Multi-target collaborative optimization scheduling control method and system for optical storage and charging micro-grid

Abstract

The invention discloses a multi-target collaborative optimization scheduling control method and system for an optical storage and charging micro-grid, and aims to solve the multi-target conflict problem of economy, environmental protection and stability in the operation of the micro-grid by constructing a micro-grid model containing photovoltaic, energy storage, load and a power grid. A multi-objective function set for economic cost minimization, carbon emission minimization and supply and demand balance optimization is constructed, and an adaptive multi-population genetic algorithm and a Pareto optimization algorithm are innovatively proposed to collaboratively iteratively solve a Pareto optimal solution set. The search efficiency is improved through an adaptive variation rate mechanism and a multi-population strategy, and the problem of multi-target conflict is effectively solved. The method is suitable for optimal scheduling of the independent micro-grid containing photovoltaic power generation, an energy storage system and a charging load, and the energy utilization efficiency and the system stability are improved.

Description

Technical Field

[0001] This invention relates to the field of new energy microgrid power generation control technology, specifically to a multi-objective collaborative optimization scheduling and control method and system for photovoltaic-storage-charging microgrids. Background Technology

[0002] With the global energy structure transitioning towards low-carbon and renewable energy, microgrids have garnered widespread attention as a novel energy management system. By integrating photovoltaic power generation, energy storage systems, and various electrical loads, microgrids construct an energy network capable of refined energy management and efficient utilization, effectively reducing dependence on fossil fuels and minimizing carbon footprint. The rapid development of distributed energy resources, particularly the widespread adoption of rooftop photovoltaics, advancements in energy storage technology, and the extensive deployment of electric vehicle charging stations, has provided a solid foundation for microgrid construction. These facilities not only promote the local development and utilization of new energy sources but also meet end-users' diverse energy needs for cooling, heating, and electricity, enhancing the flexibility and resilience of the energy system. As a key component of distributed smart grids, the efficiency of microgrid operation directly impacts the effectiveness of the entire energy system's transformation. Energy storage technology, as a core element of microgrids, is crucial for its diversity and large-scale development. The coexistence of multiple technologies, such as compressed air energy storage, electrochemical energy storage, and thermal (cold) energy storage, provides microgrids with a wealth of intraday balance regulation methods. Especially at the user side or microgrid level, the coordinated operation of distributed photovoltaic and energy storage can not only smooth out the volatility of photovoltaic power generation and achieve peak shaving and valley filling, but also effectively improve the absorption capacity of distributed photovoltaic, reduce curtailment, and thus further improve energy utilization efficiency.

[0003] However, optimizing the scheduling and control of microgrids is no easy task. Traditional microgrid scheduling often focuses on a single objective, such as minimizing costs or improving power supply reliability, resulting in a severe lack of multi-dimensional collaborative optimization. This single-objective-oriented strategy is difficult to adapt to the current diversified energy management needs; in scenarios where economic efficiency is prioritized, over-reliance on high-carbon grid power purchases will exacerbate carbon emissions, violating the global trend of carbon neutrality; in scenarios with high reliability requirements, high-cost diesel generators may be used to maintain power supply stability, resulting in resource waste. Meanwhile, traditional static scheduling strategies are severely lacking in dynamic adaptability when facing the intermittency of photovoltaic power generation and load fluctuations. Photovoltaic output is affected by sudden changes in irradiance, resulting in a certain degree of power fluctuation; electric vehicle charging load may surge during peak hours, but fixed scheduling plans cannot correct prediction deviations in real time. This rigid strategy will cause a chain reaction of problems. When photovoltaic output drops sharply, the system may be subject to urgent load or emergency startup of fuel-fired generators, leading to increased operating costs; mismatch between day-ahead scheduling and real-time control causes energy storage charging and discharging strategies to lag, reducing the utilization rate of green electricity. At the algorithm level, traditional optimization methods have structural defects, lacking sufficient flexibility and adaptability. Traditional genetic algorithms, employing a fixed mutation rate, are prone to getting trapped in local optima and lack sufficient solution diversity when solving multi-objective optimization problems, failing to effectively approximate the Pareto front. These limitations prevent the simultaneous achievement of economic efficiency, environmental friendliness, and reliability. Pursuing low costs may sacrifice stability, reducing carbon emissions, or increasing backup capacity costs; traditional algorithms cannot resolve the conflict between multiple objectives. With the deepening of energy transition, microgrid scheduling must simultaneously consider multiple objectives such as economic efficiency, environmental friendliness, and reliability. Finding the optimal balance among these conflicting objectives is a major challenge for microgrid optimal scheduling. Summary of the Invention

[0004] The purpose of this invention is to address the limitations of existing technologies in adapting to the diverse and complex energy management needs of current microgrid scheduling optimization. Therefore, this invention proposes a multi-objective optimization-based microgrid scheduling and control method and system for photovoltaic-storage-charging-utilization systems. This invention optimizes the supply and demand relationships of multiple objectives within a microgrid model, including the photovoltaic power generation system, energy storage system, load, and power grid, thereby minimizing economic costs, minimizing carbon emissions, and optimizing supply and demand balance.

