Rural new energy power generation consumption method and system based on storage and charging cooperation, and medium

Abstract

The invention relates to the technical field of digital twinning, in particular to a rural new energy power generation consumption method and system based on storage and charging cooperation and a medium. According to the method, a multi-target rural new energy power generation consumption model composed of a rural new energy local consumption evaluation model, a distributed rural new energy load self-consumption model and a charging and discharging model of a storage and charging system is constructed; and finally, based on a preset constraint condition, solving the rural new energy power generation consumption model based on storage and charging cooperation to obtain a final rural new energy power generation consumption method. Through simulation analysis, the rural new energy power generation consumption model based on storage and charging cooperation can ensure the effect of minimizing waste energy on the premise of satisfying the local consumption of the electrical load, and can realize the optimal energy storage loss under the condition of the flattest fluctuation of the new energy output power.

Description

Technical Field

[0001] This invention relates to the field of power technology, specifically to a method, system, and medium for rural renewable energy power generation and consumption based on energy storage and charging cooperation. Background Technology

[0002] Compared with traditional power systems, new energy systems differ greatly in terms of power supply structure and grid structure. Renewable energy power generation, such as solar and photovoltaic power, has strong randomness and intermittency, while wind power also has the characteristic of anti-peak shaving.

[0003] When renewable energy sources are integrated into the distribution network on a large scale, the output on the power generation side will exhibit strong uncertainty and uncontrollability. On the load side, with the increasing penetration rate of distributed renewable energy, the equivalent load composed of power load and distributed power sources will also exhibit strong uncertainty. The uncertainty on both the source and load sides increases the difficulty of real-time supply and demand balance of electricity. As a result, it is difficult to accurately match renewable energy with the demand of traditional power loads, and situations of power surplus or shortage occur frequently, seriously interfering with the supply and demand balance and power quality of the power system. Therefore, after large-scale distributed renewable energy is integrated into the power system, how to adapt to the uncertainty of instantaneous renewable energy output and promote the consumption of renewable energy will become the primary problem to be solved in the future optimization and operation of the power system.

[0004] However, current technologies still have many shortcomings. In terms of power generation prediction, traditional models based on physical principles can calculate theoretical power generation based on relevant theories and parameters, providing a basic framework for prediction. However, they are difficult to accurately capture the nonlinear changes in power generation caused by complex and ever-changing weather conditions and equipment performance variations in actual operation. While data-driven machine learning models can uncover nonlinear relationships and complex patterns in data to improve prediction accuracy, they are highly dependent on data quality and quantity, and the models have weak interpretability. In terms of energy storage system control, the problems are equally prominent. In determining charging decision constraints, the past failure to fully combine detailed historical data and accurate predictions of future electricity consumption has resulted in energy storage systems not being able to charge at the optimal time and effectively store or release electrical energy. When determining charging and discharging power, most studies only consider power generation, electricity consumption, and the energy status of the energy storage system, ignoring market electricity price factors. Under the dynamic changes in the electricity market, this makes it difficult for energy storage systems to maximize economic benefits and miss the profit opportunities brought by electricity price fluctuations. Furthermore, battery life has not been adequately considered in existing control strategies. The charging process does not fully take into account the impact of charging current, depth of charge, and charging temperature on battery life, resulting in premature battery aging, performance degradation, increased maintenance costs, and reduced overall economic benefits.

[0005] Therefore, it is essential to provide a more efficient method for rural renewable energy power generation based on energy storage-charging collaboration to achieve the absorption of renewable energy by distributed energy storage and improve the economic operating efficiency of the system. Summary of the Invention

[0006] The purpose of this invention is to provide a method and system for rural renewable energy power generation and consumption based on energy storage and charging cooperation, so as to solve the above-mentioned technical problems.

