Annual electric quantity decomposition method for power transmission and reception and hydropower and related device thereof

Abstract

The invention discloses a power transmission and reception and hydropower annual electric quantity decomposition method and a related device thereof. The method comprises the following steps: generating a load curve of a planning year according to a load characteristic curve extracted from a historical load time sequence curve, the maximum load of the planning year and an electric quantity demand; a new energy output curve of the planning year is generated according to the historical new energy output curve and the newly-added unit investment building capacity; generating a power transmitting and receiving monthly electric quantity reference decomposition curve according to the historical monthly electric quantity distribution condition or the power supply characteristics of the transmitting end; and calculating the net load electric quantity of each month according to the load, the new energy and the power transmitting and receiving monthly electric quantity reference decomposition curve, distributing the annual hydropower predicted total generating capacity to each month to obtain a hydropower monthly electric quantity reference decomposition curve, and obtaining optimized hydropower and power transmitting and receiving monthly electric quantity decomposition results of each region by solving an annual electric quantity decomposition model. The method can give full play to the complementary advantage of multi-region resources, achieves the dynamic matching of multi-region supply and demand balance, and improves the new energy consumption level and the supply guarantee capability of the whole system.

Description

Technical Field

[0001] The present application relates to the technical field of power systems, and in particular to a method for decomposing the annual electricity consumption of power transmission and reception and hydropower and related devices. Background Art

[0002] In the planning and operation of new power systems, production simulation technology is a core tool for evaluating the rationality of power supply structures and verifying system security. Planning is usually based on annual power constraints (such as the total amount of interprovincial power transmission and reception agreements and the annual hydropower generation plan for a river basin). To improve computational efficiency, production simulation often relies on monthly, weekly, daily, or even hourly power balance analysis. The accuracy of the spatiotemporal decomposition of power directly determines the feasibility of planning schemes. Traditional power decomposition methods often perform simple decomposition based on historical operating characteristics. However, with the rapid increase in the proportion of installed renewable energy capacity, the dual pressures of widening peak-to-valley differences on the load side and the volatility of renewable energy output have made the spatiotemporal characteristics of power transmission and reception, as well as hydropower, complex and changeable. Historical operating characteristics may not necessarily adapt to the needs of the new power supply structure.

[0003] The current planning and operation connection faces two major contradictions: (1) The timing mismatch between planning and operation: In the planning of load growth scenarios and large-scale grid connection of new energy, if only the annual power consumption is used to constrain slow-regulating power sources such as power transmission and reception, hydropower, etc., it may cover up the power gap on a monthly scale. (2) The challenge of dynamic coupling of multiple factors: The capacity of power transmission and reception channels is limited by the physical expansion cycle, the output of hydropower is highly seasonal due to the influence of hydrological meteorology, and there are monthly fluctuations in new energy power generation and load demand. These dynamic characteristics require that the power decomposition must simultaneously meet the multi-dimensional coupling constraints of cross-provincial mutual assistance capabilities, power supply regulation characteristics and grid security boundaries. Therefore, it is urgent to conduct a fine-grained decomposition of the annual power boundary on a smaller time scale based on the load growth scenario set by the planning scheme and the new energy planning scenario, and establish a power decomposition method for multi-source coordinated power transmission and reception, hydropower and new energy for planning scenarios. Summary of the Invention

[0004] The present application provides a method for decomposing the annual electricity consumption of power transmission and reception and hydropower and its related devices, which are used to optimize the decomposition results of the annual electricity consumption of power transmission and reception and hydropower, so as to give full play to the complementary advantages of multi-regional resources, achieve dynamic matching of multi-regional supply and demand balance, and improve the level of new energy consumption and the supply guarantee capacity of the entire system.

[0005] In view of this, the first aspect of the present application provides a method for decomposing the annual electricity consumption of power transmission, reception and hydropower, including:

[0006] Extracting a load characteristic curve according to the historical load time series curve of each region, and generating a load curve for each region in the planning year based on the load characteristic curve, the maximum load and power demand of each region in the planning year;

[0007] Generate the new energy output curve for each region in the planning year based on the historical new energy output curve and the newly invested and constructed capacity of each region;

[0008] Generate a monthly power consumption benchmark decomposition curve for each region based on the historical monthly power consumption distribution of each region or the power supply characteristics of the sending end of the transmission channel;

[0009] Determine the net load electricity of each region in each month based on the load curve, the new energy output curve, and the monthly power transmission and reception benchmark decomposition curve of each region in the planning year; allocate the total annual hydropower generation forecast of each region in the planning year to each month based on the net load electricity of each region, and obtain the monthly hydropower benchmark decomposition curve of each region;

[0010] An annual electricity decomposition model is constructed based on the monthly electricity benchmark decomposition curve of power transmission and reception and the monthly hydropower electricity benchmark decomposition curve of each region. By solving the annual electricity decomposition model, the optimized monthly hydropower electricity decomposition results and the optimized monthly electricity decomposition results of power transmission and reception are obtained for each region. The annual electricity decomposition model takes minimizing the comprehensive cost of the power system as its optimization goal.

