A distributed photovoltaic energy storage optimization scheduling method

Abstract

The present invention relates to power system optimization, and in particular to a distributed photovoltaic energy storage optimization scheduling method, which determines an objective function of the distributed photovoltaic energy storage optimization scheduling and constraints of the distributed photovoltaic energy storage optimization scheduling, constructs a distributed photovoltaic energy storage optimization scheduling model according to the objective function and constraints of the distributed photovoltaic energy storage optimization scheduling, and uses an improved multi-objective crayfish optimization algorithm to perform multi-objective solution on the distributed photovoltaic energy storage optimization scheduling model to obtain a distributed photovoltaic energy storage scheduling plan. The technical solution provided by the present invention can effectively overcome the defect of the prior art that it is difficult to accurately and quickly formulate an optimal distributed photovoltaic energy storage scheduling plan.

Description

Technical Field

[0001] The present invention relates to power system optimization, and in particular to a distributed photovoltaic energy storage optimization scheduling method. Background Art

[0002] In existing research, some scholars consider introducing energy storage loss and power prediction error into the dispatching strategy to stabilize the power fluctuation of the power grid, while some scholars focus on the improvement of distribution network loss or node voltage fluctuation by energy storage. In addition, some scholars study how to solve the peak mismatch problem between distributed generation output and load demand by controlling orderly discharge and charging of energy storage, and achieve balanced distribution of residual load margin of distribution network.

[0003] However, the above studies only consider the single interests of investors, or only aim to stabilize the power fluctuations of the power grid, or only consider single-focus factors such as economic factors. How to accurately and quickly formulate the optimal plan for distributed photovoltaic energy storage scheduling is still a technical problem that needs to be solved urgently. Summary of the invention

[0004] In view of the above-mentioned shortcomings of the prior art, the present invention provides a distributed photovoltaic energy storage optimization scheduling method, which can effectively overcome the defect of the prior art that it is difficult to accurately and quickly formulate the optimal plan for distributed photovoltaic energy storage scheduling.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions:

[0006] A distributed photovoltaic energy storage optimization scheduling method determines the objective function of distributed photovoltaic energy storage optimization scheduling and the constraints of distributed photovoltaic energy storage optimization scheduling, builds a distributed photovoltaic energy storage optimization scheduling model according to the objective function and constraints of distributed photovoltaic energy storage optimization scheduling, and uses an improved multi-objective crayfish optimization algorithm to perform multi-objective solutions on the distributed photovoltaic energy storage optimization scheduling model to obtain a distributed photovoltaic energy storage scheduling plan;

[0007] Among them, in the global exploration stage of the improved multi-objective crayfish optimization algorithm, water quality factors are introduced to evaluate the quality of the current aquatic environment, and based on this, it is decided whether to update the position. The global exploration ability in the early stage of the search is enhanced by simulating the behavior of crayfish swimming upstream to find a better aquatic environment. At the same time, global exploration is combined with the simulation of crayfish entering caves to escape the heat when the ambient temperature is high, which makes the multi-objective crayfish optimization algorithm converge quickly.

[0008] Preferably, the objective function of determining the distributed photovoltaic energy storage optimization scheduling includes:

[0009] The objective function of distributed photovoltaic energy storage optimization scheduling includes a first objective function and a second objective function. The first objective function is to minimize the network loss of the power grid system, and the second objective function is to minimize the power fluctuation of the power grid.

[0010] Preferably, the first objective function F 1 is expressed as follows:

[0011] ;

[0012] in, mn Representation Node m With Node n The connecting lines between l is the collection of all lines in the power grid, I mn For Line mn The current, R mn For Line mn Impedance;

[0013] The second objective function F 2 is expressed by the following formula:

[0014] ;

[0015] in, t Indicates the time, T For a day cycle, P is the real-time power of the power grid, is the average power of the grid.

