Photovoltaic green certificate transaction and electricity price subsidy integrated effect measurement method

Abstract

The invention relates to a photovoltaic enterprise green certificate transaction and electricity price subsidy mechanism integration effect evaluation method, and aims to optimize the cooperation process between a renewable energy source matching and forekeeping system (RPS) and a fixed electricity price system (FIT) and measure the RPS and FIT integration effect under various subsidy time limit and intensity scenes so as to support the stable operation of a photovoltaic enterprise. The method comprises the following steps: firstly, establishing an evolutionary game model of a government and a photovoltaic enterprise, and analyzing policy parameters when the game reaches a stable equilibrium condition; carrying out system dynamics modeling, and verifying the correctness of the game model; and then analyzing the influence of policy parameters on an evolution result, a green certificate transaction volume and social welfare. Research results show that the provided method is helpful for measuring integration policy effects, is helpful for the country to formulate reasonable subsidy combination or substitution policies, and realizes stable development of photovoltaic enterprises.

Description

Technical Field

[0001] The present invention relates to optimization of photovoltaic enterprise and government subsidy strategies, and in particular to a method based on evolutionary game and system dynamics model. Background Art

[0002] Energy is one of the pillars of human civilization and progress. The development of society has higher requirements for energy demand and quality. Due to the inherent "non-renewable" and "non-environmental" characteristics of traditional energy, renewable energy (clean energy) such as solar energy has entered people's field of vision. However, due to a series of factors such as higher technical requirements and unstable power supply, the development of solar power generation is limited. Photovoltaic power generation companies often need government subsidies in the initial development, installation, sales and other links to maintain business operations. Therefore, how, when and how much to subsidize photovoltaic companies is an important link in solving the problem of solar power generation replacing traditional energy power generation.

[0003] In order to reduce the burden of government subsidies, promote the consumption of renewable energy power, adjust the power structure and achieve the goals China promised in the Paris Agreement, China began to pilot the Renewable Energy Portfolio System (RPS) policy in 2017. As the most promising renewable energy companies, photovoltaic companies have become the main participants in the green certificate trading (TGC) market. However, since RPS and the fixed electricity price system (FIT) are two different subsidy systems, completing the transformation from FIT to RPS requires scientific system design. Summary of the invention

[0004] Purpose of the invention: In view of the problems existing in the above-mentioned prior art, the present invention provides a method for integrating green certificate transactions and electricity price subsidies for photovoltaic enterprises based on subsidy time limit and intensity. In view of the problems existing in the prior art, the present invention provides a method for integrating green certificate transactions and electricity price subsidies for photovoltaic enterprises based on reliability time limit and intensity. Compared with previous methods, the present invention proposes an innovative integration model by comprehensively applying game theory and system dynamics theory, and optimizes the design of existing static mechanisms, thereby enhancing the market competitiveness of photovoltaic enterprises and the economy of policy implementation.

[0005] Technical solution: To achieve the above-mentioned purpose, the technical solution adopted by the present invention is: a method for integrating green certificate trading and electricity price subsidies for photovoltaic enterprises based on subsidy time limit and intensity. Based on the institutional principles of FIT and RPS, an evolutionary game model between the government and photovoltaic enterprises was first established, and the policy parameters for the game to reach a stable equilibrium condition {implement RPS and conduct TGC transactions} were analyzed. System dynamics modeling was then carried out to verify the correctness of the game model, and then the impact of different policy parameters on evolutionary results, green certificate trading volume, and social welfare was analyzed, ultimately providing a certain theoretical basis for the country to formulate subsidy policies. Specifically, it includes the following steps:

[0006] In order to achieve the above-mentioned purpose, the technical solution adopted by the present invention is: a method for integrating green certificate trading and electricity price subsidies for photovoltaic enterprises based on subsidy time limit and intensity. Based on the institutional principles of FIT (Feed-in Tariff) and RPS (Renewable Portfolio Standards), an evolutionary game model between the government and photovoltaic enterprises was first established, and the policy parameters for the game to reach a stable equilibrium condition {implement RPS and conduct TGC (Tradable Green Certificate) transactions} were analyzed. System dynamics modeling was then carried out to verify the correctness of the game model, and then the impact of different policy parameters on evolutionary results, green certificate trading volume, and social welfare was analyzed, ultimately providing a certain theoretical basis for the country to formulate subsidy policies. Specifically, it includes the following steps:

[0007] Step 1: Establish an evolutionary game model and determine the two parties and their strategy sets. According to the different strategy combinations of the government and photovoltaic enterprises, calculate their respective benefits and construct a payment matrix; analyze the stable equilibrium conditions, and determine the policy parameters to achieve the stable equilibrium conditions {implement RPS and conduct TGC transactions} by analyzing the changes in the benefits of the two parties under different strategy combinations.

[0008] Step 2: According to the actual situation of green certificate trading and electricity price subsidies of photovoltaic enterprises, determine the system boundaries and main variables; establish a system dynamics model, and use AnyLogic to build a system dynamics model that integrates green certificate trading and electricity price subsidies for photovoltaic enterprises; simulate and verify the model. By simulating different policy parameters, verify the correctness of the evolutionary game model and analyze its impact on green certificate trading volume and social welfare.

