A method and system for renewable energy power plant construction

Abstract

The application provides a renewable energy power station construction method and system, which comprises the following steps: obtaining initial strategies of each power station site; the strategy is renewable energy power generation or non-renewable energy power generation; inputting the initial strategy of each power station site into a renewable energy power station construction model to output the optimized strategy of each power station site; wherein the renewable energy power station construction model is an evolutionary game model based on a dynamic complex network; the renewable energy power station construction model is used for iteratively updating the strategy of each power station site and the network structure of the complex network based on the expected income and cooperative income of each power station site until a preset iteration termination condition is met, so as to output the optimized strategy of each power station site. The application can optimize the strategy of each power station site, thereby promoting the construction of renewable energy power stations.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of new energy and urban construction, and particularly relates to a renewable energy power station construction method and system. BACKGROUND

[0002] With the increasing attention to climate change in the world, more and more countries have introduced renewable energy development strategies to cope with climate deterioration and achieve clean and sustainable development goals. Due to the limitations of power generation capacity or investment amount, many power generation companies cannot meet the requirements of renewable energy power generation, resulting in insufficient renewable energy power generation capacity. However, due to the clean advantage of renewable energy power generation and the huge development potential, with the further increase of the demand for electricity in the whole society, the demand for renewable energy power stations is increasing. Therefore, there is an urgent need for a renewable energy power station construction method and system to promote the construction of renewable energy power stations. SUMMARY

[0003] In view of the problems in the prior art, the present application provides a renewable energy power station construction method and system.

[0004] The present application provides a renewable energy power station construction method, comprising:

[0005] obtaining an initial strategy of each power station site; the strategy is renewable energy power generation or non-renewable energy power generation;

[0006] inputting the initial strategy of each power station site into a renewable energy power station construction model to output an optimized strategy of each power station site; wherein the renewable energy power station construction model is an evolutionary game model constructed based on a dynamic complex network; the renewable energy power station construction model is used to iteratively update the strategy of each power station site and the network structure of the complex network based on the expected income and cooperative income of each power station site until a preset iteration termination condition is met to output the optimized strategy of each power station site.

[0007] According to the renewable energy power station construction method provided by the present application, the renewable energy power station construction method further comprises:

[0008] determining the complex network based on the application scenario type of renewable energy power generation.

[0009] According to the renewable energy power station construction method provided by the present application, the determination of the complex network based on the application scenario type of renewable energy power generation comprises:

[0010] if the application scenario type is a first scenario type, the complex network is determined to be a small-world network;

[0011] If the application scenario type is a second scenario type, it is determined that the complex network is a scale-free network.

[0012] The balance degree of the connection density of each power generation site in the second scenario type is lower than the balance degree of the connection density of each power generation site in the first scenario type.

[0013] According to the renewable energy power station construction method provided by the application, the method for obtaining the expected income of the power generation site comprises the following steps:

[0014] The payment matrix is used to calculate the income of the power generation site under a plurality of different strategy distribution scenarios.

[0015] Based on the current strategy of each power generation site, the proportion of the power generation site using renewable energy power generation is determined.

[0016] Based on the income of the power generation site under a plurality of different strategy distribution scenarios and the proportion, the expected income of the power generation site is calculated.

[0017] According to the renewable energy power station construction method provided by the application, the construction of the payment matrix comprises the following steps:

[0018] Based on different strategy distribution scenarios, the elements of the payment matrix are determined; the elements of the payment matrix correspond to the strategy distribution scenarios one by one.

[0019] Based on the total demand power, the renewable energy power generation quota, the income and cost of renewable energy power generation, and the income and cost of non-renewable energy power generation, the expression of each element of the payment matrix is determined.

[0020] The payment matrix is used to calculate the income of the power generation site under a plurality of different strategy distribution scenarios, which comprises the following steps:

[0021] The income of the power generation site under the corresponding strategy distribution scenario is calculated through the expression of each element of the payment matrix.

[0022] According to the renewable energy power station construction method provided by the application, the method for obtaining the cooperative income comprises the following steps:

[0023] According to the current strategy of the power generation site and the current strategy of the cooperative site of the power generation site, a target expression is determined from the expression of each element of the payment matrix; wherein the cooperative site of the power generation site is a power generation site connected to the power generation site.

[0024] The cooperative income of the power generation site is calculated according to the target expression.

[0025] According to the renewable energy power station construction method provided by the application, the renewable energy power generation quota is a dynamic value; wherein the updating of the renewable energy power generation quota comprises:

[0026] The renewable energy power generation quota is updated based on the proportion of the power station site using renewable energy power generation.

[0027] According to the renewable energy power station construction method provided by the application, the network structure of the complex network is iteratively updated based on the expected benefits of each power station site, comprising:

[0028] When the cooperation benefits of the power station site meet the preset conditions, the connection between the power station site and the cooperation site of the power station site is disconnected;

[0029] One or more new cooperation sites are determined from the candidate sites, and the new cooperation sites are connected with the power station site; wherein the candidate sites are part or all of the power station sites that are not connected with the power station site.

