Wind and light storage load capacity optimization proportioning method of source network load storage integrated system

Abstract

The invention relates to the technical field of wind and light storage load capacity matching, in particular to a wind and light storage load capacity optimization matching method of a source network load storage integrated system, which comprises the following steps of: collecting and preprocessing data; determining an optimization target; establishing a wind-solar-storage-load integrated model; determining a capacity optimization variable; constructing an optimization model; optimization calculation: operating an optimization algorithm to solve the constructed optimization model; analyzing an optimization calculation result, and analyzing the optimization calculation result based on economy, reliability and environmental protection to obtain an optimal capacity ratio of the wind-light storage and load integrated system; verifying a result; and outputting an optimal wind and light storage load capacity matching scheme. According to the method, through the design of the particle swarm optimization algorithm and the fitness function, the optimal capacity matching scheme can be found in a complex system environment, and it can be ensured that the optimization result reaches the expected target in the aspects of economic benefits, system reliability and environmental protection performance.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of wind and solar storage capacity ratio, and is a wind and solar storage capacity optimization ratio method of a source-grid-load-storage integrated system. BACKGROUND

[0002] With the increasing demand for energy and the growing awareness of environmental protection, the development and utilization of renewable energy has become a global focus. Among them, wind energy and solar energy, as two major renewable energy sources, have rich resources, are clean and pollution-free, and are renewable, and have received widespread attention. However, due to the randomness and volatility of wind energy and solar energy, their instability and discontinuity pose a challenge to the stable operation of the power grid. Therefore, it is of great practical significance and application value to study a system that can effectively integrate wind energy and solar energy and realize wind-solar-storage integrated operation. At present, the research on wind-solar-storage integrated system mainly focuses on the establishment of wind-solar power generation model, load model and energy storage model. These models can simulate the power generation characteristics of wind and solar resources, the spatial and temporal distribution characteristics of load and the charging and discharging process of energy storage devices, providing a theoretical basis and calculation framework for system optimization.

[0003] The patent application document with publication number CN120638407A discloses a comprehensive configuration method for energy storage capacity of a source-grid-load-storage system. This method establishes an energy storage configuration capacity optimization model with the goal of minimizing the energy storage construction and the operation cost of the source-grid-load-storage system under the operation requirements of the power grid. The operation requirements of the upper-level power grid, such as the abandoned power rate, the wind power and photovoltaic power consumption proportion, and the non-sending power to the upper-level power grid, are used as constraints to obtain the energy storage configuration result of the source-grid-load-storage system that can meet the operation requirements of the upper-level power grid within the operation period. The influence of multiple types of loads on the energy storage configuration result is also considered.

[0004] The patent application document with publication number CN120579763A discloses a source-grid-load-storage integrated optimization configuration method for enterprise power grids. The method steps include: generating annual wind and solar output standard values based on historical wind and solar data, wind speed or illumination data; constructing annual typical load data of enterprises through load curve generation, k-means clustering and random sampling; establishing a mixed integer linear programming model combining investment and operation costs and energy balance constraints; calculating key indicators such as green power proportion, abandoned energy, and electricity purchase cost through the model; and checking the scheme for the maximum / minimum load day and the maximum / minimum green power proportion day.

[0005] However, the existing research still has some deficiencies in wind-solar-storage capacity optimization ratio, mainly in the following aspects: the optimization target is single, mainly focusing on investment and operation costs, while ignoring system reliability and environmental friendliness; the optimization algorithm selection is not flexible enough, and the processing capacity for complex systems is limited; and there is a lack of overall and systematic research on wind-solar-storage capacity optimization ratio methods. SUMMARY

[0006] The application provides a wind-solar-storage-load capacity optimization matching method of a source-network-load-storage integrated system, which can effectively solve the problem of single optimization target of the existing wind-solar-storage-load capacity optimization matching method, mainly focusing on investment cost and operation cost, and ignoring system reliability and environmental friendliness.

