Electric power spot transaction prediction method and system based on big data

Abstract

The invention provides an electric power spot transaction prediction method and system based on big data, and belongs to the technical field of data prediction, and the method comprises the steps: determining the power generation resource of each region based on the region type, and determining the power supply amount of a target region based on the power generation resource of each region and regional power data. The target area is an area to be predicted. And determining a first power spot transaction price based on the power supply amount of the target area and the power spot transaction type. And predicting the power spot transaction price of each region based on the first power spot transaction price and the environmental characteristics of each region to obtain a second power spot transaction price. According to the big data-based electric power spot transaction prediction method and system provided by the invention, the accuracy of electric power spot transaction prediction can be improved.

Description

Technical Field

[0001] The present disclosure belongs to the field of data prediction technology, and more specifically, to a method and system for predicting electricity spot transactions based on big data. Background Art

[0002] Electricity spot trading forecasts are in the technical background of the digital transformation of the power system and the transformation of the energy structure. With the development of smart grid technology, massive amounts of power data can be collected and stored, providing a data basis for electricity spot trading forecasts. At the same time, advanced data analysis algorithms can predict electricity spot trading prices, trading volumes, etc.

[0003] However, the existing electricity trading model is mostly based on medium- and long-term contracts, which makes it difficult to reflect the dynamic changes in electricity supply and demand in real time. At the same time, with the large-scale access of new energy to the power grid, the intermittent and uncertain nature of its power generation has increased the complexity of the power system, and the existing electricity spot trading forecasts still have the problem of low accuracy. Summary of the invention

[0004] The purpose of the present disclosure is to provide a method and system for predicting electricity spot trading based on big data, so as to improve the accuracy of electricity spot trading prediction.

[0005] A first aspect of an embodiment of the present disclosure provides a method for predicting power spot transactions based on big data, comprising: Determine the power generation resources of each region based on the region type, and determine the power supply of the target region based on the power generation resources of each region and the regional power data; the target region is the region to be predicted; determining a first electricity spot transaction price based on the power supply amount and the electricity spot transaction type of the target area; The electricity spot transaction price of each region is predicted based on the first electricity spot transaction price and the environmental characteristics of each region to obtain a second electricity spot transaction price.

[0006] A second aspect of the embodiments of the present disclosure provides a power spot transaction prediction system based on big data, including: A first calculation module is used to determine the power generation resources of each region based on the region type, and determine the power supply of the target region based on the power generation resources of each region and the regional power data; the target region is the region to be predicted; A second calculation module, configured to determine a first electricity spot transaction price based on the power supply amount and the electricity spot transaction type of the target area; A prediction module is used to predict the electricity spot transaction price of each region based on the first electricity spot transaction price and the environmental characteristics of each region to obtain a second electricity spot transaction price.

[0007] According to a third aspect of an embodiment of the present disclosure, an electronic device is provided, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor implements the steps of the above-mentioned big data-based electricity spot trading prediction method when executing the computer program.

[0008] According to a fourth aspect of an embodiment of the present disclosure, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above-mentioned big data-based electricity spot trading prediction method are implemented.

[0009] The beneficial effects of the power spot transaction prediction method and system based on big data provided by the embodiments of the present disclosure are: The disclosed embodiment accurately determines the power generation resources by regional type, which can comprehensively consider the energy advantages and limitations of different regions, make the power supply calculation more accurate, effectively avoid insufficient or excessive power supply, and ensure the stable operation of the power system. The disclosed embodiment determines the first price based on the power supply and transaction type, and can reasonably set prices based on the characteristics of different transaction modes (such as day-ahead, intraday, and real-time transactions), improve the scientificity and rationality of price formation, and enhance the effectiveness of the power market price mechanism. The disclosed embodiment predicts the second price in combination with environmental characteristics, fully considering the impact of temperature, light, wind speed, etc. on power generation efficiency, thereby more accurately reflecting changes in electricity costs, promoting the optimal allocation of power resources among different regions, and improving the operating efficiency and economic benefits of the entire power market. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] In order to more clearly illustrate the technical solutions in the embodiments of the present disclosure, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present disclosure. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0011] Figure 1 A schematic diagram of a flow chart of a power spot transaction prediction method based on big data provided by an embodiment of the present disclosure; Figure 2 A structural block diagram of a power spot transaction prediction system based on big data provided by an embodiment of the present disclosure; Figure 3 A schematic block diagram of an electronic device provided by an embodiment of the present disclosure. DETAILED DESCRIPTION

[0012] In the following description, specific details such as specific system structures and technologies are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present disclosure. However, it should be clear to those skilled in the art that the present disclosure may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid obstructing the description of the present disclosure with unnecessary details.

