Supply and demand analysis method, device and equipment for regional power grid with extremely high proportion of hydropower and wind power

Abstract

The invention discloses a supply and demand analysis method, device and equipment for a regional power grid with extremely high proportion of hydropower and wind power, relates to the technical field of power and electric quantity balance, and can solve the technical problem of how to participate in power and electric quantity balance calculation of hydropower and wind power. The method comprises the steps of determining planned outgoing electric quantity, planned thermal power generation capacity, planned hydroelectric power generation capacity, planned wind power generation capacity and predicted planned electricity consumption, and performing electric quantity balance calculation according to the planned outgoing electric quantity, the planned thermal power generation capacity, the planned hydroelectric power generation capacity, the planned wind power generation capacity and the planned electricity consumption to obtain an electric quantity balance result, the sum of water-electricity proportion and wind-electricity proportion is greater than thermal-electricity proportion; determining planned outgoing power, thermal power planned power, hydropower planned power and wind power planned power, determining peak load, and performing power balance calculation according to the planned outgoing power, the thermal power planned power, the hydropower planned power, the wind power planned power and the peak load to obtain a power balance result.

Description

Technical Field

[0001] The present invention relates to the technical field of electric power and electricity balance, and in particular to a method, device and equipment for analyzing supply and demand of a power grid in a region with extremely high proportions of hydropower and wind power. Background Art

[0002] As the proportion of hydropower and wind power in the power grid exceeds that of thermal power, and the uncertainty of hydropower and wind power is high, how hydropower and wind power can be involved in power balance calculations has become an urgent problem that needs to be solved. Summary of the invention

[0003] In view of this, the present invention provides a method, device and equipment for analyzing supply and demand of a regional power grid with a very high proportion of hydropower and wind power, which can solve the technical problem of how hydropower and wind power participate in the calculation of power and electricity balance.

[0004] According to a first aspect of the present invention, a method for analyzing supply and demand of a regional power grid with a very high proportion of hydropower and wind power is provided, the method comprising:

[0005] Determine the research horizon year for supply and demand analysis to be conducted;

[0006] Determine the planned power transmission in the research level year, determine the planned thermal power generation in the research level year, determine the planned hydropower generation according to the hydropower output characteristic curve in the historical normal water year, determine the planned wind power generation according to the historical installed capacity of wind power, the progress of wind power installation, the historical utilization hours of wind power and the ratio of the preset utilization hours of wind power, predict the planned power consumption in the research level year according to the historical power consumption information, perform power balance calculation according to the planned power transmission, the planned thermal power generation, the planned hydropower generation, the planned wind power generation and the planned power consumption, and obtain the power balance result, wherein the sum of the hydropower ratio and the wind power ratio is greater than the thermal power ratio;

[0007] Determine the wet and dry season control time in the research level year, determine the planned external power at the wet and dry season control time, determine the thermal power planned power at the wet and dry season control time, determine the hydropower planned power at the wet and dry season control time according to the historical dry year hydropower output characteristic curve, determine the wind power planned power according to the historical wind power output characteristic curve, the wind power planned installed capacity and the wind power preset output ratio, predict the planned load characteristic curve in the research level year according to the historical load characteristic curve, determine the peak load at the wet and dry season control time according to the planned load characteristic curve, perform power balance calculation according to the planned external power, the thermal power planned power, the hydropower planned power, the wind power planned power and the peak load, and obtain the power balance result.

[0008] According to a second aspect of the present invention, a device for analyzing supply and demand of a regional power grid with a very high proportion of hydropower and wind power is provided, the device comprising:

[0009] Determination module, used to determine the research level year to be conducted for supply and demand analysis;

[0010] An electricity balance calculation module is used to determine the planned external power transmission in the research level year, determine the planned thermal power generation in the research level year, determine the planned hydropower generation according to the hydropower output characteristic curve in the historical normal water year, determine the planned wind power generation according to the historical installed capacity of wind power, the progress of wind power installation, the historical utilization hours of wind power and the ratio of the preset utilization hours of wind power, predict the planned electricity consumption in the research level year according to the historical electricity consumption information, perform electricity balance calculation according to the planned external power transmission, the planned thermal power generation, the planned hydropower generation, the planned wind power generation and the planned electricity consumption, and obtain an electricity balance result, wherein the sum of the hydropower ratio and the wind power ratio is greater than the thermal power ratio;

[0011] The power balance calculation module is used to determine the wet and dry season control time in the research level year, determine the planned external power at the wet and dry season control time, determine the thermal power planned power at the wet and dry season control time, determine the hydropower planned power at the wet and dry season control time according to the historical dry year hydropower output characteristic curve, determine the wind power planned power according to the historical wind power output characteristic curve, the wind power planned installed capacity and the wind power preset output ratio, predict the planned load characteristic curve in the research level year according to the historical load characteristic curve, determine the peak load at the wet and dry season control time according to the planned load characteristic curve, perform power balance calculation according to the planned external power, the thermal power planned power, the hydropower planned power, the wind power planned power and the peak load, and obtain the power balance result.

[0012] According to the third aspect of the present application, a storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the above-mentioned method for analyzing supply and demand of a regional power grid with an extremely high proportion of hydropower and wind power is implemented.

[0013] According to the fourth aspect of the present application, a computer device is provided, including a storage medium, a processor, and a computer program stored on the storage medium and executable on the processor, wherein when the processor executes the program, the above-mentioned method for analyzing supply and demand of regional power grids with extremely high proportions of hydropower and wind power is implemented.

[0014] By means of the above technical scheme, the present invention provides a method, device and equipment for analyzing the supply and demand of a regional power grid with an extremely high proportion of hydropower and wind power. By means of the technical scheme of the present invention, for hydropower: since the water inflow in a normal water year is average, which is a general situation, and the power balance is a balance in terms of total volume, it is more accurate to use the hydropower output characteristic curve of the historical normal water year to determine the planned hydropower generation in the research normal water year, and this accurate planned hydropower generation is used to participate in the power balance calculation, so that the power balance calculation result is more accurate. Since the water inflow in the dry year is less, if power balance can be achieved in the dry year, then power balance can be achieved regardless of whether it is a dry year, a normal water year or a wet year. Therefore, it is more accurate to use the hydropower output characteristic curve of the historical dry year to determine the planned hydropower power at the wet and dry period control moment in the research normal water year, and this accurate planned hydropower power is used to participate in the power balance calculation, so that the power balance calculation result is more accurate. For wind power: The maximum power generation of wind power in the research year can be determined based on the historical installed capacity of wind power, the progress of wind power installation and the historical utilization hours of wind power. The planned power generation of wind power can be obtained by multiplying the maximum power generation by the preset utilization hours ratio of wind power. The planned power generation of wind power, rather than the maximum power generation, is involved in the power balance, which ensures the accuracy and stability of wind power when participating in power balance. The maximum output characteristic curve of wind power in the research year can be determined based on the historical wind power output characteristic curve and the planned installed capacity of wind power. The planned power of wind power can be obtained by multiplying the preset output ratio of wind power by the maximum output on the maximum output characteristic curve. The planned power of wind power, rather than the maximum output, is involved in the power balance, which ensures the accuracy and stability of wind power when participating in the power balance.

[0015] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention, it can be implemented according to the contents of the specification. In order to make the above and other purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation on the local application. In the drawings:

[0017] Figure 1 A schematic flow chart of a method for analyzing supply and demand of a regional power grid with a very high proportion of hydropower and wind power provided by an embodiment of the present invention is shown;

[0018] Figure 2 An example diagram showing a process for analyzing supply and demand of a regional power grid with a very high proportion of hydropower and wind power provided by an embodiment of the present invention is shown;

[0019] Figure 3A schematic diagram of the structure of a device for analyzing supply and demand of a regional power grid with a very high proportion of hydropower and wind power provided by an embodiment of the present invention is shown;

[0020] Figure 4 A schematic diagram of the structure of another device for analyzing supply and demand of a regional power grid with a very high proportion of hydropower and wind power provided by an embodiment of the present invention is shown;

[0021] Figure 5 An example diagram of the total electricity consumption and growth rate of Province A from 2010 to 2019 provided by an embodiment of the present invention is shown;

[0022] Figure 6 An example diagram of the changes in the electricity consumption structure of Province A from 2010 to 2019 provided by an embodiment of the present invention is shown;

[0023] Figure 7 An example diagram of per capita electricity consumption and growth rate in Province A from 2000 to 2019 provided by an embodiment of the present invention is shown;

[0024] Figure 8 An example diagram of per capita household electricity consumption and growth rate in Province A from 2000 to 2019 provided by an embodiment of the present invention is shown;

[0025] Fig. 9 An example diagram of the change process of GDP unit consumption in Province A since 2000 provided by an embodiment of the present invention is shown;

[0026] Fig.10 An example graph of a curve of maximum social load and growth rate changes in Province A from 2010 to 2019 provided by an embodiment of the present invention is shown;

[0027] Fig.11 An example diagram of a maximum load and growth rate change curve of the power grid in Province A from 2010 to 2019 provided by an embodiment of the present invention is shown;

[0028] Fig.12 An example diagram of load rate variation in province A provided by an embodiment of the present invention is shown;

[0029] Fig.13 An example diagram of the monthly trend of the maximum load of the monthly unified dispatching of the power grid in Province A from 2017 to 2019 provided by an embodiment of the present invention is shown;

[0030] Fig.14 An example diagram of the peak-to-valley difference change of the unified dispatching power grid in Province A from 2004 to 2019 provided by an embodiment of the present invention is shown;

[0031] Fig.15 An example diagram of the maximum load utilization hours of the power grid in Province A from 2000 to 2019 provided by an embodiment of the present invention is shown;

[0032] Fig.16 An example diagram of a maximum load daily curve in summer from 2012 to 2019 provided by an embodiment of the present invention is shown;

[0033] Fig.17 An example diagram of a maximum load daily curve for the winter period of 2012-2019 provided by an embodiment of the present invention is shown;

[0034] Fig.18 An example diagram of annual cooling load ratio from 2012 to 2019 provided by an embodiment of the present invention is shown;

[0035] Fig.19 An example diagram of a cooling load curve for the annual maximum load day from 2012 to 2019 provided by an embodiment of the present invention is shown;

[0036] Fig. 20 An example diagram of a maximum load daily curve from 2012 to 2019 provided by an embodiment of the present invention is shown;

[0037] Fig.21 An example diagram of a benchmark load curve from 2012 to 2019 provided by an embodiment of the present invention is shown;

[0038] Fig. 22 An example diagram of a change diagram of installed capacity and non-fossil energy installed capacity ratio in Province A provided by an embodiment of the present invention is shown;

[0039] Fig.23 An example diagram of changes in power generation and non-fossil energy power generation ratio provided by an embodiment of the present invention is shown;

[0040] Fig.24 An example diagram of the change in the amount of electricity transmitted out of the province and the proportion of the total electricity generated in the province provided by an embodiment of the present invention is shown;

[0041] Fig.25 An example diagram of an average daily load curve in summer from 2011 to 2019 provided by an embodiment of the present invention is shown;

[0042] Fig.26 An example diagram of an average daily load curve in winter from 2011 to 2019 provided by an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0043] The present invention will be described in detail below with reference to the accompanying drawings and in combination with embodiments. It should be noted that the embodiments and features in the embodiments of the present invention can be combined with each other without conflict.

[0044] At present, CN201510675264.1-Supply and demand balance analysis method and system based on electricity, electricity and peak-shaving balance analyzes the electricity and electricity balance in the next year on a monthly basis. The present invention uses wet and dry months to represent several horizontal years in future studies, rather than balancing on a monthly basis, which optimizes the calculation speed and efficiency, and is more advanced for larger-scale balance calculations.

[0045] CN201510719442.6-A method for analyzing the long-term power balance of a large-scale power station group in a hydropower-rich power grid, the technology used is to read the hourly load demand during the dispatch period of a provincial power grid, and is a power balance analysis conducted for the current power grid operation period. The present invention is an analysis of the power balance for several future research years. For the analysis of future situations, there is no "hourly load demand during the dispatch period" to read, and the time scale of the analysis is completely different. The present invention does not analyze the hourly balance situation, but adopts a more advanced method to conduct balance analysis.

[0046] CN201710256976.9-A power and electricity balance method considering air pollutant prevention and control, mainly for coal-fired power units, under the constraints of air pollutant prevention and control, to carry out power and electricity balance analysis. The present invention is aimed at clean energy power systems, and the main objects of the two methods are different, belonging to different types of technologies.

[0047] CN201710780804.1-Electricity and power balance scheme generation system and method conduct hourly electricity and power balance analysis on the day-ahead system operation. The time and spatial scales of the research problem are different from the electricity and power balance of several research years studied in the present invention, and the technology used is also different.

[0048] CN201711397659.5-A method for calculating power and electricity balance indicators based on time series operation simulation, which adopts a time series operation simulation method to obtain the profit and loss control period and peak control period of each month. However, the present invention does not adopt operation simulation, does not perform power balance analysis for each month, and the analysis time and space scales are different, and the prediction technology used is different.

[0049] CN202111346782.0-A method for energy grid planning based on dual carbon targets, with carbon peak and carbon neutrality as the boundaries, carries out calculations, while the present invention uses the objective time series of various energy development as the boundaries for calculations. CN202111346782.0 carries out three types of balance calculations for peak-shaving capacity, base load, and peak load, while this method considers the characteristics of primary energy supply and carries out more accurate balance analysis. The two technologies consider different scenarios, have different research focuses, and use different technologies.

[0050] CN202110780731.2-A method, device and terminal equipment for power balance planning, which performs power balance calculation with the goal of optimizing the total system cost, while the present invention carries out analysis and calculation with the goal of ensuring power balance and safe and stable operation of the system. The two technical objectives, methods and results are different.

[0051] CN202210337663.7-Regional power and electricity balance method for joint probability distribution of load and power source adopts the maximum load of each region within the preset years and the preset hours of output of each type of power source. The present invention adopts a variety of intelligent methods to predict the maximum load and uses measured data to obtain the output hours of various types of power sources. It has higher prediction accuracy and precision than the method proposed in CN202210337663.7.

[0052] CN202210697912.3-Energy Internet planning method considering carbon emission balance, based on the energy planning of the government energy department, carries out total energy demand forecast, and obtains the annual total energy consumption and the total electricity consumption of the whole society. The present invention predicts the total future electricity demand through a variety of intelligent methods, which is different from CN202210697912.3 using different methods and calculating different results, belonging to different technologies.

[0053] CN202210892612.0-A method for evaluating the electric quantity balance margin of an incremental distribution network oriented to a scheduling plan is proposed. It has a different research object from the present invention and adopts a different technical solution.

[0054] CN202211559129.7-A power and electricity balance calculation system and method predicts loads of different voltage levels. In order to improve efficiency and accuracy, the present invention does not distinguish between voltage level loads, but studies the load as a whole. Compared with CN202211559129.7, it is more advanced in technology and has more accurate results.

[0055] CN202310338638.5-A method for medium- and long-term power and electricity balance in an electric power system taking extreme weather into consideration, wherein the extreme weather is set so that the water inflow of hydropower in summer and autumn is reduced to half of the original amount, and the medium- and long-term power and electricity balance problem in an electric power system taking extreme weather into consideration is studied. The present invention is not targeted at specific meteorological conditions and is more universal, and the proposed method is more advanced.

[0056] CN202311373231.2-A data-driven power and electricity balance method under multi-dimensional uncertain conditions, which establishes a power and electricity balance objective function for the power system with minimum load limit, minimum power abandonment and minimum economic cost. The present invention takes safety as the boundary and balance as the goal, and does not consider the economic cost objective function. It has different research objectives and different research methods from the technology proposed in CN202311373231.2.

[0057] CN202311868692.7-Distributed photovoltaic participation in grid electricity balance regulation demand analysis method and system, the research focus is how distributed photovoltaics participate in grid electricity balance, which is different from the research object of the present invention and has different goals.

[0058] CN202410481443.0-Electricity balance risk assessment method, device, electronic device and readable storage medium. For any target power plant, based on the meteorological characteristic data of the target power plant in the same period last year and the corresponding pre-trained power generation prediction model, a first number of power generation interval probability distribution results are generated. Due to factors such as section constraints, the data from the same period last year sometimes cannot represent the actual operating conditions of the power station, and there are inaccurate calculations. Different from the technology proposed in the present invention, the present invention is more advanced and can overcome the above-mentioned defects.

[0059] CN202410618274.0-A novel power system power and electricity balance analysis method based on multi-cascade multi-process panoramic timing operation simulation, which realizes power and electricity balance by revising maintenance and reservoir boundaries. In actual operation, maintenance arrangements and reservoir boundaries are generally not adjusted, which is different from the constraints of the method proposed in the present invention and the implementation technology is different.

[0060] CN202411001034.2-A method for balancing electric power in an electric power system taking into account mutual assistance among different zones, achieving balance through combination adjustment of zoned startup units, which is different from the technology adopted in the present invention, and has different ways and methods for achieving the goals.

[0061] CN201710185028.0-A power system power and electricity balance auxiliary analysis and calculation system adapted to the characteristics of power generation of multiple types of power sources, which calculates the power balance results of power systems divided by zones and voltage levels during the planning period, is different from the present invention, which focuses on the balance of large power grids and focuses on the research of the main grid, and does not pay attention to the balance problem of voltage levels. The two technologies have different research objects.

[0062] CN201810608737.X-Power and electricity balance optimization method based on wind power capacity credibility, obtains wind power forecast credible output through historical data analysis, and optimizes power and electricity balance model based on wind power capacity credibility. Different from the present invention, the present invention conducts multiple types of power supply analysis, deeply studies the system supply guarantee capability, and adopts more advanced, comprehensive and reliable technology.

[0063] CN201910017710.8-A power and electricity balance method based on the probabilistic wind power generation scenario obtains the typical daily load curve and wind power daily generation curve of each season in the power system. In essence, this technology still conducts power and electricity balance analysis on the existing load curve. Unlike the present invention, which conducts analysis on several future research years, the research objects are different and the technologies used are different.

[0064] CN202010648322.2-A method for balancing power in a power grid, classifying different types of power sources, and proposing a method for balancing power that prioritizes renewable energy. The technology proposed in this invention prioritizes reliable power supply, but the goal is different and the technology used is different.

[0065] CN202110679678.7-A method, device and system for calculating the electricity balance of offshore wind power output, which only carries out electricity balance calculation for offshore wind power, has a different research object and research method from the present invention.

[0066] CN202110780731.2-A method, device and terminal equipment for power balance planning, which performs power balance analysis by acquiring the load planning data, wind power forecast data and photovoltaic forecast data of the target power grid. However, the present invention does not obtain load planning data, but obtains load data through calculation and analysis. The present invention also does not obtain wind power and photovoltaic forecast data, but predicts the output characteristics and output contribution of future new energy sources through analysis and calculation.

[0067] CN202111401295.X - Medium- and long-term power balance method for power systems containing large-scale renewable energy, obtaining the power generation cost of thermal power units in power systems containing large-scale renewable energy, and the objective function is to minimize the total operating production cost. It is inconsistent with the objective function of the present invention, which takes reliable power supply as the research goal.

[0068] CN202111435931.0-A method for balancing electricity and power in the grid-connected operation of new energy sources, which investigates and summarizes the total electricity consumption in the power supply area every year and every month, is different from the method adopted by the present invention. The present invention does not conduct investigation and summary.

[0069] CN202210892612.0-Incremental distribution network power balance margin assessment method for scheduling plan, the research object is the incremental distribution network, which has significantly different characteristics from the large power grid which is the research object of the present invention, and belongs to a different research category.

[0070] CN202210927187.4-A distribution network power balancing method considering energy storage configuration and demand-side management, mainly to reduce the investment cost of substations and adapt to the development of distribution networks, which is different from the research objective of the present invention.

[0071] CN202211186263.7-Power and electricity balance analysis method, device, equipment and storage medium, according to the daily load fluctuation data, determine the minimum power generation data of elastic resources. Unlike the method adopted by the present invention, the present invention does not study daily load fluctuations.

