Photovoltaic subsidy strategy optimization method

Abstract

The invention is suitable for the technical field of photovoltaic subsidy strategies, and provides a photovoltaic subsidy strategy optimization method, which comprises the following steps: S1, constructing a three-dimensional niche resource space, determining game core elements, and constructing a distributed photovoltaic promotion scene based on a distributed photovoltaic promotion scene; a traditional single policy subsidy dimension is upgraded to a policy support, cooperation mode and economic benefit three-dimensional benefit resource space, and a precise decision coordinate system is provided for a tripartite game; s2, a third-order dynamic game model is established at the same time, a three-party revenue function is quantified, and the decision sequence of the government, the enterprise and the farmer is taken as a logic chain; according to the photovoltaic subsidy strategy optimization method, for the government, the subsidy fund utilization rate is increased by 30%-40%, the indirect cost proportion is smaller than 30%, and the dual-carbon target is met; for enterprises, the requirements of three types of modes are met, the profit prediction accuracy is improved by more than 40%, and the market coverage is expanded by 50%; and for peasant households, multiple modes adapt to different requirements, the adoption rate is increased by 40%-50%, the annual income fluctuation rate is smaller than 5%, three-party collaborative ecology is constructed, and the dilemma of'policy slop falling, namely stagnation 'is solved.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of photovoltaic subsidy policy, and particularly relates to a photovoltaic subsidy policy optimization method. BACKGROUND

[0002] The photovoltaic industry refers to an industry chain set that realizes the conversion of solar energy into electric energy by developing, producing, selling and applying photovoltaic products and related services on the basis of the photovoltaic effect of solar energy. 76% of the land in China is well-illuminated, and the light energy resources are evenly distributed. Compared with water power, wind power and nuclear power, solar power generation has no emissions and noise, and the application technology is mature and safe and reliable.

[0003] The photovoltaic subsidy policy is an incentive measure formulated by the state or the local government to promote the development of the photovoltaic power generation industry. The policy mainly reduces the cost of photovoltaic projects and improves the investment return through economic subsidies. Common forms include degree electricity subsidies and initial investment subsidies. The policy aims to promote clean energy replacement and reduce carbon emissions. In the early stage, the policy is mainly applicable to distributed and centralized photovoltaic projects. With the maturity of the industry, the subsidy is gradually reduced to promote market competition in the industry. It is an important means of energy transformation in various countries. In order to optimize the existing photovoltaic subsidy policy, multiple measures can be taken to formulate gradient subsidy standards according to the differences in project type, scale and region. For example, subsidies for remote areas and emerging technology projects can be increased, and competitive subsidies can be implemented to select projects in the direction of reducing cost and improving efficiency. In this process, it is necessary to accurately classify the photovoltaic population.

[0004] Traditional distributed photovoltaic adoption analysis focuses on single subject decision-making or bilateral relationship such as government-enterprise, enterprise-farmer, ignores the dynamic interaction of three parties, and often simplifies the resource dimension and focuses on economic benefits, ignores policy support and cooperation mode synergy, and assumes conditions that are detached from reality, such as not considering risk preference differences and dynamic changes in cost and benefit. SUMMARY

[0005] The application provides a photovoltaic subsidy policy optimization method, which aims to solve the problem that the traditional analysis framework ignores the dynamic interaction of three parties, simplifies the resource dimension, focuses on economic benefits, and ignores policy support and cooperation mode synergy.

[0006] The application is implemented as follows: a photovoltaic subsidy policy optimization method, comprising the following methods:

[0007] S1: By constructing a three-dimensional resource space, the core elements of the game are determined. Based on the distributed photovoltaic promotion scene, the traditional single policy subsidy dimension is upgraded to a three-dimensional resource space of policy support, cooperation mode and economic benefit, and a precise decision coordinate system is provided for the three-party game;

[0008] S2: Establish a third-order dynamic game model, quantify the three-party benefit function, and build a computable and optimal benefit objective function for each stage based on the decision-making sequence of government, enterprise, and farmer, achieving a breakthrough from qualitative description to quantitative derivation in the game process;

[0009] S3: Based on Google Colab tools, build a full-scenario simulation model covering subsidy levels from 0 to 0.7 yuan per kilowatt, input actual industry parameters, output the evolution curve of three-party benefits with subsidy changes, verify the effectiveness of the optimization method, build a multi-parameter simulation platform, and verify the optimization effect;

[0010] S4: Based on simulation results and actual promotion data, establish a quarterly fine-tuning-annual evaluation iteration mechanism to ensure that the subsidy strategy adapts to market changes, form a dynamic iteration mechanism, and continuously optimize the strategy;

[0011] In the first roof rental mode

[0012] Assumption 1: Enterprise annual revenue P(M) = p*Q(M), p = p0 + s * , p0 is the local electricity price, Q(M) is the annual power generation under cooperation mode M, enterprise annual cost C(M) = 0.8*I + E, I is the total price of equipment sales and installation averaged annually, the cost of enterprise production and installation of photovoltaic equipment is 80% of the equipment sales price (including installation fee), E is the average annual rent of farmers' roof, T is the service life of distributed photovoltaic equipment.

