NSGA and adaptive bat algorithm combined micro-grid system multi-objective optimization method

Abstract

The invention relates to an NSGA and adaptive bat algorithm combined micro-grid system multi-objective optimization method. A micro-grid system comprising a photovoltaic cell, a wind driven generator, a gas turbine, a fuel cell and an energy storage cell is constructed; analyzing the operation cost of the fuel cell and the micro gas turbine; constructing a multi-target optimal scheduling model of the micro-grid system; solving the multi-target optimal scheduling model by adopting an adaptive bat algorithm based on NSGA-II to obtain an optimal solution; and according to the optimal solution, guiding the micro-grid system to dispatch and operate. According to the method, the multi-target optimal scheduling model of the micro-grid system containing the distributed power supply is constructed, and the optimal solution of the multi-target optimal scheduling model is solved by using the adaptive bat algorithm based on NSGA-II, so that the total operation cost of the micro-grid system and the charge and discharge quantity of an energy storage battery can be reduced; the adaptive bat algorithm based on the NSGA-II has better optimization ability and convergence.

Description

Technical Field

[0001] The invention belongs to the field of power grid dispatching, and in particular relates to a multi-objective optimization method for a microgrid system combining NSGA with an adaptive bat algorithm. Background Art

[0002] With the continuous growth of energy demand and the increasing prominence of environmental issues, microgrids, as an emerging energy supply model, have shown significant potential in improving power reliability and reducing environmental impact. Microgrids have the advantages of high flexibility, high reliability and environmental friendliness, and can effectively integrate distributed power generation resources such as wind power and solar energy. However, the operation optimization problem of microgrids is a challenging multi-objective optimization problem involving multiple conflicting goals such as economy, environmental friendliness and system reliability.

[0003] The paper "Multi-objective Optimal Dispatching of Microgrids Based on Differential Evolution Algorithm" by Huang Shuyuan et al. published in the 7th issue of "Marine Electrical Technology" in 2018 considers the three aspects of power supply reliability, environmental pollution and cost of the system, and uses a multi-objective differential evolution algorithm for optimization. This paper simplifies multi-objective optimization into a single-objective optimization problem. Although it simplifies the solution process, the diversity of solutions and the integrity of information are insufficient, and other possible excellent solutions are ignored.

[0004] The paper "Research on Multi-objective Optimization of Microgrids Based on Improved NSGAII Algorithm" by Zhang Jun et al. published in the 12th issue of "Computer Application Research" in 2019 proposed an improved NSGA-Ⅱ algorithm, introduced the information entropy mechanism to improve the operator, and approximated the crossover probability and mutation probability as a decreasing function model and a Cauchy distribution model, respectively. It shows superiority in improving the convergence speed and improving the optimization indicators, but there are still shortcomings in the algorithm that it is easy to fall into local optimality and lacks the ability to find the global optimal solution. Summary of the invention

[0005] The purpose of the present invention is to provide a multi-objective optimization method for a microgrid system combining NSGA with an adaptive bat algorithm in order to solve the above problems. The method models a microgrid system including photovoltaic cells (PV), wind turbines (WT), micro-turbines (MT), fuel cells (FC) and energy storage batteries (ES) to determine the optimization objectives of the microgrid system. The fast non-dominated sorting genetic algorithm NSGA-II is combined with the adaptive bat algorithm, and the non-dominated sorting method and multi-objective evolutionary strategy of the NSGA-II algorithm are used to effectively maintain and update the non-dominated solution set of the individual. The echolocation mechanism of the adaptive bat algorithm is used to realize the search for the global optimal solution, so as to realize the multi-objective optimization scheduling of the microgrid system and reduce the operating cost of the microgrid system and the charge and discharge capacity of the energy storage battery.

[0006] In order to achieve the above object, the technical solution provided by the present invention is: The multi-objective optimization method of microgrid system combining NSGA with adaptive bat algorithm includes the following steps: Step 1: Build a microgrid system including photovoltaic cells, wind turbines, gas turbines, fuel cells, and energy storage batteries; Step 2: Analyze the operating costs of fuel cells and micro turbines; Step 3: construct a multi-objective optimization scheduling model for the microgrid system, wherein the optimization objectives of the optimization scheduling model include minimizing the microgrid operating cost and minimizing the charge and discharge amount of the energy storage battery; Step 4: Using an adaptive bat algorithm to solve the multi-objective optimization scheduling model to obtain an optimal solution; Step 5: According to the optimal solution obtained in step 4, guide the dispatching and operation of the microgrid system.

