A distributed virtual power plant control method in power spot market environment

Abstract

The present invention relates to the technical field of smart grids, and discloses a distributed virtual power plant control method in an electricity spot market environment, comprising the following steps: step S1: acquiring power data of energy resources of a distributed virtual power plant, step S2: performing an overall benefit analysis on the power data information of the distributed virtual power plant according to an overall utility function, step S3: performing an optimization judgment on the distributed virtual power plant according to an overall benefit evaluation result, step S4: establishing a mathematical model for analyzing the rationality of profit distribution, and calculating a rationality index for profit distribution, step S5: establishing a correlation analysis model, and determining the energy correlation of the distributed virtual power plant, step S6: performing a secondary optimization judgment on the distributed virtual power plant, and step S7: receiving the optimization judgment result, optimizing and controlling the distributed virtual power plant, and realizing optimal allocation of resources, thereby improving energy utilization efficiency.

Description

Technical Field

[0001] The present invention relates to the field of smart grid technology, and more specifically to a distributed virtual power plant control method in an electricity spot market environment. Background Art

[0002] As the world pays more and more attention to environmental protection and sustainable development, the energy structure is gradually transforming from fossil energy to renewable energy. The access ratio of distributed renewable energy is increasing, which puts higher requirements on the stability and flexibility of the power system. The electricity spot market is an important part of the power market system, which can reflect the real-time supply and demand relationship and price changes of electricity commodities. Emerging market players such as virtual power plants, energy storage, and distributed power generation are encouraged to participate in power market transactions, providing the market with more choices and vitality. Emerging market players can promote the transformation and development of the power market through technological innovation and business model innovation. Summary of the invention

[0003] In order to overcome the above-mentioned defects of the prior art, the present invention provides a distributed virtual power plant control method in an electricity spot market environment to solve the problems existing in the above-mentioned background technology.

[0004] The present invention provides the following technical solution: a distributed virtual power plant control method in an electricity spot market environment, comprising the following steps:

[0005] Step S1: Obtaining power data of energy resources of a distributed virtual power plant: obtaining power data of energy resources of a distributed virtual power plant through a data transmission network, and extracting power data information;

[0006] Step S2: Performing an overall benefit analysis on the power data information of the distributed virtual power plant according to the overall utility function: inputting the extracted power data information into the overall utility function, performing model calculation based on the overall utility function and the input data, and obtaining an overall benefit evaluation result of the distributed virtual power plant;

[0007] Step S3: Perform an optimization judgment on the distributed virtual power plant based on the overall benefit evaluation result: evaluate the difference between the overall benefit of the distributed virtual power plant and the expected benefit, and perform an optimization judgment on the distributed virtual power plant based on the evaluation result;

[0008] Step S4: Establishing a mathematical model for analyzing the rationality of revenue distribution and calculating a rationality index for revenue distribution: Establishing a mathematical model for analyzing the rationality of revenue distribution and calculating a rationality index for revenue distribution of a distributed virtual power plant upon receiving an optimization instruction;

[0009] Step S5: Establish a correlation analysis model to determine the energy correlation of the distributed virtual power plant: calculate the energy correlation index through the correlation analysis model to evaluate the synergy effect of energy in the power market;

[0010] Step S6: Perform secondary optimization judgment on the distributed virtual power plant: Through the target optimization judgment model, the distributed virtual power plant benefit allocation rationality index and energy correlation index are input to perform secondary optimization judgment on the distributed virtual power plant;

[0011] Step S7: Receive the optimization judgment result and perform optimization control on the distributed virtual power plant: complete the optimization control of the distributed virtual power plant based on the first optimization judgment result and the second optimization judgment result.

[0012] Preferably, power data of energy resources of distributed virtual power plants are obtained through a data transmission network to extract power data information, wherein the power data information includes: the electric energy consumed by the virtual power plant in different time periods, the electric energy generated by the virtual power plant in different time periods, the power purchase price of the operator in different time periods, the power selling price of the operator in different time periods, and the power supply on the power supply side and the power purchase amount on the power purchasing side in different time periods.