[0016] Preferably, the determination of the constraint conditions for the optimal scheduling of distributed photovoltaic energy storage includes:

[0017] The constraints for optimal scheduling of distributed photovoltaic energy storage include energy storage charge state constraints, power grid system power balance constraints, node voltage and line current relationship equality constraints, node voltage inequality constraints, line current inequality constraints and photovoltaic output characteristics constraints.

[0018] Preferably, the energy storage state of charge constraint is expressed by the following formula:

[0019] SOC min ≤ SOC x ( t )≤ SOC max ;

[0020] in, SOC x ( t ) is the x Energy storage device tThe state of charge at the moment, SOC min , SOC max are the minimum state of charge and the maximum state of charge respectively;

[0021] The power balance constraint of the power grid system is expressed by the following formula:

[0022] P B + P P + P = P L ;

[0023] in, P B is the real-time power of the energy storage system, P P is the real-time power of the photovoltaic system, P L is the load power.

[0024] Preferably, the improved multi-objective crayfish optimization algorithm is used to perform multi-objective solution on the distributed photovoltaic energy storage optimization scheduling model to obtain a distributed photovoltaic energy storage scheduling plan, including:

[0025] The first objective function is optimized by using the improved multi-objective crayfish optimization algorithm. F 1 and the second objective function F 2 Perform multi-objective solution to obtain a distributed photovoltaic energy storage scheduling plan that meets the constraints.

[0026] Preferably, the improved multi-objective crayfish optimization algorithm is used to optimize the first objective function F 1 and the second objective function F 2. Perform multi-objective solving to obtain a distributed photovoltaic energy storage scheduling scheme that meets the constraints, including:

[0027] S1. Randomly generate an initial population of a certain size in the search space. Each crayfish individual in the initial population represents a potential solution, and initialize the parameters.

[0028] S2. In the global exploration stage, water quality factors are introduced to evaluate the quality of the current aquatic environment, and the decision on whether to update the position is made based on this. The global exploration capability in the early stage of the search is enhanced by simulating the behavior of crayfish swimming upstream to find a better aquatic environment. At the same time, the global exploration is combined with the simulation of the behavior of crayfish entering caves to escape the heat when the ambient temperature is high;

[0029] S3, in the local development stage, the location of individual crayfish is updated by simulating their foraging behavior;

[0030] S4, using the fitness function to evaluate each crayfish individual and calculate the corresponding fitness value;

[0031] S5. Record and update the global optimal solution;

[0032] S6. Determine whether the iteration termination condition is met. If the iteration termination condition is not met, return to S2. Otherwise, the current global optimal solution is used as the distributed photovoltaic energy storage scheduling solution.

[0033] Preferably, in S2, in the global exploration stage, a water quality factor is introduced to evaluate the quality of the current aquatic environment, and based on this, a decision is made whether to update the position. The global exploration capability in the initial stage of the search is enhanced by simulating the behavior of crayfish swimming upstream to find a better aquatic environment, including:

[0034] S211, according to water quality factors Q To judge the quality of the current aquatic environment, Q ≤3, it means that the crayfish perceives the quality of the current aquatic environment well and does not need to update its position. Q >3, it means that the crayfish perceives that the quality of the current aquatic environment is poor, and needs to update its position to find a better aquatic environment and enter S212;

[0035] Among them, water quality factors Q is a random number, ;

[0036] S212. Determine the ambient temperature T :

[0037] T = r 1*25+15;

[0038] in, r 1 is a random number, ;

[0039] S213, according to the ambient temperature T , the perception of individual crayfish R and the current iteration number k , calculate the adaptive water flow factor used to adjust the search step size of individual crayfish W :

[0040] ;

[0041] in, K is the maximum number of iterations, T min , T max They are the minimum and maximum ambient temperature, respectively, and the perception R is a random number, , , , All are weight coefficients;

[0042] S214, according to water quality factors Q and adaptive flow factor W , use the following formula to update the position of crayfish individuals:

[0043] ;

[0044] in, X k For crayfish individuals k The position at the iteration, X k+1 For crayfish individuals k The position at +1 iteration, X 1 is a random position within the population, which is used to guide the search direction of individual crayfish. X 2 is the optimal solution for the current population X best Current location of individual crayfish X k Random positions between , r 2 is a random number, , is a random angle used to determine the movement direction of individual crayfish.