[0007] Preferably, the constraints of the optimal scheduling model include power balance constraints, output constraints of each micro power source, charging and discharging constraints of energy storage batteries, and interactive power constraints between the microgrid and the large grid.

[0008] Furthermore, the adaptive bat algorithm improves the algorithm performance by improving the bat's flight mode and introducing the adaptive weight factor w and the winning rate P a .P a Make w adaptively adjusted with the winning rate of the individual; when the winning rate is high, the adaptive bat algorithm is in the exploration stage, and the w value needs to be reduced appropriately; when the winning rate is low, the adaptive bat algorithm is in the mining stage, and a smaller w value can quickly reduce the bat speed, enabling it to better search for the best solution near the optimal solution.

[0009] Preferably, in step 4, an adaptive bat algorithm based on NSGA-Ⅱ is used to solve the multi-objective optimization scheduling model, and the adaptive bat algorithm based on NSGA-Ⅱ includes the following steps: (1) Input forecast data of wind power, photovoltaic power and load, and set algorithm parameters; (2) Initialize the bat population P0, consider the constraints of each distributed power model, calculate its fitness value, and perform fast non-dominated sorting; (3) Randomly select any solution in the Pareto = 1 solution set as the global optimal solution, and continuously update and optimize according to the speed and position update formula of the adaptive bat algorithm to generate a new population Q0; the Pareto = 1 solution set represents the set of individuals in the bat population that are not dominated by other individuals; (4) Merge populations P0 and Q0 into R0, perform fast non-dominated sorting and crowding distance calculation, and select the dominant solution set as the new parent generation P1; (5) Determine whether the iteration end condition is met. If so, terminate the operation and output the load distribution result of the distributed generation; if not, return to step (3) and continue the iteration.

[0010] Compared with the prior art, the beneficial effects of the present invention include: 1) The present invention constructs a multi-objective optimization scheduling model for a microgrid system including distributed power sources, takes the minimum comprehensive operating cost and the minimum charge and discharge amount of the energy storage battery as the optimization objectives, and uses the adaptive bat algorithm based on NSGA-II to solve the optimal solution of the multi-objective optimization scheduling model, which can reduce the overall operating cost of the microgrid system and the charge and discharge amount of the energy storage battery.

[0011] 2) The adaptive bat algorithm of the present invention introduces adaptive weight factors and winning rates, and improves the performance of the bat algorithm by improving the flying mode of bats.

[0012] 3) The present invention combines the fast non-dominated sorting genetic algorithm NSGA-II with the adaptive bat algorithm to solve the optimal solution of the multi-objective optimization scheduling model. The non-dominated sorting and congestion distance of NSGA-II can achieve the diversity and excellence of the solution. Combined with the strong global search capability of the adaptive flying bat optimization algorithm, it has better optimization ability and convergence, and can avoid falling into the local optimum.

[0013] 4) The microgrid dispatching system provided by the present invention uses an adaptive bat algorithm based on NSGA-II to solve the multi-objective optimization dispatching model of the microgrid system, obtains the optimal dispatching scheme with the minimum overall operating cost of the microgrid system and the lowest charge and discharge amount of the energy storage battery, and sends a dispatching instruction of the distributed power output to each distributed power source, thereby realizing the automatic optimization dispatching of the microgrid dispatching system. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0015] Figure 1 Schematic diagram of the structure of a microgrid system according to an embodiment of the present invention.

[0016] Figure 2 The figure is a flow chart of an adaptive bat algorithm based on NSGA-Ⅱ according to an embodiment of the present invention.

[0017] Figure 3 This is a power curve diagram of photovoltaic cells, wind turbines and electrical load prediction according to an embodiment of the present invention.

[0018] Figure 4 A comparison chart of the Pareto front solutions obtained by the method of the present invention and the NSGA-II algorithm.

