Method for generating operation scheduling scheme of hydrogen-photovoltaic storage and charging integrated energy station

Abstract

The present invention discloses a method for generating an operation scheduling scheme for a hydrogen-light storage and charging integrated energy station, and belongs to the technical field of energy station operation scheduling scheme generation. The method comprises the following steps, setting the operation parameters of the hydrogen-light storage and charging integrated energy station, and establishing an operation scheduling model of the hydrogen-light storage and charging integrated energy station according to the operation parameters, and finally adopting an improved gray wolf optimization algorithm to iteratively solve the operation scheduling model of the hydrogen-light storage and charging integrated energy station, and obtain the individual position with the maximum predation benefit in the current wolf pack and output it as the optimal operation scheduling scheme of the hydrogen-light storage and charging integrated energy station. The present invention takes into account the correlation between various energy sources in the energy station and the uncertain factors in the operation process, and establishes a new operation scheduling model; takes into account the correlation between various energy sources in the hydrogen-light storage and charging integrated energy station, and also takes into account the uncertain factors in the operation process of the hydrogen-light storage and charging integrated energy station, and has a better effect when applied in the optimization solution algorithm.

Description

Technical Field

[0001] The present invention specifically relates to a method for generating an operation scheduling plan for a hydrogen-photovoltaic storage and charging integrated energy station, and belongs to the technical field of energy station operation scheduling plan generation. Background Art

[0002] The hydrogen-photovoltaic storage and charging integrated energy station can be regarded as a small microgrid composed of power supply and distribution system, energy storage system, photovoltaic power generation system, hydrogen power generation system, charging system, etc. In this microgrid, when the power consumption is at its peak, the charging piles can be powered by energy storage, photovoltaic and hydrogen energy together; when the power consumption is at its low point, the energy storage can store the excess electricity generated by photovoltaic and hydrogen energy, and can also be sold to the power grid to obtain additional income. The core of the hydrogen-photovoltaic storage and charging integrated energy station is to achieve unified management and unified scheduling of photovoltaic, hydrogen energy, energy storage, and car charging in the station. According to the real-time weather, electricity price, and new energy vehicle battery status information, it intelligently schedules photovoltaic, hydrogen energy, energy storage and new energy vehicles to charge in an orderly manner, and quickly forms the optimal configuration and scheduling of the hydrogen-photovoltaic storage and charging integrated energy station under the current power demand. Since the hydrogen-photovoltaic storage and charging integrated energy station involves hydrogen energy, solar energy and electric energy that influence each other, and there are many uncertain factors such as fluctuations in demand for new energy vehicles and fluctuations in distributed photovoltaic output during the operation of the hydrogen-photovoltaic storage and charging integrated energy station, the operation and scheduling problem of the hydrogen-photovoltaic storage and charging integrated energy station becomes a complex problem of random, nonlinear, multi-stage and mixed integer programming. It is difficult to quickly perform the optimal solution to obtain an optimal operation and scheduling plan under the current constraints.

[0003] Most of the existing technologies use intelligent algorithms such as particle swarm optimization, genetic algorithm, gray wolf algorithm, or data-driven algorithms such as reinforcement learning and deep reinforcement learning to solve the scheduling objective function of the hydrogen-photovoltaic storage and charging integrated energy station under certain constraints to obtain the optimal operation scheduling plan. However, the main idea of ​​these methods is to solve the above planning problem by iteratively searching for the optimal strategy in a large strategy space. The main problem faced by iterative search is that the search efficiency is low and the operation speed is slow. Summary of the invention

[0004] The technical problem to be solved by the present invention is: how to quickly obtain the optimal operation scheduling plan for the hydrogen-photovoltaic storage and charging integrated energy station.

