New energy and charging and storage collaborative optimization method for high-proportion green energy supply and consumption in rural area

Abstract

The invention discloses a rural area new energy and charging and storage collaborative optimization method for high-proportion green energy supply and consumption, and the method comprises the following steps: S1, determining and collecting the demand and benefit differences of different subjects in the area, and indexing the demand and benefit differences, the subjects including a power grid operator, a new energy power generation subject, an energy storage subject and an electric vehicle charging pile subject; s2, establishing an economic model of each subject, calculating an economic index of each subject, analyzing an economic interaction relationship among the subjects, and providing a basis for economic strategy optimization; s3, based on the index system and the weight thereof, establishing a comprehensive economic model, and calculating a comprehensive evaluation score; s4, adjusting each subject operation strategy according to the economic model, screening an optimal cooperation scheme based on the comprehensive economic model, and continuously detecting and adjusting; according to the invention, reliable, stable, safe and economic power supply and consumption in multi-subject high-proportion green energy supply and consumption rural areas can be ensured, the new energy power generation delivery cost is reduced, and the local consumption of new energy is promoted.

Description

Technical Field

[0001] This invention belongs to the field of coordinated development technology of new energy and charging and storage in rural areas, and specifically relates to a method for optimizing the coordinated development of new energy and charging and storage in rural areas with a high proportion of green energy supply and consumption. Background Technology

[0003] In recent years, all provinces in the six major regions of the company's operating area have been promoting the construction of new power systems with new energy as the main source. New energy has developed rapidly, and in rural areas with low load density, new energy has become the main source of electricity. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of the prior art and provide a method for the coordinated optimization of new energy and charging and storage in rural areas with a high proportion of green energy supply and consumption. This method can ensure a reliable, stable, safe and economical power supply and consumption in rural areas with a high proportion of green energy supply and consumption by multiple entities, while reducing the cost of transmitting new energy power generation to other areas and promoting the local consumption of new energy.

[0005] The objective of this invention is achieved as follows: a method for the coordinated optimization of new energy sources and energy storage in rural areas with a high proportion of green energy supply and utilization, comprising the following steps:

[0006] S1, identify and collect the differences in needs and interests of different entities in the region, and index them, including grid operators, new energy power generation entities, energy storage entities, and electric vehicle charging pile entities;

[0007] S2, establish economic models for each entity, calculate economic indicators for each entity, analyze the economic interaction relationships between the entities, and provide a basis for optimizing economic strategies;

[0008] S3, based on the indicator system and its weights, establishes a comprehensive economic model and calculates the comprehensive evaluation score;

[0009] S4 adjusts the operational strategies of each entity based on the economic model, selects the optimal collaborative solution based on the comprehensive economic model, and continuously monitors and adjusts it.

[0010] Preferably, step S1 is as follows:

[0011] The specific indicators for the power grid operator include power grid stability indicators and economic benefit indicators, among which the power grid stability indicator is measured by voltage fluctuation range V. fluctuation and frequency stability F stability To measure this, the economic benefit indicator is electricity sales revenue R. grid Subtract operating costs C grid Profit grid =R grid -C grid ;

[0012] The specific indicators for the main body of new energy power generation include power generation indicators, power generation revenue indicators, and power generation stability indicators, among which the power generation indicator is represented by P. new Among them, the power generation revenue indicator is the electricity sales revenue R. new Subtract equipment investment cost C inv,new and maintenance costs C maintain,new The sum of, i.e., Profit new =R new -C inv,new -C maintain,new The power generation stability index is measured by the degree of fluctuation in power output σ. Pnew To measure;

[0013] The specific indicators of the energy storage entity include charge / discharge efficiency, return on investment, and response speed, where charge / discharge efficiency is expressed as η. storage The return on investment indicator is the energy storage revenue R. storage With investment cost C storage The ratio, that is The response speed index is represented by the response time T. response ;

[0014] The specific indicators for the electric vehicle charging pile include charging service satisfaction, charging price reasonableness, charging pile utilization rate, and charging profit. The charging service satisfaction indicator is obtained through a survey, yielding a satisfaction value S. charger The reasonableness index of charging price is determined by comparison with other charging facilities in the market, and the utilization rate index of charging pile is the number of times the charging pile is used within a certain period of time, N. use With total time T total The proportion, i.e. The charging profit indicator is the charging revenue R. charger Subtract operating costs C charger Profit charger =R charger -C charger .

