Virtual power plant aggregation power supply configuration optimization method and system based on improved CCR game interaction efficiency model

Abstract

The invention discloses a virtual power plant aggregation power supply configuration optimization method and system based on an improved CCR game interaction efficiency model, and the method comprises the steps: obtaining a to-be-aggregated power supply type of a target virtual power plant, building a comprehensive evaluation index of a virtual power plant alternative unit based on the to-be-aggregated power supply type, and carrying out the optimization of the to-be-aggregated power supply type according to the comprehensive evaluation index in combination with an improved CCR model; establishing a comprehensive evaluation model of the alternative units of the virtual power plant, wherein the comprehensive evaluation model of the alternative units of the virtual power plant comprises target functions and constraint conditions of each unit type; and converting the target function of each unit type into a potential function of a full-potential game, solving the comprehensive evaluation model of the alternative units of the virtual power plant in combination with constraint conditions, and completing configuration optimization of the aggregated power supply of the virtual power plant according to a solving result. The defect that the previous research only focuses on the specific unit capacity configuration of the VPP is overcome, and the optimization problem of a power supply cooperative enterprise and the optimization configuration problem of the unit capacity can be solved for the aggregation of the VPP at the same time.

Description

Technical Field

[0001] The present invention relates to the technical field of resource aggregation modeling of virtual power plants, and in particular to a method and system for optimizing the aggregated power supply configuration of virtual power plants based on an improved CCR game interaction efficiency model. Background Art

[0002] With the increasing popularity of new energy, various renewable energy sources have brought great challenges to the safety and stability of the power grid due to defects such as unstable output and large fluctuations in electrical characteristics. The dispersion of its resource endowment also leads to great uneconomical direct physical aggregation. Virtual power plant VPP is considered to be a mainstream solution for the aggregation of distributed renewable energy, and an advanced market operation model that integrates advanced technology and resource allocation mechanism. The implementation path of VPP to solve the above problems includes two aspects. On the one hand, through the aggregation configuration of multiple categories of power sources and advanced self-dispatching technology, the uncertainty of distributed power sources is smoothed and the quality of aggregated power output is improved; on the other hand, through advanced market operation models, distributed energy owners who are difficult to participate in the market alone are attracted to join the market, thereby improving the resource allocation efficiency of the power market. Based on the above path, the first core problem faced by VPP operation is to determine not only the specific configuration of each category of resources within a certain geographical / market scope, but also which unit operators should join VPP under a given resource category. The former is a simple technical problem, and the latter is a complex business problem.

[0003] In fact, the aggregate operation of VPP is different from the project commissioning of power companies. It is not to determine the location, capacity, construction and other issues of the unit from scratch, but to select the optimal resource allocation from the existing stock resources in the market. Furthermore, due to the large-scale construction of my country's power market for more than ten years, a certain degree of market saturation has appeared on the power supply side, and there are multiple power supply companies of the same type in a market area. Therefore, when establishing a VPP, the operator of the VPP must not only determine the specific configuration of the resource analogy, but also select the best partner under the given configuration, that is, determine who to cooperate with. This puts forward a new requirement for the aggregate modeling of VPP, that is, how to evaluate the quality of the selected power supply companies in order to determine the best partner in the optional set.

[0004] In the past, many studies on VPP aggregation modeling technology have mostly explored the impact of VPP's specific unit configuration on economic benefits, but there has been less discussion on the quality of units under the same configuration conditions. During the VPP formation stage, the operator should conduct a comprehensive evaluation of units of the same category and select better units to join the VPP. The evaluation criteria should not only consider economic benefits, but also the comprehensive efficiency of the selected units. Previous studies on VPP aggregation resource evaluation methods mainly have problems such as incomplete evaluation factors, high degree of subjectivity in the evaluation standard assignment process, and inconsistent indicators and evaluation standards. Summary of the invention

[0005] The purpose of this section is to summarize some aspects of embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the specification abstract and the invention title of this application to avoid blurring the purpose of this section, the specification abstract and the invention title, and such simplifications or omissions cannot be used to limit the scope of the present invention.

[0006] In view of the above existing problems, the present invention is proposed.

[0007] Therefore, the present invention provides a virtual power plant aggregate power configuration optimization method and system based on an improved CCR game interaction efficiency model, which can solve the problems mentioned in the background technology.

[0008] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0009] In a first aspect, the present invention provides a method for optimizing the configuration of aggregated power sources of a virtual power plant based on an improved CCR game interaction efficiency model, comprising:

[0010] Obtain the category of power sources to be aggregated in the target virtual power plant, and establish comprehensive evaluation indicators for candidate units of the virtual power plant based on the category of power sources to be aggregated, wherein the category of power sources includes thermal power, distributed power sources and energy storage;

[0011] According to the comprehensive evaluation index and in combination with the improved CCR model, a comprehensive evaluation model for candidate units of a virtual power plant is established, wherein the comprehensive evaluation model for candidate units of a virtual power plant includes objective functions and constraints for each unit type;

[0012] The objective function of each unit type is converted into a potential function of the full potential game, and the comprehensive evaluation model of the virtual power plant alternative units is solved in combination with the constraints, and the optimization of the virtual power plant aggregate power configuration is completed based on the solution results.

[0013] As a preferred solution of the virtual power plant aggregated power configuration optimization method based on the improved CCR game interactive efficiency model described in the present invention, wherein: the acquisition of the target virtual power plant to be aggregated power source category and the establishment of a comprehensive evaluation index of the virtual power plant candidate units based on the to-be-aggregated power source category include:

[0014] Establish comprehensive evaluation indicators for thermal power, distributed power and energy storage alternative units, which include input indicators and output indicators;

[0015] The input indicators of the evaluation indicators of thermal power alternative units include pollutant emission concentration and maximum noise of the unit; the output indicators include planned maintenance implementation rate and key equipment integrity rate;

[0016] The input indicators of the evaluation index of the distributed power supply candidate unit include the number of voltage deviation over-limit times and the power factor over-limit ratio, and the output indicators include the high frequency ride-through ratio, low frequency ride-through ratio and low voltage ride-through ratio;

[0017] The input indicators of energy storage evaluation include battery failure rate and relative battery failure times, and the output indicator is the number of charge and discharge cycles.

