Renewable energy consumption responsibility weight distribution optimization method considering multiple factors

Abstract

A renewable energy consumption responsibility weight distribution optimization method considering multiple factors relates to the technical field of power markets, and comprises the following steps: S1, considering multiple factors, and using an entropy weight method to solve objective renewable energy power consumption responsibility weight of each market subject; s2, establishing an upper layer optimization objective function; s3, establishing a lower-layer objective function; and step S4, iteratively solving the optimal renewable energy consumption responsibility amount and consumption amount of each market subject based on the double-layer optimization model established in the step S2 and the step S3. The method takes province as a unit, comprehensively considers resource endowment, power structure and economic level of each region, and green power consumption proportion and profitability of each market subject, reasonably allocates renewable energy consumption responsibility weight, realizes optimal allocation of resources, further improves the utilization efficiency of renewable energy, reduces energy waste, and saves energy resources. And the fairness and operability of the policy can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power market, and in particular to a method for optimizing the weight allocation of provincial renewable energy consumption responsibilities by considering multiple factors. Background Art

[0002] Against the backdrop of fossil energy shortages and increasing environmental pollution, the top priority is to accelerate energy transformation and reconstruct a green and low-carbon energy system.

[0003] The renewable energy power consumption responsibility weight is the proportion of renewable energy electricity that should be achieved according to the provincial administrative region's electricity consumption regulations, including the total consumption responsibility weight and the non-hydropower consumption responsibility weight. The total consumption responsibility weight includes all types of renewable energy power generation, and the non-hydropower consumption responsibility weight includes other types of renewable energy power generation except hydropower. The minimum renewable energy power consumption responsibility weight that should be achieved is stipulated for each provincial administrative region, and the incentive consumption responsibility weight is determined by a certain degree of excess over the minimum consumption responsibility weight.

[0004] Power sales companies and power users jointly assume the responsibility of power consumption. The first type of market players include power grid companies, independent power sales companies, etc. The second type of market players include power users who purchase electricity through the power wholesale market and companies that own their own power plants.

[0005] The policy of responsible weight for renewable energy power consumption helps to ensure the consumption of renewable energy. The United States, the United Kingdom, Australia and many other countries and regions have introduced renewable energy quota system (renewable portfolio standard, RPS) policies similar to China's policy of responsible weight for renewable energy power consumption. The policy of responsible weight for renewable energy power consumption or quota system usually refers to the renewable energy consumption ratio indicators issued by government agencies to market entities, and the corresponding penalties are imposed on those who fail to meet the quota requirements.

[0006] In setting the target of renewable energy consumption responsibility weight, countries or administrative regions that implement the renewable energy quota system only set a unified renewable energy quota target. China, which implements the renewable energy power consumption responsibility weight policy, sets a renewable energy consumption responsibility weight index for each province every year. In the allocation of power consumption responsibility weight, an equal proportion allocation method is adopted without considering the characteristics of market entities. Therefore, studying the consumption responsibility weight index of each market entity is conducive to the reasonable allocation of the consumption responsibility weight responsibilities of each market entity from the region, effectively completing the assessment index requirements, and promoting the long-term development of the renewable energy consumption mechanism.

[0007] Reasonable allocation of the consumption responsibility weights of various market players can further promote the utilization and development of renewable energy. At present, the research on the allocation of consumption responsibility weights is mainly concentrated at the provincial level, attempting to establish reasonable regional consumption responsibility weight targets for national regions, but there are currently few studies on the allocation of consumption responsibility weights from the provincial level to different market players. Therefore, it is urgent to make more detailed and accurate overall considerations of different market players in the provincial region to optimize the consumption responsibility weight indicators and promote the consumption and development of renewable energy. Summary of the invention

[0008] The present invention aims to solve one of the technical problems in the related art at least to a certain extent.

[0009] To this end, one purpose of the present invention is to propose a method for optimizing the allocation of renewable energy consumption responsibility weights taking into account multiple factors, and to reasonably allocate consumption responsibility weights from the perspective of various market entities to promote the consumption and development of renewable energy.

[0010] To achieve the above object, the present invention adopts the following technical solution: a method for optimizing the weight allocation of renewable energy consumption responsibility considering multiple factors, comprising: Step S1: Considering policy factors, regional factors and enterprise factors, the objective renewable energy power consumption responsibility weight of each market player is obtained.

[0011] Step S2: Establish an upper-level optimization objective function that takes the renewable energy consumption responsibility of each market entity as the control variable and maximizes the total profit of each market entity.

[0012] Step S3: Establish a lower-level objective function that takes the renewable energy consumption of each market entity as the control variable and minimizes the total consumption reward and punishment costs of each market entity.

[0013] Step S4: Based on the two-layer optimization model established in steps S2 and S3, iteratively solve the optimal renewable energy consumption responsibility and consumption of each market player, and further obtain the renewable energy consumption responsibility weight and consumption ratio.

[0014] Furthermore, in step S1, the entropy weight method is used to obtain the objective renewable energy power consumption responsibility weight of each market entity.

[0015] Furthermore, in step S2, the upper layer optimization objective function is: In the formula, C total is the total profit of each market player; N is the number of regions in a province; N n.x is the number of market entities of the xth category in the nth region; a n.x.j Q is the unit electricity sales / consumption income of the jth market entity in the nth region of the province;n.x.j The electricity consumption of each market player; n.x.j is the incentive electricity price; M n.x.j.1 The amount of electricity that is higher than the consumption responsibility; c n.x.j To punish electricity prices; M n.x.j.2 The amount of electricity that is lower than the consumption responsibility; d n.x.j The cost of purchasing and consuming green electricity by self-generated units; q n.x.j The amount of renewable energy consumed by market entities.

[0016] Furthermore, in step S3, the lower layer objective function is: In the formula, f total It is the total reward and punishment cost of each market player.