[0011] Optionally, extracting a load characteristic curve based on a historical load time series curve of each region includes:

[0012] Based on the historical load time series curve of each region, the ratio of the maximum load in each month to the annual maximum load of each region is calculated to obtain the typical monthly load characteristic curve of each region;

[0013] Calculate the ratio of the daily maximum load of each region in each week to the maximum load of each week based on the historical load time series curve of each region to obtain the typical weekly load characteristic curve of each region; and divide the typical weekly load characteristic curve of each region into two categories: weekdays and holidays using a clustering method;

[0014] Calculate the ratio of the load at each time of each day to the maximum load of each region based on the historical load time series curve of each region to obtain the typical daily load characteristic curve of each region; and divide the typical daily load characteristic curve of each region into two categories: weekdays and holidays using a clustering method;

[0015] The load typical monthly characteristic curve, the load typical weekly characteristic curve, and the load typical daily characteristic curve are used as load characteristic curves.

[0016] Optionally, generating a load curve for each region in the planning year based on the load characteristic curve, the maximum load and power demand of each region in the planning year includes:

[0017] Calculate the initial load curve of each region in the planning year based on the peak load of each region in the planning year and the load characteristic curve of each region;

[0018] The initial load curves of each region in the planning year are corrected so that the total electricity of the initial load curves of each region in the planning year meets the electricity demand of each region in the planning year, thereby obtaining the load curves of each region in the planning year.

[0019] Optionally, generating the new energy output curve for each region in the planning year based on the historical new energy output curve and the newly invested and constructed capacity of each region includes:

[0020] According to the historical renewable energy output curves of each region, the ratio of renewable energy output to renewable energy installed capacity at each time point in each region is calculated to obtain the renewable energy output coefficient of each region.

[0021] According to the newly built capacity of units in each region, the installed capacity of each region at each moment is updated through the new energy output coefficient of each region, and the new energy output curve of each region in the planning year is obtained.

[0022] Optionally, generating a monthly power transmission and reception benchmark decomposition curve for each region based on the historical monthly power distribution of each region or the power supply characteristics of the transmission channel sending end includes:

[0023] When there are no new inter-provincial power transmission and reception channels or no new inter-regional power transmission and reception channels in each region, a clustering method is used to obtain the monthly power transmission and reception benchmark decomposition curve of each region based on the monthly power distribution of each region over many years;

[0024] When there are new inter-provincial power transmission and reception channels or new inter-regional power transmission and reception channels in various regions, a monthly power transmission and reception power benchmark decomposition curve is generated based on the power supply characteristics of the new inter-provincial power transmission and reception channels or new inter-regional power transmission and reception channels, specifically including:

[0025] For thermal power-dominated transmission channels, the monthly electricity consumption benchmark decomposition curve of thermal power units is obtained based on the historical monthly utilization hours and planned installed capacity of thermal power units with the same capacity;

[0026] For hydropower-dominated transmission channels, obtain the monthly power consumption benchmark decomposition curve of hydropower components based on the planned hydropower station water inflow forecast or utilization hours and planned capacity;

[0027] For new energy-dominated transmission channels, the monthly electricity consumption benchmark decomposition curve of new energy is obtained based on the historical new energy output curves of each region and the capacity of newly built units;

[0028] For the multi-power composite-dominant transmission channel, the monthly power consumption of each type of power is calculated according to the power type and then summed up to obtain the multi-power monthly power consumption benchmark decomposition curve.

[0029] Optionally, allocating the total annual hydropower generation forecast for each region in the planning year to each month according to the net load power of each region in each month to obtain a monthly hydropower power benchmark decomposition curve for each region includes:

[0030] Calculate the ratio of the net load electricity of each region in each month to the net load electricity of the year to obtain the hydropower electricity allocation weight of each region in each month;

[0031] Multiply the hydropower electricity allocation weight of each region in each month by the total hydropower generation forecast for the entire year in each region to obtain the initial monthly hydropower electricity allocation results for each region;

[0032] The initial monthly hydropower electricity allocation results of each region are revised by the minimum power generation requirements of each region in each month to obtain the revised monthly hydropower electricity allocation results of each region;

[0033] The monthly hydropower electricity benchmark decomposition curve for each region is generated based on the revised monthly hydropower electricity distribution results for each region.

