A microgrid optimization scheduling method

Abstract

The present invention relates to a microgrid optimization dispatching method, which comprises the following steps: 1) constructing a microgrid environmental protection and economic dispatching model that takes into account operating costs and environmental protection costs according to the power generation characteristics of distributed power sources and energy storage in the microgrid; 2) using an improved particle swarm algorithm to solve the microgrid environmental protection and economic dispatching model to obtain the optimal dispatching result. Compared with the prior art, the present invention uses an improved particle swarm algorithm to solve the proposed optimization model, improve the defects of the original method, improve the performance of the algorithm, and achieve the purpose of effectively reducing the user's electricity cost and environmental pollution, and promoting the optimized operation of the microgrid.

Description

Technical Field

[0001] The present invention relates to the field of microgrid energy dispatch optimization, and in particular to a microgrid optimization dispatch method. Background Art

[0002] At present, most of the designs of microgrid optimization scheduling methods abstract the desired goals, such as the lowest microgrid operating cost, into the results of mathematical models. The mathematical models in the methods are also established by abstracting and reasoning from various distributed power sources and loads. Finally, the results of the mathematical model are simulated using intelligent optimization algorithms to determine whether the goals have been achieved.

[0003] In the prior art, some methods start from the established mathematical model to make the parameters and expressions contained in the mathematical model more comprehensive and better simulate the actual situation. For example, photovoltaic and wind power generation are taken into consideration to propose a microgrid day-ahead scheduling method with the sum of the power generation cost and operating cost of each power generation unit as the target. This method uses an improved particle swarm algorithm to solve the mathematical model involved in the method. In addition, from the perspective of intelligent optimization algorithm, the intelligent optimization algorithm in the method and the process of solving the mathematical model by the algorithm are improved, so as to continuously approach the optimal result.

[0004] However, the above existing technologies still have the following shortcomings. First, the objectives considered are not very comprehensive. Some of them consider the operating costs but ignore the environmental protection costs. The goal of microgrid optimization scheduling requires taking multiple aspects into account to achieve better results. Second, the defects of the intelligent optimization algorithm itself will affect the solution of the mathematical model in the method, resulting in failure to achieve the desired purpose. Summary of the invention

[0005] The purpose of the present invention is to provide a microgrid optimization scheduling method in order to overcome the defects of the above-mentioned prior art. The method takes the microgrid operating cost and environmental protection cost as optimization targets at the same time, obtains the relative optimal values ​​of these two targets, thereby reducing the electricity cost and environmental pollution of the microgrid system, and promoting the optimized operation of the microgrid.

[0006] The purpose of the present invention can be achieved by the following technical solutions:

[0007] A microgrid optimization scheduling method, the method comprising the following steps:

[0008] 1) According to the power generation characteristics of distributed power sources and energy storage in the microgrid, a microgrid environmental protection and economic dispatch model considering operating costs and environmental protection costs is constructed;

[0009] 2) An improved particle swarm algorithm is used to solve the microgrid environmental protection and economic dispatch model to obtain the optimal dispatch result.

[0010] In the step 1), the power generation characteristics of the distributed power source and energy storage in the microgrid are represented by mathematical models, specifically including a wind turbine mathematical model, a photovoltaic power generation mathematical model, a diesel generator model, a micro gas turbine model and a battery model.

[0011] The mathematical model of the wind turbine generator is specifically:

[0012]

[0013] Among them, P' WT is the output power of the wind turbine, P' r is the rated power of the wind turbine, v' ci 、v' r and v' co represents the cut-in wind speed, rated wind speed and cut-out wind speed of the wind turbine, a', b', c', d' are wind speed parameters, and v is the wind speed;

[0014] The specific mathematical model of photovoltaic power generation is:

[0015]

[0016] Among them, P' pv is the output active power of the photovoltaic cell, R' pv is the photovoltaic output power under standard test conditions, q' pv is the derating factor of photovoltaic, I' T is the actual solar radiation intensity, I' STC is the solar radiation intensity under standard test conditions, α' p is the temperature coefficient of the photovoltaic panel, T' c is the photovoltaic cell temperature at the current time, T' stc is the photovoltaic cell temperature under standard testing;

[0017] Diesel Generator Model:

[0018]

