Grid-connected micro-grid capacity optimization configuration method based on improved dogvessel colony algorithm

Abstract

The invention discloses a grid-connected micro-grid capacity optimization configuration method based on an improved doliolaria group algorithm. The grid-connected micro-grid capacity optimization configuration method comprises the following steps: determining a model of a grid-connected micro-grid system; the power generation mode and output characteristics of each power supply of a grid-connected micro-grid are considered, and power balance, installed capacity of each device, transmission power interaction between a main micro-grid, output of a distributed power generation unit and charge and discharge life of an energy storage device are taken as constraint conditions; the economy and environmental protection of the micro-grid system under the grid-connected condition are taken as optimization targets, and the multi-target problem is solved by adopting an improved dogvessel colony algorithm. According to the method, the grid-connected micro-grid is optimally configured by using the improved dogvessel squirt group algorithm, the utilization efficiency of new energy is improved while the power grid quality of the micro-grid system is improved, and the dependence on non-renewable resources and the power utilization cost of the system are reduced. In addition, the method is small in calculation amount and easy to understand and operate, and has high practicability for optimization scheduling of the grid-connected micro-grid system.

Description

Technical Field

[0001] The present invention relates to the field of capacity optimization configuration of microgrids, and therefore proposes a capacity optimization configuration method for a grid-connected microgrid based on an improved salp swarm algorithm. Background Art

[0002] As the demand for energy in countries around the world grows, increasing the development and utilization of renewable energy has become an inevitable choice for countries around the world. Currently, distributed energy has become a key way to efficiently utilize renewable energy. However, as more and more distributed energy is connected to the power system, the adverse effects on the normal operation of the power system are becoming more and more obvious.

[0003] The emergence of microgrids can effectively manage distributed energy. As an important part of smart grids, microgrids can coordinate and control various distributed energy sources to meet user needs and solve problems such as renewable energy consumption. However, new energy sources such as wind and light have their own uncertainties, which poses a major challenge to the economic and stable operation of microgrids. In addition, the investment cost is mainly affected by the cost and quantity of different types of equipment. The output and load size of the system will affect the operating status of the system, and then affect the economic benefits. Therefore, the reasonable optimization and configuration of the capacity of the grid-connected microgrid system can not only reduce the construction cost and operation and maintenance cost of the microgrid, but also improve the balance of power supply and improve the environmental protection of the system operation.

[0004] Optimal configuration is essentially an optimization solution process. Taking one or more of the microgrid characteristics as the target, the optimal configuration model is established and the relevant methods are used to solve the model under the existing resource conditions and specific constraints to obtain the appropriate capacity configuration plan. Traditional mathematical methods such as enumeration method and direct method have problems such as complex calculation and slow calculation speed, while swarm intelligence optimization algorithm has been widely used due to its fast and accurate convergence performance and strong adaptability.

[0005] The Salp Swarm Algorithm (SSA) is a swarm intelligence optimization algorithm proposed by Mirjalili et al. The entire optimization process of the Salp Swarm Algorithm originates from the group foraging behavior of the salp chain, and the process of the salp group searching for the best food source is analogous to the optimization of a function. However, the low computational accuracy of the traditional Salp Swarm Algorithm is still a problem that restricts it in solving some complex problems. There are also defects in the convergence speed, and it is easy to fall into the local optimum. In the process of finding the optimal solution to the equation, the problems of insufficient development and unbalanced exploration still exist.

[0006] Chinese patent CN116485121A discloses a method for configuring a comprehensive energy system for a park based on an improved salp group optimization algorithm. The patent establishes a comprehensive demand response model for electricity and heat, and mainly establishes an objective function based on the economic operation cost of the park comprehensive energy system. The objective function does not take the environmental governance cost into account; in the improvement of the salp group optimization algorithm, the tent chaotic mapping is used for the initialization of the salp population, and the sine cosine algorithm and the differential algorithm are integrated in updating the leader and follower positions of the salp to improve the algorithm's optimization ability. The optimization goal of the present invention comprehensively considers the economic operation cost and environmental operation cost of the microgrid. In improving the optimization algorithm of the pinus brevis swarm, Logistic mapping is used to initialize the pinus brevis swarm population in view of the fact that the Tent chaotic mapping has low sensitivity to initial conditions and is prone to falling into a periodic state; the population adaptive partitioning strategy adopted alleviates the problem that the traditional pinus brevis swarm algorithm focuses on local search in the early stage and global search in the later stage; in addition, in order to avoid the algorithm from falling into the local optimum, a simulated annealing algorithm is introduced to improve the ability to jump out of the current local optimal position, thereby finding the optimal solution to the multi-objective problem, which has the advantages of fast convergence speed and high search accuracy. Summary of the invention