[0019] Figure 5 It is an output curve diagram of each distributed power source of the optimal scheduling solution obtained by the method of the present invention in the embodiment. DETAILED DESCRIPTION

[0020] like Figure 1 As shown in FIG. 1 , the multi-objective optimization method of the microgrid system combining NSGA with the adaptive bat algorithm includes the following steps: Step 1: Build a microgrid system including solar photovoltaic power stations, wind turbines, gas turbines, fuel cells, and energy storage batteries; The energy storage battery is in a charging or discharging state at time t, which can be expressed as: SOC(t) = SOC(t-1) - P ES (t)·△t·η ch (1) SOC(t) = SOC(t-1) - P ES (t)·△t / η dis (2) In the formula, SOC(t) and SOC(t-1) are the remaining power of the energy storage battery at time t and time t-1 respectively. ES (t) is the power of the energy storage battery at time t. ES When (t) is positive, the energy storage battery is discharged, P ES When (t) is negative, the energy storage battery is charged. ch , η dis are the charge and discharge efficiencies respectively, and △t is the time interval.

[0021] Step 2: Analyze the operating costs of fuel cells and micro turbines; The operating cost of a fuel cell is: CFC = C f · ; (3) In the formula, C f is the fuel unit price, η FC For power generation efficiency; is the output power of the i-th fuel cell unit in the t-th time period; I is the number of fuel cell units, i is the sequence number of the fuel cell unit, t is the time period number, and T is the total number of operating time periods of the microgrid system.

[0022] The fuel cost of the microturbine is: C MT = C f · ; (4) In the formula, C f is the unit price of fuel, P MT is the output power of the micro gas turbine in the tth period, η MT is the power generation efficiency of the micro gas turbine, C LHV is the lower calorific value of the fuel, j is the serial number of the micro gas turbine, t is the time period serial number, and J is the number of micro gas turbines.

[0023] Step 3: construct a multi-objective optimization scheduling model for the microgrid system, wherein the optimization objectives of the optimization scheduling model include minimizing the microgrid operating cost and minimizing the charge and discharge amount of the energy storage battery; The optimal function for minimizing the microgrid operating cost is: min F1= C1+ C2+ C3; (5) C1= C MT + C FC ; (6) (7) C3= ; (8) (9) (10) Where: F1 is the operating cost of the microgrid system, C1 is the fuel cost, C MT , C FC are the fuel costs of gas turbine and fuel cell respectively; K PV , K WT , K MT , K FC , K ES are the operation and maintenance unit prices of photovoltaic units, wind turbines, gas turbines, fuel cells and energy storage batteries; C buy , C set are the costs of the microgrid purchasing and selling electricity from the large power grid during period t; ebuy 、e set are the unit prices of purchasing and selling electricity from the large power grid; P buy , P set They are the power purchased and sold by the microgrid to the large power grid respectively; C2 represents the operation and maintenance cost of the microgrid system, and C3 represents the power purchase cost of the microgrid system.

[0024] The optimal function for minimizing the charge and discharge of the energy storage battery is: (11) In the formula, F2 is the charge and discharge capacity of the energy storage battery, P ES is the power of the energy storage battery at time t, η ch , η dis are the charge and discharge efficiency respectively.

[0025] The constraints of the multi-objective optimization scheduling model include: 1) Power balance constraints: = + + ; (12) Where P load is the total power required by the microgrid; P k is the output power of the kth distributed power source, P ES is the charging and discharging power of the energy storage battery; P g represents the power of interaction with the large power grid; K represents the number of distributed power sources.

[0026] 2) Output constraints of each micro power source: P k,min ≤P k ≤P k,max ; (13) Where P k,max , P k,min are the upper and lower limits of the output of the kth distributed generation respectively.

[0027] 3) Energy storage battery charging and discharging constraints: SOC min ≤ SOC ≤ SOC max ; (14) SOC start = SOC end ; (15) P ES,min ≤P ES ≤P ES,max ; (16) In the formula, SOC is the power of the energy storage battery; SOC max , SOC minThe upper and lower limits of the energy storage battery, SOC start , SOC end are the initial state and final state of the energy storage battery charge in one cycle; P ES,max , P ES,min They are the upper and lower limits of the charging and discharging power of the energy storage battery respectively.