[0009] Step 3: According to the actual situation, set different subsidy time limits and intensity parameters; simulate the system behavior under different policy parameters. Through system dynamics model simulation, analyze the impact of different policy parameters on green certificate trading volume, social welfare and photovoltaic enterprise decision-making; provide policy recommendations, and propose suggestions for optimizing subsidy policies based on simulation results, providing a theoretical basis for the country to formulate subsidy policies.

[0010] Step 4: Comprehensively analyze the simulation results, summarize the impact of different policy parameters on green certificate transactions and social welfare of photovoltaic enterprises; optimize the subsidy policy design, and propose directions and methods for further optimizing the subsidy policy.

[0011] Furthermore, in step 1, the two parties and their strategy sets are determined. The two parties are the government and the photovoltaic enterprise. The government strategy set includes {implement RPS, do not implement RPS}, and the photovoltaic enterprise strategy set includes {conduct TGC transactions, do not conduct TGC transactions}. The payment matrix is ​​constructed as follows:

[0012]

[0013] According to the game matrix and profit formula (Formula 1-8) in the previous article, we have concluded that when the government implements RPS, the profit of this type of game is:

[0014] E(G) α =βG1+(1-β)G3=P f πQ+βπP TGC Q+U1 (14)

[0015] Where, E(G) α represents the expected benefit of the government when implementing the RPS strategy; β represents the proportion of photovoltaic enterprises choosing to trade green certificates; G1 and G3 represent the benefits of the government under different circumstances; P f is the on-grid electricity price of traditional thermal power; π is the proportion of photovoltaic power generation in total power generation; Q is the total photovoltaic power generation; P TGC is the transaction price of green certificates; U1 is the additional social effect benefit of the government when implementing RPS.

[0016] Then the overall average benefit of the government group is obtained as follows:

[0017]

[0018] According to formulas 14-15, we have obtained the government replication dynamic equation:

[0019]

[0020] Similar to the government, we calculated the benefits of photovoltaic companies and obtained the benefits of green certificate trading as follows:

[0021] E(R) β =αR1+(1-α)R2=(1-π)P f Q+α(1-π)P TGC QP c Q+D1 (17)

[0022] Where, E(R) β represents the expected return of photovoltaic enterprises under the green certificate trading strategy; α represents the proportion of the government choosing to implement RPS; R1 represents the return of photovoltaic enterprises when the government implements RPS and photovoltaic enterprises conduct green certificate trading; R2 represents the return of photovoltaic enterprises when the government does not implement RPS and photovoltaic enterprises conduct green certificate trading; P c represents the cost of photovoltaic power generation; D1 represents the additional income of photovoltaic enterprises when trading green certificates.

[0023] The average revenue of photovoltaic enterprises is:

[0024] E(R)=[(1-π)Pf -P c +αβ(1-π)P TGC +(1-α)(1-β)(1-π)P sub ]Q+βD1+(1-β)D2 (18)

[0025] According to formulas 17-18, the dynamic equation for photovoltaic enterprise replication is obtained:

[0026]

[0027] In the formula, E(R) represents the average revenue of photovoltaic enterprises; π represents the proportion of photovoltaic power generation in total power generation; P f P represents the on-grid electricity price of traditional thermal power; c represents the cost of photovoltaic power generation; α represents the proportion of governments that choose to implement RPS; β represents the proportion of photovoltaic enterprises that choose to trade green certificates; P TGC Indicates the transaction price of green certificates; P sub represents the fixed electricity price subsidy; Q represents the total amount of photovoltaic power generation; D1 represents the additional income of photovoltaic enterprises when they conduct green certificate transactions; D2 represents the additional income of photovoltaic enterprises when they do not conduct green certificate transactions.

[0028] Furthermore, by solving the evolutionary game equation, the changes in the benefits and evolution paths of the two parties in the game under different strategy combinations are analyzed. The conditions for the game to reach a stable equilibrium are determined, that is, the strategy combination of the government implementing RPS and photovoltaic companies conducting TGC transactions. The changes in the strategy choices of the two parties in the game under different policy parameters are analyzed to find the optimal policy parameter combination to achieve a stable equilibrium.

[0029] Furthermore, in step 2, the system boundary is determined based on the actual situation of green certificate trading and electricity price subsidies of photovoltaic enterprises, including factors such as government, photovoltaic enterprises, market and social environment. The main variables, including subsidy time limit, subsidy intensity, green certificate price, electricity price subsidy income, green certificate trading income, policy implementation cost, etc., are determined to establish a system dynamics model.

[0030] Furthermore, a system dynamics model integrating green certificate trading and electricity price subsidies for photovoltaic enterprises is established using system dynamics modeling tools (such as Anylogic). The model includes policy layer, market layer and enterprise layer, and describes the interaction and influence between each layer through system dynamics equations; by setting different policy parameters (such as subsidy time limit and intensity), the model is simulated, the dynamic behavior of the system under different policy parameters is analyzed, and the correctness of the simulation results is verified to ensure that the model can accurately reflect the actual situation and policy effects.