[0030] The application further provides a renewable energy power station construction system, comprising:

[0031] A data acquisition module is configured to acquire the initial strategy of each power station site; the strategy is renewable energy power generation or non-renewable energy power generation;

[0032] A strategy updating module is configured to input the initial strategy of each power station site into a renewable energy power station construction model to output the optimized strategy of each power station site; wherein the renewable energy power station construction model is an evolutionary game model based on a dynamic complex network; the renewable energy power station construction model is configured to iteratively update the strategy of each power station site and the network structure of the complex network based on the expected benefits and cooperation benefits of each power station site, until the preset iteration termination condition is met, to output the optimized strategy of each power station site.

[0033] The application further provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the renewable energy power station construction method according to any of the above embodiments when executing the program.

[0034] The application further provides a non-transitory computer readable storage medium having a computer program stored thereon, wherein the computer program is executable by a processor to implement the steps of the renewable energy power station construction method according to any of the above embodiments.

[0035] The application further provides a computer program product comprising a computer program which, when executed by a processor, implements the steps of any of the above renewable energy power station construction methods.

[0036] The renewable energy power station construction method and system provided by the application can realize optimization of the strategy of each power station site by inputting the initial strategy of each power station site into an evolutionary game model constructed based on a dynamic complex network, and iteratively updating the strategy of each power station site and the network structure of the complex network according to the expected income and cooperative income of each power station site, thereby promoting the construction of renewable energy power stations. BRIEF DESCRIPTION OF DRAWINGS

[0037] In order to more clearly illustrate the technical solutions in the application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description are some embodiments of the application, and other drawings can be obtained by those skilled in the art without creative labor.

[0038] Figure 1 is a flowchart of the renewable energy power station construction method provided by the application;

[0039] Figure 2 is an analysis diagram of the renewable energy power station (land-based wind power) proportion evolution process under the small-world network and scale-free network provided by the application;

[0040] Figure 3 is an analysis diagram of the renewable energy power station (photovoltaic power generation) proportion evolution process under the small-world network and scale-free network provided by the application;

[0041] Figure 4 is an analysis diagram of the renewable energy power station (land-based wind power) proportion affected by the on-grid electricity price provided by the application;

[0042] Figure 5 is an analysis diagram of the renewable energy power station (land-based wind power) proportion affected by the renewable energy power generation cost provided by the application;

[0043] Figure 6 is a structural schematic diagram of the renewable energy power station construction system provided by the application;

[0044] Figure 7 is a structural schematic diagram of the electronic device provided by the application. DETAILED DESCRIPTION

[0045] In order to make the objectives, technical solutions and advantages of the present application clearer, the following will clearly and completely describe the technical solutions in the present application with reference to the drawings in the present application. Obviously, the described embodiments are only a part of embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the protection scope of the present application.

[0046] The following will be described with reference to the drawings Figures 1-5 The renewable energy power station construction method of the present application is executed by a computer or other electronic device or software and / or hardware therein. Figure 1 A flowchart of the renewable energy power station construction method of the present application is shown in Figure 1 The method comprises the following steps.

[0047] S100, obtaining initial strategies of each power station site; the strategy is renewable energy power generation or non-renewable energy power generation.

[0048] Specifically, each power station site, i.e., a power station site participating in the game, is at least two in number. The initial strategy of each power station site is the strategy of each power station site before participating in the game. Non-renewable energy power generation is power generation by using a traditional power generation method, for example, power generation by using fossil energy.

[0049] S200, inputting the initial strategy of each power station site into a renewable energy power station construction model to output an optimized strategy of each power station site; wherein the renewable energy power station construction model is an evolutionary game model constructed based on a dynamic complex network; the renewable energy power station construction model is used to iteratively update the strategy of each power station site and the network structure of the complex network based on the expected income and cooperative income of each power station site until a preset iteration termination condition is met, so as to output the optimized strategy of each power station site.

[0050] Specifically, evolutionary game theory is a theory developed on the basis of game theory. Evolution theory is a life science theory, which is based on Darwin's biological evolution theory and Lamarck's genetic theory. The traditional game theory usually considers that the participants are completely rational, and the participants are playing the game under the condition of complete information, but in actual application, the complete rationality of the participants and the complete information scenario cannot be met, different participants have differences, and the problems of insufficient information and limited rationality of the participants caused by the complexity of the external environment and the game problem actually exist completely. In view of the fact that the investors of each power station site do not have complete rationality, the evolutionary game model is used to obtain the strategy of each power station site in the embodiments of the present application, so as to promote the construction of the renewable energy power station.

[0051] In the embodiment of the present application, the evolutionary game model is constructed based on a dynamic complex network, considering that in the process of development and evolution, each power generation site can change the cooperative relationship with other power generation sites according to its income.