[0018] As a preferred solution of the virtual power plant aggregate power configuration optimization method based on the improved CCR game interaction efficiency model described in the present invention, the improved CCR model includes:

[0019] In the original CCR model, the evaluation index of the evaluated object is divided into an input index vector and an output index vector. Assuming that there are k evaluated objects in the same category, for evaluation object i, there are n input index categories and m output index categories, then the input index vector can be recorded as X i ={x 1i ,x 2i ,…,x ni} T , the output index vector can be recorded as Y i ={y 1i ,y 2i ,…,y mi} T , the improved CCR model is expressed as follows:

[0020]

[0021] Among them, E j represents the expected value of the interaction efficiency under the optimal weight vector of unit j, Ω dj represents the game interaction efficiency of units d and j corresponding to the expected value, and λ T Represents the weight vectors of output indicators and input indicators respectively, X d and Yd It is the input and output index sequence of the initial evaluation efficiency result of DMU obtained when the unit d is optimally solved by the original CCR model.

[0022] As a preferred solution of the virtual power plant aggregate power configuration optimization method based on the improved CCR game interactive efficiency model described in the present invention, the objective function and constraint conditions of each unit type include:

[0023] The objective function of the thermal power unit is:

[0024]

[0025] Among them, F tp represents the objective function of the thermal power unit that takes both economy and stability into consideration; Λ tp is a virtual variable, which takes the value 1 when the unit joins the virtual power plant, and takes the value 0 otherwise; R tp Represents the profit function of thermal power units, measuring economic efficiency; G tp It represents the efficiency function of thermal power units and measures the quality of output power; and Respectively represent R tp and G tp The weight of

[0026] The constraints of thermal power units are:

[0027]

[0028] in, and Respectively represent the minimum / maximum permissible output power of thermal power units; and They represent the lower and upper limits of the thermal power unit’s ramp power, represents the power output of the thermal power unit at time t, Represents the power output of the thermal power unit at time t-1.

[0029] As a preferred solution of the virtual power plant aggregate power configuration optimization method based on the improved CCR game interactive efficiency model described in the present invention, the objective function and constraint conditions of each unit type also include:

[0030] The objective function of the distributed generation unit is:

[0031]

[0032] Among them, F dg represents the objective function of the distributed generation unit that takes both economy and stability into consideration; Λ dgis a virtual variable, which takes 1 when the unit joins the virtual power plant, and takes 0 otherwise; R dg Represents the revenue function of distributed generation, which measures the economic efficiency; G dg It is the efficiency function of distributed power generation, which measures the quality of output power; and Respectively represent R dg and G dg The weight of

[0033] The constraints of distributed generation units are:

[0034]

[0035] in, It represents the predicted value given according to the output characteristics of distributed generation. Represents the output power of distributed generation at time t.

[0036] As a preferred solution of the virtual power plant aggregate power configuration optimization method based on the improved CCR game interactive efficiency model described in the present invention, the objective function and constraint conditions of each unit type also include:

[0037] The objective function of the energy storage unit is:

[0038]

[0039] Among them, F es represents the objective function of the energy storage unit that takes both economy and stability into consideration; Λ es is a virtual variable, which takes 1 when the unit joins the virtual power plant, and takes 0 otherwise; R es Represents the revenue function of energy storage, which measures the economic efficiency; G es is the efficiency function of energy storage, which measures the quality of output power; and Respectively represent R es and G es The weight of

[0040] The constraints of the energy storage unit are:

[0041]

[0042] Among them, E t represents the state of charge of the energy storage at time t, E t-1 represents the state of charge of the energy storage at time t-1; η loss Indicates the energy storage capacity loss rate; E min and E max Respectively represent the lower and upper limits of the energy storage charge state; Indicates the maximum charging power allowed by the energy storage; Indicates the maximum discharge power allowed by energy storage, Represents the net output power of energy storage.

[0043] As a preferred scheme of the virtual power plant aggregated power configuration optimization method based on the improved CCR game interactive efficiency model described in the present invention, wherein: the power category also includes controllable loads, when the target virtual power plant to be aggregated power category includes controllable loads, the controllable loads are at the demand side, and the objective function of the controllable loads only includes the profit function.

[0044] In a second aspect, the present invention provides a virtual power plant aggregated power configuration optimization system based on an improved CCR game interaction efficiency model, comprising:

[0045] An indicator acquisition module is used to obtain the category of power sources to be aggregated in the target virtual power plant and establish comprehensive evaluation indicators of candidate units of the virtual power plant based on the category of power sources to be aggregated, wherein the category of power sources includes thermal power, distributed power sources and energy storage;

[0046] A model building module, used to establish a comprehensive evaluation model of candidate units of a virtual power plant according to the comprehensive evaluation index and in combination with an improved CCR model, wherein the comprehensive evaluation model of candidate units of a virtual power plant includes an objective function and constraint conditions for each unit type;

[0047] The solution module is used to convert the objective function of each unit type into a potential function of the full potential game, and solve the comprehensive evaluation model of the virtual power plant alternative units in combination with the constraint conditions, and complete the optimization of the virtual power plant aggregate power configuration according to the solution results.

[0048] In a third aspect, the present invention provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above-mentioned method when executing the computer program.

[0049] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program implements the steps of the method described above when executed by a processor.

[0050] Compared with the prior art, the invention has the following beneficial effects: the invention proposes a virtual power plant aggregate power configuration optimization method and system based on an improved CCR game interactive efficiency model, obtains the target virtual power plant to be aggregated power source category, and establishes a comprehensive evaluation index of the virtual power plant candidate unit based on the to-be-aggregated power source category, the power source category includes thermal power, distributed power source and energy storage; according to the comprehensive evaluation index, combined with the improved CCR model, a comprehensive evaluation model of the virtual power plant candidate unit is established, the comprehensive evaluation model of the virtual power plant candidate unit includes the objective function and constraint conditions of each unit type; the objective function of each unit type is converted into the potential function of the full potential game, and the comprehensive evaluation model of the virtual power plant candidate unit is solved in combination with the constraint conditions, and the virtual power plant aggregate power configuration optimization is completed according to the solution results. It fills the defect that previous studies only focus on the specific unit capacity configuration of VPP, and can simultaneously solve the optimization problem of power supply cooperation enterprises and the optimal configuration problem of unit capacity for the aggregation of VPP. Based on the improved CCR method, through multiple iterative calculations, a more objective and comprehensive unit evaluation result can be given; this method not only plays the comprehensive evaluation mechanism of the traditional CCR method combining self-evaluation and mutual evaluation, but also increases the objectivity of the overall evaluation weight of the DMU through the mutual evaluation process by setting the Pareto condition. Finally, through the full potential game process, the economic and stability objectives of the VPP are transformed into potential functions, and the solution process must have a Nash equilibrium solution, and the convergence is faster.