[0007] The present invention aims to solve the above problems of the prior art. A method for optimizing the capacity of a grid-connected microgrid based on an improved salp swarm algorithm is proposed. The technical solution of the present invention is as follows:

[0008] A method for optimizing the capacity of a grid-connected microgrid based on an improved salp swarm algorithm comprises the following steps:

[0009] Step 1: Determine the mathematical model of the grid-connected microgrid system, including the power model of the new energy unit, the energy storage device model, and the micro gas turbine model;

[0010] Step 2: Based on the mathematical model of the grid-connected microgrid system, establish an objective function that considers the economic and environmental performance of the microgrid system;

[0011] Step 3: Establish constraints including power balance, installed capacity of each device, transmission power interaction between the main microgrid, output of distributed generation units, and charging and discharging life of energy storage devices;

[0012] Step 4: The improved salp swarm algorithm is used to solve the multi-objective problem of the objective function. The chaotic mapping method, population adaptive strategy, simulated annealing algorithm mechanism and salp swarm algorithm are combined to improve the optimization ability of the algorithm. Based on the improved salp swarm algorithm, the optimal grid-connected microgrid capacity optimization configuration is solved.

[0013] Further, step one: determine the mathematical model of the grid-connected microgrid system, including the power model of the new energy unit, the energy storage device model, and the micro gas turbine model, specifically including:

[0014] The grid-connected microgrid system is connected to an external large power grid with an AC bus as the main trunk, and uses an electronic conversion device to connect new energy units, energy storage devices, micro gas turbines and power loads in the grid, wherein the new energy units include distributed wind turbines and distributed photovoltaic power generation systems;

[0015] The wind turbine model is:

[0016]

[0017] Where: P wt (V) is the output power of the wind turbine; V is the wind speed; V ci is the cut-in wind speed; V co is the cut-out wind speed; V e is the rated wind speed; P e is the rated power of wind power.

[0018] The photovoltaic system model is:

[0019]

[0020] Where: P pv is the output power of the photovoltaic system; N pv is the number of photovoltaic cells; P STC is the rated output power; G STC is the standard light intensity; G c is the actual light intensity; k is the temperature coefficient; T C is the actual working temperature; T STC is the standard operating temperature;

[0021] The energy storage device uses a battery, and its charging and discharging model is:

[0022]

[0023] Where: SOC(t) and SOC(t0) are the state of charge of the battery at time t and t0 respectively; η ch is the charging efficiency; η dis is the discharge efficiency; P ch (t0),P dis (t0) are the charging and discharging power of the battery at time t0 respectively;

[0024] The micro gas turbine model is:

[0025]

[0026] Where: C mt (t) is the operating cost of the micro gas turbine; C g is the natural gas price; L HVg is the lower calorific value coefficient of natural gas; P mt (t) is the output power of the micro gas turbine; η mt is the output efficiency of the micro gas turbine.

[0027] Furthermore, the objective function of the grid-connected microgrid system comprehensively considers economic efficiency and environmental protection, and the overall objective function is as follows:

[0028] minF=γ1F1+γ2F2

[0029] Where: F1 is the operation cost of the grid-connected microgrid; F2 is the environmental governance cost of the grid-connected microgrid; γ1 and γ2 are weight coefficients;

[0030] The operating cost objective function F1 formula is:

[0031]

[0032] Where: C f (t) is the fuel cost; C m (t) is the equipment operation and maintenance cost; C grid (t) is the electricity interaction cost between the grid-connected microgrid and the large grid; C p (t) Cost of power outage penalty;

[0033] The formula of the environmental governance cost objective function F2 is:

[0034]

[0035] Where: M is the number of pollutant types; I is the number of equipment that produces pollutants; C k Costs for treating k types of pollutants, including CO, CO2, NO x ; r mt,k 、r grid,k are the emission coefficients of k types of pollutants generated by the interaction between the micro gas turbine and the large power grid; W buy (t) is the amount of electricity purchased by the microgrid from the large grid.