[0005] To solve the above-mentioned technical problems, the technical solution of the present invention is as follows: a multi-objective cooperative optimization scheduling and control method for photovoltaic-storage-charging microgrids, comprising: Construct a microgrid physical model that includes photovoltaic power generation system, energy storage system, load and grid interaction; Establish a multi-objective function set with the objectives of minimizing the economic cost of microgrids, minimizing the carbon emissions generated by microgrids due to electricity purchases, and minimizing the supply-demand imbalance within microgrids. By using a combined iterative optimization of an improved multi-population genetic algorithm and a Pareto optimization algorithm, a multi-objective function set is solved based on a microgrid physical model, generating the final Pareto optimal solution set. Based on preset decision preferences, a final scheduling scheme is selected from the final Pareto optimal solution set.

[0006] Preferably, the construction of the microgrid physical model, which includes a photovoltaic power generation system, an energy storage system, loads, and grid interaction, includes: Photovoltaic power generation system output power model: in, Let be the output power of a photovoltaic system at time t, which cannot exceed its maximum output power at any time t. It cannot be less than 0 (because a photovoltaic power generation system cannot generate negative power); A is the area of ​​the photovoltaic panel. Photovoltaic conversion efficiency (a dynamic value affected by factors such as temperature and light intensity); Let be the solar irradiance at time t. This formula can accurately reflect the output power of photovoltaics over a certain period of time.

[0007] Energy storage systems play a crucial role in energy buffering and regulation within microgrids. In this model, electrochemical energy storage devices are employed, and their state and power variations are considered. The energy storage system state model is as follows: in, The remaining power of the energy storage device at time t. The remaining power of the energy storage device at time t+1; To reserve the minimum remaining power for equipment; To reserve the maximum remaining power of the equipment; The charge and discharge power of the battery of the storage device at time t (positive value for charging, negative value for discharging), and its charge and discharge power at any time t must be within its specified range; Minimum charge / discharge power for the battery of the storage device; To ensure that the maximum charge and discharge power of the battery in the storage device is within the design capacity, and to prevent damage to the system during the charging and discharging process; The battery charging and discharging efficiency of the storage device (affected by factors such as battery status and temperature). To store the maximum capacity of the device's battery; Load demand is a crucial objective that microgrids must meet. The time-varying nature of load demand is considered in the model construction, ensuring that the formula accurately reflects changes in load demand at different points in time. Power balance model: This equation reflects the power balance relationship among the various components of a microgrid, namely, the photovoltaic power generation, the power exchange with the main grid (purchasing or selling electricity), and the charging and discharging power of the energy storage system together meet the load demand. This indicates the amount of electricity purchased from or sold from the main grid (the sign depends on the direction of the power exchange). For load demand.

[0008] Preferably, the establishment of a multi-objective function set aimed at minimizing the economic cost of the microgrid, minimizing the carbon emissions generated by the microgrid due to electricity purchase, and minimizing the supply-demand imbalance within the microgrid includes: Economic costs of microgrids: in, The price at which electricity is purchased from the main grid; The price at which electricity is sold from the main grid (affected by market supply and demand, policies, and other factors). The power consumption for which electricity is purchased; This refers to the power of electricity sold (in practical applications, the power sold is usually small or zero, but this term is still retained here to maintain consistency in the formula).

[0009] Carbon emissions from electricity purchases: in, This indicates the carbon emission intensity of electricity purchased from the main grid (affected by factors such as power generation method and energy structure). The formula represents the power purchased, and it calculates the total carbon emissions of the microgrid over the entire dispatch cycle.

[0010] Supply and demand imbalance within the microgrid: .

[0011] This formula calculates the supply-demand imbalance within each time step and sums them to obtain the total imbalance over the entire scheduling cycle. The goal is to ensure that the microgrid maintains a supply-demand balance as much as possible during optimized scheduling, thereby improving system stability and reliability.

[0012] Preferably, the step of generating a set of scheduling schemes by co-iterative optimization using an improved multi-population genetic algorithm and the Pareto optimization algorithm, based on a microgrid physical model, to solve for a multi-objective function set includes: In the improved multi-population genetic algorithm, an initial population is randomly generated and divided into K subpopulations. The optimization objectives of each subpopulation in the multi-objective function set are used to generate random initial solutions for each subpopulation that satisfy all microgrid physical constraints in the microgrid physical model. Each subpopulation independently performs selection, crossover, and mutation operations to adapt to the corresponding optimization objective. The mutation operation adopts an adaptive mutation rate, the probability of which is dynamically adjusted according to the individual fitness. After each generation of evolution, all individuals in all subpopulations are merged into a new candidate scheduling scheme, and each new candidate scheduling scheme is integrated into a total of candidate scheduling schemes. When the preset period is reached, the best individuals in each subpopulation that are adapted to the microgrid requirements are exchanged to update the optimization direction. The Pareto algorithm is used to optimize the candidate scheduling scheme set, and the current Pareto front is selected and fed back to each subpopulation to guide the evolutionary direction of each subpopulation until the preset termination condition is met, thus obtaining the final Pareto optimal solution set.