[0005] The technical solution proposed by the present invention is: a method for generating an operation scheduling plan for a hydrogen-photovoltaic storage and charging integrated energy station, comprising the following steps:

[0006] Step 1: Set the number of charging piles N, the maximum charging power P, and the rated capacity of hydrogen energy storage in the hydrogen-photovoltaic storage and charging integrated energy station. Maximum charge and discharge power h cap 、Charging efficiency η cand discharge efficiency η dc , Charging piles are occupied And the required charging energy Where i represents the charging pile number. and Indicates the start and end time of the numth occupancy of the i-th charging pile, Indicates the charging energy required for this occupancy;

[0007] Step 2: Establish the operation and scheduling model of the hydrogen-photovoltaic storage and charging integrated energy station, as shown in the following formula (1):

[0008]

[0009] In formula (1), C t represents the operating cost of the integrated energy station at the current moment, ω t represents the current electricity price, p t represents the total charging power in the integrated energy station, r t represents the distributed photovoltaic output at the current moment, h t,c and h t,dc They represent the maximum allowable charging power and maximum allowable discharging power of hydrogen energy storage at the current moment, p t,i Indicates the charging power of the i-th charging pile at the current moment;

[0010] The constraint conditions for obtaining the operation scheduling model of the hydrogen-photovoltaic storage and charging integrated energy station are shown in the following formula (2):

[0011]

[0012] In formula (2), b t Indicates the energy level of hydrogen storage at the current moment, h t Indicates the output power of hydrogen energy storage at the current moment, T t i Indicates the remaining charging time of the i-th charging pile at the current moment, Indicates whether there is a car charging at the i-th charging pile at the current moment. When 1 is selected, it means that there is a car charging. When the value is 0, it means there is no car charging; represents the charging time of the electric vehicle that arrives at the charging station at the next moment and uses the i-th charging pile, Indicates the remaining charging energy of the i-th charging pile at the current moment, It represents the charging energy required by the electric vehicle that arrives at the charging station at the next moment and uses the i-th charging pile;

[0013] Step 3: Use the improved gray wolf optimization algorithm to iteratively solve the operation scheduling model of the hydrogen-photovoltaic storage and charging integrated energy station, obtain the individual position with the maximum predation benefit in the current wolf pack and output it as the optimal operation scheduling plan of the hydrogen-photovoltaic storage and charging integrated energy station. The specific iterative solution process is as follows:

[0014] Step 3.1: Optimize and improve the initial wolf pack of the gray wolf optimization algorithm to obtain the final initial wolf pack. The specific steps are as follows:

[0015] Step 3.1.1: Set the rough population number g and the number of excellent lone wolves k in the iterative solution process, and initialize the number of iterations r, so that the number of iterations r=1;

[0016] Step 3.1.2: The matrix composed of the charging power of N charging piles at each moment represents the position information X of the jth wolf. j , as shown in the following formula (3),

[0017] X j =[p t,i ] 24×N (3);

[0018] Step 3.1.3: Randomly generate an initial wolf pack consisting of m wolves, and use the p in formula (2) t,i Randomly generate 24×N values ​​within the range of , and substitute them into formula (3) as the position information of a wolf in the initial wolf pack; repeat the above steps until each wolf in the initial wolf pack generates position information; collect the position information of each wolf in the initial wolf pack to form the initial wolf pack position information set X, as shown in the following formula (4):

[0019] X={X1, X2, ..., X m} (4),

[0020] In formula (4), X1 is the first position information of the first wolf in the initial wolf pack, X2 is the second position information of the second wolf in the initial wolf pack, and X m is the mth position information of the mth wolf in the initial wolf pack;

[0021] Step 3.1.4: Substitute the first position information in the initial wolf pack position information set X into the hydrogen-photovoltaic storage and charging integrated energy station operation scheduling model for calculation, and use the calculation result as the predation benefit of the first wolf in the initial wolf pack; repeat the above steps until each wolf in the initial wolf pack has a corresponding predation benefit; sort the predation benefits of all wolves in the wolf pack from large to small, and draw a first predation benefit curve; calculate the similarity between the first predation benefit curve and the five standard predation benefit curves, and select the curve type of the standard predation benefit curve with the highest similarity as the curve type of the first predation benefit curve; calculate the noise level between the standard predation benefit curve with the highest similarity and the first predation benefit curve;