[0013] In order to make the purpose, technical solutions and advantages of the present disclosure more clear, specific embodiments will be described below in conjunction with the accompanying drawings.

[0014] Please refer to Figure 1 , Figure 1 A schematic flow chart of a method for predicting electricity spot trading based on big data provided in one embodiment of the present disclosure, the method may include S101 to S103.

[0015] S101: Determine the power generation resources of each region based on the region type, and determine the power supply of the target region based on the power generation resources of each region and the regional power data. The target region is the region to be predicted.

[0016] In this embodiment, the regional type refers to the division of the target area based on geographical, economic, functional and other factors. For example, the regional type may include urban, rural, industrial, commercial and other areas. The electricity consumption characteristics and power generation resource distribution of different regional types vary greatly. For example, the electricity demand in urban areas is large and concentrated, and the electricity load in industrial areas is stable and high. The power generation resource package may include a variety of energy forms and corresponding facility parameters. For example: coal reserves, power generation equipment capacity and efficiency for thermal power generation; basin flow, drop, and installed capacity of hydropower stations for hydropower generation; wind speed data, number of wind turbines and single-unit capacity for wind power generation; illumination duration, intensity, photovoltaic panel scale, etc. for solar power generation. Regional power data may include transmission network data, substation equipment data, and distribution data.

[0017] For example, take a coastal industrial area as an example, and its regional type is industry. There is a thermal power plant in the area with a coal reserve of 500,000 tons, a power generation equipment capacity of 300 megawatts, and an efficiency of 38%. There is also a wind farm with an average wind speed of 6 meters per second, 50 wind turbines, and a single unit capacity of 3 megawatts. In terms of regional power data, the transmission network has 5 220 kV transmission lines with a resistance of 0.1 ohms per kilometer and a total capacity of 400 megavolt-amperes for substation equipment. By combining these power generation resources with regional power data, taking into account the operating status of power generation equipment, the loss of transmission lines, and the carrying capacity of substation equipment, it is determined that the power supply of the industrial area is 400 megawatts to meet the stable and large power demand of many factories in the area.

[0018] S102: Determine a first electricity spot transaction price based on the power supply amount and the electricity spot transaction type of the target area.

[0019] In this embodiment, the power supply of the target area is the value of the amount of electricity available for trading determined by comprehensively considering the regional power generation resources, the transmission capacity of the power network, etc. Factors such as the power generation power of the power generation equipment, the maximum transmission capacity of the transmission line, and the transformation capacity of the substation equipment jointly restrict the actual amount of electricity that can be supplied to the market. The types of electricity spot transactions can include day-ahead transactions, intraday transactions, and real-time transactions. Different types of electricity spot transactions have different transaction time windows and rules. For example, day-ahead transactions are transactions arranged one day in advance, intraday transactions are transactions during a specific period of the day, and real-time transactions are transactions close to the time of power delivery.

[0020] For example, the adequacy or tightness of power supply can be evaluated based on the power supply in the target area. When the power supply is sufficient, the price will tend to decline to a certain extent; otherwise, the price will rise. In combination with different types of power spot transactions, according to their corresponding transaction rules and market mechanisms, the price can be determined, for example, through the supply and demand balance model, the cost-plus method, etc.

[0021] For example, a city area determines that the power supply is 500 megawatts. If the day-ahead spot transaction type is adopted, if the overall supply of the current power market is loose and the power supply in the area is relatively stable, based on its power supply and day-ahead trading rules, the first power spot transaction price can be determined to be 300 yuan per megawatt hour. If a large industrial enterprise in the area suddenly increases its power demand, resulting in a relatively tight power supply, the price may rise to 350 yuan per megawatt hour.

[0022] S103: Predicting the electricity spot transaction price of each region based on the first electricity spot transaction price and the environmental characteristics of each region to obtain a second electricity spot transaction price.

[0023] In this embodiment, the first electricity spot transaction price is a price benchmark initially determined based on the power supply and transaction type of the target area, reflecting the basic supply and demand relationship and the price level under the transaction mode. The environmental characteristics of the area may include parameters such as temperature, light intensity, wind speed, and humidity. For example, the temperature affects the cooling efficiency and power load of thermal power equipment; the light intensity and duration determine the output of solar power generation; and the wind speed affects the power of wind power generation.