[0051] The virtual power plant aggregate power configuration optimization method and system based on the improved CCR game interactive efficiency model of the present invention not only solves the problems of incomplete evaluation factors and strong subjectivity of evaluation standards in existing research, but also improves the objectivity of evaluation and the optimization of configuration by introducing game theory and full-power game theory. In the future, with the continuous development of VPP technology and the expansion of application scenarios, the method and system of the present invention will play a greater role in the fields of energy Internet, smart grid, etc., and promote the transformation, upgrading and sustainable development of the energy industry. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative labor. Among them:

[0053] Figure 1 A method flow chart of a virtual power plant aggregate power configuration optimization method and system based on an improved CCR game interaction efficiency model provided by an embodiment of the present invention;

[0054] Figure 2A flowchart of an improved CCR model unit evaluation method for a virtual power plant aggregated power configuration optimization method and system based on an improved CCR game interactive efficiency model provided by an embodiment of the present invention;

[0055] Figure 3 An internal structural diagram of a computer device of a virtual power plant aggregate power configuration optimization method and system based on an improved CCR game interaction efficiency model provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0056] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0057] Example 1

[0058] Reference Figure 1-Figure 3 , is the first embodiment of the present invention, which provides a virtual power plant aggregate power configuration optimization method and system based on an improved CCR game interaction efficiency model, including a virtual power plant aggregate power configuration optimization method based on an improved CCR game interaction efficiency model and a virtual power plant aggregate power configuration optimization system based on an improved CCR game interaction efficiency model, wherein a virtual power plant aggregate power configuration optimization method based on an improved CCR game interaction efficiency model includes:

[0059] Before describing the embodiments of the present application in detail, some related concepts are first explained for the sake of clarity.

[0060] Virtual Power Plant (VPP): A virtual power plant is a system that integrates multiple distributed energy resources (DERs), which can be small power generation devices (such as solar panels and wind turbines), energy storage devices (such as batteries), and controllable loads (such as smart home appliances). Through advanced communication technology and software control systems, these decentralized resources are integrated to simulate the functions of a large centralized power plant, participate in power market transactions and power grid operation management. It is established to improve energy efficiency, promote the consumption of renewable energy, and enhance the flexibility and reliability of the power grid.

[0061] CCR model: The CCR model (Charnes-Cooper-Rhodes Model) is the most basic model in Data Envelopment Analysis (DEA). It is used to evaluate the relative efficiency of multiple decision making units (DMUs), each of which has multiple sets of inputs and outputs. The CCR model assumes that all decision making units operate under the same production technology and seek to maximize output while minimizing input. It uses linear programming techniques to calculate the efficiency of each decision making unit relative to other units. The CCR model usually assumes constant returns to scale (CRS), which means that efficiency is not affected by changes in scale.

[0062] Full-power game: Full-power game is a concept in game theory, which means that the interest relationship between participants is not a simple zero-sum or non-zero-sum, but involves complex interactions among multiple participants, each of whom tries to maximize his own interests through cooperation or competition with other participants. In the optimization of virtual power plant aggregate power configuration, the full-power game model can be used to simulate the cooperation and competition between different power sources to find the optimal configuration solution.

[0063] Potential function of full-potential game: In full-potential game, potential function is a mathematical expression that describes the game state between participants. It usually reflects the strategic choices of participants and the results of their interactions. In this case, potential function is used to convert the objective function of each unit type to facilitate the solution in the game process.

[0064] Aggregate power configuration optimization: Aggregate power configuration optimization refers to the process of combining and configuring different power resources to achieve the best overall performance. This may include considerations of economy, stability, and other aspects. By optimizing the configuration, the efficiency, stability, and economic benefits of the entire virtual power plant system can be improved.

[0065] There are certain limitations in the relevant technologies, namely, they mainly focus on the capacity configuration of the units within the virtual power plant (VPP), but pay less attention to the diversity of power source categories and the coordinated optimization between the power sources. In addition, when dealing with complex power systems, traditional evaluation methods often face problems such as single evaluation indicators and strong subjectivity of evaluation standards, making it difficult to fully and objectively reflect the true situation of the aggregated power configuration of the virtual power plant.

[0066] This application provides a method that can effectively solve the above-mentioned problems. Next, we will combine multiple embodiments to elaborate on how to implement the virtual power plant aggregate power supply configuration optimization method;

[0067] Figure 1A method flow chart of a virtual power plant aggregate power configuration optimization method and system based on an improved CCR game interactive efficiency model is shown, including:

[0068] S101, obtaining the category of power sources to be aggregated in the target virtual power plant, and establishing comprehensive evaluation indicators of candidate units of the virtual power plant based on the category of power sources to be aggregated, where the power source categories include thermal power, distributed power sources and energy storage;

[0069] In an embodiment of the present application, it is assumed that a target virtual power plant VPP faces four aggregable resources in its market environment, namely, thermal power, photovoltaic / wind power (hereinafter referred to as distributed power source DG), energy storage and controllable load. The controllable load mainly comes from the civilian load end and high-energy-consuming enterprises. The controllable load on the civilian end only provides information, so only the controllable load of high-energy-consuming enterprises is considered. Since the controllable load of high-energy-consuming enterprises belongs to backup resources and is largely a passive partner, the aggregation decision of the virtual power plant VPP only needs to consider the evaluation schemes of the owner companies of the three alternative units of thermal power, DG and energy storage. From the three dimensions of environmental protection, reliability and efficiency, evaluation indicators for thermal power, distributed power (DG) and energy storage alternative units can be established. For subsequent analysis, these indicators are divided into input indicators and output indicators.

[0070] Specifically, obtaining the category of power sources to be aggregated in the target virtual power plant and establishing comprehensive evaluation indicators for candidate units of the virtual power plant based on the category of power sources to be aggregated include:

[0071] Establish comprehensive evaluation indicators for thermal power, distributed power and energy storage alternative units. The comprehensive evaluation indicators include input indicators and output indicators.

[0072] Among them, the input indicators of the evaluation indicators of thermal power alternative units include pollutant emission concentration and maximum noise of the unit; the output indicators include planned maintenance implementation rate and key equipment integrity rate;

[0073] Furthermore, the input indicators of the evaluation index of the distributed generation (DG) candidate units include the number of voltage deviation over-limit times and the power factor over-limit ratio, and the output indicators include the high frequency ride-through ratio, the low frequency ride-through ratio and the low voltage ride-through ratio;

[0074] It should be noted that the high / low frequency ride-through ratio reflects the ability of DG to adapt to changes in grid frequency. When the grid frequency rises / drops outside the normal range, if the DG is not disconnected from the grid, it is recorded as a high / low frequency ride-through. The high / low frequency ride-through ratio is defined as the ratio of the number of ride-throughs of all DG units to the maximum possible number of ride-throughs. The definition of the low voltage ride-through ratio is similar.