[0031] Furthermore, in the step three, different policy parameter combinations such as subsidy time limits (such as short-term, medium-term, and long-term) and subsidy intensities (such as high intensity, medium intensity, and low intensity) are set according to actual conditions; the system dynamics model is used to simulate the changes in green certificate trading volume, social welfare, and photovoltaic enterprise decision-making under different policy parameters; the impact of different policy parameters on the green certificate trading behavior of photovoltaic enterprises is analyzed to find the optimal policy parameter combination; based on the simulation results, suggestions for optimizing the subsidy policy are put forward, such as adjusting the subsidy time limit, increasing the subsidy intensity, etc., to provide a theoretical basis for the country to formulate subsidy policies and ensure that the policies can effectively promote the development of photovoltaic enterprises and improve social welfare.

[0032] Furthermore, in step 4, the simulation results of the evolutionary game model and the system dynamics model are combined to comprehensively analyze the impact of different policy parameters on the green certificate trading of photovoltaic enterprises and social welfare. Based on the analysis results, the direction and methods for further optimizing the subsidy policy are proposed, such as setting a more flexible subsidy time limit and intensity, encouraging photovoltaic enterprises to participate in green certificate trading, etc. Specific policy implementation plans are proposed, including policy implementation plans, regulatory measures and evaluation mechanisms, etc., to ensure that the policy can be implemented smoothly and achieve the expected results.

[0033] Compared with the prior art, the present invention has the following beneficial effects:

[0034] 1. This invention uses evolutionary game theory and system dynamics model comprehensively, and analyzes the strategy evolution of photovoltaic enterprises and the government under different subsidy policies, providing a theoretical basis for the scientific setting of subsidy policies, making the subsidy policies more reasonable and effective.

[0035] 2. This paper studies the impact of the transition from the fixed electricity price subsidy system (FIT) to the renewable energy quota system (RPS). Through the evolutionary game model and system dynamics simulation, the role of policy parameters in the smooth transition of the subsidy system is analyzed, which provides a guarantee for the smooth transition of the subsidy system. Through sensitivity analysis, the impact of different subsidy strategies on social welfare, green certificate trading volume, etc. is studied, and the combination of subsidy strategies that maximize social welfare is found to improve overall social welfare.

[0036] 3. The present invention proposes a scientific subsidy strategy design to effectively reduce the government's fiscal burden, while ensuring the healthy development of photovoltaic enterprises and promoting the sustainable development of the renewable energy industry; through dynamic simulation of the green certificate trading market, the impact of different subsidy strategies on the green certificate trading volume is analyzed, promoting the healthy development of the green certificate trading market and improving market efficiency.

[0037] 4. The present invention provides a scientific basis and tools for government decision-making. By simulating and analyzing the effects of different policy parameters, it assists the government in formulating and adjusting subsidy policies and improves the effectiveness and stability of policy implementation. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the technical solution of the embodiment of the present invention, the following is a brief introduction to the drawings required for describing the embodiment. Among them:

[0039] Figure 1 It is a framework diagram of the invention;

[0040] Figure 2 This is the strategy evolution of scenario 1;

[0041] Figure 3 This is the strategy evolution of scenario 2;

[0042] Figure 4 The impact of subsidy prices on evolution;

[0043] Figure 5 is a causal loop diagram;

[0044] Figure 6 is a stock-flow diagram;

[0045] Figure 7 It is a stable change trend diagram of the evolution of both parties in the game;

[0046] Figure 8 This is the trend chart of green certificate trading volume changes;

[0047] Fig. 9 It is a trend chart of social welfare changes; DETAILED DESCRIPTION

[0048] In order to make the purpose, technical solution and advantages of the present invention more clear, the present invention is further described in detail below through the accompanying drawings and embodiments. However, it should be understood that the specific embodiments described here are only used to explain the present invention and are not used to limit the scope of the present invention.

[0049] Unless otherwise defined, all technical and scientific terms used herein have the same meanings as those commonly understood by technicians in the technical field to which the present invention belongs. The terms used in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention.

[0050] The present invention aims to establish an evolutionary game model between photovoltaic enterprises and the government, and combine it with the system dynamics simulation method to analyze the strategic evolution process of photovoltaic enterprises and the government under the fixed electricity price subsidy mechanism (FIT) and the renewable energy quota system (RPS). Through sensitivity analysis of different subsidy strategies, the optimal subsidy policy combination is explored to maximize social welfare, smoothly transition the subsidy system, reduce the government's fiscal burden, and promote the healthy development of the green certificate trading market, providing a theoretical basis and decision-making support for the government to formulate scientific and reasonable subsidy policies. The overall technical framework of the present invention is shown in the figure below. Figure 1 shown.

[0051] First, according to the environment of both parties in the game, the policy background is the evolution period from FIT to RPS. Based on evolutionary game theory, the proportion of the government and photovoltaic enterprises adopting various strategies, that is, the proportion of implementing FIT / RPS and accepting FIT / trading TGC, is taken as the research object to analyze the behavioral effects of both parties in various situations.