[0013] Preferably, in step S2, the extracted power data information is input into the overall utility function, and a model calculation is performed based on the overall utility function and the input data. The calculation formula of the overall utility function is: ,in represents the overall benefit evaluation value, T represents the data collection time period, t=1, 2, 3, ..., T, where t represents the number of the data collection time period, represents the utility coefficient, represents the load factor, represents the electric energy consumed by the virtual power plant in the tth time period, represents the electric energy generated by the virtual power plant in the tth time period, It represents the electricity purchase price of the operator in the tth time period.

[0014] Preferably, in step S3, the difference between the overall benefit of the distributed virtual power plant and the expected benefit is evaluated, and the specific content of optimizing the distributed virtual power plant according to the evaluation result is: the overall benefit evaluation value of the distributed virtual power plant is Expected benefits For comparison, if the overall benefit evaluation value Greater than or equal to the expected benefit , then the primary judgment result is that the distributed virtual power plant is operating normally. If the overall benefit evaluation value Less than the expected benefit , then a judgment result is that the distributed virtual power plant operates abnormally, and an optimization instruction is issued, and step S7 receives the optimization instruction.

[0015] Preferably, in step S4, a mathematical model for analyzing the rationality of profit distribution is established, and the specific contents of calculating the rationality index of profit distribution of the distributed virtual power plant upon receiving the optimization instruction are as follows:

[0016] Distributed virtual power plants act as the power supply side to sell electricity to the power purchase side, match the power supply of the power supply side in different time periods with the power purchase side's power purchase amount, and compare the power supply of the power supply side in different time periods with the power purchase side's power purchase amount to calculate the spot power supply and demand ratio;

[0017] A commodity price estimation model is established based on the spot power supply and demand ratio, and the spot power supply and demand ratio is input into the commodity price estimation model as an input variable to calculate the expected estimated price of electricity sold by the distributed virtual power plant in different time periods;

[0018] The expected estimated electricity sales prices in different time periods are substituted into the mathematical model of revenue distribution rationality analysis, and the revenue distribution rationality index of the distributed virtual power plant is calculated.

[0019] Preferably, the calculation formula of the power spot supply-demand ratio is: , where Es represents the spot electricity supply-demand ratio, represents the power supply on the power supply side in the tth time period, It represents the amount of electricity purchased by the electricity purchasing side in the tth time period.

[0020] Preferably, the commodity price estimation model is established based on the spot power supply-demand ratio to calculate the expected estimated price of electricity sales in different time periods. The calculation formula of the commodity price estimation model is: ,in represents the expected estimated price of electricity sold in different time periods, represents the operator's electricity selling price in the tth time period, It represents the electricity purchase price of the operator in the tth time period.

[0021] Preferably, the expected estimated prices of electricity sold in different time periods are substituted into the mathematical model for analyzing the rationality of revenue distribution, and the calculation formula for calculating the rationality index of revenue distribution is: ,in represents the rationality index of income distribution, Represents the profit distribution coefficient in the tth time period.

[0022] Preferably, in step S5, the specific content of calculating the energy relevance index by the relevance analysis model is as follows:

[0023] Step S51: Based on the power consumed by the virtual power plant in different time periods and the electricity generated by the virtual power plant in different time periods Calculate the difference ratio coefficient of the power consumption generated by the virtual power plant in different time periods. The calculation formula is: ,in It represents the proportional coefficient of the difference in electric energy consumed by the virtual power plant in the tth time period;

[0024] Step S52: Calculate the energy correlation index of the virtual power plant in different time periods, and the calculation formula is: ,in represents the energy relevance index of the virtual power plant in the tth time period, Indicates the minimum value of the difference ratio coefficient of the power consumption generated by the virtual power plant in different time periods, It indicates the maximum value of the difference ratio coefficient of the power consumption generated by the virtual power plant in different time periods. represents the correlation resolution coefficient;

[0025] Step S53: Calculate the mean of the energy correlation index of the virtual power plant in different time periods to obtain the energy correlation index of the distributed virtual power plant. The calculation formula is: ,in Represents the energy relevance index of distributed virtual power plants.