[0075] Furthermore, the input indicators of the energy storage evaluation index include battery failure rate and battery relative failure times, and the output indicator is the number of charge and discharge cycles.

[0076] It should be noted that the battery failure rate refers to the ratio of failed batteries to the total number of batteries in energy storage, and the battery relative failure count is the ratio of the battery failure count to the total number of batteries.

[0077] It should be noted that by exhaustively defining the comprehensive evaluation indicators of various candidate units, it not only provides a clear and quantitative benchmark for the subsequent optimization of the virtual power plant aggregate power configuration, but also ensures the comprehensiveness and objectivity of the evaluation process. These evaluation indicators cover multiple key dimensions such as environmental protection (such as pollutant emission concentration), reliability (such as planned maintenance implementation rate, equipment integrity rate), and efficiency (such as the number of charge and discharge cycles), so as to more accurately reflect the actual performance of each candidate unit.

[0078] In addition, subdividing the evaluation indicators into input indicators and output indicators helps to clearly identify the key factors that affect the performance of the unit. Input indicators mainly reflect the factors that need to be consumed or may have a negative impact during the operation of the unit, such as pollutant emissions, noise levels, and battery failure; while output indicators focus more on the performance of the unit under specific conditions, such as maintenance implementation efficiency, equipment integrity, frequency ride-through capability, and number of charge and discharge cycles. This classification method not only facilitates targeted optimization, but also improves the accuracy and operability of the evaluation.

[0079] S102, based on the comprehensive evaluation index and in combination with the improved CCR model, a comprehensive evaluation model for candidate units of the virtual power plant is established, wherein the comprehensive evaluation model for candidate units of the virtual power plant includes objective functions and constraints for each unit type;

[0080] It should be noted that the original CCR model is a comprehensive efficiency evaluation model jointly proposed by Charles-Cooper-Rhodes that uses the mathematical envelope analysis method DEA to solve linear programming problems. In the CCR model, the evaluation index of the evaluated object is divided into an input index vector and an output index vector. Assuming that there are k evaluated objects in the same category, for evaluation object i, there are n input index categories and m output index categories, then the input index vector can be recorded as X i ={x 1i ,x 2i ,…,x ni} T , the output index vector can be recorded as Y i ={y 1i ,y 2i ,…,y mi} T .

[0081] In the CCR model, the evaluation object to be solved is called a homogeneous decision making unit (DMU). The comprehensive evaluation result of the unit d to be solved is solved by the following linear programming equations:

[0082]

[0083] Among them, Ω d It represents the evaluation efficiency of DMU, ​​i.e. unit d; and λ T Represent the weight vectors of output indicators and input indicators respectively.

[0084] Solving the above formula can obtain the optimal weight vector of evaluation object i: and Substituting into the following formula, we can obtain its interaction efficiency under the optimal weight vector;

[0085]

[0086] Among them, X d and Y d It can be understood as the input and output index sequence of the initial evaluation efficiency result of DMU obtained by optimally solving the linear programming equation group for unit d. For the members of the same type of unit, the interaction efficiency is calculated once, and the interaction efficiency between each unit member and other members can be obtained. The final evaluation result of unit d is:

[0087]

[0088] It should also be noted that the comprehensive evaluation of CCR for evaluation objects actually includes two parts: self-evaluation and mutual evaluation of DMU objects, but the whole process may have defects such as non-unique optimal weights and low evaluation results. Therefore, this design considers the following Pareto equilibrium: the efficiency optimization of each evaluation object will at least not reduce the evaluation efficiency of other objects, so the indicator weight solution of each evaluation object should improve its evaluation results, while not making the interaction efficiency of other evaluation objects lower than their expected interaction efficiency.

[0089] In the embodiment of the present application, improving the CCR model includes:

[0090] Using the same definition, the original CCR model is improved. In the original CCR model, the evaluation index of the evaluated object is divided into an input index vector and an output index vector. Assuming that there are k evaluated objects in the same category, for evaluation object i, there are n input index categories and m output index categories, then the input index vector can be recorded as X i ={x 1i ,x 2i ,…,x ni} T , the output index vector can be recorded as Y i ={y 1i ,y 2i ,...,y mi} T , the improved CCR model is expressed as follows:

[0091]

[0092] Among them, E j represents the expected value of the interaction efficiency under the optimal weight vector of unit j, Ω dj represents the game interaction efficiency of units d and j corresponding to the expected value, and λ T Represents the weight vectors of output indicators and input indicators respectively, X d and Y d It is the input and output index sequence of the initial evaluation efficiency result of DMU obtained when the unit d is optimally solved by the original CCR model.

[0093] In the embodiment of the present application, in an evaluation object set including k units, unit d and all other units can be solved k-1 times according to the improved CCR model to obtain the game interaction efficiency of d with respect to other members respectively. The comprehensive evaluation result of unit d is:

[0094]

[0095] in, It represents the comprehensive evaluation result of unit d obtained according to the improved CCR model; Represents the optimal input index vector of unit d and unit j obtained by solving the improved CCR model.

[0096] The comprehensive evaluation results are given in After that, it is used as the E for the next round of iterative optimization j The value is substituted into the improved CCR model to obtain a new round of evaluation results. The iterative process continues until the output results are not significantly improved. For the specific implementation process, please refer to the attached Figure 2. Determine the set of candidate units 100. This is the first step of the whole process, and a set of candidate units needs to be determined. Input the index data of the candidate units 200. After the candidate units are determined, the relevant index data of these units needs to be input for subsequent calculation and comparison. Calculate the interaction efficiency with other candidate units 300. According to the input index data, the interaction efficiency between each candidate unit and other candidate units can be calculated. Output the average interaction efficiency of i and compare it with the original result 400. The calculated interaction efficiency can be used to evaluate the performance of the candidate units. What needs to be output here is the average interaction efficiency of i, and compare it with the previous original result. Evaluate whether the result has improved 500. By comparing the new and old results, it can be judged whether there is any improvement. If the result has not improved, execute the next step; otherwise, continue to execute the current step. If the result has not improved, output the evaluation results of all candidate units 600, and determine the cooperative unit 800. If there is no improvement, it means that the current candidate unit combination is not ideal. Therefore, it is necessary to output the evaluation results of all candidate units and select the best one as the cooperative unit. If the result is improved, the result of the new round of output is used as the expected value of the interaction efficiency of other units 700. If there is improvement, it means that the current candidate unit combination is feasible. Therefore, the result of this round of output needs to be used as the expected value of the interaction efficiency of other units for use in the next round.