[0007] The technical scheme of the application is realized by the following measures: a wind-solar-storage-load capacity optimization matching method of a source-network-load-storage integrated system, comprising:

[0008] S1: data collection and preprocessing, comprehensively collecting wind and solar resource data in the region, and preprocessing the wind and solar resource data;

[0009] S2: determining an optimization target, the optimization target including the lowest system investment cost, the lowest operation cost, and the highest energy utilization rate, setting a constraint condition of the optimization target, and the constraint condition including wind and solar resource limitation, load demand, and energy storage device performance;

[0010] S3: establishing a wind-solar-storage-load integrated model, the wind-solar-storage-load integrated model including a wind-solar power generation model, a load model, and an energy storage model;

[0011] S4: determining capacity optimization variables, taking the capacity of a wind-solar power generation unit, the charge and discharge power and capacity of an energy storage unit, and a load adjustment strategy as the capacity optimization variables of the wind-solar-storage-load integrated model;

[0012] S5: constructing an optimization model, constructing an optimization model of the wind-solar-storage-load integrated system based on the wind-solar-storage-load integrated model and the optimization target, and utilizing an optimization algorithm;

[0013] S6: optimization calculation, running the optimization algorithm to solve the constructed optimization model, and obtaining an optimal solution of each iteration through iterative calculation in the solving process;

[0014] S7: optimization calculation result analysis, analyzing the optimization calculation result based on economy, reliability, and environmental protection, and obtaining the optimal capacity matching of the wind-solar-storage-load integrated system;

[0015] S8: result verification, simulating and verifying the analysis result to ensure that the actual operation effect is consistent with the analysis result;

[0016] S9: outputting an optimization scheme, outputting the optimal wind-solar-storage capacity matching scheme.

[0017] The following is a further optimization or / and improvement of the above-mentioned technical scheme of the application:

[0018] Further, in the step S1, the wind and light resource data includes wind speed, light intensity, temperature information, historical load curve, load growth rate load data, charging and discharging efficiency of energy storage equipment, service life of energy storage equipment and cost of energy storage equipment; the preprocessing includes cleaning and normalization processing of the wind and light resource data.

[0019] Further, in the step S2, in the wind and light resource restriction, the maximum utilization rate and the minimum utilization time of the wind and light resource are taken as constraint conditions, and in the energy storage equipment performance, the charging and discharging times and the capacity limitation of the energy storage equipment are taken as constraint conditions.

[0020] Further, in the step S3, the wind speed and light intensity data are used to predict the power generation of the wind and light resource by combining the wind and light power generation model.

[0021] Further, in the step S4, the capacity of the wind and light power generation unit is adjusted according to the distribution and predicted power generation of the wind and light resource, and the charging and discharging power and capacity of the energy storage unit are adjusted according to the performance parameters of the energy storage equipment and the load demand.

[0022] Further, the optimization algorithm is one of a genetic algorithm, a particle swarm optimization algorithm and a simulated annealing algorithm.

[0023] Further, in the step S6, when the particle swarm optimization algorithm is used as the optimization algorithm, the particle swarm optimization algorithm specifically includes the following steps:

[0024] S61. Initialization of population: a certain number of individuals are randomly generated, each individual representing a wind and light storage capacity matching scheme; wherein each individual is coded by a group of genes, and the gene coding corresponds to a specific capacity configuration;

[0025] S62. Fitness evaluation: fitness evaluation is performed on each individual, and the fitness function is designed according to the optimization target, and the objective function value of each individual is calculated, and the target includes cost, reliability and environmental friendliness;

[0026] S63. Selection operation: according to the fitness, excellent individuals are selected into the next generation;

[0027] S64. Cross operation: randomly select two individuals to perform gene crossover to generate new individuals;

[0028] S65. Mutation operation: randomly mutate the genes of the individual;

[0029] S66. Loop iteration: repeat the fitness evaluation, selection, crossover and mutation operations until the stop condition is met;

[0030] S67. Output optimal solution: select the individual with the highest fitness as the optimal solution, and decode the genes of the optimal individual to obtain the optimal wind and light storage capacity matching scheme;

[0031] Wherein, the fitness function is the objective function, and a calculation formula of the fitness function is:

[0032] f(x)=w1·C(x)+w2·R(x)+w3·E(x)

[0033] Wherein, f(x) is the fitness function, x is the individual code, C(x) is the investment and operation cost, R(x) is the system reliability index, E(x) is the environmental friendliness index, and w1, w2 and w3 are weight coefficients.