[0052] In the evolutionary game process, the strategy of each power generation site can be updated iteratively through expected income, that is, a power generation site with low expected income can learn the strategy of a power generation site with high expected income; the network structure of the complex network can be updated iteratively through cooperative income, that is, in the process of cooperation with the corresponding power generation site, if the cooperative income is high, the cooperation can be maintained, that is, the connection relationship between the corresponding nodes in the complex network is maintained, and if the cooperative income is low, the cooperation can be stopped, that is, the connection relationship between the corresponding nodes in the complex network is disconnected to find a new power generation site for cooperation, so as to realize the iterative update of the network structure of the complex network, and the dynamic complex network is embodied as the iterative update of the network structure of the complex network. That is, the decision-making behavior and income of each power generation site are obtained through game with other power generation sites around, the process of evolutionary game is based on repeated comparison and change of strategy, and after multiple iterations, an equilibrium is reached. Through the iterative update of the strategy of each power generation site and the network structure of the complex network, the subjective initiative of each power generation site can be fully considered in the evolutionary game process, the simulation of the construction process of the renewable energy power generation station is closer to the actual situation, and thus the construction of the renewable energy power generation station is effectively promoted.

[0053] At this point, the expected income is the average value of the income obtained by the power generation site under different scenarios, and the cooperative income is the income obtained by the power generation site in the process of cooperation with another power generation site. The update of the strategy of each power generation site and the update of the network structure of the complex network can be realized synchronously, that is, the strategy of each site and the network structure of the complex network are updated synchronously in the same iteration.

[0054] Therefore, according to the embodiment of the present application, the initial strategy of each power generation site is input into the evolutionary game model constructed based on a dynamic complex network, so as to update the strategy of each power generation site and the network structure of the complex network iteratively according to the expected income and the cooperative income of each power generation site, the optimization of the strategy of each power generation site can be realized, and thus the construction of the renewable energy power generation station is promoted.

[0055] Based on the above embodiment, further comprising:

[0056] Based on the application scenario type of renewable energy power generation, the complex network is determined.

[0057] Specifically, the application scenario type can represent the balance degree of the connection density of each power generation site, and different balance degrees of the connection density represent different application scenario types. The construction of the renewable energy power generation site is greatly affected by the application scenario type, and the income of the renewable energy power generation site is related to the location and other factors. Therefore, the embodiment of the application determines the complex network based on the application scenario type of the renewable energy power generation, so that the influence of the application scenario type can be fully considered in the process of simulating the construction of the renewable energy power generation site, and the effectiveness of the optimized strategy of each power generation site obtained through the evolutionary game is further improved.

[0058] Based on any of the above embodiments, the complex network is determined based on the application scenario type of the renewable energy power generation, and the complex network includes:

[0059] If the application scenario type is a first scenario type, the complex network is determined as a small-world network.

[0060] If the application scenario type is a second scenario type, the complex network is determined as a scale-free network.

[0061] The balance degree of the connection density of each power generation site in the second scenario type is lower than the balance degree of the connection density of each power generation site in the first scenario type.

[0062] Specifically, the higher the balance degree of the connection density of each power generation site, the more balanced the number of connections of the corresponding node of each power generation site in the complex network. The balance degree of the connection density of each power generation site in the second scenario type is lower than the balance degree of the connection density of each power generation site in the first scenario type, that is, the second scenario type can reflect a monopolized application scenario, and the first scenario type can reflect a completely competitive application scenario. The complex network is determined according to the balance degree of the connection density of each power generation site, so that the connection relationship between each power generation site under different application scenario types can be effectively represented through the determined complex network, thereby providing a data basis for improving the accuracy of the strategy optimization result of each power generation site.

[0063] Based on any of the above embodiments, the method for obtaining the expected income of the power generation site includes:

[0064] The payment matrix is used to calculate the income of the power generation site under a plurality of different strategy distribution scenarios.

[0065] Based on the current strategy of each power generation site, the proportion of the power generation site using renewable energy power generation is determined.

[0066] Based on the income of the power generation site under a plurality of different strategy distribution scenarios and the proportion, the expected income of the power generation site is calculated.

[0067] Specifically, the payoff matrix is a matrix used in game theory to describe the strategies and payoffs of two or more players, and the benefits or utilities of different players are payoffs. Therefore, the benefits of each power station under a plurality of different strategy distribution scenarios can be effectively calculated through the payoff matrix. Different strategy distribution scenarios refer to scenarios in which the number distribution of power stations using renewable energy and power stations using non-renewable energy is different.

[0068] The current strategy of each power station, i.e., the strategy of each power station in the current iteration, is obtained according to the number of power stations using renewable energy and the number of power stations using non-renewable energy, i.e., the proportion of power stations using renewable energy.

[0069] Based on the benefits of the power stations under a plurality of different strategy distribution scenarios and the proportion of power stations using renewable energy, the expected benefits of the power stations can be calculated. The expected benefits U i of the power station i are calculated as shown in equation (1):

[0070]

[0071] In the equation, x is the proportion of power stations using renewable energy in the complex network; and are the expected benefits of the power station i using renewable energy and non-renewable energy, respectively; and are calculated as shown in equations (2) and (3), respectively:

[0072]

[0073]

[0074] In the equation, y i is the proportion of power stations using renewable energy among the power stations connected to the power station i in the complex network; a i , b i are the benefits of the power station i under each strategy distribution scenario corresponding to the power station i using renewable energy; c i , d i are the benefits of the power station i under each strategy distribution scenario corresponding to the power station i using non-renewable energy.