[0072] CN202211578895.8 -Method, device, electronic device and storage medium for medium- and long-term power and electricity balance, which determines the constraints of the power system at the medium- and long-term scale according to the adjustable demand range and the adjustable energy supply range, is different from the method adopted by the present invention. The present invention does not determine the constraints of the power system at the medium- and long-term scale according to the adjustable energy supply range.

[0073] CN202211204482.3-A method for optimizing the low-carbon economy of power and electricity balance, which establishes an objective function based on the coal consumption cost of thermal power plant operation, the carbon emission cost of thermal power plant, and the pollution emission cost of thermal power plant, is different from the technology used in the present invention. The present invention does not focus on the coal consumption cost of thermal power plant operation.

[0074] CN202211481042.2-A power and electricity balancing method considering short-scale frequency safety, which mainly focuses on short-scale frequency safety. Different from the research purpose of the present invention, the present invention mainly focuses on medium- and long-term power supply guarantee capabilities.

[0075] CN202211484366.1-A method for balancing electricity and power considering the coordinated operation of seasonal energy storage and short-term energy storage, mainly studies the balance of electricity and power with a daily cycle. Different from the research object of the present invention, the present invention studies the future vision and the horizontal balance of the year. Similarly, the purpose of the research is also different.

[0076] CN202211559129.7-A power and electricity balance measurement system and method, through the load density index method, combined with the typical plot load curve obtained by clustering, the grid conventional load curve is superimposed from bottom to top. Different from the method adopted by the present invention. The present invention does not perform grid processing through clustering method.

[0077] CN202211570306.1-A method and system for optimizing the scheduling of electric power balance, establishing a medium- and long-term optimization scheduling model for the system, and establishing a short-term optimization scheduling model for the system. Its purpose is to establish an optimization scheduling model. Different from the research purpose of the present invention, the present invention does not focus on scheduling issues.

[0078] CN202310338638.5-A medium- and long-term power balance method for power systems considering extreme weather conditions, with the total cost of power system operation as the objective function, is different from the research objective of the present invention. The present invention does not focus on system operation costs.

[0079] CN202310482651.8-A method and system for analyzing electric power balance considering nuclear power peak regulation, focusing on exerting the peak regulation capability of nuclear power, which has a different focus and research object from the present invention.

[0080] CN202310865544.3-Determination method and device for comprehensive evaluation of power system power balance, obtaining power gap value, peak-shaving gap value, capacity gap value, abandoned wind / solar / hydro power value and abandoned renewable energy power value of the power system, which is different from the present invention. In the present invention, the power gap value is an optimization target, not a given parameter.

[0081] CN202310852875.3-A method for optimizing the power balance of a multi-time scale and multi-resource power grid. At the day-ahead level, based on the monthly power plan, the unit output plan is compiled with a granularity of t minutes within 24 hours of the next day to obtain the day-ahead power generation plan. Different from the research object of the present invention, the present invention does not study the day-ahead power generation plan, but studies the balance problem of the horizontal year.

[0082] CN202311245615.6-A method and device for balancing electric power including an energy storage system, according to the target climbing rate obtained, the climbing rate of the generator set of the power system is controlled. Different from the research object of the present invention, the present invention does not study the problem of generator set climbing rate control. The present invention focuses on the balance problem of the horizontal year.

[0083] CN202311389566.3-Electricity balance analysis method and system for high-proportion new energy power systems, the actual output of photovoltaic power generation is represented by predicting photovoltaic output and superimposing a random fluctuation error. Different from the method used in the present invention, the present invention does not represent the actual photovoltaic output by predicting photovoltaic output and superimposing a random fluctuation error.

[0084] CN202311373231.2-A data-driven method for balancing power and electricity under multi-dimensional uncertain conditions. For new source-load prediction data, the category to which it belongs is first determined, and then the LSTM model corresponding to the category is used to make power and electricity balance decisions. Different from the technology used in the present invention, the present invention does not determine the category to which it belongs.

[0085] CN202311630443.4-A multi-time-scale power and electricity balance method applied to the power system uses the trend extrapolation method to obtain the maximum load in the future year, thereby calculating the real-time load data in the future year. Different from the method used in the present invention, the present invention does not use the trend extrapolation method to predict the real-time load data in the future year.

[0086] CN202311461895.4-A method for balancing the power and electricity of multi-regional power grid flexibility resources, with the goal of system flexibility transformation and minimum operating cost, establishes an objective function. Different from the research purpose of the present invention, the present invention does not focus on flexibility transformation or operating cost.

[0087] CN202311459637.2-Generation-reserve coordination optimization method based on time-series-probabilistic power balance. The optimization goal of the planning model is to minimize the total operating cost of the system, including thermal power generation cost and standby cost. Different from the research objective of the present invention, the present invention does not focus on operating cost.

[0088] CN202311868692.7-Distributed photovoltaic participation in grid electricity balance regulation demand analysis method and system, the research object is distributed photovoltaics, which is different from the research object of the present invention.

[0089] CN202311845149.5-A multi-energy complementary power and electricity balancing method for coping with the adverse effects of extreme weather, analyzing extreme weather, and calculating the output process of new energy in the power system. It has a different research object and adopted a different method from the present invention. The present invention does not focus on extreme weather and does not calculate the output process of new energy in the system.

[0090] CN202410230435.9-A method and device for calculating the electric power balance margin under typical weather processes, based on the actual load historical data, predicted load historical data, actual wind power output historical data, and predicted wind power output historical data in each type of typical weather process in the area to be evaluated. Different from the method adopted by the present invention, the present invention does not classify weather processes.

[0091] CN202410668518.6-Source-grid-load-storage power balancing method and system determines the set of power equipment corresponding to each power layer, which is different from the method adopted by the present invention and has a different research object.

[0092] CN202411001034.2-A method for balancing power and electricity in an electric power system taking into account mutual assistance among partitions. To avoid frequent power on and off and simplify processing, for partition i, the start and stop of its units within the planning period T are not considered. Therefore, the number of started units in each time period t of partition i in the planning period T is equal, which is inconsistent with the actual situation. The start and stop of the units are adjusted in real time according to the operation needs. This constraint is inconsistent with the actual operation scenario.

[0093] CN202410481443.0-Electricity balance risk assessment method, device, electronic device and readable storage medium, for any target power plant among the power plants, based on the first number of meteorological characteristic data of the target power plant in the same period of the previous year and the corresponding pre-trained power generation prediction model. Mainly study the prediction of the next year, which is different from the method adopted by the present invention. The present invention focuses on the balance problem of the future research level year, with different research objects and different research methods.

[0094] CN202410618274.0-A new power system power and electricity balance analysis method based on multi-cascade multi-process panoramic time series operation simulation, a scenario generation method based on a time series generative adversarial network (SGAN) and a daily state transition simulation method based on a Markov chain to generate an annual hourly wind, solar, water and load scenario set. Different from the technology adopted by the present invention, the present invention does not study the hourly scenario set.

[0095] CN202410835356.0 A multi-type energy coordinated power balance optimization method, with the goal of minimizing operating costs, effectively solves the optimization scheduling problem under a high proportion of hydropower. Different from the research objectives of the present invention, the present invention does not focus on operating costs or optimization scheduling problems.

[0096] CN202410844574.0-An electric power balancing method and device for an intelligent distribution system containing a distributed cluster uses virtual generators and virtual energy storage to aggregate and equalize the cluster to obtain an aggregated operating domain of the cluster. This is different from the method adopted by the present invention. The present invention does not use virtual generators and virtual energy storage and does not aggregate and equalize the cluster.

[0097] At present, in terms of power balance, CN201510855311.0 - Planning annual power balance method taking into account wind power output characteristics uses the correlation coefficient method to calculate the total output of the planned wind farm in the year. When there is no wind farm in a certain area, this method is invalid, which is different from the method used in the present invention, and the research object is not exactly the same.

[0098] CN201611208979.7-A method and device for balancing power between power grid partitions, firstly, the research object is not a clean energy power system, the research object is different, and secondly, the research state is the current state, not the future planning level year. Different from the present invention.

[0099] CN201810707713.X-A method for calculating the capacity of a solar thermal unit participating in power balance. The research object is a solar thermal power station. The research method is to ensure power generation output through heat conversion. It is different from the research object and research method of this province.

[0100] CN201910627835.2-Method and device for determining effective capacity and method and device for determining power balance, the effective capacity of wind power participating in power balance can be obtained only through historical wind power output curve and load curve. The power limitation problem caused by the section and the power abandonment problem caused by maintenance are not considered, which is different from the method adopted by the present invention.

[0101] CN201910897293.0-Annual power balance calculation method, device and equipment mainly focuses on wind power and thermal power systems, which is different from the research object of the present invention. The invention divides wind power into clusters and simplifies the processing by clustering, which is different from the method adopted by the present invention.

[0102] CN201910957765.7-A global energy interconnection power balance optimization method based on time-space decomposition, which conducts power balance analysis through mixed integer optimization model and linear optimization model. It is different from the method adopted in the present invention and the scope of research objects is also different.

[0103] CN201811087611.9-A method and device for calculating power balance, which calculates the energy storage capacity according to the load curve of a typical day and the energy storage charge and discharge curve to achieve power balance, is different from the method adopted by the present invention. The present invention does not focus on the daily characteristics of energy storage operation, and the main research object is also different.

[0104] CN202111274067.0-The power balancing method for source-grid-load-storage coordination of new power systems focuses on the impact of rooftop photovoltaics, distributed new energy, electric vehicles, etc. on power balance. It has a different focus from the research of the present invention and the research method used is also different.

[0105] CN202210038572.3-Electricity balance risk warning method for systems with high proportion of new energy, according to typical operation mode, calculates system balance risk. Different from the method adopted by the present invention, the purpose of the research is also different.

[0106] CN202210337663.7-Regional power and electricity balancing method for the joint probability distribution of loads and power sources, classifying coal-fired power, gas-fired power, energy storage, and pumped storage as adjustable power sources, and others as unregulated power sources. Different from the research method adopted by the present invention, the present invention believes that hydropower, wind power, photovoltaic power, etc. are all adjustable power sources. At the same time, each power source has its own peak regulation depth, or adjustment range. The invention sets the output of pumped storage and energy storage as -1, and the output of gas power as 0. Different from the method adopted by the present invention. The present invention does not preset the output value according to the actual situation.

[0107] CN202111404196.7- Photovoltaic participation in power balance availability setting method, the holiday data is taken out and sorted separately, divided into ordinary data and holiday data, and the photovoltaic output historical data at the corresponding time of each day is collected; at the same time, the photovoltaic installed capacity of the corresponding date is collected. The present invention does not process the holiday data separately, and not only sets one photovoltaic output moment. It is different from the research method adopted by the present invention.

[0108] CN202210197309.9-A method, device, electronic device and storage medium for regulating power balance, CN202211382248.X-A method and system for coordinated dispatching of power grids and provinces to promote power balance within the province, CN202310080282.X-A method, system, device and storage medium for regulating power distribution for power balance, CN202310109700.3-A method for regulating power balance to achieve short-term prediction, CN202310373226.5-A method and device for rolling dispatching of small hydropower for regional power balance, CN202310627007.5-A method, device, equipment and storage medium for coordinated dispatching of multiple resources, CN202310629352.2-A method for calculating power balance capacity Methods, devices, equipment and storage media, CN202310777976.9-A method and system for determining the wind and solar power balance capacity with energy storage, CN202311095046.1-A multi-time scale coordinated optimization scheduling method and system for power balance, CN202311249879.9-A control method and system for new energy power balance, CN202311532206.4-A power system power balance control method, system, equipment and medium, and CN202210187612.0-A multi-period coordinated power balance system and method considering medium and long-term scheduling, respectively propose a power balance scheduling control method, which is different from the research object of the present invention, the state of the research object is different, and the method adopted is also different.

[0109] CN202111159459.2-Microgrid power balance power determination method and system considering energy storage operation mode, considering the role of energy storage in microgrid, studying the maximum demand power and minimum external power when the system is balanced under the typical operation mode of energy storage. Different from the research object of the present invention, the research method is also different.

[0110] CN202210682567.6-A multi-energy complementary power balancing and allocation method establishes a power supply flexibility supply and demand and complementary demand model. Different from the research method of the present invention, the present invention does not consider the existence of a complementary demand model between different energy sources and does not perform complementary allocation of various energy forms.

[0111] CN202211277726.0-Distribution network optimization method and device based on power balance and performance cost, sets performance cost constraints and an optimization function with the goal of minimizing total cost, and uses a particle swarm algorithm to determine the maximum economic benefit. It has a different research objective from the present invention and uses a different method.

[0112] CN202111600282.5-A method and system for analyzing the active power shortage of the interconnected power grid, calculating the 24-hour dynamic difference between the load and output on the maximum load day, and determining the rotating standby of conventional units in the target area. It has a different research object, a different method, and a different research purpose from the present invention.

[0113] CN202210079972.9-A method and device for calculating power balance of an independent power grid. Through the power balance inequality, it is determined whether the flexible supply of the system meets the flexible demand of the system. It has a different research object, research purpose and research method from the present invention.

[0114] CN202310644438.2-A method and system for calculating the confident output of new energy participating in power balance. By studying the proportion of wind power and photovoltaic power participating in power balance in the same season and time period, the confident output calculation of new energy participating in power balance is carried out. It is different from the method adopted in the present invention and the purpose of the research is also different.

[0115] CN202310665934.6-A method and system for determining the proportion of new energy included in the electricity balance. Using historical data from the past three years, a method for determining the proportion of new energy included in the electricity balance is proposed. It is different from the method adopted in the present invention and the research purpose is also different.

[0116] CN202311458570.0-Multi-time scale power balance early warning method, device, equipment and storage medium, use data on power supply and demand levels and early warning indicators to train the DeepAR model, and predict the supply forecast value and demand forecast value through the trained DeepAR model. It is different from the method used in the present invention and has a different research purpose.

[0117] CN202311357901.1-A power balance calculation method and system based on large-scale distributed photovoltaics calculates the 10kV voltage level in the target area distribution network and the number of public power supply lines required to complete the power balance calculation based on large-scale distributed. It has a different research object from the present invention, uses a different method, and has a different research objective.

[0118] CN202311725940.2-A power balancing method, system, device and storage medium for distributed resources, based on the stage of the distributed resources, determines the credibility coefficient of each type of resource participating item in the stage. It has a different research object, a different method and a different research objective from the present invention.

[0119] CN202311567089.5 A method and system for generating an output curve of a photovoltaic power source on a typical day of power balance, providing a method for generating an output curve of a photovoltaic power source on a typical day of power balance, which has a different research object, adopted different methods, and has different research objectives from the present invention.

[0120] CN202410297778.7-A method and system for evaluating power balance risk considering wind power output coefficient. A method and system for evaluating power balance risk considering wind power output coefficient is proposed. It has a different research object, a different method and a different research objective from the present invention.

[0121] This embodiment provides a method for analyzing supply and demand of a regional power grid with a very high proportion of hydropower and wind power. Figure 1 As shown, the method includes:

[0122] 101. Determine the research horizon year for supply and demand analysis to be conducted.

[0123] 102. Determine the planned amount of electricity transmitted in the research level year, determine the planned amount of thermal power generated in the research level year, determine the planned amount of hydropower generated based on the hydropower output characteristic curve in the historical normal water year, determine the planned amount of wind power generated based on the historical installed capacity of wind power, the progress of wind power installation, the historical utilization hours of wind power and the ratio of the preset utilization hours of wind power, predict the planned electricity consumption in the research level year based on the historical electricity consumption information, perform electricity balance calculation based on the planned amount of electricity transmitted, the planned thermal power generation, the planned hydropower generation, the planned wind power generation and the planned electricity consumption, and obtain the electricity balance result, wherein the sum of the hydropower ratio and the wind power ratio is greater than the thermal power ratio.

[0124] As an implementation method, determining the planned power transmission amount in the research level year includes: obtaining historical power transmission capacity and historical power transmission utilization hours, and obtaining historical power transmission amount by multiplying the historical power transmission utilization hours by the historical power transmission capacity; determining preset planned power transmission utilization hours and preset additional power transmission capacity, and obtaining additional power transmission amount by multiplying the planned power transmission utilization hours by the additional power transmission capacity; and obtaining the planned power transmission amount in the research level year based on the historical power transmission and the additional power transmission.

[0125] Among them, the historical transmission capacity and the historical transmission utilization hours correspond to the historical transmission projects, are already available, and can be directly obtained (for example, taking a historical year of 2019 as an example, the cross-provincial and cross-regional transaction electricity situation is analyzed: the maximum cross-provincial power transmission capacity continues to rise, and the transmission capacity in 2019 is 132.4 billion kWh, accounting for 35.4% of the total power generation). The newly added transmission capacity is the new addition in the research level year compared with the historical level year. Since the newly added transmission capacity and the planned transmission utilization hours are determined by the newly added transmission projects, they are preset and can be determined. The sum of the historical transmission power and the newly added transmission power is the planned transmission power.

[0126] As an implementation method, determining the planned thermal power generation in the research level year includes: determining the planned thermal power installed capacity in the research level year based on the historical installed capacity of thermal power and the thermal power installation progress, predicting the planned thermal power utilization hours in the research level year based on the historical utilization hours of thermal power, and obtaining the planned thermal power generation by multiplying the planned thermal power installed capacity by the planned utilization hours of thermal power.

[0127] Among them, the planned installed capacity of thermal power in the research level year is determined based on the historical installed capacity of thermal power and the progress of thermal power installation, including: firstly, analyzing the total installed capacity and structure of historical power sources. For example, in the historical year closest to the research level year, the total installed capacity of power sources was 99.29 million kilowatts. Hydropower installed capacity reached 78.46 million kilowatts, accounting for 79.0%; thermal power installed capacity was 15.7 million kilowatts, accounting for 15.8%; wind power installed capacity was 3.25 million kilowatts, accounting for 3.3%; photovoltaic installed capacity was 1.88 million kilowatts, accounting for 1.9%. It can be seen that the historical installed capacity of thermal power is 15.7 million kilowatts. Then, by analyzing the thermal power resources, we can know the thermal power installed capacity that can be developed by technology. From the thermal power installed capacity that has been put into production, we can know the thermal power installed capacity that can be developed, so we can plan the development of thermal power. Therefore, the thermal power installed capacity schedule can be preset. It should be noted that the thermal power installed capacity schedule is the newly added thermal power installed capacity in the research level year, and the principle of determining the planned production units to be included in the balance capacity includes: thermal power installed capacity is in accordance with the planned production schedule, and the units put into production in the current year are included in the balance according to the monthly production schedule. Finally, the planned installed capacity of thermal power in the research level year is obtained by adding the historical installed capacity of thermal power to the thermal power installed capacity schedule.

[0128] Among them, for predicting the planned utilization hours of thermal power in the research level year based on the historical utilization hours of thermal power, it includes: taking each historical year and the corresponding historical utilization hours of thermal power as a coordinate point, fitting a curve, and the corresponding planned utilization hours of thermal power in the research level year can be determined by this curve. It should be noted that taking a historical year of 2019 as an example, the same is true for other historical years. The steps for determining the historical utilization hours of thermal power are: first analyze the full-caliber power generation. For example: the full-caliber power generation in 2019 is 390.3 billion kWh. Among them, hydropower is 331.6 billion kWh; thermal power is 48.8 billion kWh; wind power and photovoltaic power generation are 7.13 billion kWh and 2.82 billion kWh respectively. Then analyze the utilization hours of power generation equipment, where the utilization hours of power generation equipment are the installed capacity divided by the power generation. For example: in 2019, the utilization hours of power generation equipment were 3954 hours. Among them, hydropower utilization hours were 4,235 hours; thermal power utilization hours were 3,086 hours; wind power utilization hours were 2,553 hours; and solar power utilization hours were 1,524 hours.