[0019] Among them, C DE.OM (t), C DE.F (t), C DE.EN (t) are the operation and maintenance cost, fuel cost, and pollutant treatment cost of the diesel engine at time t, respectively. DE (t) is the power generation of the diesel engine at time t, K DE.OM is the operation and maintenance cost coefficient of the diesel engine, γ de,k is the emission of type k pollutants generated by diesel engine operation, C k is the cost coefficient for treating type k pollutants, α, β, and γ are the coefficients of diesel engines;

[0020] The micro gas turbine model is as follows:

[0021]

[0022]

[0023] Among them, P MT (t) is the active output power of the micro gas turbine, η MT (t) is the operating efficiency of the micro gas turbine, C MT.OM (t), C MT.F (t), C MT.EN (t) are the operation and maintenance cost, fuel cost, and pollutant treatment cost of the diesel engine at time t, respectively. MT (t) is the power generation of the diesel engine at time t, K MT.OM is the operation and maintenance cost coefficient of the diesel engine, γ mt,k is the emission of type k pollutants generated by diesel engine operation, C k is the cost coefficient for treating type k pollutants;

[0024] The battery model is as follows:

[0025]

[0026] Among them, SOC(t) is the remaining capacity of the battery at time t, P bess (t) is the charge and discharge power of the battery at time t, positive indicates charging, negative indicates discharging, η + , η - are the charge and discharge efficiency respectively.

[0027] The objective function of the microgrid environmental protection and economic dispatch model is:

[0028] f(x)=min[f1(x),f2(x)]

[0029] Among them, f1 and f2 represent the operating cost target and environmental protection cost target respectively, and x is the optimization variable of the model.

[0030] The expression of the operation cost of the microgrid in grid-connected mode is:

[0031]

[0032]

[0033] Among them, C grid (t), C bess (t), C MT (t), C DE(t) are the total cost of interaction between the microgrid and the main grid, the maintenance cost of energy storage, the total operating cost of the micro gas turbine and the total operating cost of the diesel generator during period t, respectively. bess (t) is the power of the energy storage at time t, P sell (t), P buy (t) are the power sold and purchased by the microgrid and the main grid at time t, respectively, c buy (t), c sell (t) are the electricity purchase and sales prices of the microgrid and the main grid at time t.

[0034] The expression of the environmental protection cost of the microgrid is:

[0035]

[0036] Among them, C GRID.EN (t) is the pollutant treatment cost of the large power grid, γ grid,k is the emission of type k pollutants generated by the operation of the large power grid, C k is the cost coefficient for treating type k pollutants.

[0037] The constraints of the microgrid environmental protection and economic dispatch model include:

[0038] (1) Power balance constraints:

[0039] P' PV (t)+P' WT (t)+P grid (t)+P DE (t)+P MT (t)+P bess (t) = P L (t)

[0040] (2) Diesel generator output constraints:

[0041]

[0042] (3) Micro gas turbine output constraints:

[0043]

[0044] (4) Transmission power constraints of tie lines:

[0045]

[0046] (5) Energy storage device constraints:

[0047]

[0048] in, are the upper and lower limits of diesel engine output and micro gas turbine output, r DE 、r MT are the upper limits of the climbing power of diesel engines and micro gas turbines, are the upper and lower limits of the transmission power of the tie line, It is the upper and lower limits of the energy storage device output. A positive value indicates power input, and a negative value indicates power output. SOC max (t), SOC min (t) is the upper and lower limits of the energy storage capacity at time t.

[0049] In the step 2), the improved particle swarm algorithm is used to solve the microgrid environmental protection and economic dispatch model, which specifically includes the following steps:

[0050] 21) Data initialization: input the system composition, structural parameters and model parameters of the microgrid as well as the particle swarm algorithm parameters, and initialize the particle population;

[0051] 22) Calculate the fitness value of the initialized particle population, i.e., the operating cost and environmental protection cost;

[0052] 23) According to the fitness value, the particle speed and position are updated to obtain the offspring population, and the dynamic inertia weight factor is set;

[0053] 24) Determine the individual extreme value pbest, and use pbest as the initial individual extreme value of the particle. If the current particle dominates pbest, then the current particle is used as the individual extreme value of pbest. If the two cannot be compared, calculate the number of other particles dominated by the two in the group, and the one that dominates more is used as the individual extreme value pbest;