[0022] Step 3.1.5: Obtain the values ​​of the optimization parameters Z1, Z2, Z3 and Z4 of the initial wolf pack according to the curve type and noise level of the first predation benefit curve obtained in step 3.1.4, and calculate the number s of the final initial population by the following formula (5):

[0023]

[0024] In formula (5), e is the natural base;

[0025] Step 3.2: Standardize the first three wolves in the final initial wolf pack as α wolf, β wolf and δ wolf respectively; calculate the distance between each wolf in the final initial wolf pack except α wolf, β wolf and δ wolf and α wolf, β wolf and δ wolf according to the following formula (6):

[0026]

[0027] In formula (6), j is a natural number greater than or equal to 4 and less than or equal to s; D α (j) is the distance between the jth wolf in the final initial wolf pack and the α wolf; D β (j) is the distance between the jth wolf in the final initial wolf pack and the β wolf; D δ (j) is the distance between the jth wolf in the final initial wolf pack and the δ wolves; X α (r), X β (r) and X δ (r) are the location information of wolf α, wolf β and wolf δ respectively; X j (r) is the location information of the jth wolf; C α , C β and C δ are the distance coefficients of α wolf, β wolf and δ wolf respectively; U α,1 , U β,1 and U δ,1They are random numbers generated randomly and uniformly distributed between the interval [0,1];

[0028] Step 3.3: Update the position information of each wolf in the final initial wolf pack except wolf α, wolf β and wolf δ in the next iteration according to the following formula (7):

[0029]

[0030] In formula (7), X j (r+1) is the position information of the jth wolf in the next iteration; X α (r+1), X β (r+1) and X δ (r+1) are the position information of α wolf, β wolf and δ wolf in the next iteration process; A α , A β and A δ are the distance update coefficients of α wolf, β wolf and δ wolf respectively; U α,2 , U β,2 , U δ,2 are random numbers randomly generated and uniformly distributed between the interval [0,1]; r is the current number of iterations; R is the maximum number of iterations;

[0031] When all the remaining wolves in the final initial wolf pack except wolf α, wolf β and wolf δ have calculated the position information of the next iteration, the current iteration number r is increased by 1, and a judgment is made to see whether the current iteration number r is greater than or equal to the maximum iteration number R; if the judgment condition is met, the position information of all wolves in the final initial wolf pack obtained under the current iteration number is output; if the judgment condition is not met, the position information of all wolves in the final initial wolf pack obtained under the current iteration number is substituted into step 3.2 and step 3.3 to perform iterative calculation again until the judgment condition is met;

[0032] Step 3.4: Substitute the position information of all wolves in the final initial wolf pack output in step 3.3 into the operation and scheduling model of the hydrogen-photovoltaic storage and charging integrated energy station in turn for calculation to obtain the predation benefits of all wolves in the final initial wolf pack; select the position information of the wolf with the largest predation benefit in the final initial wolf pack as the optimal operation and scheduling plan of the hydrogen-photovoltaic storage and charging integrated energy station.

[0033] Beneficial effects of the present invention: 1. The present invention takes into account the correlation between various energy sources in the hydrogen-photovoltaic storage and charging integrated energy station and the uncertain factors in the operation process, and establishes a new operation and scheduling model for the hydrogen-photovoltaic storage and charging integrated energy station; the model takes into account the correlation between various energy sources in the hydrogen-photovoltaic storage and charging integrated energy station, and also takes into account the uncertain factors in the operation process of the hydrogen-photovoltaic storage and charging integrated energy station, and the effect is better when applied in the optimization solution algorithm. 2. The present invention adopts an improved gray wolf optimization algorithm, which improves the existing gray wolf optimization algorithm that adopts the method of randomly generating an initial wolf pack, and adopts a special initial wolf pack generation method to obtain a better initial wolf pack number, avoiding the problem of slow algorithm iteration solution speed caused by the excessively large initial wolf pack number in the existing gray wolf optimization algorithm, and can quickly generate an operation and scheduling plan for the hydrogen-photovoltaic storage and charging integrated energy station. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 Flow chart of the method for generating an operation scheduling plan for a hydrogen-photovoltaic storage and charging integrated energy station of the present invention.