[0034] Optionally, the objective function of the annual electricity decomposition model is:

[0035]

[0036] Where, I is the set of provinces or regions; i is the index number of the province or region; are the thermal power cost coefficient, hydropower cost coefficient, transmission and reception point cost coefficient, and wind power / photovoltaic cost coefficient of province i or region i, respectively; is the thermal power generation of province i or region i in month m; is the wind power / photovoltaic power generation of province i or region i in month m; is the upper limit of wind power / photovoltaic power generation in province i or region i in month m; It is the hydropower electricity adjustment value and the power supply and reception adjustment value of province i or district i in month m.

[0037] A second aspect of the present application provides a device for decomposing annual electricity consumption of electricity transmission and reception and hydropower, comprising:

[0038] A first generating unit is configured to extract a load characteristic curve according to a historical load time series curve of each region, and generate a load curve for each region in the planning year based on the load characteristic curve, the maximum load and power demand of each region in the planning year;

[0039] The second generating unit is used to generate the new energy output curve of each region in the planning year based on the historical new energy output curve of each region and the newly invested and constructed capacity of the units;

[0040] The third generating unit is used to generate a monthly power supply and receiving power benchmark decomposition curve for each region based on the historical monthly power distribution of each region or the power supply characteristics of the transmission channel sending end;

[0041] a fourth generating unit, configured to determine the net load electricity of each region in each month based on the load curve, the new energy output curve, and the monthly power transmission and reception benchmark decomposition curve of each region in the planning year; and allocate the total annual hydropower generation forecast of each region in the planning year to each month based on the net load electricity of each region, thereby obtaining the monthly hydropower benchmark decomposition curve of each region;

[0042] An optimization unit is used to construct an annual electricity decomposition model based on the monthly electricity transmission and reception benchmark decomposition curve and the monthly hydropower benchmark decomposition curve of each region, and obtain the optimized hydropower monthly electricity decomposition results and the optimized electricity transmission and reception monthly electricity decomposition results of each region by solving the annual electricity decomposition model. The annual electricity decomposition model takes minimizing the comprehensive cost of the power system as the optimization goal.

[0043] A third aspect of the present application provides an electronic device, the device comprising a processor and a memory;

[0044] The memory is used to store program code and transmit the program code to the processor;

[0045] The processor is used to execute the annual electricity consumption decomposition method of power transmission and reception and hydropower according to any one of the first aspects according to the instructions in the program code.

[0046] In a fourth aspect, the present application provides a computer-readable storage medium for storing program code. When the program code is executed by a processor, the annual electricity decomposition method for power transmission and reception and hydropower as described in any one of the first aspects is implemented.

[0047] It can be seen from the above technical solutions that this application has the following advantages:

[0048] The annual electricity decomposition method for transmission and reception and hydropower provided in this application maps the load curve and the timing of new energy commissioning into monthly power balance constraints, generates a transmission and reception / hydropower benchmark decomposition curve that dynamically matches the planning scenario, and realizes dynamic coupling of multiple factors; allows transmission and reception, and hydropower to redistribute electricity between months according to the principle of optimal adjustment cost, and quantifies the marginal relationship between the adjustment range and the benefit of thermal power substitution; coordinates economic indicators such as thermal power operating costs, transmission and reception adjustment costs, and hydropower abandonment losses to provide an operational monthly electricity allocation strategy for the planning scheme, realizes monthly-scale electricity allocation, and helps improve the accuracy of monthly electricity allocation through multi-objective optimization. This application optimizes the annual electricity decomposition results of transmission and reception and hydropower, which can give full play to the complementary advantages of multi-regional resources, realize dynamic matching of multi-regional supply and demand balance, and improve the level of new energy consumption and the supply guarantee capacity of the entire system. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0050] Figure 1 A flowchart of a method for decomposing annual electricity consumption of electricity transmission, reception, and hydropower provided in an embodiment of the present application;

[0051] Figure 2 A schematic diagram of the structure of a device for decomposing annual electricity consumption for electricity transmission, reception, and hydropower provided in an embodiment of the present application;

[0052] Figure 3 A schematic structural diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0053] In order to help those skilled in the art better understand the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of this application.