[0075] Therefore, in the case of calculating the benefits of power generation sites under a plurality of different strategy distribution scenarios, by determining the proportion of power generation sites using renewable energy power generation, the expected benefits of each power generation site can be quickly and accurately calculated, thereby providing a data basis for improving the accuracy and effectiveness of the optimized strategy output by the evolutionary game model.

[0076] Based on any of the above embodiments, the construction of the payment matrix includes:

[0077] Based on different strategy distribution scenarios, the elements of the payment matrix are determined; the elements of the payment matrix correspond one-to-one to the strategy distribution scenarios;

[0078] Based on the total demand for electricity, the renewable energy power generation quota, the income and cost of renewable energy power generation, and the income and cost of non-renewable energy power generation, the expression of each element of the payment matrix is determined;

[0079] The payment matrix is used to calculate the benefits of the power generation sites under a plurality of different strategy distribution scenarios, including:

[0080] The benefits of the power generation sites under the corresponding strategy distribution scenarios are calculated respectively based on the expression of each element of the payment matrix.

[0081] Specifically, the elements of the payment matrix correspond one-to-one to the strategy distribution scenarios, that is, different elements in the payment matrix are used to calculate the benefits of the power generation sites under different strategy distribution scenarios. Therefore, the elements of the payment matrix are determined based on different strategy distribution scenarios. For example, the payment matrix can be determined according to Table 1.

[0082] Table 1

[0083]

[0084] In Table 1, power generation site j is a power generation site connected to power generation site i; (a i ,a j ), (b i ,b j ), (c i ,c j ) and (d i ,d j ) are elements corresponding to different strategy distribution scenarios, representing the benefits of power generation site i and power generation site j under the corresponding strategy distribution scenarios, and the corresponding expressions are determined based on the total demand for electricity, the renewable energy power generation quota, the income and cost of renewable energy power generation, and the income and cost of non-renewable energy power generation.

[0085] For (a i ,a jThe determination of the expression of (a i ,a j ) can be performed in the following manner:

[0086] If the power generation site i uses renewable energy to generate electricity, and any other power generation site j connected thereto also uses renewable energy to generate electricity, the electricity demand (i.e., the total demand power) and the profit will be shared by the power generation site i and the other power generation sites. Assuming that the total demand power in the considered range is Q, the average electricity demand of each power generation site is q = Q / N, where N is the total number of power generation sites in the complex network. The income of renewable energy generation can be composed of the on-grid electricity price p r and the green certificate price p g , and the cost of renewable energy generation is denoted as c r . The revenues (a i ,a j ) corresponding to the power generation site i and the power generation site j are shown in equation (4):

[0087]

[0088] The determination of the expression of (b i ,b j ) can be performed in the following manner:

[0089] If the power generation site i uses renewable energy to generate electricity, and any other power generation site j connected thereto uses non-renewable energy to generate electricity, all the power generation sites using renewable energy to generate electricity in the complex network will share all the renewable energy generation demand power kQ (i.e., the renewable energy generation quota), where k is the proportion of the renewable energy generation demand power in the total demand power; then the demand power corresponding to the power generation site i is kQ / xN, where x is the proportion of the power generation sites using renewable energy to generate electricity in the complex network, and N is the total number of power generation sites in the complex network. Correspondingly, the average demand power of the power generation sites using non-renewable energy to generate electricity is (1-k)Q / (1-x)N. The income of non-renewable energy generation is mainly determined by the price p f of non-renewable energy generation, and the cost of non-renewable energy generation is denoted as c f . The revenues (b i ,b j ) corresponding to the power generation site i and the power generation site j are shown in equation (5):

[0090]

[0091] The determination of the expression of (c i ,c j ) can be performed in the following manner:

[0092] If the power generation site i uses non-renewable energy to generate electricity, and any other power generation site j connected thereto uses renewable energy to generate electricity, the scenario corresponding to formula (5) is reversed, and the benefits (c i ,c j ) of the corresponding power generation site i and power generation site j are as shown in formula (6):

[0093]

[0094] For the determination of the expression of (d i ,d j ), the following method can be used:

[0095] If the power generation site i uses non-renewable energy to generate electricity, and any other power generation site j connected thereto also uses non-renewable energy to generate electricity, then all power generation sites using non-renewable energy to generate electricity need to purchase green certificates to achieve the renewable energy generation quota kQ / N, and the green certificate price is denoted as p g , then the benefits (d i ,d j ) of the corresponding power generation site i and power generation site j are as shown in formula (7):

[0096]

[0097] Therefore, by inputting the total demand power, the renewable energy generation quota, the income and cost of renewable energy generation, and the income and cost of non-renewable energy generation into formulas (4)-(7), respectively, the benefits of the power generation site under the corresponding strategy distribution scenario can be obtained.