[0017] On the other hand, the present invention also proposes a renewable energy consumption responsibility weight allocation optimization system considering multiple factors, including: an objective renewable energy power consumption responsibility weight obtaining module, which considers policy factors, regional factors and enterprise factors, and uses the entropy weight method to obtain the objective renewable energy power consumption responsibility weight of each market entity; an upper-level optimization objective function establishment module, which establishes an upper-level optimization objective function with the renewable energy consumption responsibility of each market entity as the control variable and the total profit of each market entity maximized; a lower-level objective function establishment module, which establishes a lower-level objective function with the renewable energy consumption of each market entity as the control variable and the total consumption reward and punishment fee of each market entity minimized; an iteration module, which solves the optimal renewable energy consumption responsibility and consumption of each market entity based on the model established by the two objective functions, and further obtains the renewable energy consumption responsibility weight and consumption ratio; an evaluation module, which establishes a renewable energy consumption responsibility weight allocation fairness evaluation model based on the Theil index to verify the fairness of the consumption responsibility weight allocation plan of each market entity.

[0018] In the third aspect, the present invention also proposes a device for optimizing the allocation of renewable energy consumption responsibility weights taking into account multiple factors, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the method for optimizing the allocation of renewable energy consumption responsibility weights taking into account multiple factors are implemented.

[0019] Beneficial effects: (1) Improve resource utilization efficiency. The present invention takes the province as the unit, comprehensively considers the resource endowment, power structure, economic level of each region, and the proportion of green electricity consumption and profitability of each market player, reasonably allocates the responsibility weights for renewable energy consumption, realizes the optimal allocation of resources, further improves the utilization efficiency of renewable energy, and reduces energy waste. (2) Reduces policy implementation resistance. The consumption responsibility weights are allocated based on multiple factors, fully reflecting the actual situation of each market player, reducing the policy implementation resistance caused by unreasonable allocation of consumption targets, and improving the fairness and operability of the policy. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0021] Figure 1 It is a flow chart of a renewable energy consumption responsibility weight allocation optimization method considering multiple factors proposed by the present invention; Figure 2 It is the objective renewable energy power consumption responsibility weight map of each market player in a certain region of a province; Figure 3 It is the upper and lower objective function value graph of each market entity in a certain region of a province for T iterations; Figure 4 It is the Pareto solution diagram of each market player in a certain region of a certain province for T iterations; Figure 5 It is the final renewable energy power consumption responsibility weight map of each market player in a certain region of a province. DETAILED DESCRIPTION

[0022] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present invention. However, it should be clear to those skilled in the art that the present invention may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present invention.

[0023] In order to make the purpose, technical solutions and advantages of the present invention more clear, specific embodiments will be described below in conjunction with the accompanying drawings.

[0024] The present invention proposes a flowchart of a method for optimizing the weight allocation of renewable energy consumption responsibility considering multiple factors, see Figure 1 , including the following steps: Step S1: Considering policy factors, regional factors and enterprise factors, the objective renewable energy power consumption responsibility weight of each market player is obtained.

[0025] Step S2: Establish an upper-level optimization objective function that takes the renewable energy consumption responsibility of each market entity as the control variable and maximizes the total profit of each market entity.

[0026] Step S3: Establish a lower-level objective function that takes the renewable energy consumption of each market entity as the control variable and minimizes the total consumption reward and punishment costs of each market entity.

[0027] Step S4: Based on the two-layer optimization model established in steps S2 and S3, iteratively solve the optimal renewable energy consumption responsibility and consumption of each market player, and further obtain the renewable energy consumption responsibility weight and consumption ratio.

[0028] In order to obtain the objective renewable energy power consumption responsibility weights of each market player, it is necessary to establish a quantitative model of the objective renewable energy power consumption responsibility weights based on different policy factors, regional factors and enterprise factors. You can choose to use hierarchical analysis method, expert scoring method, grey correlation analysis method, weighted combination method and other methods to empower different indicator factors and solve them.

[0029] The entropy weight method is an objective weighting method based on the principle of information entropy. It determines the weight of each indicator in the comprehensive evaluation by measuring the degree of variation of each indicator.

[0030] The core concept of the entropy weight method is information entropy, which originates from physics and information theory and is used to measure the degree of uncertainty of a system. In decision analysis, if the data difference of an indicator is large, it means that it provides a lot of information; on the contrary, if the data difference is small, it means that it provides little information.

[0031] The entropy weight method calculates the entropy value of the indicator based on the impact of the numerical changes of each indicator on the whole, and then determines the weight. The larger the entropy value, the smaller the amount of information, and the smaller the impact of the indicator on the whole.

[0032] The entropy weight method is widely used in environmental assessment, corporate performance evaluation, investment decision-making, energy utilization and other fields. It can objectively determine the weight of each indicator, thereby obtaining a more scientific and reasonable evaluation result. For example, in environmental assessment, the entropy weight method can evaluate the importance of environmental factors and determine the priority of environmental protection measures.

[0033] The advantages of the entropy weight method include objectivity and adaptability. It can avoid the interference of subjective factors and improve the objectivity and accuracy of the evaluation results.

[0034] This embodiment selects the entropy weight method, which is highly objective, can objectively reflect the information contribution of each indicator, and considers the variability and discrimination of each indicator for solution, as follows.

[0035] In step S1, the annual renewable energy power consumption responsibility weight index of each provincial administrative region issued by the National Energy Administration can be obtained from the Internet. The provincial administrative regions here include provinces, autonomous regions, and municipalities directly under the central government.

[0036] The renewable energy power consumption responsibility weight index of each provincial administrative region in a certain year can be expressed as L, for example, L=[ω 1 1 , ω 1 2 ,…,ω 1 J ,…,ω 1 M ], J=(1,2,…,M), where M represents the total number of provincial-level administrative regions.

[0037] The regional factors of each region in each province can be obtained by combining the network, provincial trading centers and power grid, which can be expressed as: .