[0026] Preferably, in step S6, a target optimization judgment model is used to input a distributed virtual power plant profit distribution rationality index and an energy correlation index to perform secondary optimization judgment on the distributed virtual power plant. The calculation formula of the target optimization judgment model is: ,in represents the optimization evaluation value of the distributed virtual power plant, represents the weight of the profit distribution rationality index in the optimization evaluation of distributed virtual power plants, represents the weight of the energy correlation index in the optimization evaluation of distributed virtual power plants, where ;

[0027] Optimizing the evaluation value of distributed virtual power plants Compared with the preset optimization judgment threshold For comparison, if the distributed virtual power plant optimizes the evaluation value Greater than or equal to the preset optimization judgment threshold , then the secondary judgment result is that the distributed virtual power plant evaluation is normal. If the distributed virtual power plant optimization evaluation value Less than the preset optimization judgment threshold , the secondary judgment result is that the distributed virtual power plant assessment is abnormal, and an optimization instruction is issued, and step S7 receives the optimization instruction.

[0028] Preferably, the step S7 receives the optimization judgment results of step S3 and step S6, and adjusts the operation strategy according to the first optimization judgment result and the second optimization judgment result. If no optimization instruction is received, the current operation strategy remains unchanged; if an optimization instruction is received, the operation strategy of the virtual power plant is adjusted according to the optimization instruction.

[0029] Technical effects and advantages of the present invention:

[0030] The present invention is provided with step S1: acquiring power data of energy resources of distributed virtual power plants, step S2: performing overall benefit analysis on power data information of distributed virtual power plants according to the overall utility function, step S3: performing an optimization judgment on distributed virtual power plants according to the overall benefit evaluation result, step S4: establishing a mathematical model for analyzing the rationality of profit distribution and calculating the rationality index of profit distribution, step S5: establishing a correlation analysis model and determining the energy correlation of distributed virtual power plants, step S6: performing a secondary optimization judgment on distributed virtual power plants, step S7: receiving the optimization judgment result, optimizing and controlling distributed virtual power plants, and realizing optimal allocation of resources. In short, a distributed virtual power plant control method in a power spot market environment performs an optimization judgment on distributed virtual power plants by evaluating the difference between the overall benefit of distributed virtual power plants and the expected benefit, and performs a secondary optimization judgment on distributed virtual power plants by inputting the rationality index of distributed virtual power plant profit distribution and the energy correlation index through a target optimization judgment model, thereby ensuring efficient utilization of power resources and stable operation of the power market, which not only improves the operation efficiency of the power system, but also enhances the flexibility and response speed of the power market. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 The figure is a flow chart of a distributed virtual power plant control method in a power spot market environment. DETAILED DESCRIPTION

[0032] The technical solution of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the present invention. In addition, the forms of the various structures recorded in the following implementation modes are merely illustrative. The distributed virtual power plant control method in a power spot market environment involved in the present invention is not limited to the various structures recorded in the following implementation modes. All other implementation modes obtained by ordinary technicians in this field without making creative work belong to the scope of protection of the present invention.