[0036] Furthermore, the constraints of the grid-connected microgrid system are as follows, including power balance, installed capacity of each device, transmission power interaction between the main microgrids, output of distributed generation units, and charging and discharging life of energy storage devices;

[0037] The power balance constraint is that the sum of the output power of each power generation unit and the interaction power between the microgrid and the large grid must match the power load, and the expression is:

[0038] P wt +P pv +P bat +P mt +P grid =P load

[0039] Where: P bat is the battery output power; P grid is the interaction power between the microgrid and the large grid; P load The power load.

[0040] The installed capacity constraints of each device are:

[0041] 0≤N n ≤N n,max

[0042] Where: N n 、N n,max They are the actual installed capacity and maximum installed capacity of each device respectively.

[0043] The power transmission interaction constraint between the main microgrids is:

[0044] P grid,min ≤P grid (t)≤P grid,max

[0045] Where: P grid,min , P grid,max are the minimum transmission power and the maximum transmission power between the main microgrids respectively.

[0046] The output constraint of the distributed generation unit is:

[0047] P n,min ≤P n (t)≤P n,max

[0048] Where: P n,min , P n,max are the minimum output power and maximum output power of each power generation unit respectively.

[0049] The energy storage device charging and discharging constraints are:

[0050]

[0051] Where: SOC min , SOC max Respectively represent the minimum state of charge and maximum state of charge of the battery; P ch,max , P dis,max They are the maximum charging power and maximum discharging power of the battery respectively.

[0052] Furthermore, the improved salp swarm algorithm is used to solve the multi-objective problem of the objective function, and the steps include:

[0053] Step 1: Initialize the position of the salp population using the chaotic mapping method;

[0054] Step 2: Calculate the fitness value of each salp individual;

[0055] Step 3: Determine the location of the food source, sort the salps according to their fitness values, select the best individual, and determine the location as the food source;

[0056] Step 4: Introduce the population adaptive adjustment strategy to divide the population size;

[0057] Step 5: Update the positions of leaders and followers;

[0058] Step 6: Recalculate the fitness value of each salp individual and update the position of the salp individual and the position of the food source;

[0059] Step 7: Perturb the optimal position and optimize the objective function according to the simulated annealing algorithm;

[0060] Step 8: If the conditions are met, the iteration ends, otherwise continue the iteration.

[0061] An electronic device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the method for optimizing the capacity of a grid-connected microgrid based on an improved Salp Unica swarm algorithm as described in any one of the items is implemented.

[0062] A non-transitory computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements a grid-connected microgrid capacity optimization configuration method based on an improved salp swarm algorithm as described in any one of the items.

[0063] The advantages and beneficial effects of the present invention are as follows:

[0064] According to the existing grid-connected microgrid system framework, the present invention establishes an objective function that considers the economy and environmental protection of the microgrid system. The optimization model also considers a variety of constraints including the installed capacity of the equipment, and provides a grid-connected microgrid capacity optimization configuration method based on the improved Salp Sea Squamite Swarm Algorithm. Most of the existing capacity optimization configuration methods use a single optimization algorithm for solution. The improvement of the Salp Sea Squamite Swarm Optimization Algorithm in the present invention is mainly in step one, step four, and step seven of claim 5. Corresponding to step one of claim 5, the present invention introduces chaotic mapping into the traditional Salp Sea Squamite Swarm Algorithm in the initialization stage to optimize the random initialization of the population; corresponding to step four of claim 5, a population adaptive adjustment strategy is introduced, and the global optimization ability in the early stage of iteration and the local optimization ability in the later stage of iteration are enhanced by adjusting the iterative relationship between the number of leaders and followers; corresponding to step seven of claim 5, the simulated annealing algorithm is combined with the Salp Sea Squamite Swarm Algorithm, which overcomes the situation that the Salp Sea Squamite Swarm Algorithm is prone to fall into the local optimum to a certain extent, thereby also improving the convergence speed of the algorithm. The capacity optimization configuration scheme obtained by solving the problem not only improves the grid quality of the microgrid system, but also improves the utilization efficiency of new energy, reduces the dependence on non-renewable resources and the electricity cost of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Figure 1 It is a flowchart of a method for optimizing capacity configuration of a grid-connected microgrid based on an improved salp swarm algorithm according to a preferred embodiment of the present invention.