[0054] 25) Sort the population in layers, store the optimal non-dominated Pareto solution into an external archive set, remove non-Pareto solutions, and determine whether the external archive set exceeds the specified capacity. If so, select m particles according to the crowding distance;

[0055] 26) Determine the global optimal value gbest, use the Pareto optimal solution saved in the external archive set, and select gbest from the external set based on the crowding distance of the optimal solution by referencing the roulette method;

[0056] 27) Perform small probability mutation. In order to prevent the particle swarm algorithm from converging to the local optimal frontier rather than the global optimal frontier too early, a small probability random mutation mechanism is introduced to generate a small probability disturbance of ±30% on the original position of the particle, thereby increasing the particle's ability to find the global optimal frontier;

[0057] 28) Return to step 23) until the termination condition, i.e. the maximum number of iterations, is met, and the final optimization scheduling result is output.

[0058] In the step 2), the inertia weight factor in the improved particle swarm algorithm is improved to prevent falling into the local optimal value, then:

[0059]

[0060] Among them, gen is the current iteration number, gen max is the total number of iterations, ω s and ω e are the initial and final values ​​of the inertia weight factor.

[0061] A microgrid optimization scheduling method, and a microgrid optimization scheduling system for implementing the microgrid optimization scheduling method comprises:

[0062] Input module: used to obtain the parameters of microgrid environmental protection and economic dispatch model and improved particle swarm algorithm;

[0063] Mathematical model module: including mathematical models of wind turbines, photovoltaic power generation, diesel generators, micro gas turbines, and energy storage and loads. Microgrid environmental protection and economic dispatch models are constructed through mathematical models.

[0064] Dispatching module: used to solve the microgrid environmental protection and economic dispatch model using an improved particle swarm algorithm;

[0065] Output module: used to output the solution results of the scheduling module, including the optimal value of operating cost and environmental protection cost, and the optimal configuration results between each distributed power source, energy storage unit and load, that is, the scheduling result.

[0066] Compared with the prior art, the present invention has the following advantages:

[0067] The present invention adopts an improved particle swarm algorithm to solve the proposed optimization model, improves the defects of the original method, improves the performance of the algorithm, and achieves the purpose of effectively reducing users' electricity costs and environmental pollution and promoting the optimized operation of the microgrid. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] Figure 1 This is a flow chart of the optimized scheduling of the present invention.

[0069] Figure 2 It is a system principle block diagram of the present invention. DETAILED DESCRIPTION

[0070] The present invention is described in detail below with reference to the accompanying drawings and specific embodiments.

[0071] Example

[0072] like Figure 2As shown, in order to achieve the optimal dispatching of the microgrid, the present invention first designs a microgrid optimal dispatching system, which includes:

[0073] Input module: The main function of this module is to obtain the specific parameters of the mathematical model and intelligent optimization algorithm to prepare for the overall simulation solution.

[0074] Mathematical model module: The mathematical model specifically includes: wind turbine mathematical model, photovoltaic power generation mathematical model, diesel generator mathematical model, micro gas turbine mathematical model, and energy storage and load mathematical models. Finally, through the above mathematical models, a scheduling mathematical model that can simulate the real situation is constructed.

[0075] Dispatch module: This module plays the role of simulating and solving the dispatch mathematical model. The microgrid optimization dispatch model is a nonlinear, multi-model, multi-objective complex dispatch model. Because the particle swarm algorithm (PSO) has strong optimization ability. At the same time, it is easier to apply when solving optimization problems. The method considers the dual objectives of operating cost and environmental protection cost. Therefore, the dispatch module uses the particle swarm algorithm in the intelligent optimization algorithm to simulate and solve.

[0076] Output module: The output results include not only the optimal values ​​after taking into account the operating cost and environmental protection cost, but also the optimal configuration results between each distributed power source, energy storage unit and load, that is, the scheduling result.

[0077] Based on the above-mentioned microgrid optimization scheduling system, the present invention provides a microgrid optimization scheduling method, which is used to determine the processing allocation of each distributed power source, realize the optimal configuration between each distributed power source, energy storage unit and load in the microgrid, make the output power of each distributed power source and energy storage device in the microgrid meet the load demand of the microgrid, ensure the safety and stability of the microgrid, and realize the economic optimization operation of the microgrid.