[0028] 4) Interaction power constraints between microgrid and large grid: P g,min ≤P g ≤P g,max ; (17) Where P g,max , P g,min They are the upper and lower limits of the interaction power between the microgrid and the large grid, respectively.

[0029] Step 4: Use the adaptive bat algorithm to solve the multi-objective optimization scheduling model and obtain the optimal solution; Step 5: According to the optimal solution obtained in step 4, guide the dispatching and operation of the microgrid system.

[0030] The Bat Algorithm (BA) was proposed by Professor Xin-She Yang in 2010. It is a meta-heuristic algorithm based on bat echolocation behavior. It imitates the mechanism of bats finding prey or avoiding obstacles through echolocation to solve optimization problems. Due to its simplicity and effectiveness, it has been widely used in many optimization problems.

[0031] The mathematical formula of the bat algorithm is as follows: The frequency update formula is: (18) In the formula represents the frequency of individual m, , Respectively represent the maximum and minimum values ​​of frequency.

[0032] speed The update formula is: (19) Location The update formula is: = + (20) Among them, β∈[0,1] is a random vector drawn from a uniform distribution, X * Represents the global optimal solution found so far.

[0033] The update formulas for loudness and pulse rate are: =α (twenty one) (twenty two) Among them, α and γ are constants, 0<α<1, γ>0. When t→∞, →0 and → .

[0034] Local Search Update Formula = +ε (twenty three) Among them, ε is a random number, , A t represents the average loudness of all bats at time t.

[0035] When a bat is searching for a target, object, or prey, it will m At speed V m Random flight with a static frequency f min , the wavelength λ changes, the loudness A0. The frequency range is f min to f max The loudness of the sound can be adjusted between A0 and A min A series of rules are used to update the bat's speed, position, and loudness as it searches for prey.

[0036] Traditional bat algorithms rely on historical speed to deviate from the optimal position of the group for search. This deviation strategy helps to cover a wider area in the search space. However, this strategy has limited effect on local search in the later stage of the algorithm.

[0037] In the embodiment, the adaptive bat algorithm improves the algorithm performance by improving the bat's flight mode, introducing an adaptive weight factor w and a winning rate P a , defined as follows: (twenty four) = ; (25) = ; (26) Where N is the number of bat populations, is the average value of the bat group's winning rate, w represents the adaptive inertia weight of the bat group, which changes with the individual fitness value; represents the pulse occurrence rate of individual m; P aMake w adaptively adjusted with the winning rate of the individual; when the winning rate is high, the adaptive bat algorithm is in the exploration stage, and the w value needs to be reduced appropriately; when the winning rate is low, the adaptive bat algorithm is in the mining stage, and a smaller w value can quickly reduce the bat speed, enabling it to better search for the best solution near the optimal solution; The speed and position update formula of the adaptive bat algorithm is as follows: (27) (28) In the formula , They represent the speed of the sth and s+1th iterations of individual m respectively; represents the frequency of individual m; is the global optimal position; , , They represent the positions of individual m at the s-1th, sth, and s+1th iterations respectively.

[0038] Non-dominated Sorting Genetic Algorithm II (NSGA-II) is a widely used multi-objective optimization algorithm. It was proposed by Kalyanmoy Deb et al. in 2002 and has been widely used in multi-objective optimization.

[0039] Among many multi-objective optimization algorithms, the NSGA-Ⅱ algorithm is simple, effective and has obvious advantages. In the solution process, the fast non-dominated sorting method is used to classify individuals: first find all individuals in the population that are not dominated by other individuals, store them in a set, and use them as a Pareto=1 individual set; then for each individual in the current set, examine the set of individuals it dominates, reduce the number of solutions of the dominated individuals by 1, store them in another set, continue to perform the above classification operation and assign corresponding Pareto numbers until all individuals are graded. M is the target number, N is the population size, and the computational complexity is O(MN 2 ).

[0040] To ensure the diversity of the population, the crowding distance is used to obtain a density estimate near a specific solution in the population. Calculation of crowding distance: The population is sorted in ascending order according to the size of each objective function value. For each objective function, the boundary solution is assigned an infinite distance value, and all other solutions in the middle are assigned a distance value that is the same as the absolute value of the normalized difference of the function values ​​of two adjacent solutions.