[0024] In this embodiment, the first electricity spot transaction price is used as the basis to analyze the impact of environmental characteristics on power generation resources, and then adjust the price forecast. When the environment is conducive to power generation, such as strong winds that greatly increase wind power output, the power supply increases and the price may decrease; conversely, if the environment is not conducive to power generation, such as high temperatures that reduce thermal power efficiency and increase electricity demand, the price may increase. The second electricity spot transaction price is predicted by using historical data and a model of the relationship between environment and power generation, such as by statistically analyzing the changes in wind power generation under different wind speeds and their impact on prices.

[0025] For example, the first electricity spot transaction price in a certain area is determined to be 350 yuan / MWh. The wind speed in the area reached 8 meters per second on that day. According to historical data and the environment and power generation relationship model, this wind speed can increase the wind power generation capacity in the region by 30%, and the power supply will increase significantly. At the same time, the temperature is 25°C and the power load is relatively stable. Taking all factors into consideration, the increase in wind power generation is sufficient to affect the balance of market supply and demand, and it is predicted that the second electricity spot transaction price will drop to 320 yuan / MWh. On the contrary, if the temperature is minus 10°C in winter, the efficiency of thermal power will decrease and the heating power load will increase significantly. Based on the first electricity spot transaction price, the second electricity spot transaction price may rise sharply to 400 yuan / MWh.

[0026] It can be concluded from the above that this embodiment determines the power generation resources by dividing the regional types, fully considering the power consumption characteristics and resource distribution differences in different regions, so that the power supply calculation is more practical. This embodiment determines the first price in combination with the power supply and transaction type, and can flexibly set prices according to the characteristics of different transaction models and market supply and demand conditions, enhances the rationality of the price formation mechanism, and helps to balance the supply and demand of the electricity market. This embodiment introduces regional environmental characteristics to predict the second price, taking into account the impact of the environment on power generation resources, such as the effect of wind speed on wind power, temperature on thermal power and power load, so that price prediction is more accurate. Accurate price prediction is conducive to the formulation of reasonable strategies by power market participants, power generation companies can optimize power generation plans, power sales companies can make reasonable quotations, and users can adjust their power consumption behavior, thereby improving the operating efficiency and stability of the entire power market and promoting the optimal allocation of power resources.

[0027] In one embodiment of the present disclosure, the regional power data includes power transmission network data.

[0028] Determine the power supply to the target area based on the power generation resources and regional power data of each area, including: The first power supply amount of each region is calculated based on the power generation resources and the power transmission network data of each region. The power supply amount of the target region is determined based on the first power supply amount of each region.

[0029] In this embodiment, the regional power data also includes: substation equipment data and power distribution data.

[0030] Determining the power supply amount of the target area based on the first power supply amount of each area includes: A first influencing factor is determined based on historical power network fault data.

[0031] A power supply adjustment parameter is determined based on the first influencing factor.

[0032] The first power supply is adjusted based on the power supply adjustment parameter, the power transformation equipment data and the power distribution data to obtain the power supply of the target area.

[0033] In this embodiment, the transmission network data may include parameters such as the length of the transmission line, the material and cross-sectional area of ​​the conductor, the rated voltage, the maximum current carrying capacity, and information such as the location, capacity, and transformation ratio of the substation. For example, the resistance of a 220 kV transmission line with a length of 100 kilometers and a cross-sectional area of ​​500 square millimeters will affect the power transmission loss, and the maximum current carrying capacity determines the maximum power that the line can safely transmit.

[0034] Substation equipment data may include transformer model, capacity, loss rate, operating life, load rate and other parameters. For example, if the load rate of a 50MVA transformer is at a high level (greater than the preset load rate) for a long time, it will affect the power supply stability and equipment life, and also restrict the distribution of power supply.

[0035] Power distribution data may include the branching, length, and wire diameter of distribution lines, the capacity and distribution of distribution boxes and cabinets, as well as the distribution of users and power demand characteristics within the distribution area. For example, in a certain area, the distribution lines have many and complex branches and small wire diameters, which may easily cause voltage drops during peak power consumption, limiting the power supply.

[0036] Historical power network fault data can include information such as the time, location, type, cause, scope and duration of power outages caused by past faults. Historical power network fault data can be used to assess the reliability and stability of power networks in different regions and determine the first influencing factor.