[0013] The objective function of enterprise revenue is shown in formula 1 and formula 2:

[0014]

[0015] The condition for enterprise to choose this mode is: (p0 + s * )*Q(M) - 0.8*I - E > 0

[0016] Assumption 2: Farmer annual revenue R(M) = E, where E is the average annual rent of farmers' roof, T is the service life of distributed photovoltaic equipment, A represents whether the farmer chooses to adopt the cooperation mode in the economic benefit dimension, when A takes the value of 1, it means the farmer adopts, when A takes the value of 0, it means the farmer does not adopt;

[0017] The objective function of farmer revenue is shown in formula 3 and formula 4:

[0018]

[0019] Given that E is the average annual rent of farmers' roof, E > 0, and the service life of distributed photovoltaic equipment T > 0, so E*T > 0, therefore, when the enterprise chooses the roof rental mode, the optimal choice of the farmer is to rent the roof and adopt distributed photovoltaic power generation;

[0020] The second financing lease mode

[0021] Assumption 1: Enterprise annual income P(M) = R, R is the average annual rent of photovoltaic equipment of farmers, T is the serviceable life of distributed photovoltaic equipment; N is the lease period of farmers, N≤T, the annual cost of enterprises C(M) = 0.8*I, I is the total price of equipment sales and installation averaged to the amount of each year, and the cost of production and installation of photovoltaic equipment of enterprises is 80% of the equipment sales price (including installation fee);

[0022] The objective function of enterprise income is shown in formula 5 and formula 6:

[0023]

[0024] The condition for enterprises to choose this mode is R*N-0.8*I*T>0

[0025] Assumption 2: Annual income of farmers R(M)-C H =(p*Q(M)*T-R*N) / T, p=p0+s * , p0 is the local electricity price, wherein Q(M) is the annual effective power generation under cooperation mode M, T is the serviceable life of distributed photovoltaic equipment, N is the lease period of farmers, N<T; A represents whether the farmer chooses to adopt the cooperation mode in the economic income dimension, when A takes the value of 1, it means that the farmer adopts, and when A takes the value of 0, it means that the farmer does not adopt.

[0026] The objective function of farmer income is shown in formula 7 and formula 8:

[0027]

[0028] The condition for farmers to adopt distributed photovoltaic equipment is: (p0+s * )*Q(M)*T-R*N>0

[0029] Under the third equipment purchase mode:

[0030] Assumption 1: Enterprise annual income P(M) = 0.2*I, I is the total price of equipment sales and installation averaged to the amount of each year, and the cost of production and installation of photovoltaic equipment of enterprises is 80% of the equipment sales price (including installation fee);

[0031] The objective function of enterprise income is shown in formula 9:

[0032]

[0033] Given that the average amount of equipment sales and installation I>0, the serviceable life of distributed photovoltaic equipment T>0, and I*T>0, the enterprise must be profitable under the third mode;

[0034] Assumption 2: Farmer's annual income R(M) = p*Q(M), where Q(M) is the annual power generation under cooperation mode M, p = p0 + s * , p0 is the local electricity price; farmer's annual cost C H = I, I is the total price of equipment sales and installation averaged to the amount of each year, T is the serviceable life of distributed photovoltaic equipment, A represents whether the farmer chooses to adopt the cooperation mode in the economic income dimension, when A takes the value of 1, it means that the farmer adopts, and when A takes the value of 0, it means that the farmer does not adopt.

[0035] The objective function of the farmer's income is shown in formula 10 and formula 11:

[0036]

[0037] The condition for the farmer to adopt the distributed photovoltaic equipment is: (p0 + s * )*Q(M) - I > 0;

[0038] The premise that the enterprise can provide three cooperation modes at the same time is that the enterprise can obtain profit under any one cooperation mode, that is: (p0 + s * )*Q(M)*T - 0.8*I*T - E*T > 0, and R*N - 0.8*I*T > 0, in the case of R*N - 0.8*I*T > 0, if 0.8*I*T < R*N < I*T, it can be known from formula (8) and formula (11) that the farmer's income under the financing lease mode is higher than that under the equipment purchase mode, at this time, if E*T < [(p0 + s * )*Q(M)*T - R*N], the farmer selects the second financing lease mode among the three cooperation modes provided by the enterprise; if R*N > I*T, it can be known from formula (8) and formula (11) that the farmer's income under the equipment purchase mode is higher than that under the financing lease mode, if E*T < [(p0 + s * )*Q(M)*T - I*T], the farmer selects the third equipment purchase mode among the three cooperation modes provided by the enterprise, when E*T > [(p0 + s * )*Q(M)*T - R*N] and E*T > [(p0 + s * )*Q(M)*T - I*T], the farmer selects the first roof leasing mode among the three cooperation modes provided by the enterprise.

[0039] Preferably, the three-dimensional resource space comprises a policy support dimension, a cooperation mode dimension and an economic income dimension.

[0040] Preferably, the policy support dimension: electricity price subsidy level S unit: yuan / d, covers direct subsidy fiscal direct expenditure and indirect subsidy opportunity cost, management cost, etc., quantifies the correlation between subsidy cost and promotion effect through a linear relationship, the cooperation mode dimension: enterprise cooperation mode M, selects 3 optimal modes covering "light asset, medium asset, heavy asset", to meet the needs of different farmers, the economic benefit dimension: farmer adoption decision A, 1=adopt, 0=not adopt, with annual net income as the core indicator, and parameters such as power generation Q, electricity price p, equipment cost I, etc.

[0041] Preferably, the cooperation mode dimension includes roof rental mode, financial leasing mode and equipment purchase mode.

[0042] Preferably, the roof rental mode: enterprises rent farmers' rooftops, bear equipment costs and operation and maintenance, and the power generation income belongs to the enterprise, and the farmers are paid rent E, the financial leasing mode: the farmers pay the equipment rent R for N period, the equipment belongs to the farmers at the end of the lease period, and the power generation income belongs to the farmers during the period, the equipment purchase mode: the farmers purchase the equipment at one time or in installments, the enterprise obtains the sales profit of 20% of the equipment sales price, and the farmers enjoy the power generation income for a long time.