[0045] Preferably, S2 simulates the behavior of crayfish entering caves to escape the heat when the ambient temperature is high to conduct a global exploration, including:

[0046] S221. Determine the ambient temperature T :

[0047] T = r 1*25+15;

[0048] in, r 1 is a random number, ;

[0049] S222, according to the ambient temperature T Determine whether to enter the cave to escape the heat. T ≤30℃, it indicates the ambient temperature T Low, crayfish individuals do not need to enter the cave to avoid the heat, if T >30℃, it indicates the ambient temperature T At higher temperatures, crayfish individuals need to enter caves to avoid the heat and enter S223;

[0050] S223. Determine the location of the caveX c :

[0051] X c =( X BSET - X best ) / 2;

[0052] in, X BSET is the current global optimal solution, X best is the optimal solution for the current population;

[0053] S224, if the random number r 3<0.5, it means that there are no other crayfish individuals competing for the cave, and the position of the crayfish individuals is updated using the following formula:

[0054] ;

[0055] in, X k For crayfish individuals k The position at the iteration, X k+1 For crayfish individuals k The position at +1 iteration, C 1 is a decreasing factor, which is used to balance global exploration and local development. , K is the maximum number of iterations, r 3 is a random number, ;

[0056] If the random number r 3≥0.5, it indicates that there are other crayfish individuals competing for the cave, and the position of the crayfish individuals is updated using the following formula:

[0057] ;

[0058] in, X k i The crayfish individuals were randomly selected i In the k The position at the iteration, i = int [ r 3*( N -1)]+1, N For the population size.

[0059] Preferably, in S3, in the local development stage, the position of the individual crayfish is updated by simulating the foraging behavior of the individual crayfish, including:

[0060] S31. Determine the ambient temperature T :

[0061] T = r 1*25+15;

[0062] in, r 1 is a random number, ;

[0063] S32, according to the ambient temperature T Calculating foraging p :

[0064] ;

[0065] in, T’ is the optimum temperature for individual crayfish. To control different ambient temperatures T The parameters of individual crayfish intake are as follows, C 2 is the control factor;

[0066] S33, the current global optimal solution X BSET As food location X f ;

[0067] S34. Determine the size of the food S :

[0068] ;

[0069] in, is the fitness value of the crayfish individual, is the fitness value of the food, C 3 is the food factor, r 4 is a random number, ;

[0070] S35, if S ≤( C 3+1) / 2, it means that the food size is appropriate, and the position of the crayfish is updated using the following formula:

[0071] ;

[0072] in, X k For crayfish individuals k The position at the iteration, X k+1 For crayfish individuals k +1 position at iteration;

[0073] like S >( C 3+1) / 2, it means that the food is larger. A combination of sine and cosine functions is used to simulate the alternating process of crayfish individuals tearing up food and eating, so as to update the position of crayfish individuals.

[0074] Compared with the prior art, the distributed photovoltaic energy storage optimization scheduling method provided by the present invention has the following beneficial effects:

[0075] 1) Determine the objective function of distributed photovoltaic energy storage optimization scheduling and the constraints of distributed photovoltaic energy storage optimization scheduling, build a distributed photovoltaic energy storage optimization scheduling model based on the objective function and constraints of distributed photovoltaic energy storage optimization scheduling, and use the improved multi-objective crayfish optimization algorithm to solve the distributed photovoltaic energy storage optimization scheduling model for multiple objectives, so as to accurately formulate the optimal plan for distributed photovoltaic energy storage scheduling;