[0033] like Figure 1 As shown, the present invention provides a distributed virtual power plant control method in an electricity spot market environment, comprising the following steps:

[0034] Step S1: Obtaining power data of energy resources of a distributed virtual power plant: obtaining power data of energy resources of a distributed virtual power plant through a data transmission network, and extracting power data information;

[0035] Step S2: Performing an overall benefit analysis on the power data information of the distributed virtual power plant according to the overall utility function: inputting the extracted power data information into the overall utility function, performing model calculation based on the overall utility function and the input data, and obtaining an overall benefit evaluation result of the distributed virtual power plant;

[0036] Step S3: Perform an optimization judgment on the distributed virtual power plant based on the overall benefit evaluation result: evaluate the difference between the overall benefit of the distributed virtual power plant and the expected benefit, and perform an optimization judgment on the distributed virtual power plant based on the evaluation result;

[0037] Step S4: Establishing a mathematical model for analyzing the rationality of revenue distribution and calculating a rationality index for revenue distribution: Establishing a mathematical model for analyzing the rationality of revenue distribution and calculating a rationality index for revenue distribution of a distributed virtual power plant upon receiving an optimization instruction;

[0038] Step S5: Establish a correlation analysis model to determine the energy correlation of the distributed virtual power plant: calculate the energy correlation index through the correlation analysis model to evaluate the synergy effect of energy in the power market;

[0039] Step S6: Perform secondary optimization judgment on the distributed virtual power plant: Through the target optimization judgment model, the distributed virtual power plant benefit allocation rationality index and energy correlation index are input to perform secondary optimization judgment on the distributed virtual power plant;

[0040] Step S7: Receive the optimization judgment result and perform optimization control on the distributed virtual power plant: complete the optimization control of the distributed virtual power plant based on the first optimization judgment result and the second optimization judgment result.

[0041] In this embodiment, it should be specifically explained that in step S2, the extracted power data information is input into the overall utility function, and the model calculation is performed based on the overall utility function and the input data. The calculation formula of the overall utility function is: ,in represents the overall benefit evaluation value, T represents the data collection time period, t=1, 2, 3, ..., T, where t represents the number of the data collection time period, represents the utility coefficient, represents the load factor, represents the electric energy consumed by the virtual power plant in the tth time period, represents the electric energy generated by the virtual power plant in the tth time period, It represents the electricity purchase price of the operator in the tth time period.

[0042] In this embodiment, it should be specifically explained that in step S3, the difference between the overall benefit of the distributed virtual power plant and the expected benefit is evaluated, and the specific content of optimizing the distributed virtual power plant based on the evaluation result is: the overall benefit evaluation value of the distributed virtual power plant is Expected benefits For comparison, if the overall benefit evaluation value Greater than or equal to the expected benefit , then the primary judgment result is that the distributed virtual power plant is operating normally. If the overall benefit evaluation value Less than the expected benefit , then the primary judgment result is that the distributed virtual power plant is operating abnormally, and an optimization instruction is issued.

[0043] In this embodiment, it should be specifically explained that in step S4, a mathematical model for analyzing the rationality of profit distribution is established, and the specific contents of calculating the rationality index of profit distribution of the distributed virtual power plant upon receiving the optimization instruction are as follows:

[0044] Distributed virtual power plants act as the power supply side to sell electricity to the power purchase side, match the power supply of the power supply side in different time periods with the power purchase side's power purchase amount, and compare the power supply of the power supply side in different time periods with the power purchase side's power purchase amount to calculate the spot power supply and demand ratio;

[0045] A commodity price estimation model is established based on the spot power supply and demand ratio, and the spot power supply and demand ratio is input into the commodity price estimation model as an input variable to calculate the expected estimated price of electricity sold by the distributed virtual power plant in different time periods;

[0046] The expected estimated electricity sales prices in different time periods are substituted into the mathematical model of revenue distribution rationality analysis, and the revenue distribution rationality index of the distributed virtual power plant is calculated.

[0047] In this embodiment, it should be specifically explained that the calculation formula of the power spot supply-demand ratio is: , where Es represents the spot electricity supply-demand ratio, represents the power supply on the power supply side in the tth time period, It represents the amount of electricity purchased by the electricity purchasing side in the tth time period.

[0048] In this embodiment, it should be specifically explained that the commodity price estimation model is established based on the spot power supply and demand ratio to calculate the expected estimated price of electricity sales in different time periods. The calculation formula of the commodity price estimation model is: ,in represents the expected estimated price of electricity sold in different time periods, represents the operator's electricity selling price in the tth time period, It represents the electricity purchase price of the operator in the tth time period.