[0097] In summary, this result can be used as a basis for VPP to select units to cooperate with. For example, if there are k thermal power units, VPP will give priority to incorporating the unit with the best evaluation result within the unit into the VPP.

[0098] It should be noted that the improved CCR model retains the advantages of the original CCR model mathematical envelope analysis (DEA) method, and significantly optimizes and expands the model by introducing the concept of Pareto equilibrium in game theory. This improvement not only enhances the fairness and accuracy of the model in the comprehensive evaluation process, but also improves its flexibility and adaptability in practical applications.

[0099] Specifically, the improved CCR model sets the expected value of the interaction efficiency of each evaluation object (i.e., unit) and solves it based on this constraint, ensuring that each evaluation object will not harm the interests of other objects while optimizing its own efficiency. This mechanism effectively avoids problems such as "non-unique optimal weight" and "low evaluation results" that may occur in the traditional CCR model, making the evaluation results closer to the actual situation and better reflecting the real competition and cooperation status between the evaluation objects.

[0100] In addition, through iterative optimization, the improved CCR model can gradually approach the optimal solution, making the final comprehensive evaluation results more stable and reliable. This iterative process not only improves the accuracy of the model, but also gives the model a certain degree of adaptive ability, enabling it to be flexibly adjusted according to the specific needs of different situations.

[0101] In the embodiment of the present application, the objective function and constraint conditions of each unit type include:

[0102] The objective function of the thermal power unit is:

[0103]

[0104] Among them, F tp represents the objective function of the thermal power unit that takes both economy and stability into consideration; Λ tp is a virtual variable, which takes the value 1 when the unit joins the virtual power plant, and takes the value 0 otherwise; R tp Represents the profit function of thermal power units, measuring economic efficiency; G tp It represents the efficiency function of thermal power units and measures the quality of output power; and Respectively represent R tp and G tp The weight of

[0105] Among them, the profit function R tp The expression is:

[0106]

[0107] in, It represents the unit electricity price income that can be obtained by VPP agent operation participating in the market; represents the power output of the thermal power unit at time t; μ coal represents the coal price, ζ represents the amount of coal consumed per unit power output of thermal power; γ tp represents the unit operation and maintenance cost of thermal power units, Indicates the maximum installed capacity of the unit.

[0108] Efficiency function G tp The expression is:

[0109]

[0110] Among them, Ω tp It represents the final game interaction efficiency of the thermal power unit obtained by iteratively calculating the comprehensive evaluation result formula of unit d.

[0111] In the embodiment of the present application, the constraints of the thermal power unit are:

[0112]

[0113] in, and Respectively represent the minimum / maximum permissible output power of thermal power units; and They represent the lower and upper limits of the thermal power unit’s ramp power, represents the power output of the thermal power unit at time t, Represents the power output of the thermal power unit at time t-1.

[0114] In the embodiment of the present application, the objective function and constraint conditions of each unit type also include:

[0115] The objective function of the distributed generation unit is:

[0116]

[0117] Among them, F dg represents the objective function of the distributed generation unit that takes both economy and stability into consideration; Λ dg is a virtual variable, which takes 1 when the unit joins the virtual power plant, and takes 0 otherwise; R dg Represents the revenue function of distributed generation, which measures the economic efficiency; G dg It is the efficiency function of distributed power generation, which measures the quality of output power; and Respectively represent R dg and G dg The weight of

[0118] Among them, the profit function R dg The expression is:

[0119]

[0120] in, represents the output power of DG at time t; γ dg represents the unit operating cost of the DG unit, Indicates the maximum installed capacity of the unit.

[0121] Efficiency function G dg The expression is:

[0122]

[0123] Among them, Ω dg It represents the final game interaction efficiency of the DG unit obtained by iterating the comprehensive evaluation result formula of unit d.

[0124] In the embodiment of the present application, the constraints of the distributed power generation unit are:

[0125]

[0126] in, It represents the predicted value given according to the output characteristics of distributed generation. Represents the output power of distributed generation at time t.

[0127] In the embodiment of the present application, the objective function and constraint conditions of each unit type also include:

[0128] The objective function of the energy storage unit is:

[0129]

[0130] Among them, F es represents the objective function of the energy storage unit that takes both economy and stability into consideration; Λ es is a virtual variable, which takes 1 when the unit joins the virtual power plant, and takes 0 otherwise; R es Represents the revenue function of energy storage, which measures the economic efficiency; G es is the efficiency function of energy storage, which measures the quality of output power; and Respectively represent R es and G es The weight of

[0131] Among them, the profit function R es The expression is:

[0132]

[0133] in, Represents the net output power of energy storage, when Indicates the discharge state; when Indicates the charging state. To simplify the analysis, it is assumed that the energy storage charging still follows Purchase electricity from the main grid. es represents the unit cost of battery loss; γ es Represents the unit cost of energy storage operation and maintenance; Indicates the maximum installed capacity of energy storage.

[0134] Efficiency function G es The expression is

[0135]

[0136] Among them, Ω es It represents the final game interaction efficiency of the energy storage unit obtained by iterating the comprehensive evaluation result formula of unit d.

[0137] In the embodiment of the present application, the constraints of the energy storage unit are:

[0138]

[0139] Among them, E t represents the state of charge of the energy storage at time t, E t-1 represents the state of charge of the energy storage at time t-1; η loss Indicates the energy storage capacity loss rate; E min and E max Respectively represent the lower and upper limits of the energy storage charge state; Indicates the maximum charging power allowed by the energy storage; Indicates the maximum discharge power allowed by energy storage, Represents the net output power of energy storage.

[0140] It should be noted that as the traditional main power generation method, the stable output power of thermal power units provides a solid guarantee for the power grid. Distributed power units further enhance the diversity and flexibility of the power system. Although the output power of these units is greatly affected by natural factors, the unified dispatch and management in the virtual power plant can effectively alleviate the peak-to-valley difference of the power grid and reduce the dependence on traditional thermal power units. As an indispensable means of regulation in the power system, the energy storage unit's rapid charging and discharging capabilities enable the power grid to respond quickly to load fluctuations, smooth power fluctuations, and improve the reliability and stability of the system. Especially in extreme weather or emergencies, energy storage units can play a key role in ensuring the safe operation of the power grid.

[0141] Therefore, it is necessary to at least build a diversified power supply structure including thermal power units, distributed power units and energy storage units in order to fully utilize the advantages of various energy sources and achieve optimal allocation of resources.

[0142] In an optional embodiment, the power source category also includes controllable loads. If controllable loads are considered in the embodiment of the present application, when the power source category to be aggregated in the target virtual power plant includes controllable loads, the controllable loads are on the demand side, and the objective function of the controllable loads only includes the profit function.