[0038] In the formula, A J is the regional factor data of each region in each province, which is represented by a matrix in this embodiment; n=(1,2,…,N J ), where N J represents the total number of regions in the Jth provincial administrative region; m 1 =(1,2,…,M 1 ), where M 1 represents the total number of regional factor indicators, a J n.m1 The mth region in the nth region of province J 1 Regional factor data.

[0039] The enterprise factors of each market player in each region of each province can be obtained by combining the network, each enterprise, each provincial trading center and the power grid, which can be expressed as: .

[0040] In the formula, A J.n is the enterprise factor data of each market entity in each region of each province, which is represented by a matrix in this embodiment; j=(1,2,…,N J.n ), where N J.n represents the total number of market entities in the nth region of the Jth provincial administrative region; m 2 =(1,2,…,M 2 ), where M2 Indicates the total number of enterprise factor indicators. J.n j.m2 The mth market entity in the jth region of the Jth province 2 Enterprise factor data.

[0041] It should be noted that the number of regional factor indicators in each region remains consistent, and the number of enterprise factor indicators for each market entity in each region remains consistent.

[0042] The entropy weight method is used to obtain the objective renewable energy power consumption responsibility weights of each market player, which can include: Step S101: Determine policy factors: Determine policy factors ω based on the minimum renewable energy power consumption responsibility weight index of the province 1 , a quantitative model of objective renewable energy power consumption responsibility weight is established based on different policy factors.

[0043] ω 1 is the minimum renewable energy power consumption responsibility weight index issued by the National Energy Administration of the province, which can be obtained from L, and specifically corresponds to the renewable energy power consumption responsibility weight index ω of Province J 1 J ,ω 1= ω 1 J .

[0044] Step S102: Determine regional factors, and establish a quantitative model of objective renewable energy power consumption responsibility weights based on different regional factors.

[0045] Regional factors include resource endowment, power structure and economic level of each region. The calculation formula is as follows: Quantitative indicators of resource endowment: Due to the differences in renewable resources such as wind and light in different regions of the province, the development of renewable energy varies greatly, resulting in large differences in the weights of consumption responsibilities, which can be expressed by the available hours of wind power and photovoltaic power, wind speed, irradiation, etc.

[0046] Quantitative indicators of power supply structure: Due to differences in installed power capacity in different regions of the province, there are also differences in the weight of consumption responsibility, mainly due to differences in installed capacity of renewable energy.

[0047] Quantitative indicators of economic level: Market entities in regions with better economic development within the province are expected to assume greater consumption weight responsibilities, which can be represented by one year's GDP.

[0048] Where N is the number of regions divided into a province; H new is the average renewable energy utilization hours in the province; H new.n is the average renewable energy utilization hours in the nth region; N is the number of regions in the province; G new is the installed capacity of renewable energy in the province (kW); G new.n is the installed capacity of renewable energy in the nth region (kW); Y is the GDP of the province in one year (yuan); Y n It is the GDP (yuan) of the nth region in one year.

[0049] Step S103: Determine enterprise factors, and establish a quantitative model of objective renewable energy power consumption responsibility weights based on different enterprise factors.

[0050] Enterprise factors mainly include the types of market players in the region, the proportion of green electricity consumption and profitability, etc. The calculation formula is as follows: Types of market entities: Market entities can be divided into power grid enterprises and power sales companies in the first category, and power users and self-owned power plant enterprises in the second category. The sales / consumption structure of different market entities varies greatly, resulting in large differences in the proportion of renewable energy and the weight of consumption responsibility.

[0051] Proportion of green electricity consumption: Since the historical proportion of renewable energy sales / consumption in the electricity sales / consumption of market entities varies greatly, the weight of absorption responsibility varies greatly.

[0052] Profitability: Market players with stronger profitability are expected to assume greater responsibility for consumption.

[0053] In the formula, Q n is the electricity sales / consumption (kWh) of the nth region; X is the number of market entity types. In this embodiment, X=4; N n.x Q is the number of market entities of the xth category in the nth region; n.x.j is the electricity sales / consumption of the jth market entity in the xth category in the nth region (kWh); r n.x is the proportion of green electricity consumption of the xth type of market entity in the nth region; r n.x.j Q is the proportion of green electricity consumption of the jth market entity in the xth category in the nth region; n.x is the electricity sales / consumption of the xth type of market entity in the nth region (kWh); R n.x is the profit margin of the xth type of market players in the nth region; R n.x.jis the profit margin of the jth market player in the xth category in the nth region.

[0054] Step S104: Using the entropy weight method, considering the wind and solar energy resource endowment, power structure and economic level of each region, the comprehensive score s of each region considering regional factor indicators is obtained. n 2 ; Using the entropy weight method, the proportion of green electricity consumption and profitability of each market entity are considered to obtain the comprehensive score of each market entity of the same market entity type in the region considering the enterprise factor index. 3 n.x.j .

[0055] In this embodiment, the power supply structure specifically refers to the installed capacity of wind and solar power sources.

[0056] The main core of the entropy weight method is to assign weights according to the amount of information of each indicator, which can objectively reflect the importance and discrimination of each indicator.

[0057] The specific process of the entropy weight method is as follows: 1) First, determine the decision-making problem and indicator system: determine the problem and indicator system that need to be decided, and describe and define each indicator.

[0058] 2) Next, normalize the indicator data: normalize the data of each indicator so that they can be compared under the same dimension. Assuming that n samples and m indicators are given: {Y 1 , Y 2 , ..., Y m}, where Y j ={y 1j ,y 2j , ..., y nj}, the standardized value is Z j ={z 1j , z 2j , ..., z nj}.

[0059] where z ij For normalized data, the following formulas are positive and negative indicators, respectively, p ij It is the proportion of the i-th sample value under the j-th indicator to the indicator.