[0078] like Figure 1 As shown, the method comprises the following steps:

[0079] 1. Build a model. The mathematical model is ultimately expressed in the form of a function, and the result of the function is the goal to be achieved, which is called the objective function.

[0080] 1. Characteristics of distributed power sources and energy storage power generation in microgrids

[0081] (1) Mathematical model of wind turbine:

[0082]

[0083] Where: P' WT is the output power of the wind turbine, P' r is the rated power of the wind turbine, v' ci、v' r and v' co It represents the cut-in wind speed, rated wind speed and cut-out wind speed of the wind turbine; a', b', c', d' are wind speed parameters, and v is the wind speed;

[0084] (2) Mathematical model of photovoltaic power generation:

[0085]

[0086] Where: P' pv is the output active power of the photovoltaic cell, R' pv is the photovoltaic output power under standard test conditions, q' pv is the derating factor of photovoltaic, I' T is the actual solar radiation intensity, I' STC is the solar radiation intensity under standard test conditions, α' p is the temperature coefficient of the photovoltaic panel, T' c is the photovoltaic cell temperature at the current time, T' s tc is the photovoltaic cell temperature under standard test;

[0087] (3) Diesel generator model:

[0088]

[0089] Where: C DE.OM (t), C DE.F (t), C DE.EN (t) are the operation and maintenance cost, fuel cost, and pollutant treatment cost of the diesel engine at time t, respectively. DE (t) is the power generation of the diesel engine at time t, K DE.OM is the diesel engine operation and maintenance cost coefficient, γ de,k is the emission of type k pollutants generated by the operation of the diesel engine, C k is the cost coefficient for treating type k pollutants, α, β, and γ are the coefficients for diesel engines;

[0090] (4) Micro gas turbine

[0091]

[0092] Where: P MT (t) is the active output power of the micro gas turbine; η MT (t) is the operating efficiency of the micro gas turbine. The gas turbine generates electricity by consuming fuel. During operation, fuel costs, operation and maintenance costs, and pollutant treatment costs will be incurred. The expression is as follows:

[0093]

[0094] Where: C MT.OM (t), C MT.F (t), C MT.EN (t) are the operation and maintenance cost, fuel cost, and pollutant treatment cost of the diesel engine at time t, respectively. MT (t) is the power generation of the diesel engine at time t, K MT.OM is the diesel engine operation and maintenance cost coefficient, γ mt,k is the emission of type k pollutants generated by the operation of the diesel engine, C k is the cost coefficient for treating type k pollutants.

[0095] (5) Battery model:

[0096]

[0097] Where: SOC(t) is the remaining capacity of the battery at time t, P bess (t) is the charge and discharge power of the battery at time t, positive indicates charging, negative indicates discharging, η + , η - are the charge and discharge efficiency respectively.

[0098] 2. Objective Function

[0099] The present invention considers operating cost and environmental protection cost as the objective function:

[0100] f(x)=min[f1(x),f2(x)] (7)

[0101] Among them: f1 and f2 represent the operation cost target and environmental protection cost target respectively, and x is the optimization variable of the model. Specifically, it refers to the output power of the distributed power source and the charging and discharging power of the energy storage device in each period of the scheduling cycle;

[0102] (1) Microgrid operating costs

[0103] The operating cost of the microgrid in grid-connected mode:

[0104]

[0105]

[0106] Where: C grid (t), C bess (t), C MT (t), C DE (t) are the total cost of interaction between the microgrid and the main grid, the maintenance cost of energy storage, the total operating cost of the micro gas turbine and the total operating cost of the diesel generator during period t, respectively. bess (t) is the power of the energy storage at time t, P sell(t), P buy (t) are the power sold and purchased by the microgrid and the main grid at time t, respectively, c buy (t), c sell (t) are the electricity purchase and sales prices of the microgrid and the main grid at time t.

[0107] (2) Environmental protection costs of microgrids:

[0108]

[0109] Where: C GRID.EN (t) is the pollutant treatment cost of the large power grid, γ grid,k is the emission of type k pollutants generated by the operation of the large power grid, C k is the cost coefficient for treating type k pollutants.

[0110] 3. Constraints

[0111] The variables in the mathematical model have certain constraints and their values ​​cannot exceed the specified range.