[0052] The present invention assumes that the two parties in the game are the government and photovoltaic enterprises, and thermal power enterprises do not participate in the game and fully accept TGC transactions; in FIT, the government's electricity price subsidy for renewable energy can be understood as the difference between the benchmark electricity price of renewable energy and the parity price; photovoltaic enterprises are the only suppliers of green certificates, and thermal power enterprises are the only buyers; it is assumed that the proportion of a certain strategy adopted by photovoltaic enterprises is linked to the power generation, that is, the proportion of adopting a certain strategy is also the proportion of the power generation of photovoltaic enterprises adopting this strategy to the power generation of the game group; there is no time delay in the government's fixed electricity price subsidy, that is, photovoltaic enterprises can obtain subsidies in a timely manner.

[0053] Based on the above assumptions and analysis of the actual situation, combined with the dynamic equations in the previous article, we can find a stable equilibrium point under different strategies, as follows:

[0054] For the stable case of a single strategy, let its replicated dynamic equation equal to 0 and its first-order derivative be less than 0, and then a stable equilibrium point can be found.

[0055] (1) Targeting the government

[0056] Let dα / dt=0, and we can get α1=0, α2=1. According to the results, when When dα / dt=0, (dα / dt)'=0, the government's strategy is stable, and the fixed electricity price subsidy and the price of green certificates will not affect the government's choice strategy, that is, it will not affect whether the government implements the RPS policy. * ∈(0,1), and the price of green certificate is P TGC by As the boundary, When , the first-order derivative of dα / dt at 1 is less than 0, the proportion of the government's strategy of choosing to implement RPS will gradually approach 1. When , the first-order derivative of dα / dt at 0 is less than 0, that is, the proportion of the government's strategy of choosing to implement RPS will gradually approach 0.

[0057] (2) Targeting photovoltaic enterprises

[0058] Let dβ / dt=0, and we can get β1=0, β2=1. Similar to the government, according to the solution results, when When α , the price of fixed electricity price subsidies and green certificates will not affect the selection strategy of photovoltaic enterprises, that is, it will not affect whether photovoltaic enterprises choose to implement green certificate transactions or choose to accept national fixed electricity price subsidies. * ∈(0,1) and when the price of green certificate is P TGC by As the boundary, When , the first-order derivative of dβ / dt at 1 is less than 0, the proportion of photovoltaic companies choosing to conduct TGC transactions will gradually approach 1. When the first-order derivative of dβ / dt at 0 is less than 0, the strategic proportion of photovoltaic enterprises choosing to trade green certificates will gradually approach 0.

[0059] For the mixed strategy stability, consider that the government and photovoltaic enterprises evolve into a dynamic system. According to the principle of evolutionary game, when the replication dynamic equation is equal to zero, that is, when the learning speed is equal to zero, the evolution reaches a local equilibrium state. According to the replication dynamic equation of the government and photovoltaic enterprises, we can get 5 local equilibrium points, namely E1(0,0), E2(0,1), E3(1,0), E4(1,1), E5(α * ,β * ) Since the equilibrium point obtained by copying the dynamic equation is not necessarily a stable point, it is necessary to use the Jacobian matrix to judge. According to formulas (16) and (19), the dynamic equation can be copied and its Jacobian matrix can be obtained as follows:

[0060]

[0061] In the Jacobian matrix, when the determinant of the matrix is ​​greater than 0 and the trace is less than 0, the equilibrium point is a stable point, when the determinant of the matrix is ​​greater than 0 and the trace is greater than 0, the equilibrium point is an unstable point, and when the determinant of the matrix is ​​less than 0, the equilibrium point is a saddle point. According to the Jacobian matrix, the determinant and trace of each equilibrium point in the Jacobian matrix are obtained, as shown in Table 1.

[0062] Table 1 Jacobian matrix determinant of equilibrium point

[0063]

[0064] For the convenience of analysis, the gains of both parties in the game are not substituted, and the determinant and trace of the simplified equilibrium point are obtained as shown in Table 3.4.

[0065] Table 2 Simplified Jacobian matrix determinant

[0066]

[0067] From the table, we can see that the determinant of the Jacobian matrix is ​​determined by the sign of the return difference. According to the previous return formula, we can get R1-R3=(1-π)P TGC Q+D1+D2 is greater than 0, R2-R4=-(1-π)P sub Q-D1-D2 is less than 0, G1-G2=P TGC +U1+U2 is greater than 0, G3-G4=(1-π)P sub Q-U1-U2 is uncertain.

[0068] According to the Jacobian matrix, it can be divided into the following two cases, as shown in Table 3.

[0069] Table 3 Local equilibrium point stability table

[0070]

[0071]

[0072] From the table, we can see that when G3-G4>0, that is, when photovoltaic enterprises choose to accept fixed electricity price subsidies, the government's benefits from implementing RPS are higher than the benefits from implementing electricity price subsidies. At this time, the stable equilibrium point is E4(1,1), and the final evolution result is {implement RPS and conduct green certificate transactions}. This shows that in this case, no matter what the initial situation of the government and enterprises is, in the process of game evolution, the final evolution is towards the national goal, that is, the government implements RPS and photovoltaic enterprises conduct green certificate transactions.