[0049] In this embodiment, it should be specifically explained that the expected estimated price of electricity sold in different time periods is substituted into the mathematical model for analyzing the rationality of revenue distribution to calculate the rationality index of revenue distribution. The calculation formula is: ,in represents the rationality index of income distribution, Represents the profit distribution coefficient in the tth time period.

[0050] In this embodiment, it should be specifically explained that in step S5, the specific content of calculating the energy relevance index by the relevance analysis model is as follows:

[0051] Step S51: Based on the power consumed by the virtual power plant in different time periods and the electricity generated by the virtual power plant in different time periods Calculate the difference ratio coefficient of the power consumption generated by the virtual power plant in different time periods. The calculation formula is: ,in It represents the proportional coefficient of the difference in electric energy consumed by the virtual power plant in the tth time period;

[0052] Step S52: Calculate the energy correlation index of the virtual power plant in different time periods, and the calculation formula is: ,in represents the energy relevance index of the virtual power plant in the tth time period, Indicates the minimum value of the difference ratio coefficient of the power consumption generated by the virtual power plant in different time periods, It indicates the maximum value of the difference ratio coefficient of the power consumption generated by the virtual power plant in different time periods. represents the correlation resolution coefficient;

[0053] Step S53: Calculate the mean of the energy correlation index of the virtual power plant in different time periods to obtain the energy correlation index of the distributed virtual power plant. The calculation formula is: ,in Represents the energy relevance index of distributed virtual power plants.

[0054] In this embodiment, it should be specifically explained that in step S6, the distributed virtual power plant is subjected to secondary optimization judgment by inputting the distributed virtual power plant profit distribution rationality index and the energy correlation index through the target optimization judgment model. The calculation formula of the target optimization judgment model is: ,in represents the optimization evaluation value of the distributed virtual power plant, represents the weight of the profit distribution rationality index in the optimization evaluation of distributed virtual power plants, represents the weight of the energy correlation index in the optimization evaluation of distributed virtual power plants, where ;

[0055] Optimizing the evaluation value of distributed virtual power plants Compared with the preset optimization judgment threshold For comparison, if the distributed virtual power plant optimizes the evaluation value Greater than or equal to the preset optimization judgment threshold , then the secondary judgment result is that the distributed virtual power plant evaluation is normal. If the distributed virtual power plant optimization evaluation value Less than the preset optimization judgment threshold , then the secondary judgment result is that the distributed virtual power plant assessment is abnormal, and an optimization instruction is issued.

[0056] In this embodiment, it should be specifically explained that, in step S7, the optimization judgment results of step S3 and step S6 are received, and the operation strategy is adjusted according to the first optimization judgment result and the second optimization judgment result. If no optimization instruction is received, the current operation strategy is kept unchanged; if an optimization instruction is received, the operation strategy of the virtual power plant is adjusted according to the optimization instruction;

[0057] Adjustment strategies include reallocating power generation resources, adjusting energy consumption plans, and optimizing electricity trading strategies. Through adjustment strategies, virtual power plants can be flexibly changed in the electricity spot market, achieving optimal resource allocation and maximizing benefits.

[0058] The difference between this embodiment and the prior art lies in that this embodiment is provided with step S1: acquiring the power data of the energy resources of the distributed virtual power plant, step S2: conducting an overall benefit analysis on the power data information of the distributed virtual power plant according to the overall utility function, step S3: conducting an optimization judgment on the distributed virtual power plant according to the overall benefit evaluation result, step S4: establishing a mathematical model for analyzing the rationality of profit distribution and calculating the rationality index of profit distribution, step S5: establishing a correlation analysis model and determining the energy correlation of the distributed virtual power plant, step S6: conducting a secondary optimization judgment on the distributed virtual power plant, and step S7: receiving Collect the optimization judgment results, optimize the control of the distributed virtual power plant, and realize the optimal allocation of resources. In short, a distributed virtual power plant control method in the electricity spot market environment makes an optimization judgment on the distributed virtual power plant by evaluating the difference between the overall benefit and the expected benefit of the distributed virtual power plant. Through the target optimization judgment model, the distributed virtual power plant profit distribution rationality index and energy correlation index are input to make a secondary optimization judgment on the distributed virtual power plant, which ensures the efficient utilization of power resources and the stable operation of the power market, and not only improves the operating efficiency of the power system, but also enhances the flexibility and response speed of the power market.

