Multi-objective collaborative optimization method for photovoltaic-storage-charging micro-grid

Abstract

The invention discloses a multi-objective collaborative optimization method for a photovoltaic-energy storage-charging micro-grid. The method comprises the following steps: configuring a photovoltaic-energy storage-charging facility micro-grid system; evaluating the autonomous operation capability of the micro-grid through the dynamic internal energy autonomy index; a multi-objective optimization model is established, and the new energy consumption rate, the operation cost and the system autonomy are collaboratively optimized; an improved decomposition type multi-objective evolutionary algorithm is adopted to solve a Pareto optimal solution set; dynamically adjusting a system operation strategy through the energy management system; according to the method, a probabilistic IEA index is provided, the autonomy risk of the micro-grid is quantified, and energy storage optimization configuration is guided; a self-adaptive weight MOEA / D algorithm is designed, and multi-target efficient collaborative optimization is achieved; and a light-storage-charging multi-target cooperative control strategy is developed, and the economical efficiency and reliability of the system are improved. The invention provides a systematic solution for autonomous operation of the renewable energy microgrid, and is suitable for scenes such as intelligent charging stations, industrial parks and the like.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems and microgrids, and in particular to a multi-objective collaborative optimization method for a photovoltaic-storage-charging microgrid. Background Art

[0002] With the acceleration of global energy transformation and the increasing demand for renewable energy, the penetration of photovoltaic power generation, energy storage systems, and electric vehicle charging facilities has rapidly increased. As a clean energy source, photovoltaic power generation is significantly affected by sunlight conditions and is characterized by volatility and intermittency. To effectively utilize photovoltaic power generation and enhance the flexibility of the power system, energy storage systems and electric vehicle charging facilities are widely used in microgrids as important regulatory measures. With the large-scale application of renewable energy in microgrids, the integrated "photovoltaic-storage-charging" microgrid composed of photovoltaic power generation systems, energy storage systems, and electric vehicle charging facilities faces the following technical challenges:

[0003] 1. Insufficient energy autonomy: The volatility of photovoltaic power generation caused by weather makes it difficult for existing technologies to maintain a continuous and stable energy supply for microgrids without the support of the main grid. In typical isolated operation scenarios, traditional systems are highly dependent on the power of the main grid.

[0004] 2. Lack of multi-objective collaborative optimization: Current methods mostly adopt single-objective optimization strategies, such as:

[0005] a. If we simply pursue the maximization of the new energy consumption rate, the average operating cost of the system will increase significantly;

[0006] b. When only operating costs are optimized, the photovoltaic abandonment rate is high;

[0007] c. Focusing on autonomy optimization will lead to a significant decrease in the cycle life of the energy storage system.

[0008] The above single optimization model is difficult to achieve the coordinated goals of "high consumption - low cost - strong autonomy".

[0009] 2. Limited dynamic response capabilities: Faced with minute-level fluctuations in electric vehicle charging loads and second-level fluctuations in photovoltaic power generation, the decision-making cycle of traditional optimization algorithms (such as linear programming) cannot achieve real-time and precise control.

[0010] 3. System coupling optimization gap: Existing technologies manage photovoltaic power generation, energy storage charging and discharging, and electric vehicle V2G (vehicle-to-grid) separately, ignoring the energy and time-space complementarity between the three. This isolated management mode easily leads to system efficiency loss. Summary of the Invention

[0011] The purpose of this section is to summarize some aspects of the embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the abstract and title of this application to avoid obscuring the purpose of this section, the abstract and the title of the invention, and such simplifications or omissions should not be used to limit the scope of the present invention.

[0012] Therefore, in order to solve the above technical problems, the present invention provides the following technical solution: a multi-objective collaborative optimization method for a solar-storage-charging microgrid, comprising the following specific steps:

[0013] S1: Configure a photovoltaic-storage-charging microgrid system, which includes a photovoltaic power generation system, an energy storage system, and electric vehicle charging facilities;

[0014] S2: The internal energy autonomy index (IEA) is used to evaluate the energy autonomous operation capability of the microgrid without relying on the main grid.

[0015] S3: Establish a multi-objective optimization model that comprehensively considers multiple objectives, including maximizing the new energy consumption rate, minimizing system operating costs, and maximizing system autonomy;

[0016] S4: Use decomposition multi-objective evolutionary algorithm (MOEA / D) to solve the multi-objective optimization model and obtain the optimal solution set;

[0017] S5: Apply the optimization results to the actual operation of the microgrid system and dynamically adjust the operating status of the photovoltaic power generation system, energy storage system and electric vehicle charging facilities through the energy management system (EMS).