[0034] A calculation formula of the environmental friendliness index is:

[0035]

[0036] Wherein, E(x) is the environmental friendliness index, Ei(x) is the environmental impact score of the i th energy, RE is the renewable energy set, and ALL is the set of all energies.

[0037] Further, in the step S7, the investment cost, the operation cost and the energy utilization rate corresponding to the optimal solution of each iteration are calculated, the economy, the reliability and the environmental protection of the optimal solution of each iteration are evaluated, and the optimal capacity ratio of the wind-solar-storage integrated system is obtained.

[0038] Further, in the step S8, the simulation software is used to simulate and verify the analysis results to simulate the actual operation; in this process, whether the power generation, the load supply and the charge-discharge state key indicators of the energy storage device of the system meet the expectation is verified; if the deviation is found, the optimization strategy needs to be adjusted according to the actual situation; if the simulation result shows that the load supply of the system is insufficient in some period, the load adjustment strategy is adjusted, and the charge-discharge power of the energy storage device is increased to improve the stability of the load supply.

[0039] Further, in the step S9, in the output optimization scheme, in addition to the optimal wind-solar-storage capacity ratio scheme, the power generation prediction and the load supply situation are also included.

[0040] 1) The existing method often focuses on a single target (such as the lowest cost), and it is difficult to effectively balance the system investment cost, operation cost, energy utilization rate, power supply reliability and environmental friendliness and other multiple conflicting targets. The present application can systematically find a capacity ratio scheme that meets multiple optimization targets by constructing a multi-objective optimization model that comprehensively considers economy, reliability and environmental protection, and using the weighting method to unify it in the fitness function, thereby solving the problem that the traditional configuration method is difficult to coordinate multiple objective optimization.

[0041] 2) The randomness, volatility of wind and light resources, and the dynamic changes of load make the system highly complex and uncertain. The deterministic model or simplified assumption adopted by the traditional method is difficult to accurately reflect the actual operation characteristics. The present application effectively deals with the complexity and uncertainty of the system by establishing refined wind and light generation models, load models and energy storage models, and using intelligent algorithms such as particle swarm optimization algorithm to process high-dimensional and nonlinear optimization problems, and improves the robustness and practicality of the configuration scheme. The problem of insufficient consideration of system complexity and uncertainty in the existing configuration process is solved.

[0042] 3) The configuration scheme obtained by the existing method often lacks sufficient simulation verification, which may lead to the theoretical optimal scheme performing poorly in actual operation. The present application specially sets up a verification and adjustment step after optimization calculation, simulates the performance of the system in the actual operation environment through simulation software, verifies the effectiveness of the optimization result, and adjusts the optimization strategy according to the simulation result, ensuring the high consistency of the configuration scheme between theoretical analysis and actual operation effect. The problem of disconnection between configuration scheme and actual operation effect is solved.

[0043] The beneficial effects of the present application are:

[0044] In the present application, the particle swarm optimization algorithm is used to construct the optimization model, which can effectively deal with the complexity and uncertainty of the system. The particle swarm optimization algorithm can quickly find the global optimal solution by simulating the foraging behavior of bird flocks in nature, which makes it possible to consider multiple objectives in the optimization process, such as system investment cost, operation cost, energy utilization rate, system reliability and environmental friendliness. The fitness function can quantify the pros and cons of different capacity allocation schemes. The calculation formula of the fitness function considers investment and operation cost, system reliability and environmental friendliness, which helps to find a balance point in multi-objective optimization. In addition, the calculation formula of the environmental friendliness index further strengthens the attention to environmental protection in the optimization process, ensuring that economic benefits are pursued while achieving the goal of sustainable development. Through the design of particle swarm optimization algorithm and fitness function, a scientific and efficient methodology is provided for the wind and light storage capacity optimization allocation method of source-grid-load integrated system. It not only helps to find the optimal capacity allocation scheme in a complex system environment, but also ensures that the optimization result achieves the expected goal in economic benefit, system reliability and environmental friendliness.