[0059] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

[0060] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A distributed virtual power plant control method in a power spot market environment, characterized by: The following steps are involved: Step S1: Obtaining power data of energy resources of a distributed virtual power plant: obtaining power data of energy resources of a distributed virtual power plant through a data transmission network, and extracting power data information; Step S2: Performing an overall benefit analysis on the power data information of the distributed virtual power plant according to the overall utility function: inputting the extracted power data information into the overall utility function, performing model calculation based on the overall utility function and the input data, and obtaining an overall benefit evaluation result of the distributed virtual power plant; The calculation formula of the overall utility function is: ,in represents the overall benefit evaluation value, T represents the data collection time period, t=1, 2, 3, ..., T, where t represents the number of the data collection time period, represents the utility coefficient, represents the load factor, represents the electric energy consumed by the virtual power plant in the tth time period, represents the electric energy generated by the virtual power plant in the tth time period, represents the electricity purchase price of the operator in the tth time period; Step S3: Perform an optimization judgment on the distributed virtual power plant based on the overall benefit evaluation result: evaluate the difference between the overall benefit of the distributed virtual power plant and the expected benefit, and perform an optimization judgment on the distributed virtual power plant based on the evaluation result; Step S4: Establishing a mathematical model for analyzing the rationality of revenue distribution and calculating a rationality index for revenue distribution: Establishing a mathematical model for analyzing the rationality of revenue distribution and calculating a rationality index for revenue distribution of a distributed virtual power plant upon receiving an optimization instruction; Step S5: Establish a correlation analysis model to determine the energy correlation of the distributed virtual power plant: calculate the energy correlation index through the correlation analysis model to evaluate the synergy effect of energy in the power market; The specific contents of calculating the energy correlation index through the correlation analysis model are as follows: Step S51: Based on the power consumed by the virtual power plant in different time periods and the electricity generated by the virtual power plant in different time periods Calculate the difference ratio coefficient of the power consumption generated by the virtual power plant in different time periods. The calculation formula is: ,in It represents the proportional coefficient of the difference in electric energy consumed by the virtual power plant in the tth time period; Step S52: Calculate the energy correlation index of the virtual power plant in different time periods, and the calculation formula is: ,in represents the energy relevance index of the virtual power plant in the tth time period, Indicates the minimum value of the difference ratio coefficient of the power consumption generated by the virtual power plant in different time periods, It indicates the maximum value of the difference ratio coefficient of the power consumption generated by the virtual power plant in different time periods. represents the correlation resolution coefficient; Step S53: Calculate the mean of the energy correlation index of the virtual power plant in different time periods to obtain the energy correlation index of the distributed virtual power plant. The calculation formula is: ,in represents the energy relevance index of distributed virtual power plants; Step S6: Perform secondary optimization judgment on the distributed virtual power plant: Through the target optimization judgment model, the distributed virtual power plant benefit allocation rationality index and energy correlation index are input to perform secondary optimization judgment on the distributed virtual power plant; Step S7: Receive the optimization judgment result and perform optimization control on the distributed virtual power plant: complete the optimization control of the distributed virtual power plant based on the first optimization judgment result and the second optimization judgment result.