[0007] This invention provides a method for rural renewable energy power generation consumption based on energy storage-charging cooperation, comprising the following steps: S1, obtain the output parameters of distributed rural renewable energy, and establish an assessment model for on-site consumption of rural renewable energy with the objective of minimizing the amount of abandoned distributed rural renewable energy. The objective function is: ; T represents the total amount of abandoned distributed rural renewable energy within the area during time period T; N represents the total number of distributed rural renewable energy nodes within the area. Let i be the photovoltaic power output at time t. Let i be the wind power output at time t. Let i be the photovoltaic power absorbed at time t. Let i be the wind power output absorbed by node i at time t; S2, based on the load output parameters for local consumption, and with the goal of maximizing the self-consumption of distributed rural renewable energy, a load self-consumption model for distributed rural renewable energy is established, with the objective function being: ; This represents the total load self-consumption of distributed rural renewable energy within the area during time period T. Let M represent the response status of user l within the area at time t, with values ​​[-1, 0, 1] indicating load reduction, non-participation in scheduling, and load increase, respectively; M represents the total number of users. This represents the response power of user l at time t; S3. Based on maximizing the volatility of distributed rural new energy while minimizing energy storage lifespan loss, a charging and discharging model for the energy storage and charging system is established, with the objective function being: Where J represents the objective function, Let be the satisfaction rate of the new energy output fluctuation at time t. Let J be the equivalent lifetime loss of energy storage system j, and H be the number of energy storage systems; S4, a multi-objective rural new energy power generation and consumption model composed of the rural new energy local consumption assessment model, the distributed rural new energy load self-consumption model, and the charging and discharging model of the energy storage and charging system; S5. Based on the preset constraints, the rural new energy power generation consumption model is solved to obtain the final rural new energy power generation consumption method.

[0008] Preferably, the constraints in step S1 include: the preset voltage range of each node voltage load, the negative current of each branch being less than the safe current, and the safe range of the maximum output power load of distributed rural new energy.

[0009] Preferably, the constraints in S2 include: , t represents the scheduling time step within the scheduling period, ranging from 1 to 3 hours; The maximum number of responses for user l whose load is reduced within a scheduling cycle.

[0010] Preferably, in step S3, , Where B is the total number of times when the output power of new energy is not zero before energy storage smoothing, and b is the total number of times when the output power of new energy is satisfied after energy storage smoothing. Let t be the damping value of the energy storage system on the fluctuations of distributed rural new energy sources. , , and These refer to the energy storage charging and discharging efficiency, respectively. Let t be the charging and discharging power of the energy storage system at time t.

[0011] Preferably, in step S3, ,in, Let Q be the change in the state of charge of energy storage system j before and after the qth charge and discharge cycle (%), Q be the total number of energy storage cycles of energy storage system j, DOD be the depth of charge and discharge of energy storage system j, cycle be the total number of energy storage cycles of energy storage system j, and Cap be the percentage of available capacity of energy storage system j.

[0012] Preferably, the constraints in step S3 include: energy storage charge / discharge rate constraint, energy storage capacity constraint, inverter output power fluctuation constraint, and energy storage charge / discharge power constraint.

[0013] Preferably, step S5 includes: S51, an initial solution set is obtained through random generation or chaotic mapping and the population is initialized; S52, evaluate each solution in the initial solution set, calculate its value on each objective function, and select the solutions that meet the preset conditions as the parent. S53, Based on the parent generation, generate new solutions as offspring through a population update strategy, then use the solutions of the parent generation and offspring generation as a new solution set and population, and return to step S2 for repeated iteration; S4, after reaching the preset number of iterations or the preset convergence condition, obtains the optimal solution set.

[0014] The present invention also provides a rural new energy power generation consumption system based on storage-charging cooperation, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the rural new energy power generation consumption method based on storage-charging cooperation as described in any of the preceding claims.

[0015] The present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the rural new energy power generation and consumption method based on storage-charging cooperation as described in any of the preceding claims.

[0016] This invention provides a rural renewable energy power generation consumption method based on energy storage and charging cooperation. It constructs a multi-objective rural renewable energy power generation consumption model, consisting of a rural renewable energy local consumption assessment model, a distributed rural renewable energy load self-consumption model, and a charging and discharging model of the energy storage and charging system. Finally, based on preset constraints, the rural renewable energy power generation consumption model based on energy storage and charging cooperation is solved to obtain the final rural renewable energy power generation consumption method. Simulation analysis shows that the rural renewable energy power generation consumption model based on energy storage and charging cooperation can achieve optimal energy storage loss while satisfying the premise of local electricity load consumption, minimizing waste energy, and minimizing fluctuations in renewable energy output power. Attached Figure Description

[0017] The accompanying drawings, as part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments and descriptions of the invention are used to explain the invention, but do not constitute an undue limitation of the invention. Obviously, the drawings described below are merely some embodiments, and those skilled in the art can obtain other drawings based on these drawings without creative effort. In the drawings: Figure 1 This is a flowchart illustrating a method for rural renewable energy power generation and consumption based on energy storage and charging cooperation in one embodiment of the present invention.