[0060] (Positive indicator) (Reverse indicator) .

[0061] 3) Then calculate the information entropy value: Calculate the information entropy of each indicator to reflect the discrete degree and uncertainty of each indicator. jis the information entropy of index j, and the calculation formula is as follows, where if p ij =0, then define e ij =0.

[0062] .

[0063] 4) Finally, determine the weight and comprehensive score of each indicator: the comprehensive score can be used to measure the comprehensive level of each sample. The higher the comprehensive score, the better the sample. The details are as follows: Where, d j is the information entropy redundancy of index j; w j is the weight of index j; s j is the comprehensive score of sample i.

[0064] In this embodiment, the final formula for the comprehensive score of each region based on regional factors and each market entity of the same market entity type based on enterprise factors is as follows: In the formula, s n 2 is the comprehensive score of the nth region based on the multi-region factor indicators, m 2 is the number of regional factor indicators, m 2 =3, where i=1 represents resource endowment, i=2 represents power structure, and i=3 represents economic level.

[0065] y k.i is the data of the i-th regional factor index in the k-th region; y i is the data of the i-th regional factor index. Similarly, y n.i is the data of the factor index of the ith region in the nth region; y k.l is the data of the factor index of the lth region in the kth region; y l is the data of the lth regional factor indicator.

[0066] s 3 n.x.j is the comprehensive score of the jth market entity in the xth category of the nth region based on multiple enterprise factor indicators, m 3 is the number of enterprise factor indicators, m 3 =2, where t=1 is the proportion of green electricity and t=2 is profitability.

[0067] y j.t n.x is the data of the t-th enterprise factor indicator of the j-th market entity in the x-th category of the n-th region;t n.x is the data of the t-th enterprise factor index of the x-th market entity in the n-th region. Similarly, y k.t n.x is the data of the t-th enterprise factor indicator of the k-th market entity of the x-th category in the n-th region; k.l n.x is the data of the lth enterprise factor index of the kth market entity of the xth category in the nth region; l n.x It is the data of the lth enterprise factor indicator of the xth type of market entity in the nth region.

[0068] Constraints: Constraints include total quantity constraints and upper and lower limit constraints.

[0069] The total renewable energy consumption responsibility of each region is equal to the renewable energy power of the province: The total renewable energy consumption responsibility of each market entity of the same type in the same region is equal to the renewable energy electricity of each market entity of the same type in the region: The objective consumption responsibility weight of each market player has upper and lower limits: τ 1 .r n.x.j ≥ω n.x.j ≥τ 2 .r n.x.j .

[0070] In the formula, τ 1 , τ 2 They are respectively the upper and lower limit ratios of the objective absorption responsibility weights of each market player.

[0071] Step S105: Calculate the objective renewable energy consumption responsibility weight: Based on the quantitative models of policy factors, regional factors and enterprise factors, the entropy weight method is used to solve the objective consumption responsibility weights of each market entity in each region of the province. The final formula is as follows: ω 2.n =s n 2 .β , ω 3.n.x.j = s 3 n.x.j .γ ω n.x.j =ω 1 .ω 2.n .ω 3.n.x.j In the formula, ω 2.n is the objective renewable energy consumption responsibility weight ratio of the nth region based on regional factors; ω 3.n.x.jis the objective renewable energy consumption responsibility weight ratio of the jth market entity in the xth category in the nth region based on enterprise factors; ω n.x.j is the objective renewable energy consumption responsibility weight of the j-th market entity of the n-th category considering multiple factors; β and γ are the consumption responsibility weight constraint coefficients.

[0072] In this example, the minimum renewable energy power consumption responsibility weight index of a province issued by the National Energy Administration is used as the government factor, and the average wind speed (m / s) and annual average horizontal irradiation (kWh / m 2 ), wind power installed capacity (MW), and photovoltaic installed capacity (MW) are regional factors, and the market entity type, green electricity share, and profitability of each market entity in each region of the province are corporate factors. Then, the objective power consumption responsibility weights of different market entities in different regions of the province are obtained through the entropy weight method. Figure 2 is the objective power consumption responsibility weight of a certain type of market entity in a certain area of ​​the province. In this embodiment, the market entity type is power sales companies, with a total of 24 market entities, among which the upper and lower limit ratios of the objective power consumption responsibility weights of each market entity are τ 1 , τ 2 Taking 0.95 and 1.05, the regional factor consumption responsibility weight constraint coefficient β is calculated to be 1.1319, and the regional factor consumption responsibility weight constraint coefficient γ is calculated to be 0.9865. In the figure, the horizontal axis is the number of market entities, and the left side of the vertical axis is the "green electricity consumption ratio r n.x.j " and "objective consumption responsibility weight ω n.x.j "; To the right of the vertical axis is the "change ratio". Figure 2 The entropy weight method, which can objectively reflect the importance and differentiation of each indicator, is used to determine the objective absorption responsibility weight of each market player, which can reflect the objective impact of objective policy, regional and enterprise factors on the absorption responsibility weight indicators of each market player.

[0073] Step S2: Establish an upper-level optimization objective function that takes the renewable energy consumption responsibility of each market entity as the control variable and maximizes the total profit of each market entity.

[0074] The profits of different market players are affected by many factors, including the player's own electricity consumption, the proportion of green electricity, profitability, and the weight of consumption responsibility. At the same time, there are certain differences in these factors among all market players.

[0075] In this example, an objective function based on the profit maximization of market entities is established, in which various factors affecting the profits of market entities are taken as part of the objective function, and the consumption responsibility of each market entity is taken as an unknown quantity and solved using the entropy weight method and PSO algorithm. Based on the objective function of maximizing the profit of market entities, the entropy weight method and PSO algorithm are used to obtain the consumption responsibility of each market entity, which can include: The objective function is established by taking the maximum profit of all market players as the optimization goal. The objective function can be expressed as: .