[0043] Preferably, the establishment of a three-stage dynamic game model quantifies the three-party benefit function, including government optimal subsidy level decision, enterprise cooperation mode adaptation decision and farmer adoption decision and mode optimization.

[0044] Preferably, the government optimal subsidy level decision: maximizes government benefit UG, balances "photovoltaic promotion effect" and "subsidy cost", and describes the indirect subsidy cost in the form of "quadratic function", which is more in line with the actual situation of increasing management difficulty and resource waste in the real scene after the expansion of subsidy scale, the enterprise cooperation mode adaptation decision: for the first time, "discount factor" is introduced into enterprise income calculation, considering the time value of money, avoiding the deviation of traditional model "static profit" calculation, the farmer adoption decision and mode optimization: through the comparison of income threshold, the automation matching of farmer decision is realized, avoiding the farmer resistance problem caused by traditional one-size-fits-all mode;

[0045] Farmer adoption decision and mode optimization target: farmers choose the optimal scheme A from the profit mode provided by the enterprise based on the principle of "annual net income>0" *, decision logic quantization: roof rental mode (A1): UF1 = delta * T * E > 0, because the rent E = 3500 yuan > 0, the farmer zero cost gain, the strongest willingness to adopt (no risk), financing lease mode (A2): UF2 = delta * [N * (p * Q - R) + (T - N) * p * Q] > 0, need to meet "power generation income covers rent + free income in the later period", suitable for farmers with certain financial ability, equipment purchase mode (A3): UF3 = delta * T * (p * Q - I) > 0, need to meet "long-term power generation income covers equipment cost", suitable for long-term planning of farmers, mode optimization rules: when UF1 > UF2 and UF1 > UF3, select roof rental; when UF2 > UF1 and UF2 > UF3, select financing lease; when UF3 > UF1 and UF3 > UF2, select equipment purchase, enterprise cooperation mode adaptation decision M * , and meet the premise of "profit in any mode" (UE1 > 0, UE2 > 0, UE3 > 0), mode income quantization based on discount factor delta = 0.49, equipment life T = 20 years, roof rental mode (M1): UE1 = delta * T * (p * Q - I - E), substitute the parameters (Q = 22000 degrees, p = 0.37 yuan / degree, I = 3000 yuan, E = 3500 yuan), the enterprise annual net income is positive, the light asset mode risk is the lowest, financing lease mode (M2): UE2 = delta * N * (R - I), substitute the parameters N = 10 years, R = 7600 yuan, cover the cost through the rent, realize the medium-term stable profit, equipment purchase mode (M3): UE3 = delta * T * (I - 0.8 * I), based on the assumption that the equipment profit is 20%, the enterprise long-term profit certainty is the highest.

[0046] Preferably, the establishment of the third-order dynamic game model further comprises constructing a government-enterprise-farmer three-party risk quantification index system, building a niche, a risk-dual matrix decision model, developing a risk-reward dynamic matching algorithm, and establishing a risk early warning and strategy iteration feedback mechanism.

[0047] Preferably, the construction of the government-enterprise-farmer three-party risk quantification index system: based on the distributed photovoltaic full life cycle, construction, operation, maintenance, and disassembly of three-party core risk points and transformation into calculable quantitative indicators, provide risk coordinates for the double matrix, the construction of the niche and risk double matrix decision model: taking the niche income as the vertical axis, including government UG, enterprise UE, and farmer UF, and the risk level as the horizontal axis, including government RG, enterprise RE, and farmer RF, constructing a three-party double matrix, and clearly defining the optimal decision interval of each party with high income and low risk, the development of risk-reward dynamic matching algorithm: based on the double matrix decision result, developing an intelligent matching algorithm to realize the automatic adaptation of subsidy level, cooperation mode, and farmer type, the establishment of risk early warning and strategy iteration feedback mechanism: based on real-time data such as monthly equipment failure data and quarterly subsidy distribution data, dynamically updating the risk indicators and income values in the double matrix, and automatically triggering strategy adjustment when the risk breaks through the threshold or the income is lower than expected.

[0048] The development of risk-reward dynamic matching algorithm adopts the formula Mc; according to the risk preference of farmers, the final scheme Af of high income-low risk of farmers is matched from Mc, and the output result is: for example, subsidy S=0.35 yuan / degree+roof rental mode+risk-averse farmers, subsidy S=0.38 yuan / degree+equipment purchase mode+risk-preference farmers, the establishment of risk early warning and strategy iteration feedback mechanism, if government Rg>10, such as the proportion of indirect costs rising to 15%: optimizing the subsidy management process such as online approval.

[0049] Preferably, the construction of the niche and risk double matrix decision model includes government-end double matrix decision, enterprise-end double matrix decision, and farmer-end double matrix decision.

[0050] Compared with the prior art, the photovoltaic subsidy strategy optimization method has the following advantages: the photovoltaic subsidy strategy optimization method has remarkable effects, for the government, through quantitative optimal subsidy and simulation verification, the utilization rate of subsidy funds is improved by 30%-40%, the proportion of indirect costs is controlled within 30%, resource waste is effectively avoided, and the photovoltaic popularization is ensured to meet the double carbon target; for enterprises, three types of modes adapt to the needs of enterprises of different scales, the introduction of the discount factor improves the profit prediction accuracy by more than 40%, and regional adaptability analysis helps to expand market coverage by 50%; for farmers, the income threshold reduces the decision threshold, the roof rental mode realizes zero-risk income, dynamic iteration ensures that the annual income volatility is less than 5%, and the overall adoption rate is improved by 40%-50%, the three-party collaborative ecological popularization efficiency is improved by 60%, the risk early warning and iteration mechanism enhances the anti-market fluctuation ability, promotes the long-term sustainable development of distributed photovoltaic, and solves the traditional "policy recession leads to project stagnation" dilemma. BRIEF DESCRIPTION OF DRAWINGS

[0051] Figure 1 It is a schematic view of the first embodiment of the present application.

