VPP aggregation power supply configuration optimization method and system based on network evolutionary game

Abstract

The invention discloses a VPP aggregation power supply configuration optimization method based on a network evolutionary game, and relates to the technical field of resource aggregation modeling of virtual power plant VPP. Comprising the following steps: establishing a game model of VPP including wind and light storage and combustion based on a network evolutionary game; according to a specific game combination, giving an equilibrium solution under different game scenes; and giving a revenue distribution mechanism of unit investors in the VPP polymer based on an improved Shapley value method. According to the method, the resource aggregation problem of the VPP is solved through the network evolutionary game path, and the relationship essence between a VPP operation main body and each unit investor is better met. A plurality of game combination strategies are formed by the network evolutionary game, so that the design has a wider application range and flexibility in solving problems. Based on an improved Shapley value method, the defects of a traditional method can be overcome, and a unit income distribution mechanism is more reasonable and fair through a multi-dimensional evaluation method. By means of the method, the problem of the unit configuration scheme in the initial stage of VPP building can be solved.

Description

Technical Field

[0001] The present invention relates to the technical field of resource aggregation modeling of a virtual power plant (VPP), and in particular to a VPP aggregate power supply configuration optimization method and system based on network evolution game. Background Art

[0002] After more than a decade of development, my country's renewable energy construction has entered a mature stage, many projects have entered the investment payback cycle, and the market focus has shifted from incremental construction to stock allocation. The current renewable energy allocation problem mainly lies in the unstable output of renewable energy represented by photovoltaic and wind power at the technical level and the uneven distribution of resources, which has brought new challenges to the safety, stability and economy of the power system.

[0003] As a comprehensive operation mode of multi-resource aggregation, Virtual Power Plants are recognized as the mainstream solution to the problem of distributed energy configuration. The operation of virtual power plants is generally undertaken by market entities that meet the market access qualifications. The operating entity and the investors of each unit build the VPP operation as a whole through an agreement, participate in the market in a clear form and determine the profit distribution mechanism of each unit. Therefore, the construction of a VPP must first solve the problem of resource aggregation. At the operational level, it is necessary to determine the specific unit configuration of each category of resources aggregated as a whole and the allocation mechanism of each unit.

[0004] Unlike general resource allocation or investment decision-making problems, the VPP operating entity and each unit are not in the same property relationship, so it is not applicable to the traditional resource planning problem under given boundary conditions, but a dynamic game problem of multiple decision-making entities, including both the game problem between the VPP operating entity and the investors of each unit, and the game between the investors of each unit. Furthermore, the market has seen a new operating model in which various VPPs cooperate to form a larger VPP alliance to participate in the market. Therefore, the aforementioned game process should also be extended to multiple types of VPP aggregates. In addition, there is an agency relationship between the operating entity of the VPP and the investors. Because the ownership of the unit belongs to its investors, the unit investors need to weigh the difference in benefits between participating in the VPP and not participating in the VPP. The VPP operator needs to weigh whether incorporating a certain unit into the aggregate can increase the overall benefits. There is a typical conflict between individual rationality and overall rationality in economics. For research in this field, most traditional research methods study the optimal capacity configuration under the conditions of given resource categories and total number of units, treating the configuration problem as a traditional resource planning problem, either without considering the decision factors of the unit owners, or without considering the game relationship between the unit owners and the operating entities.

[0005] In fact, the choice of whether to cooperate, with whom to cooperate, how to cooperate, and how long to cooperate will affect the final unit configuration of the VPP. Therefore, the unit configuration of the VPP should not be a given prerequisite, but a problem that needs to be solved. It is precisely because of the various game relationships in the VPP aggregate that game theory is undoubtedly a powerful tool that is very suitable for solving the VPP configuration problem. For this kind of game process with multiple decision-makers, classical game theory is difficult to handle, because game relationships can be generated between each participant in the game process, and the equilibrium conditions also change.

[0006] At the same time, when determining the specific benefit distribution mechanism, the uncertainty brought by the output characteristics of distributed energy must be considered. Therefore, it is necessary to introduce the risk characterization in the game process, that is, the impact of risk preference on the subject's utility. For the risk-benefit distribution problem in the game process, the industry mainly uses the Shapley value method, but this method has the defects of a single distribution basis and does not consider the differences in risk preferences of participants.

[0007] This design will propose a network game method for a typical VPP aggregation of wind power, photovoltaics, energy storage, and micro gas turbines to solve the unit configuration optimization problem and profit distribution mechanism problem in the VPP aggregation stage. Summary of the invention

[0008] In view of the problems existing in the prior art, the present invention is proposed.