[0112] (1) Power balance constraints:

[0113] P' PV (t)+P' WT (t)+P grid (t)+P DE (t)+P MT (t)+P bess (t) = P L (t) (11)

[0114] (2) Diesel generator output constraints:

[0115]

[0116] (3) Micro gas turbine output constraints:

[0117]

[0118] (4) Transmission power constraints of tie lines:

[0119]

[0120] (5) Energy storage device constraints:

[0121]

[0122] in: are the upper and lower limits of diesel engine output and micro gas turbine output respectively; r DE 、r MTare the upper limits of the climbing power of diesel engines and micro gas turbines, are the upper and lower limits of the transmission power of the tie line, It is the upper and lower limits of the energy storage device output. A positive value indicates power input, and a negative value indicates power output. SOC max (t), SOC min (t) is the upper and lower limits of the energy storage capacity at time t.

[0123] Second, the model is solved in the scheduling module. Specifically, the improved particle swarm algorithm is used. The core of the particle swarm algorithm is the particle velocity update formula. Assuming that the population size of the particle swarm algorithm is N, at time t, the coordinate position of a single particle in the D-dimensional space is: j (t)=(x1,x2,...x i ...,x D ), the particle velocity is expressed as: v j (t)=(v1,v2,...,v i ...,v D ), the velocity v of a single particle at time t+1 j (t+1) and position x j (t+1) is:

[0124] v j (t+1)=ωv j (t)+C1Φ1[p best -x j (t)]+C2Φ2[g best -x j (t)] (16)

[0125] x j (t+1)=x j (t)+v j (t+1) (17)

[0126] Among them: C1 and C2 are learning factors, which reflect the self-learning ability and social learning ability of particles respectively; Φ1 and Φ2 are random numbers between (0, 1); ω is the inertia weight, which represents the influence of particle inertia on velocity.

[0127] Since the inertia weight factor and learning factor of the existing particle swarm algorithm are fixed and easy to fall into the local optimal value, the particle swarm algorithm is improved so that the expected scheduling result can be obtained.

[0128] The solution process is as follows:

[0129] 1) Data initialization (input module). Input the system composition, structural parameters and model parameters of the microgrid as well as the particle swarm algorithm parameters, and initialize the particle population;

[0130] (Scheduling module)

[0131] 2) Calculate the fitness value of the initialized particle population, i.e., the operating cost and environmental protection cost;

[0132] 3) According to the fitness value, the particle speed and position are updated by formula (16) (17) to obtain the offspring population, and the dynamic inertia weight factor is set;

[0133] 4) Determine the individual extreme value pbest. Take pbest as the initial individual extreme value of the particle. If the current particle dominates pbest, then take the current particle as the individual extreme value of pbest. If the two cannot be compared, calculate the number of other particles dominated by the two in the group. The one that dominates more is taken as the individual extreme value pbest.

[0134] 5) Sort the population in layers, store the optimal non-dominated Pareto solution into an external archive set, remove non-Pareto solutions, and determine whether the external archive set exceeds the specified capacity. If so, select m particles according to the crowding distance.

[0135] 6) Global optimal value gbest: The Pareto optimal solution saved in the external archive set is used, and the roulette wheel method is used to select gbest from the external set according to the crowding distance of the optimal solution.

[0136] 7) Small probability mutation. In order to prevent the particle swarm algorithm from converging to the local optimal frontier rather than the global optimal frontier too early, a small probability random mutation mechanism is introduced to generate a small probability disturbance of ±30% on the original position of the particle, thereby increasing the particle's ability to find the global optimal frontier.

[0137] 8) Return to step 3) until the termination condition is met. The present invention takes the termination condition as the maximum number of iterations and outputs the final optimization scheduling result. (Output module)

[0138] The unimproved particle swarm algorithm may not get the expected scheduling results due to the defects of the algorithm itself. The original shortcomings are overcome by improving the particle swarm algorithm. The improved strategy is as follows:

[0139] Improvements to the inertia weight factor:

[0140]