[0018] As a preferred solution of the multi-objective collaborative optimization method of the photovoltaic-storage-charging microgrid described in the present invention, the photovoltaic-storage-charging microgrid system also includes an energy management system (EMS) for unified scheduling and control of the operation of the photovoltaic power generation system, energy storage system and electric vehicle charging facilities.

[0019] As a preferred solution of the multi-objective collaborative optimization method of the solar-storage-charging microgrid described in the present invention, in step S2, the calculation formula of the internal energy autonomy index (IEA) is:

[0020]

[0021] Where T represents the total number of time intervals in the evaluation period; t represents the time point index; P grid (t) represents the power obtained from the main grid at time t; P Load (t) represents the total load demand at time t, Represents the probability of power loss.

[0022] As a preferred solution of the multi-objective collaborative optimization method of the solar-storage-charging microgrid described in the present invention, in step S2, the evaluation process of the internal energy autonomy index (IEA) includes:

[0023] S201: Data collection, obtaining photovoltaic power generation forecast data P within the evaluation period PV (t), load demand data P Load (t) and the initial state SOC of the energy storage system ESS (0).

[0024] S202: Power balance calculation: For each time interval t, calculate the power balance:

[0025] P Balance (t) = P PV (t)+P ESS (t)-P Load (t);

[0026] Among them, P ESS (t) represents the charge and discharge power of the energy storage system at time t;

[0027] S203: Energy storage status update: Update the energy storage system status based on the power balance result:

[0028]

[0029] S204: Main grid interactive calculation, when P Balance When (t)<0, the power P is obtained from the main grid. grid (t)=-P Balance (t).

[0030] S205: IEA calculation: Based on the above results, the internal energy autonomy index within the entire evaluation period is calculated.

[0031] As a preferred solution of the multi-objective collaborative optimization method of the solar-storage-charging microgrid of the present invention, in step S3, a multi-objective optimization model is established, which includes the following objective functions:

[0032] Maximizing the new energy consumption rate:

[0033] Among them, P PV (t) represents the photovoltaic power generation at time t, which is based on predicted data or actual measurement values; Δt represents the length of the time interval;

[0034] Minimize system operating costs:

[0035] Among them, C inv Indicates the initial investment cost of the system; Cop (t) represents the operating cost at time t;

[0036] Maximize system autonomy:

[0037] As a preferred solution of the multi-objective collaborative optimization method of the solar-storage-charging microgrid described in the present invention, in step S3, the constraints of the multi-objective optimization model include:

[0038] Power balance constraint: P PV (t)+P ESS (t)+P grid (t) = P Load (t)+P EV (t);

[0039] Where: P EV (t) represents the charge and discharge power of the electric vehicle at time t, where a positive value indicates discharge and a negative value indicates charge;

[0040] Energy storage equipment capacity constraints:

[0041] in, Indicates the maximum charge and discharge power of the energy storage system;

[0042] Charging facility operation constraints:

[0043] in, Indicates the maximum charging power of electric vehicle charging facilities.

[0044] As a preferred solution of the multi-objective collaborative optimization method of the solar-storage-charging microgrid described in the present invention, in step S4, the solution process of the decomposition multi-objective evolutionary algorithm (MOEA / D) includes:

[0045] S401: Problem decomposition: decompose the multi-objective optimization problem into multiple single-objective sub-problems. Each sub-problem corresponds to a weighted objective function, such as:

[0046]

[0047] Among them, λ represents the weight vector, f i (x) is the i-th objective function, by adjusting the weight λ i Different sub-problems can be generated.

[0048] S402: Initialization, generating the initial population and assigning a subproblem to each individual;

[0049] S403: Evolution operation, performing selection, crossover and mutation operations on individuals in the population to generate new individuals;

[0050] S404: Solve the subproblems, update the solution of each subproblem, compare it with the solutions of adjacent subproblems, and retain the optimal solution;

[0051] S405: Pareto frontier construction, generating Pareto optimal solution set and constructing Pareto frontier;

[0052] S406: Optimal solution selection: select the optimal solution from the Pareto front according to actual needs to guide the operation strategy of the microgrid.