[0015] Preferably, step S2 is as follows:

[0016] The economic model for the power grid operator is as follows:

[0017] Objective function:

[0018] Maximize(Profit grid =Maximize(R) grid -C grid +k1P new ); where k1 is the weighting coefficient for new energy absorption capacity, Pnew This refers to the amount of new energy consumed;

[0019] Constraints: V fluctuation,min ≤V fluctuation ≤V fluctuation,max ;

[0020] F stability,min ≤F stability ≤F stablity,max ;

[0021] The economic model for the main source of new energy power generation is as follows:

[0022] Objective function:

[0023] Maximize(Profit new =Maximize(R) new -C inv,new -C maintain,new +k2σ Pnew ); where k2 is the power generation stability weighting coefficient;

[0024] Constraints: P new,min ≤P new ≤P new,max ;

[0025] The economic model for the energy storage entity is as follows:

[0026] Objective function: Where k3 is the response speed weighting coefficient;

[0027] Constraints: η storage,min ≤η storage ≤η new,storage ;

[0028] The economic model for the main body of the electric vehicle charging station is as follows:

[0029] Objective function: Maximize(Profit) charger =Maximize(R) charger -C charger +

[0030] k4Utilization charger ); where k4 is the weighting coefficient for charging pile utilization rate;

[0031] Constraints: P rate,min ≤P rate ≤P rate,max ;

[0032] S charger,min ≤S charger ≤S charger,max ;where Prate The charging price for electric vehicle charging stations.

[0033] Preferably, step S3 is as follows:

[0034] S31, Determine the indicator system:

[0035] Economic indicators: These are the economic indicators for each entity, including Profit. grid Profit new Return storage and Profit charger ;

[0036] Environmental indicators: including emission reductions from renewable energy power generation and environmental impact assessment values ​​for energy storage equipment, with renewable energy power generation emission reduction E env1 =P new ×k env , where k env E represents the emission reduction factor and the environmental impact assessment value of energy storage equipment. env2 The value range is [0,1], determined by expert scoring.

[0037] Social indicators: including rural energy supply stability and charging service satisfaction, and rural energy supply stability. Where N is the number of power outages, and T is the duration of the power outage. max and T max The preset maximum number of power outages and longest power outage duration; charging service satisfaction E soc2 Satisfaction scores were obtained through user surveys, with a range of [0,1].

[0038] S32, Determine the weights of each indicator in the indicator system:

[0039] The weights ω of economic indicators are determined using the analytic hierarchy process. econ Environmental indicator weights ω env Social indicator weight ω soc , and ω econ +ω env +ω soc =1;

[0040] In economic indicators, the economic benefit weight ω of power grid operators is determined. grid ω, the main revenue weight of new energy power generation new Weight of return on investment for energy storage entities ω storage Profit weight ω of electric vehicle charging pile main body charger In environmental indicators, the weight ω for emission reduction from renewable energy power generation is determined. env1 Environmental impact assessment weight ω for energy storage equipment env2 In the social indicators, the weight ω for the stability of rural energy supply is determined.soc1 And the weight ω of charging service satisfaction soc2 ;

[0041] S33, Comprehensive evaluation score calculation, the calculation formula is as follows:

[0042]

[0043] Preferably, step S4 is as follows:

[0044] S41, Operational strategies of each entity: Grid operators adopt the method of reasonably arranging the discharge of new energy power generation and energy storage equipment during peak electricity consumption periods; new energy power generation entities adopt the method of improving power generation efficiency; energy storage entities adopt the method of arranging charging and discharging times according to the peak and valley periods of local electricity prices; and electric vehicle charging pile entities adopt the method of setting reasonable charging prices.

[0045] S42, calculate the comprehensive evaluation score of different collaborative schemes, and select the scheme with the highest comprehensive score as the optimal scheme;

[0046] S43 continuously monitors changes in various indicators during actual operation and adjusts the coordination plan in a timely manner based on feedback to ensure the effectiveness and sustainability of the plan.