[0013] In multi-objective dynamic optimization scheduling, the adaptive multi-population genetic algorithm employs the Pareto optimization strategy to handle conflicts between multiple objectives. This algorithm dynamically adjusts the genetic process through an adaptive mutation rate to enhance search capabilities. Building upon this, a multi-population strategy is introduced, dividing the total population into several subpopulations, allowing the populations to evolve independently in a distributed space. After each iteration, individuals undergo dynamic information exchange and mutation operations with a certain probability. This method not only maintains population diversity but also increases the probability of escaping local optima, thus making it more likely to find the global optimum. Therefore, compared to traditional genetic algorithms, this method effectively improves optimization efficiency by flexibly adjusting the mutation rate and population structure after each genetic operation, solving the problems of premature convergence and low search efficiency that traditional genetic algorithms often encounter when dealing with multi-objective optimization problems.

[0014] Preferably, each subpopulation independently undergoes mutation operations to adapt to the corresponding optimization objective, employing an adaptive mutation rate and dynamically adjusting the mutation probability based on individual fitness, as detailed below: Traditional genetic algorithms suffer from several problems when solving multi-objective optimization problems, such as early convergence and low search efficiency. In solving microgrid optimal scheduling problems, a fixed mutation rate and a single population strategy are typically employed. A fixed mutation rate may prevent the algorithm from flexibly balancing exploration and utilization capabilities during the search process, leading to getting trapped in local optima. Conversely, a single population strategy may limit population diversity, causing premature convergence. To address these issues, this paper proposes improvements to existing genetic algorithms.

[0015] To overcome the problems associated with a fixed mutation rate, an adaptive mutation rate is proposed. It dynamically adjusts the mutation probability based on the fitness of an individual, allowing the algorithm to flexibly balance exploration and exploitation capabilities during the search process. Adaptive Mutation Rate This refers to the parameter adjustment probability for a given candidate scheduling scheme. The candidate scheme needs to explicitly include the 24-hour photovoltaic power output allocation strategy, the energy storage charging and discharging time periods and power parameters, and the grid power purchase and sale time periods and power parameters. The core scheduling parameters directly determine whether the scheme needs to be optimized, and are the binding carrier between algorithm optimization and the actual operation requirements of the microgrid. This represents the minimum probability of variation. The value needs to be set in conjunction with the load fluctuation range of the microgrid. The purpose is to avoid over-adjusting the "high-quality scheme that has been adapted to photovoltaic output and load" and to prevent damage to the operation stability of the microgrid. This represents the maximum probability of variation. Its value needs to be set in conjunction with the hardware constraints of the energy storage system. The purpose is to force parameter optimization for "inferior solutions with excessively high electricity purchase costs and excessive carbon emissions" to avoid microgrids from shedding loads or purchasing high-carbon electricity due to solution defects. The maximum fitness value in the population (i.e., the fitness of the best individual) refers to the comprehensive score of the optimal scheduling scheme in the current iteration. The score is calculated based on the multi-objective function of "minimizing economic cost, minimizing carbon emissions, and minimizing supply and demand balance" as defined in claim 3 of this invention, and represents the current scheduling level that best meets the multi-objective requirements of the microgrid. The fitness of the current individual (calculated based on the objective function) represents the overall score of a candidate scheduling scheme. The average fitness of the population (i.e., the arithmetic mean of the fitness of all individuals) is represented by the average comprehensive score of all candidate scheduling schemes in the current iteration.

[0016] Preferably, the Pareto algorithm is used to optimize the candidate scheduling scheme set, and the current Pareto front is selected and fed back to each subpopulation to guide the evolutionary direction of each subpopulation until a preset termination condition is met, resulting in the final Pareto optimal solution set, including: Using the physical constraints in the microgrid physical model as boundaries, and with the optimization objectives of minimizing the economic cost of the microgrid, minimizing the carbon emissions generated by the microgrid due to electricity purchase, and minimizing the supply and demand imbalance within the microgrid, a set of candidate scheduling schemes is input. Calculate all objective function values ​​for each scheduling scheme in the candidate scheduling scheme set, sort them hierarchically according to dominance relationship, and select the highest-ranking non-dominated solution set as the contemporary Pareto front. Calculate the crowding distance of each solution in the contemporary Pareto front, and filter and eliminate solutions based on the crowding distance to retain the optimal solution set; If the preset termination condition is not met, the optimal solution set selected in the current Pareto frontier will be used as the initial seed or elite individuals for the next generation of adaptive multi-population genetic algorithm, and fed back to each subpopulation to update the optimization direction. If the preset termination condition is met, the final Pareto optimal solution set will be output.