[0009] Therefore, the problem to be solved by the present invention is to characterize the economic relationship between the VPP operating entity and each resource entity, refer to the method of network evolutionary game, and transform the economic goal of maximizing the benefits of the VPP as a whole and each entity into a set of multi-dimensional game strategies that can interact freely. The equilibrium solution of this set can be used to obtain the global optimal solution through the PSO algorithm, and finally, the optimal profit distribution mechanism of the unit under the equilibrium solution is given through the improved Shapley value method.

[0010] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0011] In a first aspect, an embodiment of the present invention provides a VPP aggregate power configuration optimization method based on network evolution game, which includes, based on network evolution game, establishing a game model of VPP including wind, solar, storage and fuel;

[0012] According to the specific game combination, the equilibrium solutions under different game scenarios are given;

[0013] Based on the improved Shapley value method, the profit distribution mechanism of unit investors in VPP aggregation is proposed.

[0014] As a preferred solution of the VPP aggregate power configuration optimization method based on network evolution game of the present invention, wherein: the network evolution game model includes game participants, game strategies, game combinations and participant benefits;

[0015] The game combination is expressed as:

[0016] Γ={N,Ω,Λ={Λ ij} i,j∈Ω}

[0017] Among them, Γ represents all game combinations, N = {1, 2, 3…, n} represents the set of all participants, Ω represents the set of pairwise combinations of participants, and Λ represents the cooperation status between participants, which is set as a dummy variable. The value is as follows

[0018]

[0019] Among them, when all four resource types choose to cooperate, Λ={1, 1, 1, 1}, the game combination can be regarded as a resource aggregation process within the VPP; when wind power and photovoltaics cooperate, energy storage and gas turbines cooperate, but there is no cooperation between the two groups, Λ={0, 1, 1, 0}, the game combination can be regarded as an alliance process of a VPP that only includes wind power and photovoltaics and another VPP that only includes energy storage and gas turbines.

[0020] As a preferred solution of the VPP aggregate power configuration optimization method based on network evolution game of the present invention, the game strategy refers to the participants determining the capacity of the units participating in the aggregation, and the strategy set is:

[0021]

[0022] in, They represent the maximum participating capacity of wind power, photovoltaic power, energy storage, and micro gas turbine resources respectively. They represent the minimum participating capacity of each category of resources: wind power, photovoltaic power, energy storage, and micro gas turbine.

[0023] As a preferred solution of the VPP aggregated power configuration optimization method based on network evolution game of the present invention, the benefits of participants include the electricity benefits obtained through bidding, ancillary service benefits, initial costs, operation and maintenance costs, power generation costs and output deviation costs. The benefit calculation formula is:

[0024]

[0025] Among them, R i represents the net benefit of power source i, It indicates the actual output benefit of the VPP winning bid electricity decomposed into the i power source; represents the revenue obtained by power source i from participating in ancillary services; represents the present value of the cost of commissioning power source i; Indicates operation and maintenance cost; represents the cost of electricity generation; Indicates the output deviation cost;

[0026] Among them, scalar return The calculation formula is:

[0027]

[0028] in, Indicates the winning bid price for VPP electricity to be connected to the grid. It indicates the amount of electricity that the VPP bidder decomposes according to the actual timing output of the i power supply;

[0029] Among them, the processing deviation cost The calculation formula is:

[0030]

[0031] in, Indicates the penalty coefficient of VPP due to actual output deviation; △P t Represents the overall output deviation of VPP at time t.

[0032] As a preferred solution of the VPP aggregate power configuration optimization method based on network evolution game of the present invention, the game equilibrium solution includes:

[0033] The VPP internal aggregation game model is solved, and its equilibrium strategy solution is:

[0034]

[0035] in, They respectively represent the optimal total installed capacity of wind power, photovoltaic power, energy storage and micro gas turbines determined when the game is in equilibrium.

[0036] As a preferred solution of the VPP aggregate power configuration optimization method based on network evolution game of the present invention, the improved Shapley value method includes the following evaluation dimensions:

[0037] Investment ratio

[0038]

[0039] Risk preference differences

[0040]

[0041] and marginal contribution

[0042]

[0043] in, represents the installed capacity of investor i, P Σ represents the total installed capacity of VPP, η i represents the difference between investor i’s risk preference and the average risk preference of VPP, θ i Represents the marginal contribution of investor i to VPP.

[0044] As a preferred solution of the VPP aggregate power configuration optimization method based on network evolution game of the present invention, the parameter weight ω is solved so that the income obtained by investors under the VPP internal cooperative game combination is not lower than the income under other game combinations, that is:

[0045]

[0046] Thus, the final profit distribution coefficient is determined;

[0047] Among them, U represents the internal cooperative game combination of VPP, R i (U) represents the return that investor i can obtain under U, Represents the profit obtained by i under other game combinations.

