Micro-grid multi-objective optimization scheduling method and device

Abstract

The invention relates to a micro-grid multi-objective optimization scheduling method and device, and belongs to the technical field of micro-grid optimization scheduling, and the method comprises the following steps: determining the structure of a micro-grid system, and collecting the equipment parameters of each distributed power supply; establishing a micro-grid optimization scheduling model according to the characteristics of the micro-grid and the collected equipment parameters, wherein the micro-grid optimization scheduling model takes minimization of user operation cost and environment cost as an objective function; generating an initial population of the MOPSO algorithm by using a chaotic equation, and improving the value of a learning factor by using a dynamic adjustment parameter to obtain an improved MOPSO algorithm; and performing optimization solution on the micro-grid optimization scheduling model by using an improved MOPSO algorithm to obtain the output power of each distributed power supply. According to the invention, the cost and environmental factors of the micro-grid are considered at the same time, and effective scheduling and management of the micro-grid are realized.

Description

Technical Field

[0001] The invention relates to a microgrid multi-objective optimization scheduling method and device, and belongs to the technical field of microgrid optimization scheduling. Background Art

[0002] A microgrid is a small power generation and distribution system consisting of distributed power sources, energy storage devices, energy conversion devices, loads, etc. It can be connected to the grid or operated in an isolated manner. It aims to achieve efficient application of distributed power sources and improve power supply reliability. In order to give full play to the advantages of microgrids, they need to be optimized and dispatched.

[0003] The optimal dispatch of microgrids generally refers to the reasonable configuration and adjustment of various energy resources in microgrids to achieve the best economic benefits and operating efficiency. The significance of studying the optimal dispatch of microgrids is mainly as follows: (1) Optimal dispatch can effectively configure and utilize energy resources in microgrids, reduce energy waste, and improve the operating efficiency of the overall system. (2) Through the precise dispatch of microgrids, energy procurement and operating costs can be reduced to maximize economic benefits. (3) Optimal dispatch helps to improve the power supply reliability of microgrids, ensuring that the system can operate stably and provide stable power supply when power demand fluctuates or power grid failures occur. (4) Optimal dispatch can better integrate and utilize renewable energy (such as wind power and solar energy), reduce dependence on traditional fossil energy, and promote green and low-carbon development.

[0004] Existing research on microgrid optimization scheduling often faces problems such as overly complex optimization models, large amount of calculations, and poor real-time performance. In addition, many studies fail to fully consider production costs and environmental factors, making it difficult to achieve ideal results in practical applications. In contrast, the use of an improved particle swarm algorithm for optimization scheduling has significant advantages. This algorithm can effectively handle complex optimization problems, has strong global search capabilities and fast convergence speed. When comprehensively considering production costs and environmental factors, the particle swarm algorithm can find the optimal scheduling solution through intelligent search, balance economic benefits and environmental protection, and achieve dual optimization of microgrid operation. Summary of the invention

[0005] The present invention proposes a microgrid multi-objective optimization scheduling method and device, which can balance economic benefits and environmental protection and achieve dual optimization of microgrid operation.

[0006] The technical solution adopted by the present invention to solve the technical problem is:

[0007] In a first aspect, an embodiment of the present invention provides a microgrid multi-objective optimization scheduling method, comprising the following steps:

[0008] Determine the structure of the microgrid system and collect equipment parameters of each distributed power source;

[0009] A microgrid optimization scheduling model is established according to the characteristics of the microgrid and the collected equipment parameters, wherein the microgrid optimization scheduling model takes minimizing user operating costs and environmental costs as an objective function;

[0010] The initial population of the MOPSO (Multi-Objective Particle Swarm Optimization) algorithm is generated by using the chaos equation, and the value of the learning factor is improved by using dynamic adjustment parameters to obtain the improved MOPSO algorithm.

[0011] The improved MOPSO algorithm is used to optimize and solve the microgrid optimal scheduling model to obtain the output power of each distributed power source.

[0012] As a possible implementation of this embodiment, the microgrid system is composed of a photovoltaic cell PV, a wind turbine WT, a micro gas turbine MT, a diesel generator DE and an energy storage battery ES. The equipment parameters include: the upper and lower power limits, operation and maintenance costs, service life, pollutant gas emission coefficients and corresponding processing parameters of the photovoltaic cell PV, wind turbine WT, micro gas turbine MT and diesel generator DE, and the rated power, cut-in wind speed, cut-out wind speed and rated wind speed of WT.

[0013] As a possible implementation of this embodiment, the step of establishing a microgrid optimization scheduling model according to the characteristics of the microgrid and the collected device parameters includes:

[0014] Step S21, establishing models for each distributed power source and battery, wherein the established models include a wind turbine model, a photovoltaic power generation model, a micro gas turbine model, a diesel generator model and a battery model;

[0015] Step S22, minimizing the operating cost and environmental cost as the objective function:

[0016]

[0017] Among them, C buy (t), C sell (t) are the electricity purchase cost and electricity sales cost of the microgrid and the large grid at time t, respectively, c buy (t), c sell (t) are the purchase price and sales price of electricity for the microgrid and the large grid at time t, respectively. sell (t), P buy (t) are the power sold and purchased by the microgrid and the large grid at time t; C MT (t), C DE(t) are the total operating costs of the micro-turbine and diesel generator at time t, C MT,OM (t), C MT,F (t) are the operation and maintenance cost and fuel cost of the micro gas turbine at time t, C DE,OM (t), C DE,F (t) are the operation and maintenance cost and fuel cost of the diesel engine at time t respectively.