[0041] The basic idea of ​​NSGA-Ⅱ algorithm: (1) Randomly generate an initial population P of size N t, after non-dominated sorting, selection, crossover and mutation, the offspring population Q is generated t , combining the parents and offspring into a population R of size 2N t ,; (2) R t Perform fast non-dominated sorting, calculate the crowding degree of individuals in each non-dominated layer, and select the dominant individual set as the new parent population P t+1 ﹔ (3) Generate a new offspring population Q through the basic operations of the genetic algorithm t+1 , P t+1 With Q t+1 Combine to form a new population R t+1 , repeat the above steps until the end condition is met.

[0042] In the embodiment, an adaptive bat algorithm based on NSGA-Ⅱ is used to solve the multi-objective optimization scheduling model, such as Figure 2 , the NSGA-Ⅱ based adaptive bat algorithm comprises the following steps: (1) Input forecast data of wind power, photovoltaic power and load, and set the parameters of bat algorithm; (2) Initialize the bat population P0, consider the constraints of each distributed power model, calculate its fitness value, and perform fast non-dominated sorting; (3) Randomly select any solution in the Pareto = 1 solution set as the global optimal solution, and continuously update and optimize according to equations (27) and (28) to generate a new population Q0; the Pareto = 1 solution set represents the set of individuals in the bat population that are not dominated by other individuals; (4) Merge populations P0 and Q0 into R0, perform fast non-dominated sorting and crowding distance calculation, and select the dominant solution set as the new parent generation P1; (5) Determine whether the iteration end condition is met. If so, terminate the operation and output the load distribution result of the distributed generation; if not, return to step (3) and continue the iteration.

[0043] Figure 2 In the example, rand1 and rand2 are both random variables. rand1 is used to randomly generate a new position near the current optimal solution to increase the diversity of the search space and prevent the algorithm from falling into the local optimum. rand2 is used to determine whether the newly generated position solution is better than the current global optimal solution. By comparing the conditions related to the fitness value, it is decided whether to accept the new solution, thereby optimizing the search process.

[0044] Case analysis: The dispatching cycle of the microgrid is 1 day, which is divided into 24 time periods. The operating parameters of the microgrid system are shown in Table 1. The photovoltaic, wind power, and load forecast power are as follows: Figure 3 As shown. The rated capacity of the energy storage battery is 300kw, the charging and discharging efficiency is 75%, the initial state of SOC is 50%, the upper limit is 80%, and the lower limit is 25%. The parameters of the adaptive bat algorithm NSGA-Ⅱ-BA based on NSGA-Ⅱ are set as follows: pulse frequency f min =0,f max =1, frequency r=0.5, loudness A=1. The NSGA-Ⅱ algorithm parameters are set as: crossover probability P c =0.5, mutation probability P m =0.1. Both algorithms set the population size N=50 and the number of iterations T=100.

[0045] Table 1 Operation parameters of each distributed power source ; The transaction electricity prices between the microgrid system and the large grid during normal, peak and valley periods are shown in Table 2.

[0046] Table 2 Transaction electricity prices between microgrid and large grid at different time periods ; In order to verify the feasibility and superiority of the adaptive bat algorithm based on NSGA-II, a comparative experiment was conducted between it and the standard NSGA-II algorithm. Substitute the above micro-power source operating parameters and algorithm parameters into the example, share an initialization population, and solve in the same simulation environment. The following is obtained: Figure 4 The Pareto solution set shown in the figure. The solution results show that the optimization result of the adaptive bat algorithm based on NSGA-II is significantly better than that of the NSGA-II algorithm, with a faster convergence speed and a more comprehensive and uniform distribution of solution sets.

[0047] The NSGA-II algorithm and the adaptive bat algorithm based on NSGA-II are used to solve the comprehensive optimization of the two objectives of operating cost and energy storage battery charge and discharge, and the corresponding target values ​​are shown in Table 3. The operating cost obtained by the adaptive bat algorithm based on NSGA-II saves 4.6% compared with the optimization result of the standard NSGA-II algorithm, and the charge and discharge of the energy storage battery is reduced by 5.2%.