[0035] Figure 2 It is a convergence diagram of the operating results of the method adopted by the present invention in a specific implementation manner.

[0036] Figure 3 It is the operation structure convergence diagram of the existing operation scheduling method. DETAILED DESCRIPTION

[0037] The following is a further description of a method for generating an operation scheduling scheme for a hydrogen-photovoltaic storage and charging integrated energy station according to the present invention in conjunction with the accompanying drawings and specific embodiments.

[0038] The present invention provides a method for generating an operation scheduling plan for a hydrogen-photovoltaic storage and charging integrated energy station, such as Figure 1 As shown, the following steps are included:

[0039] Step 1: Set the number of charging piles N, the maximum charging power P, and the rated capacity of hydrogen energy storage in the hydrogen-photovoltaic storage and charging integrated energy station. Maximum charge and discharge power h cap 、Charging efficiency η c and discharge efficiency η dc , Charging piles are occupied And the required charging energy Where i represents the charging pile number. and Indicates the start and end time of the numth occupancy of the i-th charging pile, Indicates the charging energy required for this occupancy;

[0040] Step 2: Establish the operation and scheduling model of the hydrogen-photovoltaic storage and charging integrated energy station, as shown in the following formula (1):

[0041]

[0042] In formula (1), C t represents the operating cost of the integrated energy station at the current moment, ω t represents the current electricity price, p t represents the total charging power in the integrated energy station, r t represents the distributed photovoltaic output at the current moment, h t,c and h t,dc They represent the maximum allowable charging power and maximum allowable discharging power of hydrogen energy storage at the current moment, p t,i Indicates the charging power of the i-th charging pile at the current moment;

[0043] The constraint conditions for obtaining the operation scheduling model of the hydrogen-photovoltaic storage and charging integrated energy station are shown in the following formula (2):

[0044]

[0045] In formula (2), b t Indicates the energy level of hydrogen storage at the current moment, h t Indicates the output power of hydrogen energy storage at the current moment, T t i Indicates the remaining charging time of the i-th charging pile at the current moment, Indicates whether there is a car charging at the i-th charging pile at the current moment. When 1 is selected, it means that there is a car charging. When the value is 0, it means there is no car charging; represents the charging time of the electric vehicle that arrives at the charging station at the next moment and uses the i-th charging pile, Indicates the remaining charging energy of the i-th charging pile at the current moment, It represents the charging energy required by the electric vehicle that arrives at the charging station at the next moment and uses the i-th charging pile;

[0046] Step 3: Use the improved gray wolf optimization algorithm to iteratively solve the operation scheduling model of the hydrogen-photovoltaic storage and charging integrated energy station, obtain the individual position with the maximum predation benefit in the current wolf pack and output it as the optimal operation scheduling plan of the hydrogen-photovoltaic storage and charging integrated energy station. The specific iterative solution process is as follows:

[0047] Step 3.1: Optimize and improve the initial wolf pack of the gray wolf optimization algorithm to obtain the final initial wolf pack. The specific steps are as follows:

[0048] Step 3.1.1: Set the rough population number g and the number of excellent lone wolves k in the iterative solution process, and initialize the number of iterations r, so that the number of iterations r=1;

[0049] Step 3.1.2: The matrix composed of the charging power of N charging piles at each moment represents the position information X of the jth wolf. j , as shown in the following formula (3),

[0050] X j =[p t,i ] 24×N (3);

[0051] Step 3.1.3: Randomly generate an initial wolf pack consisting of m wolves, and use the p in formula (2) t,i Randomly generate 24×N values ​​within the range of , and substitute them into formula (3) as the position information of a wolf in the initial wolf pack; repeat the above steps until each wolf in the initial wolf pack generates position information; collect the position information of each wolf in the initial wolf pack to form the initial wolf pack position information set X, as shown in the following formula (4):

[0052] X={X1, X2, ..., X m} (4),

[0053] In formula (4), X1 is the first position information of the first wolf in the initial wolf pack, X2 is the second position information of the second wolf in the initial wolf pack, and X m is the mth position information of the mth wolf in the initial wolf pack;