[0098] At present, the main obstacles to promoting the construction of renewable energy power generation sites are as follows: (1) The investment cost and operation cost of renewable energy power generation are still relatively high compared with traditional power generation, including renewable energy generators, supporting energy storage devices, power electronic devices, etc., thereby causing excessive investment pressure on power generation companies; (2) From the perspective of renewable energy types, the on-grid price of wind energy and the green certificate transaction price of solar energy are different, and should be differentiated for analysis, but the current renewable energy transaction scale is small, and the profit space is limited; (3) The application scenario type also has a certain influence on the strategy of power generation companies.

[0099] The embodiment of the present application determines the elements of the payment matrix based on different strategy distribution scenarios, and determines the expression of each element of the payment matrix based on the total demand power, the renewable energy power generation quota, the income and cost of renewable energy power generation, and the income and cost of non-renewable energy power generation. The cost, income, and market type influence of each power generation site can be reflected through the income of the payment matrix. Through the analysis of the strategy of each power generation site and the mathematical description thereof, the income value thereof is analyzed and quantified, and the complex evolutionary game problem is equivalent to a mathematical model problem. Thus, through the simulation of the strategy evolution process of each power generation site, the actual construction process of the renewable energy power generation station can be simulated, and the construction of the renewable energy power generation station can be promoted through the evolution process analysis.

[0100] Based on any of the above embodiments, the method for obtaining the cooperation income comprises:

[0101] determining a target expression from the expression of each element of the payment matrix according to the current strategy of the power generation site and the current strategy of the cooperation site of the power generation site; wherein the cooperation site of the power generation site is a power generation site connected to the power generation site;

[0102] calculating the cooperation income of the power generation site according to the target expression.

[0103] Specifically, a target expression is determined from the expression of each element of the payment matrix according to the current strategy of the power generation site and the current strategy of the cooperation site of the power generation site. For example, if the strategy of the power generation site is renewable energy power generation and the strategy of the cooperation site thereof is also renewable energy power generation, the expression of (a i ,a j ) is determined as the target expression. If the strategy of the power generation site is renewable energy power generation and the strategy of the cooperation site thereof is non-renewable energy power generation, the expression of (b i ,b j ) is determined as the target expression. If the strategy of the power generation site is non-renewable energy power generation and the strategy of the cooperation site thereof is renewable energy power generation, the expression of (c i ,c j ) is determined as the target expression. If the strategy of the power generation site is non-renewable energy power generation and the strategy of the cooperation site thereof is also non-renewable energy power generation, the expression of (d j ) is determined as the target expression. The cooperation income of the power generation site can be calculated according to the determined target expression, so that the decision evolution process of the cooperation relationship of the power generation site can be simulated according to the cooperation income, the simulation process is more in line with the actual situation, and the accuracy and effectiveness of the optimized strategy output by the evolutionary game model are improved.

[0104] Based on any of the above embodiments, the renewable energy power generation quota is a dynamic value; wherein the updating of the renewable energy power generation quota comprises:

[0105] Based on the proportion of power generation sites using renewable energy power generation, the renewable energy power generation quota is updated.

[0106] Specifically, in the evolutionary game process, the embodiments of the present application also consider the dynamic change of the renewable energy power generation quota to ensure that the simulation process is more realistic. In each iteration, based on the proportion of power generation sites using renewable energy power generation in the total power generation sites in the complex network, the renewable energy power generation quota is updated to obtain a renewable energy power generation station construction model under the dynamic quota standard. Based on the proportion of power generation sites using renewable energy power generation in the total power generation sites in the complex network, the renewable energy power generation quota is updated, which can be updated according to formula (8) as follows: assuming that the renewable energy power generation quota is positively correlated with the proportion of power generation sites using renewable energy power generation, the renewable energy power generation quota can be updated according to formula (8):

[0107] k' = k u *x (8)

[0108] In the formula, k' is the updated value of the proportion of renewable energy power generation demand in the total demand; k u is the upper limit of the proportion of renewable energy power generation demand; and x is the proportion of power generation sites using renewable energy power generation in the complex network.

[0109] In the case of dynamic change of the renewable energy power generation quota, the calculation of the expression of each element of the payment matrix also needs to be changed accordingly. Replace k in formulas (4)-(7) with k', that is, the update of the expression of each element of the payment matrix can be completed, and the updated expressions are shown in formula (9):

[0110]

[0111] In the formula, a' i , a' j , b' i b' j , c' i , c' j , d' i , and d' j are the updated values of the income of power generation site i and power generation site j under the corresponding strategy distribution scenario.

[0112] The expected income U i of power generation site i also needs to be updated accordingly, and the updated expected income U' i is shown in formula (10):

[0113]

[0114] According to any one of the above embodiments, the strategy of each power generation site is iteratively updated based on the expected revenue of each power generation site, including:

[0115] According to any one of the above embodiments, the strategy of each power generation site is iteratively updated based on the expected revenue of each power generation site, including:

[0116] Specifically, in each iteration, the strategy update of power generation site i is the probability P(i→j) of the strategy of power generation site j (i.e., power generation site i learns the strategy of power generation site j) as shown in formula (11):

[0117]

[0118] In the formula, U i and U j are the expected revenues of power generation site i and power generation site j, respectively; s is the decision strength, generally taken as 0.1.