[0076] In the formula, C total is the total profit of each market player; N is the number of regions in a province; X is the number of market player types. In this embodiment, the number of market player types X=4, where 1 represents a power grid company, 2 represents an independent power sales company, 3 represents a power user who purchases electricity through the power wholesale market, and 4 represents an enterprise with a self-owned power plant.

[0077] N n.x is the number of market entities of the xth category in the nth region; a n.x.j Q is the revenue per unit of electricity sales / consumption of the jth market entity in the nth region of the province (yuan / MWh); n.x.j is the electricity consumption of each market player (kWh); b n.x.j is the incentive electricity price (yuan / kWh); M n.x.j.1 is the amount of electricity (kWh) higher than the consumption responsibility; c n.x.j is the penalty electricity price (yuan / kWh); M n.x.j.2 The amount of electricity (kWh) lower than the consumption responsibility; d n.x.j is the cost of purchasing and consuming green electricity by self-generated units (yuan / kWh); n.x.j It refers to the consumption amount of market entities (kWh).

[0078] Among them, M n.x.j.1 =max(0, q n.x.j ‒M n.x.j ), M n.x.j.2 =max(0, M n.x.j ‒ q n.x.j ), d n.x.j =D / D n.x.j .

[0079] M n.x.j is the renewable energy consumption responsibility (kWh); D is the basic consumption electricity price (yuan / kWh); D n.x.j It is a comprehensive score that takes into account the market entity’s electricity consumption, renewable energy consumption, electricity consumption responsibility and profitability.

[0080] At the same time, considering that the electricity price of each market player is the same, the profit C of all market players is total Maximization is equivalent to minimizing the expenditure function C of each market entity, as shown below: .

[0081] Constraints include upper and lower limit constraints and total quantity constraints.

[0082] The weight of each market player's renewable energy consumption responsibility M n.x.j / Q n.x.j There are upper and lower limits: ω n.x.j .η 1 ≥M n.x.j / Q n.x.j ≥ω n.x.j .η 2 The total amount of renewable energy consumption responsibility of market players in each region is equal to the total renewable energy electricity: Where η 1 , η 2 are the upper and lower limits of the consumption responsibility weights of each market entity, Q new.n.x.j The amount of electricity sold / used by each market player is renewable energy.

[0083] The entropy weight method and PSO algorithm are used to solve the consumption responsibility of each market player.

[0084] The solution process of the PSO algorithm is as follows: First, initialize: randomly generate the position and velocity of particles in the D dimension of the problem space.

[0085] Secondly, evaluate the particles: for each particle, evaluate the applicability of the D-dimensional optimization function.

[0086] Then update the optimal: a. Compare the particle's fitness value with its individual optimal value pbest. If it is better than pbest, set its pbest position to the current particle position. b. Compare the particle's fitness value with the group's optimal value gbest. If the current value is better than gbest, set the gbest position to the current particle position.

[0087] Then update the particles: change the velocity and position of the particles.

[0088] Final stopping condition: loop back to the evaluation particle step until the termination condition is met, usually the applicable value and the maximum iteration number t are met.

[0089] The PSO algorithm is modeled and solved iteratively on the MATLAB platform, and the number of iterations is t.

[0090] The speed and position formulas of each particle are as follows: In the formula, v i k is the speed of the i-th particle at the k-th iteration; i=1,2,…,I, I is the total number of particles; k=1,2,…,t, t is the number of iterations; w is the inertia weight; c 1 is the individual learning factor; rand is a random number between (0,1); pbest i k is the personal best position; x i k is the position of the i-th particle at the k-th iteration; c 2 is the social learning factor; gbest i k is the global best position.

[0091] The formulas for calculating the personal best position and the global best position are as follows: In the formula, m(x i k ) is the objective function to be optimized.

[0092] Step S3: Establish a lower-level objective function that takes the renewable energy consumption of each market entity as the control variable and minimizes the total consumption reward and punishment costs of each market entity.

[0093] The upper function is from the perspective of market entities, and the lower function is from the perspective of the government. The government hopes that the consumption responsibility weights of each entity are reasonable, and on the basis of reasonable weights, it also hopes that each entity fulfills this weight. Therefore, it hopes that the consumption responsibility weights and the actual consumption volume of users are as small as possible, that is, the sum of the absolute value of rewards and the absolute value of penalties is minimized. If each user increases the actual consumption volume due to the improvement of their own profitability, they will also be rewarded.

[0094] The reward and punishment costs of different market players are mainly affected by the amount of electricity consumed, the weight of consumption responsibility and the reward and punishment electricity price, among which these factors vary among all market players.

[0095] In this example, an objective function based on minimizing the total consumption reward and punishment fees of market entities is established, in which various factors affecting the reward and punishment fees of market entities are taken as part of the objective function, and the consumption of each market entity is taken as an unknown quantity and solved using the PSO algorithm. Based on the objective function of minimizing the total consumption reward and punishment fees of market entities, the PSO algorithm is used to obtain the consumption of each market entity, which may include: The objective function is established by minimizing the total consumption reward and punishment fees of all market players as the optimization goal. The objective function can be expressed as: .

[0096] Constraints include upper and lower limit constraints and total quantity constraints.

[0097] The proportion of renewable energy consumption by each market player in electricity sales / consumption q n.x.j / Q n.x.j There are upper and lower limits: ω n.x.j .ϕ 1 ≥q n.x.j / Q n.x.j ≥ω n.x.j .ϕ 2 .

[0098] The total amount of renewable energy consumed by market players in each region is equal to the total renewable energy electricity: Where, 1 , ϕ 2 They are respectively the upper and lower limit ratios of electricity consumption of each market player.

[0099] The PSO algorithm is used to calculate the consumption capacity of each market player that meets the upper and lower limits and total amount constraints of renewable energy consumption.