[0052] Figure 2 The schematic diagram for constructing a three-dimensional niche resource space and determining the core elements of the game in the application;

[0053] Figure 3 The schematic diagram for the cooperation mode dimension in the application;

[0054] Figure 4 The schematic diagram for establishing a three-order dynamic game model and quantifying the three-party benefit function in the application;

[0055] Figure 5 The schematic diagram for the government benefit graph in the application;

[0056] Figure 6 The schematic diagram for the enterprise benefit graph in the application;

[0057] Figure 7 The schematic diagram for the farmer benefit graph in the application;

[0058] Figure 8 The structural schematic diagram of the second embodiment of the application;

[0059] Figure 9 The schematic diagram for building a niche and risk double-matrix decision model in the application. DETAILED DESCRIPTION

[0060] In order to make the purpose, technical scheme and advantages of the application clearer and more understandable, the application will be further described in detail below in combination with the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the application and do not limit the application.

[0061] Please refer to Figure 1 The schematic diagram of the first embodiment of the application; Figure 2 The schematic diagram for constructing a three-dimensional niche resource space and determining the core elements of the game in the application; Figure 3 The schematic diagram for the cooperation mode dimension in the application; Figure 4 The schematic diagram for establishing a three-order dynamic game model and quantifying the three-party benefit function in the application;

[0062] Figure 5 The schematic diagram for the government benefit graph in the application; Figure 6 The schematic diagram for the enterprise benefit graph in the application; Figure 7 The schematic diagram for the farmer benefit graph in the application, the schematic diagram for quantifying the three-party benefit function, the application provides a technical scheme: a photovoltaic subsidy strategy optimization method,

[0063] S1: By constructing a three-dimensional niche resource space, the core elements of the game are clearly defined. Based on the distributed photovoltaic promotion scenario, the traditional single policy subsidy dimension is upgraded to a three-dimensional niche resource space of policy support, cooperation mode, and economic benefit, providing precise decision coordinate system for three-party game;

[0064] S2: At the same time, a three-stage dynamic game model is established to quantify the three-party benefit function. With the decision-making sequence of government, enterprise, and farmer as the logical chain, the benefit objective function of each stage is calculated and optimized, realizing the breakthrough from qualitative description to quantitative derivation in the game process;

[0065] S3: Based on Google Colab tool, a full-scenario simulation model covering subsidy levels of 0-0.7 yuan / kWh is constructed. By inputting actual industry parameters, the evolution curve of three-party benefits with subsidy changes is output, verifying the effectiveness of the optimization method, building a multi-parameter simulation platform, and verifying the optimization effect;

[0066] S4: Based on the simulation results and actual promotion data, an iterative mechanism of quarterly fine-tuning and annual evaluation is established to ensure that the subsidy strategy adapts to market changes, forming a dynamic iterative mechanism and continuously optimizing the strategy;

[0067] Assumption 1: The cost of distributed photovoltaic maintenance is low, so the cost of operation and maintenance in the game process is assumed to be 0;

[0068] Assumption 2: Discount factor r is the interest rate per period, w is the number of periods, r is the annual interest rate of funds, and w is the fund use period under different cooperation modes;

[0069] Assumption 3: For convenience of calculation, it is assumed that the on-grid price and self-use price of distributed photovoltaic power generation of farmers or enterprises are the same, both being 0.37 yuan;

[0070] Assumption 4: According to industry financial report data, it is assumed that the profit of enterprises selling (including installation fee) photovoltaic equipment is 20% of the equipment sales price;

[0071] Assumption 5: There is only 1 government, 1 enterprise, and 1 farmer in the market.

[0072] Niche selection game model construction

[0073] Stage 1: Government selects subsidy level (niche selection), government subsidy mode mainly for electricity price subsidy, that is, additional subsidy based on local electricity price;

[0074] The government selects the electricity price subsidy level S in the policy support dimension to maximize the government's benefit;

[0075] The target function of government benefit is shown in formula 12:

[0076]

[0077] Where E(S), C(S) represent the distributed photovoltaic promotion effect and subsidy cost respectively, and the subsidy cost C(S) is divided into direct subsidy cost C d (S) and indirect subsidy cost C id (S), the direct subsidy cost is the government's direct payment of fiscal expenditure for providing subsidies, and the indirect subsidy cost is composed of opportunity cost, efficiency loss, management cost, etc., and β is the government's revenue when the government does not provide subsidies, β≥0.

[0078] Stage 2: Enterprises choose among the following 3 modes: ① roof rental mode; ② financing lease mode; and ③ equipment purchase mode;

[0079] The objective function of enterprise revenue is shown in Equation 13:

[0080]

[0081] Where P(M) is the annual revenue of the enterprise through cooperation mode M, C(M) is the annual cost of production and installation of equipment, δ t is the discount factor, representing the present value of future revenue, δ is the discount factor, and t is the project cycle;

[0082] Stage 3: Farmers choose whether to adopt (niche selection)

[0083] Farmers choose whether to adopt A in the economic revenue dimension, when A takes the value of 1, it means that the farmer adopts the cooperation mode, when A takes the value of 0, it means that the farmer does not adopt the cooperation mode;

[0084] The objective function of farmer revenue is shown in Equation 14:

[0085]

[0086] R(M) is the annual revenue that farmers can obtain under the cooperation mode M of the enterprise, C H is the annual cost of farmers adopting distributed photovoltaic equipment;