[0119] After calculating P(i→j), a random number r between 0 and 1 can be generated by using a random function, if r is greater than P(i→j), the strategy of power generation site i remains unchanged, otherwise, power generation site i learns the strategy of power generation site j, to realize the update of the strategy of power generation site.

[0120] According to any one of the above embodiments, the network structure of the complex network is iteratively updated based on the expected revenue of each power generation site, including:

[0121] When the cooperation revenue of the power generation site meets the preset condition, disconnect the connection between the power generation site and the cooperation site of the power generation site;

[0122] Determine one or more new cooperation sites from the candidate sites, and connect the new cooperation sites to the power generation site; wherein the candidate sites are part or all of the power generation sites that are not connected to the power generation site.

[0123] Specifically, in each iteration, any power generation site i can select its neighbor, i.e., cooperative site, according to the cooperative benefit. First, according to the calculated cooperative benefit, it is determined whether the cooperative benefit obtained by connecting the power generation site i with the power generation site j satisfies a preset condition. If yes, the power generation site i disconnects with the power generation site j, i.e., stops cooperation with the power generation site j. Here, the preset condition can be set according to actual conditions, for example, the cooperative benefits of the power generation site i with the current cooperative sites can be sorted from more to less, and the connection with one or more cooperative sites at the end of the sorting is disconnected. Second, one or more new cooperative sites are determined from the candidate sites, and the power generation site i establishes a connection with the new cooperative site. The candidate sites can be part or all of all power generation sites that are not currently connected with the power generation site i, and the specific setting can be made according to actual needs. Since the power generation site i can change the strategy in multiple iterations, there is a certain probability that the power generation site i cooperates with the power generation site j again after disconnecting with the power generation site j.

[0124] Therefore, according to the cooperative benefit, the network structure of the complex network is updated, so that the simulation process is more in line with the actual situation, and the accuracy and effectiveness of the output optimized strategy are ensured.

[0125] The effectiveness of the renewable energy power generation station construction method of the present application is verified by specific examples as follows.

[0126] First, a scale-free network and a small-world network model are constructed, and a scale-free network and a small-world network structure are formed by a scale-free network and a small-world network growth rule, N=100 is taken, the initial value of the proportion x of the power generation site using renewable energy power generation is set to 0.2, and the strategy of each node is randomly given. The construction process of the renewable energy power generation station is simulated according to the parameters given in Table 2.

[0127] Table 2

[0128]

[0129] In the process of iterative updating of the strategy of each power generation site and the network structure of the complex network, the Monte Carlo method is used to repeatedly generate 100 groups of data for averaging in each iteration to eliminate the influence of randomness, and the corresponding evolution process curve under each application scenario is obtained. Among them, Figure 2 is an analysis diagram of the proportion of renewable energy power generation stations (land-based wind power) under the small-world network and the scale-free network; Figure 3 is an analysis diagram of the proportion of renewable energy power generation stations (photovoltaic power generation) under the small-world network and the scale-free network; Figure 4 is an analysis diagram of the proportion of renewable energy power generation stations (land-based wind power) affected by the on-grid electricity price; Figure 5The analysis diagram of the proportion of renewable energy power stations (land-based wind power) affected by the renewable energy power generation cost. From Figure 2 and Figure 3 It can be seen that, by iteratively updating the strategy of each power station and the network structure of the complex network, the proportion of renewable energy power stations gradually tends to be balanced, from Figure 4 and Figure 5 It can be seen that the higher the on-grid electricity price, the higher the proportion of renewable energy power stations, the higher the renewable energy power generation cost, and the lower the proportion of renewable energy power stations, which is consistent with the actual situation, thereby proving the effectiveness and accuracy of the method, which can best fit the actual situation and meet the needs of engineering analysis. At the same time, from Figure 4 and Figure 5 It can be seen that the main factors affecting the construction and development of renewable energy power stations can be adjusted to promote the construction of renewable energy power stations.

[0130] The renewable energy power station construction system provided by the present application is described below, and the renewable energy power station construction system described below can be correspondingly referred to the renewable energy power station construction method described above. As Figure 6 shown, the system comprises:

[0131] A data acquisition module 610 is configured to acquire an initial strategy of each power station; the strategy is renewable energy power generation or non-renewable energy power generation;

[0132] A strategy updating module 620 is configured to input the initial strategy of each power station into a renewable energy power station construction model to output an optimized strategy of each power station; wherein the renewable energy power station construction model is an evolutionary game model constructed based on a dynamic complex network; the renewable energy power station construction model is configured to iteratively update the strategy of each power station and the network structure of the complex network based on the expected income and cooperative income of each power station, until a preset iteration termination condition is met, to output the optimized strategy of each power station.

[0133] Based on the above embodiments, the system further comprises:

[0134] Based on the application scenario type of renewable energy power generation, the complex network is determined.

[0135] Based on any of the above embodiments, the complex network is determined based on the application scenario type of renewable energy power generation, comprising:

[0136] If the application scenario type is a first scenario type, the complex network is determined to be a small-world network;

[0137] if the application scenario type is a second scenario type, determining that the complex network is a scale-free network;

[0138] a balance degree of a connection density of each of the power generation sites in the second scenario type is lower than a balance degree of a connection density of each of the power generation sites in the first scenario type.