[0018] As a possible implementation of this embodiment, the wind turbine model is:

[0019]

[0020] Among them, P WT , P r are the actual output power and rated power of the wind turbine respectively; v ci 、v co 、v r They are the cut-in wind speed, cut-out wind speed and rated wind speed of the fan respectively;

[0021] The photovoltaic power generation model is:

[0022] P PV =ζη m A p η p cosθ (2)

[0023] Among them, P PV is the actual output power of the photovoltaic cell; ζ is the sunlight intensity; η m is the efficiency in maximum power point tracking mode; A p is the area of ​​the solar panel; η p is the efficiency of the photovoltaic cell; θ is the incident angle of light;

[0024] The micro gas turbine model is:

[0025] The efficiency of the micro gas turbine is:

[0026]

[0027] Among them, P MT (t) is the output power of the micro gas turbine at time t, η MT is the working efficiency of the micro gas turbine at time t;

[0028] The fuel costs, operation and maintenance costs, and pollutant treatment costs incurred by the gas turbine during operation are:

[0029]

[0030] In the formula, 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 micro gas turbine at time t; P MT (t) is the power generation of the micro gas turbine at time t; K MT,OM is the operation and maintenance cost coefficient of the micro gas turbine; γ mt,k is the emission of type k pollutants generated by the operation of the micro gas turbine;

[0031] The diesel generator model is:

[0032]

[0033] In the formula, 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; P 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 the operation of diesel engines;

[0034] The battery model is:

[0035]

[0036] Among them, E SB (t) is the capacity of the battery at time t, P bess (t) is the charge and discharge power of the battery at time t. When it is greater than 0, it means the battery is charging, and when it is less than 0, it means the battery is discharging. + , η - Represent the charge and discharge efficiency respectively.

[0037] As a possible implementation of this embodiment, the constraint conditions of the microgrid optimization scheduling model include:

[0038] Power balance constraints:

[0039] P PV (t)+P WT (t)+P DE (t)+P MT (t)+P bess (t) = P L (t) (9)

[0040] Among them, P L (t) is the minimum power of the microgrid system at time t;

[0041] Diesel generator output constraints:

[0042]

[0043] P DE (t)-P DE (t-1)|≤r DE (11)

[0044] Among them, r DE Indicates the upper limit of the diesel engine's climbing power; are the minimum and maximum power generation of the diesel engine respectively;

[0045] Micro gas turbine output constraints:

[0046]

[0047] P MT (t)-P MT (t-1)|≤r MT (13)

[0048] Among them, r MT It represents the upper limit of the ramp power of the micro gas turbine; are the minimum and maximum power generation of the micro gas turbine respectively;

[0049] Energy storage device constraints:

[0050]

[0051] in, are the minimum charge and discharge power and maximum charge and discharge power of the battery respectively. are the minimum and maximum capacities of the battery respectively.

[0052] As a possible implementation of this embodiment, the use of the chaotic equation to generate the initial population of the MOPSO algorithm and the use of dynamic adjustment parameters to improve the value of the learning factor to obtain the improved MOPSO algorithm includes:

[0053] Step S31, setting parameters in the MOPSO algorithm, the parameters including dimension, number of particles, number of iterations, inertia weight and range of learning factor;

[0054] Step S32, using chaotic population initialization to calculate the fitness value of each particle, and storing the non-inferior solution in an external file;

[0055] Step S33, using dynamic adjustment parameters to improve individual learning factors and social learning factors;

[0056] Step S34, updating the speed and position of the particle;

[0057] Step S35, using crowding distance to maintain external archives and update the global optimal position;

[0058] Step S36, judging whether the MOPSO algorithm has reached the set number of iterations and judging whether the number of non-inferior solutions in the external archive has reached the set maximum capacity. If so, the operation is terminated and the non-inferior solution is output.

[0059] As a possible implementation of this embodiment, the calculation of the fitness value of each particle by using the chaotic population initialization includes:

[0060] Initialize the particle position: Assume that the particle dimension is D, x max,j and x min,j are the maximum and minimum values ​​of the particle's search position in the j dimension, respectively, and m0 and n0 are two arbitrary initialized chaotic variables;

[0061] Generate two chaotic variables m k,j and n k,j :

[0062] m k,j =μm k-1,j (1-m k-1,j ) (17)

[0063] n k,j =μn k-1,j (1-n k-1,j ) (18)

[0064] x i,j =x min,j +m k,j (x max,j -x min,j ) (19)

[0065] p i,j =p min,j +n k,j (p max,j -p min,j ) (20)

[0066] Among them, μ is the control parameter;

[0067] Use the generated chaotic variables to update the particle positions;

[0068] Compare the fitness of any two individual best positions of the particle x(D) and p(D), and select the smaller particle as the global best position of the particle.

[0069] As a possible implementation of this embodiment, the formula for improving the individual learning factor and the social learning factor by using dynamic adjustment parameters is:

[0070]

[0071] η=5+3rand (23)

[0072] Among them, c1 is the individual learning factor, c2 is the social learning factor, η is the coefficient, rand represents a random number between 0 and 1, c 1max and c 1min represents the maximum and minimum values ​​of the individual learning factor, c 2max and c 2min represents the maximum and minimum value of the social learning factor, T max Indicates the set number of iterations;

[0073] The formula for updating the velocity and position of the particle is:

[0074] v id (t+1)=ωv id (t)+c1rand1(p id (t)-x id (t))+c2rand2(g d (t)-x id (t)) (24)

[0075] x id (t+1)=x id (t)+v id (t+1) (25)

[0076] where v id (t) is the velocity of the ith particle at time t, x id (t) is the position of the i-th particle at time t, ω is the inertia weight; c1 and c2 are learning factors; rand1 and rand2 are random numbers in the range [0,1]; p id (t) is the d-th dimension component in the historical optimal solution vector of the i-th particle at time t; g d (t) is the d-th dimension component in the global optimal solution vector at time t.