[0017] Preferably, the step of selecting the final scheduling scheme from the final Pareto optimal solution set based on preset decision preferences includes: If an economic priority mode is adopted, the scheduling scheme with the lowest total operating economic cost of the microgrid system is selected from the final Pareto optimal solution set. If an environmental protection priority mode is adopted: select the dispatch scheme with the lowest carbon emissions from purchasing electricity from the grid from the final Pareto optimal solution set; If a comprehensive trade-off model is adopted: the objective function values ​​of each scheme in the solution set are weighted and standardized by setting the preference weights of three objectives: economic cost, carbon emissions, and supply-demand imbalance, and the scheduling scheme with the highest comprehensive score is selected.

[0018] Another objective of this invention is to provide a multi-objective collaborative optimization scheduling and control system for photovoltaic-storage-charging microgrids, comprising: The microgrid physical model building module is used to build a microgrid physical model that includes photovoltaic power generation system, energy storage system, load and grid interaction; The multi-objective function set establishment module is used to establish a multi-objective function set with the objectives of minimizing the economic cost of the microgrid, minimizing the carbon emissions generated by the microgrid due to electricity purchase, and minimizing the supply and demand imbalance within the microgrid. The Pareto optimal solution set generation module is used to solve the multi-objective function set based on the microgrid physical model by using a cooperative iterative optimization of an improved multi-population genetic algorithm and the Pareto optimization algorithm to generate the final Pareto optimal solution set. The scheduling scheme selection module selects the final scheduling scheme from the final Pareto optimal solution set based on preset decision preferences.

[0019] The present invention also provides a computer storage medium storing a computer program, which, when executed by a processor, implements the steps of the multi-objective collaborative optimization scheduling and control method for photovoltaic-storage-charging microgrids as described above.

[0020] The present invention also provides an electronic device, including a memory and a processor: the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions, wherein when the computer-executable instructions are executed by the processor, the steps of the multi-objective collaborative optimization scheduling and control method for photovoltaic-storage-charging microgrids as described above are implemented.

[0021] The beneficial effects of this invention are as follows: This invention first divides the total population into subpopulations for parallel evolution using multiple population strategies, ensuring the diversity of the solution space. Second, it employs an adaptive mutation rate to dynamically adjust genetic operations, balancing global exploration and local exploitation capabilities to improve the accuracy of the evolutionary direction. Finally, it updates the non-dominated solution set through a Pareto optimization strategy, outputting an economical, environmentally friendly, and supply-demand balanced solution. This improves the overall optimization efficiency and solves the problems of premature convergence and low search efficiency in traditional genetic algorithms.

[0022] The above description is merely an overview of the technical solution of the present invention. In order to better understand the technical means of the present invention and to implement it according to the contents of the specification, the preferred embodiments of the present invention are described in detail below with reference to the accompanying drawings. Specific embodiments of the present invention are given in detail below with reference to the accompanying drawings. Attached Figure Description

[0023] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate exemplary embodiments of the invention and, together with their description, serve to explain the invention and do not constitute an undue limitation thereof. In the drawings: Figure 1 The flowchart of the multi-objective collaborative optimization scheduling and control method for photovoltaic-storage-charging microgrids provided in Example 1 is shown below. Figure 2 This is a flowchart of the collaborative iterative optimization solution of the improved multi-population genetic algorithm and Pareto optimization algorithm in Example 1; Figure 3 The power curves for photovoltaic power generation, energy storage system, grid and load demand in the microgrid system simulated in Example 1 are shown. Figure 4 This is a block diagram of the multi-objective collaborative optimization scheduling and control method for photovoltaic-storage-charging microgrids provided in Example 2. Detailed Implementation

[0024] The principles and features of the present invention are described below with reference to the accompanying drawings. The examples given are only for explaining the present invention and are not intended to limit the scope of the present invention.

[0025] Example 1 like Figure 1As shown, this embodiment provides a multi-objective collaborative optimization scheduling and control method for photovoltaic-storage-charging microgrids. It simulates an independent microgrid system consisting of a photovoltaic power generation system, an energy storage system, loads, and a power grid. The photovoltaic power generation system serves as the primary source of renewable energy generation. The energy storage system is used to mitigate the volatility of photovoltaic power generation and the uncertainty of load demand. The power grid acts as a backup power source, providing power support when necessary. The case data includes photovoltaic power generation data, energy storage system data, load demand data, and power grid data. The photovoltaic power generation data simulates the solar irradiance over 24 hours and calculates the photovoltaic power output. The energy storage system data simulates the charging and discharging power of the energy storage system, considering battery charging and discharging efficiency, maximum capacity, and power limitations. The load demand data simulates the load demand over 24 hours, reflecting the time-varying nature of electricity load. The power grid data simulates the power purchase and sale of electricity by the grid, considering electricity prices and carbon emission intensity. Running the simulation calculations yields power curves for photovoltaic power generation, the energy storage system, the power grid, and load demand. The process includes the following steps: S1. Construct a microgrid physical model that includes photovoltaic power generation system, energy storage system, load and grid interaction; Photovoltaic power generation system output power model: in, Let be the output power of a photovoltaic system at time t, which cannot exceed its maximum output power at any time t. It cannot be less than 0 (because a photovoltaic power generation system cannot generate negative power); A is the area of ​​the photovoltaic panel. Photovoltaic conversion efficiency (a dynamic value affected by factors such as temperature and light intensity); Let be the solar irradiance at time t.