[0047] Due to the adoption of the above technical solution, the beneficial effects of the present invention are:

[0048] (1) This invention adopts the study of the behavior patterns and decision-making processes of different subjects, establishes a corresponding economic model based on the differences in the needs and interests of different subjects, and considers the interaction and interest game between different subjects to accurately reflect the multi-subject economy in rural areas, establishes a comprehensive evaluation model, and then comprehensively considers economic, environmental and social factors to formulate a suitable indicator system and a synergistic optimization and trade-off method for new energy and charging and storage in rural areas, so as to ensure that the high proportion of green energy supply and consumption by multiple subjects in rural areas is reliable, stable, safe and economical, while reducing the cost of new energy power generation and transmission and promoting the local consumption of new energy.

[0049] (2) In terms of economic benefits, this invention proposes a high-proportion green energy supply and consumption optimization scheme for new energy and charging / storage in rural areas. This scheme can realize coordinated planning of new energy and charging / storage in rural areas, multi-entity economic analysis, reduce investment and operation costs of new energy and charging / storage facilities in rural areas, improve system operation efficiency and economy, and increase the economic benefits of different investment and operation entities. It can realize coordinated planning of new energy and charging / storage in typical rural areas, innovate operation models, reduce rural investment costs, and increase the operating income of rural power grids. At the same time, it can provide technical guidance and reference for the planning, construction and operation of new energy and charging / storage facilities in different regions, improve planning and operation efficiency, effectively reduce investment costs, increase the operating income of power grid companies, and provide a high-proportion green power supply and consumption technology guarantee for the stable economic development of rural areas.

[0050] (3) In terms of social benefits, this invention will form a new solution for high-proportion green energy supply and use in rural areas, and promote the transformation of energy supply and use operation mode in rural areas. Attached Figure Description

[0051] Figure 1 This is a schematic diagram of the method flow of the present invention. Detailed Implementation

[0052] The technical solution of the present invention will be further described in detail below through embodiments and in conjunction with the accompanying drawings.

[0053] like Figure 1 As shown, this invention provides a method for the coordinated optimization of new energy sources and energy storage in rural areas with a high proportion of green energy supply and consumption, comprising the following steps:

[0054] S1 identifies and collects the differences in needs and interests among different entities in the region, such as power grid operators, new energy power generation entities, energy storage entities, and electric vehicle charging pile entities, and then quantifies these differences.

[0055] A thorough analysis of the different needs and interests of various stakeholders is needed to clarify the specific needs of grid operators, new energy power generation entities, energy storage entities, and electric vehicle charging pile entities, such as grid stability, power generation revenue, return on investment, and charging service satisfaction. The focus should also be on identifying the points of interest and conflicts of interest for each stakeholder, such as the conflict between the instability of new energy power generation and the need for grid stability.

[0056] S2 establishes economic models for each entity, calculates economic indicators for each entity, analyzes the economic interactions between entities, and provides a basis for optimizing economic strategies.

[0057] Calculate the economic indicators of each entity, such as the economic benefits of grid operators (electricity sales revenue minus operating costs), the revenue of new energy power generation entities (electricity sales revenue minus power generation costs), the return on investment of energy storage entities (the ratio of energy storage revenue to investment costs), and the profit of electric vehicle charging pile entities (charging revenue minus operating costs); analyze the economic interaction relationships among the entities to provide a basis for optimizing economic strategies.

[0058] S3, based on the indicator system and its weights, establishes a comprehensive economic model and calculates the comprehensive evaluation score.

[0059] An indicator system encompassing economic, environmental, and social dimensions is constructed. Economic indicators cover the economic benefits of each entity; environmental indicators include emission reductions from new energy power generation and environmental impact assessments of energy storage equipment; and social indicators include the stability of rural energy supply and satisfaction with charging services. The weights of each indicator are determined, and methods such as the analytic hierarchy process (AHP) are used to comprehensively consider the importance of the three dimensions. Each indicator is quantitatively scored, and a comprehensive evaluation score is calculated to compare the overall benefits of different collaborative solutions.

[0060] S4 adjusts the operational strategies of each entity based on the economic model, selects the optimal collaborative solution based on the comprehensive economic model, and continuously monitors and adjusts it.