[0076] 2) In the global exploration stage of the improved multi-objective crayfish optimization algorithm, water quality factors are introduced to evaluate the quality of the current aquatic environment, and the decision on whether to update the position is made based on this. The global exploration capability in the initial stage of the search is enhanced by simulating the behavior of crayfish swimming upstream to find a better aquatic environment. At the same time, the global exploration is combined with the simulation of the behavior of crayfish entering caves to escape the heat when the ambient temperature is high, so that the multi-objective crayfish optimization algorithm can converge quickly, thereby quickly formulating the optimal scheduling plan for distributed photovoltaic energy storage. BRIEF DESCRIPTION OF THE DRAWINGS

[0077] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0078] Figure 1 It is a schematic diagram of the process of the present invention;

[0079] Figure 2 The improved multi-objective crayfish optimization algorithm is used in the present invention to solve multiple objectives and obtain a flow chart of a distributed photovoltaic energy storage scheduling scheme. DETAILED DESCRIPTION

[0080] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0081] A distributed photovoltaic energy storage optimization scheduling method, such as Figure 1 As shown, the objective function of distributed photovoltaic energy storage optimization scheduling and the constraints of distributed photovoltaic energy storage optimization scheduling are determined, and a distributed photovoltaic energy storage optimization scheduling model is constructed according to the objective function and constraints of distributed photovoltaic energy storage optimization scheduling. The distributed photovoltaic energy storage optimization scheduling model is solved by multi-objective using the improved multi-objective crayfish optimization algorithm to obtain a distributed photovoltaic energy storage scheduling plan;

[0082] Among them, in the global exploration stage of the improved multi-objective crayfish optimization algorithm, water quality factors are introduced to evaluate the quality of the current aquatic environment, and based on this, it is decided whether to update the position. The global exploration ability in the early stage of the search is enhanced by simulating the behavior of crayfish swimming upstream to find a better aquatic environment. At the same time, global exploration is combined with the simulation of crayfish entering caves to escape the heat when the ambient temperature is high, which makes the multi-objective crayfish optimization algorithm converge quickly.

[0083] ① Determine the objective function of distributed photovoltaic energy storage optimization scheduling, including:

[0084] The objective function of distributed photovoltaic energy storage optimization scheduling includes a first objective function and a second objective function. The first objective function is to minimize the network loss of the power grid system, and the second objective function is to minimize the power fluctuation of the power grid.

[0085] 1) First objective function F 1 is expressed as follows:

[0086] ;

[0087] in, mn Representation Node m With Node n The connecting lines between l is the collection of all lines in the power grid, I mn For Line mn The current, R mn For Line mn impedance.

[0088] 2) Second objective function F 2 is expressed by the following formula:

[0089] ;

[0090] in, t Indicates the time, T For a day cycle, P is the real-time power of the power grid, is the average power of the grid.

[0091] ② Determine the constraints for optimal dispatch of distributed photovoltaic energy storage, including:

[0092] The constraints for optimal scheduling of distributed photovoltaic energy storage include energy storage charge state constraints, power grid system power balance constraints, node voltage and line current relationship equality constraints, node voltage inequality constraints, line current inequality constraints and photovoltaic output characteristics constraints.

[0093] 1) The energy storage charge state constraint is expressed as follows:

[0094] SOC min ≤ SOC x ( t )≤ SOC max ;

[0095] in, SOC x ( t ) is the x Energy storage device t The state of charge at the moment, SOC min , SOC max They are the minimum state of charge and the maximum state of charge respectively.

[0096] 2) The power balance constraint of the power grid system is expressed as follows:

[0097] P B + P P + P = P L ;

[0098] in, P B is the real-time power of the energy storage system, P P is the real-time power of the photovoltaic system, P L is the load power.

[0099] ③ The improved multi-objective crayfish optimization algorithm is used to solve the distributed photovoltaic energy storage optimization scheduling model for multi-objective solutions, and the distributed photovoltaic energy storage scheduling scheme is obtained, including:

[0100] The first objective function is optimized by using the improved multi-objective crayfish optimization algorithm. F 1 and the second objective function F 2 Perform multi-objective solution to obtain a distributed photovoltaic energy storage scheduling plan that meets the constraints.