[0048] Table 3 Comparison of objective function values ​​of different algorithms ; The adaptive bat algorithm based on NSGA-II obtains the optimal output of each micro-power source during the dispatch cycle: During the valley period of 1:00-6:00 and 23:00-24:00, the microgrid load demand is small, and the load demand is met by dispatching wind power, gas turbines, and fuel cells. The insufficient load is met by purchasing electricity from the large power grid, and the energy storage battery is charged during this period. During the peak period of 18:00-21:00, the energy storage battery discharges to balance the output of each micro-power source, and sells electricity to the large power grid to reduce costs. The output of each distributed power source in the remaining periods is as follows Figure 5 As shown in the figure, Grid represents the interactive power with the large power grid.

[0049] The simulation results show that the adaptive bat algorithm based on NSGA-II provided by the present invention has better optimization ability and convergence than the solution results of the standard NSGA-II algorithm.

[0050] The microgrid dispatching system of the multi-objective optimization method of the microgrid system includes: A fuel cell cost calculation module is used to calculate the operating cost of the fuel cell; Gas turbine cost calculation module, used to calculate the fuel cost of the micro gas turbine; The energy storage battery charge and discharge calculation module is used to calculate the charge and discharge amount of the energy storage battery; Microgrid operation and maintenance cost calculation module, used to calculate the operation and maintenance cost of the microgrid system; The microgrid power purchase and sales calculation module is used to calculate the cost or benefit of power purchase and sales between the microgrid system and the large power grid; The adaptive bat algorithm module based on NSGA-Ⅱ realizes the combination of NSGA-Ⅱ algorithm and adaptive bat algorithm to solve the multi-objective optimization scheduling model; The optimal dispatching solution module is used to build a multi-objective optimization dispatching model for the microgrid system, call the adaptive bat algorithm module based on NSGA-Ⅱ, obtain the optimal solution, and generate the optimal dispatching solution; The command and dispatch module issues dispatch instructions for the output of each distributed power source to the operators of photovoltaic cells, wind turbines, gas turbines, fuel cells and energy storage batteries according to the optimal dispatch plan obtained by the optimal dispatch plan solving module.

Claims

1. The multi-objective optimization method of microgrid system combining NSGA and adaptive bat algorithm is characterized by: The following steps are involved: Step 1: Build a microgrid system including photovoltaic cells, wind turbines, gas turbines, fuel cells, and energy storage batteries; Step 2: Analyze the operating costs of fuel cells and micro turbines; Step 3: construct a multi-objective optimization scheduling model for the microgrid system, wherein the optimization objectives of the optimization scheduling model include minimizing the microgrid operating cost and minimizing the charge and discharge amount of the energy storage battery; Step 4: Using an adaptive bat algorithm to solve the multi-objective optimization scheduling model to obtain an optimal solution; Step 5: According to the optimal solution obtained in step 4, guide the scheduling and operation of the microgrid system.

2. The multi-objective optimization method for a microgrid system according to claim 1, characterized in that: In step 2, the operating cost of the fuel cell is: C FC = C f · ; (3) In the formula, C f is the fuel unit price, η FC For power generation efficiency; is the output power of the i-th fuel cell unit in the t-th period; I is the number of fuel cell units, i is the sequence number of the fuel cell unit, t is the sequence number of the period, and T is the total number of operating periods of the microgrid system; The fuel cost of the microturbine is: C MT = C f · ; (4) In the formula, C f is the unit price of fuel, P MT is the output power of the micro gas turbine in the tth period, η MT is the power generation efficiency of the micro gas turbine, C LHV is the lower calorific value of the fuel, j is the serial number of the micro gas turbine, t is the time period serial number, and J is the number of micro gas turbines.

3. The multi-objective optimization method for a microgrid system according to claim 2, characterized in that: In step 3, the optimization function for minimizing the microgrid operating cost is: min F1 = C1 + C2 + C3; (5) C1= C MT + C FC ;(6) ；(7) C3 = ；(8) ；(9) ； (10) Where: F1 is the operating cost of the microgrid system, C1 is the fuel cost, C MT , C FC are the fuel costs of gas turbine and fuel cell respectively; K PV , K WT , K MT , K FC , K ES are the operation and maintenance unit prices of photovoltaic battery units, wind turbines, micro gas turbines, fuel cells and energy storage batteries; C buy , C set are the costs of the microgrid purchasing and selling electricity from the large power grid during period t; e buy 、e set are the unit prices of purchasing and selling electricity from the large power grid; P buy , P set They are the power purchased and sold by the microgrid to the large power grid respectively; C2 represents the operation and maintenance cost of the microgrid system, and C3 represents the power purchase cost of the microgrid system.