[0077] As a possible implementation of this embodiment, maintaining external archives by using crowding distance includes:

[0078] The crowding distance of the mth non-inferior solution is defined as 1 / 2 of the perimeter of the quadrilateral with the m-1th non-inferior solution and the m+1th non-inferior solution as diagonal vertices; the new solution generated by the particle is defined as P new , f i (x) represents the value of solution x on the i-th objective function, and the external archive is N p, the number of non-inferior solutions in the external archive is limited to N;

[0079] When P new It does not form a dominant relationship with the solution x, and |N P |≤N, then P new Store to N p middle;

[0080] like Satisfy that x is better than P in all objective function values new , then ignore P new , do not store it in N p middle;

[0081] If P new Able to control N p If there are some non-inferior solutions x in the solution, delete these dominated solutions and then new Store to N p middle;

[0082] If |N p |>N, and for All have P new does not dominate x and P new Not dominated by x, then sort N in descending order of congestion p The non-inferior solutions in , and eliminate the non-inferior solutions with the lowest congestion one by one, until |N p |≤N.

[0083] In a second aspect, an embodiment of the present invention provides a microgrid multi-objective optimization scheduling device, comprising:

[0084] Data acquisition module, used to determine the structure of the microgrid system and collect equipment parameters of each distributed power source;

[0085] A model building module is used to build a microgrid optimization scheduling model according to the characteristics of the microgrid and the collected equipment parameters, wherein the microgrid optimization scheduling model takes minimizing user operating costs and environmental costs as an objective function;

[0086] The algorithm improvement module is used to generate the initial population of the MOPSO algorithm using the chaotic equation and to improve the value of the learning factor using dynamic adjustment parameters to obtain the improved MOPSO algorithm;

[0087] The model solving module is used to optimize and solve the microgrid optimization scheduling model using the improved MOPSO algorithm to obtain the output power of each distributed power source.

[0088] The beneficial effects of the technical solution of the embodiment of the present invention are as follows:

[0089] The present invention uses precise data collection and modeling, comprehensively considers the user's electricity cost and environmental protection factors, and ensures the comprehensiveness and practicality of the optimization solution; the improved MOPSO algorithm is used to effectively overcome the problems of premature convergence and local optimality of traditional algorithms, and improves the global search capability and storage efficiency of the solution. The present invention also considers the cost and environmental factors of the microgrid, and realizes the effective scheduling and management of the microgrid.

[0090] The present invention has significant advantages in using an improved particle swarm algorithm for optimization scheduling. This algorithm can effectively handle complex optimization problems, has strong global search capabilities and fast convergence speed. When comprehensively considering production costs and environmental factors, the particle swarm algorithm can find the optimal scheduling solution through intelligent search, balance economic benefits and environmental protection, and achieve dual optimization of microgrid operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0091] Figure 1 is a flow chart of a microgrid multi-objective optimization scheduling method according to an exemplary embodiment;

[0092] Figure 2 It is a structural diagram of a microgrid multi-objective optimization scheduling device according to an exemplary embodiment. DETAILED DESCRIPTION

[0093] In order to more clearly illustrate the technical features of the solution of the present invention, the present invention is described in detail below through specific implementation methods and in conjunction with the accompanying drawings.

[0094] like Figure 1 As shown, a microgrid multi-objective optimization scheduling method provided by an embodiment of the present invention includes the following steps:

[0095] Step S1, determine the structure of the microgrid system and collect equipment parameters of each distributed power source;

[0096] Step S2, establishing a microgrid optimization scheduling model according to the characteristics of the microgrid and the collected equipment parameters, wherein the microgrid optimization scheduling model takes minimizing user operating costs and environmental costs as an objective function;

[0097] Step S3, using the chaos equation to generate the initial population of the MOPSO algorithm, and using dynamic adjustment parameters to improve the value of the learning factor to obtain an improved MOPSO algorithm;

[0098] Step S4, using the improved MOPSO algorithm to optimize and solve the microgrid optimization scheduling model to obtain the output power of each distributed power source.

[0099] As a possible implementation of this embodiment, the microgrid system is composed of a photovoltaic cell PV, a wind turbine WT, a micro gas turbine MT, a diesel generator DE and an energy storage battery ES. The equipment parameters include: the upper and lower power limits, operation and maintenance costs, service life, pollutant gas emission coefficients and corresponding processing parameters of the photovoltaic cell PV, wind turbine WT, micro gas turbine MT and diesel generator DE, and the rated power, cut-in wind speed, cut-out wind speed and rated wind speed of WT.

[0100] As a possible implementation of this embodiment, the step of establishing a microgrid optimization scheduling model according to the characteristics of the microgrid and the collected device parameters includes:

[0101] Step S21, establishing models for each distributed power source and battery, wherein the established models include a wind turbine model, a photovoltaic power generation model, a micro gas turbine model, a diesel generator model and a battery model;

[0102] Step S22, minimizing the operating cost and environmental cost as the objective function:

[0103]

[0104] Among them, C buy (t), C sell (t) are the electricity purchase cost and electricity sales cost of the microgrid and the large grid at time t, respectively, c buy (t), c sell (t) are the purchase price and sales price of electricity for the microgrid and the large grid at time t, respectively. sell (t), P buy (t) are the power sold and purchased by the microgrid and the large grid at time t; C MT (t), C DE (t) are the total operating costs of the micro-turbine and diesel generator at time t, C MT,OM (t), C MT,F (t) are the operation and maintenance cost and fuel cost of the micro gas turbine at time t, C DE,OM (t), C DE,F (t) are the operation and maintenance cost and fuel cost of the diesel engine at time t respectively.