[0143] In the embodiment of the present application, the controllable load in the design mainly considers the interruptible load in high-energy-consuming enterprises, which is divided into continuous interruptible load and discrete interruptible load. For the convenience of analysis, it is assumed that the controllable load in high-energy-consuming enterprises is continuous interruptible load. Since the controllable load is on the demand side, the evaluation of the controllable load does not need to be considered when making VPP cooperation decisions. The objective function of the controllable load only includes the benefit function, and its objective function is:

[0144]

[0145] Among them, μ c represents the product price of high energy-consuming enterprises, qc Indicates the output of the product; μ up and μ down Indicates the price of controllable load up and down spinning reserve; and They represent the reserve power for upward and downward rotation at time t respectively; and are all dummy variables. Indicates that IL ("Interruptible Load") starts at time t, which counts once. Indicates that IL is in shutdown state at time t, and counts once; μ on and μ off Respectively represent the single startup / shutdown cost of IL; μ ie represents the industrial electricity price, μ env It represents the unit environmental governance cost of high energy consumption products; Represents the active power output of IL at time t.

[0146] The power constraint of the controllable load is:

[0147]

[0148] Among them, IL t It is a virtual variable. When it is 1, it means that IL is in the calling state, and when it is 0, it means that IL is not in the calling state. and Indicates the minimum and maximum allowed power of IL.

[0149] At the same time, the controllable load is also subject to the production conditions of high-energy-consuming enterprises. The constraints are as follows:

[0150]

[0151] Among them, υ represents the power consumption per unit output of high energy-consuming enterprises; q order represents the daily order volume of the product, q max Indicates the enterprise's maximum production capacity for the day.

[0152] It should be noted that by incorporating controllable loads into the aggregation scope of virtual power plants (VPPs), especially for the continuous interruptible loads of high-energy-consuming enterprises, VPPs can more flexibly adjust their power supply and demand balance. During peak power demand periods, VPPs can call on these controllable loads to reduce power consumption, thereby alleviating the pressure on the power grid; during off-peak periods, these loads can be restored to normal operation, ensuring the overall flexibility of the system. The participation of controllable loads enables VPPs to comprehensively consider the characteristics and constraints of various power types (including energy storage, distributed generation, etc.) in the decision-making process, and achieve the best resource allocation through optimization algorithms. This can not only improve energy utilization efficiency, but also reduce overall operating costs. The objective function of controllable loads includes a profit function, which means that in the process of participating in VPPs, high-energy-consuming enterprises can obtain economic compensation by providing interruptible load services. At the same time, VPPs can also obtain higher profits in the power market by reasonably dispatching these load resources, thereby enhancing the economic benefits of the entire system. By introducing controllable loads as one of the power categories to be aggregated by VPPs, not only the flexibility of the system and the efficiency of resource allocation are improved, but also the economic benefits and energy-saving and emission reduction effects are enhanced, which is of great significance for promoting the healthy development of the power market.

[0153] S103, converting the objective function of each unit type into the potential function of the full potential game, solving the comprehensive evaluation model of the virtual power plant candidate units in combination with the constraint conditions, and completing the optimization of the virtual power plant aggregate power configuration based on the solution results.

[0154] In the embodiment of the present application, the full potential game process is used to solve the model. Considering thermal power, DG, energy storage and high-energy-consuming enterprises as game participants, the output of each unit, the charging and discharging of energy storage and the production power consumption decision of high-energy-consuming enterprises are their respective feasible strategy sets. For participant i, the game utility function is constructed as follows:

[0155] U i (x i ,x -i )=F i (x i ,x -i )-F P (x i ,x -i )

[0156] Among them, U i represents the utility function of participant i; χ i ,x -i represents the game state in the feasible strategy of participant i; F i (x i ,x -i ) is the objective function of participant i; F P (xi ,x -i ) represents the penalty function constructed based on the power balance target, and its expression is:

[0157]

[0158] Among them, P i represents the output of each participant, P represents the sum of the uncontrollable load of high-energy-consuming enterprises and the civilian load; θ is a parameter to measure power balance, P (x i ,x -i )=0, and the specific value can be obtained by solving, which means that the system meets the power balance condition.

[0159] According to the above two formulas (game utility function, penalty function), the utility function of VPP can be constructed, which is called the potential function of the game, and the expression is as follows:

[0160]

[0161] According to the full-game theory, we have:

[0162] ΔΦ=Φ(χ′ i ,x -i )-Φ(χ″ i ,x -i )=U i (χ′ i ,x -i )-U i (x″ i ,x -i )=ΔU i

[0163] Among them, Φ(χ′ i , χ -i ) represents the pair Φ to χ i The first-order partial derivative of Φ(χ″ i ,x -i ) represents the i The second-order partial derivative of U i (χ′ i ,x -i ) and U i (x″ i ,x -i The above formula is the derivation result. The specific derivation process is very complicated and will not be described in detail due to space limitations.

[0164] At this point, the above game process is called a full-potential game, and the full-potential game has a Nash equilibrium solution after a finite number of improvements.

[0165] Therefore, by combining the above equations, we can find the specific power supply configuration scheme of the member selection variables and power decision variables that maximize the value of the VPP potential function. The specific solution can be performed through the CPLEX module in Python or other mathematical software.

[0166] In an optional embodiment, after obtaining the above-mentioned virtual power plant (VPP) aggregated power configuration optimization model based on full-potential game, its implementation and effect evaluation, as well as possible improvement directions, can be further explored. The specific steps can be as follows:

[0167] First, collect historical operation data, load forecast data, market price information, etc. of thermal power, distributed generation (DG), energy storage systems, and high-energy-consuming enterprises, and perform necessary cleaning, integration, and standardization. According to the collected data, set the cost function of each unit, the parameters in the constraints (such as power consumption per unit output, daily order volume, maximum production capacity, etc.), and the penalty function parameters in the full-potential game. Using optimization tools such as Python or CPLEX, input the preprocessed data and the set parameters into the full-potential game model for solution. During the solution process, multiple iterations may be required to approach the optimal solution. According to the solution results, formulate the aggregated power configuration plan of the VPP, including the output plan of each unit, the charging and discharging strategy of the energy storage system, and the production power adjustment plan of high-energy-consuming enterprises. The formulated plan will be issued to all participants for execution, including thermal power plants, DG operators, energy storage system managers, and high-energy-consuming enterprises, to ensure that all parties operate according to the established plan.