[0045] In this invention, data collection and preprocessing provide an accurate and reliable data foundation for subsequent analysis, ensuring the quality and efficiency of the entire optimization process. Determining the optimization objective and establishing an integrated wind-solar-storage model provides clear guidance and a computational framework for the subsequent optimization process. Capacity optimization variables are identified, clarifying the key parameters that need adjustment and optimization during the process. Optimization calculations are performed, and the optimal solution for each iteration is recorded for analysis of the optimization process. Result analysis yields the optimal capacity ratio of the integrated wind-solar-storage system, and the economic efficiency, reliability, and environmental friendliness of the optimization results are evaluated. Verification and adjustment involve simulation verification of the optimization results to ensure that the actual operating effect matches the theoretical analysis, and the optimization strategy is adjusted according to the actual situation to improve system performance. Attached Figure Description

[0046] Appendix Figure 1 A flowchart illustrating the method for optimizing the allocation of wind, solar, and energy storage capacity in an integrated power generation, grid, load, and energy storage system. Detailed Implementation

[0047] The present invention is not limited to the following embodiments, and the specific implementation can be determined according to the technical solution of the present invention and the actual situation.

[0048] The present invention will be further described below with reference to embodiments:

[0049] Example 1: As Figure 1 As shown, a method for optimizing the wind, solar, and storage capacity ratio in an integrated power generation, grid, load, and storage system includes:

[0050] S1: Data collection and preprocessing, comprehensively collect wind and solar resource data in the region, and preprocess the wind and solar resource data;

[0051] S2: Determine the optimization objectives, which include the lowest system investment cost, the lowest operating cost, and the highest energy utilization rate. Set the constraints for the optimization objectives, which include wind and solar resource limitations, load demand, and energy storage equipment performance.

[0052] S3: Establish an integrated wind-solar-storage-load model, which includes a wind-solar power generation model, a load model, and an energy storage model. The wind-solar power generation model is established to simulate the power generation characteristics of wind and solar resources; the load model is established to reflect the spatiotemporal distribution characteristics of the load; and the energy storage model is established to describe the charging and discharging process of the energy storage equipment.

[0053] S4: Determine the capacity optimization variables, using the capacity of the wind and solar power generation unit, the charging and discharging power and capacity of the energy storage unit, and the load regulation strategy as the capacity optimization variables for the integrated wind, solar, energy storage and load model.

[0054] S5: Constructing an optimization model, based on the wind-solar-storage integrated model and the optimization objective, using an optimization algorithm to construct an optimization model of the wind-solar-storage integrated system; setting the optimization algorithm parameters, according to the characteristics of the optimization algorithm, reasonably setting the initial population size, iteration times, crossover probability, mutation probability; and need to ensure that the algorithm converges in a limited time;

[0055] S6: Optimization calculation, running the optimization algorithm to solve the constructed optimization model, in the solving process, through iterative calculation, the optimal solution of each iteration is obtained;

[0056] S7: Optimization calculation result analysis, based on the economy, reliability and environmental protection, the optimization calculation result is analyzed, and the optimal capacity ratio of the wind-solar-storage integrated system is obtained;

[0057] S8: Result verification, simulation verification is carried out on the analysis result, to ensure that the actual running effect is consistent with the analysis result;

[0058] S9: Output optimization scheme, output the optimal wind-solar-storage capacity ratio scheme, and provide system operation strategy and suggestion.

[0059] Embodiment 2: As the optimization of the above embodiment, in step S1, the wind and light resource data includes wind speed, light intensity, temperature information, historical load curve, load growth rate load data, charging and discharging efficiency of energy storage device, service life of energy storage device and cost of energy storage device; the preprocessing includes cleaning and normalization processing of wind and light resource data.