[0105] As a possible implementation of this embodiment, the wind turbine model is:

[0106]

[0107] Among them, P WT , P r are the actual output power and rated power of the wind turbine respectively; v ci 、vco 、v r They are the cut-in wind speed, cut-out wind speed and rated wind speed of the fan respectively;

[0108] The photovoltaic power generation model is:

[0109] P PV =ζη m A p η p cosθ (2)

[0110] Among them, P PV is the actual output power of the photovoltaic cell; ζ is the sunlight intensity; η m is the efficiency in maximum power point tracking mode; A p is the area of ​​the solar panel; η p is the efficiency of the photovoltaic cell; θ is the incident angle of light;

[0111] The micro gas turbine model is:

[0112] The efficiency of the micro gas turbine is:

[0113]

[0114] Among them, P MT (t) is the output power of the micro gas turbine at time t, η MT is the working efficiency of the micro gas turbine at time t;

[0115] The fuel costs, operation and maintenance costs, and pollutant treatment costs incurred by the gas turbine during operation are:

[0116]

[0117] In the formula, 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 micro gas turbine at time t; P MT (t) is the power generation of the micro gas turbine at time t; K MT,OM is the operation and maintenance cost coefficient of the micro gas turbine; γ mt,k is the emission of type k pollutants generated by the operation of the micro gas turbine;

[0118] The diesel generator model is:

[0119]

[0120] In the formula, 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; P 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 the operation of diesel engines;

[0121] The battery model is:

[0122]

[0123] Among them, E SB (t) is the capacity of the battery at time t, P bess (t) is the charge and discharge power of the battery at time t. When it is greater than 0, it means the battery is charging, and when it is less than 0, it means the battery is discharging. + , η - Represent the charge and discharge efficiency respectively.

[0124] As a possible implementation of this embodiment, the constraint conditions of the microgrid optimization scheduling model include:

[0125] (1) Power balance constraints:

[0126] P PV (t)+P WT (t)+P DE (t)+P MT (t)+P bess (t) = P L (t) (9)

[0127] Among them, P L (t) is the minimum power of the microgrid system at time t;

[0128] (2) Diesel generator output constraints:

[0129]

[0130] P DE (t)-P DE (t-1)|≤r DE (11)

[0131] Among them, r DE Indicates the upper limit of the diesel engine's climbing power; are the minimum and maximum power generation of the diesel engine respectively;

[0132] (3) Micro gas turbine output constraints:

[0133]

[0134] PMT (t)-P MT (t-1)|≤r MT (13)

[0135] Among them, r MT It represents the upper limit of the ramp power of the micro gas turbine; are the minimum and maximum power generation of the micro gas turbine respectively;

[0136] (4) Energy storage device constraints:

[0137]

[0138] in, are the minimum charge and discharge power and maximum charge and discharge power of the battery respectively. are the minimum and maximum capacities of the battery respectively.

[0139] As a possible implementation of this embodiment, the use of the chaotic equation to generate the initial population of the MOPSO algorithm and the use of dynamic adjustment parameters to improve the value of the learning factor to obtain the improved MOPSO algorithm includes:

[0140] Step S31, setting parameters in the MOPSO algorithm, the parameters including dimension, number of particles, number of iterations, inertia weight and range of learning factor;

[0141] Step S32, using chaotic population initialization to calculate the fitness value of each particle, and storing the non-inferior solution in an external file;

[0142] Step S33, using dynamic adjustment parameters to improve individual learning factors and social learning factors;

[0143] Step S34, updating the speed and position of the particle;

[0144] Step S35, using crowding distance to maintain external archives and update the global optimal position;

[0145] Step S36, judging whether the MOPSO algorithm has reached the set number of iterations and judging whether the number of non-inferior solutions in the external archive has reached the set maximum capacity. If so, the operation is terminated and the non-inferior solution is output.

[0146] As a possible implementation of this embodiment, the calculation of the fitness value of each particle by using the chaotic population initialization includes:

[0147] Initialize the particle position: Assume that the particle dimension is D, x max,j and x min,jare the maximum and minimum values ​​of the particle's search position in the j dimension, respectively, and m0 and n0 are two arbitrary initialized chaotic variables;

[0148] Generate two chaotic variables m k,j and n k,j :

[0149] m k,j =μm k-1,j (1-m k-1,j ) (17)

[0150] n k,j =μn k-1,j (1-n k-1,j ) (18)

[0151] x i,j =x min,j +m k,j (x max,j -x min,j ) (19)

[0152] p i,j =p min,j +n k,j (p max,j -p min,j ) (20)

[0153] Among them, μ is the control parameter;

[0154] Use the generated chaotic variables to update the particle positions;

[0155] Compare the fitness of any two individual best positions of the particle x(D) and p(D), and select the smaller particle as the global best position of the particle.

[0156] As a possible implementation of this embodiment, the formula for improving the individual learning factor and the social learning factor by using dynamic adjustment parameters is:

[0157]

[0158] η=5+3rand (23)

[0159] Among them, c1 is the individual learning factor, c2 is the social learning factor, η is the coefficient, rand represents a random number between 0 and 1, c 1max and c 1min represents the maximum and minimum values ​​of the individual learning factor, c 2max and c 2min represents the maximum and minimum value of the social learning factor, T max Indicates the set number of iterations;

[0160] The formula for updating the velocity and position of the particle is:

[0161] v id (t+1)=ωv id (t)+c1rand1(p id (t)-x id (t))+c2rand2(g d (t)-x id (t)) (24)

[0162] x id (t+1)=x id (t)+v id (t+1) (25)

[0163] where v id (t) is the velocity of the ith particle at time t, x id (t) is the position of the i-th particle at time t, ω is the inertia weight; c1 and c2 are learning factors; rand1 and rand2 are random numbers in the range [0,1]; p id (t) is the d-th dimension component in the historical optimal solution vector of the i-th particle at time t; g d (t) is the d-th dimension component in the global optimal solution vector at time t.