[0053] As a preferred solution of the multi-objective collaborative optimization method of the photovoltaic-storage-charging microgrid described in the present invention, the energy management system (EMS) monitors the system operation status in real time based on the optimization results, and dynamically adjusts the energy scheduling strategy according to changes in load demand and photovoltaic power generation.

[0054] Beneficial effects of the present invention:

[0055] 1. This invention significantly enhances the autonomous operation capability of microgrids without relying on the main grid through quantitative evaluation and dynamic optimization of the internal energy autonomy index (IEA), effectively solving the energy supply challenges brought about by the volatility of photovoltaic power generation and load uncertainty.

[0056] 2. This invention overcomes the limitations of traditional single-objective optimization by establishing a multi-objective optimization model that comprehensively considers the new energy absorption rate, operating costs, and system autonomy, achieving the collaborative optimization goals of "high absorption, low cost, and strong autonomy" and improving the overall performance of the system.

[0057] 3. The present invention adopts a decomposition-based multi-objective evolutionary algorithm (MOEA / D) to decompose complex multi-objective problems into multiple single-objective sub-problems and solve them in parallel, which significantly improves the optimization efficiency, can quickly respond to real-time changes in photovoltaic power generation and load, and ensure the dynamic optimality of system operation.

[0058] 4. The present invention uses an energy management system (EMS) to uniformly dispatch and dynamically adjust photovoltaic power generation, energy storage systems, and electric vehicle charging facilities, thereby achieving flexible configuration and efficient utilization of system resources and enhancing the robustness of the microgrid in responding to emergencies.

[0059] 5. The present invention significantly improves the local absorption rate of renewable energy, reduces energy waste, and supports the construction of a green and low-carbon energy system by optimizing the coordinated operation of photovoltaic power generation, energy storage systems, and electric vehicle charging facilities.

[0060] 6. While ensuring the autonomy of the system, the present invention reduces the long-term operating expenses of the microgrid and improves its economic efficiency by optimizing the operating costs; and the constraint condition design of the multi-objective optimization model further ensures the stability and reliability of the system operation.

[0061] 7. This invention provides effective technical support for the deep integration of photovoltaic power generation, energy storage systems and electric vehicle charging facilities, helps promote the large-scale application of "photovoltaic-storage-charging" microgrids, and has broad engineering practice value. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. Those skilled in the art can also derive other drawings based on these drawings without inventive effort. Among them:

[0063] Figure 1 It is the workflow diagram of the present invention. DETAILED DESCRIPTION

[0064] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0065] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0066] Secondly, the term "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in various places throughout this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive of other embodiments.

[0067] Reference Figure 1 , which is the first embodiment of the present invention, provides a multi-objective collaborative optimization method for a solar-storage-charging microgrid, including the following specific steps:

[0068] Step 1: Configuration of the solar-storage-charging microgrid system;

[0069] First, a photovoltaic-storage-charging microgrid system is constructed, which includes a photovoltaic power generation system, an energy storage system and electric vehicle charging facilities; the photovoltaic power generation system uses solar panels to convert solar energy into electrical energy as the main energy input; the energy storage system is composed of high-efficiency battery packs, which are used to store the surplus electricity generated by photovoltaic power generation and release electricity when photovoltaic power generation is insufficient or load demand peaks, ensuring the power supply stability of the system; the electric vehicle charging facilities provide charging services for electric vehicles and support bidirectional charging functions, so that electric vehicle batteries can feed back electricity to the microgrid when needed, enhancing the flexibility and autonomy of the microgrid.

[0070] The specific steps are as follows:

[0071] 1. Photovoltaic power generation system (PV): Install an appropriate number of solar panels to convert solar energy into electricity, which serves as the main clean energy source for the microgrid;

[0072] It consists of solar panels and inverters that convert solar energy into electrical energy.

[0073] The installed capacity can be optimized according to the scale of the microgrid and local sunshine conditions, and can be set as

[0074] Maximum power point tracking (MPPT) technology is used to improve photovoltaic power generation efficiency.

[0075] 2. Energy Storage System (ESS): A battery energy storage system with appropriate capacity is configured to store excess power from photovoltaic power generation and release power when photovoltaic power generation is insufficient or when the load is peak, ensuring stable power supply.

[0076] It consists of a high-efficiency battery pack, a bidirectional converter, and a battery management system (BMS), supporting charge and discharge management.

[0077] Assume that the energy storage system capacity is And the maximum charge and discharge power is

[0078] The charging and discharging strategies are optimized based on load demand and photovoltaic power generation to ensure the energy balance of the microgrid.