[0054] For easier understanding, please refer to Figure 1 The embodiment of the present application provides a method for decomposing the annual electricity consumption of power transmission and reception and hydropower, including:

[0055] Step 110: extract a load characteristic curve according to the historical load time series curve of each region, and generate a load curve for each region in the planning year based on the load characteristic curve, the maximum load and power demand of each region in the planning year;

[0056] The embodiments of this application require the acquisition of various data, including unit technical parameters, planning scenario parameters, and historical operating data. Unit technical parameters include dynamic operating constraint parameters such as the generating capacity, ramp rate, start-up and shutdown costs, and fuel cost coefficients for existing and incremental units. Planning scenario parameters include the capacity of newly added units and their commissioning sequence, as well as the planned annual maximum load and power demand. Historical operating data includes historical load time series curves, historical wind power output curves, and historical photovoltaic output curves based on a time series resolution of 8760 hours. For historical operating data, missing data can be repaired using cubic spline interpolation to ensure the spatiotemporal consistency of the input data.

[0057] According to the historical load time series curves of each region, load characteristic curves are extracted, including monthly scale load characteristic curves, weekly scale load characteristic curves and daily scale load characteristic curves.

[0058] The process of extracting the monthly load characteristic curve is as follows: Calculate the ratio of the maximum load of each region in each month to the annual maximum load based on the historical load time series curve of each region. , we can get the typical monthly load characteristic curve of each region, namely:

[0059]

[0060] Where, is the maximum load in month m.

[0061] The process of extracting the weekly load characteristic curve is as follows: Calculate the ratio of the daily maximum load of each region in each week to the maximum load of each week based on the historical load time series curve of each region. , obtain the typical weekly load characteristic curve of each region; use the clustering method to divide the typical weekly load characteristic curve of each region into two categories: working days and holidays;

[0062]

[0063] Where, is the maximum daily load on the dth day of the wth week.

[0064] The process of extracting the daily load characteristic curve is as follows: Calculate the ratio of the load of each region at each time of the day to the maximum load of each day based on the historical load time series curve of each region. , obtain the typical daily load characteristic curve of each region; use the clustering method to divide the typical daily load characteristic curve of each region into two categories: working days and holidays;

[0065]

[0066] in, is the load at time t on day d.

[0067] According to the maximum load given by each region in the planning year The initial load curve of each region in the planning year is calculated based on the load characteristic curve of each region, namely:

[0068]

[0069] The initial load curve of each region in the planning year is modified so that the total electricity of the initial load curve of each region in the planning year meets the electricity demand of each region in the planning year, that is:

[0070]

[0071] The initial load curve is corrected through the above process to obtain the final load curve.

[0072] Step 120: Generate a new energy output curve for each region in the planning year based on the historical new energy output curve and the newly invested and constructed capacity of each region;

[0073] According to the historical renewable energy output curves of each region (such as wind power output curve and photovoltaic output curve), the ratio of renewable energy output at each time in each region to the renewable energy installed capacity at each time is calculated to obtain the renewable energy output coefficient of each region, that is:

[0074]

[0075] Where, is the wind power / photovoltaic output of region i at time t, is the wind power / photovoltaic installed capacity of region i at time t;

[0076] According to the newly added capacity of each region, the installed capacity of each region at each moment is updated through the new energy output coefficient of each region, and the new energy output curve of each region in the planning year is obtained. Based on the incremental unit construction plan, the installed capacity of region i is dynamically updated. ,Right now:

[0077]

[0078] Where, is the wind power / PV output of region i at time t in the planning year.

[0079] Step 130: Generate a monthly power transmission and reception benchmark decomposition curve for each region based on the historical monthly power distribution of each region or the power supply characteristics of the transmission channel sending end;

[0080] The monthly power transmission and reception benchmark decomposition curve needs to be generated using different methods depending on whether there are new power transmission and reception channels outside the province (region) in each region. Specifically:

[0081] When no new inter-provincial or inter-regional power transmission or reception channels are established in each region, a clustering method is used to obtain a benchmark decomposition curve for each region's monthly power transmission and reception, based on the region's monthly power distribution over many years. For example, based on the historical monthly power distribution over many years, a k-means clustering method is used to obtain typical monthly power distribution characteristics, thereby generating a benchmark decomposition curve for the monthly power transmission and reception.