[0061] The operational strategies of each entity are adjusted according to the economic model, such as pricing strategies, power generation and energy storage dispatch strategies, to maximize economic benefits; the optimal collaborative scheme in terms of economic, environmental and social benefits is selected based on the comprehensive evaluation model; during actual operation, changes in various indicators are continuously monitored, and the collaborative scheme is adjusted in a timely manner based on feedback to ensure the effectiveness and sustainability of the scheme.

[0062] Step S1 is as follows:

[0063] For power grid operators, the primary need is to ensure the stable operation of the power grid and meet user electricity demands; the benefit lies in generating revenue through sales outlets while simultaneously reducing operating costs and improving their own economic efficiency. Specific indicators include power grid stability indicators and economic efficiency indicators, with power grid stability indicators measured by voltage fluctuation range V. fluctuation and frequency stability F stability To measure this, the economic benefit indicator is electricity sales revenue R. grid Subtract operating costs C grid Profit grid =R grid -C grid .

[0064] For renewable energy power generation entities, the demand is to improve the utilization rate and power generation of renewable energy sources while reducing power generation costs to obtain a more favorable return. Specific indicators include power generation, power generation revenue, and power generation stability, with power generation denoted as P. new Among them, the power generation revenue indicator is the electricity sales revenue R. new Subtract equipment investment cost C inv,new and maintenance costs C maintain,new The sum of, i.e., Profit new =R new -C inv,new -C maintain,new The power generation stability index is measured by the degree of fluctuation in power output σ. Pnew To measure.

[0065] For energy storage providers, the demand is to store electricity when prices are low and release it when prices are high to profit from the price difference, while simultaneously improving the stability of the power system. Specific indicators include charge / discharge efficiency, return on investment, and response speed, with charge / discharge efficiency denoted as η. storage The return on investment indicator is the energy storage revenue R. storage With investment cost C storage The ratio, that is The response speed index is represented by the response time T. response .

[0066] For electric vehicle charging station operators, the demand is to provide convenient charging services for electric vehicles, meet users' travel needs, and generate reasonable revenue through charging fees. Specific indicators include charging service satisfaction, charging price reasonableness, charging station utilization rate, and charging profit. The charging service satisfaction indicator is obtained through a survey, yielding a satisfaction value S. charger The reasonableness index of charging price is determined by comparison with other charging facilities in the market, and the utilization rate index of charging pile is the number of times the charging pile is used within a certain period of time, N. use With total time T total The proportion, i.e. The charging profit indicator is the charging revenue R. charger Subtract operating costs C charger Profit charger =R charger -C charger .

[0067] Step S2 is as follows:

[0068] The economic model for the power grid operator is as follows:

[0069] Objective function:

[0070] Maximize(Profit grid =Maximize(R) grid -C grid +k1P new ); where k1 is the weighting coefficient for new energy absorption capacity, P new This refers to the amount of new energy consumed;

[0071] Constraints: V fluctuation,min ≤V fluctuation ≤V fluctuation,max ;

[0072] F stability,min ≤F stability ≤F stablity,max .

[0073] The economic model for the main source of new energy power generation is as follows:

[0074] Objective function:

[0075] Maximize(Profit new =Maximize(R) new -C inv,new -C maintain,new +k2σ Pnew ); where k2 is the power generation stability weighting coefficient;

[0076] Constraints: P new,min ≤P new ≤P new,max .

[0077] The economic model for the energy storage entity is as follows:

[0078] Objective function: Where k3 is the response speed weighting coefficient;

[0079] Constraints: η storage,min ≤η storage ≤η new,storage .

[0080] The economic model for the main body of the electric vehicle charging station is as follows:

[0081] Objective function: Maximize(Profit) charger =Maximize(R) charger -C charger +

[0082] k4Utilization charger ); where k4 is the weighting coefficient for charging pile utilization rate;

[0083] Constraints: P rate,min ≤P rate ≤P rate,max ;

[0084] S charger,min ≤S charger ≤S charger,max ;where P rate The charging price for electric vehicle charging stations.

[0085] Step S3 is as follows:

[0086] S31, Determine the indicator system:

[0087] Economic indicators: These are the economic indicators for each entity, including Profit. grid Profitnew Return storage and Profit charger .

[0088] Environmental indicators: including emission reductions from renewable energy power generation and environmental impact assessment values ​​for energy storage equipment, with renewable energy power generation emission reduction E env1 =P new ×k env , where k env E represents the emission reduction factor and the environmental impact assessment value of energy storage equipment. env2 The value range is [0,1], determined by expert scoring.