[0079] 3. Electric Vehicle Charging System (EVCS): Equipped with charging piles that support bidirectional charging, so that electric vehicles can not only obtain power from the microgrid, but also feed power back to the microgrid when needed, improving system flexibility;

[0080] Set the total power of the charging pile to And intelligently schedule charging and discharging strategies based on load conditions.

[0081] 4. Energy Management System (EMS): Adopts distributed intelligent control algorithms to achieve coordinated control of photovoltaic power generation, energy storage systems and charging facilities to ensure efficient and stable operation of the system.

[0082] The main functions include: load forecasting, power scheduling, energy distribution, anomaly detection, etc.

[0083] The system parameters are set as follows:

[0084] Load demand (Load): Set the power load curve P of the microgrid Load(t) , including fixed loads and adjustable loads;

[0085] Meteorological data: collect data such as light intensity and ambient temperature to establish a photovoltaic power generation prediction model;

[0086] Dispatching strategy: Dynamically adjust the operating status of photovoltaic power generation, energy storage and charging facilities based on load demand and meteorological data.

[0087] Step 2: Evaluation of the internal energy autonomy indicator (IEA);

[0088] The internal energy autonomy index (IEA) refers to the extent to which a microgrid can meet load demand through its own renewable energy and energy storage system within a specific time period (such as a day or a year);

[0089] This embodiment uses the internal energy autonomy index (IEA) to measure the energy self-sufficiency of the microgrid. The IEA calculation formula is as follows:

[0090]

[0091] Where T represents the total number of time intervals in the evaluation period (such as one day or one year); t represents the time point index; P grid (t) represents the power obtained from the main grid at time t (kW); P Load (t) represents the total load demand at time t (kW), represents the probability of power loss;

[0092] By using historical data and scenario simulations, the probability distribution of power loss can be determined and the IEA value calculated for different energy storage system capacities. The relationship between energy storage system capacity and IEA value can be used to optimize the configuration of the energy storage system to ensure energy autonomy in various operating scenarios. The evaluation steps are as follows:

[0093] Step 201: Data collection: Acquire photovoltaic power generation forecast data, load demand data, and energy storage system initial status.

[0094] Step 202: Power balance calculation: P Balance (t) = PPV (t)+P ESS (t)-P Load (t);

[0095] Among them, P ESS (t) represents the charge and discharge power of the energy storage system at time t;

[0096] Step 203: Energy storage status update:

[0097] Based on the power balance results, update the status of the energy storage system:

[0098]

[0099] Step 204: Main grid interaction calculation:

[0100] When P Balance When (t)<0, the power P is obtained from the main grid. grid (t)=-P Balance (t).

[0101] Step 205: IEA calculation:

[0102] Based on the above results, the internal energy autonomy index for the entire evaluation period is calculated.

[0103] Step 3: Establishment of multi-objective optimization model;

[0104] After the microgrid system is built, this embodiment establishes an optimization model that comprehensively considers multiple objectives to achieve optimal performance of system operation. The model mainly includes the following objective functions:

[0105] Maximizing the new energy consumption rate improves the utilization efficiency of renewable energy by maximizing the proportion of photovoltaic power generation in total electricity consumption. This goal can be expressed as:

[0106]

[0107] Among them, P PV (t) represents the photovoltaic power generation at time t, which is based on predicted data or actual measurement values; Δt represents the length of the time interval;

[0108] Minimize system operating costs: This includes equipment investment costs, operation and maintenance costs, and electricity purchase costs. This goal can be expressed as:

[0109]

[0110] Among them, C inv Indicates the initial investment cost of the system; C op (t) represents the operating cost at time t;

[0111] Maximizing system autonomy: By optimizing energy scheduling, reducing dependence on the main grid, and improving the independent operation capability of the microgrid, the autonomy indicators can be defined as:

[0112]

[0113] The constraints of the target optimization model include:

[0114] Power balance constraint: P PV (t)+P ESS (t)+P grid (t) = P Load (t)+P EV (t);

[0115] Where: P EV (t) represents the charge and discharge power of the electric vehicle at time t, where a positive value indicates discharge and a negative value indicates charge;

[0116] Energy storage equipment capacity constraints:

[0117] in, Indicates the maximum charge and discharge power of the energy storage system;

[0118] Charging facility operation constraints:

[0119] in, Indicates the maximum charging power of electric vehicle charging facilities.