[0073] When G3-G4<0, that is, when the PV enterprises do not trade green certificates, the government's benefits from implementing RPS are lower than the benefits from implementing electricity price subsidies. At this time, there are two stable equilibrium points, namely E1(0,0) and E4(1,1). The evolution will eventually lead to two results, namely {implementing fixed electricity price subsidies and accepting fixed electricity price subsidies} and {implementing RPS and conducting TGC transactions}.

[0074] In the case of G3-G4<0, when the probability of both parties choosing strategies is at E5(α * ,β * ), that is, in the E5E2E1E3 region, the final evolution of both parties will be {implementing fixed electricity price subsidies, accepting fixed electricity price subsidies}, and when the probability of both parties choosing strategies is in E5 (α * ,β * ) is at the upper right, that is, E5E3E4E2. At this time, the final situation of the evolution of both parties will be {implement RPS and conduct TGC transactions}. According to my country's current policies, the final national goal is {implement RPS and conduct TGC transactions}. Therefore, in order to achieve this goal, the initial strategy point must be placed as much as possible on E5 (α * ,β* ) to the upper right, this goal can be achieved, that is, E5 (α * ,β * )The area of ​​the upper right corner E5E3E4E2 should be as large as possible.

[0075] According to references and data from the National Bureau of Statistics, we set the price of green certificates to 0.22 yuan / KWh and the initial price of subsidies to 0.17 yuan / KWh. The transaction volume of green certificates is 1053.88 million.

[0076] (1) G3-G4>0 scenario

[0077] To make the image clear, we select 5 representative initial state points (0.1, 0.1), (0.3, 0.3), (0.5, 0.5), (0.7, 0.7), (0.9, 0.9) Through MATLAB simulation, we find that no matter what the initial values ​​of α and β are, Figure 2 As shown, it will eventually evolve into the situation E4(1,1).

[0078] G3-G4<0 scenario,

[0079] To make the image clear, we continue to select 5 representative initial state points (0.1, 0.1), (0.3, 0.3), (0.5, 0.5), (0.7, 0.7), (0.9, 0.9) through matlab simulation, as shown in Figure 3 As shown, we find that when α∈(α * ,1),β∈(β * ,1), both sides will eventually evolve to the situation of E4(1,1); when α∈(0,α * ), β∈(0,β * ), the evolution direction will eventually evolve into E1(0,0).

[0080] (3) Impact of changes in subsidies.

[0081] In order to study the impact of subsidies on the evolution of the government and photovoltaic enterprises, the initial subsidy price was set at 0.17 yuan / KWh, and three comparison prices were set at 0.07 yuan / KWh, 0.27 yuan / KWh, and 0.37 yuan / KWh. Three different evolutions were obtained. The government's additional social benefits selected scenario 2, that is, scenario selection G3-G4<0, and the evolution was as follows Figure 4 shown.

[0082] From the figure, we find that when the price of subsidies gradually rises, the evolutionary strategies will gradually approach the stable point of E4(1,1). According to the relationship and evolution, we believe that this situation occurs because although photovoltaic companies have gained huge benefits from subsidies, the government will bear a large amount of subsidy costs, which will lead to a significant reduction in government revenue. In order to maximize profits, the government must change this situation by implementing the RPS government. Therefore, the government will gradually evolve to implement the RPS strategy, and when the subsidy price is higher, the government will evolve faster. In order to obtain more benefits, photovoltaic companies will also gradually evolve in the direction of RPS, but the evolution speed will be slower than that of the government, but they will eventually trade green certificates, that is, reach the stable point of E4(1,1).

[0083] So far, the present invention has analyzed the decision-making of both parties in the game and obtained the profit results of both parties in various situations. However, the present invention also pays attention to the development of social welfare and green certificate trading. Based on simple static game analysis, it is difficult to study the changes in variables such as social welfare and green certificate trading volume under different parameters, and it is impossible to conduct horizontal comparative analysis, so it is necessary to find a new method to make up for the shortcomings of the game. System dynamics is an efficient method for studying variable relationships and macroeconomics; and its visualized change charts can also observe the evolution of targets over time. In this case, the present invention chooses to combine the system dynamics model with the game model, and uses system dynamics to analyze the development of social welfare and green certificate trading volume under different parameter changes, providing a theoretical basis for national policy making.

[0084] Since the system dynamics model is used to verify the game model, its main assumptions should be the same as the game assumptions. Photovoltaic enterprises are the only suppliers of green certificates in the market, while thermal power enterprises are the only demanders of green certificates in the market. The government and photovoltaic enterprises are the main players in the game. In addition, the system dynamics model has many parameters and variables. In order to facilitate the processing of the model, and the installed capacity of power generation has been growing steadily in recent years, according to the fitting of historical data, it is assumed that the installed capacity of photovoltaic power generation conforms to the Logistic growth model. In order to facilitate the study of system mechanisms, economic crises, inflation and other issues are not considered.