[0082] When new inter-provincial transmission and reception channels or inter-regional transmission and reception channels are added to each region, a monthly transmission and reception power benchmark decomposition curve is generated based on the power supply characteristics of the new inter-provincial transmission and reception channels or the new inter-regional transmission and reception channels. Specifically, for thermal power-dominated transmission channels, the monthly power benchmark decomposition curve of thermal power units is obtained based on the historical monthly utilization hours and planned installed capacity of thermal power units of the same capacity;

[0083] For hydropower-dominated transmission channels, obtain the monthly power consumption benchmark decomposition curve of hydropower components based on the planned hydropower station water inflow forecast or utilization hours and planned capacity;

[0084] For new energy-dominated transmission channels, the monthly electricity consumption benchmark decomposition curve of new energy is obtained based on the historical new energy output curves of each region and the capacity of newly built units;

[0085] For the multi-power composite-dominant transmission channel, the monthly power consumption of each type of power is calculated according to the power type and then summed up to obtain the multi-power monthly power consumption benchmark decomposition curve.

[0086] Step 140: Determine the net load electricity of each region in each month based on the load curve, renewable energy output curve, and monthly power transmission and reception benchmark decomposition curve of each region in the planning year; allocate the total annual hydropower generation forecast of each region in the planning year to each month based on the net load electricity of each region, and obtain the monthly hydropower benchmark decomposition curve of each region;

[0087] Based on the load curves, new energy output curves and monthly power transmission and reception benchmark decomposition curves of each region in the planning year, the net load power of each region in each month is determined, namely:

[0088]

[0089] Where, is the net load electricity in month m, is the load power in month m, is the wind power / photovoltaic power in month m, is the amount of electricity sent and received in month m;

[0090] Calculate the ratio of the net load electricity of each region in each month to the net load electricity of the year, and obtain the hydropower distribution weight of each region in each month, that is:

[0091]

[0092] Where, Assign weights to the hydropower consumption in month m;

[0093] Multiply the hydropower electricity allocation weight of each region in each month by the total hydropower generation forecast for each region in the planning year to obtain the initial monthly hydropower electricity allocation results for each region, namely:

[0094]

[0095] Where, Forecast total hydroelectric power generation for the entire year; is the initial monthly hydropower electricity allocation result for month m;

[0096] The initial monthly hydropower electricity allocation results of each region are corrected by the minimum power generation demand of each region in each month to obtain the revised monthly hydropower electricity allocation results of each region; based on the revised monthly hydropower electricity allocation results of each region, the monthly hydropower electricity benchmark decomposition curve of each region is generated. In order to meet the minimum power generation demand of each month , the monthly electricity consumption of water and electricity obtained in the previous steps needs to be Compared, take the larger value as the modified hydropower distribution result to get the hydropower benchmark power for each month In other months, the total hydropower consumption is converted proportionally under the premise of ensuring that the total hydropower consumption remains unchanged throughout the year. .

[0097] Step 150: Construct an annual electricity decomposition model based on the monthly electricity consumption benchmark decomposition curves for power transmission and reception and the monthly hydropower electricity benchmark decomposition curves for each region, and obtain the optimized monthly hydropower electricity decomposition results and the optimized monthly electricity consumption decomposition results for power transmission and reception for each region by solving the annual electricity decomposition model.

[0098] Based on the monthly electricity consumption benchmark decomposition curves for power transmission and reception in each region and the monthly hydropower electricity consumption benchmark decomposition curves, an annual electricity consumption decomposition model is constructed that takes into account the cross-provincial power transmission and reception characteristics and the source-load coordination strategy. The optimized hydropower and power transmission and reception annual electricity consumption decomposition results are obtained by solving the problem. The objective function of the long-term operation simulation of the power system is to minimize the comprehensive cost of the power system, namely the fuel cost of thermal power units, the power curtailment cost of new energy units, and the monthly adjustment cost of hydropower / power transmission and reception. The objective function of the annual electricity consumption decomposition model is specifically:

[0099]

[0100] The constraints of the annual power decomposition model include: System power balance constraints for each month:

[0101]

[0102] Upper and lower limit constraints for hydropower electricity adjustment:

[0103]

[0104] The upper and lower limits of hydropower electricity adjustment are used to constrain the amount of hydropower electricity adjustment from deviating too much from the hydropower monthly electricity benchmark decomposition curve;

[0105] Upper and lower limits of power transmission and reception:

[0106]

[0107] By constraining the upper and lower limits of the power transmission and reception, the amount of power transmission and reception adjustment cannot deviate too much from the monthly power transmission and reception benchmark decomposition curve;

[0108] Annual hydropower consumption limit constraints:

[0109]

[0110] Annual upper limit constraints on power supply and reception:

[0111]

[0112] Monthly upper and lower limit constraints for each type of unit power (18)-(22):

[0113]

[0114]

[0115]

[0116]

[0117]