[0129] As an implementation mode, determining the planned hydropower power generation based on the hydropower output characteristic curve in historical normal water years includes: determining the planned hydropower installed capacity in the research normal water year based on the historical installed capacity of hydropower and the progress of hydropower installation, predicting the hydropower output characteristic curve in the research normal water year in the research normal water year based on the hydropower output characteristic curve in historical normal water years and the planned hydropower installed capacity, and determining the planned hydropower power generation based on the hydropower output characteristic curve in the research normal water year.

[0130] Among them, the planned installed capacity of hydropower in the research level year is determined based on the historical installed capacity of hydropower and the progress of hydropower installation, including: firstly, analyzing the water resources reserves and water inflow conditions. When the water volume is known, the technically exploitable installed capacity of hydropower can be known. The installed capacity of hydropower that can be exploited can be known from the installed capacity of hydropower that has been put into production, so that the development of hydropower can be planned. Therefore, the progress of hydropower installation can be preset. It should be noted that the progress of hydropower installation is the newly added hydropower installed capacity in the research level year, and the principle of including the planned units into the balance capacity is determined, including: the hydropower installed capacity is in accordance with the planned production progress, the units put into production in the current year are included in the balance according to the monthly production progress, and the Jiehe power station is included in the balance according to the capacity connected to the provincial power grid. Then the planned installed capacity of hydropower in the research level year is obtained by adding the historical installed capacity of hydropower to the progress of hydropower installation. (Specific: Analyze the water supply situation: For example, in the historical year closest to the research level year, the average water supply of the whole network was 8.5% less than the same period last year, and 13% more than the same period in many years. By quarter: In the first quarter, the water supply of each basin was relatively abundant, and the average water flow was 2.3% less than the same period last year, and 2.5% more than the multi-year level; in the second quarter, the average water flow of each basin was 7.2% less than the same period last year, and 24% more than the multi-year level; in the third quarter, the water supply of each basin was relatively abundant, and the average water flow was 12.1% less than the same period last year, and 15.4% more than the multi-year level; in the fourth quarter, the average water flow was 8.4% more than the multi-year level, and 3% less than the same period last year. By month, in January, the water supply of the whole network was 1.1% less than the multi-year level, and 5.8% less than the same period last year. In February, the water supply of the whole network was 4.5% more than the multi-year level, and 2.7% more than the same period last year. In March, the water supply of the whole network increased by 5.3% compared with the multi-year level, and the water supply of the same period last year increased by 1.3%. In April, the water supply of the whole network was 20.6% higher than the level of the same period in many years, and 0.6% lower than the level of the same period last year. In May, the water supply of the whole network was 58.5% higher than the level of the same period in many years, and 13.9% higher than the level of the same period last year. In June, the water supply of the whole network was 9.4% higher than the level of the same period in many years, and 18.5% lower than the level of the same period last year. In July, the average water supply of the whole network was 15.9% higher than the level of the same period in many years, and 26.3% lower than the same period last year; in August, the average water supply of the whole network was It is 1% more than the multi-year period and 13% less than the same period last year; in September, the average water inflow of the whole network was 36.6% more than the multi-year period and 31.3% more than the same period last year; in October, the average water inflow of the whole network was 8.9% more than the multi-year period and 9.3% less than the same period last year; in November, the average water inflow of the whole network was 14.5% more than the multi-year period and 10.1% more than the same period last year; in December, the average water inflow of the whole network was 0.1% less than the multi-year period and 2.1% less than the same period last year).

[0131] Among them, for the hydropower output characteristic curve for the research average water year predicted in the research average water year based on the hydropower output characteristic curve for the historical average water year and the hydropower planned installed capacity, it includes: first determining the historical average water year, then obtaining the historical hydropower installed capacity and the hydropower output characteristic curve for the historical average water year, each output on the hydropower output characteristic curve for the historical average water year corresponds to the same historical hydropower installed capacity, and each output corresponding to the hydropower planned installed capacity is calculated in equal proportion as the hydropower output characteristic curve for the research average water year. Take three points on the historical hydropower output characteristic curve in normal water years as an example (the historical hydropower output characteristic curve in normal water years corresponds to the historical installed capacity of hydropower Q1): point A (time 1, output 1), point B (time 2, output 2), point C (time 3, output 3). It is known that the planned installed capacity of hydropower is Q2, and the corresponding output at time 1 is: (Q2*output 1) / Q1, the output at time 2 is: (Q2*output 2) / Q1, and the output at time 3 is: (Q2*output 3) / Q1, so the corresponding three points are: point A′(time 1, (Q2*output 1) / Q1)), point B′(time 2, (Q2*output 2) / Q1)), point C′(time 3, (Q2*output 3) / Q1)). In this way, the hydropower output characteristic curve in normal water years can be obtained.

[0132] Wherein, determining the planned hydropower generation according to the studied hydropower output characteristic curve in a normal water year includes: calculating the area enclosed by the studied hydropower output characteristic curve in a normal water year, and determining it as the planned hydropower generation.

[0133] As an implementation mode, the method of determining the planned wind power generation based on the historical installed capacity of wind power, the progress of wind power installation, the historical utilization hours of wind power and the ratio of preset utilization hours of wind power includes: determining the planned installed capacity of wind power in the research level year based on the historical installed capacity of wind power and the progress of wind power installation, predicting the planned utilization hours of wind power in the research level year based on the historical utilization hours of wind power, and obtaining the planned wind power generation based on the planned installed capacity of wind power, the planned utilization hours of wind power and the ratio of preset utilization hours of wind power.

[0134] Among them, the historical installed capacity of wind power and the progress of wind power installation are both known. The sum of the two is the planned installed capacity of wind power in the research level year. The historical installed capacity of wind power in each historical year and the corresponding historical utilization hours of wind power are used as a coordinate point to fit a curve. The planned utilization hours of wind power corresponding to the research level year can be obtained on this curve. The planned installed capacity of wind power is multiplied by the planned utilization hours of wind power to obtain the maximum power generation of wind power in the research level year. The planned power generation of wind power is obtained by multiplying the ratio of wind power preset utilization hours by this maximum power generation.

[0135] The historical electricity consumption information includes historical electricity elasticity coefficient, historical electricity consumption, historical gross domestic product, historical per capita electricity consumption, and historical power consumption per unit of output value. As an implementation method, the planned electricity consumption in the research level year is predicted based on the historical electricity consumption information, including: predicting the planned electricity elasticity coefficient in the research level year based on the historical electricity elasticity coefficient, obtaining the electricity consumption growth rate by multiplying the planned electricity elasticity coefficient by a preset economic growth rate, and obtaining the first planned electricity consumption in the research level year by multiplying the historical electricity consumption by the electricity consumption growth rate; predicting the second planned electricity consumption in the research level year based on the historical electricity consumption; predicting the third planned electricity consumption in the research level year based on the historical gross domestic product and the historical electricity consumption; predicting the planned per capita electricity consumption in the research level year based on the historical per capita electricity consumption, and obtaining the fourth planned electricity consumption in the research level year by multiplying the planned per capita electricity consumption by a preset number of permanent residents; predicting the fifth planned electricity consumption in the research level year based on the historical power consumption per unit of output value and the historical electricity consumption; and obtaining the planned electricity consumption based on the first planned electricity consumption, the second planned electricity consumption, the third planned electricity consumption, the fourth planned electricity consumption, and the fifth planned electricity consumption.

[0136] Among them, the first planned electricity consumption is obtained by predicting electricity consumption based on the electricity elasticity coefficient method. Taking the electricity balance calculation in years as an example, each historical year and the corresponding historical electricity elasticity coefficient are used as a coordinate point to fit a curve. Based on this curve, the planned electricity elasticity coefficient corresponding to the research level year can be determined. The preset economic growth rate is the GDP growth rate. It should be noted that the GDP growth rate and the historical electricity consumption correspond to the same year.

[0137] Among them, the second planned electricity consumption is obtained by predicting electricity consumption based on the trend extrapolation method. The trend extrapolation method takes time as the independent variable and the annual total electricity consumption as the dependent variable. Taking the electricity balance calculation in years as an example, the historical electricity consumption is the annual total electricity consumption in the historical years. The second planned electricity consumption in the research level year based on the historical electricity consumption forecast includes: obtaining the annual total electricity consumption of multiple historical years, taking each historical year and the corresponding annual total electricity consumption as a coordinate point, and fitting a curve, so that the annual total electricity consumption corresponding to the research level year can be obtained on the curve, and it is used as the second planned electricity consumption.

[0138] Among them, the third planned electricity consumption is obtained by predicting electricity consumption based on the regression analysis method. The regression analysis method uses gross domestic product as the independent variable and the annual total electricity consumption as the dependent variable. Taking the electricity balance calculation in years as an example, the historical gross domestic product is the gross domestic product of the historical year, and the historical electricity consumption is the annual total electricity consumption of the historical year. The third planned electricity consumption in the research level year is predicted based on the historical gross domestic product and the historical electricity consumption, including: obtaining historical electricity consumption and historical gross domestic product for multiple historical years, taking the historical electricity consumption and the corresponding historical gross domestic product of each historical year as a coordinate point, and fitting a curve. The gross domestic product of the research level year can be obtained according to the historical gross domestic product and the preset economic growth rate, so that the annual total electricity consumption of the research level year corresponding to the gross domestic product of the research level year can be obtained on the curve, and it is used as the third planned electricity consumption.

[0139] Among them, the fourth planned electricity consumption is obtained by predicting electricity consumption based on the per capita electricity consumption method, taking the electricity balance calculation in years as an example, the historical per capita electricity consumption is the per capita electricity consumption in the historical year, and the planned per capita electricity consumption in the research level year is predicted based on the historical per capita electricity consumption. The fourth planned electricity consumption in the research level year is obtained by multiplying the planned per capita electricity consumption by the preset permanent population, including: taking each historical year and the corresponding historical per capita electricity consumption as a coordinate point, fitting a curve, and then determining the planned per capita electricity consumption corresponding to the research level year based on the curve.

[0140] Among them, the electricity consumption of the fifth plan is obtained by predicting the electricity consumption based on the unit consumption method of output value. Taking the electricity balance calculation in years as an example, the historical unit output value electricity consumption is the electricity consumption per unit GDP in the historical years. The fifth plan electricity consumption in the research level year is predicted based on the historical unit output value electricity consumption and the historical electricity consumption, including: taking each historical year and the corresponding historical unit output value electricity consumption as a coordinate point, fitting a curve, so that the unit output value electricity consumption corresponding to the research level year can be determined according to the curve, the gross domestic product of the research level year, that is, GDP, can be obtained according to the historical gross domestic product and the preset economic growth rate, and the fifth plan electricity consumption is obtained by multiplying the unit output value electricity consumption corresponding to the research level year by the gross domestic product of the research level year.

[0141] Among them, obtaining the planned electricity consumption according to the first planned electricity consumption, the second planned electricity consumption, the third planned electricity consumption and the fourth planned electricity consumption includes: calculating the average values ​​of the first planned electricity consumption, the second planned electricity consumption, the third planned electricity consumption and the fourth planned electricity consumption, respectively calculating the deviations of the first planned electricity consumption, the second planned electricity consumption, the third planned electricity consumption and the fourth planned electricity consumption from the average value, deleting those with deviations greater than a preset threshold, and then calculating the average value until the deviation is less than or equal to the preset threshold, and the calculated average value is the planned electricity consumption.

[0142] Among them, for the planned electricity consumption in the research level year predicted according to the historical electricity consumption information, the electricity balance calculation is performed according to the planned external transmission electricity, the planned thermal power generation, the planned hydropower generation, the planned wind power generation and the planned electricity consumption, and the electricity balance result is obtained, including: the planned external transmission electricity, the planned electricity consumption and the preset standby electricity are all electricity consumption, the planned thermal power generation, the planned hydropower generation and the planned wind power generation are all power generation, and the electricity balance is carried out between electricity consumption and power generation. If electricity consumption is greater than power generation to a first preset value, it is considered that power generation is insufficient and needs to be adjusted to ensure electricity consumption. If power generation is greater than electricity consumption to a second preset value, it is considered that power generation is in excess and needs to be adjusted to reduce power abandonment.

[0143] 103. Determine the wet and dry season control time in the research level year, determine the planned external power transmission at the wet and dry season control time, determine the thermal power planning power at the wet and dry season control time, determine the hydropower planning power at the wet and dry season control time according to the historical dry year hydropower output characteristic curve, determine the wind power planning power according to the historical wind power output characteristic curve, the wind power planning installed capacity and the wind power preset output ratio, predict the planned load characteristic curve in the research level year according to the historical load characteristic curve, determine the peak load at the wet and dry season control time according to the planned load characteristic curve, perform power balance calculation according to the planned external power transmission, the thermal power planning power, the hydropower planning power, the wind power planning power and the peak load, and obtain the power balance result.

[0144] Among them, the control time refers to the maximum load time, and the wet and dry season control time refers to the wet season control time and the dry season control time. The wet season control time of the historical year can be used as the wet season control time of the research level year, and the dry season control time of the historical year can be used as the dry season control time of the research level year. For example, the wet season control time is 21:15, and the dry season control time is 11:15.

[0145] As an implementation mode, the determination of the planned power transmission at the wet and dry period control moment includes: determining the planned power transmission capacity in the research level year based on historical power transmission capacity and newly added power transmission capacity; predicting the planned power transmission curve in the research level year based on historical power transmission curve and the planned power transmission capacity; and determining the planned power transmission at the wet and dry period control moment based on the planned power transmission curve.

[0146] Among them, the historical power transmission curve corresponds to the historical power transmission capacity, each point on the historical power transmission curve is a moment and the historical power transmission, each historical power transmission on the historical power transmission curve corresponds to the same historical power transmission capacity, and each planned power transmission corresponding to the planned power transmission capacity is calculated in equal proportion as the planned power transmission curve. Take three points on the historical transmission curve as an example (the historical transmission curve corresponds to the historical transmission capacity S1): point D (time 1, historical transmission power 1), point E (time 2, historical transmission power 2), point F (time 3, historical transmission power 3). It is known that the planned installed capacity of hydropower is S2, and the corresponding planned transmission power at time 1 is: (S2*historical transmission power 1) / S1, the planned transmission power at time 2 is: (S2*historical transmission power 2) / S1, and the planned transmission power at time 3 is: (S2*historical transmission power 3) / S1, so the corresponding three points are: point D′(time 1, (S2*historical transmission power 1) / S1)), point E′(time 2, (S2*historical transmission power 2) / S1)), point F′(time 3, (S2*historical transmission power 3) / S1)). In this way, the planned transmission curve can be obtained.

[0147] Among them, for determining the planned external power transmission at the wet season control time according to the planned external power transmission curve, it includes: since each point on the planned external power transmission curve is a time and the planned external power transmission, then when the planned external power transmission curve is determined, if the wet season control time is known, the planned external power transmission corresponding to the wet season control time can be determined on the planned external power transmission curve; similarly, if the dry season control time is known, the planned external power transmission corresponding to the dry season control time can be determined on the planned external power transmission curve.

[0148] As an implementation mode, the determination of the planned thermal power generation at the wet and dry season control moment includes: predicting a planned thermal power generation output characteristic curve in the research level year based on a historical thermal power generation output characteristic curve and the planned thermal power installed capacity, and determining the planned thermal power generation at the wet and dry season control moment based on the planned thermal power generation output characteristic curve.

[0149] Among them, the historical thermal power output characteristic curve corresponds to the historical installed capacity of thermal power. Each point on the historical thermal power output characteristic curve is a moment and the historical thermal power output. Each historical thermal power output on the historical thermal power output characteristic curve corresponds to the same historical installed capacity of thermal power. Under equal proportion, each planned thermal power output corresponding to the planned installed capacity of thermal power is calculated as the planned thermal power output characteristic curve. Take three points on the historical thermal power output characteristic curve as an example (the historical thermal power output characteristic curve corresponds to the historical installed capacity of thermal power P1): point G (time 1, historical thermal power output 1), point H (time 2, historical thermal power output 2), and point J (time 3, historical thermal power output 3). It is known that the planned installed capacity of thermal power is P2. The corresponding planned thermal power output at time 1 is: (P2*historical thermal power output 1) / P1, the planned external power at time 2 is: (P2*historical thermal power output 2) / P1, and the planned external power at time 3 is: (P2*historical thermal power output 3) / P1. The corresponding three points are: point G′(time 1, (P2*historical thermal power output 1) / P1)), point H′(time 2, (P2*historical thermal power output 2) / P1)), and point J′(time 3, (P2*historical thermal power output 3) / P1)). Thus, the planned thermal power output characteristic curve can be obtained.

[0150] Among them, for determining the planned thermal power at the wet and dry season control time according to the planned thermal power output characteristic curve, it includes: since each point on the planned thermal power output characteristic curve is a time and the planned thermal power output, then when the planned thermal power output characteristic curve is determined, if the wet season control time is known, the thermal power planned power corresponding to the wet season control time can be determined on the planned thermal power output characteristic curve; similarly, if the dry season control time is known, the thermal power planned power corresponding to the dry season control time can be determined on the planned thermal power output characteristic curve.

[0151] As an implementation mode, the method of determining the planned hydropower power at the wet and dry season control time based on the historical dry year hydropower output characteristic curve includes: predicting the research dry year hydropower output characteristic curve in the research level year based on the historical dry year hydropower output characteristic curve and the hydropower planned installed capacity, and determining the planned hydropower power at the wet and dry season control time based on the research dry year hydropower output characteristic curve.

[0152] Among them, for the research dry year hydropower output characteristic curve predicted in the research level year based on the historical dry year hydropower output characteristic curve and the hydropower planned installed capacity, it includes: first determining the historical dry year, then obtaining the historical hydropower installed capacity and the historical dry year hydropower output characteristic curve of the historical dry year, each output on the historical dry year hydropower output characteristic curve corresponds to the same historical hydropower installed capacity, and each output corresponding to the hydropower planned installed capacity is calculated in equal proportion as the research dry year hydropower output characteristic curve. Take three points on the historical dry year hydropower output characteristic curve as an example (the historical dry year hydropower output characteristic curve corresponds to the historical hydropower installed capacity of Q4): point R (time 1, output 4), point Y (time 2, output 5), point U (time 3, output 6), it is known that the planned hydropower installed capacity is Q5, the corresponding output at time 1 is: (Q5*output 4) / Q4, the output at time 2 is: (Q5*output 5) / Q4, the output at time 3 is: (Q5*output 6) / Q4, and the corresponding three points are: point R'(time 1, (Q5*output 4) / Q4)), point Y'(time 2, (Q5*output 5) / Q4)), point U'(time 3, (Q5*output 6) / Q4)). In this way, the dry year hydropower output characteristic curve can be obtained.

[0153] Among them, for determining the hydropower planned power at the wet and dry season control time according to the studied hydropower output characteristic curve in the dry year, it includes: since each point on the studied hydropower output characteristic curve in the dry year is a time and the hydropower planned power, then when the studied hydropower output characteristic curve in the dry year is determined, if the wet season control time is known, the hydropower planned power corresponding to the wet season control time can be determined on the studied hydropower output characteristic curve in the dry year; similarly, if the dry season control time is known, the hydropower planned power corresponding to the dry season control time can be determined on the studied hydropower output characteristic curve in the dry year.

[0154] As an implementation mode, the method of determining the planned wind power electricity according to the historical wind power output characteristic curve, the planned wind power installed capacity and the preset wind power output ratio includes: predicting the planned wind power output characteristic curve in the research level year according to the historical wind power output characteristic curve and the planned wind power installed capacity, and determining the planned wind power electricity at the wet and dry period control moment according to the planned wind power output characteristic curve and the preset wind power output ratio.