[0100] Step S4: Based on the two-layer optimization model established in steps S2 and S3, iteratively solve the optimal renewable energy consumption responsibility and consumption of each market player, and further obtain the renewable energy consumption responsibility weight and consumption ratio.

[0101] The absorption responsibility and absorption volume of different market players will affect the total profit and total reward and punishment costs of all market players. Moreover, the absorption responsibility and absorption volume of each market player serve as input and output of each other, and ultimately an ideal absorption responsibility weight needs to be obtained.

[0102] In this embodiment, two objective functions having a coupling relationship are respectively used as an upper layer function and a lower layer function and are solved iteratively, and finally the optimal upper and lower layer objective function values ​​are solved.

[0103] Based on the two-layer optimization model, the optimal renewable energy consumption responsibility weight and consumption ratio of each market player can be solved iteratively, which can include: 1) The upper and lower models of two objective functions with a coupled relationship are continuously iterated to solve the renewable energy consumption responsibility and consumption.

[0104] The purpose of considering establishing a two-layer model is to consider the economic and environmental goals of market entities and the government respectively, but there is a coupling relationship between the two constructed objective function models. Therefore, this embodiment uses a two-layer model that originated in 1950 and aims to solve the problem between two decision makers. The problem of allocating the consumption responsibility targets of each market entity is two-level and can be regarded as a leader-follower game. The leader is the provincial government, which needs to evaluate the profits of each market entity by adjusting the renewable energy consumption responsibility. The followers are each market entity, and each market entity needs to evaluate the reward and punishment costs of the consumption responsibility weight policy by adjusting its renewable energy consumption. This two-level decision-making process is not independent, because the superior decision will affect the realization of the subordinate goal, and the subordinate decision will affect the realization of the superior goal. Therefore, the most ideal renewable energy consumption responsibility weight and consumption ratio are mainly based on the renewable energy consumption responsibility and consumption amount solved by the upper and lower models of continuous iteration. The two-layer model is modeled and solved iteratively under the MATLAB platform, and the iteration algebra is T.

[0105] 2) Solve the optimal combination of the objective function of the upper layer optimization and the objective function of the lower layer optimization.

[0106] The two-layer optimization model requires a large number of iterations. If the iterations finally converge, the optimal combination of the objective function of the upper layer optimization and the objective function of the lower layer optimization is the final converged objective function value.

[0107] If the iteration cannot converge at the end, in order to find the most ideal combination of upper and lower layers, the Pareto optimal solution can be considered. Its definition is that without making a certain goal worse, it is impossible to further optimize the solution of other goals. Correspondingly, the Pareto Front represents the set of solutions that can be achieved when there are no other solutions that can improve both goals at the same time. At the same time, if the main purpose is to obtain the most ideal renewable energy consumption responsibility weight indicators of each market entity, it is possible to consider giving priority to satisfying the minimum value of the upper function; however, in order to make the values ​​of the upper and lower objective functions as close as possible to the optimal values ​​of the most ideal upper and lower functions, it is considered to select the solution with the smallest Euclidean distance between the two objective function values ​​and the ideal point, so as to find the optimal combination of an upper-level optimized objective function and a lower-level optimized objective function.

[0108] In this embodiment, taking the data of all market entities of a certain type in a certain region of a province as an example, through the successive iteration of the upper and lower layer functions, the upper and lower limit ratios of the consumption responsibility weights of each market entity are η 1 , η 2 The upper and lower limits of the power consumption ratio of each market player are 0.9 and 1.1 respectively. 1 , ϕ 2 The basic electricity price D is 1 yuan / kWh, and the reward electricity price b is n.x.j and penalty electricity price c n.x.j Take 0.5 yuan / kWh, the inertia weight w of PSO is 0.7, and the personal learning coefficient c 1 Take 1.5, the global learning coefficient c 2 Take 1.5, the PSO iteration number t is 5, and the two-layer optimization model iteration number T is 5000.

[0109] Figure 3 The function value graph of the upper and lower objective functions for each market entity in a certain region of a certain province for 5000 iterations, i.e., the iteration value, Figure 4 This is the Pareto solution diagram for 5000 iterations of each market player in a certain region of a province.

[0110] Figure 3 , 4 The result shows that during the iteration process, the ideal consumption responsibility weights of various market players did not converge in the end. The selection of Pareto frontier points provides a series of balance points for the optimization process, which prevents the algorithm from falling into local oscillations or being unable to iterate. At the same time, further selecting the point with the minimum Euclidean distance among the Pareto frontier points as the optimal combination of the upper-level optimization objective function and the lower-level optimization objective function can also balance the optimization results of the upper and lower-level objective functions to a certain extent, so that the interests of both reach the optimal combination and avoid the deviation caused by single-objective optimization.

[0111] In order to evaluate the rationality and fairness of the required consumption responsibility weight allocation scheme, this embodiment also proposes step S5: based on the Theil index, a fairness evaluation model for renewable energy consumption responsibility weight allocation is established to verify the fairness of the consumption responsibility weight allocation schemes of various market entities.

[0112] In this embodiment, the Theil index of the initial green electricity consumption ratio allocation plan, the objective consumption responsibility weight allocation plan and the final consumption responsibility weight allocation plan are calculated and compared. Based on the Theil index, a fairness evaluation model for renewable energy consumption responsibility weight allocation is established to verify the fairness of the consumption responsibility weight allocation plans of various market entities, which may include: 1) Establish a fairness evaluation model for the distribution of renewable energy consumption responsibility weights based on the Theil index.

[0113] Theil index is an indicator for evaluating fairness proposed by Dutch economist Theil using the concept of entropy in information theory. It can be regarded as a comprehensive benchmark for inequality. It can also be used as an indicator to reflect regional income disparity. The larger the value, the greater the income disparity between regions. Therefore, a Theil index formula for evaluating the fairness of distribution is proposed with reference to it, where the smaller the Theil index value, the higher the fairness of distribution.