[0085] Based on the assumptions, we constructed Figure 5The causal loop diagram shows that there are three main entities: the government, thermal power companies, and photovoltaic companies. The three are mainly connected through green certificate transactions and fixed electricity price subsidies. Based on the causal loop diagram, in order to specifically describe the behaviors of thermal power companies, photovoltaic companies, and the government in the game process, and in order to deeply study the impact of various parameter changes on the evolution of the two sides of the game, the green certificate transaction volume, and social welfare, a system dynamics model of the game between the government and photovoltaic companies was established, such as Figure 6 shown.

[0086] In this system, we study the strategic evolution of social welfare, government and photovoltaic enterprises. Only the connection of lines cannot output a specific image, nor can it complete the verification of the game. It is necessary to verify the relationship between the variables in the stock-flow diagram. By referring to papers, national statistical yearbooks, newspapers and other materials, and fitting some relationships, the dynamic equation relationship shown in Table 4 is obtained.

[0087] Table 4 Kinetic equations

[0088]

[0089]

[0090]

[0091] In addition, there are other expressions such as G1-G4, R1-R4 and other variables, which are the benefits of the government and photovoltaic enterprises under various strategies, which are the same as the calculation formula in the evolutionary game model in the previous article. In this SD model, the state variables mainly include installed capacity, green certificate price, proportion of implementation of various strategies, green certificate holdings, etc. Dynamic variables include thermal power enterprise profits, green certificate trading volume, government average revenue, carbon emission costs, etc. Exogenous parameters include renewable energy quota ratio, grid-connected electricity price, government fixed electricity price subsidy, effective utilization time, etc.

[0092] This paper will take the photovoltaic grid-connected electricity price of 0.47 yuan / KWh in Class I areas as the initial value of the model green electricity grid-connected electricity price; the initial value of the subsidy price is set to 0.17 yuan / KWh. According to the China Low Carbon Industry Network and the paper reference, the carbon trading price is set to 40 yuan / t. Coal is not the focus of this paper, so it is set to the average price of 600 yuan / t in recent years. According to the performance of the green certificate market, the initial price of the green certificate is set to 0.22 yuan / KWh, and the thermal power grid-connected electricity price is set to 0.3 yuan / KWh based on literature and historical data. The simulation starts in 2017, so according to the data of the National Bureau of Statistics, the photovoltaic installed capacity is set to 77.42 million KWh. It is assumed that the initial government adopts the RPS system at a rate of 0.2, the government implements the RPS at a rate of 0.5, and the photovoltaic enterprise initially implements the green certificate trading at a rate of 0.5. In addition, the unit power generation carbon dioxide emissions, effective utilization time, etc. are all referenced to the average level of previous years, as shown in Table 5.

[0093] Table 5 Variable data description

[0094]

[0095]

[0096] Bring the parameters in the table above into AnyLogic software for simulation. The simulation period is 2017-2025, and the simulation step is 1 month. The simulation can be effectively run by adjusting the parameters. When analyzing the green certificate trading market, we mainly observe the strategic evolution of the government and photovoltaic companies, the number of green certificate transactions, and the market price of green certificates. In terms of analyzing social effects, we can analyze by observing the trend of social welfare changes. Due to the fast evolution speed, here we count the images of 50 time periods, such as Figure 7-9 shown.

[0097] Through the evolution graph, we found that when the initial ratio was 0.5, the evolution speed of the government was higher than that of the photovoltaic enterprises, and the ratio of photovoltaic enterprises first decreased and then increased. In the final evolutionary stable state, the evolutionary stable state of both was (1, 1), that is, the government implemented RPS and photovoltaic enterprises traded TGC, which was consistent with the conclusion of the previous game model.

[0098] from Figure 8 From the simulation results, we found that the overall trend of green certificate trading volume is on the rise, that is, more and more photovoltaic companies will adopt green certificate strategies. The number of green certificate transactions increased rapidly in the early stage and slowly in the later stage, because the evolution in the later stage tends to be stable, without too much fluctuation, and the growth is fixed in each period. In terms of social welfare, social welfare gradually increased during the simulation period, which is in line with the overall development of society.

[0099] Through the evolution analysis of the government and photovoltaic enterprises under the SD model, this SD model verifies the game model very well. In order to study under what circumstances social welfare is maximized and the impact of parameter changes on the evolution of both parties in the game, this paper analyzes the impact of two parameters, government fixed subsidies and initial evolution state ratio, on the number of green certificate transactions, social welfare and the strategies of each subject. See Table 6 for specific parameter settings.

[0100] Table 6 Scenario settings

[0101]

[0102]

[0103] In the previous game model, we found that the initial ratio of the two parties in the game has a great influence on the evolutionary game, which is divided into three scenarios: 0.3, 0.5, and 0.7. When the basic ratio is higher than a certain value, the evolution will eventually tend to (1, 1), and when it is less than a certain ratio, such as 0.3, the evolution will eventually tend to (0, 0), which is consistent with the results of the second scenario in the evolutionary game model, that is, G3-G4<0. The trading volume of green certificates also gradually increases with the increase of the initial ratio. In addition, we found that the initial ratio has basically no effect on social welfare.