[0155] Among them, taking a historical year as an example, each output on the historical wind power output characteristic curve corresponds to the same wind power historical installed capacity. Under equal proportion, each wind power output corresponding to the planned wind power installed capacity is calculated, and such calculation is performed for all historical years. The average value of the wind power output corresponding to all historical years at the same time is calculated to obtain the average wind power output corresponding to each moment as the planned wind power output characteristic curve. Take three points on the historical wind power output characteristic curve as an example (the historical installed capacity of wind power is Y1): point M (time 1, wind power output 1), point N (time 2, wind power output 2), and point K (time 3, wind power output 3). It is known that the planned installed capacity of wind power is Y2, and the corresponding wind power output at time 1 is: (Y2*wind power output 1) / Y1, the wind power output at time 2 is: (Y2*wind power output 2) / Y1, and the wind power output at time 3 is: (Y2*wind power output 3) / Y1, so the corresponding three points are: point M′(time 1, (Y2*wind power output 1) / Y1), point N′(time 2, (Y2*wind power output 2) / Y1), and point K′(time 3, (Y2*wind power output 3) / Y1. In this way, each wind power output corresponding to the planned installed capacity of wind power can be obtained.

[0156] The preset wind power output ratio refers to multiplying each wind power output on the planned wind power output characteristic curve by the preset wind power output ratio to obtain the corresponding planned wind power power, that is, not directly using all wind power outputs on the planned wind power output characteristic curve for power balance calculation, but according to the preset wind power output ratio, for example, 15%. The wind power output corresponding to the peak control moment on the planned wind power output characteristic curve is multiplied by the preset wind power output ratio to obtain the planned wind power power at the peak control moment, and the wind power output corresponding to the dry control moment on the planned wind power output characteristic curve is multiplied by the preset wind power output ratio to obtain the planned wind power power at the dry control moment.

[0157] As an implementation method, a planned load characteristic curve in the research level year is predicted based on a historical load characteristic curve, including: the historical load characteristic curve includes a historical annual load characteristic curve, a historical monthly load characteristic curve and a historical daily load characteristic curve. The planned daily load characteristic curve in the research level year is predicted by taking the historical daily load characteristic curve as an example. The planned annual load characteristic curve is the same as the planned monthly load characteristic curve, and will not be repeated here. The representative months of the wet and dry seasons are determined. For example, the representative month of the wet season is August, and the representative month of the dry season is December. Taking August as an example, for each historical year, the following calculation is performed: the daily load characteristic curve of each day in August is obtained (the horizontal axis is the time, such as 1-24 hours, the vertical axis is the daily load, and there are 31 daily load characteristic curves), the daily loads of these 31 daily load characteristic curves at the same time are calculated as the average, and the average daily load characteristic curve of August is obtained (the horizontal axis is the time, such as 1-24 hours, the vertical axis is the average daily load, and there is 1 average daily load characteristic curve for August). Such calculation is performed for each historical year, so each historical year corresponds to 1 average daily load characteristic curve in August. For each moment, the following calculation is performed: the average daily load of the historical year and the moment on the average daily load characteristic curve in August of the historical year is taken as a coordinate point, and a curve is fitted. Then, on this curve, the average daily load in August corresponding to the research level year at that moment can be determined. Thus, the average daily load in August corresponding to the research level year at each moment can be determined and used as the planned daily load characteristic curve for August.

[0158] As a preferred implementation, the load characteristics are analyzed according to the historical load characteristic curve to determine whether they meet the daily load characteristics. If they meet the requirements, they are used as the planned daily load characteristic curve for August. If they do not meet the requirements, they are adjusted using the daily load characteristics to obtain the planned daily load characteristic curve for August.

[0159] Among them, the load characteristics are analyzed according to the historical load characteristic curve, including annual load characteristics, monthly load characteristics and daily load characteristics. The purpose of the analysis is that the load characteristics in the historical year are similar to the research level year. Therefore, the load characteristics of the historical year can be used to adjust the predicted planning load characteristic curve, so that the predicted planning load characteristic curve is more accurate.

[0160] Specifically, the annual load characteristics are analyzed: First, the changes in power load in each month of the year are mainly affected by the natural growth of load and climate change during the year. Among them, climate (temperature and humidity) changes determine the amount of air conditioning turned on, which is the main factor; while the natural growth of load during the year is mainly affected by economic growth, which is a secondary factor. The target area is located in a humid climate, hot and humid in summer, and air conditioning is mainly used for cooling and cooling; in addition, there are no central heating facilities, and it is wet and cold in winter, so air conditioning is mostly used for heating. Therefore, the air conditioning startup volume in summer and winter is large, and the air conditioning load appears almost equally, accounting for about 1 / 3 of the maximum load. The double peak time occurs in August in summer and December in winter, and the ratio of the double peak is between 0.96 and 0.98. The temperature in spring and autumn is relatively suitable, and the air conditioning startup volume is greatly reduced. The load during this period is relatively low, and the minimum load month of the year generally occurs in February to April in the first half of the year. As the proportion of electricity consumption in the tertiary industry increases, the summer cooling and winter heating loads continue to increase, and the seasonal imbalance coefficient of the target area power grid generally shows a downward trend. Each historical year and the corresponding seasonal imbalance coefficient are used as a coordinate point to fit a curve, so that the seasonal imbalance coefficient corresponding to the research level year on the curve can be obtained. Similarly, the monthly imbalance coefficient can be predicted.

[0161] Then, the annual cooling load is analyzed: the annual maximum temperature, annual cooling load, annual maximum load, and cooling load ratio of each year are taken as a set of data. Multiple sets of data can be obtained from many years of history. It can be concluded that the cooling load ratio is increasing, which is related to the increase in air-conditioning load due to high temperature.

[0162] Specifically, the monthly load characteristics are analyzed: the annual maximum load occurs in August, and the maximum load of each month has been gradually increasing over the years. In January, the temperature is low, and the heating of offices, hotels, restaurants and residents is large, and the load remains at a high level; from February to May, the temperature warms up, and the climate is within a relatively comfortable range. The power load of industrial electricity, urban and rural residents' daily electricity, agricultural irrigation, etc. is also basically relatively stable, and the power load is at a relatively low level throughout the year. From June to September, the temperature rises, the summer is hot and humid, and air conditioners and other cooling equipment are turned on in large quantities, and the cooling load increases. According to calculations, the cooling load can be as high as 1 / 3 of the maximum load in continuous high temperature weather; at the same time, the water demand of crops in summer is large, and the agricultural drainage and irrigation load increases. From October to November, the temperature drops, and the power load of urban and rural residents' daily electricity, agricultural irrigation, etc. gradually drops; in December, the temperature continues to drop, the climate is cold and humid, and air conditioners and other heating and temperature equipment are turned on in large quantities; coupled with the rush of various production enterprises at the end of the year, the load of the provincial power grid has increased, forming the second peak of the year.

[0163] Specifically, the daily load characteristics are analyzed: first, the electricity consumption structure is analyzed to analyze the relationship between the annual average daily load rate and the annual average daily minimum load rate and the electricity consumption structure. Among them, the electricity consumption of the tertiary industry and residents' lives has grown rapidly, becoming the main force driving the electricity consumption of the whole society. Due to the impact of energy conservation and emission reduction and industrial structure transformation, the electricity consumption of the secondary industry has maintained a slowing growth rate, and the electricity consumption of the primary industry has maintained a low growth rate. The proportion of electricity consumption in the secondary industry is still high, which is an important factor driving the growth of electricity consumption. As the growth rate of electricity consumption in the secondary industry slows down and its proportion decreases, the annual average daily load rate and the annual average daily minimum load rate show a continuous downward trend, which is consistent with the trend of the change in the proportion of electricity consumption in the secondary industry. Take each historical year and the corresponding annual average daily load rate as a coordinate point, and fit a curve, so that the annual average daily load rate corresponding to the research level year on the curve can be predicted. Similarly, the annual average daily minimum load rate of the research level year can be predicted. Then analyze the typical daily load curve in summer and the typical daily load curve in winter. Among them, the typical daily load curve in summer: presents 2 peaks and 2 troughs. The load peak is 13:00-15:00 noon and 21:00-22:00 at night; the load trough is 7:00-8:00 in the morning and 19:00-20:00 in the evening. The typical daily load curve in winter: presents 1 peak, 1 flat section and 1 trough. The load peak is 11:00-12:00 noon, the flat section is 13:00-21:00, the load is kept at a high level during the flat section, and the load trough is 4:00-5:00 in the morning. The annual average daily load rate of typical days is distributed between 0.80-0.86. The annual average daily load rate and the annual average daily minimum load rate in summer are higher than those in winter, which is related to the hot summer and the large proportion of air conditioning load. Finally, the daily cooling load is analyzed: the cooling load curve of summer working days shows a "W" type change characteristic, and the cooling load accounts for a higher proportion at night. There are two troughs around 7:00-8:00 in the morning and 19:00-20:00 in the evening, and there are two peaks after 13:00-15:00 in the afternoon and 21:00-22:00 in the evening. This is closely related to the local load composition, meteorological changes and people's living habits. For urban residents, they have the habit of turning off the air conditioner and ventilating naturally after getting up around 7 o'clock in the morning, so the cooling load is relatively small; after 8 o'clock, as people start to go to work one after another, the cooling load gradually increases. The highest daily temperature in summer generally occurs between 13 o'clock and 15 o'clock, so the cooling load reaches its maximum value during this time period; while 18 o'clock to 20 o'clock is the peak time for getting off work, and the cooling load is relatively small. After 20:00, the amount of air conditioning used by residential users begins to increase, and the cooling load also increases accordingly. Especially after 21-22 o'clock, people gradually start to sleep and turn on the air conditioner to cool the room. The cooling load increases rapidly and reaches another peak around 24 o'clock. Then, as the temperature in the room decreases, the power absorbed by the air conditioner load from the power grid decreases, and the cooling load also gradually decreases.

[0164] Among them, determining the peak load at the wet season control time according to the planned load characteristic curve includes: the peak load at the wet season control time corresponding to the wet season control time on the planned load characteristic curve, and the peak load at the wet season control time corresponding to the dry season control time on the planned load characteristic curve.

[0165] Among them, power balance calculation is performed according to the planned external power, the thermal power planned power, the hydropower planned power, the wind power planned power and the peak load to obtain a power balance result, including: the planned external power, peak load and preset standby power are all power consumption (wherein, the standby capacity is determined, preferably, it can also include determining the maintenance capacity, for example, the preset standby power is 12% of the peak load, and the maintenance is arranged according to the preset arrangement), the thermal power planned power, the hydropower planned power and the wind power planned power are all power generation, and power balance is carried out between power consumption and power generation. If power consumption is greater than power generation to the third preset value, it is considered that power generation is insufficient and needs to be adjusted to ensure power consumption. If power generation is greater than power consumption to the fourth preset value, it is considered that power generation is in excess and needs to be adjusted to reduce power abandonment.

[0166] Preferably, the power balance calculation also includes: first predicting the maximum load of the research year in the research level year, and then performing the power balance calculation. As an implementation method, the sum of the planned power of thermal power, the planned power of hydropower, and the planned power of wind power is calculated, and the difference between the sum and the maximum load of the research year is calculated to obtain the power balance result. For predicting the maximum load of the research year in the research level year, it includes: (1) predicting the annual maximum load based on the trend extrapolation method. The trend extrapolation method takes time as the independent variable and the annual maximum load as the dependent variable. Each historical year and the corresponding historical year maximum load are used as a coordinate point to obtain a curve, and the corresponding research year maximum load of the research level year can be determined on the curve. (2) Based on the utilization hours method, the maximum load is predicted, and the utilization hours of the maximum load of the research level year are predicted according to the utilization hours of the historical year maximum load; specifically, each historical year and the corresponding historical year maximum load utilization hours are used as a coordinate point to obtain a curve, and the corresponding research year maximum load utilization hours of the research level year can be determined on the curve. Then, the maximum load of the research level year in the research level year is obtained according to the planned power consumption and the utilization hours of the maximum load of the research year. Specifically, the planned electricity consumption is divided by the number of hours of maximum load utilization in the study year to obtain the maximum load in the study year. (3) Based on the regression analysis method, the maximum load is predicted, with the gross domestic product as the independent variable and the annual maximum load as the dependent variable. Each historical gross domestic product and the corresponding historical annual maximum load are used as a coordinate point to obtain a curve. The gross domestic product of the study level year can be obtained based on the historical gross domestic product and the preset economic growth rate. On this curve, the maximum load of the study year corresponding to the gross domestic product of the study level year can be determined. The maximum load of the study year obtained by these three methods can be calculated as an average value, and this average value is used for power balance calculation. It is also possible to use any maximum load of the study year for power balance calculation, without any restriction here.

[0167] As another implementation method, since the maximum load duration of the study year is short, if the power is increased to meet the maximum load of the study year, a large amount of power will be abandoned at other times. Therefore, it is necessary to remove a part of the maximum load of the study year. The removed part is the peak load. The maximum load of the study year minus the peak load is used to replace the maximum load of the study year to participate in the power balance. Therefore, the most important thing is to predict the peak load. Specifically: first analyze the historical peak load duration and power, and then determine the preset duration range corresponding to each peak load percentage. Take each historical year and the corresponding historical duration as a coordinate point to obtain a curve, so that the duration corresponding to the study level year on the curve can be obtained, and the peak load percentage corresponding to the preset duration range where the duration is located is obtained, that is, the peak load percentage of the study level year. The peak load of the study level year is equal to the maximum load of the study year multiplied by the peak load percentage.

[0168] Preferably, it is also necessary to analyze the peak-valley difference. Specifically, historical peak-valley difference information is obtained, including the historical average peak-valley difference, the historical average peak-valley difference rate, and the historical maximum peak-valley difference rate, etc. The peak-valley difference information of the research level year is predicted based on the historical peak-valley difference information. Taking the historical average peak-valley difference in the historical peak-valley difference information as an example, each historical year and the corresponding historical average peak-valley difference are used as a coordinate point to fit a curve. The average peak-valley difference corresponding to the research level year can be obtained on the curve, and then the planned power of thermal power, hydropower and wind power can be used to determine whether the peak-shaving balance can be achieved.

[0169] Preferably, it is determined whether it is necessary to ensure the supply of electricity according to the result of the power balance. If so, the preset power supply guarantee is performed, and the power balance calculation is performed again until the power balance result does not require the supply of electricity, wherein the preset power supply guarantee includes at least one of increasing the installed capacity of thermal power, introducing external power, reducing external transmission, and reducing standby. Among them, if the power consumption is greater than the power generation to the first preset value, it is considered that the power generation is insufficient and needs to be adjusted to ensure the power consumption. As an implementation method, measure one: for example, increasing the installed capacity of thermal power by 4 to 5 million kilowatts can ensure the basic balance of electricity during the dry season. Measure two: large hydropower increases the power reserve to reduce external transmission: for example, a large power station is required to reserve 10 billion kilowatt-hours of electricity during the dry season. In comparison with the output curve of the power station and the corresponding DC external transmission curve, only 4.3 billion kilowatt-hours of power are actually considered to be reserved by the power station. According to the 10 billion kilowatt-hours of power reserve, the two DC power stations of the power station need to send 5.7 billion kilowatt-hours less during the dry season. In this case, the annual power shortage during the dry season is 7.7-14 billion kilowatt-hours.

[0170] Preferably, it is determined whether power supply guarantee is needed according to the power balance result. If so, a preset power supply guarantee is performed, and the power balance calculation is performed again until the power balance result does not require power supply guarantee, wherein the preset power supply guarantee includes at least one of increasing thermal power installed capacity, introducing external power, reducing external transmission, and reducing standby. Among them, if the power consumption is greater than the power generation to the third preset value, it is considered that the power generation is insufficient and needs to be adjusted to ensure power consumption. As an implementation method, first, the system reserve is selected as 12%, and the reserve capacity is appropriately reduced during peak hours. Third, without considering the addition of new units, the external transmission channels are considered to be 90% external, rather than 100% external. Combining the above measures, the power supply guarantee during the peak period of the flood season can be guaranteed under the basic plan. Under the high plan, it is necessary to further reduce the standby or external transmission.

[0171] like Figure 2 The figure shows an example of the supply and demand analysis process of the power grid in a region with a very high proportion of hydropower and wind power.

[0172] Step 1: Determine the research horizon year (e.g. 2021-2025); Step 2: Analyze the annual total electricity consumption of provinces in the region (e.g. Figure 5 , from 2016 to 2020, the total electricity consumption in Province A has grown steadily and rapidly, from 199.2 billion kWh in 2015 to 263.6 billion kWh in 2019, with an average annual growth rate of 7.25%. Among the total electricity consumption in society, the fastest growing are tertiary industry electricity consumption and residential electricity consumption, reaching 46.2 billion kWh and 48.4 billion kWh in 2019, with an average annual growth rate of 13.5% and 9.2%, respectively. The rest are: secondary industry electricity consumption 167.7 billion kWh, an average annual growth rate of 5.4%; primary industry electricity consumption 1.3 billion kWh, an average annual growth rate of 1.5%. From the growth trend, the overall growth rate of electricity consumption has shown a "V"-shaped change trend since 2010, and a "double-digit" high-speed growth from 2010 to 2011. Since 2012, affected by factors such as industrial structure adjustment, downward industrial production, energy conservation and emission reduction, and temperature, electricity consumption has been moving forward at a low level, and the growth rate of electricity in 2015 was as low as negative growth. In 2016 and 2017, the economy rebounded, and electricity consumption rebounded, maintaining a medium-speed growth of more than 5%. In 2018 and 2019, stimulated by the recovery of industrial electricity consumption, electricity consumption resumed a relatively high growth rate, with the growth rate reaching double digits again in 2018 and a year-on-year growth of 7.2% in 2019); Step 3: Analyze the electricity consumption structure of provinces in the region (in recent years, the electricity consumption of the tertiary industry and residents has grown rapidly, becoming the main force driving the electricity consumption of the whole society, the electricity consumption of the secondary industry has maintained a slowing growth rate, and the electricity consumption of the primary industry has maintained a relatively low growth rate. Figure 6 As shown in Table 1, the electricity consumption structure was adjusted from 0.6:68.3:14.0:17.1 in 2015 to 0.5:63.6:17.5:18.4 in 2019. The proportion of the primary industry decreased by 0.1 percentage point, the proportion of the secondary industry decreased by 4.7 percentage points, the proportion of the tertiary industry increased by 3.5 percentage points, and the electricity consumption of residents increased by 1.3 percentage points. From the perspective of electricity consumption structure, the proportion of electricity consumption in the secondary industry is still relatively high, which is an important factor driving the growth of electricity consumption. The electricity consumption and growth rate of Province A are shown in Table 1, unit: billion kWh);

[0173] Table 1:

[0174]

[0175]