[0037] The first impact factor is a parameter used to measure the adjustment range of power supply caused by historical power network failures.

[0038] The parameters of the first impact factor may include the number of faults, which reflects the frequency of the faults. The more faults, the greater the impact on power supply stability; the duration of the fault, which indicates that the fault has a serious impact; the scope of the fault, which indicates that the consequences of the fault are serious; the type of fault, such as short circuit, open circuit and other types of faults have different degrees of damage to the power supply system; the length of maintenance time, which means that the fault has a large continuous interference with the power supply. The first impact factor is determined based on the number of faults, duration of the fault, scope of the fault, type of fault and maintenance time to reasonably adjust the power supply.

[0039] In this embodiment, the first power supply of each region is calculated based on the regional power generation resources and transmission network data. This is a preliminary power assessment based on ideal power generation and transmission conditions. The first influencing factor is determined by the number, duration, range, type and maintenance time of the historical power network fault data. The first influencing factor comprehensively reflects the potential impact of the past reliability problems of the power network on the power supply, and then the power supply adjustment parameters are obtained. Finally, the first power supply is adjusted in combination with the substation equipment data and the distribution data, so as to obtain a target area power supply that is more in line with reality and takes into account the full range of the power network, ensuring the safety, stability and rationality of the power supply.

[0040] This embodiment comprehensively considers a variety of power data to make the power supply assessment more realistic and avoid over- or under-power supply. This embodiment adjusts the power supply by determining the influencing factors based on historical fault data, which can respond to potential risks of the power network in advance and ensure the stability of power supply. This embodiment is further optimized in combination with substation and distribution data to improve the safety and rationality of power supply, meet the power demand of users in different regions, and promote the effective allocation of power resources and the reliable operation of the power system.

[0041] In one embodiment of the present disclosure, calculating the first power supply amount of each region based on the power generation resources and transmission network data of each region includes: A first resource identifier is determined based on the power generation resource.

[0042] A power generation-supply association characteristic corresponding to the power generation resource is determined based on the first resource identifier.

[0043] The power generation efficiency is determined based on the power generation-power supply correlation characteristics corresponding to the power generation resources, and the first power supply amount for each area is calculated based on the power generation efficiency and the power transmission network data.

[0044] In this embodiment, the power transmission network data includes physical parameters of the power transmission lines, substation parameters and power transmission network operation data.

[0045] The first power supply amount of each region is calculated based on the power generation resources and transmission network data of each region, including: The first power supply amount of each region is calculated based on the power generation resources of each region, physical parameters of the transmission line, substation parameters and transmission network operation data.

[0046] In this embodiment, the first resource identifier is a classification mark for different power generation resources. For example, thermal power generation resources can be marked as "FP"; hydropower generation resources can be marked as "HP"; wind power generation resources can be marked as "WP"; solar power generation resources can be marked as "SP", etc. The first resource identifier helps to quickly distinguish different types of power generation resources so as to subsequently determine their specific power generation-power supply association characteristics.

[0047] Different power generation resources have different characteristics that affect power supply. Taking thermal power generation as an example, its associated characteristics may include factors that affect power generation efficiency, such as coal combustion efficiency and steam turbine efficiency, as well as power supply-related characteristics such as the need for a stable water supply for cooling; the associated characteristics of hydropower generation may include the impact of seasonal changes in river runoff on power generation, the relationship between dam water storage capacity and power generation sustainability, etc.; the associated characteristics of wind power generation may include the relationship between wind speed and wind turbine power curve, that is, the change in wind turbine power generation under different wind speeds, and the availability of wind turbines, etc.; the associated characteristics of solar power generation may include the relationship between light intensity and photovoltaic panel conversion efficiency, the impact of daytime light duration on power generation, etc.

[0048] Power generation efficiency refers to the ratio of power generation resources to convert other forms of energy (such as chemical energy of coal, wind energy, hydropower, solar energy, etc.) into electrical energy. The physical parameters of transmission lines can include line length, conductor material, conductor cross-sectional area, etc. Substation parameters can include substation capacity, transformation ratio, etc.