[0018] Figure 2 This is a schematic diagram of the hardware structure of a system that runs a rural new energy power generation and consumption method based on storage-charging cooperation in one embodiment of the present invention.

[0019] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0020] The technical problems solved by the embodiments of the present invention, the technical solutions adopted, and the technical effects achieved will be clearly and completely described below with reference to the accompanying drawings and specific embodiments. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. Based on the embodiments in this application, all other equivalent or obvious variations of embodiments obtained by those skilled in the art without creative effort fall within the protection scope of the present invention. The embodiments of the present invention can be embodied in various different ways as defined and covered by the claims.

[0021] It should be noted that many specific details are given in the following description for ease of understanding. However, it is obvious that the present invention may be implemented without these specific details.

[0022] It should be noted that, in the absence of explicit limitations or conflicts, the various embodiments and their technical features in this invention can be combined with each other to form a technical solution.

[0023] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0024] In the following description, the use of suffixes such as "module," "part," or "unit" to denote elements is solely for the purpose of illustrative purposes and has no specific meaning in itself. Therefore, "module," "part," or "unit" may be used interchangeably.

[0025] The invention will now be described in further detail with reference to the accompanying drawings.

[0026] Please combine Figure 1 and Figure 2 , Figure 1 A method for rural renewable energy power generation consumption based on energy storage and charging cooperation is provided in one embodiment of the present invention, including steps S1-S5.

[0027] S1, obtain the output parameters of distributed rural renewable energy, and establish an assessment model for on-site consumption of rural renewable energy with the objective of minimizing the amount of abandoned distributed rural renewable energy. The objective function is: ; T represents the total amount of abandoned distributed rural renewable energy within the area during time period T; N represents the total number of distributed rural renewable energy nodes within the area. Let i be the photovoltaic power output at time t. Let i be the wind power output at time t. Let i be the photovoltaic power absorbed at time t. Let i be the wind power output absorbed by node i at time t; The constraints in step S1 include: the preset voltage range of each node voltage load, the negative current of each branch being less than the safe current, and the safe range of the maximum output power load of distributed rural new energy.

[0028] S2, based on the load output parameters for local consumption, and with the goal of maximizing the self-consumption of distributed rural renewable energy, a load self-consumption model for distributed rural renewable energy is established, with the objective function being: ; This represents the total self-consumption capacity of distributed rural renewable energy within the area during time period T. Let M represent the response status of user l within the area at time t, with values ​​[-1, 0, 1] indicating load reduction, non-participation in scheduling, and load increase, respectively; M represents the total number of users. This represents the response power of user l at time t; The constraints in S2 include: , t represents the scheduling time step within the scheduling period, ranging from 1 to 3 hours; The maximum number of responses for user l whose load is reduced within a scheduling cycle.

[0029] S3. Based on maximizing the volatility of distributed rural new energy while minimizing energy storage lifespan loss, a charging and discharging model for the energy storage and charging system is established, with the objective function being: Where J represents the objective function, Let be the energy output fluctuation satisfaction rate at time t. Let J be the equivalent lifetime loss of energy storage system j, and H be the number of energy storage systems; Preferably, in step S3, B represents the total number of times when the output power of new energy sources is not zero before energy storage smoothing, and b represents the total number of times when the output power of new energy sources is satisfied after energy storage smoothing. Let t be the damping value of the energy storage system on the fluctuations of distributed rural new energy sources. , , and These refer to the energy storage charging and discharging efficiency, respectively. Let t be the charging and discharging power of the energy storage system at time t.

[0030] Preferably, in step S3, ,in, Let Q be the change in the state of charge of energy storage system j before and after the qth charge and discharge cycle (%), Q be the total number of energy storage cycles of energy storage system j, DOD be the depth of charge and discharge of energy storage system j, cycle be the total number of energy storage cycles of energy storage system j, and Cap be the percentage of available capacity of energy storage system j.

[0031] Preferably, the constraints in step S3 include: energy storage charge / discharge rate constraint, energy storage capacity constraint, inverter output power fluctuation constraint, and energy storage charge / discharge power constraint.