[0048] In a second aspect, an embodiment of the present invention provides a VPP aggregated power supply configuration optimization system based on network evolution game, which includes a game model construction module, an equilibrium solution module and a revenue distribution module;

[0049] The game model building module is used to collect capacity data, output characteristic data, cost data and market price data of wind power, photovoltaics, energy storage and micro gas turbines, and establish a VPP network evolution game model;

[0050] The equilibrium solution module is used to solve the equilibrium strategy of the VPP internal aggregation game model and the inter-VPP aggregation game model based on different game combinations;

[0051] The profit distribution module is used to adopt the improved Shapley value method, taking into account the investment ratio, risk preference difference and marginal contribution, calculate the profit distribution coefficient of each unit investor, and regularly update and optimize the VPP aggregation plan.

[0052] In a third aspect, an embodiment of the present invention provides a computer device, including a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program instructions are executed by the processor, the steps of the VPP aggregate power supply configuration optimization method based on network evolution game as in the first aspect of the present invention are implemented.

[0053] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program instructions are executed by a processor, the steps of the VPP aggregate power supply configuration optimization method based on network evolution game as in the first aspect of the present invention are implemented.

[0054] The beneficial effects of the present invention are: solving the resource aggregation problem of VPP through the network evolution game path, which is more in line with the essence of the relationship between the VPP operating entity and the investors of each unit. At the same time, the various game combination strategies formed by the network evolution game make this design have a wider scope of application and flexibility in solving problems. It can not only be used for resource aggregation decisions within the VPP, but also for cooperation decisions between VPPs. The improved Shapley value method can overcome the shortcomings of the traditional method, reflecting the risks brought by the uncertainty of distributed energy, and at the same time making the unit profit distribution mechanism more reasonable and fair through a multi-dimensional evaluation method. This method can not only solve the unit configuration plan problem faced by VPP in the early stage of its establishment, but also further solve the profit distribution and risk sharing problems of each unit, thereby increasing the attractiveness of VPP to distributed energy. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of 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 work.

[0056] Figure 1 It is a schematic diagram of the overall process of the VPP aggregate power configuration optimization method based on network evolution game;

[0057] Figure 2 A schematic diagram of the specific implementation process of solving the game equilibrium of the VPP aggregate power configuration optimization system based on the network evolution game;

[0058] Figure 3 It is a schematic diagram of the overall process of the VPP aggregate power configuration optimization system based on network evolution game; DETAILED DESCRIPTION

[0059] In order to make the above-mentioned objects, 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 accompanying drawings.

[0060] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0061] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0062] Example 1

[0063] Reference Figure 1 to Figure 3 , which is the first embodiment of the present invention, and provides a VPP aggregate power supply configuration optimization method based on network evolution game, including:

[0064] S1: Based on the network evolution game, establish a game model of VPP including wind, solar, storage and fuel;

[0065] Furthermore, the network evolution game model includes game participants, game strategies, game combinations, and participant benefits;

[0066] The game combination is expressed as:

[0067] Γ={N,Ω,Λ={Λ ij} i,j∈Ω}

[0068] Among them, Γ represents all game combinations, N = {1, 2, 3…, n} represents the set of all participants, Ω represents the set of pairwise combinations of participants, and Λ represents the cooperation status between participants, which is set as a dummy variable. The value is as follows

[0069]

[0070] Among them, when all four resource types choose to cooperate, Λ={1, 1, 1, 1}, the game combination can be regarded as a resource aggregation process within the VPP; when wind power and photovoltaics cooperate, energy storage and gas turbines cooperate, but there is no cooperation between the two groups, Λ={0, 1, 1, 0}, the game combination can be regarded as an alliance process of a VPP that only includes wind power and photovoltaics and another VPP that only includes energy storage and gas turbines.

[0071] It should also be noted that for the game participants, we consider a game scenario that includes wind power, photovoltaics, energy storage and micro gas turbines. Since the interruptible compliance is at the user end, it is not considered in our consideration. For the convenience of analysis, we assume that each type of unit has a corresponding investor / owner, so the specific game participants can be understood as different types of units, represented as WT, PV, ES and MT.

[0072] Furthermore, the game strategy refers to the participants determining the capacity of the units participating in the aggregation, and the strategy set is:

[0073]

[0074] in, They represent the maximum participating capacity of wind power, photovoltaic power, energy storage, and micro gas turbine resources respectively. They represent the minimum participating capacity of each category of resources: wind power, photovoltaic power, energy storage, and micro gas turbine.