[0120] Step 4: Decomposition multi-objective evolutionary algorithm (MOEA / D) solution;

[0121] To solve the multi-objective optimization problem, this embodiment adopts a decomposition-based multi-objective evolutionary algorithm (MOEA / D), and the steps are as follows:

[0122] Step 401: Problem decomposition: Decompose the multi-objective optimization problem into multiple single-objective sub-problems, each of which corresponds to a weighted objective function, such as:

[0123]

[0124] Among them, λ represents the weight vector, f i (x) is the i-th objective function, by adjusting the weight λ i Different sub-problems can be generated.

[0125] Step 402: Initialization: Generate an initial population and assign a subproblem to each individual;

[0126] Individuals in the population represent different configurations or operation strategies of the microgrid;

[0127] Step 403: Evolution operation: performing selection, crossover and mutation operations on individuals in the population to generate new individuals;

[0128] The fitness of each individual is evaluated by the objective function of its corresponding subproblem.

[0129] Step 404: Subproblem solving: In each iteration, the solution of each subproblem is updated and compared with the solutions of adjacent subproblems, and the optimal solution is retained;

[0130] By exchanging information between adjacent individuals, the convergence speed of the algorithm and the diversity of solutions are improved;

[0131] Step 405: Pareto front construction: After multiple iterations, generate a Pareto optimal solution set and construct the Pareto front;

[0132] Solutions on the Pareto front represent the best trade-offs between different objectives;

[0133] Step 406: Optimal solution selection: Select the optimal solution from the Pareto front based on actual needs (the decision maker's preferences may also be considered) to guide the operation strategy of the microgrid to maximize energy autonomy, minimize operating costs, and minimize power fluctuations.

[0134] Step 5: Operation and optimization implementation of the microgrid system;

[0135] Based on the optimization results (optimal solution), they are applied to the actual operation of the microgrid. Specifically, the microgrid's energy management system (EMS) monitors the status of photovoltaic power generation, energy storage systems, and charging facilities in real time and dynamically adjusts the energy scheduling strategy. The specific implementation includes:

[0136] Real-time optimization and scheduling: Adjust charging, discharging, and V2G strategies based on weather and load changes;

[0137] Abnormal handling mechanism: When photovoltaic power generation suddenly decreases or load suddenly increases, the energy storage system is prioritized or the charging load is adjusted;

[0138] Long-term strategy adjustment: Optimize equipment capacity configuration based on historical data to improve economy and autonomy.

[0139] This solution proposes an internal energy autonomy indicator (IEA) to quantitatively evaluate the independent operation capability of microgrids under different energy storage configurations. Compared with existing empirical configuration methods, the IEA indicator can accurately measure the power supply capability of energy storage systems in various scenarios, and rationally configure energy storage capacity through optimization algorithms to improve system autonomy and economy. In addition, this solution constructs a multi-objective optimization model that comprehensively considers new energy consumption, operating costs, and autonomy, and adopts a decomposition-based multi-objective evolutionary algorithm (MOEA / D) for solution. Compared with traditional heuristic algorithms (such as particle swarm optimization and genetic algorithms), the MOEA / D optimization method can more efficiently find the optimal solution set, reduce computational overhead, and provide a better energy scheduling strategy.

[0140] In terms of intelligent scheduling, this solution combines load forecasting, PV power prediction, and energy storage state estimation to achieve adaptive optimization based on real-time data. Unlike existing scheduling methods based on fixed rules, this solution's intelligent EMS can dynamically adjust the microgrid's operating strategy based on external environmental changes, thereby improving the system's adaptability and stability. Especially under uncertain factors such as electricity price fluctuations and weather changes, this solution's adaptive optimization method can effectively reduce operating costs, improve PV utilization, and enhance the system's autonomy.

[0141] In summary, this solution, by proposing the IEA indicator, optimizing energy storage configuration, and employing a multi-objective optimization algorithm and intelligent scheduling strategy, achieves technological breakthroughs in the autonomy, economy, and intelligence of solar-storage-charging microgrid systems. Compared with existing technologies, this solution not only resolves the microgrid system's dependence on the main grid but also provides an optimization method that balances economy and independence.