4. The multi-objective optimization method for a microgrid system according to claim 3, characterized in that: In step 3, the optimization function for minimizing the charge and discharge amount of the energy storage battery is: ；(11) In the formula, F2 is the charge and discharge capacity of the energy storage battery, P ES is the power of the energy storage battery at time t, η ch , η dis are the charge and discharge efficiency respectively.

5. The multi-objective optimization method for a microgrid system according to claim 4, characterized in that: In step 3, the constraints of the multi-objective optimization scheduling model include power balance constraints: = + + ；(12) Where P load is the total power required by the microgrid; P k is the output power of the kth distributed power source, P ES is the charging and discharging power of the energy storage battery; P g represents the power of interaction with the large power grid; K represents the number of distributed power sources.

6. The multi-objective optimization method for a microgrid system according to claim 5, characterized in that: In step 3, the constraints of the multi-objective optimization scheduling model also include the output constraints of each micro-source: P k,min ≤P k ≤P k,max ;(13) Where P k,max , P k,min are the upper and lower limits of the output of the kth distributed generation respectively.

7. The multi-objective optimization method for a microgrid system according to claim 6, characterized in that: In step 3, the constraints of the multi-objective optimization scheduling model also include energy storage battery charging and discharging constraints: SOCIETY min ≤ SOC ≤ SOC max (14) SOCIETY start = SOC end (15) P ES,min ≤P ES ≤P ES,max ; (16) In the formula, SOC is the power of the energy storage battery; SOC max , SOC min SOC is the upper and lower limits of the energy storage battery. start , SOC end are the initial and final state power of the energy storage battery in a cycle; P ES,max , P ES,min They are the upper and lower limits of the charging and discharging power of the energy storage battery respectively.

8. The multi-objective optimization method for a microgrid system according to claim 7, characterized in that: In step 3, the constraints of the multi-objective optimization scheduling model also include the interactive power constraints between the microgrid and the large grid: P g,min ≤P g ≤P g,max ;(17) Where P g,max , P g,min They are the upper and lower limits of the interaction power between the microgrid and the large grid, respectively.

9. The multi-objective optimization method for a microgrid system according to claim 8, characterized in that: In step 4, the adaptive bat algorithm improves the algorithm performance by improving the bat's flight mode, introducing an adaptive weight factor w and a winning rate P a , defined as follows: ；（24） = ；（25） = ；（26） Where N is the number of bat populations, is the average value of the bat group's winning rate, w represents the adaptive inertia weight of the bat group, which changes with the individual fitness value; represents the pulse occurrence rate of individual m; The speed and position update formula of the adaptive bat algorithm is as follows: ；（27） ； （28） In the formula , They represent the speed of the sth and s+1th iterations of individual m respectively; represents the frequency of individual m; is the global optimal position; , , They represent the positions of individual m at the s-1th, sth, and s+1th iterations respectively.

10. The multi-objective optimization method for a microgrid system according to claim 9, characterized in that: In step 4, an adaptive bat algorithm based on NSGA-Ⅱ is used to solve the multi-objective optimization scheduling model. The adaptive bat algorithm based on NSGA-Ⅱ includes the following steps: (1) Input forecast data of wind power, photovoltaic power and load, and set algorithm parameters; (2) Initialize the bat population P0, consider the constraints of each distributed power model, calculate its fitness value, and perform fast non-dominated sorting; (3) Randomly select any solution in the Pareto = 1 solution set as the global optimal solution, and continuously update and optimize according to equations (27) and (28) to generate a new population Q0; the Pareto = 1 solution set refers to the set of individuals in the bat population that are not dominated by other individuals; (4) Merge populations P0 and Q0 into R0, perform fast non-dominated sorting and crowding distance calculation, and select the dominant solution set as the new parent generation P1; (5) Determine whether the iteration end condition is met. If so, terminate the operation and output the load distribution result of the distributed generation; if the judgment result is no, return to step (3) to continue iteration.

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