[0176] Step 4: Analyze the per capita electricity consumption of provinces in the region (such as Figure 7 and Figure 8As shown, in 2000, the per capita domestic electricity consumption in Province A was only 128.7 kWh. In 2007, the per capita domestic electricity consumption exceeded 200 kWh for the first time. By 2011, the per capita domestic electricity consumption in Province A exceeded 300 kWh, and it reached 579 kWh in 2019. However, there is still a large gap between the current per capita domestic electricity consumption in Province A and the national average. In 2019, the national per capita domestic electricity consumption was 694 kWh, which was 1.2 times the per capita electricity consumption in Province A. The per capita electricity consumption in Province A has increased significantly. In 2000, the per capita electricity consumption was only 657.6 kWh. In 2010, the per capita electricity consumption was 1909.3 kWh. In 2019, it reached 2897 kWh, which is also a certain gap compared with the national per capita electricity consumption); Step 5: Analyze the electricity consumption per unit output value of provinces in the region (such as Fig. 9 As shown, the electricity consumption per unit of GDP has generally been on a downward trend since 2000. In 2000, the electricity consumption per 10,000 yuan of GDP was 933.7 kWh, in 2010 it was 859.5 kWh, and in 2019 it dropped to 565.4 kWh per 10,000 yuan of GDP. From 2001 to 2005, high-energy-consuming industries developed rapidly, and GDP electricity consumption increased by an average of 1.3% per year; from 2006 to 2010, the province's economy developed in the direction of green GDP, with an average annual decrease of 2.9%; from 2011 to 2015, capacity reduction measures were further intensified, and high-energy-consuming industries such as steel were further compressed, with an average annual decrease of 4.3%; from 2016 to 2020, electricity consumption per 10,000 yuan of GDP has decreased by 3.9% per year); Step 6: Analyze the maximum social load of the provinces in the region (from 2010 to 2019, the maximum social load continued to grow rapidly, with an average annual increase of 6.9%; the change curve of the maximum social load of the province from 2010 to 2019 is as follows Fig.10 Among them, from 2016 to 2020, the maximum load of the province has increased from 37.3 million kilowatts in 2015 to 49.35 million kilowatts in 2019, with an average annual growth of 7.2%, and an average annual increase of more than 3 million kilowatts); Step 7: Analyze the maximum load of the provinces in the region (from 2010 to 2019, the growth rate of the maximum power load of the power grid in Province A has been at a high level, with an average annual growth of 7.91%. The change curve of the maximum load of the province from 2010 to 2019 is shown in Fig.11As shown. Among them, from 2016 to 2020, the maximum load of the unified dispatching increased from 28.26 million kilowatts in 2015 to 41.54 million kilowatts in 2019, with an average annual growth of 10.1%, and an average annual increase of more than 3.3 million kilowatts); Step 8: Analyze the annual, monthly, and daily load characteristics of the provinces in the region ((1) Annual load characteristics: The changes in power load in each month of the year are mainly affected by the natural growth of load and climate change during the year. Among them, climate (temperature and humidity) changes determine the amount of air conditioning turned on, which is the main factor; while the natural growth of load during the year is mainly affected by economic growth, which is a secondary factor. From 2008 to 2019, the monthly maximum load of the power grid in Province A is as follows Fig.12 As shown. Province A is located in the south of the Qinling Mountains. It has a humid climate and is hot and muggy in summer. Air conditioning is mainly used for cooling and cooling. In addition, there are no central heating facilities in the province. It is cold and damp in winter, so air conditioning is mostly used for heating. Therefore, the air conditioning startup volume is large in summer and winter, and the air conditioning load is almost the same, accounting for about 1 / 3 of the maximum load. The double peak time occurs in August to September in summer and November to December in winter. The ratio of the two peaks is between 0.96 and 0.98. The temperature in spring and autumn is more suitable, and the air conditioning startup volume is greatly reduced. The load during this period is relatively low. The minimum load month of the year generally occurs in February to April in the first half of the year. (2) As Fig.13 As shown, monthly load characteristics: the maximum load occurs in August, and has been gradually increasing in the past three years. (3) Daily load characteristics: The annual average daily load rate and the annual average daily minimum load rate show a continuous downward trend, which is consistent with the changing trend of the proportion of electricity consumption in the secondary industry. Since 2010-2019, the growth rate of electricity consumption in the secondary industry has slowed down, and its proportion has decreased. The annual average daily load rate γ has decreased from 87.0% in 2010 to 86.8% in 2019, and the annual average daily minimum load rate β has decreased from 74.2% in 2010 to 69.9% in 2019); Step 9: Analyze the peak-to-valley difference of provinces in the region (with the annual increase in electricity consumption in the tertiary industry and residents, the peak-to-valley difference of the unified power grid in Province A has increased year by year. From 2006 to 2010, the average peak-to-valley difference rate was due to the proportion of industrial electricity consumption. From 2004 to 2019, the average peak-to-valley difference rate and the maximum peak-to-valley difference rate increased. From 2008 to 2009, some industrial loads stopped production, and the average peak-to-valley difference rate and the maximum peak-to-valley difference rate also increased. From 2010 to 2011, industrial electricity consumption resumed, and the maximum peak-to-valley difference rate and the average peak-to-valley difference rate declined to a certain extent. Since 2012, the growth of electricity consumption in the secondary industry has been weak, while the electricity consumption in the tertiary industry has grown against the trend. The growth rate of residential electricity consumption has accelerated, and the average peak-to-valley difference rate has continued to increase. The trend of the average peak-to-valley difference, the maximum peak-to-valley difference rate, and the average peak-to-valley difference rate from 2004 to 2019 is as follows: Fig.14 Step 10: Analyze the annual maximum load utilization hours of the provinces in the region (as shown in Fig.15As shown in the data, the proportion of electricity consumption in the secondary industry changes synchronously with the annual maximum load utilization hours. Since 2011-2015, as the proportion of electricity consumption in the secondary industry has decreased year by year, the daily load rate, monthly imbalance coefficient and seasonal imbalance coefficient have decreased year by year, and the annual maximum load utilization hours have decreased year by year. In 2019, the maximum load utilization hours of the power grid in Province A was 5374 hours, a decrease of 642 hours year-on-year); Step 11: Analyze the peak load duration and power consumption of the provinces in the region year by year (the peak load and power consumption are shown in Table 2. In 2015, affected by the cool summer, the load curve was relatively flat, the peak load lasted for a long time, and the power consumption was high; from 2016 to 2018, the peak load and peak power consumption increased year by year, and the maximum load showed an increasing trend year by year. In 2019, affected by the cool summer, the growth rates of load and power consumption were relatively low, but the maximum load still hit a record high, the maximum load duration was short, and the peak power consumption was small. The 90% and 95% peak loads and power consumption decreased significantly compared with 2018, and the 97% peak load still showed an increasing trend. The units are 10,000 kWh, 10,000 kW, hours, and days respectively);

[0177] Table 2:

[0178]

[0179]

[0180] Step 12: Analyze the daily load curves of provinces in the region (such as Fig.16 As shown in the figure, it is a typical daily load curve in summer, showing two peaks and two troughs. The load peak is from 13:00 to 15:00 at noon and from 21:00 to 22:00 at night; the load trough is from 7:00 to 8:00 in the morning and from 19:00 to 20:00 at night. Fig.17 As shown, it is a typical daily load curve in winter, showing 1 peak, 1 flat section, and 1 trough. The load peak is from 11:00 to 12:00 noon, and the flat section is from 13:00 to 21:00. The load remains at a high level during the flat section, and the load trough is from 4:00 to 5:00 in the morning. The typical daily load indicators in summer and winter of the unified dispatching caliber from 2015 to 2019 are shown in Table 3. The daily load rates on typical days are distributed between 0.80 and 0.86, and the average daily load rate and the daily minimum load rate in summer are higher than those in winter. The average daily load rate in summer is about 0.84, and the minimum daily load rate is about 0.62, which are about 2 to 5 percentage points higher than that in winter, which is related to the hot summer in Chongqing and the large proportion of air-conditioning load. Unit: 10,000 kilowatts);

[0181] Table 3:

[0182]

[0183] Step 13: Analyze the annual cooling load curves of the provinces in the region (such as Fig.18The figure shows the annual cooling load ratio from 2012 to 2019, and the summer cooling load from 2012 to 2019 is shown in Table 4. In 2015, the maximum temperature was relatively low, and the cooling load accounted for 40%; from 2016 to 2018, the cooling load accounted for about 55%. In 2019, due to the influence of weather, the cooling load accounted for 52%. From 2016 to 2020, the proportion of cooling load increased significantly, and the impact of cooling load on the load of the entire network became more and more obvious. Unit: ℃, 10,000 kW, %);

[0184] Table 4:

[0185] years Annual maximum temperature Cooling load Annual maximum load Cooling load ratio Other loads 2012 40 495.71 1149.7 43 653.98 2013 40 605.28 1385.4 44 780.09 2014 40 653.48 1444.9 45 791.37 2015 38 519.48 1301.8 40 782.36 2016 40 1010.70 1798.4 56 787.69 2017 40 1032.10 1925.7 54 893.59 2018 40 1121.60 2042.1 55 920.62 2019 39 1117.00 2131.4 52 1014.36

[0186] Step 14: Analyze the daily cooling load curves of provinces in the region (such as Figure 19-21 As shown in the figure, the cooling load curve on summer working days shows a "W"-shaped change characteristic, and the cooling load accounts for a higher proportion at night. There are two troughs at around 7-8 in the morning and 19-20 in the evening, and two peaks at 13-15 in the afternoon and after 21-22 in the evening. This is closely related to the local load composition, meteorological changes, and people's living habits. For urban residents, they have the habit of turning off the air conditioner and natural ventilation after getting up around 7 in the morning, so the cooling load is relatively small; after 8 o'clock, as people start to go to work one after another, the cooling load gradually increases. The highest daily temperature in summer generally occurs between 13 and 15 o'clock, so the cooling load reaches the maximum value during this time period; while 18-20 o'clock is the peak time for getting off work, the cooling load is relatively small, and after 20:00, the amount of air conditioning used by residential users begins to increase, and the cooling load also rises accordingly. Especially after 21-22 o'clock, people gradually start to go to bed and turn on the air conditioner to cool the room. The cooling load increases rapidly and reaches another peak at around 24 o'clock. Then, as the temperature in the room decreases, the power absorbed by the air conditioning load from the power grid decreases, and the cooling load gradually decreases. The cooling load has a greater impact on the characteristics of the unified load. Compared with the benchmark load curve, the cooling load curve has an obvious downward process around 19-20 o'clock, and the unified load curve also maintains the same change trend as the cooling load curve); Step 15: Are there any provinces that have not been analyzed in the region; if not, proceed to step 16, if so, jump to step 2, and continue to execute steps 2 to 15 until there are no provinces that have not been analyzed in the region, and then proceed to step 16: Analyze the total installed power capacity and structure of the provinces in the region (such as Fig. 22As shown in the figure, by the end of 2019, the installed capacity of power generation in Province A was 99.29 million kilowatts, an increase of 12.56 million kilowatts from 2015. Hydropower installed capacity reached 78.46 million kilowatts, accounting for 79.0%; thermal power added biomass power generation capacity, and shut down a number of small high-coal-consuming thermal power units, with an installed capacity of 15.70 million kilowatts, accounting for 15.8%; with the development of new energy, wind power and photovoltaic power generation in Province A have been built from scratch, with installed capacities of 3.25 million kilowatts and 1.88 million kilowatts, accounting for 3.3% and 1.9% respectively. The power structure continues to be optimized, and the green proportion has increased. The installed capacity of clean energy power generation has reached 83.59 million kilowatts, ranking first in the country, accounting for 84.2% of the total installed capacity, an increase of 2.9 percentage points from 2015); Step 17: Analyze the full-caliber power generation of provinces in the region (such as Fig.23As shown in the data, the total power generation in 2019 was 390.3 billion kWh, an increase of nearly 700 kWh compared with 2015. Among them, hydropower was 331.6 billion kWh, accounting for 85.0% of the power generation; thermal power was 48.8 billion kWh, accounting for 12.5%; wind power and photovoltaic power generation were 7.13 billion kWh and 2.82 billion kWh respectively. Non-fossil energy power generation has exceeded 341.5 billion kWh, accounting for 87.5%, an increase of 0.9 percentage points from 2015); Step 18: Analyze the utilization hours of power generation equipment in the provinces in the region (in 2019, the average utilization hours of power generation equipment was 3954 hours, an increase of 95 hours year-on-year. Among them, the utilization hours of hydropower were 4235 hours, an increase of 49 hours year-on-year; the utilization hours of thermal power were 3086 hours, an increase of 362 hours year-on-year; the utilization hours of wind power were 2553 hours, an increase of 220 hours year-on-year; the utilization hours of solar energy were 1524 hours, a decrease of 83 hours year-on-year); Step 19: Analyze the water supply situation in the provinces in the region And wind conditions (In 2019, the average water flow of the whole network was 8.5% less than the same period last year, and 13% more than the same period in many years. In terms of quarters: In the first quarter, the water flow of each basin was relatively abundant, and the average water flow was 2.3% less than the same period last year, and 2.5% more than the same period in many years; In the second quarter, the average water flow of each basin was 7.2% less than the same period last year, and 24% more than the same period in many years; In the third quarter, the water flow of each basin was relatively abundant, and the average water flow was 12.1% less than the same period last year, and 15.4% more than the same period in many years; The average water flow in the fourth quarter was 8.4% more than the same period in many years, and 3% less than the same period in the same period last year. In terms of months, the water flow of the whole network in January was more than the same period in many years. In February, the water supply of the whole network was 4.5% higher than the level of the same period in many years, and 2.7% higher than the same period last year. In March, the water supply of the whole network increased by 5.3% over the same period in many years, and was 2.2% lower than the same period last year. In April, the water supply of the whole network was 20.6% higher than the level of the same period in many years, and was 0.6% lower than the level of the same period last year. In May, the water supply of the whole network was 58.5% higher than the level of the same period in many years, and was 13.9% higher than the level of the same period last year. In June, the water supply of the whole network was 9.4% higher than the level of the same period in many years, and was 18.5% lower than the level of the same period last year. In July, the average water supply of the whole network was 15.9% higher than the level of the same period in many years, and was 2.2% lower than the same period last year. 6.3%; in August, the average water inflow of the whole network was 1% higher than the same period of many years, and 13% lower than the same period of last year; in September, the average water inflow of the whole network was 36.6% higher than the same period of many years, and 31.3% higher than the same period of last year; in October, the average water inflow of the whole network was 8.9% higher than the same period of many years, and 9.3% lower than the same period of last year; in November, the average water inflow of the whole network was 14.5% higher than the same period of many years, and 10.1% higher than the same period of last year; in December, the average water inflow of the whole network was 0.1% lower than the same period of many years, and 2.1% lower than the same period of last year. According to the wind resource monitoring data, the wind situation in the province in that year was analyzed. According to the irradiation resource monitoring data, the irradiance of photovoltaic power generation in the province in that year was analyzed.According to the wind resource monitoring data, analyze the wind conditions in the province in that year); Step 20: Analyze the supply and consumption of power coal in the provinces in the region (in 2019, the total coal intake of the whole network was 16.64 million tons, with an average daily coal intake of 45,600 tons, an increase of 5.6% year-on-year; the total coal consumption was 16.31 million tons, with an average daily coal consumption of 44,700 tons, an increase of 6.7% year-on-year. As of the end of 2019, the whole network had a coal reserve of 3.4 million tons, an increase of 330,000 tons year-on-year, and the number of days the coal reserve in the whole network can be used is about 51 days (calculated on a single machine)); Step 21: Are there any provinces that have not been analyzed in the region; if not, proceed to step 22, if so, jump to step 16, and continue to execute steps 16 to 21 until there are no provinces that have not been analyzed in the region, and then proceed to step 22: Analyze the cross-provincial and cross-regional trading electricity of provinces in the region (such as. Fig.24As shown in the figure, during the flood season in 2019, the maximum inter-provincial power transmission capacity of the power grid in Province A continued to rise. In 2014, the power transmission exceeded 100 billion kWh, and in 2019, the power transmission reached 132.4 billion kWh, accounting for 35.4% of the total power generation. From 2016 to 2020, the average annual power transmission reached 133.3 billion kWh); Step 23: Analyze the changing trend of power supply and demand in the provinces in the region (from 2011 to 2015, the installed capacity of clean energy in Province A grew rapidly, with the installed capacity of power sources growing by 43.5 million kilowatts (of which hydropower grew by 38.69 million kilowatts), while the load grew by only 10.3 million kilowatts during the same period, and the transmission capacity grew by about 15.2 million kilowatts, exceeding the growth of power consumption within the province and the power transmission. The supply of electricity exceeds demand, which also caused a large amount of hydropower abandonment during the peak-shaving period from 2011 to 2015 to 2016 to 2020. Since 2016-2020, affected by the supply and demand situation and the guidance of relevant provincial policies, the development of power generation capacity has slowed down significantly, with 12.5 million kilowatts of new power generation capacity (9.07 million kilowatts of which are hydropower), while the load growth has accelerated during the same period, with an additional load of about 12.05 million kilowatts and an increase in transmission capacity of about 2 million kilowatts. At the same time, the construction of 500 kV hydropower transmission channels in the province has been accelerated, and the construction of ultra-high voltage direct current transmission projects has started. The problem of hydropower consumption has been gradually alleviated, and the amount of hydropower abandonment has decreased year by year. In 2019, the new installed capacity of the power grid slowed down, and the electricity load increased, especially in During the peak winter and summer periods, the power supply was tight. The total grid power consumption for the whole year was 205.68 billion kWh, an increase of 8.66% year-on-year, of which the first quarter increased by 9.53% year-on-year, the second quarter increased by 12.65% year-on-year, the third quarter increased by 6.52% year-on-year, and the fourth quarter increased by 6.51% year-on-year. There was no peak avoidance throughout the year. In terms of quarters, the first quarter was in the dry season, and the company achieved full acquisition of clean energy such as hydropower, wind power and photovoltaics. In the second quarter, the power load maintained a rapid growth trend, and the online power of the generator set increased significantly. In the third quarter, the overall supply and demand of electricity was balanced, but the power supply was tight during peak hours, and the high temperature weather lasted for a short time. In the fourth quarter, due to the increase in demand for bulk off-grid load and air Modulated heat increases, electricity load maintains a high growth level, water and coal storage is in good condition, and electricity supply and demand are balanced); Step 24: Are there any provinces in the region that have not been analyzed? If not, proceed to Step 25. If so, jump to Step 22 and continue to execute Steps 22 to 24 until there are no provinces in the region that have not been analyzed. Then proceed to Step 25: Analyze the economic policy environment and industrial structure of the provinces in the region; Step 26: Analyze the development of key industries in the provinces in the region; Step 27: Analyze energy conservation, emission reduction and electricity substitution in the provinces in the region; Step 28: Carry out economic growth forecasts for the provinces in the region (the average annual growth rate of the gross domestic product (GDP) of Province A from 2016 to 2020 is above 7%.In 2016, the GDP growth rate of Province A was 7.7%. In 2017, the total GDP reached 3698.02 billion yuan, with a growth rate of 8.1%. In 2018, the total GDP reached 4067.81 billion yuan, with a growth rate of 8.0%. In 2019, the total GDP reached 4661.58 billion yuan, with a growth rate of 7.5%. The annual GDP forecasts for 2020, 2022 and 2025 are shown in Table 5. Unit: 100 million yuan);.

[0187] Table 5:

[0188]

[0189] Step 29: Use the power elasticity coefficient method to predict electricity consumption (the power elasticity coefficient is an important indicator reflecting the development of the national economy and the growth of electricity demand. Its relationship is the ratio of the growth rate of electricity consumption to the growth rate of the national economy. Table 6 shows the power elasticity coefficient and the power consumption forecast value from 2020 to 2025. As shown in Table 6, except for 2001-2005, the average power elasticity coefficient in the remaining periods is less than 1. The power elasticity coefficient for energy saving and consumption reduction in 2006-2010 dropped to 0.76, which is significantly lower than that in 2001-2005. Since 2011-2015, the economic growth rate has shifted from high speed to medium-high speed growth mode, the secondary industry dominated by high energy consumption has weak growth, and the power consumption has continued to be sluggish, and the elasticity coefficient has dropped to 0.59. Unit: 100 million yuan, 100 million kWh. According to the economic forecast and the power elasticity coefficient method, the total social power consumption in 2020 and 2025 will reach 277.8 billion and 344.6 billion kWh respectively);

[0190] Table 6:

[0191]

[0192] Step 30: Use the output value unit consumption method to predict electricity consumption (as shown in Table 7, which is the historical data of unit GDP electricity consumption and the predicted value of electricity from 2020 to 2025. Since 2016-2020, the unit consumption of output value in Province A has maintained a gradual downward trend. According to the output value electricity consumption method, the total social electricity consumption in 2020 and 2025 will reach 274.9 billion kWh and 354 billion kWh respectively. Unit: 100 million yuan, 100 million kWh, 100 million kWh / 10,000 yuan, tons of standard coal / 10,000 yuan);

[0193] Table 7:

[0194]

[0195]

[0196] Step 31: Use the trend extrapolation method to predict electricity consumption (time is the independent variable, annual electricity consumption is the dependent variable, and a certain mathematical model is selected based on historical data to express the changing trend of time and electricity consumption. According to the historical electricity consumption of Province A, the following formula is used for prediction: Quadratic fitting curve: y = at 2 +bt+c; quadratic logarithmic fitting curve: y=αln 2 (t)+βln(t)+γ;

[0197] Among them, t is the year, y is the electricity consumption, a, b, c and α, β, γ are the parameters to be determined for the model. As shown in Table 8, for the fitting accuracy of the trend extrapolation method, 1995-2019 and 2000-2019 are taken as the input original data for curve fitting. Among them, the determination coefficients of models 1 and 2 are lower than those of models 3 and 4, indicating that the fitting accuracy of models 1 and 2 for the original data is relatively low.