[0118] Logical constraint (23) on the amount of electricity sent and received (i.e., without considering grid losses, the sum of the amount of electricity sent and received by each province and the amount of electricity outside the region is approximately equal to 0):

[0119]

[0120] Where, I is the set of provinces or regions; i is the index number of the province or region; are the thermal power cost coefficient, hydropower cost coefficient, transmission and reception point cost coefficient, and wind power / photovoltaic cost coefficient of province i or region i, respectively; They are the upper limit of hydropower, thermal power, wind power, photovoltaic power, and power transmission and reception for province i or region i in month m; They are the minimum limits of hydropower, thermal power, and power transmission and reception for province i or region i in month m, respectively; The maximum adjustment ratio of hydropower and electricity supply and reception in province i or region i in month m; The out-of-region electricity of province i or region i in month m (incoming electricity is positive, outgoing electricity is negative); is the load electricity of province i or region i in month m; The hydropower benchmark electricity volume and the power transmission and reception benchmark electricity volume of province i or region i in month m (receipt is positive, transmission is negative); The adjusted value of hydropower and electricity supply and reception in province i or district i in month m; are the thermal power, wind power, and photovoltaic power of province i or region i in month m respectively; They are respectively the total predicted hydropower generation and the total power transmission and reception for the whole year in province i or region i.

[0121] Solve the above annual electricity decomposition model and obtain the monthly optimized and adjusted power transmission and reception and hydropower electricity from the operation simulation results.

[0122] This application maps the load curve and the timing of new energy commissioning into monthly power balance constraints, generates a transmission and reception / hydropower benchmark decomposition curve that dynamically matches the planning scenario, and realizes dynamic coupling of multiple factors; allows transmission and reception, and hydropower to redistribute electricity between months according to the principle of optimal adjustment cost, quantifies the marginal relationship between the adjustment range and the benefit of thermal power substitution, and adjusts transmission and reception and hydropower between months within the range allowed by the constraints, which can optimize the consumption of new energy on a longer time scale, reduce the amount of new energy power curtailment, and thus reduce thermal power generation; coordinates economic indicators such as thermal power operation costs, transmission and reception adjustment costs, and hydropower curtailment losses, provides an operational monthly electricity allocation strategy for the planning scheme, realizes monthly electricity allocation, and helps to improve the accuracy of monthly electricity allocation through multi-objective optimization.

[0123] Please refer to Figure 2 The embodiment of the present application further provides a device for decomposing annual electricity consumption of electricity transmission and reception and hydropower, including:

[0124] The first generating unit 210 is configured to extract a load characteristic curve according to the historical load time series curve of each region, and generate a load curve for each region in the planning year based on the load characteristic curve, the maximum load and power demand of each region in the planning year;

[0125] The second generating unit 220 is used to generate the new energy output curve of each region in the planning year based on the historical new energy output curve of each region and the newly invested and constructed capacity of the units;

[0126] The third generating unit 230 is used to generate a monthly power supply and receiving power benchmark decomposition curve for each region based on the historical monthly power distribution of each region or the power supply characteristics of the transmission channel sending end;

[0127] The fourth generating unit 240 is configured to determine the net load electricity of each region in each month based on the load curve, the new energy output curve, and the monthly power transmission and reception benchmark decomposition curve of each region in the planning year; allocate the total annual hydropower generation forecast of each region in the planning year to each month based on the net load electricity of each region, and obtain the monthly hydropower benchmark decomposition curve of each region;

[0128] The optimization unit 250 is used to construct an annual electricity decomposition model based on the monthly electricity consumption benchmark decomposition curve of power transmission and reception and the monthly hydropower electricity benchmark decomposition curve of each region, and obtain the optimized hydropower monthly electricity decomposition results and the optimized monthly electricity consumption decomposition results of power transmission and reception in each region by solving the annual electricity decomposition model. The annual electricity decomposition model takes minimizing the comprehensive cost of the power system as the optimization goal.

[0129] Please refer to Figure 3 , an embodiment of the present application further provides an electronic device, the device including a processor 310 and a memory 320;

[0130] The memory 320 is used to store program codes and transmit the program codes to the processor 310;

[0131] The processor 310 is configured to execute the annual electricity consumption decomposition method for electricity transmission, reception, and hydropower in the aforementioned method embodiment according to the instructions in the program code.

[0132] An embodiment of the present application also provides a computer-readable storage medium, which is used to store program code. When the program code is executed by a processor, it implements the annual electricity decomposition method of power transmission and reception and hydropower in the aforementioned method embodiment.