[0168] Secondly, calculate the total revenue of VPP after implementing the new configuration plan, including electricity sales revenue, energy conservation and emission reduction subsidies, demand response rewards, etc., and compare them with those before implementation to evaluate the improvement of economic benefits. Observe the balance of electricity supply and demand of VPP during peak and trough periods of electricity demand to evaluate its ability to flexibly respond to market changes. Count the total carbon emissions of VPP after implementing the new plan and compare them with those before implementation to evaluate its contribution to energy conservation and emission reduction. It is also possible to collect satisfaction feedback on the new plan from thermal power plants, DG operators, energy storage system managers, and high-energy-consuming enterprises through questionnaires or interviews to understand the actual needs and improvement suggestions of all parties.

[0169] Finally, the technical solution is readjusted based on the evaluation results until the effect of the technical solution meets the needs.

[0170] Exemplary: Calculate the total revenue A of the VPP after implementing the new configuration plan, including electricity sales revenue, energy conservation and emission reduction subsidies, demand response rewards, etc., and compare it with B before implementation to evaluate the improvement of economic benefits. If A > B + 50 (in ten thousand yuan), the effect evaluation is good; if A < B + 30 (in ten thousand yuan), the effect evaluation is poor; if A > B + 150 (in ten thousand yuan), the effect evaluation is excellent. Among them, 30 (the cost in the entire design process), 50 (the amount at which profit starts after excluding all other expenses), and 150 (the amount of full profit) are all the actual funds encountered during the implementation of this application. Other technicians can also design according to actual needs;

[0171] In an alternative embodiment, the full-potential game model can also be further optimized according to the feedback of the implementation effect evaluation, including adjusting the objective function, constraint conditions, or game rules, etc., to improve the accuracy and practicability of the model. The data collection and processing capabilities can also be strengthened, and more-dimensional data (such as weather data, policy changes, etc.) can be introduced. More valuable information can be discovered through data mining techniques to provide more comprehensive inputs for the model. Through the exploration and practice of the above steps and directions, the optimization level of the aggregated power source configuration of the VPP can be further improved, promoting the healthy development of the power market and the realization of the energy conservation and emission reduction goals.

[0172] In summary, the present invention proposes an optimization method for the aggregated power source configuration of a virtual power plant based on an improved CCR game interaction efficiency model, obtains the types of power sources to be aggregated for the target virtual power plant, and establishes a comprehensive evaluation index for the alternative units of the virtual power plant based on the types of power sources to be aggregated. The types of power sources include thermal power, distributed power sources, and energy storage; according to the comprehensive evaluation index, combined with the improved CCR model, a comprehensive evaluation model for the alternative units of the virtual power plant is established. The comprehensive evaluation model for the alternative units of the virtual power plant includes the objective function and constraint conditions of each unit type; the objective function of each unit type is converted into a potential function of the full-potential game, and the comprehensive evaluation model for the alternative units of the virtual power plant is solved in combination with the constraint conditions, and the optimization of the aggregated power source configuration of the virtual power plant is completed according to the solution results. It fills the defect that previous studies only focused on the specific unit capacity configuration of the VPP, and can simultaneously solve the problems of the optimal selection of power supply cooperation enterprises and the optimal configuration of unit capacity for the aggregation of the VPP. Based on the improved CCR method, through multiple iterative calculations, more objective and comprehensive unit evaluation results can be given; this method not only gives full play to the comprehensive evaluation mechanism of the traditional CCR method combining self-evaluation and mutual evaluation, but also makes the process of mutual evaluation more objective by setting the Pareto condition and increasing the overall evaluation weight of the DMU. Finally, through the full-potential game process, the economic and stability goals of the VPP are transformed into potential functions, and there must be a Nash equilibrium solution in the solution process, and the convergence is faster.

[0173] The virtual power plant aggregate power configuration optimization method and system based on the improved CCR game interactive efficiency model of the present invention not only solves the problems of incomplete evaluation factors and strong subjectivity of evaluation standards in existing research, but also improves the objectivity of evaluation and the optimization of configuration by introducing game theory and full-power game theory. In the future, with the continuous development of VPP technology and the expansion of application scenarios, the method and system of the present invention will play a greater role in the fields of energy Internet, smart grid, etc., and promote the transformation, upgrading and sustainable development of the energy industry.

[0174] This embodiment also provides a virtual power plant aggregate power configuration optimization system based on an improved CCR game interaction efficiency model, including:

[0175] An indicator acquisition module is used to obtain the category of power sources to be aggregated in the target virtual power plant and establish comprehensive evaluation indicators for candidate units of the virtual power plant based on the category of power sources to be aggregated. The power source categories include thermal power, distributed power sources and energy storage.

[0176] A model building module is used to build a comprehensive evaluation model for candidate units of a virtual power plant based on comprehensive evaluation indicators and in combination with an improved CCR model. The comprehensive evaluation model for candidate units of a virtual power plant includes objective functions and constraints for each unit type.

[0177] The solution module is used to convert the objective function of each unit type into the potential function of the full potential game, and solve the comprehensive evaluation model of the alternative units of the virtual power plant in combination with the constraints, and complete the optimization of the aggregated power configuration of the virtual power plant based on the solution results.

[0178] The above-mentioned unit modules may be embedded in or independent of a processor in a computer device in the form of hardware, or may be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to the above-mentioned modules.

[0179] This embodiment also provides a computer device, which may be a terminal, and its internal structure diagram may be as shown in FIG. Figure 3As shown. The computer device includes a processor, a memory, a communication interface, a display screen and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. When the computer program is executed by the processor, a virtual power plant aggregate power configuration optimization method based on an improved CCR game interaction efficiency model is implemented. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covered on the display screen, or a button, trackball or touchpad set on the computer device housing, or an external keyboard, touchpad or mouse.

[0180] This embodiment further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the following steps are implemented:

[0181] Obtain the categories of power sources to be aggregated in the target virtual power plant, and establish comprehensive evaluation indicators for candidate units of the virtual power plant based on the categories of power sources to be aggregated. The power source categories include thermal power, distributed power sources, and energy storage.

[0182] According to the comprehensive evaluation index and the improved CCR model, a comprehensive evaluation model for candidate units of virtual power plants is established. The comprehensive evaluation model for candidate units of virtual power plants includes the objective function and constraint conditions of each unit type.

[0183] The objective function of each unit type is converted into the potential function of the full potential game, and the comprehensive evaluation model of the alternative units of the virtual power plant is solved in combination with the constraints. Based on the solution results, the optimization of the aggregated power configuration of the virtual power plant is completed.

[0184] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

[0185] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of complete hardware embodiments, complete software embodiments, or embodiments in combination with software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code. The scheme in the embodiments of the present application can be implemented in various computer languages, for example, object-oriented programming language Java and literal scripting language JavaScript, etc.