[0075] It should also be noted that under the current market rules, each aggregation unit participates in the market through VPP, and its supply of electricity is profitable according to the bidding price. At the same time, the aggregation unit can also obtain income by participating in auxiliary services; when the real-time output of VPP cannot meet the load demand, it needs to be fined in the settlement or make up for it by purchasing electricity from the main grid. Therefore, for power source i, its participant income is the net income from its participation in the market through VPP, which is the difference between income and cost. Its cost mainly includes initial investment cost, operation and maintenance cost, power generation cost, and output deviation cost, among which the initial cost is amortized according to the discounted value.

[0076] Furthermore, the benefits of participants include the electricity revenue obtained through bidding, ancillary service revenue, initial cost, operation and maintenance cost, power generation cost and output deviation cost. The benefit calculation formula is:

[0077]

[0078] Among them, R i represents the net benefit of power source i, It indicates the actual output benefit of the VPP winning bid electricity decomposed into the i power source; represents the revenue obtained by power source i from participating in ancillary services; represents the present value of the cost of commissioning power source i; Indicates operation and maintenance cost; represents the cost of electricity generation; Indicates the output deviation cost;

[0079] Among them, scalar return The calculation formula is:

[0080]

[0081] in, Indicates the winning bid price for VPP electricity to be connected to the grid. It indicates the amount of electricity that the VPP bidder decomposes according to the actual timing output of the i power supply;

[0082] The decomposition power value is determined as follows

[0083]

[0084] In the above formula, P Σ represents the total installed capacity of VPP, P Σ (t) represents the total actual output of each power source at time t, P ∑i represents the total installed capacity of i, P load (t) represents the total required power on the load side at time t, Indicates the maximum transmission power allowed by the line, P i (t) represents the actual output of power source i at time t.

[0085] The benefits of power generation participating in ancillary services are mainly the benefits obtained by energy storage and micro gas turbines from smoothing peak and valley fluctuations, while the ancillary service benefits of wind power and photovoltaic power can be regarded as 0. It can be expressed as

[0086]

[0087] In the above formula, It represents the price of the period of time during which power source i provides ancillary services; It represents the backup capacity that power source i can provide at time t.

[0088] Present value of initial cost of power supply for

[0089]

[0090] In the above formula, represents the production cost of the installed capacity of power source i; r represents the annualized discount rate; L i Indicates the service life or depreciation period of power supply i.

[0091] Power supply operation and maintenance costs for

[0092]

[0093] in, It represents the cost of installed capacity per unit of power source i.

[0094] Cost of power supply output deviation The total cost of the VPP output deviation is apportioned among the units based on the installed capacity ratio of each output unit;

[0095] Among them, the processing deviation cost The calculation formula is:

[0096]

[0097] in, Indicates the penalty coefficient of VPP due to actual output deviation; △P t Represents the overall output deviation of VPP at time t.

[0098] The value can be determined by the following formula:

[0099]

[0100] Specific game combinations formed by the VPP network evolution game:

[0101]

[0102] S2: Based on the specific game combination, give equilibrium solutions for different game scenarios.

[0103] First, consider the game scenario of VPP internal aggregation. The set of participants is {wt, pv, es, mt}. At this time, VPP can be regarded as a game combination of wind, solar and fuel storage; the set of cooperative states is Λ = {1, 1, 1, 1}. At this time, the game mode is cooperative game, and the decision-making goal is to maximize the overall aggregated benefits of VPP. The strategy set is

[0104]

[0105] The elements in the set in the above formula are the minimum / maximum installed capacity of wind power, photovoltaic, energy storage and micro gas turbine respectively.

[0106] At this time, the profit function of the model can be expressed as:

[0107] R wpem (P Σwt ,P Σpv ,P Σes ,P Σmt )=R wt +R pv +R es +R mt

[0108] Furthermore, the game equilibrium solution includes:

[0109] The VPP internal aggregation game model is solved, and its equilibrium strategy solution is:

[0110]

[0111] in, They respectively represent the optimal total installed capacity of wind power, photovoltaic power, energy storage and micro gas turbines determined when the game is in equilibrium.

[0112] It should also be noted that photovoltaic and wind power are regarded as a cooperation group, which is regarded as a wind-solar VPP, and energy storage and micro gas turbines are regarded as another cooperation group, which is regarded as a fuel-storage VPP. According to formula (3), the cooperation state at this time is Λ = {0, 1, 1, 0}. At this time, for the two VPPs, the game mode is a non-cooperative game. Under the non-cooperative game, the two VPPs each pursue the maximization of their own interests, but at the same time, they still meet the conditions of the cooperative game internally, pursuing the maximization of the overall benefits of the VPP.