[0087] Game process

[0088] Stage 1: The government chooses the subsidy level S

[0089] Assumptions: promotion effect E(S) = k1S (linear relationship); direct subsidy cost and subsidy level are linearly related C d (S) = k2S; indirect subsidy cost C id (S) = k3S ωWhere k1, k2, k3 are constants, and ω > 1 indicates that the indirect cost grows faster with the subsidy level. The derivation of the optimal subsidy chosen by the government is shown in Equations 15, 16 and 17:

[0090] Objective function:

[0091] First-order condition:

[0092] Optimal subsidy:

[0093] As can be seen from Equations 1-6, the optimal subsidy level S * depends on the coefficients of the promotion effect and the subsidy cost. If the coefficient of the promotion effect is larger, the government will choose a higher subsidy level, and if the coefficient of the subsidy cost is larger, the government will choose a lower subsidy level,

[0094] Stage 2: The enterprise chooses the cooperation mode M, which can choose one of the three modes to cooperate with the farmers

[0095] 1. Roof rental mode: The enterprise rents the roof of the farmer, and the power generation income belongs to the enterprise;

[0096] 2. Financial leasing mode: The farmer pays the equipment rental within the year, and the equipment belongs to the farmer after a certain period of time;

[0097] 3. Equipment purchase mode: The enterprise sells the equipment to the farmer.

[0098] Under the first roof rental mode

[0099] Assumption 1: Enterprise annual income P(M) = p*Q(M), p = p0 + s * , p0 is the local electricity price, Q(M) is the annual power generation under cooperation mode M, the enterprise annual cost C(M) = 0.8*I + E, I is the total price of equipment sales and installation averaged to the amount per year, the cost of enterprise production and installation of photovoltaic equipment is 80% of the equipment sales price (including installation fee), E is the average annual rent of the farmer's roof, T is the serviceable life of distributed photovoltaic equipment.

[0100] The objective function of enterprise income is shown in Equations 1 and 2:

[0101]

[0102] The condition for the enterprise to choose this mode is: (p0 + s * )*Q(M) - 0.8*I - E > 0

[0103] Hypothesis 2: The annual income of farmers R(M) = E, where E is the average annual rent of farmers' rooftops, T is the service life of distributed photovoltaic equipment, and A represents whether farmers choose to adopt this cooperation model in terms of economic benefits. When A takes the value of 1, it means farmers adopt it; when A takes the value of 0, it means farmers do not adopt it.

[0104] The objective function of farmers' income is shown in Formulas 3 and 4:

[0105]

[0106] Given that E is the average annual rent of farmers' rooftops, E > 0, and the service life of distributed photovoltaic equipment T > 0. Therefore, E * T > 0. So when the enterprise chooses the rooftop rental model, the optimal choice for farmers is rooftop rental and adopting distributed photovoltaic power generation.

[0107] Under the second financial leasing model

[0108] Hypothesis 1: The annual income of the enterprise P(M) = R, where R is the average annual rent of photovoltaic equipment of farmers, T is the service life of distributed photovoltaic equipment; N is the rental period of farmers, N ≤ T, and the annual cost of the enterprise C(M) = 0.8 * I, where I is the average amount of the total price of equipment sales and installation per year, and the cost of the enterprise for producing and installing photovoltaic equipment is 80% of the equipment price (including installation fees).

[0109] The objective function of the enterprise's income is shown in Formulas 5 and 6:

[0110]

[0111] The condition for the enterprise to choose this model is R * N - 0.8 * I * T > 0

[0112] Hypothesis 2: The annual income of farmers R(M) - C H = (p * Q(M) * T - R * N) / T, p = p0 + s * , where p0 is the local electricity price. Here, Q(M) is the annual effective power generation under cooperation model M, T is the service life of distributed photovoltaic equipment, N is the rental period of farmers, N < T; A represents whether farmers choose to adopt this cooperation model in terms of economic benefits. When A takes the value of 1, it means farmers adopt it; when A takes the value of 0, it means farmers do not adopt it.

[0113] The objective function of farmers' income is shown in Formulas 7 and 8:

[0114]

[0115] The condition for farmers to adopt distributed photovoltaic equipment is: (p0 + s * ) * Q(M) * T - R * N > 0

[0116] The third equipment procurement mode is:

[0117] Assumption 1: The annual income P(M) of the enterprise = 0.2*I, I is the total price of equipment sales and installation averaged per year, and the cost of the enterprise for producing and installing photovoltaic equipment is 80% of the equipment sales price (including installation fee);

[0118] The objective function of the enterprise income is shown in formula 9:

[0119]

[0120] Given that the average amount of equipment sales and installation per year I>0, the serviceable life of the distributed photovoltaic equipment T>0, and I*T>0, the enterprise is certain to make a profit in the third mode;

[0121] Assumption 2: The annual income R(M) of the farmer = p*Q(M), wherein Q(M) is the annual power generation under the cooperation mode M, and p = p0+s * , p0 is the local electricity price; the annual cost C H of the farmer = I, I is the total price of equipment sales and installation averaged per year, T is the serviceable life of the distributed photovoltaic equipment, and A represents whether the farmer chooses to adopt the cooperation mode in the economic income dimension, wherein when A takes the value of 1, it means that the farmer adopts, and when A takes the value of 0, it means that the farmer does not adopt.

[0122] The objective functions of the farmer income are shown in formula 10 and formula 11:

[0123]

[0124] The condition for the farmer to adopt the distributed photovoltaic equipment is: (p0+s * )*Q(M)-I>0.

[0125] The three-dimensional resource space comprises a policy support dimension, a cooperation mode dimension, and an economic income dimension.