[0060] The collected wind and light resource data is cleaned and normalized to ensure data accuracy and usability; the data is cleaned using average, median and standard deviation statistical methods, and different dimension data is converted to the same range using normalization method for subsequent analysis.

[0061] Embodiment 3: As the optimization of the above embodiment, in step S2, in the wind and light resource restriction, the maximum utilization rate and the minimum utilization time of wind and light resource are used as constraint conditions, and in the energy storage device performance, the charging and discharging times and capacity limit of energy storage device are used as constraint conditions.

[0062] Embodiment 4: As the optimization of the above embodiment, in step S3, the wind speed and light intensity data are used to predict the power generation of wind and light resources combined with the wind and light power generation model.

[0063] Embodiment 5: As the optimization of the above embodiment, in step S4, the capacity of the wind and light power generation unit is adjusted according to the distribution of wind and light resources and the predicted power generation, and the charging and discharging power and capacity of the energy storage unit are adjusted according to the performance parameters (charging and discharging efficiency, service life and energy storage device cost) of the energy storage device and the load demand.

[0064] Embodiment 6: As an optimization of the above embodiment, the optimization algorithm is one of a genetic algorithm, a particle swarm optimization algorithm, and a simulated annealing algorithm. Ensure that the algorithm converges within a limited time and finds a satisfactory solution; for the genetic algorithm, set the population size to 100, the number of iterations to 100, the crossover probability to 0.8, and the mutation probability to 0.2.

[0065] Embodiment 7: As an optimization of the above embodiment, in step S6, when the optimization algorithm uses a particle swarm optimization algorithm, the particle swarm optimization algorithm specifically includes the following steps:

[0066] S61. Initialize the population: randomly generate a certain number of individuals (solutions), each individual representing a wind-light-storage capacity allocation scheme; wherein each individual is encoded by a set of genes, and the gene code corresponds to a specific capacity configuration;

[0067] S62. Evaluate fitness: evaluate the fitness of each individual, and the fitness function is designed according to the optimization target, and calculate the objective function value of each individual, the target including cost, reliability, and environmental friendliness;

[0068] S63. Selection operation: select excellent individuals into the next generation according to the fitness; adopt selection methods such as roulette and tournament;

[0069] S64. Crossover operation: randomly select two individuals for gene crossover to generate new individuals; the crossover position and method are random or based on certain rules;

[0070] S65. Mutation operation: randomly mutate the genes of the individual to increase the diversity of the population; the mutation probability is low;

[0071] S66. Loop iteration: repeat the evaluation of fitness, selection, crossover, and mutation operations until the stopping condition is met;

[0072] S67. Output the optimal solution: select the individual with the highest fitness as the optimal solution, and decode the genes of the optimal individual to obtain the optimal wind-light-storage capacity allocation scheme;

[0073] wherein the fitness function is the objective function, and the calculation formula of the fitness function is:

[0074] f(x) = w1·C(x) + w2·R(x) + w3·E(x)

[0075] where f(x) is the fitness function, x is the individual code, C(x) is the investment and operation cost, R(x) is the system reliability index, E(x) is the environmental friendliness index, and w1, w2, w3 are weight coefficients;

[0076] The calculation formula of the environmental friendliness index is:

[0077]

[0078] Wherein, E(x) is the environmental friendliness index; Ei(x) is the environmental impact score of the i-th energy; RE is the renewable energy set; ALL is the set of all energies.

[0079] Embodiment 8: As an optimization of the above embodiments, in step S7, the investment cost, operation cost and energy utilization rate corresponding to the optimal solution of each iteration are calculated, the economy, reliability and environmental friendliness of the optimal solution of each iteration are evaluated, and the optimal capacity ratio of the wind-solar-storage integrated system is obtained.