[0089] Social indicators: including rural energy supply stability and charging service satisfaction, and rural energy supply stability. Where N is the number of power outages, and T is the duration of the power outage. max and T max The preset maximum number of power outages and longest power outage duration; charging service satisfaction E soc2 Satisfaction values ​​are obtained through user surveys, with a range of [0,1].

[0090] S32, Determine the weights of each indicator in the indicator system:

[0091] The weights ω of economic indicators are determined using the analytic hierarchy process. econ Environmental indicator weights ω env Social indicator weight ω soc , and ω econ +ω env +ω soc =1.

[0092] In economic indicators, the economic benefit weight ω of power grid operators is determined. grid ω, the main revenue weight of new energy power generation new Weight of return on investment for energy storage entities ω storage Profit weight ω of electric vehicle charging pile main body charger In environmental indicators, the weight ω for emission reduction from renewable energy power generation is determined. env1 Environmental impact assessment weight ω for energy storage equipment env2 In the social indicators, the weight ω for the stability of rural energy supply is determined. soc1 And the weight ω of charging service satisfaction soc2 .

[0093] S33, Comprehensive evaluation score calculation, the calculation formula is as follows:

[0094]

[0095] Step S4 is as follows:

[0096] S41, Operational strategies of each entity: Grid operators adopt the method of reasonably arranging the discharge of new energy power generation and energy storage equipment during peak electricity consumption periods; new energy power generation entities adopt the method of improving power generation efficiency; energy storage entities adopt the method of arranging charging and discharging times according to the peak and valley periods of local electricity prices; and electric vehicle charging pile entities adopt the method of setting reasonable charging prices.

[0097] S42, calculate the comprehensive evaluation score of different collaborative schemes, and select the scheme with the highest comprehensive score as the optimal scheme;

[0098] S43 continuously monitors changes in various indicators during actual operation and adjusts the coordination plan in a timely manner based on feedback to ensure the effectiveness and sustainability of the plan.

[0099] The following is a specific data embodiment provided by the present invention.

[0100] Here is a specific example:

[0101] I. Set relevant data for each entity:

[0102] 1. Power grid operators:

[0103] Electricity sales revenue R grid = 5 million yuan; operating costs C grid =3 million yuan; weighting coefficient for new energy absorption capacity k1 = 0.2; assuming new energy absorption capacity P new = 1000 kilowatts.

[0104] 2. Main body of new energy power generation:

[0105] Electricity sales revenue R new =3 million yuan; Equipment investment C inv,new = 1.5 million yuan; maintenance cost C maintain,new = 500,000 yuan; power generation stability weighting coefficient k2 = 0.3; assumed fluctuation in power generation output.

[0106] 3. Energy storage unit:

[0107] Energy storage revenue R storage =800,000 yuan; Investment cost C storage = 1 million yuan; response speed weighting coefficient k3 = 0.2; assuming charge / discharge efficiency η storage =80%; Response time T response = 2 hours.

[0108] 4. Main body of electric vehicle charging station:

[0109] Charging revenue R charger = 1.2 million yuan; operating costs Ccharger =800,000 yuan; Charging pile utilization rate weighting coefficient k4 = 0.3; Assuming charging pile utilization rate Utilization charger =0.6; User satisfaction with charging services S obtained from the survey charger =0.8.

[0110] II. Economic Model Calculation:

[0111] 1. Economic model for power grid operators:

[0112] Profit grid =R grid -C grid +k1P new =500-300+0.2×1000=4 million yuan.

[0113] 2. Main economic model for new energy power generation:

[0114]

[0115] 3. Economic Model for Energy Storage Entities:

[0116]

[0117] 4. Main economic model for electric vehicle charging stations:

[0118] Profit charger =R charger -C charger +k4Utilization charger =120-80+0.3×0.6=401,800 yuan.