[0066] Figure 2 A schematic diagram of the structure of a grid-connected microgrid is provided for the present invention. DETAILED DESCRIPTION

[0067] The following will describe the technical solutions in the embodiments of the present invention in detail in conjunction with the accompanying drawings in the embodiments of the present invention. The described embodiments are only part of the embodiments of the present invention.

[0068] The technical solution of the present invention to solve the above technical problems is:

[0069] like Figure 1 As shown, the method process of optimizing the capacity of a grid-connected microgrid based on an improved salp swarm algorithm provided by the present invention includes: determining a mathematical model of a grid-connected microgrid system; establishing an objective function and constraints that consider the economy and environmental protection of the microgrid system according to the model; using an improved salp swarm algorithm to solve the multi-objective problem of the objective function; and finally obtaining the optimal grid-connected microgrid capacity optimization configuration.

[0070] like Figure 2As shown, the structure of the grid-connected microgrid system provided by the present invention is: the grid-connected microgrid system is connected to the external large power grid with the AC bus as the main trunk, and the new energy units, batteries, micro gas turbines and power loads are connected in the grid by electronic conversion devices, wherein the new energy units include distributed wind turbines and distributed photovoltaic power generation systems.

[0071] The wind turbine model is:

[0072]

[0073] Where: P wt (V) is the output power of the wind turbine; V ci is the cut-in wind speed; V co is the cut-out wind speed; V e is the rated wind speed; P e is the rated power of wind power.

[0074] The photovoltaic system model is:

[0075]

[0076] Where: P pv is the output power of the photovoltaic system; N pv is the number of photovoltaic cells; P STC is the rated output power; G STC is the standard light intensity; G c is the actual light intensity; k is the temperature coefficient; T C is the actual working temperature; T STC is the standard operating temperature.

[0077] The battery charging and discharging model is:

[0078]

[0079] Where: SOC(t) and SOC(t0) are the state of charge of the battery at time t and t0 respectively; η ch is the charging efficiency; η dis is the discharge efficiency; P ch (t0),P dis (t0) are the charging and discharging powers of the battery at time t0 respectively.

[0080] The micro gas turbine model is:

[0081]

[0082] Where: C mt (t) is the operating cost of the micro gas turbine; C g is the natural gas price; L HVgis the lower calorific value coefficient of natural gas; P mt (t) is the output power of the micro gas turbine; η mt is the output efficiency of the micro gas turbine.

[0083] The objective function of the grid-connected microgrid system comprehensively considers economic efficiency and environmental protection, and the overall objective function is:

[0084] minF=γ1F1+γ2F2

[0085] Where: F1 is the operation cost of the grid-connected microgrid; F2 is the environmental governance cost of the grid-connected microgrid; γ1 and γ2 are weight coefficients.

[0086] The expression of the operating cost objective function F1 is:

[0087]

[0088] Where: C f (t) is the fuel cost; C m (t) is the equipment operation and maintenance cost; C grid (t) is the electricity interaction cost between the grid-connected microgrid and the large grid; C p (t) is the power outage penalty cost.

[0089] The fuel cost is the cost of fuel consumed by the micro gas turbine, and its expression is:

[0090]

[0091] The equipment operation and maintenance cost expression is:

[0092]

[0093] Where: N is the number of power supply equipment; k n is the maintenance cost coefficient; P n (t) is the output power of the power supply equipment.

[0094] The expression of electric energy interaction cost is:

[0095] C grid (t) = C buy (t)W buy (t)-C sell (t)W sell (t)

[0096] Where: C buy (t), C sell (t) are the prices of electricity purchased and sold by the grid-connected microgrid to the large grid; W buy (t), W sell(t) are the electricity prices purchased and sold by the grid-connected microgrid to the main grid.