[0080] Embodiment 9: As an optimization of the above embodiments, in step S8, simulation software is used to simulate and verify the analysis results to simulate the actual operation; In this process, whether the key indicators of the power generation, load supply and charging and discharging state of the energy storage device of the system meet the expectations will be verified; If deviations are found, the optimization strategy needs to be adjusted according to the actual situation; If the simulation results show that the load supply of the system is insufficient in some period, adjust the load regulation strategy and increase the charging and discharging power of the energy storage device to improve the stability of the load supply; Through multiple iterations and adjustments, the performance of the system is continuously improved to make it more suitable for the actual operating environment.

[0081] Embodiment 10: As an optimization of the above embodiments, in step S9, in the output optimization scheme, in addition to the optimal wind-solar-storage capacity ratio scheme, it also includes power generation prediction and load supply situation key data, then combined with the actual situation, the system is provided with operation strategy and suggestion; Provide operation strategy under different seasons and different weather conditions, and how to adjust the power generation and charging and discharging state of the energy storage device according to the actual load demand; In addition, it will also provide maintenance and fault handling suggestions to ensure the long-term stable operation of the system; These outputs will provide important reference basis for actual engineering application, guide actual operation and decision-making; Provide detailed optimization scheme report including optimal capacity ratio, operation strategy and suggestion to guide actual engineering application.

[0082] The above technical features respectively constitute embodiments of the present application, which have strong adaptability and implementation effect, and unnecessary technical features can be added or subtracted according to actual needs to meet the needs of different situations.

Claims

1. A method for optimizing the proportion of wind and solar storage capacity in a source-network-load-storage integrated system, characterized in that, Comprise: S1: data collection and preprocessing, comprehensive collection of wind and light resource data in the region, preprocessing the wind and light resource data; S2: determine the optimization target, the optimization target includes the lowest system investment cost, the lowest operation cost, the highest energy utilization rate, set the constraint condition of optimization target, the constraint condition includes wind and light resource limit, load demand, energy storage device performance; S3: establish wind and light storage load integration model, wind and light storage load integration model includes wind and light power generation model, load model and energy storage model; S4: determine the capacity optimization variable, the capacity of wind and light power generation unit, the charge and discharge power and capacity of energy storage unit, and the load adjustment strategy are taken as the capacity optimization variable of wind and light storage load integration model; S5: build optimization model, based on the wind and light storage load integration model and optimization target, the optimization model of wind and light storage load integration system is constructed by using optimization algorithm; S6: optimization calculation, the optimization algorithm is run to solve the constructed optimization model, in the solving process, the optimal solution of each iteration is obtained through iterative calculation; S7: optimization calculation result analysis, the optimization calculation result is analyzed based on economy, reliability and environmental protection, and the optimal capacity ratio of wind and light storage load integration system is obtained; S8: result verification, the simulation verification is carried out on the analysis result, and it is ensured that the actual operation effect is consistent with the analysis result; S9: output optimization scheme, output the optimal wind and light storage capacity ratio scheme.

2. The wind-solar-storage capacity optimization matching method of the source-grid-storage integrated system according to claim 1, characterized in that, In step S1, the wind and light resource data includes wind speed, light intensity, temperature information, historical load curve, load growth rate load data, charge and discharge efficiency of energy storage device, service life of energy storage device and cost of energy storage device; the preprocessing includes cleaning and normalization processing of wind and light resource data; Or / and, in step S2, in the wind and light resource limit, the maximum utilization rate and the minimum utilization time of wind and light resource are taken as the constraint condition, and in the energy storage device performance, the charge and discharge times and capacity limit of energy storage device are taken as the constraint condition.

3. The method for optimizing the allocation of wind, solar, and energy storage capacity in an integrated power generation, grid, load, and energy storage system according to claim 1 or 2, characterized in that, In step S3, the wind speed and light intensity data are used to predict the power generation capacity of wind and light resource by combining the wind and light power generation model; Or / and, in step S4, the capacity of wind and light power generation unit is adjusted according to the distribution of wind and light resource and the predicted power generation capacity, and the charge and discharge power and capacity of energy storage unit are adjusted according to the performance parameters of energy storage device and load demand.

4. The wind-solar-storage capacity optimization matching method of the source-grid-storage integrated system according to claim 3, characterized in that, The optimization algorithm is one of genetic algorithm, particle swarm optimization algorithm and simulated annealing algorithm.