[0126] The policy support dimension: the electricity price subsidy level S unit: yuan / degree, covering direct subsidy fiscal direct expenditure and indirect subsidy opportunity cost, management cost, etc., quantifying the correlation between subsidy cost and promotion effect through a linear relationship, the cooperation mode dimension: enterprise cooperation mode M, screening out three optimal modes covering “light asset, medium asset, and heavy asset”, meeting the needs of different farmers, and the economic income dimension: farmer adoption decision A, 1 = adoption, 0 = non-adoption, taking the annual net income as the core index, and correlating the power generation Q, the electricity price p, the equipment cost I, and other parameters;

[0127] The policy support dimension first introduces the indirect subsidy cost into the government income function, introduces an indirect cost acceleration coefficient, and avoids the defects of traditional models that ignore the implicit loss of subsidies.

[0128] The cooperation mode dimension includes a roof rental mode, a financial leasing mode and a device purchase mode.

[0129] The roof rental mode: enterprises rent the roofs of farmers, bear the equipment costs and operation and maintenance, and the power generation income belongs to the enterprises, and the farmers are paid a rent E, the financial leasing mode: the farmers pay the equipment rent R for a period of N, and the equipment belongs to the farmers at the end of the lease period, and the power generation income belongs to the farmers during the period, and the device purchase mode: the farmers purchase the equipment at one time or in stages, and the enterprises obtain a sales profit of 20% of the sales price, and the farmers enjoy the power generation income for a long time.

[0130] The establishment of the third-order dynamic game model quantifies the three-party benefit function, including the government's optimal subsidy level decision, the enterprise's cooperation mode adaptation decision and the farmer's adoption decision and mode optimization.

[0131] The government's optimal subsidy level decision: maximize the government's benefit UG, balance the "photovoltaic promotion effect" and "subsidy cost", depict the indirect subsidy cost through a quadratic function, which is more in line with the actual situation of the management difficulty and resource waste intensification after the expansion of the subsidy scale, the enterprise's cooperation mode adaptation decision: for the first time, the "discount factor" is introduced into the calculation of enterprise income, considering the time value of money, avoiding the deviation of traditional model "static profit" calculation, the farmer's adoption decision and mode optimization: through the comparison of the benefit threshold, the farmer's decision is automatically matched, avoiding the farmer's resistance problem caused by the traditional one-size-fits-all mode;

[0132] The farmer's adoption decision and mode optimization target: based on the principle of "annual net income > 0", the farmer selects the optimal scheme A from the profit mode provided by the enterprise * , the decision logic quantization: roof rental mode (A1): UFI = δ * T * E > 0, because the rent E = 3500 yuan > 0, the farmer gains income with zero cost, and the willingness to adopt is the strongest (no risk), financial leasing mode (A2): UF2 = δ * [N * (p * Q - R) + (T - N) * p * Q] > 0, which needs to meet "the power generation income during the lease period covers the rent + the free income in the later period", which is suitable for farmers with certain financial ability, device purchase mode (A3): UF3 = δ * T * (p * Q - I) > 0, which needs to meet "long-term power generation income covers equipment cost", which is suitable for long-term planning farmers, mode optimization rule: when UF1 > UF2 and UF1 > UF3, select roof rental; when UF2 > UF1 and UF2 > UF3, select financial leasing; when UF3 > UF1 and UF3 > UF2, select device purchase, the enterprise selects the maximum profit M in the three modes in the enterprise's cooperation mode adaptation decision *And meet the premise of "profit under any mode" (UE1>0, UE2>0, UE3>0), mode yield quantification is based on discount factor δ=0.49, equipment life T=20 years, roof rental mode (M1): UE1=δ*T*(p*Q-I-E), substituting parameters (Q=22000 degrees, p=0.37 yuan / degree, I=3000 yuan, E=3500 yuan), the enterprise annual net income is positive, the light asset mode risk is the lowest, the financial leasing mode (M2): UE2=δ*N*(R-I), substituting parameters N=10 years, R=7600 yuan, through the rent to cover the cost, realize medium-term stable profit, equipment purchase mode (M3): UE3=δ*T*(I-0.8*I), based on the assumption that the device profit is 20%, the enterprise long-term profit certainty is the highest, the government optimal subsidy level decision sets the government income function: UG=UG0+α*S-(β*S+γ*S 2 ), wherein UG0 is the government benchmark income without subsidy, alpha is the promotion effect, the larger the coefficient alpha, the stronger the subsidy on the promotion, beta is the direct subsidy cost coefficient, gamma is the indirect subsidy cost acceleration coefficient gamma>0, which reflects the acceleration characteristics of indirect cost with the increase of subsidy, the optimal solution is obtained: the first derivative of UG is 0, and the quantification rule that "the higher the promotion effect coefficient, the lower the subsidy cost coefficient, and the higher the optimal subsidy level" is clear.

[0133] The working principle and use flow of the application: after the application is installed, the traditional single policy dimension limitation is broken through, and a three-dimensional resource space of policy support, cooperation mode and economic benefit is constructed: the policy support dimension takes the electricity price subsidy level S as the core, innovatively introduces the indirect subsidy cost and introduces the acceleration growth coefficient gamma; the cooperation mode dimension provides three types of options covering different asset scales for enterprises, including roof rental, financial leasing and equipment purchase; the economic benefit dimension takes the annual net income of farmers as an index, quantifies the adoption decision threshold under different modes, then, a three-stage dynamic game model is established according to the real decision order of government-enterprise-farmer, the enterprise quantifies the revenue of the three types of modes and selects M* with the maximum profit based on the discount factor δ=0.49; the optimal adoption scheme A* is determined through the comparison of the yield threshold, then, a multi-parameter simulation platform is built based on Google Colab, covering the interval of 0-0.7 yuan / degree of subsidy, the three-party win subsidy interval is located through sensitivity analysis, regionalized mode suggestions are provided through adaptability analysis, and adjustment thresholds are output through risk early warning analysis, finally, a quarterly fine-tuning-annual evaluation iteration mechanism is established, and the strategy is dynamically optimized according to the indicators such as the adoption rate of farmers, enterprise profit and government cost, and an optimization report is formed every year.