[0054] Step 3.1.4: Substitute the first position information in the initial wolf pack position information set X into the hydrogen-photovoltaic storage and charging integrated energy station operation scheduling model for calculation, and use the calculation result as the predation benefit of the first wolf in the initial wolf pack; repeat the above steps until each wolf in the initial wolf pack has a corresponding predation benefit; sort the predation benefits of all wolves in the wolf pack from large to small, and draw a first predation benefit curve; calculate the similarity between the first predation benefit curve and the five standard predation benefit curves, and select the curve type of the standard predation benefit curve with the highest similarity as the curve type of the first predation benefit curve; calculate the noise level between the standard predation benefit curve with the highest similarity and the first predation benefit curve;

[0055] Step 3.1.5: Obtain the values ​​of the optimization parameters Z1, Z2, Z3 and Z4 of the initial wolf pack according to the curve type and noise level of the first predation benefit curve obtained in step 3.1.4, and calculate the number s of the final initial population by the following formula (5):

[0056]

[0057] In formula (5), e is the natural base;

[0058] Step 3.2: The first three wolves in the final initial wolf pack are standardized as α wolf, β wolf and δ wolf respectively; the distance between each wolf in the final initial wolf pack except α wolf, β wolf and δ wolf and α wolf, β wolf and δ wolf is calculated according to the following formula (5):

[0059]

[0060] In formula (6), j is a natural number greater than or equal to 4 and less than or equal to s; D α (j) is the distance between the jth wolf in the final initial wolf pack and the α wolf; D β (j) is the distance between the jth wolf in the final initial wolf pack and the β wolf; D δ (j) is the distance between the jth wolf in the final initial wolf pack and the δ wolves; X α (r), X β (r) and X δ (r) are the location information of wolf α, wolf β and wolf δ respectively; X j (r) is the location information of the jth wolf; C α , C β and C δ are the distance coefficients of α wolf, β wolf and δ wolf respectively; U α,1 , U β,1 and U δ,1 They are random numbers generated randomly and uniformly distributed between the interval [0,1];

[0061] Step 3.3: Update the position information of each wolf in the final initial wolf pack except wolf α, wolf β and wolf δ in the next iteration according to the following formula (6):

[0062]

[0063] In formula (7), X j (r+1) is the position information of the jth wolf in the next iteration; X α (r+1), X β (r+1) and X δ (r+1) are the position information of α wolf, β wolf and δ wolf in the next iteration process; A α , A β and A δ are the distance update coefficients of α wolf, β wolf and δ wolf respectively; U α,2 , U β,2 , U δ,2 are random numbers randomly generated and uniformly distributed between the interval [0,1]; r is the current number of iterations; R is the maximum number of iterations;

[0064] When all the remaining wolves in the final initial wolf pack except wolf α, wolf β and wolf δ have calculated the position information of the next iteration, the current iteration number r is increased by 1, and a judgment is made to see whether the current iteration number r is greater than or equal to the maximum iteration number R; if the judgment condition is met, the position information of all wolves in the final initial wolf pack obtained under the current iteration number is output; if the judgment condition is not met, the position information of all wolves in the final initial wolf pack obtained under the current iteration number is substituted into step 3.2 and step 3.3 to perform iterative calculation again until the judgment condition is met;

[0065] Step 3.4: Substitute the position information of all wolves in the final initial wolf pack output in step 3.3 into the operation and scheduling model of the hydrogen-photovoltaic storage and charging integrated energy station in turn for calculation to obtain the predation benefits of all wolves in the final initial wolf pack; select the position information of the wolf with the largest predation benefit in the final initial wolf pack as the optimal operation and scheduling plan of the hydrogen-photovoltaic storage and charging integrated energy station.