5. The wind-solar-storage capacity optimization matching method of the source-grid-storage integrated system according to claim 4, characterized in that, In step S6, when the optimization algorithm adopts particle swarm optimization algorithm, the particle swarm optimization algorithm specifically comprises the following steps: S61: initialize population: a certain number of individuals are randomly generated, each individual represents a wind and light storage capacity ratio scheme; wherein each individual is coded by a group of genes, and the gene coding corresponds to specific capacity configuration; S62: evaluate fitness: evaluate the fitness of each individual, the fitness function is designed according to the optimization target, and the objective function value of each individual is calculated, the objective includes cost, reliability and environmental friendliness; S63: selection operation: select excellent individuals into next generation according to fitness; S64: cross operation: randomly select two individuals to perform gene crossover to generate new individuals; S65: variation operation: randomly mutate the genes of the individual; S66: loop iteration: repeat the evaluation of fitness, selection, crossover and variation operation until the stop condition is met; S67: output the optimal solution: select the individual with the highest fitness as the optimal solution, and decode the genes of the optimal individual to obtain the optimal wind-solar-storage capacity allocation scheme; wherein the fitness function is the objective function, and the calculation formula of the fitness function is: f(x) = w1-C(x) + w2-R(x) + w3-E(x) wherein f(x) is the fitness function, x is the individual code, C(x) is the investment and operation cost, R(x) is the system reliability index, E(x) is the environmental friendliness index, and w1, w2, w3 are weight coefficients; The calculation formula of the environmental friendliness index is: wherein E(x) is the environmental friendliness index; Ei(x) is the environmental impact score of the i-th energy; RE is the set of renewable energies; and ALL is the set of all energies.

6. The wind-solar-storage capacity optimization matching method of the source-network-storage integrated system according to claim 1 or 2 or 4 or 5, characterized in that, In step S7, the investment cost, operation cost and energy utilization rate corresponding to the optimal solution of each iteration are calculated, the economy, reliability and environmental protection of the optimal solution of each iteration are evaluated, and the optimal capacity allocation of the wind-solar-storage integrated system is obtained.

7. The wind-solar-storage capacity optimization matching method of the source-grid-storage integrated system according to claim 3, characterized in that, In step S7, the investment cost, operation cost and energy utilization rate corresponding to the optimal solution of each iteration are calculated, the economy, reliability and environmental protection of the optimal solution of each iteration are evaluated, and the optimal capacity allocation of the wind-solar-storage integrated system is obtained.

8. The wind-solar-storage capacity optimization matching method of the source-network-storage integrated system according to claim 1 or 2 or 4 or 5 or 7, characterized in that, In step S8, simulation software is used to simulate the analysis results to simulate the actual operation; in this process, whether the power generation, load supply, and charging and discharging state of the energy storage device of the system meet the expectations is verified; If deviations are found, the optimization strategy needs to be adjusted according to the actual situation; if the simulation results show that the load supply of the system is insufficient in some periods, the load adjustment strategy is adjusted, and the charging and discharging power of the energy storage device is increased to improve the stability of the load supply.

9. The wind-solar-storage capacity optimization matching method of the source-grid-storage integrated system according to claim 6, characterized in that, In step S8, simulation software is used to simulate the analysis results to simulate the actual operation; in this process, whether the power generation, load supply, and charging and discharging state of the energy storage device of the system meet the expectations is verified; If deviations are found, the optimization strategy needs to be adjusted according to the actual situation; if the simulation results show that the load supply of the system is insufficient in some periods, the load adjustment strategy is adjusted, and the charging and discharging power of the energy storage device is increased to improve the stability of the load supply.

10. The method for optimizing the allocation of wind, solar, and energy storage capacity in an integrated power generation, grid, load, and storage system according to claim 9, is characterized in that... In step S9, in addition to the optimal wind-solar-storage capacity allocation scheme, the power generation prediction and load supply situation are also included in the output optimization scheme.

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