[0049] In this embodiment, the first resource identifier is determined according to the power generation resource, and the corresponding power generation-power supply association characteristics are searched according to the first resource identifier to determine the power generation efficiency. For example, after it is determined to be a wind power generation resource, its power generation efficiency is determined according to the local average wind speed, wind turbine performance, etc. The first power supply is calculated in combination with the power generation efficiency and the transmission network data. Taking a region as an example, if there is a wind farm, the first power supply that can be output in the region under the current conditions is calculated based on the power generation efficiency of the wind turbine and the maximum current carrying capacity of the transmission line, the capacity of the substation, etc., taking into account the loss and limitation of electric energy during transmission. If the power generation efficiency is high (greater than the preset power generation efficiency corresponding to the first resource identifier) ​​and the transmission network operates well and has sufficient capacity, the first power supply will be relatively large; otherwise it will be small.

[0050] For example, there is a wind farm in a certain area, which is marked as "WP". The average wind speed in the area is 6 meters per second. According to the power curve and performance of the wind turbine, its power generation efficiency is determined to be 38%. In terms of the transmission network, there are three 110 kV transmission lines, the conductor material is aluminum, the cross-sectional area is 300 square millimeters, the length is 30 kilometers, and the substation capacity is 150 megavolt-amperes. By calculating the line resistance and considering the substation capacity limitation and the power generation efficiency of the wind farm, it is concluded that the first power supply in the area is 80 megawatts. In actual operation, if the wind speed increases, the power generation efficiency is improved, and the transmission network operation data shows that the line load is low, then the first power supply may increase to 90 megawatts; if the transmission line fails or the substation is maintained, the first power supply will be reduced accordingly.

[0051] In this embodiment, calculating the first power supply amount of each region based on the power generation resources, physical parameters of the transmission line, substation parameters and transmission network operation data of each region may include: The first power supply of each area is calculated based on the power supply calculation formula, the power generation resources of each area, the physical parameters of the transmission line, the substation parameters and the transmission network operation data.

[0052] The power supply calculation formula is:

[0053] in, Indicates the power supply. represents the power generation of the power generation resource, L represents the length of the transmission line, A represents the cross-sectional area of ​​the transmission line conductor, It represents the resistivity of the conductor material of the transmission line, V represents the rated voltage of the transmission line, Indicates the maximum current carrying capacity of the transmission line. represents the substation capacity, Indicates the transformer ratio adjustment coefficient of the substation, Indicates the real-time load power when the transmission network is running, represents the comprehensive transmission efficiency coefficient of the transmission network, and min{} represents the minimum function.

[0054] In this embodiment, the first power supply amount is determined by taking the minimum value of the calculation results of the three formulas in the power supply amount calculation formula. This is because the actual power supply amount is limited by multiple factors such as the maximum current carrying capacity of the transmission line, the capacity of the substation, and the loss during the transmission process. Taking the minimum value can accurately reflect the actual amount of power that can be supplied under these restrictions.

[0055] In this embodiment, the power generation of the power generation resources may include the maximum power generation that can be generated by combining various power generation methods in the region, such as thermal power, hydropower, wind power, solar power, etc., according to resource conditions such as installed capacity, wind speed, light, water flow, and the efficiency of power generation equipment. Considering the impact of substations on transmission efficiency under different transformation ratios, the value range of the transformation ratio adjustment coefficient of the substation is determined according to the characteristics of the actual substation, for example, generally between 0.8 and 1.2. The closer the transformation ratio is to the ideal state, the closer the coefficient is to 1. The comprehensive transmission efficiency coefficient of the transmission network can be determined based on multiple factors such as the corona loss of the transmission line and the loss of the transformer. The value range of the comprehensive transmission efficiency coefficient of the transmission network can be set between 0.9-0.98. The closer the comprehensive transmission efficiency coefficient of the transmission network is to 1, the higher the transmission efficiency of the transmission network.

[0056] In this embodiment, the power supply calculation formula determines the first power supply by taking the minimum value of the calculation results of the three formulas. This is because the actual power supply is limited by multiple factors such as the maximum current carrying capacity of the transmission line, the capacity of the substation, and the loss during the transmission process. Taking the minimum value can accurately reflect the actual amount of power that can be supplied under these restrictions.

[0057] This embodiment clarifies the power generation efficiency by determining the first resource identifier and the corresponding power generation-power supply correlation characteristics, which can accurately consider the characteristics of different power generation resources and make the power supply calculation more realistic. The power supply calculation formula integrates a variety of key factors, such as transmission lines, substations, and operating data, and comprehensively evaluates the impact of various links in power transmission on power supply. Taking the minimum value of the results of the three formulas to determine the power supply can accurately reflect the actual amount of power available under various constraints, effectively avoid over-estimation, and ensure the rationality and reliability of power supply arrangements.