[0097] The power outage penalty cost expression is:

[0098]

[0099] Where: l is the power outage penalty coefficient.

[0100] The expression of the environmental governance cost objective function F2 is:

[0101]

[0102] Where: M is the number of pollutant types; I is the number of equipment that produces pollutants; C k Costs for treating k types of pollutants, including CO, CO2, NO x ; r mt,k 、r grid,k are the emission coefficients of k types of pollutants generated by the interaction between the micro gas turbine and the large power grid; W buy (t) is the amount of electricity purchased by the microgrid from the large grid.

[0103] The constraints of the grid-connected microgrid system are as follows, including power balance, installed capacity of each device, transmission power interaction between the main microgrids, output of distributed generation units, and battery charge and discharge life.

[0104] The power balance constraint is that the sum of the output power of each power generation unit and the interaction power between the microgrid and the large grid must match the power load, and the expression is:

[0105] P wt +P pv +P bat +P mt +P grid =P load

[0106] When the output power of each power generation unit is sufficient to match the power load, the microgrid transmits power to the large grid; conversely, the large grid transmits power to the microgrid.

[0107] The installed capacity constraints of each device are:

[0108] 0≤N n ≤N n,max

[0109] The power transmission interaction constraint between the main microgrids is:

[0110] P grid,min ≤P grid (t)≤P grid,max

[0111] The output constraint of distributed generation units is:

[0112] P n,min ≤P n (t)≤P n,max

[0113] The battery charge and discharge constraints are:

[0114]

[0115] The improved salp swarm algorithm is used to solve this multi-objective optimization problem.

[0116] The Salp Swarm Algorithm (SSA) is a novel intelligent optimization algorithm designed to simulate the leader and follower behavior of salps in the ocean. In the Salp Swarm Algorithm, the first half of the salps in the salp chain are leaders, and the rest are followers. The leader guides the followers to move forward, and as the number of iterations increases, the optimal food source is gradually found, thus completing the evolution process.

[0117] The salp swarm algorithm is widely used in optimization problems in the industrial field. However, the salp swarm algorithm has disadvantages such as slow convergence and easy to fall into local optimum. In order to meet the above objective function and constraints, a salp swarm algorithm with a hybrid chaotic mapping, population adaptive adjustment and simulated annealing algorithm mechanism is used to improve it.

[0118] 1) Initialize the population. Assume that the predation space is an N×D-dimensional space, where the population size of the salp group is N and the spatial dimension is D. The food in the space is represented by F = [F1, F2, ..., F D ] T The upper bound of the search space is ub = [ub1, ub2, ..., ub D ], the lower bound of the search space is lb=[lb1,lb2,...,lb D ]. On the basis of meeting the upper and lower limits, we have:

[0119]

[0120] The population initialization of salps is randomly generated, so the chaotic mapping population initialization is introduced, and the chaotic sequence used is Logistic mapping, and its formula is:

[0121]

[0122] Where i represents the population size and j represents the sequence number.

[0123] So the population is initialized as:

[0124]

[0125] 2) Individual fitness of salps

[0126]

[0127] Where f i is the objective function value of salp individual i.

[0128] 3) In the salp swarm algorithm, the number of leaders and followers each accounts for half of the total population, which will lead to a greater emphasis on local search in the early stages of the algorithm iteration, which is prone to falling into local extremes; and a greater emphasis on global search in the later stages of the algorithm iteration, resulting in lower optimization accuracy. Therefore, a population adaptive adjustment strategy is introduced to improve the global search capability in the early stages of the iteration and the local search capability in the later stages of the iteration. After the improvement, the number of leaders and followers is ωN and (1-ω)N, respectively, where ω is the control factor, expressed as:

[0129]

[0130] Where l is the current iteration number of the algorithm; L is the maximum iteration number of the algorithm; b is the proportional coefficient, which is 0.75; k is the disturbance factor, which is 0.2.

[0131] 4) Update leader position

[0132]

[0133] In the formula is the position of the leader in the jth dimension; F j is the location of the food source; c1, c2, and c3 are control parameters, where c2 and c3 are random numbers between [0,1], which are used to enhance the global search capability and the diversity of individuals in the population; c1 is the convergence factor, which occupies a major position in the control parameters, and its expression is:

[0134]

[0135] 5) Update follower location

[0136]

[0137] In the formula is the position of the i-1th individual in the salp chain in the jth dimension.