[0113] For the game model within the VPP, we can still refer to equations (15)-(17) to solve it. Between the wind and solar VPP and the fuel storage VPP, the participant sets are {wt, pv} and {es, mt}, and the strategy set is

[0114]

[0115] At this time, the profit function is

[0116]

[0117] Furthermore, the game equilibrium solution includes:

[0118] The VPP internal aggregation game model is solved, and its equilibrium strategy solution is:

[0119]

[0120] in, They respectively represent the optimal total installed capacity of wind power, photovoltaic power, energy storage and micro gas turbines determined when the game is in equilibrium.

[0121] S3: Based on the improved Shapley value method, the profit distribution mechanism of unit investors in the VPP aggregate is given.

[0122] The Shapley value is a classic method in cooperative game theory to quantify the individual contribution of each member within a group. It achieves fair distribution of cooperative benefits by calculating the utility contribution of each participant to all possible subsets to which he belongs. The Shapley value meets the basic requirements for fair reward distribution, including effectiveness, symmetry, additivity, and irrelevant variables. Specifically, effectiveness requires that the total reward should be fully distributed to all participants. Symmetry stipulates that if two participants have the same marginal contribution, they should receive the same reward. Additivity indicates that the reward value of two tasks should be the sum of the reward values ​​of the individual tasks.

[0123] The Shapley value can be expressed as follows:

[0124]

[0125] Among them, φ i is the Shapley value of participant i; S is any subset of the set of participants N, excluding participant i; n is the total number of participants; v(S∪{i}) and v(S) represent the overall benefits or utility of the alliance or team before and after participant i joins the set S, respectively.

[0126] This formula can be explained as follows: For a player, his benefit for the entire game should be the average of his contributions in every possible combination. That is, in all possible combinations containing the player, the value that can be contributed minus the value that cannot be obtained in, and then these contributions are averaged. Since the contribution of each player is symmetrical, that is, for any two players and, if and only if their positions are exchanged, their contributions should also be exchanged. Therefore, this formula ensures the fairness and uniqueness of the Shapley value.

[0127] Furthermore, the improved Shapley value method includes the following evaluation dimensions:

[0128] Investment ratio

[0129] Risk preference differences

[0130]

[0131] and marginal contribution

[0132]

[0133] in, represents the installed capacity of investor i, P Σ represents the total installed capacity of VPP, η i represents the difference between investor i’s risk preference and the average risk preference of VPP, θ i Represents the marginal contribution of investor i to VPP.

[0134] By solving the parameter weight ω, the returns obtained by investors under the VPP internal cooperative game combination are not lower than the returns under other game combinations, that is:

[0135]

[0136] Thus, the final profit distribution coefficient is determined;

[0137] Among them, U represents the internal cooperative game combination of VPP, R i (U) represents the return that investor i can obtain under U, Represents the profit obtained by i under other game combinations.

[0138] It should also be noted that, compared with other types of resources, wind power and photovoltaic units have higher uncertainty, so their investors have lower risk preferences. In the VPP aggregation, since the resource categories of each unit are different, under the condition of bearing common risks, units with higher risk preferences should be given greater returns to attract more units to join the aggregation. Therefore, the difference in risk preference can represent the attractiveness of VPP to investors of different units, and its expression is:

[0139]

[0140] In the above formula, Φ i ∈[0,1], represents the risk preference coefficient of the i-th unit investor before participating in VPP; τ j is a dummy variable, which takes the value of 1 when investor j other than investor i participates in the VPP, and 0 otherwise; It represents the maximum benefit that investor j can obtain if he does not participate in VPP.

[0141] Among them, the marginal contribution of investor i to the aggregate is expressed as

[0142]

[0143] In the above formula, R represents the overall benefit after VPP aggregation; R j represents the income of other investors j after joining VPP except i. Normalized, we have:

[0144]

[0146] Get the parameter set that represents the basis for investor allocation At this point, the specific weights of the elements in K must be determined to obtain the final allocation basis for investor i.

[0147] First, for the specific configuration of wind power, photovoltaic, energy storage and micro gas turbine of investor i, each type of unit can be substituted as a variable to obtain its parameter set K. At this time, a parameter matrix can be obtained as follows:

[0148]

[0149] Assume that the weight of each parameter in K is Then the final coefficient sequence of investor i's income distribution can be expressed as

[0150]

[0151] Then the income that investor i should distribute is

[0152]

[0153] The above formula represents the income that investor i should distribute, because is a 4×1 matrix, so here It represents a 1×4 matrix that separates the revenues of wind power, photovoltaic power, energy storage, and micro gas turbines, as follows

[0154]

[0155] R in the above formula wt ,R pv ,R es ,R mt They respectively represent the total benefits actually obtained by wind power, photovoltaic power, energy storage, and micro gas turbines through VPP.