[0101] Specifically, the improved multi-objective crayfish optimization algorithm is used to optimize the first objective function F 1 and the second objective function F 2. Perform multi-objective solving to obtain a distributed photovoltaic energy storage scheduling scheme that meets the constraints, including:

[0102] S1. Randomly generate an initial population of a certain size in the search space. Each crayfish individual in the initial population represents a potential solution, and initialize the parameters.

[0103] S2. In the global exploration stage, water quality factors are introduced to evaluate the quality of the current aquatic environment, and the decision on whether to update the position is made based on this. The global exploration capability in the early stage of the search is enhanced by simulating the behavior of crayfish swimming upstream to find a better aquatic environment. At the same time, the global exploration is combined with the simulation of the behavior of crayfish entering caves to escape the heat when the ambient temperature is high;

[0104] S3, in the local development stage, the location of individual crayfish is updated by simulating their foraging behavior;

[0105] S4, using the fitness function to evaluate each crayfish individual and calculate the corresponding fitness value;

[0106] S5. Record and update the global optimal solution;

[0107] S6. Determine whether the iteration termination condition is met. If the iteration termination condition is not met, return to S2. Otherwise, the current global optimal solution is used as the distributed photovoltaic energy storage scheduling solution.

[0108] 1) In the global exploration phase of S2, water quality factors are introduced to evaluate the quality of the current aquatic environment, and the decision on whether to update the position is made based on this. The global exploration capability in the early stage of the search is enhanced by simulating the behavior of crayfish swimming upstream to find a better aquatic environment, including:

[0109] S211, according to water quality factors Q To judge the quality of the current aquatic environment, Q ≤3, it means that the crayfish perceives the quality of the current aquatic environment well and does not need to update its position. Q>3, it means that the crayfish perceives that the quality of the current aquatic environment is poor, and needs to update its position to find a better aquatic environment and enter S212;

[0110] Among them, water quality factors Q is a random number, ;

[0111] S212. Determine the ambient temperature T :

[0112] T = r 1*25+15;

[0113] in, r 1 is a random number, ;

[0114] S213, according to the ambient temperature T , the perception of individual crayfish R and the current iteration number k , calculate the adaptive water flow factor used to adjust the search step size of individual crayfish W :

[0115] ;

[0116] in, K is the maximum number of iterations, T min , T max They are the minimum and maximum ambient temperature, respectively, and the perception R is a random number, , , , All are weight coefficients;

[0117] S214, according to water quality factors Q and adaptive flow factor W , use the following formula to update the position of crayfish individuals:

[0118] ;

[0119] in, X k For crayfish individuals k The position at the iteration, X k+1 For crayfish individuals k The position at +1 iteration, X 1 is a random position within the population, which is used to guide the search direction of individual crayfish. X 2 is the optimal solution for the current populationX best Current location of individual crayfish X k Random positions between , r 2 is a random number, , is a random angle used to determine the movement direction of individual crayfish.

[0120] 2) S2 simulates the behavior of crayfish entering caves to escape the heat when the ambient temperature is high, and conducts a global exploration, including:

[0121] S221. Determine the ambient temperature T :

[0122] T = r 1*25+15;

[0123] in, r 1 is a random number, ;

[0124] S222, according to the ambient temperature T Determine whether to enter the cave to escape the heat. T ≤30℃, it indicates the ambient temperature T Low, crayfish individuals do not need to enter the cave to avoid the heat, if T >30℃, it indicates the ambient temperature T At higher temperatures, crayfish individuals need to enter caves to avoid the heat and enter S223;

[0125] S223. Determine the location of the cave X c :

[0126] X c =( X BSET - X best ) / 2;

[0127] in, X BSET is the current global optimal solution, X best is the optimal solution for the current population;