[0141] Among them: gen is the current iteration number, gen max is the total number of iterations, ω s and ω eare the initial and final values ​​of the inertia weight factor. In the early stage of iteration, a larger ω makes the algorithm less likely to fall into the local minimum and facilitates global search. In the later stage of iteration, a smaller ω is conducive to local search and convergence of the algorithm. And the mutation operation is introduced in the particle swarm algorithm to improve the particle swarm algorithm. Adaptive mutation is based on the mutation idea in genetic algorithm, that is, some variables are reinitialized with a certain probability. The mutation operation expands the population search space that is shrinking in iteration, so that particles can jump out of the optimal value position previously searched and search in a larger space, while maintaining population diversity and increasing the possibility of the algorithm finding the optimal value. Therefore, a simple mutation operator is introduced on the basis of the ordinary particle swarm algorithm, and the particles are reinitialized with a certain probability after each particle update.

[0142] In summary, the present invention starts from the economic and environmental protection aspects of the microgrid, establishes a microgrid environmental protection and economic dispatch model, the objectives of the model are operating costs and environmental protection costs, and uses an improved particle swarm algorithm to solve the optimization model to obtain the optimal dispatch result, and improves the inertia weight of the particle swarm algorithm to make it dynamic rather than fixed in the solution process. The mutation operation is introduced into the particle swarm algorithm, that is, some variables are reinitialized with a certain probability, which improves the possibility of the algorithm finding the optimal value. The present invention can effectively reduce the electricity cost and environmental pollution of the system, and promote the optimized operation of the microgrid.