[0104] In the scenario of G3-G4<0, fixed electricity price subsidies have a great impact on the evolution, green certificate trading volume, and social welfare. When the fixed electricity price subsidy gradually increases, the evolution speed will accelerate for both parties in the game, but the specific effects are different. For the government, the evolution speed will gradually accelerate, but for photovoltaic companies, the proportion of photovoltaic companies trading green certificates will first decrease and then gradually increase, and when the subsidy is getting higher and higher, the speed of the first decrease will increase. This is because subsidies have a lot of benefits for photovoltaic companies and will increase their profits, but this will seriously increase the burden on the government, causing the government to quickly evolve to implement RPS, and photovoltaic companies at this time must change their strategies to maximize their profits and conduct TGC transactions to avoid losses, resulting in the above curve. For the green certificate trading volume, in the short term, when the subsidy price is higher, the green certificate trading volume will be less, and in the long term, the higher the price, the greater the green certificate trading volume. In terms of social welfare, whether in the short term or long term, the higher the subsidy price, the greater the social welfare.

[0105] When subsidies decrease period by period, the evolution speed of both the government and photovoltaic enterprises will be reduced. When subsidies increase period by period, the evolution speed of both parties will gradually increase, but photovoltaic enterprises will evolve towards FIT in the initial stage and then evolve towards RPS.

[0106] Through the comparative analysis of these three scenarios, it can be seen that the larger the initial ratio of the two parties in the game, the higher the government's fixed electricity price subsidy, the faster the evolution of the two parties in the game, the higher the trading volume of green certificates in the long run, and the more prosperous the green certificate market. Social welfare is only related to the government's fixed electricity price subsidy. When the fixed electricity price subsidy is higher, the social welfare is greater.

[0107] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modification, equivalent substitution or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for integrating green certificate trading and electricity price subsidies for photovoltaic enterprises based on subsidy time limit and intensity, characterized in that By combining the evolutionary game model with system dynamics simulation, the strategy optimization and social welfare maximization of photovoltaic enterprises and the government under different subsidy policies are achieved. The method includes the following steps: Step 1: Construct an evolutionary game model between photovoltaic enterprises and the government. Based on the policy background of the fixed electricity price subsidy mechanism (FIT) and the renewable energy quota system (RPS), analyze the game relationship between photovoltaic enterprises and the government under these two subsidy mechanisms, and construct an evolutionary game model to describe the process of strategic evolution of both parties. Step 2: Conduct system dynamics simulation. Use system dynamics methods to simulate and verify the evolutionary game model, and analyze the impact of different subsidy policy parameters on photovoltaic companies, government strategy evolution, and social welfare; Step 3: Conduct sensitivity analysis of policy parameters. Through simulation models, study the impact of different subsidy intensities and durations on photovoltaic enterprises and government strategies, green certificate markets, and social welfare, and identify key policy parameters; Step 4: Propose policy recommendations. Based on the simulation and optimization results and the actual situation, propose optimization policy recommendations for photovoltaic companies and the government under the fixed electricity price subsidy mechanism and renewable energy quota system.

2. The method for integrating green certificate trading and electricity price subsidy for photovoltaic enterprises based on subsidy time limit and intensity according to claim 1 is characterized in that : The evolutionary game model between photovoltaic enterprises and the government described in step 1 first defines the strategy set of both parties in the game; the strategy of photovoltaic enterprises includes choosing fixed electricity price subsidies (FIT) and conducting green certificate trading (TGC), and the government's strategy includes implementing a fixed electricity price subsidy mechanism (FIT) and implementing a renewable energy quota system (RPS). The specific forms are as follows: R1=[(1-π)(P TGC +P f )-P c ]Q+D1(1) R2=[(1-π)P f -P c ]Q-D1(2) R3=[(1-π)P f -P c ]Q-D2(3) R4=[(1-π)(P sub +P f )-P c ]Q+D2(4) In the formula, R1 represents the income of photovoltaic enterprises when they choose to trade green certificates and obtain subsidies; R2 represents the income of photovoltaic enterprises when they choose to trade green certificates but do not obtain subsidies; R3 represents the income of photovoltaic enterprises when they choose fixed electricity price subsidies but do not trade green certificates; R4 represents the income of photovoltaic enterprises when they choose fixed electricity price subsidies and trade green certificates. (1-π) represents the proportion of photovoltaic enterprises receiving subsidies; P TGC Indicates the transaction price of green certificates; P f P represents the grid-parity electricity price; c Represents the unit cost of photovoltaic power generation; Q represents photovoltaic power generation; D1 represents the additional income of photovoltaic enterprises from trading green certificates and obtaining subsidies; D2 represents the additional income of photovoltaic enterprises from choosing fixed electricity price subsidies and trading green certificates. G1=(P TGC +P f )πQ+U1(5) G2=P f πQ-U2(6) G3=P f πQ-U1(7) G4=P sub (π-1)Q+P f πQ+U2(8) In the formula, G1 represents the benefits when the government chooses to implement RPS and conduct green certificate transactions; G2 represents the benefits when the government chooses to implement RPS but does not conduct green certificate transactions; G3 represents the benefits when the government chooses fixed electricity price subsidies but does not conduct green certificate transactions; G4 represents the benefits when the government chooses fixed electricity price subsidies and conducts green certificate transactions. PTGC represents the green certificate transaction price; π represents the probability that the government chooses to implement RPS; U1 represents the additional benefits of the government implementing RPS and conducting green certificate transactions; U2 represents the additional benefits of the government implementing RPS but not conducting green certificate transactions; P sub Indicates government fixed electricity price subsidy.