[0114] .

[0115] Where TI is the Theil index; X is the number of market players; r x is the consumption responsibility weight of market entity x; r ‒ is the average value of the consumption responsibility weights.

[0116] 2) Compare the fairness evaluation models of power consumption responsibility weight allocation under different schemes, and verify the fairness of the power consumption responsibility weight allocation scheme from the provincial level to each market player.

[0117] The Theil index of the initial green electricity consumption ratio allocation plan, the objective consumption responsibility weight allocation plan and the final consumption responsibility weight allocation plan is calculated, and the fairness evaluation models of renewable energy consumption responsibility weight allocation of different plans are compared to verify the fairness of the consumption responsibility weight allocation plans from the provincial level to each market entity.

[0118] In this embodiment, taking the data of all market entities in a certain region of a province as an example, the upper and lower functions are iterated in sequence, and the most ideal power consumption responsibility weights of the upper and lower objective functions are obtained, where the Theil index of the initial green electricity consumption ratio allocation scheme is 0.1775, the Theil index of the objective consumption responsibility weight allocation scheme is 0.1677, and the Theil index of the final consumption responsibility weight allocation scheme is 0.1637. Therefore, whether it is the objective consumption responsibility weight or the final consumption responsibility weight, the fairness of the allocation is improved. At the same time, the Theil index of the final consumption responsibility weight allocation scheme is the lowest, indicating that its scheme is better than other allocation schemes. Therefore, it shows that the scheme of the consumption responsibility weight allocation method based on double-layer optimization proposed in this paper has certain feasibility.

[0119] Figure 5 The final power consumption responsibility weight diagram of different types of market entities in a certain region of a province is Figure 2 It is further optimized based on the objective renewable energy consumption responsibility weights, taking into account the objective influence of policy, regional and enterprise factors, while balancing the economic and environmental goals of market players and the government to adapt to the specific conditions of different regions and market players.

[0120] The present invention also proposes an embodiment of a renewable energy consumption responsibility weight allocation optimization system considering multiple factors, including: The objective renewable energy power consumption responsibility weight calculation module takes into account policy factors, regional factors and enterprise factors, and uses the entropy weight method to calculate the objective renewable energy power consumption responsibility weight of each market player.

[0121] The upper-level optimization objective function establishment module establishes the upper-level optimization objective function with the renewable energy consumption responsibility of each market entity as the control variable and the total profit maximization of each market entity.

[0122] The lower-level objective function establishment module establishes a lower-level objective function that takes the renewable energy consumption of each market entity as the control variable and minimizes the total consumption reward and punishment fees of each market entity.

[0123] The iterative module solves the optimal renewable energy consumption responsibility and consumption of each market player based on the model established by the two objective functions, and further obtains the renewable energy consumption responsibility weight and consumption ratio.

[0124] The evaluation module establishes a fairness evaluation model for the distribution of renewable energy consumption responsibility weights based on the Theil index to verify the fairness of the consumption responsibility weight distribution plans of various market players.

[0125] The present invention also proposes an embodiment of a device for optimizing the allocation of renewable energy consumption responsibility weights taking into account multiple factors, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of a method for optimizing the allocation of renewable energy consumption responsibility weights taking into account multiple factors are implemented.

Claims

1. A multi-factor renewable energy consumption responsibility weight allocation optimization method, characterized in that: include: Step S1: Considering policy factors, regional factors and enterprise factors, the objective renewable energy power consumption responsibility weight of each market player is obtained; Step S2: Establishing an upper optimization objective function that takes the renewable energy consumption responsibility of each market entity as a control variable and maximizes the total profit of each market entity; Step S3: Establishing a lower-level objective function that takes the renewable energy consumption of each market entity as the control variable and minimizes the total consumption reward and penalty fees of each market entity; Step S4: Based on the two-layer optimization model established in steps S2 and S3, iteratively solve the optimal renewable energy consumption responsibility and consumption of each market player, and further obtain the renewable energy consumption responsibility weight and consumption ratio.

2. The optimization method according to claim 1, characterized in that: In step S1, the entropy weight method is used to obtain the objective renewable energy power consumption responsibility weight of each market entity.

3. The optimization method according to claim 1, characterized in that: In step S2, the upper layer optimization objective function is: Where, C total is the total profit of each market player; N is the number of regions in a province; X is the number of market player types, N n.x is the number of market entities of the xth category in the nth region; a n.x.j Q is the unit electricity sales / consumption income of the jth market entity in the nth region of the province; n.x.j The electricity consumption of each market player; n.x.j is the incentive electricity price; M n.x.j.1 The amount of electricity that is higher than the consumption responsibility; c n.x.j To punish electricity prices; M n.x.j.2 The amount of electricity that is lower than the consumption responsibility; d n.x.j The cost of purchasing and consuming green electricity by self-generated units; q n.x.j The amount of renewable energy consumed by market entities; Where: M n.x.j.1 =max(0, q n.x.j ‒M n.x.j ), M n.x.j.2 =max(0, M n.x.j ‒q n.x.j ) Where M n.x.j Responsible for absorbing renewable energy; The constraints are: The weight of each market player's renewable energy consumption responsibility M n.x.j / Q n.x.j There are upper and lower limits: oh n.x.j .η1≥M n.x.j / Q n.x.j ≥ω n.x.j .η2 The total amount of renewable energy consumption responsibility of market players in each region is equal to the total renewable energy electricity: In the formula, ω n.x.j is the objective renewable energy consumption responsibility weight of the jth market entity of the nth category considering multiple factors; η1 and η2 are the upper and lower limit ratios of the consumption responsibility weight of each market entity respectively; Q new.n.x.j The amount of electricity sold / used by each market player is renewable energy.