Claims

1. A microgrid optimization scheduling method, characterized in that: The method comprises the following steps: 1) According to the power generation characteristics of distributed power sources and energy storage in the microgrid, a microgrid environmental protection and economic dispatch model considering operating costs and environmental protection costs is constructed; 2) Use the improved particle swarm algorithm to solve the microgrid environmental protection and economic dispatch model to obtain the optimal dispatch result; In the step 1), the power generation characteristics of the distributed power source and energy storage in the microgrid are represented by mathematical models, specifically including a wind turbine mathematical model, a photovoltaic power generation mathematical model, a diesel generator model, a micro gas turbine model and a battery model; The mathematical model of the wind turbine generator is specifically: Among them, P' WT is the output power of the wind turbine, P' r is the rated power of the wind turbine, v' ci 、v' r and v' co represents the cut-in wind speed, rated wind speed and cut-out wind speed of the wind turbine, a', b', c', d' are wind speed parameters, and v is the wind speed; The specific mathematical model of photovoltaic power generation is: Among them, P' pv is the output active power of the photovoltaic cell, R' pv is the photovoltaic output power under standard test conditions, q' pv is the derating factor of photovoltaic, I' T is the actual solar radiation intensity, I' STC is the solar radiation intensity under standard test conditions, α' p is the temperature coefficient of the photovoltaic panel, T' c is the photovoltaic cell temperature at the current time, T' stc is the photovoltaic cell temperature under standard testing; Diesel Generator Model: Among them, C DE.OM (t), C DE.F (t), C DE.EN (t) are the operation and maintenance cost, fuel cost, and pollutant treatment cost of the diesel engine at time t, respectively. DE (t) is the power generation of the diesel engine at time t, K DE.OM is the operation and maintenance cost coefficient of the diesel engine, γ de,k is the emission of type k pollutants generated by diesel engine operation, C k is the cost coefficient for treating type k pollutants, α, β, and γ are the coefficients of diesel engines; The micro gas turbine model is as follows: Among them, P MT (t) is the active output power of the micro gas turbine, η MT (t) is the operating efficiency of the micro gas turbine, C MT.OM (t), C MT.F (t), C MT.EN (t) are the operation and maintenance cost, fuel cost, and pollutant treatment cost of the diesel engine at time t, respectively. MT (t) is the power generation of the diesel engine at time t, K MT.OM is the operation and maintenance cost coefficient of the diesel engine, γ mt,k is the emission of type k pollutants generated by diesel engine operation, C k is the cost coefficient for treating type k pollutants; The battery model is as follows: Among them, SOC(t) is the remaining capacity of the battery at time t, P bess (t) is the charge and discharge power of the battery at time t, positive indicates charging, negative indicates discharging, η + , η - are charge and discharge efficiency respectively; The objective function of the microgrid environmental protection and economic dispatch model is: f(x)=min[f1(x),f2(x)] Among them, f1 and f2 represent the operating cost target and environmental protection cost target respectively, and x is the optimization variable of the model; The expression of the operation cost of the microgrid in grid-connected mode is: Among them, C grid (t), C bess (t), C MT (t), C DE (t) are the total cost of interaction between the microgrid and the main grid, the maintenance cost of energy storage, the total operating cost of the micro gas turbine and the total operating cost of the diesel generator during period t, respectively. bess (t) is the power of the energy storage at time t, P sell (t), P buy (t) are the power sold and purchased by the microgrid and the main grid at time t, respectively, c buy (t), c sell (t) are the electricity purchase and sales prices of the microgrid and the main grid at time t; The expression of the environmental protection cost of the microgrid is: Among them, C GRID.EN (t) is the pollutant treatment cost of the large power grid, γ grid,k is the emission of type k pollutants generated by the operation of the large power grid, C k is the cost coefficient for treating type k pollutants; The constraints of the microgrid environmental protection and economic dispatch model include: (1) Power balance constraints: P P ' V (t)+P W ' T (t)+P grid (t)+P DE (t)+P MT (t)+P bess (t)=P L (t) (2) Diesel generator output constraints: (3) Micro gas turbine output constraints: (4) Transmission power constraints of tie lines: (5) Energy storage device constraints: in, are the upper and lower limits of diesel engine output and micro gas turbine output, respectively. DE 、r MT are the upper limits of the climbing power of diesel engines and micro gas turbines, are the upper and lower limits of the transmission power of the tie line, It is the upper and lower limits of the energy storage device output. A positive value indicates power input, and a negative value indicates power output. SOC max (t), SOC min (t) is the upper and lower limits of the energy storage capacity at time t; In the step 2), the improved particle swarm algorithm is used to solve the microgrid environmental protection and economic dispatch model, which specifically includes the following steps: 21) Data initialization: input the system composition, structural parameters and model parameters of the microgrid as well as the particle swarm algorithm parameters, and initialize the particle population; 22) Calculate the fitness value of the initialized particle population, i.e., the operating cost and environmental protection cost; 23) According to the fitness value, the particle speed and position are updated to obtain the offspring population, and the dynamic inertia weight factor is set; 24) Determine the individual extreme value pbest, and use pbest as the initial individual extreme value of the particle. If the current particle dominates pbest, then the current particle is used as the individual extreme value of pbest. If the two cannot be compared, calculate the number of other particles dominated by the two in the group, and the one that dominates more is used as the individual extreme value pbest; 25) Sort the population in layers, store the optimal non-dominated Pareto solution into an external archive set, remove non-Pareto solutions, and determine whether the external archive set exceeds the specified capacity. If so, select m particles according to the crowding distance; 26) Determine the global optimal value gbest, use the Pareto optimal solution saved in the external archive set, and select gbest from the external set based on the crowding distance of the optimal solution by referencing the roulette method; 27) Perform small probability mutation. In order to prevent the particle swarm algorithm from converging to the local optimal frontier rather than the global optimal frontier too early, a small probability random mutation mechanism is introduced to generate a small probability disturbance of ±30% on the original position of the particle, thereby increasing the particle's ability to find the global optimal frontier; 28) Return to step 23) until the termination condition, i.e. the maximum number of iterations, is met, and the final optimization scheduling result is output; In the step 2), the inertia weight factor in the improved particle swarm algorithm is improved to prevent falling into the local optimal value, then: Among them, gen is the current iteration number, gen max is the total number of iterations, ω s and ω e are the initial and final values ​​of the inertia weight factor.

2. A microgrid optimization scheduling method according to claim 1, characterized in that: The microgrid optimization dispatching system for implementing the microgrid optimization dispatching method includes: Input module: used to obtain the parameters of microgrid environmental protection and economic dispatch model and improved particle swarm algorithm; Mathematical model module: including mathematical models of wind turbines, photovoltaic power generation, diesel generators, micro gas turbines, and energy storage and loads. Microgrid environmental protection and economic dispatch models are constructed through mathematical models. Dispatching module: used to solve the microgrid environmental protection and economic dispatch model using an improved particle swarm algorithm; Output module: used to output the solution results of the scheduling module, including the optimal value of operating cost and environmental protection cost, and the optimal configuration results between each distributed power source, energy storage unit and load, that is, the scheduling result.

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