[0032] S4, a multi-objective rural new energy power generation and consumption model composed of the rural new energy local consumption assessment model, the distributed rural new energy load self-consumption model, and the charging and discharging model of the energy storage and charging system; S5. Based on the preset constraints, the rural new energy power generation consumption model is solved to obtain the final rural new energy power generation consumption method.

[0033] Preferably, step S5 includes: S51, an initial solution set is obtained through random generation or chaotic mapping and the population is initialized; S52, evaluate each solution in the initial solution set, calculate its value on each objective function, and select the solutions that meet the preset conditions as the parent. S53, Based on the parent generation, generate new solutions as offspring through a population update strategy, then use the solutions of the parent generation and offspring generation as a new solution set and population, and return to step S2 for repeated iteration; S4, after reaching the preset number of iterations or the preset convergence condition, obtains the optimal solution set.

[0034] Furthermore, step S52 includes: Non-dominated ranking and crowding calculation are performed on all individuals in the population: First, the dominance relationship between individuals is determined based on the objective function value of each individual in the population; then, the non-dominated levels are divided according to the dominance relationship between individuals, and non-dominated ranking is performed; finally, the crowding of each individual in the same level is calculated. Based on the non-dominated sorting and crowding calculation results, all individuals in the population are divided into elite individuals and ordinary individuals, with the elite individuals serving as the parents. Step S53 includes: Update the parameters of the individuals in the parent population and perform differential evolution operations to obtain the offspring population; combine the individuals in the parent population and the offspring population to generate a new population, and the size of the new population is 2*N; N represents the number of individuals in the parent population; perform non-dominated sorting and crowding degree calculation on the new population; according to the non-dominated sorting and crowding degree calculation results of the new population, use the elitist retention mechanism to screen the new population, and the size of the screened new population is N; for the current new population, first remove N-S individuals with low non-dominated sorting levels and small crowding degrees until the population size reaches S, S < N; then, store the removed individuals in the external set; next, randomly initialize N-S individuals within the search range and perform non-dominated sorting again, and calculate the crowding degree; finally, according to the non-dominated sorting and crowding degree calculation results, use the elitist retention mechanism to screen the new population; the maximum size of the external set is N, and if it is greater than N, randomly delete individuals to make it equal to N; finally, obtain a new solution set and population. Return to step S2 for repeated iteration.

[0035] The rural new energy power generation consumption method based on storage and charging cooperation provided by the present invention constructs a multi-objective rural new energy power generation consumption model composed of a rural new energy local consumption evaluation model, a load self-consumption model of distributed rural new energy, and a charge-discharge model of the storage and charging system; finally, based on the preset constraint conditions, solve the rural new energy power generation consumption model based on storage and charging cooperation to obtain the final rural new energy power generation consumption method. Through simulation analysis, the rural new energy power generation consumption model based on storage and charging cooperation can achieve the best energy storage loss under the premise of meeting the local consumption of the electricity load, ensuring the minimum waste energy, and at the same time the flattest fluctuation of the new energy output power.

[0036] The present invention also provides a rural new energy power generation consumption system based on storage and charging cooperation. The rural new energy power generation consumption system based on storage and charging cooperation runs on the basis of a computer system, and specifically includes a memory 61, a processor 62, and a computer program 63 stored in the memory 61 and executable on the processor 62. When the processor 62 executes the computer program 63, it implements the steps of the rural new energy power generation consumption method based on storage and charging cooperation as described in any one of the above.

[0037] This embodiment also provides a computer-readable storage medium storing a computer program, and when the computer program is executed by a processor, it implements the steps of the rural new energy power generation consumption method based on storage and charging cooperation as described in any one of the above.

[0038] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0039] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0040] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0041] In the embodiments provided by this invention, it should be understood that the disclosed apparatus / terminal devices and methods can be implemented in other ways. For example, the apparatus / terminal device embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0042] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0043] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0044] If the integrated module / unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content included in the computer-readable medium can be appropriately added or removed according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electrical carrier signals and telecommunication signals.

[0045] Therefore, the embodiments should be considered as exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of the equivalents of the application are intended to be included within the invention.