[0198] Table 8:

[0199]

[0200] In order to evaluate the forecasting model, the trend extrapolation method is used to make a virtual forecast for 2016. The forecast deviations of models 1 and 2 are also large because the older the data is, the less predictive value it has for future trends. In summary, in order to improve the forecasting accuracy, the data from 2000 to 2019 are used as the original data to forecast the future. The average value of models 3-4 is used as the forecast value of the trend extrapolation method, that is, the total social electricity consumption in 2020, 2022 and 2025 will reach 275 billion, 310.5 billion and 367.8 billion kWh respectively; Step 32: Use regression analysis to forecast electricity consumption (with regional GDP as the independent variable and annual electricity consumption as the dependent variable, and according to historical electricity consumption and economic development trends, the following formula is used for forecasting: First-order regression fitting curve: y=gx+f; Second-order logarithmic fitting curve: y=αln 2 (x)+βln(x)+γ; cubic logarithmic fitting curve: y=aln 3 (x)+bln 2 (x)+cln(x)+d;

[0201] Among them, x is the regional GDP, y is the electricity consumption, a, b, c, d, f, g and α, β, γ are the parameters to be determined for the model. Table 9 shows the prediction fitting accuracy of the regression method, taking 2010-2019 and 2000-2019 as the input original data for fitting. The determination coefficient R2 of each model exceeds 0.99, indicating that the model has a high degree of fitting to the historical data.

[0202] Table 9:

[0203]

[0204] As shown in Table 10, for the virtual prediction verification of the regression method, the trend extrapolation method was used to make a virtual prediction for 2017 in order to evaluate the prediction model. The prediction deviation of model 10 was the largest.

[0205] Table 10:

[0206]

[0207] In summary, in order to improve the prediction accuracy, models 8 and 9 are used to predict the future. The average values ​​of models 8, 9 and models 11 and 12 are used as the prediction values ​​of the regression method, that is, the total social electricity consumption in 2020 and 2025 will reach 2680 billion kWh and 351 billion kWh respectively); Step 33: Use the per capita electricity consumption method to predict electricity consumption (it is expected that the annual average growth rate of the permanent population from 2016 to 2020 will be 0.25%, as shown in Table 11, which is the historical data of per capita electricity consumption and the electricity forecast value from 2020 to 2025. According to the per capita electricity consumption method, the total social electricity consumption in 2020 and 2025 will reach 274.7 billion kWh and 345.1 billion kWh respectively, unit: billion kWh, ten thousand people);

[0208] Table 11:

[0209]

[0210]

[0211] Step 34: whether it is necessary to eliminate the results with large prediction deviations (calculate the average value of the predicted electricity consumption of each method, calculate the deviation of each prediction result from the average value, and when the deviation is greater than the set threshold, eliminate the prediction result. The threshold is set to 10% here); if not, jump to step 36, if yes, proceed to step 35: eliminate the results with large prediction deviations, recalculate the mean, until there is no need to eliminate the results with large prediction deviations, and then proceed to step 36: obtain the predicted value of total social electricity consumption (Table 12 is the predicted value of total social electricity consumption from 2020 to 2025. Combining the prediction results of various methods, it is estimated that the electricity consumption in 2020 will reach 273 billion kWh, and the electricity consumption in 2025 will reach 348 billion kWh. The high plan considers that the electricity consumption will reach 360 billion kWh, unit: billion kWh);

[0212] Table 12:

[0213]

[0214] Step 37: Are there any provinces that have not been analyzed in the region? If not, proceed to step 38. If yes, jump to step 25 and continue to execute steps 25 to 37 until there are no provinces that have not been analyzed in the region. Then proceed to step 38: Use trend extrapolation method to predict the maximum load (trend extrapolation method uses time as the independent variable and annual maximum load as the dependent variable. According to historical data, a certain mathematical model is selected to express the changing trend of time and load. According to the historical electricity load of Province A, the following formula is used for prediction);

[0215] First-order fitting curve: y=gt+f; second-order fitting curve: y=at 2 +bt+c; first-order logarithmic fitting curve: y=kln(t)+h; second-order logarithmic fitting curve: y=αln 2 (t)+βln(t)+γ;

[0216] Among them: t is the year, y is the maximum load of each year, a, b, c, g, f, k, h and α, β, γ are the unknown parameters of the model.

[0217] As shown in Table 13, for the trend extrapolation method prediction fitting accuracy, 2000-2019 and 2005-2019 were taken as input raw data for curve fitting. The determination coefficient R2 of each model exceeded 0.982, indicating that the model has a high degree of fitting to the historical data.

[0218] Table 13:

[0219]

[0220]

[0221] As shown in Table 14, for the verification of virtual prediction using the trend extrapolation method, in order to evaluate the prediction model, the trend extrapolation method was used to make virtual predictions for 2017. The prediction deviations of models 14 and 16 were smaller than those of other models, that is, the prediction errors of the predictions using the one-time and one-time logarithmic models were smaller.

[0222] Table 14:

[0223]

[0224] In summary, in order to improve the prediction accuracy, models 14 and 16 are used to predict the future. That is, according to the trend extrapolation method, the maximum load in 2020 and 2025 will reach 50.13 and 67.37 million kilowatts respectively. Step 39: Use the utilization hours method to predict the maximum load (as shown in Table 15, the utilization hours method is used to predict the maximum load from 2020 to 2025. The load utilization hours are mainly determined by factors such as electricity consumption structure, orderly electricity consumption measures and air-conditioning load. The proportion of electricity consumption in the secondary industry is positively correlated with the utilization hours. From 2001 to 2005, the industry in Province A developed rapidly, the proportion of electricity consumption in the secondary industry continued to increase, and the load utilization hours continued to rise. From 2006 to 2010, the growth rate of electricity consumption in the tertiary industry and residents was relatively fast, and the proportion of electricity consumption in the secondary industry in the total social electricity consumption increased from 2005 to 2010. It has dropped from 76.1% in 2010 to 73.7% in 2010, and the maximum load utilization hours have been declining year by year. With the decrease in the proportion of electricity consumption in the secondary industry, the load utilization hours will continue to decline. The growth rate of electricity consumption in the secondary industry will maintain a low growth trend, and the proportion of electricity consumption in the tertiary industry and residents will increase. It is expected that the utilization hours will drop to about 5,300 in 2020 and about 5,250 in 2025. According to the load utilization hours method, the maximum loads in 2020 and 2025 will reach 51.51 million kilowatts and 66.29 million kilowatts respectively. Unit: 10,000 kilowatts, hours);

[0225] Table 15:

[0226]

[0227]

[0228] Step 40: Use regression analysis to predict the maximum load (with regional GDP as the independent variable and annual maximum load as the dependent variable, and use the following formula to predict based on historical load and economic development trends); first-order regression fitting curve: y = gx + f; second-order logarithmic fitting curve: y = αln 2 (x)+βln(x)+γ; cubic logarithmic fitting curve: y=aln 3 (x)+bln 2 (x)+cln(x)+dwhere: x is the GDP value of each year, y is the maximum load of each year, a, b, c, d, g, f, and α, β, γ are the unknown parameters of the model.

[0229] Taking 2010-2018 and 2000-2018 as input raw data for fitting, the determination coefficients R2 of models 23-26 are all over 0.99, indicating that the model has a high degree of fit to the historical data. The determination coefficient of model 22 is 0.98, which is relatively low. As shown in Table 16, the maximum load in 2020-2025 is predicted by regression method.

[0230] Table 16:

[0231]

[0232] As shown in Table 17, the regression method is used for virtual prediction. To evaluate the prediction model, the trend extrapolation method is used to make a virtual prediction for 2017. The prediction deviation of model 22 is the largest.

[0233] Table 17:

[0234]

[0235] In summary, in order to improve the prediction accuracy, models 23-26 are used to predict the future. According to the regression method, the maximum loads in 2020 and 2025 will reach 52.34 million kilowatts and 68.47 million kilowatts respectively. Step 41: Combining various methods to obtain the final prediction results (combining the prediction results of various methods, it is expected that the power load will reach 51 million kilowatts in 2020 and 67 million kilowatts in 2025. Similarly, consider the high scenario of 70 million kilowatts in 2025. As shown in Table 18, it is the predicted value of the maximum power load from 2020 to 2025);

[0236] Table 18:

[0237]

[0238] Taking into account the uncertainties of economy and electricity consumption, combined with the forecast plan proposed by Province A’s energy department from 2016 to 2020, the electricity demand forecast plan for 2020-2025 is formulated as shown in Table 19, unit: billion kWh, ten thousand kilowatts.

[0239] Table 19:

[0240]

[0241] Step 42: Analyze the annual, monthly, and daily load characteristics of the provinces in the region (1) Annual load characteristics: According to historical data, the annual power load of the power grid in Province A is obviously affected by climate change and shows a certain regularity. The unified load shows a double peak in August in summer and December in winter. The specific situation is: in January, the temperature is low, and the heating of offices, hotels, restaurants and residents is large, and the load remains at a high level; from February to May, the temperature rises, and the climate is within a relatively comfortable range. The power load of industrial electricity, urban and rural residents’ living electricity, agricultural irrigation, etc. also basically remains relatively stable, and the power load is at a relatively low level throughout the year. From June to September, the weather is hot. Temperatures rise, summers are hot and humid, air conditioners and other cooling equipment are turned on in large quantities, and the cooling load increases. According to calculations, the cooling load can be as high as 1 / 3 of the maximum load during continuous high temperature weather. At the same time, summer crops require a lot of water, and the agricultural irrigation load increases. From October to November, the temperature dropped, and the electricity load for urban and rural residents' daily use and agricultural irrigation gradually dropped. In December, the temperature continued to drop, the climate was cold and humid, and air conditioners and other heating and cooling equipment were turned on in large quantities. In addition, the rush to work by various production enterprises at the end of the year caused the load of the provincial power grid to rise, forming the second peak of the year. The historical annual load characteristics of the power grid in Province A from 2011 to 2019 are shown in Table 20.

[0242] Table 20:

[0243]

[0244] (2) Monthly load characteristics: With the increase in the proportion of electricity consumption in the tertiary industry, the cooling load in summer and the heating load in winter continue to increase. The seasonal imbalance coefficient ρ of the power grid in Province A is generally on a downward trend. It is predicted that the seasonal imbalance coefficient ρ will basically remain between 0.80 and 0.85 from 2020 to 2030. At the same time, its monthly imbalance coefficient σ is between 0.83 and 0.88. It is predicted that the double peak characteristics of summer (about August) and December in winter will still be basically maintained from 2020 to 2030. The peak load in summer is greater than the load in winter, and the minimum value of the annual load curve appears around April in spring. Based on the above characteristics, the annual load curves for 2020 and 2025 are predicted as shown in Table 21.

[0245] Table 21:

[0246]

[0247]

[0248] (3) Daily load characteristics: In recent years, with the adjustment of electricity consumption structure, the proportion of electricity consumption in daily life and the tertiary industry has gradually increased, and the daily minimum load rate and daily average load rate have a downward trend; in addition, the increase in air-conditioning load in summer will increase the evening power load, causing the time of the summer maximum load to be pushed back; however, with the continuous expansion of the scale of the power grid, the application of demand-side management technology, the widespread promotion of time-of-use electricity prices, and power outages, the load rate has increased. The daily load characteristics from 2004 to 2019 are shown in Table 22);

[0249] Table 22:

[0250] years Average annual load factor Annual minimum load factor 2011 87.2％ 74.0％ 2012 86.3％ 72.0％ 2013 84.2％ 72.5％ 2014 86.1％ 71.0％ 2015 85.3％ 69.5％ 2016 85.0％ 67.5％ 2017 84.8％ 66.2％ 2018 85.11％ 66.60％ 2019 85.6％ 67.3％

[0251] The average daily load curves in summer and winter from 2011 to 2019 are as follows: Fig.25 and Fig.26 As shown in Table 23, the daily load characteristics are predicted, and as shown in Table 24, the typical daily load curve is predicted. It is expected that the daily load curve will basically maintain the above characteristics in the future. At the same time, affected by the increase in residents' electricity consumption and industrial structure adjustment, it is predicted that the daily load rate γ and the minimum load rate β will decline slightly from 2020 to 2025, and the daily load rate γ will be maintained between 0.8-0.85, and the minimum daily load rate β will be maintained between 0.65 and 0.70.

[0252] Table 23:

[0253]

[0254] Table 24:

[0255]

[0256]