[0058] In one embodiment of the present disclosure, the power spot transaction prediction method based on big data further includes: A regional power dispatching strategy is determined based on the second power spot transaction price.

[0059] In this embodiment, determining a regional power dispatching strategy based on the second power spot transaction price includes: The environmental impact factor prediction data corresponding to the second time period and the first time period is determined based on the first time period. The first time period is the time period to be predicted, and the second time period is the historical time period corresponding to the first time period.

[0060] Get the historical scheduling strategy corresponding to the second time period.

[0061] A regional power dispatch strategy is determined based on the environmental impact factor prediction data corresponding to the first time period and the historical dispatch strategy.

[0062] In this embodiment, the second electricity spot trading price is a price predicted after considering the first electricity spot trading price and the environmental characteristics of each region. The second electricity spot trading price comprehensively reflects the dynamic changes in the electricity supply and demand relationship under the influence of environmental factors, and the value of the second electricity spot trading price directly affects the formulation of subsequent power dispatching strategies. For example, a higher price (higher than the preset price) may mean that the supply is tight or the cost of power generation has increased due to environmental factors, while a lower price may mean that the supply is relatively abundant.

[0063] Regional power dispatch strategy refers to the arrangement plan for the allocation, deployment and operation of power resources in a specific region, with the aim of ensuring the stable operation of the power system, meeting power demand and maximizing economic benefits. Regional power dispatch strategy can include the power adjustment range of each power station, the transmission capacity allocation of transmission lines, and the arrangement of power transfer between different regions.

[0064] The first time period refers to a certain period in the future to be predicted, such as the next week, month, etc., which can be specifically set from a specific date and time to another specific date and time. The second time period refers to the past historical period corresponding to the first time period, and its time span, start and end time, etc. are similar to the first time period for comparison and analysis. For example, if the first time period is July 1st to July 7th in the future, the second time period may be July 1st to July 7th in the same period of the past three years.

[0065] Environmental impact factor prediction data may include the expected changes in environmental factors such as temperature, light intensity, wind speed, humidity, etc. within the first time period and the degree of impact on power generation and electricity consumption. For example, if it is predicted that the temperature in a certain area will rise within the first time period, the air conditioning power load will increase, which will also affect the cooling efficiency of thermal power plants.

[0066] In this embodiment, the second time period (historical time period of the same period) corresponding to the first time period (time period to be predicted) is determined, and the historical dispatching strategy corresponding to the second time period is obtained. This is based on the idea that historical experience may have a certain reference value for the current situation. The environmental impact factor prediction data corresponding to the first time period is analyzed to understand the impact characteristics of environmental factors on power generation and power consumption in the future time period. The environmental impact factor prediction data is combined with the historical dispatching strategy, and the historical dispatching strategy is adjusted and optimized to determine the regional power dispatching strategy suitable for the first time period. For example, it is predicted that the wind speed in a certain area will increase in the first time period, the wind power output will increase, and the power load will increase or decrease due to changes in temperature. According to these conditions, the dispatching strategies such as power generation power allocation and power transfer arrangement in the same period in history are adjusted to better adapt to future changes in power supply and demand.

[0067] This embodiment can use past experience and reduce the blindness of strategy formulation by relying on historical time periods and dispatch strategies. Combined with the prediction data of environmental impact factors, it fully considers the dual impact of future environmental changes on power generation and power consumption, making the dispatch strategy more in line with actual demand changes. By optimizing and adjusting historical dispatch strategies, the rationality of power resource allocation can be improved, the ability of the power system to cope with changes in supply and demand can be enhanced, and stable power supply and economical and efficient operation can be guaranteed.

[0068] Corresponding to the power spot transaction prediction method based on big data in the above embodiment, Figure 2This is a block diagram of a power spot trading forecasting system based on big data provided by an embodiment of the present disclosure. For ease of explanation, only the parts related to the embodiment of the present disclosure are shown. Figure 2 The big data-based electricity spot trading prediction system 20 includes: a first calculation module 21, a second calculation module 22 and a prediction module 23.

[0069] The first calculation module 21 is used to determine the power generation resources of each region based on the region type, and determine the power supply of the target region based on the power generation resources of each region and the regional power data. The target region is the region to be predicted.

[0070] The second calculation module 22 is used to determine a first electricity spot transaction price based on the power supply of the target area and the electricity spot transaction type.