[0128] S224, if the random number r 3<0.5, it means that there are no other crayfish individuals competing for the cave, and the position of the crayfish individuals is updated using the following formula:

[0129] ;

[0130] in,X k For crayfish individuals k The position at the iteration, X k+1 For crayfish individuals k The position at +1 iteration, C 1 is a decreasing factor, which is used to balance global exploration and local development. , K is the maximum number of iterations, r 3 is a random number, ;

[0131] If the random number r 3≥0.5, it indicates that there are other crayfish individuals competing for the cave, and the position of the crayfish individuals is updated using the following formula:

[0132] ;

[0133] in, X k i The crayfish individuals were randomly selected i In the k The position at the iteration, i = int [ r 3*( N -1)]+1, N For the population size.

[0134] 3) In the local development stage of S3, the location of individual crayfish is updated by simulating their foraging behavior, including:

[0135] S31. Determine the ambient temperature T :

[0136] T = r 1*25+15;

[0137] in, r 1 is a random number, ;

[0138] S32, according to the ambient temperature T Calculating foraging p :

[0139] ;

[0140] in, T’ is the optimum temperature for individual crayfish. To control different ambient temperatures T The parameters of individual crayfish intake are as follows, C2 is the control factor;

[0141] S33, the current global optimal solution X BSET As food location X f ;

[0142] S34. Determine the size of the food S :

[0143] ;

[0144] in, is the fitness value of the crayfish individual, is the fitness value of the food, C 3 is the food factor, r 4 is a random number, ;

[0145] S35, if S ≤( C 3+1) / 2, it means that the food size is appropriate, and the position of the crayfish is updated using the following formula:

[0146] ;

[0147] in, X k For crayfish individuals k The position at the iteration, X k+1 For crayfish individuals k +1 position at iteration;

[0148] like S >( C 3+1) / 2, it means that the food is larger. A combination of sine and cosine functions is used to simulate the alternating process of crayfish individuals tearing up food and eating, so as to update the position of crayfish individuals.

[0149] The technical solution of the present application determines the objective function of distributed photovoltaic energy storage optimization scheduling and the constraints of distributed photovoltaic energy storage optimization scheduling, constructs a distributed photovoltaic energy storage optimization scheduling model based on the objective function and constraints of distributed photovoltaic energy storage optimization scheduling, and uses an improved multi-objective crayfish optimization algorithm to perform multi-objective solution on the distributed photovoltaic energy storage optimization scheduling model, so as to accurately formulate the optimal plan for distributed photovoltaic energy storage scheduling.

[0150] In the global exploration stage of the improved multi-objective crayfish optimization algorithm, water quality factors are introduced to evaluate the quality of the current aquatic environment, and based on this, it is decided whether to update the position. The global exploration capability in the initial stage of the search is enhanced by simulating the behavior of crayfish swimming upstream to find a better aquatic environment. At the same time, the global exploration is combined with the simulation of the behavior of crayfish entering caves to escape the heat when the ambient temperature is high, so that the multi-objective crayfish optimization algorithm converges quickly, thereby enabling the rapid formulation of the optimal distributed photovoltaic energy storage scheduling plan.