[0026] Energy storage system state model: in, The remaining power of the energy storage device at time t. The remaining power of the energy storage device at time t+1; To reserve the minimum remaining power for equipment; To reserve the maximum remaining power of the equipment; The charge and discharge power of the battery of the storage device at time t (positive value for charging, negative value for discharging), and its charge and discharge power at any time t must be within its specified range; Minimum charge / discharge power for the battery of the storage device; To ensure that the maximum charge and discharge power of the battery in the storage device is within the design capacity, and to prevent damage to the system during the charging and discharging process; The battery charging and discharging efficiency of the storage device (affected by factors such as battery status and temperature). This is the maximum capacity of the battery in the storage device.

[0027] Power balance model: This equation reflects the power balance relationship among the various components of a microgrid, namely, the photovoltaic power generation, the power exchange with the main grid (purchasing or selling electricity), and the charging and discharging power of the energy storage system together meet the load demand. This indicates the amount of electricity purchased from or sold from the main grid (the sign depends on the direction of the power exchange). For load demand.

[0028] S2. Establish a multi-objective function set with the objectives of minimizing the economic cost of microgrids, minimizing the carbon emissions generated by microgrids due to electricity purchases, and minimizing the supply-demand imbalance within microgrids. Economic costs of microgrids: in, The price at which electricity is purchased from the main grid; The price at which electricity is sold from the main grid (affected by market supply and demand, policies, and other factors). The power consumption for which electricity is purchased; This refers to the power of electricity sold (in practical applications, the power sold is usually small or zero, but this term is still retained here to maintain consistency in the formula).

[0029] Carbon emissions from electricity purchases: in, This indicates the carbon emission intensity of electricity purchased from the main grid (affected by factors such as power generation method and energy structure). The formula represents the power purchased, and it calculates the total carbon emissions of the microgrid over the entire dispatch cycle.

[0030] Supply and demand imbalance within the microgrid: .

[0031] This formula calculates the supply-demand imbalance within each time step and sums them to obtain the total imbalance over the entire scheduling cycle. The goal is to ensure that the microgrid maintains a supply-demand balance as much as possible during optimized scheduling, thereby improving system stability and reliability.

[0032] S3, such as Figure 2As shown, through the collaborative iterative optimization of an improved multi-population genetic algorithm and the Pareto optimization algorithm, the multi-objective function set is solved based on the microgrid physical model, generating the final Pareto optimal solution set, as detailed below: 3.1 In the improved multi-population genetic algorithm, an initial population is randomly generated and divided into K subpopulations, where K≥3, corresponding to the microgrid scheduling objectives of "economic priority", "environmental protection priority", and "supply and demand balance priority" respectively. An initial solution is randomly generated for each subpopulation, including photovoltaic output. Energy storage charging and discharging power Power grid interaction Furthermore, the following physical constraints of the microgrid physical model established by S1 must be satisfied: Photovoltaic power constraints: To ensure that the initial solution does not exceed the maximum output of the photovoltaic system; Energy storage state constraints: , This avoids the initial solution causing overcharging / over-discharging of energy storage.

[0033] Power balance constraints: This ensures that the initial solution meets the basic balance between supply and demand in the microgrid.

[0034] 3.2 Setting the adaptive variability parameter: Setting σ min (Minimum variability rate, adapting to load fluctuations), σ max (Maximum mutation rate, adapting to energy storage hardware constraints); Iteration parameters: setting the maximum number of iterations, information exchange interval N generations (e.g., N=10, corresponding to actual scheduling 2-hour update), and Pareto solution set preset capacity.

[0035] 3.3 Each subpopulation should independently perform selection, crossover, and mutation operations to adapt to the corresponding optimization objectives, and the evolutionary operations of each subpopulation should be directionally adapted to its corresponding microgrid scheduling objectives to avoid indiscriminate evolution.

[0036] Selection operation: Selecting high-quality individuals based on the fitness function of the subpopulation objective, for example: Economic priority population: Using the economic cost of microgrids as the core fitness indicator, select the dispatch scheme with lower cost; Environmental priority subpopulation: Using the amount of carbon emissions generated from electricity purchases as the core fitness indicator, select dispatch schemes with lower carbon emissions; Supply and demand balance priority subpopulation: Using the supply and demand imbalance within the microgrid as the core fitness indicator, select the dispatch scheme with better balancing effect.