[0119] III. Comprehensive Evaluation Model Setting and Calculation:

[0120] 1. Setting of comprehensive evaluation model indicators:

[0121] Economic indicator weights ω econ =0.4; Economic benefit weight ω for power grid operators grid =0.3; Revenue weight ω of new energy power generation entities new =0.3; Weight ω of the return on investment for energy storage entities storage =0.2; Profit weight ω of electric vehicle charging pile main body charger =0.2;

[0122] Environmental indicator weights ω env =0.3; Weight ω for emission reduction from new energy power generation env1 =0.6; Environmental impact assessment weight ω for energy storage equipment env2 =0.4; Assuming the environmental impact assessment value E of the energy storage deviceenv2 =0.4;

[0123] Social indicator weight ω soc =0.3; Rural energy supply stability weight ω soc1 =0.5. Assume rural energy supply stability E soc1 =0.8; Charging service satisfaction weight ω soc2 =0.5.

[0124] 2. Environmental Indicator Calculation:

[0125] Assume that the renewable energy generation is P new =1000 kilowatts, emission reduction coefficient k env =0.5 tons of carbon dioxide / kilowatt, then the emission reduction E from new energy power generation env1 =P new ×k env =1000 × 0.5 = 500 tons of carbon dioxide.

[0126] 3. Calculation of comprehensive evaluation score:

[0127] Economic indicator scores:

[0128] Normalized score of economic benefits for power grid operators Assuming minimum profit grid,min =0, maximum value Profit grid,max =1000, then the score is

[0129] Normalized score of revenue from new energy power generation Assuming the minimum profit is Profit new,min =0, maximum value Profit new,max =500, then the score is

[0130] Normalized score of return on investment for energy storage entities Assuming the minimum rate of return is Return storage,min =0, maximum value Return storage,max =2, then the score is 2.

[0131] Normalized score of main profits of electric vehicle charging piles Assuming minimum profit charger,min =0, maximum value Profit charger,max =200, then the score is

[0132] The total score for the economic indicators is 0.4×0.3×0.4+0.4×0.3×0.266+0.4×0.2×0.6+0.4×0.2×0.2009=0.2748.

[0133] Environmental indicator scores Assuming the minimum emission reduction is E env1,min =0, maximum value E env1,max =1000, then the score is

[0134] Social indicator score: 0.3 × 0.5 × S soc1 +0.3×0.5×S charger =0.3×0.5×0.8+0.3×0.5×0.8=0.24.

[0135] Therefore, the overall evaluation score is 0.2748 + 0.144 + 0.24 = 0.6588.

[0136] The above example demonstrates how to use economic models and comprehensive evaluation models to analyze rural new energy and energy storage integrated systems and obtain a specific comprehensive evaluation result.

[0137] In practical applications, data and parameters can be adjusted according to different situations to better optimize the collaborative solution.

[0138] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of the claims of the present invention.

Claims

1. A new energy and charging and storage collaborative optimization method for rural areas with high proportion of green energy supply, characterized in that, The method comprises the following steps: S1, determining and collecting the demand and interest differences of different subjects in the region, and indexing them, wherein the subjects include power grid operators, new energy power generation subjects, energy storage subjects, and electric vehicle charging pile subjects; S2, establishing economic models of the subjects, calculating economic indicators of the subjects, and analyzing economic interaction relationships between the subjects to provide a basis for economic strategy optimization; S3, establishing a comprehensive economic model based on the index system and its weight, and calculating a comprehensive evaluation score; S4, adjusting the operation strategies of the subjects according to the economic model, screening an optimal coordination scheme based on the comprehensive economic model, and continuously detecting and adjusting.

2. The method of claim 1, wherein the method is characterized by, The step S1 is specifically as follows: The specific indicators of the power grid operator include power grid stability indicators and economic benefit indicators, wherein the power grid stability indicators are measured by voltage fluctuation range V fluctuation and frequency stability F stability , wherein the economic benefit indicators are the electricity sales revenue R grid minus the operation cost C grid , that is, Profit grid = R grid -C grid ; The specific indicators for the main body of new energy power generation include power generation indicators, power generation revenue indicators, and power generation stability indicators, among which the power generation indicator is represented by P. new Among them, the power generation revenue indicator is the electricity sales revenue R. new Subtract equipment investment cost C inv,new and maintenance costs C maintain,new The sum of, i.e., Profit new =R new -C inv,new -C maintain,new Among them, the power generation stability index is measured by the degree of fluctuation in power generation output. To measure; The specific indicators of the energy storage main body include a charge-discharge efficiency indicator, an investment return rate indicator, and a response speed indicator, wherein the charge-discharge efficiency indicator is represented as η storage , wherein the investment return rate indicator is the ratio of energy storage income R storage to investment cost C storage , that is, , wherein the response speed indicator is represented as response time T response ; The specific indexes of the electric vehicle charging pile body include a charging service satisfaction index, a charging price rationality index, a charging pile utilization rate index and a charging profit index, wherein the charging service satisfaction index is obtained by investigating the satisfaction value S charger , wherein the charging price rationality index is determined compared with other charging facilities in the market, wherein the charging pile utilization rate index is the number of uses N use of the charging pile within a certain time total , that is , wherein the charging profit index is the charging income R charger minus the operating cost C charger , that is Profit charger = R charger -C charger .

