Hydrogen energy storage system optimization method based on improved whale optimization algorithm

Abstract

The invention requests to protect a hydrogen energy storage system optimization method based on an improved whale optimization algorithm. Optimization design is carried out by taking total electric power, upper and lower limits of power of each power generation unit, a ramp rate of a generator set, hydrogen storage and output, and charge and discharge power constraints of a hydrogen fuel cell as constraint conditions; according to the method, the comprehensive cost after empowerment of the equipment operation cost and the environmental pollution treatment cost is taken as an optimization target; the whale optimization algorithm is improved by introducing a Skew Tent chaos sequence, nonlinear convergence factors in different time periods and a Cauchy variation strategy, scheduling of the hydrogen energy storage system is researched by applying the improved whale optimization algorithm, and the method has excellent performance in the aspects of high latitude problem, optimization capacity improvement and local optimum prevention. In addition, the method has relatively high practicability for realizing optimal scheduling of the hydrogen energy storage system.

Description

Technical Field

[0001] The present invention belongs to the field of energy planning, and mainly relates to a hydrogen energy storage system optimization method based on an improved whale optimization algorithm. Background Art

[0002] The problem of global warming is becoming increasingly serious, and reducing greenhouse gas emissions has become a common goal of the international community. As a major carbon emitter, my country is actively promoting energy transformation, reducing dependence on fossil energy, and developing low-carbon and zero-carbon energy is an inevitable choice.

[0003] my country is rich in renewable energy resources, and has made significant progress in the development and utilization of renewable energy such as solar energy and wind energy in recent years. However, the generation of renewable energy is intermittent and volatile. For example, photovoltaic power generation cannot generate electricity at night, and wind power generation is greatly affected by wind speed, which leads to unstable power supply. Hydrogen energy storage can convert excess renewable energy electricity into hydrogen and store it. When needed, hydrogen can be converted into electricity through fuel cells and other methods, realizing the effective consumption and stable supply of renewable energy.

[0004] The Whale Optimization Algorithm was proposed by Mirjalili in 2016. The algorithm is based on the hunting behavior of humpback whales and uses three different methods, including encirclement hunting, bubble hunting, and random search, to determine the best capture path. The hunting skills of humpback whales are amazing, and one of the most eye-catching behaviors is the use of a capture strategy called bubble net hunting. The whale's hunting behavior can be divided into two stages: search and predation. The algorithm has the advantages of few initial parameters, simple search mechanism, easy to understand implementation process, and strong optimization ability. It has been widely used in economic scheduling, fault diagnosis, parameter optimization, image segmentation and other fields.

[0005] Due to the randomness of the whale algorithm and its dependence on the initial population, the results may vary greatly at different times of running, and lack stability. The algorithm's search mechanism and update strategy are random to a certain extent, and it is difficult to ensure that each iteration can move in the direction of improving the accuracy of the solution, especially when dealing with high-dimensional problems. This problem of insufficient accuracy is more prominent. It is easy to fall into local optimality. During the search process, the whale algorithm mainly guides the movement of individuals based on the current optimal solution. However, this dependence makes the algorithm easily attracted by the local optimal solution and trapped in it when facing a complex search space, and it is difficult to jump out of the local optimal area to explore a wider search space, thus affecting the algorithm's ability to find the global optimal solution.

[0006] The present invention improves the whale algorithm by introducing the Skew Tent chaotic sequence, nonlinear convergence factors in different time periods and the Cauchy mutation strategy to improve the whale optimization algorithm, and uses the improved whale optimization algorithm to study the scheduling of the hydrogen energy storage system. This method has superior performance in high-dimensional problems, improving the ability to find the best solution and preventing falling into local optimality.