[0164] As a possible implementation of this embodiment, maintaining external archives by using crowding distance includes:

[0165] The crowding distance of the mth non-inferior solution is defined as 1 / 2 of the perimeter of the quadrilateral with the m-1th non-inferior solution and the m+1th non-inferior solution as diagonal vertices; the new solution generated by the particle is defined as P new , f i (x) represents the value of solution x on the i-th objective function, and the external archive is N p , the number of non-inferior solutions in the external archive is limited to N;

[0166] When P new It does not form a dominant relationship with the solution x, and N P |≤N, then P new Store to N p middle;

[0167] like Satisfy that x is better than P in all objective function values new , then ignore P new , do not store it in N p middle;

[0168] If P new Able to control N pIf there are some non-inferior solutions x in the solution, delete these dominated solutions and then new Store to N p middle;

[0169] If |N p |>N, and for All have P new does not dominate x and P new Not dominated by x, then sort N in descending order of congestion p The non-inferior solutions in , and eliminate the non-inferior solutions with the lowest congestion one by one, until |N p |≤N.

[0170] like Figure 2 As shown, a microgrid multi-objective optimization scheduling device provided by an embodiment of the present invention includes:

[0171] Data acquisition module, used to determine the structure of the microgrid system and collect equipment parameters of each distributed power source;

[0172] A model building module is used to build a microgrid optimization scheduling model according to the characteristics of the microgrid and the collected equipment parameters, wherein the microgrid optimization scheduling model takes minimizing user operating costs and environmental costs as an objective function;

[0173] The algorithm improvement module is used to generate the initial population of the MOPSO algorithm using the chaotic equation and to improve the value of the learning factor using dynamic adjustment parameters to obtain the improved MOPSO algorithm;

[0174] The model solving module is used to optimize and solve the microgrid optimization scheduling model using the improved MOPSO algorithm to obtain the output power of each distributed power source.

[0175] The specific process of the present invention for performing multi-objective optimization scheduling of microgrids is as follows:

[0176] Step 1: Determine the structure of the microgrid system and obtain the equipment parameters of each distributed power source and battery through data acquisition technology.

[0177] The microgrid system studied in the present invention is mainly composed of photovoltaic cells PV, wind turbines WT, micro gas turbines MT, diesel generators DE and energy storage batteries ES. The acquired equipment parameters mainly include: the upper and lower power limits, operation and maintenance costs, service life, pollutant gas emission coefficients and corresponding processing parameters of photovoltaic cells PV, wind turbines WT, micro gas turbines MT and diesel generators DE, and the rated power, cut-in wind speed, cut-out wind speed and rated wind speed of WT.

[0178] Step 2: Establish a microgrid optimization scheduling model based on the characteristics of the microgrid and the acquired data, with minimizing user operating costs and environmental costs as the objective function.

[0179] First, each distributed power source and battery is modeled. The specific mathematical model is as follows:

[0180] (1) Wind turbine model

[0181] The output power model of the wind turbine can be expressed as:

[0182]

[0183] Among them, P WT , P r are the actual output power and rated power of the wind turbine respectively; v ci 、v co 、v r They are the cut-in wind speed, cut-out wind speed and rated wind speed of the fan respectively.

[0184] (2) Photovoltaic power generation model

[0185] The output power of photovoltaic cells can be expressed as:

[0186] P PV =ζη m A p η p cosθ (2)

[0187] Among them, P PV is the actual output of PV; ζ is the solar intensity; η m is the efficiency in maximum power point tracking mode; A p is the area of ​​the solar panel; η p is the efficiency of the photovoltaic cell; θ is the incident angle of light.

[0188] (3) Micro gas turbine

[0189] The efficiency expressions of the micro gas turbine are as follows:

[0190]

[0191] Among them, P MT (t) is the output power of MT at time t, η MT is the efficiency of MT at time t. The fuel cost, operation and maintenance cost, and pollutant treatment cost generated by the gas turbine during operation are expressed as follows:

[0192]

[0193] In the formula, 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 micro gas turbine at time t; P MT (t) is the power generation of the micro gas turbine at time t; K MT,OM is the operation and maintenance cost coefficient of the micro gas turbine; γ mt,k It is the emission of type K pollutants generated by the operation of micro gas turbine.

[0194] (4) Diesel generator

[0195] The fuel costs, operation and maintenance costs, and pollutant treatment costs generated by diesel generators during operation are expressed as follows:

[0196]

[0197] In the formula, 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. 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 It is the emission of type K pollutants generated by the operation of diesel engines.

[0198] (5) Battery

[0199] When the output power of the distributed generation is greater than the total load, the ES is charged, otherwise, the ES is discharged. The mathematical expression of battery charging and discharging is as follows:

[0200]

[0201] Among them, E SB (t) is the capacity of the battery at time t, P bess (t) is the charge and discharge efficiency of the battery at time t. When it is greater than 0, it means the battery is charging, and when it is less than 0, it means the battery is discharging. + , η - Represent the charge and discharge efficiency respectively.

[0202] Then, the objective function is to minimize the operating cost and environmental cost. The operating cost is calculated as follows:

[0203]

[0204]

[0205] Among them, C MT (t), C DE(t) are the total operating costs of the micro-turbine and diesel generator at time t, P sell (t), P buy (t) are the power sold and purchased by the microgrid and the large grid at time t, respectively, c buy (t), c sell (t) are the purchase price and sales price of electricity of the microgrid and the large grid at time t respectively.

[0206] The constraints considered in the model are as follows:

[0207] The power balance constraints are as follows:

[0208] P PV (t)+P WT (t)+P DE (t)+P MT (t)+P bess (t) = P L (t) (9)

[0209] Diesel generator output constraints:

[0210]

[0211] P DE (t)-P DE (t-1)|≤r DE (11)

[0212] Among them, r DE Indicates the upper limit of the diesel engine's climbing power.

[0213] Micro gas turbine output constraints:

[0214]

[0215] |P MT (t)-P MT (t-1)|≤r MT (13)

[0216] Among them, r MT Indicates the upper limit of the climbing power of the micro gas turbine.