[0071] The prediction module 23 is used to predict the electricity spot transaction price of each region based on the first electricity spot transaction price and the environmental characteristics of each region to obtain a second electricity spot transaction price.

[0072] In one embodiment of the present disclosure, the regional power data includes transmission network data. The first calculation module 21 is specifically used to calculate the first power supply of each region based on the power generation resources and transmission network data of each region. The power supply of the target region is determined based on the first power supply of each region.

[0073] In an embodiment of the present disclosure, the regional power data further includes: substation equipment data and power distribution data. The first calculation module 21 is further configured to determine a first impact factor based on historical power network fault data.

[0074] A power supply adjustment parameter is determined based on the first influencing factor.

[0075] The first power supply is adjusted based on the power supply adjustment parameter, the power transformation equipment data and the power distribution data to obtain the power supply of the target area.

[0076] In an embodiment of the present disclosure, the first calculation module 21 is further configured to determine a first resource identifier based on power generation resources.

[0077] A power generation-supply association characteristic corresponding to the power generation resource is determined based on the first resource identifier.

[0078] The power generation efficiency is determined based on the power generation-power supply correlation characteristics corresponding to the power generation resources, and the first power supply amount for each area is calculated based on the power generation efficiency and the power transmission network data.

[0079] In one embodiment of the present disclosure, the transmission network data includes physical parameters of the transmission line, substation parameters and transmission network operation data. The first calculation module 21 is further used to calculate the first power supply of each area based on the power generation resources, physical parameters of the transmission line, substation parameters and transmission network operation data of each area.

[0080] In one embodiment of the present disclosure, the big data-based electricity spot trading prediction system 20 further includes: a dispatching strategy module, which is used to determine a regional electricity dispatching strategy based on the second electricity spot trading price.

[0081] In one embodiment of the present disclosure, the scheduling strategy module is specifically used to determine the environmental impact factor prediction data corresponding to the second time period and the first time period based on the first time period. The first time period is the time period to be predicted, and the second time period is the historical time period corresponding to the first time period.

[0082] Get the historical scheduling strategy corresponding to the second time period.

[0083] A regional power dispatch strategy is determined based on the environmental impact factor prediction data corresponding to the first time period and the historical dispatch strategy.

[0084] See also Figure 3 , Figure 3 A schematic block diagram of an electronic device provided by an embodiment of the present disclosure. Figure 3 The electronic device 300 in the embodiment shown may include: one or more processors 301, one or more input devices 302, one or more output devices 303 and one or more memories 304. The processors 301, input devices 302, output devices 303 and memories 304 communicate with each other via a communication bus 305. The memory 304 is used to store computer programs, which include program instructions. The processor 301 is used to execute the program instructions stored in the memory 304. The processor 301 is configured to call the program instructions to execute the functions of the modules in the above-mentioned system embodiments, such as Figure 2 The functions of modules 21 to 23 are shown.

[0085] It should be understood that in the embodiment of the present disclosure, the processor 301 may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0086] The input device 302 may include a touch panel, a fingerprint collection sensor (for collecting the user's fingerprint information and fingerprint direction information), a microphone, etc., and the output device 303 may include a display (LCD, etc.), a speaker, etc.

[0087] The memory 304 may include a read-only memory and a random access memory, and provide instructions and data to the processor 301. A portion of the memory 304 may also include a non-volatile random access memory. For example, the memory 304 may also store information about the device type.

[0088] In a specific implementation, the processor 301, input device 302, and output device 303 described in the embodiments of the present disclosure can execute the implementation methods described in the first and second embodiments of the big data-based electricity spot trading prediction method provided in the embodiments of the present disclosure, and can also execute the implementation methods of the electronic device 300 described in the embodiments of the present disclosure, which will not be repeated here.

[0089] In another embodiment of the present disclosure, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, wherein the computer program includes program instructions, and when the program instructions are executed by the processor, all or part of the processes in the above-mentioned embodiment method are implemented, and the computer program can also be completed by instructing the relevant hardware through the computer program. The computer program can be stored in a computer-readable storage medium, and when the computer program is executed by the processor, the steps of each of the above-mentioned method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form, etc. The computer-readable medium can include: any entity or device capable of carrying computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium, etc.