[0007] After searching, the application publication number CN116632869A, a hybrid energy storage system optimization configuration solution method based on a multi-strategy improved whale optimization algorithm, this invention uses multiple strategies to improve the algorithm to address the problems of low convergence accuracy and easy to fall into local optimality of the whale optimization algorithm, and proposes an adaptive dynamic whale optimization algorithm. In the early stage of the algorithm, the cubic chaos map is introduced to improve the ergodicity of the initial solution; the adaptive inertia weight coefficient is introduced and the convergence factor is improved nonlinearly to balance the ability of global search and local search; the spiral search equation is improved to enable the whale to dynamically adjust the search shape to improve the global search ability of the algorithm, and the generalized reverse learning mechanism is introduced to enhance the ability of the algorithm to jump out of the local optimality, respectively taking the lowest cost, the highest suppression of renewable energy power fluctuations and the establishment of the maximum utilization of the interconnection line as the objective functions, and the optimization configuration problem of the hybrid energy storage system is solved with high accuracy.

[0008] The above invention uses cubic chaotic mapping to improve the traversability of the initial solution in the early stage of the algorithm, and uses dynamic adjustment of the search shape to improve the global search capability, but the specific implementation and parameter adjustment may be relatively complicated. And the impact of environmental costs is ignored. The present invention is further improved by using the Skew Tent chaotic sequence with better traversability and chaos, which can provide a more uniform distribution of initial solutions in the early stage of the algorithm, thereby improving the efficiency of global search. By using different nonlinear convergence factors in different iteration periods, the present invention can more flexibly balance global and local searches, avoid premature convergence to local optimality, and improve the convergence speed. The Cauchy mutation strategy helps the algorithm to jump out of the local optimal solution during the search process by generating a larger variable step length, further improving the algorithm's optimization ability and robustness, and the weighted comprehensive cost of equipment operating costs and environmental pollution control costs is used as the objective function. The present invention applies the improved whale optimization algorithm to the scheduling research of hydrogen energy storage systems. By considering the characteristics and constraints of hydrogen energy storage systems, it realizes the effective solution of high-dimensional problems and improves the practicality and pertinence of the algorithm. Summary of the invention

[0009] The present invention aims to solve the above problems of the prior art. A method is proposed. The technical solution of the present invention is as follows:

[0010] A hydrogen energy storage system optimization method based on an improved whale optimization algorithm comprises the following steps:

[0011] A model of the hydrogen energy storage system is obtained, and the comprehensive cost after weighting the equipment operation cost and the environmental pollution control cost is used as the objective function; the electric power, the upper and lower power limits of each power generation unit, the climbing rate of the generator set, the hydrogen storage and output, and the hydrogen fuel cell charging and discharging power constraints are used as constraints; the whale optimization algorithm is improved by introducing the Skew Tent chaotic sequence, the nonlinear convergence factor in different time periods and the Cauchy mutation strategy, and the improved whale optimization algorithm is used to solve the optimal solution for the hydrogen energy storage system, and scheduling is performed according to the optimal solution.

[0012] Furthermore, the model of the hydrogen energy storage system specifically includes: a wind power generation model, a photovoltaic power generation model, and a hydrogen fuel cell model; the energy storage model is: a hydrogen production model.

[0013] Furthermore, the weighted comprehensive cost of equipment operation cost and environmental pollution control cost is used as the objective function, and the objective function is:

[0014] mmin M=β 1 ·M 1 +β 2 ·M 2

[0015] In the formula, β 1 and β 2 is the weight coefficient, 0<β 1 β 2 <1, β 1 +β 2 <1, by changing the weight value, M 1 is the equipment operating cost, M 2 It is the cost of environmental governance.

[0016] Furthermore, the objective function of the equipment operation cost is:

[0017]

[0018] In the formula, C W , C PV , C D , C H , C F They are the costs of wind power, photovoltaic power, thermal power, hydrogen production, and fuel cells at time t;

[0019] Environmental cost, the objective function is:

[0020]

[0021] In the formula is the sum of various governance costs, P D (t) is the output power at time t. D,enIt is the pollution cost of thermal power generation.

[0022] Furthermore, the electric power constraint is:

[0023] P Load (t) = P W (t)+P PV (t)+P D (t)+P F (t)

[0024] Where P Load (t) is the load power of the power grid at time t; P W (t) is wind power; P PV (t) is the photoelectric power; P D (t) is thermal power; P F (t) is the power of hydrogen fuel cell.