[0046] In the description of this specification, references to terms such as "one embodiment," "another embodiment," "other embodiments," or "first embodiment to Xth embodiment," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, method steps, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0047] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0048] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A method for rural renewable energy power generation consumption based on energy storage-charging cooperation, characterized in that, Including the following steps: S1, obtain the output parameters of distributed rural renewable energy, and establish an assessment model for on-site consumption of rural renewable energy with the objective of minimizing the amount of abandoned distributed rural renewable energy. The objective function is: ; T represents the total amount of abandoned distributed rural renewable energy within the area during time period T; N represents the total number of distributed rural renewable energy nodes within the area. Let i be the photovoltaic power output at time t. Let i be the wind power output at time t. Let i be the photovoltaic power absorbed at time t. Let i be the wind power output absorbed by node i at time t; S2, based on the load output parameters for local consumption, and with the goal of maximizing the self-consumption of distributed rural renewable energy, a load self-consumption model for distributed rural renewable energy is established, with the objective function being: ; This represents the total self-consumption capacity of distributed rural renewable energy within the area during time period T. Let M represent the response status of user l within the area at time t, with values ​​[-1, 0, 1] indicating load reduction, non-participation in scheduling, and load increase, respectively; M represents the total number of users. This represents the response power of user l at time t; S3. Based on maximizing the volatility of distributed rural new energy while minimizing energy storage lifespan loss, a charging and discharging model for the energy storage and charging system is established, with the objective function being: Where J represents the objective function, Let be the satisfaction rate of the new energy output fluctuation at time t. Let J be the equivalent lifetime loss of energy storage system j, and H be the number of energy storage systems; S4, a multi-objective rural new energy power generation and consumption model composed of the rural new energy local consumption assessment model, the distributed rural new energy load self-consumption model, and the charging and discharging model of the energy storage and charging system; S5. Based on the preset constraints, the rural new energy power generation consumption model is solved to obtain the final rural new energy power generation consumption method.

2. The method for rural renewable energy power generation consumption based on energy storage and charging cooperation according to claim 1, characterized in that, The constraints in step S1 include: the preset voltage range of each node voltage load, the negative current of each branch being less than the safe current, and the safe range of the maximum output power load of distributed rural new energy.

3. The method for rural renewable energy power generation consumption based on energy storage and charging cooperation according to claim 1, characterized in that, The constraints in S2 include: , t represents the scheduling time step within the scheduling period, ranging from 1 to 3 hours; The maximum number of responses for user l whose load is reduced within a scheduling cycle.

4. The method for rural renewable energy power generation consumption based on energy storage-charging cooperation according to claim 1, characterized in that, In step S3 , Where B is the total number of times when the output power of new energy is not zero before energy storage smoothing, and b is the total number of times when the output power of new energy is satisfied after energy storage smoothing. Let t be the damping value of the energy storage system on the fluctuations of distributed rural new energy sources. ， , and These refer to the energy storage charging and discharging efficiency, respectively. Let t be the charging and discharging power of the energy storage system at time t.

5. The method for rural renewable energy power generation consumption based on energy storage and charging cooperation according to claim 1, characterized in that, In step S3 ,in, Let Q be the change in the state of charge of energy storage system j before and after the qth charge and discharge cycle, Q be the total number of energy storage cycles of energy storage system j, DOD be the depth of charge and discharge of energy storage system j, cycle be the total number of energy storage cycles of energy storage system j, and Cap be the percentage of available capacity of energy storage system j.

6. The method for rural renewable energy power generation consumption based on energy storage-charging cooperation according to claim 1, characterized in that, The constraints in step S3 include: energy storage charge / discharge rate constraint, energy storage capacity constraint, inverter output power fluctuation constraint, and energy storage charge / discharge power constraint.

7. The method for rural renewable energy power generation consumption based on energy storage-charging cooperation according to claim 1, characterized in that, Step S5 includes: S51, an initial solution set is obtained through random generation or chaotic mapping and the population is initialized; S52, evaluate each solution in the initial solution set, calculate its value on each objective function, and select the solutions that meet the preset conditions as the parent. S53, Based on the parent generation, generate new solutions as offspring through a population update strategy, then use the solutions of the parent generation and offspring as a new solution set and population, and return to step S2 for repeated iteration; S4, after reaching the preset number of iterations or the preset convergence condition, obtains the optimal solution set.

8. A rural renewable energy power generation and consumption system based on energy storage and charging cooperation, characterized in that, It includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the rural new energy power generation consumption method based on storage-charging cooperation as described in any one of claims 1-7.

9. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the rural new energy power generation consumption method based on storage-charging cooperation as described in any one of claims 1 to 7.

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