[0257] Step 43: Are there any provinces in the region that have not been analyzed? If not, proceed to step 44. If yes, jump to step 38 and continue to execute steps 38 to 43 until there are no provinces in the region that have not been analyzed. Then proceed to step 44: Analyze the reserves of water, wind, coal, natural gas and other resources in the provinces in the region (Province A is rich in energy resources, mainly water, coal and natural gas, with water resources accounting for about 75%, coal resources accounting for about 23.5%, and natural gas and oil resources accounting for about 1.5%. Province A has many rivers and rich hydropower resources, and has unique advantages in developing hydropower. The theoretical reserves of hydropower resources in Province A are 1,287.9 ​​billion kWh per year, with a corresponding average power of 147 million kilowatts. The technically exploitable installed capacity is 148 million kilowatts, with an annual power generation of 676.4 billion kilowatt-hours; the economically exploitable capacity is 145 million kilowatts, with an annual power generation of 659.4 billion kilowatt-hours. The province currently has 12.27 billion tons of coal resources, mainly distributed in the southern part of Province A. The types of coal are relatively complete, including anthracite, lean coal, lean coal, bituminous coal, lignite, and peat. Oil and gas resources are mainly natural gas, and the reserves of oil resources are very small. Province A is rich in natural gas resources, with discovered reserves of more than 7 trillion cubic meters of natural gas resources, accounting for about 19% of the country's total natural gas resources, mainly distributed in the southern, northwestern, central, and northeastern regions. Bioenergy is relatively abundant, with 31.4853 million tons of human and animal feces that can be exploited each year. Firewood is 11.8903 million tons, straw is 42.1224 million tons, and biogas is about 1 billion cubic meters. Solar energy, wind energy, and geothermal resources are also relatively abundant and need to be developed and utilized. (1) Hydropower resources: The hydropower resources in Province A are unevenly distributed in the region, with more in the west and less in the east. According to its geographical characteristics, Province A can be divided into two major parts, east and west, with the M River as the boundary. The west of the M River is the western region, and the east of the M River is the eastern region. The western region is mainly plateaus and mountainous areas. The mountains in the region overlap, and the altitude is mostly above 3,000 meters. The terrain gradually decreases from northwest to southeast, and the mountains and rivers are mostly northwest-southeast oriented; the rivers are severely eroded, and high mountains and valleys alternate, with a relative height difference of more than 2,000 meters. The east is a basin and surrounding mountainous areas, with an altitude of more than 1,000 meters. ~3000m; the basin is 300~600m above sea level, and the terrain is mostly rolling hills. The distribution characteristics of hydropower resources in Province A are more in the west and less in the east. In the eastern region, there are mostly small and medium-sized power stations, and fewer large power stations, and the exploitable resources account for 11.3% of the province; there are many large, medium and small power stations in the west, and the exploitable resources account for 88.7% of the province. The rivers in the western region flow from the plateau mountains to the hilly basins, with a large drop, abundant water, and extremely rich hydropower resources. The river drop in the eastern region of Province A is relatively small, and the hydropower resources are relatively less. Although the proportion of hydropower resources in the east is relatively small in the province, its absolute value is still considerable. Because Province A has extremely unique natural and geographical conditions, the conditions for the development and utilization of hydropower resources are particularly favorable.Its main characteristics are as follows: a. The rivers in Province A have a large controlled basin area, abundant runoff, large and concentrated drop, and extremely rich hydropower resources; b. The resources are more in the west than in the east, and the hydropower resources in the western region are particularly rich. The province is relatively scattered, and the local areas are relatively concentrated. c. There are a full range of large, medium and small types, and the scale advantage of large power stations is prominent; d. There are conditions for planning leading reservoirs, and the overall joint operation and regulation performance of the basin or cluster is good; e. The resource area is far away from the load center, and there are prominent problems such as the channel for large-capacity and long-distance centralized transmission of electricity. (2) Coal resources: Province A has relatively poor coal resources. There are coal resources in 75 counties and cities in the province, but they are most concentrated in the southern region, accounting for about two-thirds of the province. From the perspective of coal resource development potential, it is estimated that the province's coal production capacity can reach 80 to 100 million tons / year. (3) Natural gas resources: Province A is extremely rich in natural gas, mainly distributed in the south, west, central and eastern regions. In recent years, large gas fields in Province A have increased day by day, and reserves have doubled. With the discovery of large gas fields such as L gas field, P gas field and G gas field, the northeastern region is expected to become one of the regions with the largest natural gas reserves in Province A, which will enable Province A to maintain its position as one of the three major natural gas resource areas in my country. In the past 10 years, the rapid development of natural gas exploration and development in Province A is mainly manifested in: a. Rapid growth of proven natural gas reserves; b. Continuous increase in annual natural gas production; c. Discovery of a number of large and extra-large gas fields; d. Exploration continues to expand to deep, ultra-deep layers and new areas. (4) Wind energy resources: According to the terrain and circulation characteristics of Province A, the wind energy resources of Province A are mainly concentrated in the mountains in the southwest of Province A and the western plateau area. The southwest of Province A is located on the south branch of the westerly wind channel. The airflow in the channel is affected by the uplift of the terrain, forming a large wind speed. In particular, due to the uplift of the LN mountain range perpendicular to the airflow channel, rich wind energy resources are formed. The average wind speed of most wind farms is above 7m / s, and the average wind speed of some wind farms even reaches above 9m / s. It is currently one of the main battlefields for the development of wind energy resources in Province A. The western plateau area of ​​Province A is mainly located on the north branch of the westerly wind channel. The overall terrain is relatively high. Affected by the north branch of the airflow, there are certain wind energy resources above a certain height (4000m). At present, the development of this area is relatively slow due to the limitation of technological maturity. The near-ground layer of most of the basins in Province A is controlled by the sinking airflow of the north branch of the westerly wind. The upper layer is warm and humid airflow, with weak wind, stable weather and small wind. It is one of the poor wind areas in my country and basically has no wind energy development value. The mountains in the eastern part of Province A are mainly located in the convergence zone of the north and south branches of the westerly wind and the range of the East Asian monsoon. The airflow is generally stable, and it is difficult to form a large-scale wind power development area. In some places, due to the influence of the terrain, there are certain wind resources, but generally low-speed wind farms are dominant. Province A is generally located at the junction of the westerly belt, the southeast monsoon and the southwest monsoon belt. In addition, the plateau monsoon caused by the thermal difference between the plateau and the surrounding free atmosphere also has a great impact on Province A. Several monsoons have their main control areas in different seasons, but they often blend with each other to form complex and changeable weather in Province A.In winter, in the northern hemisphere, because the western part of Province A is backed by the plateau, the westerly airflow below three or four thousand meters is divided into two jet streams, the south and north. The southern branch comes from Central Asia and South Asia, with high temperature and low humidity, sunny weather and little rain, especially dry and warm. This branch of airflow has a greater impact on the southwestern mountainous area of ​​Province A; the northern branch of airflow, after bypassing the northern part of Province X, merges with the polar continental air mass moving southward, which is cold and dry, and has a greater impact on the northern plateau of Province A; the basin area is in the convergence zone of the two westerly winds on the east side of the plateau. The lower layer is the northern branch of cold airflow, the upper layer is the southern branch of warm current, and the near-ground layer is controlled by the sinking airflow of the northern branch of westerly wind, with weak wind and stable weather. Therefore, the winter wind force in Province A is mainly distributed in the southwestern mountains of Province A and the northern plateau of Province A. During this period, the entire area is dry and has little rain. In summer, the subtropical westerly belt moves northward, the Pacific subtropical high pressure moves northward and westward, the southern branch airflow disappears, and the summer monsoon (southeast monsoon and southwest monsoon) from the southern ocean advances northward. This airflow brings a lot of precipitation, and the whole province enters a rainy season with weak wind. The southerly airflow in the basin of Province A is mainly the southeast monsoon, while the southerly airflow in the southwest of Province S belongs to the southwest monsoon. In spring and autumn, the climate of Province A is greatly affected by the retreat or advancement of the westerly wind and monsoon, and is controlled by the plateau monsoon. Overall, the above-mentioned climatic conditions form the annual characteristics of drought and little rain in winter and spring, strong wind, increased precipitation in summer and autumn, and weak wind in Province A. Wind and precipitation have a certain relationship of increase and decrease. From the perspective of time distribution, since the westerly airflow is strong in the winter half of the year and brings relatively dry airflow; in the summer half of the year, the southeast monsoon and southwest monsoon mainly bring warm and humid airflow, so the wind energy resources in Province A have obvious two monsoon characteristics, and the time distribution is mainly concentrated in the winter half of the year. According to statistics, the maximum monthly wind power density in the winter half of the year is 8 to 12 times that in the summer half of the year. Based on the 35-year wind speed data from 1980 to 2015 at the meteorological station in Province A, the year is used as the horizontal axis and the ratio of the average wind speed of each year to its multi-year average is used as the vertical axis to characterize the multi-year trend of wind speed. From the interannual distribution diagram, the multi-year average wind speed of meteorological stations in various regions has shown a downward trend. The wind speed reached the lowest around 2000, and then rebounded slightly; from the annual distribution, the wind speed was relatively high from January to June, and the lowest from July to September); Step 45: Analyze the progress of power generation capacity installation in provinces within the region ((1) Hydropower installation progress: Province A is rich in hydropower resources, and hydropower development is mainly concentrated in the three major river basins in the west. As of the end of 2019, the installed capacity of the three main streams was 12.3 million, 14.7 million and 14.8 million kilowatts, respectively, with a total installed capacity of about 41.8 million kilowatts, accounting for more than half of the total installed capacity of hydropower in Province A. At present, Province A has approved more than 35 million kilowatts of hydropower under construction, including 12 hydropower stations on the three main streams, with an installed capacity of 32.43 million kilowatts. It is estimated that the province will add about 32 million kilowatts of hydropower installed capacity from 2021 to 2025, reaching 112.34 million kilowatts by 2025.(2) Thermal power generation capacity: According to the current boundary conditions, the installed capacity of thermal power generation is expected to increase by 6.46 million kilowatts from 2021 to 2025, including: Gas-fired power generation. As of the end of 2019, the installed capacity of gas-fired power generation was 1.26 million kilowatts. From 2021 to 2025, the construction of gas-fired power generation will be steadily promoted in combination with the gas source situation. It is planned to increase by 4 million kilowatts; by 2025, the installed capacity of gas-fired power generation will reach 5.27 million kilowatts. Coal-fired power generation. As of the end of 2019, the installed capacity of coal-fired power generation was 12.16 million kilowatts. From 2021 to 2025, according to the need for balance, a certain scale of clean and efficient coal-fired power generation with supporting functions will be reasonably arranged. At present, 2.12 million kilowatts (including suspended and slowed construction) are approved for construction and are planned to be put into operation in the early stage of 2021-2025. By 2025, the installed capacity of coal-fired power generation will be 15.93 million kilowatts. Other thermal power generation. In 2019, other thermal power (mainly waste power generation) was 2.28 million kilowatts. It is expected that 340,000 kilowatts will be added during the period of 2021-2025, reaching 2.62 million kilowatts in 2025. (3) Progress of wind power installation: Province A is a Class IV wind energy resource area. The province has abundant wind energy resources, and the exploitable amount under the current economic and technical conditions exceeds 200 million kilowatts. From 2016 to 2020, Province A will optimize the layout of wind power development, control the total amount, and develop moderately. Other areas such as the basin-surrounding mountains will develop wind power moderately in a decentralized manner according to local conditions. Strengthen wind power project management, deepen and refine wind energy resource surveys, optimize micro-site selection and design, improve the quality of wind power equipment and the level of operation and maintenance, improve wind energy utilization efficiency, promote the reduction of wind power costs, and reduce the impact on the environment. From 2021 to 2025, wind power development will still be mainly concentrated in the western and southwestern regions of Province A and some cities. It is estimated that by 2025, the total installed capacity of wind power will exceed 10 million kilowatts); Step 46: Analyze the planning and construction of cross-provincial and cross-regional channels in the provinces in the region (according to the basic plan of the planning boundary of Province A, the scale of cross-provincial and cross-regional transmission is as follows. (1) Power flow from 2016 to 2020: In 2019 and 2020, the power grid of Province A delivered 30.34 million kilowatts of power in the peak season and 12.04 million kilowatts in the dry season. (2) New power flow from 2021 to 2025: 1) Approved projects under construction: YH ultra-high voltage direct current transmission project, with a designed transmission capacity of 8 million kilowatts, is scheduled to be put into operation by the end of 2021. Considering the construction of the power source at the sending end and the acceptance capacity of the receiving end power grid, a power flow of 3 million kilowatts is arranged in 2022 and a power flow of 5 million kilowatts is arranged in 2025. 2) Preliminary projects underway: JS UHV DC transmission project, with a designed transmission capacity of 8 million kilowatts, is scheduled to be approved in 2020 and put into operation in 2022. The power flow is scheduled to be 6 million kilowatts in 2022 and 8 million kilowatts in 2025. JT UHV DC transmission project, with a designed transmission capacity of 8 million kilowatts, is scheduled to be approved in 2020 and put into operation in 2022. The power flow is scheduled to be 6 million kilowatts in 2022 and 8 million kilowatts in 2025. 3) Research and demonstration projects: JY UHV DC transmission project, with a designed transmission capacity of 8 million kilowatts.Considering the construction progress of the power supply and transmission channel at the sending end, 2 million kilowatts of power flow will be arranged in 2025, and full transmission will be gradually achieved from 2026 to 2030. 4) Projects supported by the two provinces of AB: The AC UHV target grid of AB Province is an effective measure to meet the demand for new hydropower transmission in the western part of Province A, improve the power supply capacity of the load center of AB Province, and systematically solve the risk of excessive short-circuit current in the load center area. The 1000 kV UHV AC transmission channel of AB Province is an important part of the target grid. It is expected that the AC UHV AC transmission channel of AB Province will be completed in 2024. By then, the section of AB Province will form a "1+3" networking scale of 1 1000 kV channel and 3 500 kV channels, and the AC section transmission capacity will reach 10 million kilowatts. According to the issued boundary conditions, the power verification between provinces AB is temporarily considered to be 6 million kilowatts); Step 47: Analyze the arrangement of cross-provincial and cross-regional power flows (from 2021 to 2025, the power grid of Province A will add three UHV DC transmission capacities totaling 24 million kilowatts, and the new JY UHV DC transmission capacity will be 5 million kilowatts. In the abundant season of 2022, the YH, JS, and JT UHV DCs are arranged to send out 6.7 million, 6 million, and 5 million kilowatts respectively, and the other channels are fully transmitted, with a total power flow of 45.04 million kilowatts; in the dry season, combined with the hydrological characteristics of the hydropower station, the total power flow sent out is 20.69 million kilowatts. In 2025, in the abundant season, the AC Province-H Province UHV DC is arranged to send out 5 million kilowatts, and the other channels are fully transmitted, with a total power flow of 56.34 million kilowatts; in the dry season, the total power flow sent out is 25.34 million kilowatts. As shown in Table 25, this is the power flow arrangement of the power grid in Province A);

[0258] Table 25:

[0259]

[0260]

[0261] Step 48: Are there any provinces in the region that have not been analyzed? If not, proceed to step 49. If so, jump to step 44 and continue to execute steps 44 to 48 until there are no provinces in the region that have not been analyzed. Then proceed to step 49: perform load forecasting and determine the load level involved in the balance calculation (first, use 95% of the recommended basic load level for balance calculation, considering a 5% load response); Step 50: Determine the hydrological year data for power and electricity balance calculation (second, use the hydrological data of the dry year for power balance calculation, and use the hydrological data of the normal year for electricity balance calculation); Step 51: Determine the representative months for the wet and dry seasons (according to the load characteristics, power supply structure and supply and demand balance characteristics of Province A, the representative month for the wet season is August, The representative month of the dry season is December); Step 52: Determine the principle of including the planned units into the balancing capacity (thirdly, hydropower and thermal power installed capacity shall be included in the balancing capacity according to the planned production schedule, the units put into operation in the current year shall be included in the balancing capacity according to the monthly production schedule, and the boundary river power station shall be included in the balancing capacity according to the capacity connected to the provincial power grid); Step 53: Determine the inter-provincial and inter-regional power transmission and reception flow (fourth, the external power flow is as mentioned above, considering that from 2021 to 2025, the new YH, JS, and JT UHV DC transmission capacity will be 24 million kilowatts, and the new JY UHV DC transmission will be 5 million kilowatts. Considering that the current power transmission capacity of the AC in Province AB has reached 6 million kilowatts and is temporarily transmitted at this capacity, 6 million kilowatts will be temporarily considered in 2025); Step 54: Determine the standby and maintenance capacity (fifth, the standby power flow The capacity is considered to be about 12% of the maximum load; maintenance is arranged according to relevant regulations); Step 55: Analyze the output characteristics and utilization hours of various power sources (first, hydropower output characteristics. According to the regulations, hydropower uses the output characteristics of dry years and normal years to calculate the balance of electricity and electricity. Existing hydropower refers to its hydropower output curve in recent years, and the output characteristics of new mainstream hydropower in its feasibility study report are used to simulate the power generation curve. New small and medium-sized hydropower refers to the output of existing small and medium-sized hydropower to simulate the power generation curve. The overall utilization hours of hydropower exceed 4,300 hours, and the electricity consumption in the dry and normal periods accounts for about 45% of the whole year. Second, thermal power generates electricity according to demand in the balancing program. According to the coal supply and power generation of thermal power in the province in recent years, the utilization hours are set to 4,000 hours. Around. Third, new energy output: Wind power participates in the balance by ensuring the output coefficient at the peak load moment. According to historical operating data, wind power participates in the balance at 15% of the installed capacity. In the power balance, 2000 hours of wind power utilization hours are selected to participate in the balance, and the output characteristics refer to the existing wind power output); Step 56: Determine the utilization hours of cross-provincial and cross-regional DC transmission and reception (based on the current boundaries, FF, JS, and BJ DC are 5000, 5000, and 4750 hours respectively, and the AB provincial section considers a power transmission capacity of 6 million kilowatts, and the utilization hours reach 5600 hours; the newly added 4 DC loops are calculated based on 4000 hours. The power transmission capacity and power transmission are shown in Table 26. The specific typical power transmission curves are simulated and issued in a unified manner with reference to the existing power transmission curves);

[0262] Table 26:

[0263] aisle Power transmission (billion kWh) Power transmission capacity (10,000 kW) Utilization hours (hours) AB Province Exchange 337 600 5613 FF DC 320 640 5000 JS DC 360 720 5000 BJ DC 380 800 4750 DB DC -100 300 3333 YH DC 320 800 4000 JS DC 320 800 4000 JT DC 320 800 4000 JY DC 200 500 4000

[0264] Step 57: Conduct power balance analysis and calculation (the power grid of Province A can basically be balanced with a slight surplus during the peak season from 2021 to 2025, and there will be a small gap of about 1.83 million kilowatts during the peak season in 2025. The specific power balance results of the power grid of Province A from 2021 to 2025 are shown in Table 27, unit: 10,000 kilowatts);

[0265] Table 27:

[0266]

[0267]

[0268]

[0269] Step 58: Conduct power balance analysis and calculation (there is a small gap in the basic balance of power during the dry season in the province from 2021 to 2025, with the maximum gap being about 2.69 billion kWh. The power balance results of the power grid in Province A from 2021 to 2025 are shown in Table 28, unit: billion kWh);

[0270] Table 28:

[0271]

[0272]

[0273]

[0274] Step 59: Study measures to ensure power supply during the flood season (during the flood season, there is a large power supply gap in Province A during peak load moments. The following measures can be taken: First, the output of renewable energy power generation is not considered in the balance. The peak load moment during the flood season is around 21:15 in the evening. At this time, 10% of wind power output is considered (but according to the output characteristics, 0% of photovoltaic power output). Second, the system reserve is selected as 12%, and the reserve capacity is appropriately reduced during peak hours, considering a reduction of 3 percentage points to 9%. Third, without considering the addition of new units, consider the external transmission channel at 90% external transmission (in recent years, the three UHV DCs that have been put into operation have all transmitted power at a maximum of 90%). Combining the above measures, the basic plan can ensure the power supply during the peak hours of the flood season. The calculation results of the power balance of the power grid in Province A during the flood season in 2025 are shown in Table 29. Unit: 10,000 kilowatts. Under the high plan, the reserve or external transmission needs to be further reduced);

[0275] Table 29:

[0276]

[0277]

[0278]

[0279] Step 60: Study measures to ensure electricity supply during the dry season (Measure 1: Increase thermal power installed capacity. According to the above research, increasing thermal power installed capacity by 4 to 5 million kilowatts can ensure basic balance of electricity during the dry season. Measure 2: Increase the reserved electricity of large hydropower. As mentioned above, the preliminary feasibility study of the AB Province-H Province UHV DC project is progressing smoothly, so no sensitivity analysis is conducted on the reserved electricity of related power stations. Step 1: According to the preset requirements, a large power station reserves 10 billion kWh of electricity during the dry season. In comparison with the output curve of the power station and the corresponding DC transmission curve, only 4.3 billion kWh of electricity is actually considered for the power station (as shown in Table 30). According to the reserved electricity of 10 billion kWh, the two DC circuits of the power station need to send 5.7 billion kWh less during the dry season. In this case, the electricity gap of Province A in the dry season in 2025 will be 7.7-14 billion kWh (basic load plan-high load plan, the same below).

[0280] Table 30:

[0281] project Flood season Flat water period Dry Season Low water season The power station's electricity generation 339.0 84.8 187.3 272.0 JS DC power transmission 195.6 45.0 79.4 124.4 JT DC power transmission 214.9 40.0 65.1 105.1 Total of two DC circuits 410.6 85.0 144.5 229.5 Reserve power / supplement DC power -71.5 -0.2 42.8 42.6

[0282] As shown in Table 31, this is the electricity balance result table of the power grid in Province A in 2025 (10 billion kWh of electricity retained during the dry season), unit: 10,000 kilowatts.

[0283] Table 31:

[0284]

[0285]

[0286]

[0287] Measure 3: Introduce external electricity. The main consideration is to distribute electricity to Province A during the dry season with KY DC under the condition that large power stations retain 10 billion kWh of electricity. Considering KY DC for 5,500 hours, the annual electricity consumption is about 44 billion kWh. In order to meet the electricity shortage in Province A during the dry season, 8-14 billion kWh of electricity needs to be distributed to Province A in 2025, accounting for about 20-30% of the annual electricity consumption. As shown in Table 32, this is the KY DC electricity distribution balance result table in 2025);

[0288] Table 32:

[0289]

[0290]

[0291]

[0292] Step 61: Conduct power security sensitivity program analysis (taking into account demand-side response and other measures to optimize load, and calculating according to 95% of the maximum load. The power balance results for 2023-2025 are shown in Table 33, unit: 10,000 kilowatts. According to the calculation results, after considering temporary additional power purchases and 5% demand-side response measures for 2023-2025, the power is basically balanced);

[0293] Table 33:

[0294]

[0295] Electricity balance: Under 95% maximum load, the calculation results of electricity balance from 2023 to 2025 are shown in Table 34, unit: billion kWh. In 2023, 31.9 billion kWh of electricity will be purchased through the AB province channel; in 2024, 18.4 billion kWh of electricity will be purchased through the AB province channel, and 23.5 billion kWh of electricity will be purchased from Province X; in 2025, 10.4 billion kWh of electricity will be purchased through the AB province channel, and 41.6 billion kWh of electricity will be purchased from Province X. According to the calculation results, the electricity balance from 2023 to 2025 can be basically achieved, but there will be a small amount of peak-shaving and abandoned hydropower in both 2023 and 2025. It is appropriate to strive to purchase more electricity from Province A to participate in peak-shaving or optimize the output of units within the network.

[0296] Table 34:

[0297] years 2023 2024 2025 1. Electricity consumption of the whole society 1495 1590 1690 2. Purchased Electricity 509 609 710 Long-term agreement 190 190 190 KY DC 0 235 416 other 319 184 104 3. Thermal power generation 693 693 693 Hours of use 3800 3800 3800 4. Hydropower Generation 252 253 253 Hours of use 3217 3218 3217 5. Total power generation from pumped storage and generation 0 0 0 VI. Wind power generation 37 37 37 Hours of use 1800 1800 1800 7. Solar power generation 0 0 0 8. Electricity Profit and Loss 0 0 0 IX. Peak load regulation and curtailment 0.6 0.0 0.3

[0298] Step 62: Are there any provinces in the region that have not been analyzed? If not, end; if so, jump to step 49 and continue to execute steps 49 to 62 until there are no provinces in the region that have not been analyzed. End.

[0299] The present invention provides a method, device and equipment for analyzing supply and demand of a regional power grid with an extremely high proportion of hydropower and wind power. Through the technical solution of the present invention, for hydropower: since the water inflow in a normal water year is average, which is a general situation, and the power balance is a balance in total volume, it is more accurate to use the hydropower output characteristic curve of the historical normal water year to determine the planned hydropower generation in the research normal year, and this accurate hydropower planned generation is used to participate in the power balance calculation, so that the power balance calculation result is more accurate. Since the water inflow in the dry year is less, if power balance can be achieved in the dry year, then power balance can be achieved regardless of whether it is a dry year, a normal water year or a wet year. Therefore, it is more accurate to use the hydropower output characteristic curve of the historical dry year to determine the planned hydropower power at the wet and dry period control moment in the research normal year, and this accurate hydropower planned power is used to participate in the power balance calculation, so that the power balance calculation result is more accurate. For wind power: The maximum power generation of wind power in the research year can be determined based on the historical installed capacity of wind power, the progress of wind power installation and the historical utilization hours of wind power. The planned power generation of wind power can be obtained by multiplying the maximum power generation by the preset utilization hours ratio of wind power. The planned power generation of wind power, rather than the maximum power generation, is involved in the power balance, which ensures the accuracy and stability of wind power when participating in power balance. The maximum output characteristic curve of wind power in the research year can be determined based on the historical wind power output characteristic curve and the planned installed capacity of wind power. The planned power of wind power can be obtained by multiplying the preset output ratio of wind power by the maximum output on the maximum output characteristic curve. The planned power of wind power, rather than the maximum output, is involved in the power balance, which ensures the accuracy and stability of wind power when participating in the power balance.