[0090] The computer-readable storage medium may be an internal storage unit of the electronic device of any of the aforementioned embodiments, such as a hard disk or memory of the electronic device. The computer-readable storage medium may also be an external storage device of the electronic device, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (SecureDigital, SD) card, a flash card (Flash Card), etc. equipped on the electronic device. Furthermore, the computer-readable storage medium may also include both an internal storage unit of the electronic device and an external storage device. The computer-readable storage medium is used to store computer programs and other programs and data required by the electronic device. The computer-readable storage medium may also be used to temporarily store data that has been output or is to be output.

[0091] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this disclosure.

[0092] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the electronic devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0093] In the several embodiments provided in the present application, it should be understood that the disclosed electronic device and method can be implemented in other ways. For example, the system embodiments described above are only schematic. For example, the division of units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces or units, or it can be an electrical, mechanical or other form of connection.

[0094] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the embodiments of the present disclosure.

[0095] In addition, each functional unit in each embodiment of the present disclosure may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0096] The above are only specific embodiments of the present disclosure, but the protection scope of the present disclosure is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed in the present disclosure, and these modifications or replacements should be included in the protection scope of the present disclosure. Therefore, the protection scope of the present disclosure should be based on the protection scope of the claims.

Claims

1. A method for predicting power spot transactions based on big data, characterized in that: include: Determine the power generation resources of each region based on the region type, and determine the power supply of the target region based on the power generation resources of each region and the regional power data; The target area is an area to be predicted; determining a first electricity spot transaction price based on the power supply amount and the electricity spot transaction type of the target area; The electricity spot transaction price of each region is predicted based on the first electricity spot transaction price and the environmental characteristics of each region to obtain a second electricity spot transaction price.

2. The method for predicting power spot transactions based on big data according to claim 1, characterized in that: The regional power data includes transmission network data; The determining of the power supply amount of the target area based on the power generation resources and regional power data of each area includes: Calculating a first power supply amount for each region based on power generation resources and transmission network data for each region; The power supply amount of the target area is determined based on the first power supply amount of each area.

3. The method for predicting power spot transactions based on big data according to claim 2, characterized in that: The regional power data also includes: substation equipment data and power distribution data; The step of determining the power supply amount of the target area based on the first power supply amount of each area includes: Determining a first influencing factor based on historical power network fault data; Determining a power supply adjustment parameter based on the first influencing factor; The first power supply is adjusted based on the power supply adjustment parameter, the power transformation equipment data and the power distribution data to obtain the power supply of the target area.

4. The method for predicting power spot transactions based on big data according to claim 2, characterized in that: The calculating the first power supply amount of each region based on the power generation resources and transmission network data of each region includes: determining a first resource identifier based on the power generation resource; Determining a power generation-supply association characteristic corresponding to the power generation resource based on the first resource identifier; The power generation efficiency is determined based on the power generation-power supply correlation characteristics corresponding to the power generation resources, and the first power supply amount for each area is calculated based on the power generation efficiency and the power transmission network data.

5. The method for predicting power spot transactions based on big data according to claim 2, characterized in that: The transmission network data includes physical parameters of transmission lines, substation parameters and transmission network operation data; The calculating the first power supply amount of each region based on the power generation resources and transmission network data of each region includes: The first power supply amount of each area is calculated based on the power generation resources of each area, the physical parameters of the transmission line, the substation parameters and the transmission network operation data.

6. The method for predicting power spot transactions based on big data according to claim 1, characterized in that: Also includes: A regional power dispatching strategy is determined based on the second power spot trading price.

7. The method for predicting power spot transactions based on big data according to claim 6, characterized in that: The determining of the regional power dispatching strategy based on the second power spot transaction price includes: Determine, based on the first time period, environmental impact factor prediction data corresponding to the second time period and the first time period; the first time period is the time period to be predicted, and the second time period is the historical time period corresponding to the first time period; Obtaining a historical scheduling strategy corresponding to the second time period; A regional power dispatching strategy is determined based on the environmental impact factor prediction data corresponding to the first time period and the historical dispatching strategy.

8. A power spot trading prediction system based on big data, characterized in that: include: A first calculation module is used to determine the power generation resources of each region based on the region type, and determine the power supply of the target region based on the power generation resources of each region and the regional power data; The target area is an area to be predicted; A second calculation module, configured to determine a first electricity spot transaction price based on the power supply amount and the electricity spot transaction type of the target area; A prediction module is used to predict the electricity spot transaction price of each region based on the first electricity spot transaction price and the environmental characteristics of each region to obtain a second electricity spot transaction price.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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