[0037] Crossover operation: Exchange the "critical time segment parameters" of two parent scheduling schemes. For example, for peak load periods, exchange the energy storage discharge power and power purchase power parameters of the parent scheme to generate a child scheme that takes into account the advantages of the parent scheme, ensuring that the crossover result still conforms to the constraints of the microgrid model.

[0038] The mutation operation employs an adaptive mutation rate, the probability of which is dynamically adjusted based on the individual's fitness, as detailed below: For example, when a certain scheme is in the economic priority subpopulation The cost is much higher than the average level of the population. When ), (High mutation rate) Adjust its (Electricity purchase period / power) to drive the optimization of the solution towards lower cost. f max This represents the maximum fitness value in the population (i.e., the fitness of the best individual). f(i) The fitness of the current individual (calculated based on the objective function) represents the overall score of a candidate scheduling scheme. f avg The average fitness of the population (i.e., the arithmetic mean of the fitness of all individuals) is represented by the average comprehensive score of all candidate scheduling schemes in the current iteration.

[0039] 3.4 After each generation of evolution, all individuals in all subpopulations are merged into a new candidate scheduling scheme, and each new candidate scheduling scheme is integrated into a total of candidate scheduling schemes. At the predetermined interval (e.g., every 10 generations, corresponding to an actual scheduling update every 2 hours), the best individuals in each subpopulation that are adapted to the microgrid's needs are exchanged to update their optimization direction, sharing high-quality scheduling parameters (such as energy storage charging and discharging strategies and grid power purchase timing) to avoid a single subpopulation falling into local optima. The exchange can be done by randomly selecting several individuals or by selecting individuals based on a certain strategy (such as fitness ranking).

[0040] 3.5 The Pareto algorithm is applied to optimize the candidate scheduling scheme set, and the current Pareto front is selected and fed back to each subpopulation to guide the evolutionary direction of each subpopulation in step 3.3 until a preset termination condition is met, and the final Pareto optimal solution set is obtained, as follows: Using the physical constraints in the microgrid physical model as boundaries, and with the optimization objectives of minimizing the economic cost of the microgrid, minimizing the carbon emissions generated by the microgrid due to electricity purchase, and minimizing the supply and demand imbalance within the microgrid, a set of candidate scheduling schemes is input. Calculate all objective function values ​​for each scheduling scheme in the candidate scheduling scheme set, traverse the candidate schemes, and calculate the three major objective values ​​for each scheme; determine the dominance relationship; classify the undominated schemes into the first front layer (core non-dominated solutions), remove the first layer and repeat the sorting, complete the hierarchical sorting of all schemes, and select the set of non-dominated solutions with the highest level as the contemporary Pareto front (i.e., the first front layer). Calculate the crowding distance of each solution in the current Pareto front (i.e., the first front layer) (to measure the uniqueness of the solution; the larger the distance, the stronger the representativeness); if the number of individuals in the layer exceeds the preset capacity, delete the individual with the smallest crowding distance, and retain the "economic optimal", "low-carbon optimal", "balance optimal" and compromise solutions to ensure that the solution set covers the entire trade-off region; If the preset termination condition is not met, the optimal solution set selected in the current Pareto frontier will be used as the initial seed or elite individuals for the next generation of adaptive multi-population genetic algorithm, and fed back to each subpopulation in step 3.3 to update the optimization direction. If the preset termination condition is met, the final Pareto optimal solution set will be output.

[0041] S4. Based on preset decision preferences, select the final scheduling scheme from the final Pareto optimal solution set.

[0042] If an economic priority mode is adopted, the scheduling scheme with the lowest total operating economic cost of the microgrid system is selected from the final Pareto optimal solution set. If an environmental protection priority mode is adopted: select the dispatch scheme with the lowest carbon emissions from purchasing electricity from the grid from the final Pareto optimal solution set; If a comprehensive trade-off model is adopted: the objective function values ​​of each scheme in the solution set are weighted and standardized by setting the preference weights of three objectives: economic cost, carbon emissions, and supply-demand imbalance, and the scheduling scheme with the highest comprehensive score is selected.

[0043] Through simulation calculations, power curves for photovoltaic power generation, energy storage systems, power grid, and load demand were obtained, such as... Figure 3 As shown.

[0044] As shown in the figure, the photovoltaic (PV) power output is high during the day (6:00 to 18:00) and zero at night (18:00 to 6:00), reflecting both the intermittent nature of PV power generation and a good match with load demand. The energy storage system discharges when sunlight is insufficient and charges when sunlight is sufficient, effectively playing a role in energy buffering and regulation. Simultaneously, the amount of electricity purchased from the main grid by the load is significantly reduced after optimization, especially during peak hours. Through the discharge of the energy storage system and the supplementation by PV power generation, dependence on the main grid is reduced. The optimized dispatching method achieves supply and demand balance in the microgrid, improving system stability and reliability. This further verifies that the proposed method performs excellently in improving the economy, environmental friendliness, and operational stability of microgrids, providing a new solution and theoretical support for the optimized dispatching of independent microgrids.