[0156] Furthermore, by solving the parameter weight ω, the returns obtained by investors under the VPP internal cooperative game combination are not lower than the returns under other game combinations, that is:

[0157]

[0158] Thus, the final profit distribution coefficient is determined;

[0159] Among them, U represents the internal cooperative game combination of VPP, R i (U) represents the return that investor i can obtain under U, Represents the profit obtained by i under other game combinations.

[0160] Through the above three steps, we have achieved the two major optimization goals of VPP internal resource allocation and profit distribution mechanism.

[0161] Furthermore, this embodiment also provides a VPP aggregate power configuration optimization system based on network evolution game, including a game model construction module, an equilibrium solution module and a revenue distribution module;

[0162] The game model building module is used to collect capacity data, output characteristic data, cost data and market price data of wind power, photovoltaics, energy storage and micro gas turbines, and establish a VPP network evolution game model;

[0163] The equilibrium solution module is used to solve the equilibrium strategy of the VPP internal aggregation game model and the inter-VPP aggregation game model based on different game combinations;

[0164] The profit distribution module is used to adopt the improved Shapley value method, taking into account the investment ratio, risk preference difference and marginal contribution, calculate the profit distribution coefficient of each unit investor, and regularly update and optimize the VPP aggregation plan.

[0165] This embodiment also provides a computer device, which is applicable to the VPP aggregate power supply configuration optimization method based on network evolution game, including a memory and a processor; the memory is used to store computer executable instructions, and the processor is used to execute computer executable instructions to implement the VPP aggregate power supply configuration optimization method based on network evolution game proposed in the above embodiment.

[0166] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. 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. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0167] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, the method for optimizing the configuration of VPP aggregated power supply based on network evolution game proposed in the above embodiment is implemented.

[0168] In summary, the present invention solves the resource aggregation problem of VPP through the network evolution game path, which is more in line with the essence of the relationship between the VPP operating entity and the investors of each unit. At the same time, the various game combination strategies formed by the network evolution game make this design have a wider scope of application and flexibility in solving problems. It can not only be used for VPP internal resource aggregation decisions, but also for cooperation decisions between VPPs. The improved Shapley value method can overcome the shortcomings of traditional methods, reflecting the risks brought by the uncertainty of distributed energy, and at the same time making the unit profit distribution mechanism more reasonable and fair through a multi-dimensional evaluation method. This method can not only solve the unit configuration plan problem faced by VPP in the early stage of its establishment, but also further solve the profit distribution and risk sharing problems of each unit, thereby increasing the attractiveness of VPP to distributed energy.

[0169] Example 2

[0170] This is the second embodiment of the present invention. This embodiment provides a VPP aggregate power supply configuration optimization method based on network evolution game. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0171] In order to verify the effectiveness of the VPP aggregate power configuration optimization method based on network evolution game proposed in this paper, a typical VPP project was selected in the distribution network area of ​​a provincial power company for experiment. The VPP project includes four types of distributed energy: wind power, photovoltaic power, energy storage and micro gas turbine.

[0172] The experimental process is as follows:

[0173] First, the historical operation data of various types of distributed energy sources were collected, including output characteristics, cost structure, and market price information. Based on these data, a network evolution game model was constructed. In the model, the game participants are four types of distributed energy investors, and the game strategy is to decide the installed capacity of participating in the VPP. The game combination represents different aggregation scenarios through the cooperation state matrix Λ.

[0174] Next, three typical game scenarios were designed: Scenario 1 is full cooperation within the VPP (Λ={1,1,1,1}), Scenario 2 is cooperation between wind and solar and energy storage and gas turbines but no cooperation between the two groups (Λ={0,1,1,0}), and Scenario 3 is independent operation (Λ={0,0,0,0}). For each scenario, the particle swarm optimization algorithm is used to solve the game equilibrium and obtain the optimal installed capacity configuration plan.

[0175] Then, based on the improved Shapley value method, the profit distribution coefficient of each investor is calculated by considering the three dimensions of investment ratio, risk preference difference and marginal contribution. Among them, the risk preference coefficient is determined according to the volatility characteristics of each type of energy. The risk preference of wind power and photovoltaic power is relatively low, set at 0.3 and 0.4 respectively, while the risk preference of energy storage and gas turbine is relatively high, set at 0.7 and 0.8 respectively.