[0139] Based on any of the above embodiments, the method for obtaining the expected revenue of the power generation site comprises:

[0140] using a payoff matrix to calculate the revenue of the power generation site under a plurality of different strategy distribution scenarios;

[0141] based on the current strategy of each of the power generation sites, determining a proportion of power generation sites that use renewable energy;

[0142] based on the revenue of the power generation site under a plurality of different strategy distribution scenarios and the proportion, calculating the expected revenue of the power generation site.

[0143] Based on any of the above embodiments, the construction of the payoff matrix comprises:

[0144] based on different strategy distribution scenarios, determining elements of the payoff matrix; the elements of the payoff matrix correspond one-to-one to the strategy distribution scenarios;

[0145] based on total demand power, renewable energy generation quota, income and cost of renewable energy generation, and income and cost of non-renewable energy generation, determining an expression of each element of the payoff matrix;

[0146] The using of the payoff matrix to calculate the revenue of the power generation site under a plurality of different strategy distribution scenarios comprises:

[0147] using the expression of each element of the payoff matrix to calculate the revenue of the power generation site under the corresponding strategy distribution scenario.

[0148] Based on any of the above embodiments, the method for obtaining the cooperation revenue comprises:

[0149] from the expression of each element of the payoff matrix, determining a target expression according to the current strategy of the power generation site and the current strategy of the cooperative site of the power generation site; wherein the cooperative site of the power generation site is a power generation site connected to the power generation site;

[0150] calculating the cooperation revenue of the power generation site according to the target expression.

[0151] Based on any of the above embodiments, the renewable energy generation quota is a dynamic value; wherein the updating of the renewable energy generation quota comprises:

[0152] Based on the proportion of the power generation site using renewable energy power generation, the renewable energy power generation quota is updated.

[0153] Based on any of the above embodiments, the network structure of the complex network is iteratively updated based on the expected revenue of each power generation site, including:

[0154] When the cooperative revenue of the power generation site meets the preset condition, disconnect the power generation site from the cooperative site of the power generation site.

[0155] From the candidate site, determine one or more new cooperative sites, and connect the new cooperative sites to the power generation site; wherein the candidate site is part or all of all power generation sites that are not connected to the power generation site.

[0156] Figure 7 An example of an electronic device entity structure diagram is shown as Figure 7 The electronic device can include a processor 710, a communications interface 720, a memory 730, and a communications bus 740, wherein the processor 710, the communications interface 720, and the memory 730 communicate with each other through the communications bus 740. The processor 710 can invoke the logic instructions in the memory 730 to execute a renewable energy power station construction method, which includes: obtaining the initial strategy of each power generation site; the strategy is renewable energy power generation or non-renewable energy power generation;

[0157] Input the initial strategy of each power generation site into the renewable energy power station construction model to output the optimized strategy of each power generation site; wherein the renewable energy power station construction model is an evolutionary game model based on a dynamic complex network; the renewable energy power station construction model is used to iteratively update the strategy of each power generation site and the network structure of the complex network based on the expected revenue and the cooperative revenue of each power generation site, until the preset iteration termination condition is met, to output the optimized strategy of each power generation site.

[0158] Further, the logic instructions in the memory 730 described above can be implemented in the form of software functional units and sold or used as independent products, and can be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present application essentially or the parts that make contributions to the prior art or parts of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium, and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a magnetic disk or an optical disk, and various media that can store program codes.

[0159] In another aspect, the present application also provides a computer program product, which comprises a computer program, the computer program can be stored on a non-transitory computer readable storage medium, and the computer program can be executed by a processor to enable a computer to execute the renewable energy power station construction method provided by the above-mentioned methods. The method comprises: obtaining an initial strategy of each power station site; the strategy is renewable energy power generation or non-renewable energy power generation;

[0160] inputting the initial strategy of each power station site into a renewable energy power station construction model to output an optimized strategy of each power station site; wherein the renewable energy power station construction model is an evolutionary game model constructed based on a dynamic complex network; the renewable energy power station construction model is used to iteratively update the strategy of each power station site and the network structure of the complex network based on the expected income and the cooperative income of each power station site, until a preset iteration termination condition is met, to output the optimized strategy of each power station site.

[0161] In another aspect, the present application also provides a computer program product, which comprises a computer program, the computer program can be stored on a non-transitory computer readable storage medium, and the computer program can be executed by a processor to enable a computer to execute the renewable energy power station construction method provided by the above-mentioned methods. The method comprises: obtaining an initial strategy of each power station site; the strategy is renewable energy power generation or non-renewable energy power generation;

[0162] The initial strategy of each power generation site is input into a renewable energy power generation station construction model to output the optimized strategy of each power generation site; wherein the renewable energy power generation station construction model is an evolutionary game model based on a dynamic complex network; the renewable energy power generation station construction model is used to iteratively update the strategy of each power generation site and the network structure of the complex network based on the expected income and cooperative income of each power generation site, until a preset iteration termination condition is met, to output the optimized strategy of each power generation site.