2. The distributed virtual power plant control method in a power spot market environment according to claim 1 is characterized by: In step S3, the difference between the overall benefit and the expected benefit of the distributed virtual power plant is evaluated, and the specific content of optimizing the distributed virtual power plant based on the evaluation result is: the overall benefit evaluation value of the distributed virtual power plant is Expected benefits For comparison, if the overall benefit evaluation value Greater than or equal to the expected benefit , then the primary judgment result is that the distributed virtual power plant is operating normally. If the overall benefit evaluation value Less than the expected benefit , then the primary judgment result is that the distributed virtual power plant is operating abnormally, and an optimization instruction is issued.

3. The distributed virtual power plant control method in a power spot market environment according to claim 1, characterized in that: In step S4, a mathematical model for analyzing the rationality of profit distribution is established, and the specific contents of calculating the rationality index of profit distribution of the distributed virtual power plant upon receiving the optimization instruction are as follows: Distributed virtual power plants act as the power supply side to sell electricity to the power purchase side, match the power supply of the power supply side in different time periods with the power purchase side's power purchase amount, and compare the power supply of the power supply side in different time periods with the power purchase side's power purchase amount to calculate the spot power supply and demand ratio; A commodity price estimation model is established based on the spot power supply and demand ratio, and the spot power supply and demand ratio is input into the commodity price estimation model as an input variable to calculate the expected estimated price of electricity sold by the distributed virtual power plant in different time periods; The expected estimated electricity sales prices in different time periods are substituted into the mathematical model of revenue distribution rationality analysis, and the revenue distribution rationality index of the distributed virtual power plant is calculated.

4. The distributed virtual power plant control method in a power spot market environment according to claim 3 is characterized by: The calculation formula of the power spot supply-demand ratio is: , where Es represents the spot electricity supply-demand ratio, represents the power supply on the power supply side in the tth time period, It represents the amount of electricity purchased by the electricity purchasing side in the tth time period.

5. The distributed virtual power plant control method in a power spot market environment according to claim 4 is characterized by: The commodity price estimation model is established based on the spot power supply and demand ratio to calculate the expected estimated price of electricity sales in different time periods. The calculation formula of the commodity price estimation model is: ,in represents the expected estimated price of electricity sold in different time periods, represents the operator's electricity selling price in the tth time period, It represents the electricity purchase price of the operator in the tth time period.

6. The distributed virtual power plant control method in a power spot market environment according to claim 5, characterized in that: Substituting the expected estimated price of electricity sold in different time periods into the mathematical model for analyzing the rationality of revenue distribution, the calculation formula for calculating the rationality index of revenue distribution is: ,in represents the rationality index of income distribution, Represents the profit distribution coefficient in the tth time period.

7. The distributed virtual power plant control method in a power spot market environment according to claim 1, characterized in that: In step S6, the distributed virtual power plant is subjected to secondary optimization judgment by inputting the distributed virtual power plant profit distribution rationality index and the energy correlation index through the target optimization judgment model. The calculation formula of the target optimization judgment model is: ,in represents the optimization evaluation value of the distributed virtual power plant, represents the weight of the profit distribution rationality index in the optimization evaluation of distributed virtual power plants, represents the weight of the energy correlation index in the optimization evaluation of distributed virtual power plants, where ; Optimizing the evaluation value of distributed virtual power plants Compared with the preset optimization judgment threshold For comparison, if the distributed virtual power plant optimizes the evaluation value Greater than or equal to the preset optimization judgment threshold , then the secondary judgment result is that the distributed virtual power plant evaluation is normal. If the distributed virtual power plant optimization evaluation value Less than the preset optimization judgment threshold , then the secondary judgment result is that the distributed virtual power plant assessment is abnormal, and an optimization instruction is issued.

8. The distributed virtual power plant control method in a power spot market environment according to claim 1, characterized in that: The step S7 receives the optimization judgment results of step S3 and step S6, and adjusts the operation strategy according to the first optimization judgment result and the second optimization judgment result. If no optimization instruction is received, the current operation strategy remains unchanged; If an optimization instruction is received, the operation strategy of the virtual power plant is adjusted according to the optimization instruction.

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