[0133] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described devices and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0134] In the specification of this application and the above-mentioned drawings, the terms "first," "second," "third," "fourth," etc. (if any) are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. In addition, the terms "including" and "having," and any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or apparatus that includes a series of steps or elements is not necessarily limited to those steps or elements explicitly listed, but may include other steps or elements not explicitly listed or inherent to such process, method, product, or apparatus.

[0135] It should be understood that in this application, "at least one (item)" means one or more, and "plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships can exist. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the previous and next associated objects are in an "or" relationship. "At least one of the following items" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or plural.

[0136] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0137] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0138] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0139] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for executing all or part of the steps of the method described in each embodiment of the present application through a computer device (which can be a personal computer, server, or network device, etc.). The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (full name: Read-Only Memory, English abbreviation: ROM), random access memory (full name: Random Access Memory, English abbreviation: RAM), disk or optical disk, and other media that can store program code.

[0140] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for decomposing the annual electricity consumption of electricity transmission, reception and hydropower, characterized in that: include: Extracting a load characteristic curve according to the historical load time series curve of each region, and generating a load curve for each region in the planning year based on the load characteristic curve, the maximum load and power demand of each region in the planning year; Generate the new energy output curve for each region in the planning year based on the historical new energy output curve and the newly invested and constructed capacity of each region; Generate a monthly power consumption benchmark decomposition curve for each region based on the historical monthly power consumption distribution of each region or the power supply characteristics of the sending end of the transmission channel; Determine the net load electricity of each region in each month based on the load curve, the new energy output curve, and the monthly power transmission and reception benchmark decomposition curve of each region in the planning year; allocate the total annual hydropower generation forecast of each region in the planning year to each month based on the net load electricity of each region, and obtain the monthly hydropower benchmark decomposition curve of each region; An annual electricity decomposition model is constructed based on the monthly electricity benchmark decomposition curve of power transmission and reception and the monthly hydropower electricity benchmark decomposition curve of each region. By solving the annual electricity decomposition model, the optimized monthly hydropower electricity decomposition results and the optimized monthly electricity decomposition results of power transmission and reception are obtained for each region. The annual electricity decomposition model takes minimizing the comprehensive cost of the power system as its optimization goal.

2. The annual electricity consumption decomposition method of power transmission and reception and hydropower according to claim 1 is characterized in that: The extracting of load characteristic curves based on the historical load time series curves of each region includes: Based on the historical load time series curve of each region, the ratio of the maximum load in each month to the annual maximum load of each region is calculated to obtain the typical monthly load characteristic curve of each region; Calculate the ratio of the daily maximum load of each region in each week to the maximum load of each week based on the historical load time series curve of each region to obtain the typical weekly load characteristic curve of each region; and divide the typical weekly load characteristic curve of each region into two categories: weekdays and holidays using a clustering method; Calculate the ratio of the load at each time of each day to the maximum load of each region based on the historical load time series curve of each region to obtain the typical daily load characteristic curve of each region; and divide the typical daily load characteristic curve of each region into two categories: weekdays and holidays using a clustering method; The load typical monthly characteristic curve, the load typical weekly characteristic curve, and the load typical daily characteristic curve are used as load characteristic curves.

3. The annual electricity consumption decomposition method of power transmission and reception and hydropower according to claim 1 is characterized in that: Generating a load curve for each region in the planning year based on the load characteristic curve, the maximum load and power demand of each region in the planning year includes: Calculate the initial load curve of each region in the planning year based on the peak load of each region in the planning year and the load characteristic curve of each region; The initial load curves of each region in the planning year are corrected so that the total electricity of the initial load curves of each region in the planning year meets the electricity demand of each region in the planning year, thereby obtaining the load curves of each region in the planning year.

4. The annual electricity consumption decomposition method of power transmission and reception and hydropower according to claim 1 is characterized in that: The generation of the new energy output curve for each region in the planning year based on the historical new energy output curve and the newly invested and constructed capacity of each region includes: According to the historical renewable energy output curves of each region, the ratio of renewable energy output to renewable energy installed capacity at each time point in each region is calculated to obtain the renewable energy output coefficient of each region. According to the newly built capacity of units in each region, the installed capacity of each region at each moment is updated through the new energy output coefficient of each region, and the new energy output curve of each region in the planning year is obtained.