[0138] 6) In order to overcome the situation that the salp swarm algorithm is prone to fall into the local optimal solution in the later stage of iteration, the salp swarm algorithm is combined with the simulated annealing algorithm. After the combination, it can jump out of the current local optimal position with a certain probability, which overcomes the problem of the algorithm falling into the local optimal solution to a certain extent.

[0139] The sampling formula of the simulated annealing algorithm is:

[0140]

[0141] Where the fitness function d f =f(S')-f(S), if d f <0, the new solution is accepted, otherwise the new solution is accepted with probability.

[0142] If the conditions are not met during the iteration process, the salp swarm algorithm is used to optimize the calculation. After the calculation of the salp swarm algorithm, the simulated annealing algorithm is used for optimization, which reduces the overall calculation amount.

[0143] Improved salp group algorithm process:

[0144] Step 1: Initialize the position of the salp population using the chaotic mapping method;

[0145] Step 2: Calculate the fitness value of each salp individual;

[0146] Step 3: Determine the location of the food source, sort the salps according to their fitness values, select the best individual, and determine the location as the food source;

[0147] Step 4: Introduce the population adaptive adjustment strategy to divide the population size;

[0148] Step 5: Update the positions of leaders and followers;

[0149] Step 6: Recalculate the fitness value of each salp individual and update the position of the salp individual and the position of the food source;

[0150] Step 7: Perturb the optimal position and optimize the objective function according to the simulated annealing algorithm;

[0151] Step 8: If the conditions are met, the iteration ends, otherwise continue the iteration.

[0152] The systems, devices, modules or units described in the above embodiments may be implemented by computer chips or entities, or by products with certain functions.

[0153] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0154] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0155] The above embodiments should be understood to be only used to illustrate the present invention and not to limit the protection scope of the present invention. After reading the contents of the present invention, technicians can make various changes or modifications to the present invention, and these equivalent changes and modifications also fall within the scope defined by the claims of the present invention.

Claims

1. A method for optimizing the capacity of a grid-connected microgrid based on an improved salp swarm algorithm, characterized in that: The following steps are involved: Step 1: Determine the mathematical model of the grid-connected microgrid system, including the power model of the new energy unit, the energy storage device model, and the micro gas turbine model; Step 2: Based on the mathematical model of the grid-connected microgrid system, establish an objective function that considers the economic and environmental performance of the microgrid system; Step 3: Establish constraints including power balance, installed capacity of each device, transmission power interaction between the main microgrid, output of distributed generation units, and charging and discharging life of energy storage devices; Step 4: The improved salp swarm algorithm is used to solve the multi-objective problem of the objective function. The chaotic mapping method, population adaptive strategy, simulated annealing algorithm mechanism and salp swarm algorithm are combined to improve the optimization ability of the algorithm. Based on the improved salp swarm algorithm, the optimal grid-connected microgrid capacity optimization configuration is solved.

2. A method for optimizing the capacity of a grid-connected microgrid based on an improved salp swarm algorithm according to claim 1, characterized in that: Step 1: Determine the mathematical model of the grid-connected microgrid system, including the power model of the new energy unit, the energy storage device model, and the micro gas turbine model, specifically including: The grid-connected microgrid system is connected to an external large power grid with an AC bus as the main trunk, and uses an electronic conversion device to connect new energy units, energy storage devices, micro gas turbines and power loads in the grid, wherein the new energy units include distributed wind turbines and distributed photovoltaic power generation systems; The wind turbine model is: Where: P wt (V) is the output power of the wind turbine; V is the wind speed; V ci is the cut-in wind speed; V co is the cut-out wind speed; V e is the rated wind speed; P e is the rated power of wind power. The photovoltaic system model is: Where: P pv is the output power of the photovoltaic system; N pv is the number of photovoltaic cells; P STC is the rated output power; G STC is the standard light intensity; G c is the actual light intensity; k is the temperature coefficient; T C is the actual working temperature; T STC is the standard operating temperature; The energy storage device uses a battery, and its charging and discharging model is: Where: SOC(t) and SOC(t0) are the state of charge of the battery at time t and t0 respectively; η ch is the charging efficiency; η dis is the discharge efficiency; P ch (t0),P dis (t0) are the charging and discharging power of the battery at time t0 respectively; The micro gas turbine model is: Where: C mt (t) is the operating cost of the micro gas turbine; C g is the natural gas price; L HVg is the lower calorific value coefficient of natural gas; P mt (t) is the output power of the micro gas turbine; η mt is the output efficiency of the micro gas turbine.