[0217] Energy storage device constraints:

[0218]

[0219] Step 3: To solve the problem that particle swarm optimization is prone to premature convergence and easy to fall into local optimality, the chaotic equation is introduced to generate the initial population of the MOPSO algorithm and the dynamic adjustment parameters are used to improve the value of the learning factor. At the same time, an external archive maintenance method based on crowding distance is proposed to improve the storage efficiency of the solution set of the multi-objective algorithm. The improved MOPSO algorithm has the following steps:

[0220] Step 3.1, first set the parameters in the MOPSO algorithm, such as dimension, number of particles, number of iterations, inertia weight, and range of learning factor.

[0221] In step 3.2, the chaotic population is initialized, the fitness value of each particle is calculated, and the non-inferior solution is stored in an external archive.

[0222] Chaos is a random motion state obtained by deterministic equations. It is a ubiquitous nonlinear phenomenon with complex behavior, similar to random motion, and has the characteristics of initial value sensitivity and ergodicity. The basic idea of ​​initializing a chaotic population is to use the randomness and unpredictability in chaos theory to distribute the individuals of the initial population in multiple locations in the search space to increase the search range of the solution space, thereby increasing the probability of finding the global optimal solution. As a basic chaotic mapping, the Logistic map has the characteristics of good convergence performance and easy implementation. Its mathematical expression is as follows:

[0223] x n+1 =μx n (1-x n ) (n=0,1,2,...) (16)

[0224] Among them, μ is the control parameter. When the value of μ is 4, the generated chaotic sequence is most evenly distributed. When x0∈(0,1), the chaotic variable x n ∈(0,1).

[0225] The present invention designs a population initialization method based on Logistic chaotic mapping to initialize the position of particles. Assuming that the particle dimension is D, x max,j and x min,j are the maximum and minimum values ​​of the particle's search position in the j-dimension, respectively, and m0 and n0 are two arbitrary initialized chaotic variables. First, generate two chaotic variables m k,j and n k,j ; Then, the generated chaotic variables are used to update the particle positions; Finally, the fitness of x(D) and p(D) is compared and the smaller particle is selected.

[0226] m k,j =μm k-1,j (1-m k-1,j ) (17)

[0227] n k,j =μn k-1,j (1-n k-1,j ) (18)

[0228] x i,j =x min,j +m k,j (x max,j -x min,j ) (19)

[0229] p i,j =p min,j +n k,j (p max,j -p min,j ) (20)

[0230] Taking into account that general optimization algorithms all use randomly generated initial populations, the idea of ​​chaotic mapping can be used to generate two populations and select the better individuals from the two populations to achieve the purpose of initializing the population, making the individuals in the population closer to the optimal solution and improving the convergence performance of the algorithm.

[0231] Step 3.3, introduce dynamic adjustment parameters to improve individual learning factors and social learning factors.

[0232] In the particle swarm algorithm, the value of the learning factor has a great influence on the performance of the algorithm. Therefore, dynamic adjustment parameters are introduced to improve the individual learning factor c1 and the social learning factor c2 to achieve better results. The expression is as follows:

[0233]

[0234] η=5+3rand (23)

[0235] Among them, rand represents a random number between 0 and 1, c 1max and c 1min represents the maximum and minimum values ​​of the individual learning factor, c 2max and c 2min represents the maximum and minimum value of the social learning factor, T max Indicates the set number of iterations.

[0236] Step 3.4, update the speed and position. The update formulas for speed and position are as follows:

[0237] v id (t+1)=ωv id (t)+c1rand1(p id (t)-x id (t))+c2rand2(g d(t)-x id (t)) (24)

[0238] x id (t+1)=x id (t)+v id (t+1) (25)

[0239] Among them, ω is the inertia weight; c1 and c2 are learning factors; rand1 and rand2 are random numbers in the range of [0,1]; p id (t) is the d-th dimension component in the historical optimal solution vector of the i-th particle at time t; g d (t) is the d-th dimension component in the global optimal solution vector at time t.

[0240] In step 3.5, crowding distance is introduced to maintain the external archive and update the global optimal position.

[0241] Unlike the single-objective particle swarm algorithm, the MOPSO algorithm needs to use an external archive to store and maintain each generation of non-inferior solutions. Through the test analysis of the standard MOPSO algorithm, it was found that in some test functions, the solutions obtained by the algorithm are unevenly distributed in the target space and gathered in one place. In order to maintain the diversity of solutions in the external archive and improve the global search performance of the algorithm, the present invention introduces the crowding distance to improve the external archive maintenance strategy. The crowding distance of the mth non-inferior solution is defined as 1 / 2 of the perimeter of the quadrilateral with the m-1th non-inferior solution and the m+1th non-inferior solution as diagonal vertices.

[0242] Define the new solution of particle generation as P new , f i (x) represents the value of solution x on the i-th objective function, and the external archive is N p , the number of non-inferior solutions in the external archive is limited to N. The external archive maintenance strategy designed in this chapter is as follows:

[0243] 1). Store to the new archive: When P new It does not form a dominant relationship with the solution x, and |N P |≤N, then P new Store to N p middle.

[0244] 2). Ignore the solutions dominated by Pareto non-inferior solutions: If Satisfy that x is better than P in all objective function values new , then ignore P new , do not store it in N p The mathematical expression of this process is as follows:

[0245]

[0246] 3) Handling the situation where the newly generated solution can dominate the stored non-inferior solution: If P new Able to control N p If there are some non-inferior solutions x in the solution, delete these dominated solutions and then new Store to N p The mathematical expression of this process is as follows:

[0247]

[0248] 4). If |N p |>N, and for All have P new does not dominate x and P new Not dominated by x, then sort N in descending order of congestion p The non-inferior solutions in , and eliminate the non-inferior solutions with the lowest congestion one by one, until N p |≤N.

[0249] Step 3.6, determine whether the MOPSO algorithm has reached the set number of iterations and whether the number of non-inferior solutions in the external archive has reached the set maximum capacity. If so, terminate the operation and output the non-inferior solution.