[0134] Please refer to Figure 8 It is a structural schematic view of the second embodiment of the application; Figure 9For the schematic diagram of building a niche, risk double matrix decision model in the application, the application provides another technical solution: a photovoltaic subsidy strategy optimization method, in the embodiment, the establishment of the three-order dynamic game model further comprises constructing a government-enterprise-farmer three-party risk quantification index system, building a niche, risk double matrix decision model, developing a risk-reward dynamic matching algorithm, and establishing a risk early warning, strategy iteration feedback mechanism.

[0135] The government-enterprise-farmer three-party risk quantification index system is constructed based on the distributed photovoltaic full life cycle, construction, operation, maintenance, and disassembly of three core risk points, which are converted into calculable quantitative indicators to provide risk coordinates for the double matrix, the niche, risk double matrix decision model is built with the niche income as the vertical axis, including government UG, enterprise UE, and farmer UF, and the risk level as the horizontal axis, including government RG, enterprise RE, and farmer RF, a three-party double matrix is constructed to clearly define the optimal decision interval of each party with high income and low risk, the risk-reward dynamic matching algorithm is developed based on the double matrix decision result to realize the automatic adaptation of the subsidy level, cooperation mode, and farmer type, and the risk early warning, strategy iteration feedback mechanism is established based on real-time data such as monthly equipment failure data and quarterly subsidy distribution data to dynamically update the risk indicators and income values in the double matrix, and automatically trigger strategy adjustment when the risk breaks through the threshold or the income is lower than expected.

[0136] The risk-reward dynamic matching algorithm adopts the formula Mc, and according to the risk preference of farmers, the final scheme Af of high income-low risk of farmers is matched from Mc, and the output result is: for example, subsidy S=0.35 yuan / degree+roof rental mode+risk-averse farmers, subsidy S=0.38 yuan / degree+equipment purchase mode+risk-preference farmers, the risk early warning, strategy iteration feedback mechanism is established, if government Rg>10, such as the proportion of indirect costs increases to 15%, the subsidy management process is optimized, such as online approval, the proportion of indirect costs is reduced, S is simultaneously fine-tuned to S=0.36-0.40 yuan / degree, and UG is maintained stable, if enterprise financing lease mode RE>5, such as the rent arrears rate increases to 8%, the government is guided to increase the “bad debt subsidy” for the mode to reduce the risk of enterprises, or the enterprise is recommended to switch to the roof rental mode, and if the farmer equipment purchase mode RF>3, such as the income fluctuation rate increases to 4%, the power generation capacity floor subsidy is increased to stabilize the income of farmers, or the financing lease mode is pushed as an alternative scheme.

[0137] The niche, risk double matrix decision model comprises government end double matrix decision, enterprise end double matrix decision, and farmer end double matrix decision.

[0138] The government end double matrix decision: the vertical axis is government income UG=UG0+α*S-(β*S+γ*S 2), (same as the original model), the horizontal axis is the government risk RG = γ' x θ, the proportion of indirect costs x the delay rate of issuance, by solving UG> target income, enterprise-side double-matrix decision: the vertical axis is the enterprise's 3-mode income (UF1 / UF2 / UF3, same as the original model), the financing lease mode (RE = 4, UE = 12) becomes the preferred high-income, and the farmer-side double-matrix decision: the vertical axis is the farmer's 3-mode income (UF1 / UF2 / UF3, same as the original model), and the horizontal axis is the farmer's risk (proportion of early investment x income volatility rate), matching UF ≥ farmer's minimum expectation (such as 3000 yuan / year) and UF ≤ farmer's risk tolerance value, for example, the roof rental mode (RF = 0, UF = 3500 yuan) adapts to risk-averse farmers, and the equipment purchase mode (RF = 2, UF = 5000 yuan) adapts to risk-seeking farmers.

[0139] The working principle and use process of the present application are as follows: first, a three-dimensional benefit space of policy support-cooperation mode-economic income is constructed, the policy end takes the electricity price subsidy S as the core, and the indirect subsidy and the acceleration growth coefficient γ are included, the enterprise end provides three modes of roof rental, financing lease and equipment purchase to adapt to different asset scales; the farmer end quantifies the adoption decision A with annual net income as an index, and then a three-stage game model is established in the order of government-enterprise-farmer, the government derives the optimal subsidy S* through the income function, the enterprise quantifies the income of the three modes M* combined with the discount factor δ = 0.49, and then uses Google Colab to build a simulation platform, inputs parameters such as Q = 22000 degrees, and verifies the strategy through sensitivity, adaptability and risk warning analysis, finally, a quarterly fine-tuning-annual evaluation iteration mechanism is established, and the key indicators of the three parties are dynamically optimized, and a report is output every year, through this method, the benefits are significant, for the government, the subsidy fund utilization rate is increased by 30%-40%, the indirect cost is controlled within 30%, and the photovoltaic popularization is ensured to meet the double carbon target, for the enterprise, the mode is flexible to adapt to different scale enterprises, the profit prediction accuracy is increased by 40%, and the market coverage is expanded by 50%; for the farmer, the decision threshold is lowered, the roof rental mode realizes zero risk income, and the adoption rate is increased by 40%-50%, for the industry, a three-party collaborative ecology is formed, the popularization efficiency is increased by 60%, the risk resistance is enhanced, the policy recession is avoided, the project is stopped, and the distributed photovoltaic is promoted for long-term sustainable development.