[0151] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements will not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A distributed photovoltaic energy storage optimization scheduling method, characterized by: Determine the objective function of distributed photovoltaic energy storage optimization scheduling and the constraints of distributed photovoltaic energy storage optimization scheduling, build a distributed photovoltaic energy storage optimization scheduling model based on the objective function and constraints of distributed photovoltaic energy storage optimization scheduling, and use the improved multi-objective crayfish optimization algorithm to solve the distributed photovoltaic energy storage optimization scheduling model for multiple objectives, and obtain a distributed photovoltaic energy storage scheduling plan; The objective function for determining the optimal scheduling of distributed photovoltaic energy storage includes: The objective function of distributed photovoltaic energy storage optimization scheduling includes a first objective function and a second objective function. The first objective function is to minimize the grid loss of the power grid system, and the second objective function is to minimize the power fluctuation of the power grid. The first objective function F 1 is expressed as follows: ； in, mn Representation Node m With Node n The connecting lines between l is the collection of all lines in the power grid, I mn For Line mn The current, R mn For Line mn Impedance; The second objective function F 2 is expressed by the following formula: ； in, t Indicates the time, T For a day cycle, P is the real-time power of the power grid, is the average power of the power grid; The improved multi-objective crayfish optimization algorithm is used to optimize the first objective function F 1 and the second objective function F 2. Perform multi-objective solving to obtain a distributed photovoltaic energy storage scheduling scheme that meets the constraints, including: S1. Randomly generate an initial population of a certain size in the search space. Each crayfish individual in the initial population represents a potential solution, and initialize the parameters. S2. In the global exploration stage, water quality factors are introduced to evaluate the quality of the current aquatic environment, and the decision on whether to update the position is made based on this. The global exploration capability in the early stage of the search is enhanced by simulating the behavior of crayfish swimming upstream to find a better aquatic environment. At the same time, the global exploration is combined with the simulation of the behavior of crayfish entering caves to escape the heat when the ambient temperature is high; S3, in the local development stage, the location of individual crayfish is updated by simulating their foraging behavior; S4, using the fitness function to evaluate each crayfish individual and calculate the corresponding fitness value; S5. Record and update the global optimal solution; S6, judging whether the iteration termination condition is met, if not, returning to S2, otherwise taking the current global optimal solution as the distributed photovoltaic energy storage scheduling scheme; Among them, in the global exploration stage of the improved multi-objective crayfish optimization algorithm, water quality factors are introduced to evaluate the quality of the current aquatic environment, and based on this, it is decided whether to update the position. The global exploration ability in the early stage of the search is enhanced by simulating the behavior of crayfish swimming upstream to find a better aquatic environment. At the same time, global exploration is combined with the simulation of crayfish entering caves to escape the heat when the ambient temperature is high, which makes the multi-objective crayfish optimization algorithm converge quickly.

2. The distributed photovoltaic energy storage optimization scheduling method according to claim 1 is characterized in that: The constraint conditions for determining the optimal scheduling of distributed photovoltaic energy storage include: The constraints for optimal scheduling of distributed photovoltaic energy storage include energy storage charge state constraints, power grid system power balance constraints, node voltage and line current relationship equality constraints, node voltage inequality constraints, line current inequality constraints and photovoltaic output characteristics constraints.

3. The distributed photovoltaic energy storage optimization scheduling method according to claim 2 is characterized in that: The energy storage charge state constraint is expressed by the following formula: SOC min ≤ SOC x ( t )≤ SOC max ； in, SOC x ( t ) is the x Energy storage device t The state of charge at the moment, SOC min , SOC max are the minimum state of charge and the maximum state of charge respectively; The power balance constraint of the power grid system is expressed by the following formula: P B + P P + P = P L ； in, P B is the real-time power of the energy storage system, P P is the real-time power of the photovoltaic system, P L is the load power.

4. The distributed photovoltaic energy storage optimization scheduling method according to claim 1 is characterized in that: The improved multi-objective crayfish optimization algorithm is used to solve the distributed photovoltaic energy storage optimization scheduling model for multi-objectives, and a distributed photovoltaic energy storage scheduling plan is obtained, including: The first objective function is optimized by using the improved multi-objective crayfish optimization algorithm. F 1 and the second objective function F 2 Perform multi-objective solution to obtain a distributed photovoltaic energy storage scheduling plan that meets the constraints.