[0250] Step 4: Use the improved MOPSO algorithm to optimize and solve the model to obtain the output power of each distributed power source.

[0251] The present invention provides a microgrid optimization scheduling method, which can simultaneously consider the cost and environmental factors of the microgrid and realize effective scheduling and management of the microgrid.

[0252] The present invention first determines the structure of the microgrid system, and the selection of the structure needs to take into account the characteristics of the microgrid and the actual operation. To this end, the present invention will use data acquisition technology to obtain the device parameters of each distributed power source to facilitate subsequent analysis and modeling. In this way, the performance and characteristics of each device can be understood, so as to better formulate a reasonable microgrid scheduling strategy.

[0253] Secondly, the present invention will establish a microgrid optimization scheduling model based on the characteristics of the microgrid and the acquired data. The goal of this model is to maximize the minimization of user operating costs while also considering the minimization of environmental costs. The present invention will use some mathematical tools and methods, such as linear programming, integer programming, etc., to construct this model. Through this model, the present invention can obtain an optimized scheduling plan that can not only meet the user's electricity demand, but also minimize environmental pollution as much as possible.

[0254] Then, a problem needs to be solved, that is, how to solve the problem of early convergence and local optimality of particle swarm algorithm. To this end, the present invention introduces chaos equations into the MOPSO algorithm to generate an initial population of the MOPSO algorithm. In this way, the present invention can ensure the global search capability of the algorithm and avoid the occurrence of local optimality. At the same time, the present invention will also dynamically adjust the value of the learning factor so that the convergence speed and accuracy of the algorithm can be effectively controlled.

[0255] Finally, the present invention needs to propose an external archive maintenance method based on crowding distance to improve the storage efficiency of the solution set of the multi-objective algorithm. In the MOPSO algorithm, each particle will constantly try to find a better solution. However, as the search process proceeds, it will be found that some solutions are no longer optimal. At this time, it is necessary to remove these solutions from the solution set to make room for the formation of new solutions. To this end, the present invention proposes an archive maintenance method based on crowding distance, that is, when the distance of a solution from the current optimal solution exceeds a certain threshold, it is considered that this solution is no longer valuable and is removed from the solution set. This can effectively improve the storage efficiency of the solution set and avoid wasting too much computing resources.

[0256] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A multi-objective optimization scheduling method for a microgrid, characterized in that: The steps include: Determine the structure of the microgrid system and collect equipment parameters of each distributed power source; A microgrid optimization scheduling model is established according to the characteristics of the microgrid and the collected equipment parameters, wherein the microgrid optimization scheduling model takes minimizing user operating costs and environmental costs as an objective function; The initial population of the MOPSO algorithm is generated by using the chaotic equation, and the value of the learning factor is improved by using dynamic adjustment parameters to obtain the improved MOPSO algorithm. The improved MOPSO algorithm is used to optimize and solve the microgrid optimal scheduling model to obtain the output power of each distributed power source.

2. The microgrid multi-objective optimization scheduling method according to claim 1 is characterized in that: The microgrid system consists of a photovoltaic cell PV, a wind turbine WT, a micro gas turbine MT, a diesel generator DE and an energy storage battery ES. The equipment parameters include: the upper and lower power limits, operation and maintenance costs, service life, pollutant gas emission coefficients and corresponding processing parameters of the photovoltaic cell PV, wind turbine WT, micro gas turbine MT and diesel generator DE, the rated power, cut-in wind speed, cut-out wind speed and rated wind speed of WT.

3. The microgrid multi-objective optimization scheduling method according to claim 1 is characterized in that: The microgrid optimization scheduling model is established according to the characteristics of the microgrid and the collected equipment parameters, including: Step S21, establishing models for each distributed power source and battery, wherein the established models include a wind turbine model, a photovoltaic power generation model, a micro gas turbine model, a diesel generator model and a battery model; Step S22, minimizing the operating cost and environmental cost as the objective function: Among them, C buy (t), C sell (t) are the electricity purchase cost and electricity sales cost of the microgrid and the large grid at time t, respectively, c buy (t), c sell (t) are the purchase price and sales price of electricity for the microgrid and the large grid at time t, respectively. sell (t), P buy (t) are the power sold and purchased by the microgrid and the large grid at time t; C MT (t), C DE (t) are the total operating costs of the micro-turbine and diesel generator at time t, C MT,OM (t), C MT,F (t) are the operation and maintenance cost and fuel cost of the micro gas turbine at time t, C DE,OM (t), C DE,F (t) are the operation and maintenance cost and fuel cost of the diesel engine at time t respectively.

4. The microgrid multi-objective optimization scheduling method according to claim 3 is characterized in that: The wind turbine model is: Among them, P WT , P r are the actual output power and rated power of the wind turbine respectively; v ci 、v co 、v r They are the cut-in wind speed, cut-out wind speed and rated wind speed of the fan respectively; The photovoltaic power generation model is: P PV =live m A p or p cosθ (2) Among them, P PV is the actual output power of the photovoltaic cell; ζ is the sunlight intensity; η m is the efficiency in maximum power point tracking mode; A p is the area of ​​the solar panel; η p is the efficiency of the photovoltaic cell; θ is the incident angle of light; The micro gas turbine model is: The efficiency of the micro gas turbine is: Among them, P MT (t) is the output power of the micro gas turbine at time t, η MT is the working efficiency of the micro gas turbine at time t; The fuel costs, operation and maintenance costs, and pollutant treatment costs incurred by the gas turbine during operation are: In the formula, 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 micro gas turbine at time t; P MT (t) is the power generation of the micro gas turbine at time t; K MT,OM is the operation and maintenance cost coefficient of the micro gas turbine; γ mt,k is the emission of type k pollutants generated by the operation of the micro gas turbine; The diesel generator model is: In the formula, 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; P 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 the operation of diesel engines; The battery model is: Among them, E SB (t) is the capacity of the battery at time t, P bess (t) is the charge and discharge power of the battery at time t. When it is greater than 0, it means the battery is charging, and when it is less than 0, it means the battery is discharging. + , η - Represent the charge and discharge efficiency respectively.