[0186] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0187] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0188] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0189] Although the preferred embodiments of the present application have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0190] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims

1. A virtual power plant aggregate power configuration optimization method based on an improved CCR game interactive efficiency model, characterized in that: include: Obtain the category of power sources to be aggregated in the target virtual power plant, and establish comprehensive evaluation indicators for candidate units of the virtual power plant based on the category of power sources to be aggregated, wherein the category of power sources includes thermal power, distributed power sources and energy storage; According to the comprehensive evaluation index and in combination with the improved CCR model, a comprehensive evaluation model for candidate units of a virtual power plant is established, wherein the comprehensive evaluation model for candidate units of a virtual power plant includes objective functions and constraints for each unit type; The objective function of each unit type is converted into a potential function of the full potential game, and the comprehensive evaluation model of the virtual power plant alternative units is solved in combination with the constraints, and the optimization of the virtual power plant aggregate power configuration is completed based on the solution results.

2. The virtual power plant aggregate power configuration optimization method based on the improved CCR game interaction efficiency model according to claim 1 is characterized in that: The step of obtaining the category of power sources to be aggregated in the target virtual power plant and establishing comprehensive evaluation indicators of candidate units of the virtual power plant based on the category of power sources to be aggregated includes: Establish comprehensive evaluation indicators for thermal power, distributed power and energy storage alternative units, which include input indicators and output indicators; The input indicators of the evaluation indicators of thermal power alternative units include pollutant emission concentration and maximum noise of the unit; the output indicators include planned maintenance implementation rate and key equipment integrity rate; The input indicators of the evaluation index of the distributed power supply candidate unit include the number of voltage deviation over-limit times and the power factor over-limit ratio, and the output indicators include the high frequency ride-through ratio, low frequency ride-through ratio and low voltage ride-through ratio; The input indicators of energy storage evaluation include battery failure rate and relative battery failure times, and the output indicator is the number of charge and discharge cycles.

3. The virtual power plant aggregate power configuration optimization method based on the improved CCR game interaction efficiency model according to claim 2 is characterized in that: The improved CCR model includes: In the original CCR model, the evaluation index of the evaluated object is divided into an input index vector and an output index vector. Assuming that there are k evaluated objects in the same category, for evaluation object i, there are n input index categories and m output index categories, then the input index vector can be recorded as X i ={x 1i ,x 2i ,…,x ni } T , the output index vector can be recorded as Y i ={y 1i ,y 2i ,…,y mi } T , the improved CCR model is expressed as follows: Among them, E j represents the expected value of the interaction efficiency under the optimal weight vector of unit j, Ω dj represents the game interaction efficiency of units d and j corresponding to the expected value, and λ T Represents the weight vectors of output indicators and input indicators respectively, X d and Y d It is the input and output index sequence of the initial evaluation efficiency result of DMU obtained when the unit d is optimally solved by the original CCR model.

4. The virtual power plant aggregate power configuration optimization method based on the improved CCR game interaction efficiency model according to claim 3 is characterized in that: The objective functions and constraints of each unit type include: The objective function of the thermal power unit is: Among them, F tp represents the objective function of the thermal power unit that takes both economy and stability into consideration; Λ tp is a virtual variable, which takes the value 1 when the unit joins the virtual power plant, and takes the value 0 otherwise; R tp Represents the profit function of thermal power units, measuring economic efficiency; G tp It represents the efficiency function of thermal power units and measures the quality of output power; and Respectively represent R tp and G tp The weight of The constraints of thermal power units are: in, and Respectively represent the minimum / maximum permissible output power of thermal power units; and Δ They represent the lower and upper limits of the thermal power unit’s ramp power, represents the power output of the thermal power unit at time t, Represents the power output of the thermal power unit at time t-1.

5. The virtual power plant aggregate power configuration optimization method based on the improved CCR game interaction efficiency model according to claim 4 is characterized in that: The objective functions and constraints of each unit type also include: The objective function of the distributed generation unit is: Among them, F dg represents the objective function of the distributed generation unit that takes both economy and stability into consideration; Λ dg is a virtual variable, which takes 1 when the unit joins the virtual power plant, and takes 0 otherwise; R dg Represents the revenue function of distributed generation, which measures the economic efficiency; G dg It is the efficiency function of distributed power generation, which measures the quality of output power; and Respectively represent R dg and G dg The weight of The constraints of distributed generation units are: in, Represents the predicted value given according to the output characteristics of distributed generation, P t dg Represents the output power of distributed generation at time t.

6. The virtual power plant aggregate power configuration optimization method based on the improved CCR game interaction efficiency model according to claim 5 is characterized in that: The objective functions and constraints of each unit type also include: The objective function of the energy storage unit is: Among them, F es represents the objective function of the energy storage unit that takes both economy and stability into consideration; Λ es is a virtual variable, which takes 1 when the unit joins the virtual power plant, and takes 0 otherwise; R es Represents the revenue function of energy storage, which measures the economic efficiency; G es is the efficiency function of energy storage, which measures the quality of output power; and Respectively represent R es and G es The weight of The constraints of the energy storage unit are: Among them, E t represents the state of charge of the energy storage at time t, E t-1 represents the state of charge of the energy storage at time t-1; η loss Indicates the energy storage capacity loss rate; E min and E max Respectively represent the lower and upper limits of the energy storage charge state; Indicates the maximum charging power allowed by the energy storage; Indicates the maximum discharge power allowed by energy storage, Represents the net output power of energy storage.

7. The virtual power plant aggregate power configuration optimization method based on the improved CCR game interaction efficiency model according to claim 6 is characterized in that: The power source category also includes controllable loads. When the power source category to be aggregated in the target virtual power plant includes controllable loads, the controllable loads are at the demand side, and the objective function of the controllable loads only includes the benefit function.

8. A virtual power plant aggregate power configuration optimization system based on an improved CCR game interactive efficiency model, characterized in that: include: An indicator acquisition module is used to obtain the category of power sources to be aggregated in the target virtual power plant and establish comprehensive evaluation indicators of candidate units of the virtual power plant based on the category of power sources to be aggregated, wherein the category of power sources includes thermal power, distributed power sources and energy storage; A model building module, used to establish a comprehensive evaluation model of candidate units of a virtual power plant according to the comprehensive evaluation index and in combination with an improved CCR model, wherein the comprehensive evaluation model of candidate units of a virtual power plant includes an objective function and constraint conditions for each unit type; The solution module is used to convert the objective function of each unit type into a potential function of the full potential game, and solve the comprehensive evaluation model of the virtual power plant alternative units in combination with the constraint conditions, and complete the optimization of the virtual power plant aggregate power configuration according to the solution results.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: 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 having a computer program stored thereon, 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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