5. The distributed photovoltaic energy storage optimization scheduling method according to claim 1 is characterized in that: In the global exploration phase of S2, water quality factors are introduced to evaluate the quality of the current aquatic environment, and the decision on whether to update the position is made based on this. The global exploration capability in the early stage of the search is enhanced by simulating the behavior of crayfish swimming upstream to find a better aquatic environment, including: S211, according to water quality factors Q To judge the quality of the current aquatic environment, Q ≤3, it means that the crayfish perceives the quality of the current aquatic environment well and does not need to update its position. Q >3, it means that the crayfish perceives that the quality of the current aquatic environment is poor, and needs to update its position to find a better aquatic environment and enter S212; Among them, water quality factors Q is a random number, ; S212. Determine the ambient temperature T : T = r 1*25+15； in, r 1 is a random number, ; S213, according to the ambient temperature T , the perception of individual crayfish R and the current iteration number k , calculate the adaptive water flow factor used to adjust the search step size of individual crayfish W : ； in, K is the maximum number of iterations, T min , T max They are the minimum and maximum ambient temperature, respectively, and the perception R is a random number, , , , All are weight coefficients; S214, according to water quality factors Q and adaptive flow factor W , use the following formula to update the position of crayfish individuals: ； in, X k For crayfish individuals k The position at the iteration, X k+1 For crayfish individuals k The position at +1 iteration, X 1 is a random position within the population, which is used to guide the search direction of individual crayfish. X 2 is the optimal solution for the current population X best Current location of individual crayfish X k Random positions between , r 2 is a random number, , is a random angle used to determine the movement direction of individual crayfish.

6. The distributed photovoltaic energy storage optimization scheduling method according to claim 1 is characterized by: S2 simulates the behavior of crayfish entering caves to escape the heat when the ambient temperature is high, and conducts a global exploration, including: S221. Determine the ambient temperature T : T = r 1*25+15； in, r 1 is a random number, ; S222, according to the ambient temperature T Determine whether to enter the cave to escape the heat. T ≤30℃, it indicates the ambient temperature T Low, crayfish individuals do not need to enter the cave to avoid the heat, if T >30℃, it indicates the ambient temperature T At higher temperatures, crayfish individuals need to enter caves to avoid the heat and enter S223; S223. Determine the location of the cave X c : X c =( X BSET － X best ) / 2； in, X BSET is the current global optimal solution, X best is the optimal solution for the current population; S224, if the random number r 3<0.5, it means that there are no other crayfish individuals competing for the cave, and the position of the crayfish individuals is updated using the following formula: ； in, X k For crayfish individuals k The position at the iteration, X k+1 For crayfish individuals k The position at +1 iteration, C 1 is a decreasing factor, which is used to balance global exploration and local development. , K is the maximum number of iterations, r 3 is a random number, ; If the random number r 3≥0.5, it indicates that there are other crayfish individuals competing for the cave, and the position of the crayfish individuals is updated using the following formula: ； in, X k i The crayfish individuals were randomly selected i In the k The position at the iteration, i = int [ r 3*( N -1)]+1, N For the population size.

7. The distributed photovoltaic energy storage optimization scheduling method according to claim 1 is characterized by: In the local development stage in S3, the location of individual crayfish is updated by simulating their foraging behavior, including: S31. Determine the ambient temperature T : T = r 1*25+15； in, r 1 is a random number, ; S32, according to the ambient temperature T Calculating foraging p : ； in, T’ is the optimum temperature for individual crayfish. To control different ambient temperatures T The parameters of individual crayfish intake are as follows, C 2 is the control factor; S33, the current global optimal solution X BSET As food location X f ; S34. Determine the size of the food S : ； in, is the fitness value of the crayfish individual, is the fitness value of the food, C 3 is the food factor, r 4 is a random number, ; S35, if S ≤( C 3+1) / 2, it means that the food size is appropriate, and the position of the crayfish is updated using the following formula: ； in, X k For crayfish individuals k The position at the iteration, X k+1 For crayfish individuals k +1 position at iteration; like S >( C 3+1) / 2, it means that the food is larger. A combination of sine and cosine functions is used to simulate the alternating process of crayfish individuals tearing up food and eating, so as to update the position of crayfish individuals.

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