[0045] Example 2 like Figure 4 As shown, this embodiment provides a multi-objective collaborative optimization scheduling and control system for photovoltaic-storage-charging microgrids, including: The microgrid physical model building module is used to build a microgrid physical model that includes photovoltaic power generation system, energy storage system, load and grid interaction; The multi-objective function set establishment module is used to establish a multi-objective function set with the objectives of minimizing the economic cost of the microgrid, minimizing the carbon emissions generated by the microgrid due to electricity purchase, and minimizing the supply and demand imbalance within the microgrid. The Pareto optimal solution set generation module is used to solve the multi-objective function set based on the microgrid physical model by using a cooperative iterative optimization of an improved multi-population genetic algorithm and the Pareto optimization algorithm to generate the final Pareto optimal solution set. The scheduling scheme selection module selects the final scheduling scheme from the final Pareto optimal solution set based on preset decision preferences.

[0046] Example 3 The present invention also provides a computer storage medium storing a computer program, which, when executed by a processor, implements the steps of the multi-objective collaborative optimization scheduling and control method for photovoltaic-storage-charging microgrids as described in Embodiment 1.

[0047] Example 4 The present invention also provides an electronic device, including a memory and a processor: the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions, wherein when the computer-executable instructions are executed by the processor, the steps of the multi-objective collaborative optimization scheduling and control method for photovoltaic-storage-charging microgrids as described in Embodiment 1 are implemented.

[0048] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Those skilled in the art can readily implement the present invention based on the accompanying drawings and the above description. However, any modifications, alterations, or variations made by those skilled in the art without departing from the scope of the present invention, utilizing the disclosed technical content, are equivalent embodiments of the present invention. Furthermore, any modifications, alterations, or variations made to the above embodiments based on the essential technology of the present invention are still within the protection scope of the present invention.

Claims

1. A multi-objective collaborative optimization scheduling and control method for photovoltaic-storage-charging microgrids, characterized in that, include: Construct a microgrid physical model that includes photovoltaic power generation system, energy storage system, load and grid interaction; Establish a multi-objective function set with the objectives of minimizing the economic cost of microgrids, minimizing the carbon emissions generated by microgrids due to electricity purchases, and minimizing the supply-demand imbalance within microgrids. By using a combined iterative optimization of an improved multi-population genetic algorithm and a Pareto optimization algorithm, a multi-objective function set is solved based on a microgrid physical model, generating the final Pareto optimal solution set. Based on preset decision preferences, a final scheduling scheme is selected from the final Pareto optimal solution set.

2. The multi-objective collaborative optimization scheduling and control method for photovoltaic-storage-charging microgrids according to claim 1, characterized in that, The construction of the microgrid physical model, which includes a photovoltaic power generation system, an energy storage system, loads, and grid interaction, includes: Photovoltaic power generation system output power model: in, Let be the output power of the photovoltaic at time t. A represents the maximum output power of the photovoltaic panel; A represents the area of ​​the photovoltaic panel. Photovoltaic conversion efficiency; Let be the solar irradiance at time t. Energy storage system state model: in, The remaining power of the energy storage device at time t. The remaining power of the energy storage device at time t+1; To reserve the minimum remaining power for equipment; To reserve the maximum remaining power of the equipment; The charge / discharge power of the battery in the storage device at time t. Minimum charge / discharge power for the battery of the storage device; To store the maximum charge and discharge power of the device's battery. The battery charging and discharging efficiency of the storage device; To store the maximum capacity of the device's battery; Power balance model: in, This indicates the amount of electricity purchased or sold from the main grid. For load demand.

3. The multi-objective collaborative optimization scheduling and control method for photovoltaic-storage-charging microgrids according to claim 2, characterized in that, The establishment of a multi-objective function set, aimed at minimizing the economic cost of microgrids, minimizing carbon emissions from electricity purchases by microgrids, and minimizing supply-demand imbalances within microgrids, includes: Economic costs of microgrids: in, The price at which electricity is purchased from the main grid. The price at which electricity is sold from the main grid. For the power of electricity purchased, The power output for selling electricity; Carbon emissions from electricity purchases: in, This indicates the carbon emission intensity of electricity purchased from the main grid. Indicates the power consumption of the purchased electricity; Supply and demand imbalance within the microgrid: 。 4. The multi-objective collaborative optimization scheduling and control method for photovoltaic-storage-charging microgrids according to claim 1, characterized in that, The improved multi-population genetic algorithm and Pareto optimization algorithm are used in a coordinated iterative optimization process to solve a multi-objective function set based on a microgrid physical model, generating a set of scheduling schemes, including: In the improved multi-population genetic algorithm, an initial population is randomly generated and divided into K subpopulations. The optimization objectives of each subpopulation in the multi-objective function set are used to generate random initial solutions for each subpopulation that satisfy all microgrid physical constraints in the microgrid physical model. Each subpopulation independently performs selection, crossover, and mutation operations to adapt to the corresponding optimization objectives; after each generation of evolution, all individuals in all subpopulations are merged into a new candidate scheduling scheme, and each new candidate scheduling scheme is integrated into a total of candidate scheduling schemes; and when the preset period is reached, the best individuals in each subpopulation that are adapted to the microgrid requirements are exchanged to update the optimization direction. The Pareto algorithm is used to optimize the candidate scheduling scheme set, and the current Pareto front is selected and fed back to each subpopulation to guide the evolutionary direction of each subpopulation until the preset termination condition is met, thus obtaining the final Pareto optimal solution set.