[0176] Finally, the overall income of VPP and the individual income of each investor under the three scenarios were compared and analyzed to evaluate the effectiveness of this method. The experimental period is one year, and the statistics are calculated on a monthly basis. In order to better display the results, the data of four representative months are selected for detailed analysis. The experimental results are shown in the following table:

[0177] scene index January April July October Scenario 1 (Full Cooperation) Total VPP income (10,000 yuan) 2850 2420 3180 2680 Scenario 1 (Full Cooperation) Wind power income (10,000 yuan) 720 580 860 650 Scenario 1 (Full Cooperation) Photovoltaic income (10,000 yuan) 680 620 920 710 Scenario 1 (Full Cooperation) Energy storage income (10,000 yuan) 750 630 720 680 Scenario 1 (Full Cooperation) Gas turbine income (10,000 yuan) 700 590 680 640 Scenario 2 (Partial Cooperation) Total VPP income (10,000 yuan) 2580 2210 2890 2450 Scenario 2 (Partial Cooperation) Wind power income (10,000 yuan) 650 530 780 590 Scenario 2 (Partial Cooperation) Photovoltaic income (10,000 yuan) 620 570 840 650 Scenario 2 (Partial Cooperation) Energy storage income (10,000 yuan) 680 570 650 620 Scenario 2 (Partial Cooperation) Gas turbine income (10,000 yuan) 630 540 620 590 Scenario 3 (Independent Operation) Total VPP income (10,000 yuan) 2320 1980 2610 2200 Scenario 3 (Independent Operation) Wind power income (10,000 yuan) 580 470 700 530 Scenario 3 (Independent Operation) Photovoltaic income (10,000 yuan) 560 510 760 580 Scenario 3 (Independent Operation) Energy storage income (10,000 yuan) 610 510 580 550 Scenario 3 (Independent Operation) Gas turbine income (10,000 yuan) 570 490 570 540

[0178] Through the analysis of the above experimental data, the following conclusions can be drawn:

[0179] VPP overall revenue: Among the three scenarios, the total revenue of VPP in scenario 1 (full cooperation) is always the highest, with an average revenue of 27.825 million yuan in four months. Scenario 2 (partial cooperation) is second, with an average revenue of 25.325 million yuan. Scenario 3 (independent operation) is the lowest, with an average revenue of 22.775 million yuan. This shows that the VPP aggregation method based on network evolution game proposed in the present invention can effectively improve the overall revenue. Compared with independent operation, VPP in the full cooperation mode increases revenue by 22.2% on average.

[0180] Benefits of each investor: In the fully cooperative scenario, the benefits of each investor have been significantly improved. Taking July as an example, the benefits of wind power, photovoltaics, energy storage and gas turbines increased by 22.9%, 21.1%, 24.1% and 19.3% respectively compared with the independent operation scenario. This shows that this method can achieve a win-win situation for all parties and is conducive to motivating investors to participate in VPP aggregation.

[0181] Fairness of income distribution: The improved Shapley value method takes into account the input ratio, risk preference difference and marginal contribution, making the income distribution more reasonable. For example, although photovoltaics performed well in the summer (July), due to its lower risk preference (0.4), its income increase (21.1%) was slightly lower than that of energy storage with a higher risk preference (24.1%, risk preference 0.7). This reflects the advantage of this method in balancing income and risk.

[0182] Seasonal impact: Data show that VPPs perform differently in different seasons. The benefits in summer (July) are generally higher, which may be due to increased electricity demand and sufficient photovoltaic output. The benefits in spring and autumn (April and October) are relatively low. This method can better cope with seasonal changes and optimize the overall performance of VPP by dynamically adjusting the participation ratio of each type of energy.

[0183] Effect of partial cooperation: The results of scenario 2 (partial cooperation) are between full cooperation and independent operation, which shows that even the aggregation of partial resources can bring significant revenue improvement. For example, partial cooperation in October increased total revenue by 11.4% compared with independent operation. This provides a feasible path for the gradual expansion of VPP.

[0184] Synergy effect: In the full cooperation scenario, the revenue growth of each type of energy is not exactly the same, which reflects the synergy between different resources. For example, energy storage has achieved a higher revenue growth in most months, which may be because it can effectively cooperate with the intermittent characteristics of wind power and photovoltaics to provide peak and frequency regulation services.

[0185] In summary, the VPP aggregate power configuration optimization method based on network evolution game proposed in this invention shows significant advantages in practical applications. It can not only effectively improve the overall benefits of VPP, but also ensure the balance of interests of various investors, while taking into account factors such as risk preference, and achieving a more fair and reasonable distribution of benefits. In addition, this method shows good adaptability to seasonal changes and different degrees of cooperation models, providing strong support for the actual operation and promotion of VPP.

[0186] 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.

Claims

1. A VPP aggregate power configuration optimization method based on network evolution game, characterized by: Including, based on the network evolution game, establishing a game model for VPP including wind, solar, storage and fuel; According to the specific game combination, the equilibrium solutions under different game scenarios are given; Based on the improved Shapley value method, the profit distribution mechanism of unit investors in VPP aggregation is proposed.