3. A method for optimizing the capacity of a grid-connected microgrid based on an improved salp swarm algorithm according to claim 2, characterized in that: The objective function of the grid-connected microgrid system comprehensively considers economic efficiency and environmental protection, and the overall objective function is as follows: min F=γ1F1+γ2F2 Where: F1 is the operation cost of the grid-connected microgrid; F2 is the environmental governance cost of the grid-connected microgrid; γ1 and γ2 are weight coefficients; The operating cost objective function F1 formula is: Where: C f (t) is the fuel cost; C m (t) is the equipment operation and maintenance cost; C grid (t) is the electricity interaction cost between the grid-connected microgrid and the large grid; C p (t) Cost of power outage penalty; The formula of the environmental governance cost objective function F2 is: Where: M is the number of pollutant types; I is the number of equipment that produces pollutants; C k Costs for treating k types of pollutants, including CO, CO2, NO x ; r mt,k 、r grid,k are the emission coefficients of k types of pollutants generated by the interaction between the micro gas turbine and the large power grid; W buy (t) is the amount of electricity purchased by the microgrid from the large grid.

4. A method for optimizing the capacity of a grid-connected microgrid based on an improved salp swarm algorithm according to claim 3, characterized in that: The constraints of the grid-connected microgrid system are as follows, including power balance, installed capacity of each device, transmission power interaction between the main microgrids, output of distributed generation units, and charging and discharging life of energy storage devices; The power balance constraint is that the sum of the output power of each power generation unit and the interaction power between the microgrid and the large grid must match the power load, and the expression is: P wt +P pv +P bat +P mt +P grid =P load Where: P bat is the battery output power; P grid is the interaction power between the microgrid and the large grid; P load is the electrical power load; The installed capacity constraints of each device are: 0≤N n ≤N n,max Where: N n 、N n,max They are the actual installed capacity and maximum installed capacity of each device respectively; The power transmission interaction constraint between the main microgrids is: P grid,min ≤P grid (t)≤P grid,max Where: P grid,min , P grid,max are the minimum transmission power and the maximum transmission power between the main microgrids respectively; The output constraint of the distributed generation unit is: P n,min ≤P n (t)≤P n,max Where: P n,min , P n,max are the minimum output power and maximum output power of each power generation unit respectively; The energy storage device charge and discharge constraints are: Where: SOC min , SOC max Respectively represent the minimum state of charge and maximum state of charge of the battery; P ch,max , P dis,max They are the maximum charging power and maximum discharging power of the battery respectively.

5. The method for optimizing the capacity of a grid-connected microgrid based on an improved salp swarm algorithm according to claim 1, characterized in that: The improved salp swarm algorithm is used to solve the multi-objective problem of the objective function, and the steps include: Step 1: Initialize the position of the salp population using the chaotic mapping method; Step 2: Calculate the fitness value of each salp individual; Step 3: Determine the location of the food source, sort the salps according to their fitness values, select the best individual, and determine the location as the food source; Step 4: Introduce the population adaptive adjustment strategy to divide the population size; Step 5: Update the positions of leaders and followers; Step 6: Recalculate the fitness value of each salp individual and update the position of the salp individual and the position of the food source; Step 7: Perturb the optimal position and optimize the objective function according to the simulated annealing algorithm; Step 8: If the conditions are met, the iteration ends, otherwise continue the iteration.

6. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the grid-connected microgrid capacity optimization configuration method based on the improved salp swarm algorithm as described in any one of claims 1 to 5 is implemented.

7. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, the method for optimizing the capacity of a grid-connected microgrid based on the improved salp swarm algorithm as described in any one of claims 1 to 5 is implemented.

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