[0066] The hardware environment for the experiment is CPU Core(TM) i5-1340P, memory 16G. The experimental parameters are set as: N = 20, P = 3.6kW, h cap =50kW, η c =0.82, η dc =0.62, the time period when the charging pile is occupied In the interval [1,24], randomly select values ​​to generate time periods. Randomly select values ​​in the interval [P, 5P], R = 50, g = 20 and k = 10. The experiment compares the proposed method with the classic gray wolf algorithm, such as Figure 2 and Figure 3 As shown in the figure, the horizontal axis represents the number of wolf pack iterations based on the wolf pack optimization gray wolf algorithm, and the vertical axis represents the optimal predation efficiency of the wolf pack under the current iteration. It can be seen that the hydrogen-photovoltaic storage and charging integrated energy station operation scheduling method based on the wolf pack optimization gray wolf algorithm proposed in the present invention can basically converge to the optimal solution after 10 iterations, while the classic gray wolf algorithm requires about 28 iterations to converge to the optimal solution. The running time of the method proposed in the present invention is 58 seconds, while the running time of the classic gray wolf algorithm is 142 seconds. The main reason is that the present invention proposes a wolf pack optimization algorithm, which can improve the initial wolf pack quality of the gray wolf algorithm and speed up the search efficiency of the optimal operation scheduling strategy.