5. The microgrid multi-objective optimization scheduling method according to claim 4 is characterized in that: The constraints of the microgrid optimization scheduling model include: Power balance constraints: P PV (t)+P WT (t)+P DE (t)+P MT (t)+P bess (t)=P L (t) (9) Among them, P L (t) is the minimum power of the microgrid system at time t; Diesel generator output constraints: P DE (t)-P DE (t-1)|≤r DE (11) Among them, r DE Indicates the upper limit of the diesel engine's climbing power; are the minimum and maximum power generation of the diesel engine respectively; Micro gas turbine output constraints: P MT (t)-P MT (t-1)|≤r MT (13) Among them, r MT It represents the upper limit of the ramp power of the micro gas turbine; are the minimum and maximum power generation of the micro gas turbine respectively; Energy storage device constraints: in, are the minimum charge and discharge power and maximum charge and discharge power of the battery respectively. are the minimum and maximum capacities of the battery respectively.

6. The multi-objective optimization scheduling method for microgrid according to any one of claims 1 to 5, characterized in that: The method of using the chaotic equation to generate the initial population of the MOPSO algorithm and using the dynamic adjustment parameters to improve the value of the learning factor to obtain the improved MOPSO algorithm includes: Step S31, setting parameters in the MOPSO algorithm, the parameters including dimension, number of particles, number of iterations, inertia weight and range of learning factor; Step S32, using chaotic population initialization to calculate the fitness value of each particle, and storing the non-inferior solution in an external file; Step S33, using dynamic adjustment parameters to improve individual learning factors and social learning factors; Step S34, updating the speed and position of the particle; Step S35, using crowding distance to maintain external archives and update the global optimal position; Step S36, judging whether the MOPSO algorithm has reached the set number of iterations and judging whether the number of non-inferior solutions in the external archive has reached the set maximum capacity. If so, the operation is terminated and the non-inferior solution is output.

7. The microgrid multi-objective optimization scheduling method according to claim 6 is characterized in that: The calculation of the fitness value of each particle by using the chaotic population initialization includes: Initialize the particle position: Assume that the particle dimension is D, x max,j and x min,j are the maximum and minimum values ​​of the particle's search position in the j dimension, respectively, and m0 and n0 are two arbitrary initialized chaotic variables; Generate two chaotic variables m k,j and n k,j : m k,j =μm k-1,j (1-m k-1,j ) (17) a k,j =μn k-1,j (1-n k-1,j ) (18) x i,j =x min,j +m k,j (x max,j -x min,j ) (19) p i,j =p min,j +n k,j (p max,j -p min,j ) (20) Among them, μ is the control parameter; Use the generated chaotic variables to update the particle positions; Compare the fitness of any two individual best positions of the particle x(D) and p(D), and select the smaller particle as the global best position of the particle.

8. The microgrid multi-objective optimization scheduling method according to claim 6 is characterized in that: The formula for improving the individual learning factor and the social learning factor by using dynamic adjustment parameters is: η=5+3rand (23) Among them, c1 is the individual learning factor, c2 is the social learning factor, η is the coefficient, rand represents a random number between 0 and 1, c 1max and c 1min represents the maximum and minimum values ​​of the individual learning factor, c 2max and c 2min represents the maximum and minimum value of the social learning factor, T max Indicates the set number of iterations; The formula for updating the velocity and position of the particle is: v id (t+1)=ωv id (t)+c1rand1(p id (t)-x id (t))+c2rand2(g d (t)-x id (t)) (24) x id (t+1)=x id (t)+v id (t+1) (25) where v id (t) is the velocity of the ith particle at time t, x id (t) is the position of the i-th particle at time t, ω is the inertia weight; c1 and c2 are learning factors; rand1 and rand2 are random numbers in the range [0,1]; p id (t) is the d-th dimension component in the historical optimal solution vector of the i-th particle at time t; g d (t) is the d-th dimension component in the global optimal solution vector at time t.

9. The microgrid multi-objective optimization scheduling method according to claim 6, characterized in that: The use of crowding distance to maintain external archives includes: The crowding distance of the mth non-inferior solution is defined as 1 / 2 of the perimeter of the quadrilateral with the m-1th non-inferior solution and the m+1th non-inferior solution as diagonal vertices; the new solution generated by the particle is defined as P new , f i (x) represents the value of solution x on the i-th objective function, and the external archive is N p , the number of non-inferior solutions in the external archive is limited to N; When P new It does not form a dominant relationship with the solution x, and |N P |≤N, then P new Store to N p middle; like Satisfy that x is better than P in all objective function values new , then ignore P new , do not store it in N p middle; If P new Able to control N p If there are some non-inferior solutions x in the solution, delete these dominated solutions and then new Store to N p middle; If |N p |>N, and for All have P new does not dominate x and P new Not dominated by x, then sort N in descending order of congestion p The non-inferior solutions in , and eliminate the non-inferior solutions with the lowest congestion one by one, until |N p |≤N.

10. A microgrid multi-objective optimization dispatching device, characterized in that: include: Data acquisition module, used to determine the structure of the microgrid system and collect equipment parameters of each distributed power source; A model building module is used to build a microgrid optimization scheduling model according to the characteristics of the microgrid and the collected equipment parameters, wherein the microgrid optimization scheduling model takes minimizing user operating costs and environmental costs as an objective function; The algorithm improvement module is used to generate the initial population of the MOPSO algorithm using the chaotic equation and to improve the value of the learning factor using dynamic adjustment parameters to obtain the improved MOPSO algorithm; The model solving module is used to optimize and solve the microgrid optimization scheduling model using the improved MOPSO algorithm to obtain the output power of each distributed power source.

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