[0142] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A multi-objective collaborative optimization method for a solar-storage-charging microgrid, characterized by: The following specific steps are included: S1: Configure a photovoltaic-storage-charging microgrid system, which includes a photovoltaic power generation system, an energy storage system, and electric vehicle charging facilities; S2: Evaluate the energy autonomy capability of the microgrid without relying on the main grid through the internal energy autonomy indicator; S3: Establish a multi-objective optimization model that comprehensively considers multiple objectives, including maximizing the new energy consumption rate, minimizing system operating costs, and maximizing system autonomy; S4: Use decomposition-based multi-objective evolutionary algorithm to solve the multi-objective optimization model and obtain the optimal solution set; S5: Apply the optimization results to the actual operation of the microgrid system, and dynamically adjust the operating status of the photovoltaic power generation system, energy storage system, and electric vehicle charging facilities through the energy management system.

2. The multi-objective collaborative optimization method of a solar-storage-charging microgrid according to claim 1, characterized in that: The photovoltaic-storage-charging microgrid system also includes an energy management system for unified scheduling and control of the operation of photovoltaic power generation systems, energy storage systems and electric vehicle charging facilities.

3. The multi-objective collaborative optimization method of the solar-storage-charging microgrid according to claim 2, characterized in that: In step S2, the calculation formula of the internal energy autonomy index is: Where T represents the total number of time intervals in the evaluation period; t represents the time point index; P grid (t) represents the power obtained from the main grid at time t; P Load (t) represents the total load demand at time t, Represents the probability of power loss.

4. The multi-objective collaborative optimization method of a solar-storage-charging microgrid according to claim 3, characterized in that: In step S2, the evaluation process of the internal energy autonomy indicator includes: S201: Data collection, obtaining photovoltaic power generation forecast data P within the evaluation period PV (t), load demand data P Load (t) and the initial state SOC of the energy storage system ESS (0). S202: Power balance calculation: For each time interval t, calculate the power balance: P Balance (t)=P PV (t)+P ESS (t)-P Load (t); Among them, P ESS (t) represents the charge and discharge power of the energy storage system at time t; S203: Energy storage status update: Update the energy storage system status based on the power balance result: S204: Main grid interactive calculation, when P Balance When (t)<0, the power P is obtained from the main grid. grid (t)=-P Balance (t). S205: IEA calculation: Based on the above results, the internal energy autonomy index within the entire evaluation period is calculated.

5. The multi-objective collaborative optimization method of a solar-storage-charging microgrid according to claim 4, characterized in that: In step S3, a multi-objective optimization model is established, which includes the following objective functions: Maximizing the new energy consumption rate: Among them, P PV (t) represents the photovoltaic power generation at time t, which is based on predicted data or actual measurement values; Δt represents the length of the time interval; Minimize system operating costs: Among them, C inv Indicates the initial investment cost of the system; C op (t) represents the operating cost at time t; Maximize system autonomy:

6. The multi-objective collaborative optimization method of a solar-storage-charging microgrid according to claim 5, characterized in that: In step S3, the constraints of the multi-objective optimization model include: Power balance constraint: P PV (t)+P ESS (t)+P grid (t) = P Load (t)+P EV (t); Where: P EV (t) represents the charge and discharge power of the electric vehicle at time t, where a positive value indicates discharge and a negative value indicates charge; Energy storage equipment capacity constraints: in, Indicates the maximum charge and discharge power of the energy storage system; Charging facility operation constraints: in, Indicates the maximum charging power of electric vehicle charging facilities.

7. The multi-objective collaborative optimization method of a solar-storage-charging microgrid according to claim 6, characterized in that: In step S4, the solution process of the decomposition multi-objective evolutionary algorithm includes: S401: Problem decomposition: decompose the multi-objective optimization problem into multiple single-objective sub-problems. Each sub-problem corresponds to a weighted objective function, such as: Among them, λ represents the weight vector, f i (x) is the i-th objective function, by adjusting the weight λ i Generate different sub-problems. S402: Initialization, generating the initial population and assigning a subproblem to each individual; S403: Evolution operation, performing selection, crossover and mutation operations on individuals in the population to generate new individuals; S404: Solve the subproblems, update the solution of each subproblem, compare it with the solutions of adjacent subproblems, and retain the optimal solution; S405: Pareto frontier construction, generating Pareto optimal solution set and constructing Pareto frontier; S406: Optimal solution selection: select the optimal solution from the Pareto front according to actual needs to guide the operation strategy of the microgrid.

8. The multi-objective collaborative optimization method of a solar-storage-charging microgrid according to claim 7, characterized in that: Based on the optimization results, the energy management system monitors the system operation status in real time and dynamically adjusts the energy scheduling strategy according to changes in load demand and photovoltaic power generation.

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