[0163] The system embodiments described above are only illustrative, wherein the units illustrated as separate components can or can not be physically separated, and the components illustrated as units can or can not be physical units, i.e., can be located in one place or distributed on multiple network units. Part or all of the modules can be selected to achieve the purpose of the embodiment scheme according to actual needs. Those skilled in the art can understand and implement without creative labor.

[0164] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be realized by means of software and the necessary general hardware platform, and of course can also be realized by hardware. Based on such understanding, the above technical solutions can be embodied in the form of a software product, which can be stored in a computer readable storage medium, such as a ROM / RAM, a magnetic disk, an optical disk, etc., and includes a number of instructions to make a computer device (which can be a personal computer, a server, or a network device, etc.) execute the methods described in each embodiment or some parts of the embodiments.

[0165] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, and not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement for some technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method of constructing a renewable energy power plant, characterized by, The method comprises the following steps: obtaining initial strategies of each power generation site; the strategies are renewable energy power generation or non-renewable energy power generation; inputting the initial strategies of each power generation site into a renewable energy power station construction model to output optimized strategies of each power generation site; wherein the renewable energy power station construction model is an evolutionary game model based on a dynamic complex network; the renewable energy power station construction model is used to iteratively update the strategies of each power generation site and the network structure of the complex network based on the expected income and cooperative income of each power generation site until a preset iteration termination condition is met to output the optimized strategies of each power generation site; the method for obtaining the expected income of the power generation site comprises: using a payoff matrix to calculate the income of the power generation site under a plurality of different strategy distribution scenarios; determining the proportion of power generation sites using renewable energy power generation based on the current strategies of each power generation site; calculating the expected income of the power generation site based on the income of the power generation site under a plurality of different strategy distribution scenarios and the proportion; the construction of the payoff matrix comprises: determining the elements of the payoff matrix based on different strategy distribution scenarios; the elements of the payoff matrix correspond one-to-one to the strategy distribution scenarios; determining the expression of each element of the payoff matrix based on the total demand for electricity, the renewable energy power generation quota, the income and cost of renewable energy power generation, and the income and cost of non-renewable energy power generation; the method for calculating the income of the power generation site under a plurality of different strategy distribution scenarios using the payoff matrix comprises: calculating the income of the power generation site under the corresponding strategy distribution scenario through the expression of each element of the payoff matrix; the method for obtaining the cooperative income comprises: determining a target expression from the expression of each element of the payoff matrix according to the current strategy of the power generation site and the current strategy of the cooperative site of the power generation site; wherein the cooperative site of the power generation site is a power generation site connected to the power generation site; calculating the cooperative income of the power generation site according to the target expression.

2. A method of constructing a renewable energy power plant according to claim 1, wherein, Further comprising: determining the complex network based on the application scenario type of renewable energy power generation.

3. A method of constructing a renewable energy power plant according to claim 2, wherein, The method for determining the complex network based on the application scenario type of renewable energy power generation comprises: if the application scenario type is a first scenario type, determining the complex network to be a small-world network; if the application scenario type is a second scenario type, determining the complex network to be a scale-free network; the balance degree of the connection density of each power generation site in the second scenario type is lower than the balance degree of the connection density of each power generation site in the first scenario type.

4. A method of constructing a renewable energy power plant according to claim 1, wherein, The renewable energy power generation quota is a dynamic value; wherein the update of the renewable energy power generation quota comprises: updating the renewable energy power generation quota based on the proportion of power generation sites using renewable energy power generation.

5. A method of constructing a renewable energy power plant according to claim 1, wherein, iteratively updating the network structure of the complex network based on the expected income of each power generation site comprises: determining that the cooperative benefit of the power generation site meets a preset condition, disconnecting the power generation site from the cooperative site of the power generation site; determining one or more new cooperative sites from candidate sites, and connecting the new cooperative sites to the power generation site; wherein the candidate sites are part or all of all power generation sites that are not connected to the power generation site.

6. A renewable energy power plant construction system applying the renewable energy power plant construction method according to any one of claims 1 to 5, characterized by, comprising: a data acquisition module configured to acquire an initial strategy of each power generation site; the strategy is renewable energy power generation or non-renewable energy power generation; a strategy updating module configured to input the initial strategy of each power generation site into a renewable energy power station construction model to output an optimized strategy of each power generation site; wherein the renewable energy power station construction model is an evolutionary game model based on a dynamic complex network; the renewable energy power station construction model is used to iteratively update the strategy of each power generation site and the network structure of the complex network based on the expected benefit and the cooperative benefit of each power generation site until a preset iteration termination condition is met to output the optimized strategy of each power generation site.

7. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, The processor executes the program to realize the steps of the renewable energy power station construction method according to any one of claims 1 to 5.

8. A non-transitory computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to realize the steps of the renewable energy power station construction method according to any one of claims 1 to 5.

9. A computer program product comprising a computer program, characterized in that, The computer program is executed by the processor to realize the steps of the renewable energy power station construction method according to any one of claims 1 to 5.

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