5. The multi-objective collaborative optimization scheduling and control method for photovoltaic-storage-charging microgrids according to claim 4, characterized in that, Each subpopulation independently undergoes mutation operations to adapt to the corresponding optimization objective, using an adaptive mutation rate that dynamically adjusts the mutation probability based on individual fitness, as detailed below: Among them, adaptive variability rate The pointer indicates the probability of parameter adjustments for a given candidate scheduling scheme. This represents the minimum probability of mutation. This represents the maximum mutation probability. This represents the maximum fitness value in the population, and the overall score of the optimal scheduling scheme in the current iteration. This represents the fitness of the current individual, indicating the overall score of a candidate scheduling scheme; The average fitness value in the population is represented by the average comprehensive score of all candidate scheduling schemes in the current iteration.

6. The multi-objective collaborative optimization scheduling and control method for photovoltaic-storage-charging microgrids according to claim 4, characterized in that, The Pareto algorithm is used to optimize the candidate scheduling scheme set, and the current Pareto front is selected and fed back to each subpopulation to guide the evolutionary direction of each subpopulation until a preset termination condition is met, resulting in the final Pareto optimal solution set, including: Using the physical constraints in the microgrid physical model as boundaries, and with the optimization objectives of minimizing the economic cost of the microgrid, minimizing the carbon emissions generated by the microgrid due to electricity purchase, and minimizing the supply and demand imbalance within the microgrid, a set of candidate scheduling schemes is input. Calculate all objective function values ​​for each scheduling scheme in the candidate scheduling scheme set, sort them hierarchically according to dominance relationship, and select the highest-ranking non-dominated solution set as the contemporary Pareto front. Calculate the crowding distance of each solution in the contemporary Pareto front, and filter and eliminate solutions based on the crowding distance to retain the optimal solution set; If the preset termination condition is not met, the optimal solution set selected in the current Pareto frontier will be used as the initial seed or elite individuals for the next generation of adaptive multi-population genetic algorithm, and fed back to each subpopulation to update the optimization direction. If the preset termination condition is met, the final Pareto optimal solution set will be output.

7. The multi-objective collaborative optimization scheduling and control method for photovoltaic-storage-charging microgrids according to claim 1, characterized in that, The selection of the final scheduling scheme from the final Pareto optimal solution set based on preset decision preferences includes: If an economic priority mode is adopted, the scheduling scheme with the lowest total operating economic cost of the microgrid system is selected from the final Pareto optimal solution set. If an environmental protection priority mode is adopted: select the dispatch scheme with the lowest carbon emissions from purchasing electricity from the grid from the final Pareto optimal solution set; If a comprehensive trade-off model is adopted: the objective function values ​​of each scheme in the solution set are weighted and standardized by setting the preference weights of three objectives: economic cost, carbon emissions, and supply-demand imbalance, and the scheduling scheme with the highest comprehensive score is selected.

8. A multi-objective collaborative optimization scheduling and control system for photovoltaic-storage-charging microgrids, characterized in that, include: The microgrid physical model building module is used to build a microgrid physical model that includes photovoltaic power generation system, energy storage system, load and grid interaction; The multi-objective function set establishment module is used to establish a multi-objective function set with the objectives of minimizing the economic cost of the microgrid, minimizing the carbon emissions generated by the microgrid due to electricity purchase, and minimizing the supply and demand imbalance within the microgrid. The Pareto optimal solution set generation module is used to solve the multi-objective function set based on the microgrid physical model by using a cooperative iterative optimization of an improved multi-population genetic algorithm and the Pareto optimization algorithm to generate the final Pareto optimal solution set. The scheduling scheme selection module selects the final scheduling scheme from the final Pareto optimal solution set based on preset decision preferences.

9. A computer storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the multi-objective collaborative optimization scheduling and control method for photovoltaic-storage-charging microgrids as described in any one of claims 1-7.

10. An electronic device, characterized in that, Includes a memory and a processor: the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions, which, when executed by the processor, implement the steps of the multi-objective collaborative optimization scheduling and control method for photovoltaic-storage-charging microgrids as described in any one of claims 1-7.

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