Claims

1. A method for generating an operation scheduling plan for a hydrogen-photovoltaic storage and charging integrated energy station, characterized in that: The following steps are involved: Step 1: Set the number of charging piles N, the maximum charging power P, and the rated capacity of hydrogen energy storage in the hydrogen-photovoltaic storage and charging integrated energy station. Maximum charge and discharge power h cap 、Charging efficiency η c and discharge efficiency η dc , Charging piles are occupied And the required charging energy Where i represents the charging pile number. and Indicates the start and end time of the numth occupancy of the i-th charging pile, Indicates the charging energy required for this occupancy; Step 2: Establish the operation and scheduling model of the hydrogen-photovoltaic storage and charging integrated energy station, as shown in the following formula (1): In formula (1), C t represents the operating cost of the integrated energy station at the current moment, ω t represents the current electricity price, p t represents the total charging power in the integrated energy station, r t represents the distributed photovoltaic output at the current moment, h t,c and h t,dc They represent the maximum allowable charging power and maximum allowable discharging power of hydrogen energy storage at the current moment, p t,i Indicates the charging power of the i-th charging pile at the current moment; The constraint conditions for obtaining the operation scheduling model of the hydrogen-photovoltaic storage and charging integrated energy station are shown in the following formula (2): In formula (2), b t Indicates the energy level of hydrogen storage at the current moment, h t Indicates the output power of hydrogen energy storage at the current moment, Indicates the remaining charging time of the i-th charging pile at the current moment, Indicates whether there is a car charging at the i-th charging pile at the current moment. When 1 is selected, it means that there is a car charging. When the value is 0, it means there is no car charging; represents the charging time of the electric vehicle that arrives at the charging station at the next moment and uses the i-th charging pile, Indicates the remaining charging energy of the i-th charging pile at the current moment, It represents the charging energy required by the electric vehicle that arrives at the charging station at the next moment and uses the i-th charging pile; Step 3: Use the improved gray wolf optimization algorithm to iteratively solve the operation scheduling model of the hydrogen-photovoltaic storage and charging integrated energy station, obtain the individual position with the maximum predation benefit in the current wolf pack and output it as the optimal operation scheduling plan of the hydrogen-photovoltaic storage and charging integrated energy station. The specific iterative solution process is as follows: Step 3.1: Optimize and improve the initial wolf pack of the gray wolf optimization algorithm to obtain the final initial wolf pack. The specific steps are as follows: Step 3.1.1: Set the rough population number g and the number of excellent lone wolves k in the iterative solution process, and initialize the number of iterations r, so that the number of iterations r=1; Step 3.1.2: The matrix composed of the charging power of N charging piles at each moment represents the position information X of the jth wolf. j , as shown in the following formula (3), X j =[p t,i ] 24×N (3); Step 3.1.3: Randomly generate an initial wolf pack consisting of m wolves, and use the p in formula (2) t,i Randomly generate 24×N values ​​within the range of , and substitute them into formula (3) as the position information of a wolf in the initial wolf pack; repeat the above steps until each wolf in the initial wolf pack generates position information; collect the position information of each wolf in the initial wolf pack to form the initial wolf pack position information set X, as shown in the following formula (4): X={X1、X2、...、X m } (4), In formula (4), X1 is the first position information of the first wolf in the initial wolf pack, X2 is the second position information of the second wolf in the initial wolf pack, and X m is the mth position information of the mth wolf in the initial wolf pack; Step 3.1.4: Substitute the first position information in the initial wolf pack position information set X into the hydrogen-photovoltaic storage and charging integrated energy station operation scheduling model for calculation, and use the calculation result as the predation benefit of the first wolf in the initial wolf pack; repeat the above steps until each wolf in the initial wolf pack has a corresponding predation benefit; sort the predation benefits of all wolves in the wolf pack from large to small, and draw a first predation benefit curve; calculate the similarity between the first predation benefit curve and the five standard predation benefit curves, and select the curve type of the standard predation benefit curve with the highest similarity as the curve type of the first predation benefit curve; calculate the noise level between the standard predation benefit curve with the highest similarity and the first predation benefit curve; Step 3.1.5: Obtain the values ​​of the optimization parameters Z1, Z2, Z3 and Z4 of the initial wolf pack according to the curve type and noise level of the first predation benefit curve obtained in step 3.1.4, and calculate the number s of the final initial population by the following formula (5): In formula (5), e is the natural base; Step 3.2: Standardize the first three wolves in the final initial wolf pack as α wolf, β wolf and δ wolf respectively; calculate the distance between each wolf in the final initial wolf pack except α wolf, β wolf and δ wolf and α wolf, β wolf and δ wolf according to the following formula (6): In formula (6), j is a natural number greater than or equal to 4 and less than or equal to s; D α (j) is the distance between the jth wolf in the final initial wolf pack and the α wolf; D β (j) is the distance between the jth wolf in the final initial wolf pack and the β wolf; D δ (j) is the distance between the jth wolf in the final initial wolf pack and the δ wolves; X α (r), X β (r) and X δ (r) are the location information of wolf α, wolf β and wolf δ respectively; X j (r) is the location information of the jth wolf; C α , C β and C δ are the distance coefficients of α wolf, β wolf and δ wolf respectively; U α,1 , U β,1 and U δ,1 They are random numbers generated randomly and uniformly distributed between the interval [0,1]; Step 3.3: Update the position information of each wolf in the final initial wolf pack except wolf α, wolf β and wolf δ in the next iteration according to the following formula (7): In formula (7), X j (r+1) is the position information of the jth wolf in the next iteration; X α (r+1), X β (r+1) and X δ (r+1) are the position information of α wolf, β wolf and δ wolf in the next iteration process; A α , A β and A δ are the distance update coefficients of α wolf, β wolf and δ wolf respectively; U α,2 , U β,2 , U δ,2 are random numbers randomly generated and uniformly distributed between the interval [0,1]; r is the current number of iterations; R is the maximum number of iterations; When all the remaining wolves in the final initial wolf pack except wolf α, wolf β and wolf δ have calculated the position information of the next iteration, the current iteration number r is increased by 1, and a judgment is made to see whether the current iteration number r is greater than or equal to the maximum iteration number R; if the judgment condition is met, the position information of all wolves in the final initial wolf pack obtained under the current iteration number is output; if the judgment condition is not met, the position information of all wolves in the final initial wolf pack obtained under the current iteration number is substituted into step 3.2 and step 3.3 to perform iterative calculation again until the judgment condition is met; Step 3.4: Substitute the position information of all wolves in the final initial wolf pack output in step 3.3 into the operation and scheduling model of the hydrogen-photovoltaic storage and charging integrated energy station in turn for calculation to obtain the predation benefits of all wolves in the final initial wolf pack; select the position information of the wolf with the largest predation benefit in the final initial wolf pack as the optimal operation and scheduling plan of the hydrogen-photovoltaic storage and charging integrated energy station.

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