3. The method of claim 2, wherein the method is characterized by, The step S2 is specifically as follows: The economic model of the power grid operator is as follows: Objective function: Maximize(Profit grid ) = Maximize(R grid -C grid +k1P new ); wherein k1 is a new energy consumption capacity weight coefficient, P new is a new energy consumption amount; Constraint: V fluctuation,min ≤ V fluctuation ≤ V fluctuation,max ; F stability,min ≤F stability ≤F stablity,max ; The economic model of the new energy power generation subject is as follows: Objective function: where k2 is a power generation stability weight coefficient; Constraint: P new,min ≤ P new ≤ P new,max ; The economic model of the energy storage subject is as follows: Objective function: where k3 is a response speed weight coefficient; Constraint: η storage,min ≤ η storage ≤ η new,storage ; The economic model of the electric vehicle charging pile subject is as follows: Objective function: Maximize (Profit charger ) = Maximize (R charger -C charger + k4Utilization charger ); where k4 is a charging post utilization weight factor; Constraint: P rate,min ≤ P rate ≤ P rate,max ; S charger,min ≤S charger ≤S charger,max ; wherein P rate is the charging price of the electric vehicle charging pile.

4. The method of claim 3, wherein the method is characterized by, The step S3 is specifically as follows: S31, determining an index system; Economic indicators: economic indicators of each subject, including Profit grid , Profit new , Return storage and Profit charger ; The environmental index includes a new energy power generation emission reduction amount and an energy storage device environmental impact evaluation value, and the new energy power generation emission reduction amount E env1 = P new × k env , wherein k env is an emission reduction coefficient, and the energy storage device environmental impact evaluation value E env2 is determined by expert scoring, and the numerical range is [0, 1]; Social indicators: including rural energy supply stability and charging service satisfaction, rural energy supply stability where N is the number of power outages, T is the duration of power outages, N max and T max are the preset maximum number of power outages and the longest duration of power outages; Charging service satisfaction E soc2 Satisfaction values are obtained through user surveys, with values ranging from [0, 1]; S32, determining the weight of each index of the index system; The economic index weight ω is determined by using analytic hierarchy process econ The environmental index weight ω env The social index weight ω soc , and ω econ + ω env + ω soc = 1. In the economic indicators, the economic benefit weight ω of the power grid operator is determined grid , the new energy power generation subject benefit weight ω new , the energy storage subject investment return rate weight ω storage , and the electric vehicle charging pile subject profit weight ω charger ; in the environmental indicators, the new energy power generation emission reduction weight ω env1 and the energy storage device environmental impact assessment weight ω env2 ; in the social indicators, the rural energy supply stability weight ω soc1 and the charging service satisfaction weight ω soc2 ; S33, comprehensive evaluation score calculation, and the calculation formula is as follows:

5. The method of claim 1, wherein the method is characterized by, The step S4 is specifically as follows: S41, operation strategies of the subjects: the power grid operator adopts a reasonable arrangement of new energy power generation and energy storage device discharge during the peak period of electricity consumption, the new energy power generation subject adopts a way to improve power generation efficiency, the energy storage subject adopts a way to arrange charging and discharging time according to the peak and valley period of local electricity price, and the electric vehicle charging pile subject adopts a way to formulate a reasonable charging price; S42, calculating the comprehensive evaluation scores of different coordination schemes, and selecting the scheme with the highest comprehensive score as the optimal scheme; S43, in the actual operation process, continuously detecting the changes of each index, and timely adjusting the coordination scheme according to the feedback to ensure the effectiveness and sustainability of the scheme.

Images