[0300] Further, as Figure 1 The specific implementation of the method shown in the figure, the embodiment of the present invention provides a device for analyzing supply and demand of a power grid in a region with a very high proportion of hydropower and wind power, such as Figure 3 As shown, the device includes: a determination module 31, a power balance calculation module 32, and a power balance calculation module 33;

[0301] A determination module 31 is used to determine the research level year for supply and demand analysis to be conducted;

[0302] The power balance calculation module 32 is used to determine the planned external power transmission in the research level year, determine the planned thermal power generation in the research level year, determine the planned hydropower generation according to the hydropower output characteristic curve in the historical average water year, determine the planned wind power generation according to the historical installed capacity of wind power, the progress of wind power installation, the historical utilization hours of wind power and the ratio of the preset utilization hours of wind power, predict the planned power consumption in the research level year according to the historical power consumption information, perform power balance calculation according to the planned external power transmission, the planned thermal power generation, the planned hydropower generation, the planned wind power generation and the planned power consumption, and obtain the power balance result, wherein the sum of the hydropower ratio and the wind power ratio is greater than the thermal power ratio;

[0303] The power balance calculation module 33 is used to determine the wet and dry season control time in the research level year, determine the planned external power at the wet and dry season control time, determine the thermal power planned power at the wet and dry season control time, determine the hydropower planned power at the wet and dry season control time according to the historical dry year hydropower output characteristic curve, determine the wind power planned power according to the historical wind power output characteristic curve, the wind power planned installed capacity and the wind power preset output ratio, predict the planned load characteristic curve in the research level year according to the historical load characteristic curve, determine the peak load at the wet and dry season control time according to the planned load characteristic curve, perform power balance calculation according to the planned external power, the thermal power planned power, the hydropower planned power, the wind power planned power and the peak load, and obtain the power balance result.

[0304] In specific application scenarios, such as Figure 4 As shown, a device for analyzing supply and demand of power grids in areas with extremely high proportions of hydropower and wind power, the device also includes: a guarantee module 34, which can be specifically used to determine whether power supply guarantee is needed according to the power balance result, and if so, perform a preset power supply guarantee, and re-calculate the power balance until the power balance result does not require power supply guarantee, wherein the preset power supply guarantee includes at least one of increasing thermal power installed capacity, introducing external power, reducing external transmission, and reducing standby; determine whether power supply guarantee is needed according to the power balance result, and if so, perform a preset power supply guarantee, and re-calculate the power balance until the power balance result does not require power supply guarantee, wherein the preset power supply guarantee includes at least one of increasing thermal power installed capacity, introducing external power, reducing external transmission, and reducing standby.

[0305] Accordingly, in order to determine the planned power transmission amount in the research level year, the power balance calculation module 32 can be specifically used to obtain the historical power transmission capacity and the historical power transmission utilization hours, and obtain the historical power transmission amount by multiplying the historical power transmission utilization hours by the historical power transmission capacity; determine the preset planned power transmission utilization hours and the preset additional power transmission capacity, and obtain the additional power transmission amount by multiplying the planned power transmission utilization hours by the additional power transmission capacity; and obtain the planned power transmission amount in the research level year based on the historical power transmission and the additional power transmission.

[0306] Correspondingly, the historical electricity consumption information includes historical electricity elasticity coefficient, historical electricity consumption, historical gross domestic product, historical per capita electricity consumption, and historical electricity consumption per unit of output value. In order to predict the planned electricity consumption in the research level year based on the historical electricity consumption information, the electricity balance calculation module 32 can be specifically used to predict the planned electricity elasticity coefficient in the research level year based on the historical electricity elasticity coefficient, obtain the electricity consumption growth rate by multiplying the planned electricity elasticity coefficient by the preset economic growth rate, and obtain the first planned electricity consumption in the research level year by multiplying the historical electricity consumption by the electricity consumption growth rate; predict the second planned electricity consumption in the research level year based on the historical electricity consumption; predict the third planned electricity consumption in the research level year based on the historical gross domestic product and the historical electricity consumption; predict the planned per capita electricity consumption in the research level year based on the historical per capita electricity consumption, and obtain the fourth planned electricity consumption in the research level year by multiplying the planned per capita electricity consumption by the preset permanent population; predict the fifth planned electricity consumption in the research level year based on the historical unit of output value electricity consumption and the historical electricity consumption; obtain the planned electricity consumption based on the first planned electricity consumption, the second planned electricity consumption, the third planned electricity consumption, the fourth planned electricity consumption and the fifth planned electricity consumption.

[0307] Accordingly, in order to determine the planned thermal power generation in the research level year, the power balance calculation module 32 can be specifically used to determine the planned thermal power installed capacity in the research level year according to the historical installed capacity of thermal power and the progress of thermal power installation, predict the planned utilization hours of thermal power in the research level year according to the historical utilization hours of thermal power, and obtain the planned thermal power generation by multiplying the planned installed capacity of thermal power by the planned utilization hours of thermal power. In order to determine the planned hydropower generation according to the hydropower output characteristic curve in the historical average water year, the power balance calculation module 32 can be specifically used to determine the planned hydropower installed capacity in the research level year according to the historical installed capacity of hydropower and the progress of hydropower installation, predict the hydropower output characteristic curve in the research average water year in the research level year according to the hydropower output characteristic curve in the historical average water year and the planned hydropower installed capacity, and determine the planned hydropower generation according to the hydropower output characteristic curve in the research average water year. In order to determine the planned wind power generation according to the historical installed capacity of wind power, the progress of wind power installation, the historical utilization hours of wind power and the ratio of the preset utilization hours of wind power, the power balance calculation module 32 can be specifically used to determine the planned installed capacity of wind power in the research level year according to the historical installed capacity of wind power and the progress of wind power installation, predict the planned utilization hours of wind power in the research level year according to the historical utilization hours of wind power, and obtain the planned wind power generation according to the planned installed capacity of wind power, the planned utilization hours of wind power and the ratio of the preset utilization hours of wind power.

[0308] Accordingly, in order to determine the planned power transmission at the peak and low season control moment, the power balance calculation module 33 can be specifically used to determine the planned power transmission capacity in the research level year based on historical power transmission transaction information and planned power transmission transaction information; predict the planned power transmission curve in the research level year based on the historical power transmission curve and the planned power transmission capacity; and determine the planned power transmission at the peak and low season control moment based on the planned power transmission curve.

[0309] Accordingly, in order to determine the planned thermal power at the wet and dry season control time, the power balance calculation module 33 can be specifically used to predict the planned thermal power output characteristic curve in the research level year according to the historical thermal power output characteristic curve and the planned thermal power installed capacity, and determine the planned thermal power at the wet and dry season control time according to the planned thermal power output characteristic curve. In order to determine the planned hydropower at the wet and dry season control time according to the historical dry year hydropower output characteristic curve, the power balance calculation module 33 can be specifically used to predict the studied dry year hydropower output characteristic curve in the research level year according to the historical dry year hydropower output characteristic curve, and determine the planned hydropower at the wet and dry season control time according to the studied dry year hydropower output characteristic curve. In order to determine the planned wind power according to the historical wind power output characteristic curve, the planned wind power installed capacity and the preset wind power output ratio, the power balance calculation module 33 can be specifically used to predict the planned wind power output characteristic curve in the research level year according to the historical wind power output characteristic curve and the planned wind power installed capacity, and determine the planned wind power at the wet and dry period control moment according to the planned wind power output characteristic curve and the preset wind power output ratio.

[0310] It should be noted that for other corresponding descriptions of the functional units involved in the extremely high proportion of hydropower and wind power regional power grid supply and demand analysis device provided in this embodiment, reference can be made to Figure 1 The corresponding description will not be repeated here.

[0311] Based on the above Figure 1 The method shown in the embodiment, accordingly, also provides a storage medium, which can be volatile or non-volatile, and stores a computer program, which is executed by a processor to implement the above-mentioned Figure 1 The supply and demand analysis method for regional power grids with extremely high proportions of hydropower and wind power is shown.

[0312] Based on this understanding, the technical solution of the present invention can be embodied in the form of a software product, which can be stored in a storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.), and includes a number of instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods of various implementation scenarios of the present invention.

[0313] Based on the above Figure 1 The method shown and Figure 3 , Figure 4 In order to achieve the above-mentioned purpose, the present embodiment further provides a computer device, which includes a storage medium and a processor; the storage medium is used to store a computer program; the processor is used to execute the computer program to achieve the above-mentioned Figure 1 The supply and demand analysis method for regional power grids with extremely high proportions of hydropower and wind power is shown.

[0314] Optionally, the computer device may further include a user interface, a network interface, a camera, a radio frequency (RF) circuit, a sensor, an audio circuit, a WI-FI module, etc. The user interface may include a display, an input unit such as a keyboard, etc., and the optional user interface may also include a USB interface, a card reader interface, etc. The network interface may optionally include a standard wired interface, a wireless interface (such as a WI-FI interface), etc.

[0315] Those skilled in the art will appreciate that the computer device structure provided in this embodiment does not constitute a limitation on the physical device, and may include more or fewer components, or a combination of certain components, or different component arrangements.

[0316] The storage medium may also include an operating network communication module. The operating is a program that manages the hardware and software resources of the above-mentioned computer device, supporting the operation of the information processing program and other software and / or programs. The network communication module is used to communicate between the components inside the storage medium, and to communicate with other hardware and software in the information processing entity device.

[0317] Through the description of the above implementation modes, those skilled in the art can clearly understand that the present invention can be implemented by means of software plus a necessary general hardware platform, or by hardware.

[0318] The present invention provides a method, device and equipment for analyzing supply and demand of a regional power grid with an extremely high proportion of hydropower and wind power. Through the technical solution of the present invention, for hydropower: since the water inflow in a normal water year is average, which is a general situation, and the power balance is a balance in total volume, it is more accurate to use the hydropower output characteristic curve of the historical normal water year to determine the planned hydropower generation in the research normal year, and this accurate hydropower planned generation is used to participate in the power balance calculation, so that the power balance calculation result is more accurate. Since the water inflow in the dry year is less, if power balance can be achieved in the dry year, then power balance can be achieved regardless of whether it is a dry year, a normal water year or a wet year. Therefore, it is more accurate to use the hydropower output characteristic curve of the historical dry year to determine the planned hydropower power at the wet and dry period control moment in the research normal year, and this accurate hydropower planned power is used to participate in the power balance calculation, so that the power balance calculation result is more accurate. For wind power: The maximum power generation of wind power in the research year can be determined based on the historical installed capacity of wind power, the progress of wind power installation and the historical utilization hours of wind power. The planned power generation of wind power can be obtained by multiplying the maximum power generation by the preset utilization hours ratio of wind power. The planned power generation of wind power, rather than the maximum power generation, is involved in the power balance, which ensures the accuracy and stability of wind power when participating in power balance. The maximum output characteristic curve of wind power in the research year can be determined based on the historical wind power output characteristic curve and the planned installed capacity of wind power. The planned power of wind power can be obtained by multiplying the preset output ratio of wind power by the maximum output on the maximum output characteristic curve. The planned power of wind power, rather than the maximum output, is involved in the power balance, which ensures the accuracy and stability of wind power when participating in the power balance.

[0319] Those skilled in the art will appreciate that the accompanying drawings are only schematic diagrams of a preferred implementation scenario, and the modules or processes in the accompanying drawings are not necessarily necessary for implementing the present invention. Those skilled in the art will appreciate that the modules in the devices in the implementation scenario can be distributed in the devices of the implementation scenario according to the description of the implementation scenario, or can be changed accordingly and located in one or more devices different from the present implementation scenario. The modules of the above-mentioned implementation scenario can be combined into one module, or can be further split into multiple modules.

[0320] The above serial numbers of the present invention are only for description and do not represent the advantages and disadvantages of the implementation scenarios. The above disclosure is only a few specific implementation scenarios of the present invention, but the present invention is not limited thereto, and any changes that can be thought of by those skilled in the art should fall within the protection scope of the present invention.

Claims

1. A method for analyzing supply and demand of power grids in areas with extremely high proportions of hydropower and wind power, characterized in that: The method comprises: Determine the research horizon year for supply and demand analysis to be conducted; Determine the planned power transmission in the research level year, determine the planned thermal power generation in the research level year, determine the planned hydropower generation according to the hydropower output characteristic curve in the historical normal water year, determine the planned wind power generation according to the historical installed capacity of wind power, the progress of wind power installation, the historical utilization hours of wind power and the ratio of the preset utilization hours of wind power, predict the planned power consumption in the research level year according to the historical power consumption information, perform power balance calculation according to the planned power transmission, the planned thermal power generation, the planned hydropower generation, the planned wind power generation and the planned power consumption, and obtain the power balance result, wherein the sum of the hydropower ratio and the wind power ratio is greater than the thermal power ratio; Determine the wet and dry season control time in the research level year, determine the planned external power at the wet and dry season control time, determine the thermal power planned power at the wet and dry season control time, determine the hydropower planned power at the wet and dry season control time according to the historical dry year hydropower output characteristic curve, determine the wind power planned power according to the historical wind power output characteristic curve, the wind power planned installed capacity and the wind power preset output ratio, predict the planned load characteristic curve in the research level year according to the historical load characteristic curve, determine the peak load at the wet and dry season control time according to the planned load characteristic curve, perform power balance calculation according to the planned external power, the thermal power planned power, the hydropower planned power, the wind power planned power and the peak load, and obtain the power balance result.

2. The method according to claim 1, characterized in that The method further comprises: Determine whether power supply guarantee is required according to the power balance result, and if so, perform preset power supply guarantee, and recalculate power balance until the power balance result shows that power supply guarantee is not required, wherein the preset power supply guarantee includes at least one of increasing thermal power installed capacity, introducing external power, reducing external transmission, and reducing standby power; Determine whether power supply guarantee is needed based on the power balance result, and if so, perform preset power supply guarantee and recalculate power balance until the power balance result shows that power supply guarantee is not needed, wherein the preset power supply guarantee includes at least one of increasing thermal power installed capacity, introducing external power, reducing external power transmission and reducing standby power.

3. The method according to claim 1, characterized in that The planned transmission volume in the research level year is determined as follows: Obtaining historical external power transmission capacity and historical external power transmission utilization hours, and obtaining historical external power transmission quantity by multiplying the historical external power transmission utilization hours by the historical external power transmission capacity; Determine the preset planned external power transmission utilization hours and the preset additional external power transmission capacity, and obtain the additional external power transmission capacity by multiplying the planned external power transmission utilization hours by the additional external power transmission capacity; According to the historical power transmission and the newly added power transmission, the planned power transmission in the research level year is obtained.

4. The method according to claim 1, characterized in that: The historical electricity consumption information includes the historical electricity elasticity coefficient, the historical electricity consumption, the historical gross domestic product, the historical per capita electricity consumption, and the historical electricity consumption per unit output value. The planned electricity consumption in the research level year is predicted based on the historical electricity consumption information, including: Predict the planned power elasticity coefficient in the research level year according to the historical power elasticity coefficient, obtain the power consumption growth rate according to the planned power elasticity coefficient multiplied by the preset economic growth rate, and obtain the first planned power consumption in the research level year according to the historical power consumption multiplied by the power consumption growth rate; Predicting a second planned electricity consumption in the research level year based on the historical electricity consumption; Predicting the third planned electricity consumption in the research level year based on the historical gross domestic product and the historical electricity consumption; Predict the planned per capita electricity consumption in the research level year based on the historical per capita electricity consumption, and multiply the planned per capita electricity consumption by the preset permanent population to obtain the fourth planned electricity consumption in the research level year; Predict the fifth plan electricity consumption in the research level year based on the historical unit output value electricity consumption and the historical electricity consumption; The planned power consumption is obtained according to the first planned power consumption, the second planned power consumption, the third planned power consumption, the fourth planned power consumption and the fifth planned power consumption.

5. The method according to claim 1, characterized in that: The determination of the planned thermal power generation in the research level year includes: Determine the planned installed capacity of thermal power in the research level year based on the historical installed capacity of thermal power and the progress of thermal power installation, predict the planned utilization hours of thermal power in the research level year based on the historical utilization hours of thermal power, and obtain the planned thermal power generation by multiplying the planned installed capacity of thermal power by the planned utilization hours of thermal power; Determining the planned hydropower generation according to the hydropower output characteristic curve in the historical normal water year includes: Determine the planned installed capacity of hydropower in the research level year based on the historical installed capacity of hydropower and the progress of hydropower installation, predict the hydropower output characteristic curve of the research level year in the research level year based on the historical hydropower output characteristic curve and the planned installed capacity of hydropower, and determine the planned hydropower power generation based on the hydropower output characteristic curve of the research level year; The determination of the planned wind power generation according to the historical installed capacity of wind power, the progress of wind power installation, the historical utilization hours of wind power and the proportion of the preset utilization hours of wind power includes: The planned installed capacity of wind power in the research level year is determined based on the historical installed capacity of wind power and the progress of wind power installation. The planned utilization hours of wind power in the research level year are predicted based on the historical utilization hours of wind power. The planned power generation of wind power is obtained based on the planned installed capacity of wind power, the planned utilization hours of wind power and the ratio of the preset utilization hours of wind power.

6. The method according to claim 1, characterized in that The step of determining the planned external power transmission at the peak-peak period control time includes: Determine the planned power transmission capacity in the research level year according to the historical power transmission transaction information and the planned power transmission transaction information; Predicting the planned power transmission curve in the research level year based on the historical power transmission curve and the planned power transmission capacity; The planned power transmission at the peak-peak control time is determined according to the planned power transmission curve.

7. The method according to claim 5, characterized in that The step of determining the planned thermal power generation at the peak and low season control time comprises: According to the historical thermal power output characteristic curve and the thermal power planned installed capacity forecast, the planned thermal power output characteristic curve in the research level year is determined according to the planned thermal power output characteristic curve at the wet and dry season control moment; Determining the planned hydropower power at the wet and dry season control time according to the hydropower output characteristic curve in the historical dry years includes: According to the historical dry year hydropower output characteristic curve and the planned hydropower installed capacity forecast of the studied dry year hydropower output characteristic curve in the studied level year, the planned hydropower power at the wet and dry season control time is determined according to the studied dry year hydropower output characteristic curve; Determining the planned wind power according to the historical wind power output characteristic curve, the planned wind power installed capacity and the preset wind power output ratio includes: According to the historical wind power output characteristic curve and the planned wind power installed capacity forecast in the research level year, the planned wind power output characteristic curve is determined, and according to the planned wind power output characteristic curve and the preset wind power output ratio, the planned wind power at the wet and dry period control moment is determined.

8. A device for analyzing supply and demand of power grids in areas with extremely high proportions of hydropower and wind power, characterized in that: The device comprises: Determination module, used to determine the research level year to be conducted for supply and demand analysis; An electricity balance calculation module is used to determine the planned external power transmission in the research level year, determine the planned thermal power generation in the research level year, determine the planned hydropower generation according to the hydropower output characteristic curve in the historical normal water year, determine the planned wind power generation according to the historical installed capacity of wind power, the progress of wind power installation, the historical utilization hours of wind power and the ratio of the preset utilization hours of wind power, predict the planned electricity consumption in the research level year according to the historical electricity consumption information, perform electricity balance calculation according to the planned external power transmission, the planned thermal power generation, the planned hydropower generation, the planned wind power generation and the planned electricity consumption, and obtain an electricity balance result, wherein the sum of the hydropower ratio and the wind power ratio is greater than the thermal power ratio; The power balance calculation module is used to determine the wet and dry season control time in the research level year, determine the planned external power at the wet and dry season control time, determine the thermal power planned power at the wet and dry season control time, determine the hydropower planned power at the wet and dry season control time according to the historical dry year hydropower output characteristic curve, determine the wind power planned power according to the historical wind power output characteristic curve, the wind power planned installed capacity and the wind power preset output ratio, predict the planned load characteristic curve in the research level year according to the historical load characteristic curve, determine the peak load at the wet and dry season control time according to the planned load characteristic curve, perform power balance calculation according to the planned external power, the thermal power planned power, the hydropower planned power, the wind power planned power and the peak load, and obtain the power balance result.

9. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for analyzing supply and demand of a regional power grid with a very high proportion of hydropower and wind power as described in any one of claims 1 to 7 is implemented.

10. A computer device comprising a storage medium, a processor, and a computer program stored in the storage medium and executable on the processor, characterized in that: When the processor executes the computer program, the method for analyzing supply and demand of a regional power grid with an extremely high proportion of hydropower and wind power as described in any one of claims 1 to 7 is implemented.

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