3. The photovoltaic enterprise green certificate transaction and electricity price subsidy integration method based on subsidy time limit and intensity according to claim 1 is characterized in that Based on the evolutionary game model described in step 2, a simulation model that reflects the evolution of photovoltaic enterprise and government strategies is constructed using the system dynamics method. The main process is as follows: (1) Define the variables and equations in the system dynamics model. The system dynamics model includes the following main variables and equations: Q pv =Capacity total ×T util (9) In the formula, Q pv Represents photovoltaic power generation, Capacity total Indicates the total installed capacity, T util Indicates effective use of time. Q TGC =min(Q supply ,Q demand )(10) In the formula, Q TGC Indicates the trading volume of green certificates, Q supply Represents the supply of green certificates, Q demand Indicates the demand for green certificates. G=α×G1+(1-α)×G2(11) In the formula, G represents government revenue, α represents the proportion of the government choosing to implement RPS, G1 represents the government revenue when RPS is implemented and photovoltaic enterprises conduct green certificate transactions, and G2 represents the government revenue when RPS is not implemented and photovoltaic enterprises conduct green certificate transactions. R=β×R1+(1-β)×R2(12) In the formula, R represents the income of photovoltaic enterprises, β represents the proportion of photovoltaic enterprises choosing to trade green certificates, R1 represents the income of photovoltaic enterprises when they choose to trade green certificates, and R2 represents the income of photovoltaic enterprises when they do not choose to trade green certificates. (2) Setting the parameters of the simulation model: Setting the parameters of the simulation model according to actual data, including photovoltaic power generation installed capacity, green certificate price, fixed electricity price subsidy, etc.; (3) Run the simulation model: Use simulation software such as AnyLogic to run the system dynamics model to simulate the strategic evolution of photovoltaic companies and the government under different subsidy policies; (4) Analyze the simulation results; based on the simulation results, analyze the impact of different subsidy policy parameters on photovoltaic companies, government strategy evolution, and social welfare.

4. According to claim 1, a method for integrating green certificate trading and electricity price subsidies for photovoltaic enterprises based on subsidy time limit and intensity, characterized in that: In step 3, we study the impact of different subsidy intensities and durations on photovoltaic enterprises and government strategies, green certificate markets, and social welfare, identify key policy parameters, and conduct sensitivity analysis of policy parameters as follows: (1) Set different policy parameter combinations. Set a variety of policy parameter combinations based on factors such as subsidy intensity, subsidy time, and initial strategy ratio; (2) Run sensitivity analysis simulations. Use the system dynamics model to simulate each policy parameter combination and record the dynamic changes in the evolution of PV companies and government strategies; (3) Calculate social welfare indicators. The social welfare under different policy parameter combinations is calculated through simulation results. The formula is as follows: In the formula, SW represents social welfare, Profit TGC Indicates the profits of thermal power companies participating in TGC transactions, represents the average revenue of photovoltaic enterprises, Q TGC Indicates the amount of electricity generated by thermal power plants participating in TGC transactions, Q PV represents photovoltaic power generation, and CO2 represents carbon dioxide emissions. (4) Compare the effects of different policy parameter combinations; by comparing the simulation results under different policy parameter combinations, analyze the impact of factors such as subsidy intensity, subsidy time, and initial strategy ratio on the evolution of photovoltaic enterprise and government strategies and social welfare; (5) Identify key policy parameters: Based on the results of sensitivity analysis, identify the key policy parameters that have the greatest impact on the evolution of photovoltaic enterprise and government strategies and social welfare to guide policy making.

5. According to claim 1, a method for integrating green certificate trading and electricity price subsidies for photovoltaic enterprises based on subsidy time limit and intensity, characterized in that: In step 4, based on the actual situation, we put forward optimization policy suggestions for photovoltaic enterprises and the government under the fixed electricity price subsidy mechanism and renewable energy quota system. The specific steps are as follows: (1) Summarize the simulation results. Based on the results of system dynamics simulation and evolutionary game model, summarize the evolution of photovoltaic enterprise and government strategies and social welfare changes under different subsidy policy parameters; (2) Analyze the policy effects. By comparing the simulation results of different subsidy policy parameter combinations, analyze the impact of factors such as subsidy intensity, subsidy time, and initial strategy ratio on photovoltaic enterprises, government strategy evolution, and social welfare; (3) Determine the optimal subsidy strategy. Based on the analysis of different combinations of subsidy policy parameters, determine the optimal subsidy strategy combination that can maximize social welfare; (4) Propose policy recommendations. Based on the optimal subsidy strategy combination, propose optimization policy recommendations for photovoltaic companies and the government under the fixed electricity price subsidy mechanism and the renewable energy quota system to achieve a smooth transition and effective implementation of the subsidy policy.

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