5. The annual electricity consumption decomposition method of power transmission and reception and hydropower according to claim 1 is characterized in that: The generation of a monthly power supply and reception benchmark decomposition curve for each region based on the historical monthly power distribution of each region or the power supply characteristics of the transmission channel sending end includes: When there are no new inter-provincial power transmission and reception channels or no new inter-regional power transmission and reception channels in each region, a clustering method is used to obtain the monthly power transmission and reception benchmark decomposition curve of each region based on the monthly power distribution of each region over many years; When there are new inter-provincial power transmission and reception channels or new inter-regional power transmission and reception channels in various regions, a monthly power transmission and reception power benchmark decomposition curve is generated based on the power supply characteristics of the new inter-provincial power transmission and reception channels or new inter-regional power transmission and reception channels, specifically including: For thermal power-dominated transmission channels, the monthly electricity consumption benchmark decomposition curve of thermal power units is obtained based on the historical monthly utilization hours and planned installed capacity of thermal power units with the same capacity; For hydropower-dominated transmission channels, obtain the monthly power consumption benchmark decomposition curve of hydropower components based on the planned hydropower station water inflow forecast or utilization hours and planned capacity; For new energy-dominated transmission channels, the monthly electricity consumption benchmark decomposition curve of new energy is obtained based on the historical new energy output curves of each region and the capacity of newly built units; For the multi-power composite-dominant transmission channel, the monthly power consumption of each type of power is calculated according to the power type and then summed up to obtain the multi-power monthly power consumption benchmark decomposition curve.

6. The method for decomposing annual electricity consumption of electricity transmission, reception and hydropower according to claim 1, characterized in that: The total hydropower generation forecast for each region in the planning year is allocated to each month according to the net load power of each region in each month, and the monthly hydropower power benchmark decomposition curve of each region is obtained, including: Calculate the ratio of the net load electricity of each region in each month to the net load electricity of the year to obtain the hydropower electricity allocation weight of each region in each month; Multiply the hydropower electricity allocation weight of each region in each month by the total hydropower generation forecast for each region in the planning year to obtain the initial monthly hydropower electricity allocation results for each region; The initial monthly hydropower electricity allocation results of each region are revised by the minimum power generation requirements of each region in each month to obtain the revised monthly hydropower electricity allocation results of each region; The monthly hydropower electricity benchmark decomposition curve for each region is generated based on the revised monthly hydropower electricity distribution results for each region.

7. The annual electricity consumption decomposition method of power transmission and reception and hydropower according to claim 1 is characterized in that: The objective function of the annual electricity decomposition model is: Where, I is the set of provinces or regions; i is the index number of the province or region; are the thermal power cost coefficient, hydropower cost coefficient, transmission and reception point cost coefficient, and wind power / photovoltaic cost coefficient of province i or region i, respectively; is the thermal power generation of province i or region i in month m; is the wind power / photovoltaic power generation of province i or region i in month m; is the upper limit of wind power / photovoltaic power generation in province i or region i in month m; It is the hydropower electricity adjustment value and the power supply and reception adjustment value of province i or district i in month m.

8. A device for decomposing the annual electricity consumption of electricity transmission and reception and hydropower, characterized in that: include: A first generating unit is configured to extract a load characteristic curve according to a historical load time series curve of each region, and generate a load curve for each region in the planning year based on the load characteristic curve, the maximum load and power demand of each region in the planning year; The second generating unit is used to generate the new energy output curve of each region in the planning year based on the historical new energy output curve of each region and the newly invested and constructed capacity of the units; The third generating unit is used to generate a monthly power supply and receiving power benchmark decomposition curve for each region based on the historical monthly power distribution of each region or the power supply characteristics of the transmission channel sending end; a fourth generating unit, configured to determine the net load electricity of each region in each month based on the load curve, the new energy output curve, and the monthly power transmission and reception benchmark decomposition curve of each region in the planning year; and allocate the total annual hydropower generation forecast of each region in the planning year to each month based on the net load electricity of each region, thereby obtaining the monthly hydropower benchmark decomposition curve of each region; An optimization unit is used to construct an annual electricity decomposition model based on the monthly electricity transmission and reception benchmark decomposition curve and the monthly hydropower benchmark decomposition curve of each region, and obtain the optimized hydropower monthly electricity decomposition results and the optimized electricity transmission and reception monthly electricity decomposition results of each region by solving the annual electricity decomposition model. The annual electricity decomposition model takes minimizing the comprehensive cost of the power system as the optimization goal.

9. An electronic device, characterized in that: The device includes a processor and a memory; The memory is used to store program code and transmit the program code to the processor; The processor is used to execute the annual electricity consumption decomposition method of power transmission and reception and hydropower according to any one of claims 1 to 7 according to the instructions in the program code.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium is used to store program code, and when the program code is executed by the processor, it implements the annual power decomposition method of power transmission and reception and hydropower according to any one of claims 1 to 7.

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