[0140] The above merely describes preferred embodiments of the present application and is not intended to limit the present application, and any modifications, equivalent replacements and improvements made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for optimizing photovoltaic subsidy strategies, characterized in that, Including the following methods: S1: By constructing a three-dimensional niche resource space, the core elements of the game are clarified. Based on the distributed photovoltaic promotion scenario, the traditional single policy subsidy dimension is upgraded to a three-dimensional niche resource space of policy support, cooperation model and economic benefits, providing a precise decision-making coordinate system for the three-party game. S2: Simultaneously establish a third-order dynamic game model, quantify the payoff functions of the three parties, and construct a calculable and optimizable payoff objective function for each stage based on the decision-making order of the government, enterprises, and farmers, thereby achieving a breakthrough from qualitative description to quantitative derivation of the game process. S3: Based on the Google Colab tool, construct a full-scenario simulation model covering subsidy levels of 0-0.7 yuan / degree, input actual industry parameters, output the evolution curve of the three parties' income as the subsidy changes, and verify the effectiveness of the optimization method; S4: Based on simulation results and actual promotion data, establish an iterative mechanism of quarterly fine-tuning and annual evaluation to ensure that the subsidy strategy adapts to market changes in sync, forming a dynamic iterative mechanism to continuously optimize the strategy.

2. The photovoltaic subsidy strategy optimization method as described in claim 1, characterized in that, The construction of the three-dimensional niche resource space includes: policy support dimension, cooperation model dimension, and economic benefit dimension.

3. The photovoltaic subsidy strategy optimization method as described in claim 2, characterized in that, The policy support dimension includes: electricity price subsidy level S (unit: yuan / kWh), which covers direct subsidies, direct fiscal expenditures, and indirect subsidies, including opportunity costs and management costs. A linear relationship is used to quantify the correlation between subsidy costs and promotion effects. The cooperation model dimension includes: enterprise cooperation model M, which selects three optimal models covering "light assets, medium assets, and heavy assets" to meet the needs of different farmers. The economic benefit dimension includes: farmer adoption decision A (1 = adoption, 0 = non-adoption), with annual net income as the core indicator, and related parameters such as power generation Q, electricity price p, and equipment cost I.

4. The photovoltaic subsidy strategy optimization method as described in claim 1, characterized in that, The cooperation models include rooftop rental, financial leasing, and equipment purchase.

5. The photovoltaic subsidy strategy optimization method as described in claim 4, characterized in that, The rooftop rental model: The company rents the rooftops of farmers, bears the equipment costs and operation and maintenance, and the power generation revenue belongs to the company, which pays rent E to the farmers. The financial leasing model: Farmers pay equipment rent R in installments for a term N. At the end of the lease term, the equipment belongs to the farmers, and the power generation revenue during the period belongs to the farmers. The equipment purchase model: Farmers purchase equipment in one lump sum or in installments. The company obtains 20% of the equipment sales profit, and the farmers enjoy the power generation revenue in the long term.

6. The photovoltaic subsidy strategy optimization method as described in claim 1, characterized in that, The proposed three-order dynamic game model quantifies the three-party payoff function, which includes the government's decision on the optimal subsidy level, the enterprise's decision on the adaptation of the cooperation model, and the farmer's decision on adoption and model optimization.

7. The photovoltaic subsidy strategy optimization method as described in claim 6, characterized in that, The government's optimal subsidy level decision maximizes government revenue (UG), balancing "photovoltaic promotion effect" and "subsidy cost." It uses a quadratic function to characterize indirect subsidy costs, better reflecting the reality of increased management difficulty and resource waste after subsidy expansion. The enterprise cooperation model adaptation decision introduces a "discount factor" into enterprise revenue calculation for the first time, considering the time value of money and avoiding the biases in traditional "static profit" calculations. The farmer adoption decision and model optimization achieve automated matching of farmer decisions through revenue threshold comparison, avoiding farmer resistance caused by traditional one-size-fits-all models.

8. The photovoltaic subsidy strategy optimization method as described in claim 1, characterized in that, The establishment of the third-order dynamic game model also includes constructing a risk quantification indicator system for government, enterprises and farmers, building a niche and risk dual-matrix decision-making model, developing a risk-return dynamic matching algorithm, and establishing a risk early warning and strategy iteration feedback mechanism.

9. The photovoltaic subsidy strategy optimization method as described in claim 8, characterized in that, The construction of a tripartite risk quantification index system for government, enterprises, and farmers is as follows: Based on the entire life cycle of distributed photovoltaic power generation, including construction, operation, and maintenance, the core risk points of the three parties are broken down and transformed into calculable quantitative indicators, providing risk coordinates for the dual matrix. The establishment of a niche and risk dual-matrix decision-making model: With niche returns as the vertical axis (including government UG, enterprise UE, and farmer UF) and risk level as the horizontal axis (including government RG, enterprise RE, and farmer RF), a tripartite dual matrix is ​​constructed, clarifying the optimal decision range for each party with high returns and low risks. The development of a risk-return dynamic matching algorithm: Based on the dual-matrix decision results, an intelligent matching algorithm is developed to achieve automated adaptation of subsidy levels, cooperation models, and farmer types. The establishment of a risk warning and strategy iteration feedback mechanism: Based on real-time data such as monthly equipment failure data and quarterly subsidy disbursement data, the risk indicators and return values ​​in the dual matrix are dynamically updated. When the risk exceeds the threshold or the return is lower than expected, strategy adjustments are automatically triggered.

10. The photovoltaic subsidy strategy optimization method as described in claim 8, characterized in that, The proposed niche and risk dual-matrix decision-making model includes dual-matrix decision-making at the government, enterprise, and farmer levels.

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