2. The VPP aggregate power supply configuration optimization method based on network evolution game according to claim 1, characterized in that: The network evolution game model includes game participants, game strategies, game combinations and participant benefits; The game combination is expressed as: C={N,Ω,Λ={Λ ij } i,j∈Ω } Among them, Γ represents all game combinations, N = {1, 2, 3…, n} represents all the participants, Ω represents the set of two-by-two combinations of participants, and Λ represents the cooperation status between two participants, which is set as a dummy variable. The value is as follows Among them, when all four resource types choose to cooperate, Λ={1, 1, 1, 1}, the game combination can be regarded as a resource aggregation process within the VPP; when wind power and photovoltaics cooperate, energy storage and gas turbines cooperate, but there is no cooperation between the two groups, Λ={0, 1, 1, 0}, the game combination can be regarded as an alliance process of a VPP that only includes wind power and photovoltaics and another VPP that only includes energy storage and gas turbines.

3. The VPP aggregate power supply configuration optimization method based on network evolution game as claimed in claim 2, characterized in that: The game strategy refers to the participants determining the capacity of the units participating in the aggregation, and the strategy set is: in, They represent the maximum participating capacity of wind power, photovoltaic power, energy storage, and micro gas turbine resources respectively. They represent the minimum participating capacity of each category of resources: wind power, photovoltaic power, energy storage, and micro gas turbine.

4. The VPP aggregate power supply configuration optimization method based on network evolution game as claimed in claim 3, characterized in that: The participant benefits include electricity benefits obtained through bidding, ancillary service benefits, initial costs, operation and maintenance costs, power generation costs, and output deviation costs. The benefit calculation formula is: Among them, R i represents the net benefit of power source i, It indicates the actual output benefit of the VPP winning bid electricity decomposed into the i power source; represents the revenue obtained by power source i from participating in ancillary services; represents the present value of the cost of commissioning power source i; Indicates operation and maintenance cost; represents the cost of electricity generation; Indicates the output deviation cost; Among them, scalar return The calculation formula is: in, Indicates the winning bid price for VPP electricity to be connected to the grid. It indicates the amount of electricity that the VPP bidder decomposes according to the actual timing output of the i power supply; Among them, the processing deviation cost The calculation formula is: in, Indicates the penalty coefficient of VPP due to actual output deviation; △P t Represents the overall output deviation of VPP at time t.

5. The VPP aggregate power supply configuration optimization method based on network evolution game according to claim 4, characterized in that: The game equilibrium solution includes: The VPP internal aggregation game model is solved, and its equilibrium strategy solution is: in, They respectively represent the optimal total installed capacity of wind power, photovoltaic power, energy storage and micro gas turbines determined when the game is in equilibrium.

6. The VPP aggregate power supply configuration optimization method based on network evolution game according to claim 5, characterized in that: The improved Shapley value method includes the following evaluation dimensions: Investment ratio Risk preference differences and marginal contribution Among them, P Σi represents the installed capacity of investor i, P Σ represents the total installed capacity of VPP, η i represents the difference between investor i’s risk preference and the average risk preference of VPP, θ i Represents the marginal contribution of investor i to VPP.

7. The VPP aggregate power supply configuration optimization method based on network evolution game according to claim 6, characterized in that: By solving the parameter weight ω, the returns obtained by investors under the VPP internal cooperative game combination are not lower than the returns under other game combinations, that is: Thus, the final profit distribution coefficient is determined; Among them, U represents the internal cooperative game combination of VPP, R i (U) represents the return that investor i can obtain under U, Represents the profit obtained by i under other game combinations.

8. A VPP aggregate power supply configuration optimization system based on network evolution game, based on the VPP aggregate power supply configuration optimization method based on network evolution game according to any one of claims 1 to 7, characterized in that: It also includes a game model building module, an equilibrium solving module and a revenue distribution module; The game model building module is used to collect capacity data, output characteristic data, cost data and market price data of wind power, photovoltaic, energy storage and micro gas turbines, and establish a VPP network evolution game model; The equilibrium solution module is used to solve the equilibrium strategy of the VPP internal aggregation game model and the inter-VPP aggregation game model based on different game combinations; The profit distribution module is used to calculate the profit distribution coefficient of each unit investor by adopting the improved Shapley value method, taking into account the investment ratio, risk preference difference and marginal contribution, and regularly update and optimize the VPP aggregation plan.

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 VPP aggregate power supply configuration optimization method based on network evolution game 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 VPP aggregate power supply configuration optimization method based on network evolution game according to any one of claims 1 to 7 are implemented.

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