4. The optimization method according to claim 3, characterized in that: In step S3, the lower layer objective function is: In the formula, f total is the total reward and punishment costs of each market player; The constraints are: Renewable energy consumption ratio of each market playerq n.x.j / Q n.x.j There are upper and lower limits: oh n.x.j .ϕ1≥q n.x.j / Q n.x.j ≥ω n.x.j .ϕ2 The total amount of renewable energy consumed by market players in each region is equal to the total renewable energy electricity: In the formula, ϕ1 and ϕ2 are the upper and lower limit ratios of electricity consumption of each market player, respectively.

5. The optimization method according to claim 2, characterized in that: Step S1 includes: Step S101: Determine policy factors: Determine policy factor ω1 based on the minimum renewable energy power consumption responsibility weight index of the province. Step S102: Determine regional factors: Regional factors include resource endowment, power structure and economic level of each region. The calculation formula is as follows: Quantitative indicators of resource endowment: Quantitative indicators of power supply structure: Quantitative indicators of economic level: Where N is the number of regions divided into a province; H new is the average renewable energy utilization hours in the province; H new.n is the average renewable energy utilization hours in the nth region; G new is the installed capacity of renewable energy in the province; G new.n is the installed capacity of renewable energy in the nth region; Y is the GDP of the province for one year; Y n is the GDP of the nth region in one year; Step S103: Determine enterprise factors: Enterprise factors include the types of market players in the region, the proportion of green electricity consumption and profitability. The calculation formula is as follows: Market entity type: Green electricity consumption share: Profitability: In the formula, Q n is the electricity sales / consumption in the nth region; N n.x Q is the number of market entities of the xth category in the nth region; n.x.j is the electricity sales / consumption of the jth market entity of the xth category in the nth region; r n.x is the proportion of green electricity consumption of the xth type of market entity in the nth region; r n.x.j Q is the proportion of green electricity consumption of the jth market entity in the xth category in the nth region; n.x is the electricity sales / consumption of the xth type of market entity in the nth region; R n.x is the profit margin of the xth type of market player in the nth region; R n.x.j is the profit margin of the jth market player in the xth category in the nth region; Step S104: Using the entropy weight method, considering the wind and solar energy resource endowment, power structure and economic level of each region, the comprehensive score s of each region considering regional factor indicators is obtained. n 2 , Using the entropy weight method, the green electricity consumption proportion and profitability of each market entity are considered to obtain the comprehensive score of each market entity of the same market entity type in the region considering the enterprise factor index. 3 n.x.j ; Step S105: Calculate the objective renewable energy consumption responsibility weight: oh 2.n =s n 2 .b,oh 3.n.x.j =s 3 n.x.j .c oh n.x.j =ω1.ω 2.n .oh 3.n.x.j In the formula, ω 2.n is the objective renewable energy consumption responsibility weight ratio of the nth region based on regional factors; ω 3.n.x.j is the objective renewable energy consumption responsibility weight ratio of the jth market entity in the xth category in the nth region based on enterprise factors; ω n.x.j is the objective renewable energy consumption responsibility weight of the j-th market entity of the n-th category considering multiple factors; β and γ are the consumption responsibility weight constraint coefficients using the entropy weight method.

6. The optimization method according to claim 5, characterized in that: In step S104, the calculation formula of the comprehensive score is as follows: In the formula, m2 is the number of regional factor indicators, m2=3, where i=1 is resource endowment, i=2 is power structure, and i=3 is economic level; y k.i is the data of the i-th regional factor index in the k-th region; y i is the data of the i-th regional factor indicator; m3 is the number of enterprise factor indicators, m3=2, where t=1 is the proportion of green electricity and t=2 is profitability; y j.t n.x is the data of the t-th enterprise factor indicator of the j-th market entity in the x-th category of the n-th region; t n.x The data of the t-th enterprise factor indicator of the x-th market entity in the n-th region; Constraints: The total renewable energy consumption responsibility of each region is equal to the renewable energy power of the province: The total renewable energy consumption responsibility of each market entity of the same type in the same region is equal to the renewable energy electricity of each market entity of the same type in the region: The objective consumption responsibility weight of each market player has upper and lower limits; τ1.r n.x.j ≥ω n.x.j ≥τ2.r n.x.j In the formula, τ1 and τ2 are the upper and lower limit ratios of the objective consumption responsibility weights of each market player, respectively.

7. The optimization method according to any one of claims 1 to 6, characterized in that: The number of market entity types X=4, 1 represents a power grid enterprise, 2 represents an independent power sales company, 3 represents a power user who purchases electricity through the power wholesale market, and 4 represents an enterprise with a self-owned power plant.

8. The optimization method according to claim 1, characterized in that: Also includes: Step S5: Based on the Theil index, a fairness evaluation model for the allocation of renewable energy consumption responsibility weights is established to verify the fairness of the consumption responsibility weight allocation plans of various market players.

9. A renewable energy consumption responsibility weight allocation optimization system considering multiple factors, characterized in that: include: The objective renewable energy power consumption responsibility weight calculation module considers policy factors, regional factors and enterprise factors, and uses the entropy weight method to calculate the objective renewable energy power consumption responsibility weight of each market entity; The upper optimization objective function establishment module establishes the upper optimization objective function with the renewable energy consumption responsibility of each market entity as the control variable and the total profit maximization of each market entity; The lower objective function establishment module establishes the lower objective function with the renewable energy consumption of each market entity as the control variable and the total consumption reward and punishment fee of each market entity minimized; The iterative module solves the optimal renewable energy consumption responsibility and consumption of each market player based on the model established by the two objective functions, and further obtains the renewable energy consumption responsibility weight and consumption ratio; The evaluation module establishes a fairness evaluation model for the distribution of renewable energy consumption responsibility weights based on the Theil index to verify the fairness of the consumption responsibility weight distribution plans of various market players.

10. A device for optimizing the weight allocation of renewable energy consumption responsibility considering multiple factors, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 8 are implemented.

Images