[0025] Furthermore, the device's own constraints are used as constraints:

[0026] The output constraints of each generator set are:

[0027]

[0028] Where P i (t) is the output power of the ith controllable generator set at time t, are the upper and lower limits of the output power of the i-th controllable generator set respectively;

[0029] Generator set maximum ramp rate constraint:

[0030] P i (t)-P i (t-Δt)≤p i Δt

[0031] Where p i is the maximum ramp rate of each controllable generator set i; Δt is the running time increment;

[0032] Hydrogen fuel cell charging and discharging power constraints:

[0033]

[0034] In the formula, i represents charge and discharge, Indicates the upper and lower limits of charging and discharging power;

[0035] The hydrogen storage and output constraints are:

[0036] 0≤Q E ≤Q S，max

[0037] Where: Q Eis the amount of hydrogen consumed per unit output power; Q S，max is the oxygen storage capacity of the hydrogen storage device.

[0038] Furthermore, the whale optimization algorithm specifically includes:

[0039] (1) Initialize various parameters in the whale population and randomly generate l and p;

[0040] (2) Find the best individual whale and retain the best whale position by calculating the fitness of individual whales;

[0041] (3) If p ≥ 0.5, use bubble net to catch prey; otherwise, jump directly to the next step;

[0042] (4) If the coefficient vector |A|>1, use global search to capture the prey; if |A|<1, directly surround the prey; and update the position;

[0043] (5) Recalculate the fitness value. If it is better than before, it is the current optimal solution. Determine whether the current end condition is met. If it is met, output it. Otherwise, return to the second step and continue the cycle;

[0044] The improved optimized whale algorithm introduces chaotic sequences, nonlinear convergence factors in different time periods and Cauchy mutation strategies.

[0045] Furthermore, the improved whale optimization algorithm introduces chaotic sequences, nonlinear convergence factors in different time periods and Cauchy mutation strategies, specifically including:

[0046] The chaotic sequence is used to initialize the population, so that the initial solution distribution is more uniform. The expression is:

[0047]

[0048] In the formula, is a random number; Z∈[0,1], n is the number of whale populations; k is the current iteration number, combined with the chaotic sequence

[0049] Using nonlinear convergence factors at different time periods, the expression is:

[0050]

[0051] The Cauchy mutation strategy is added to prevent local optimality, and its function is as follows:

[0052]

[0053] Furthermore, the specific steps to improve the whale optimization algorithm include:

[0054] (1) Let the number of whales be N, the dimension of each whale be D, and the maximum number of iterations of the algorithm be T. max , search space [ub,lb];

[0055] (2) Using the chaotic mapping strategy to initialize the population, according to the updated nonlinear convergence factor a, according to the calculated coefficient vectors A and C, l and p are randomly generated;

[0056] (3) Calculate the fitness value of individual whales and save the current optimal whale position. If the optimal whale position has not changed for 10 consecutive generations, perform a Cauchy mutation operation on the current optimal whale, recalculate the fitness of individual whales, and update the optimal whale position.

[0057] (4) If p ≥ 0.5, use bubble net to capture and update the position; if p < 0.5, continue to judge the coefficient vector |A|, if |A| < 1, surround the prey and update the position; if |A| > 1, search for prey globally and update the position;

[0058] (5) After the position update is completed, the fitness value is calculated again. If there is a better result, the current optimal solution is updated; (6) Determine whether the algorithm meets the termination requirements. If so, output the optimal individual, indicating that the algorithm has found the optimal solution. Otherwise, return to the third step and enter the next cycle.

[0059] The advantages and beneficial effects of the present invention are as follows:

[0060] The present invention proposes a hydrogen energy storage system optimization method based on an improved whale optimization algorithm, which takes the weighted comprehensive cost of operating cost and environmental maintenance cost as the objective function and constraint condition, thereby improving the utilization rate of renewable energy to a certain extent and obtaining the best economic benefits.

[0061] The traditional whale algorithm has poor population diversity and a defective algorithm search mechanism, which leads to slow convergence in the early stage and fast convergence in the later stage, and is prone to falling into the local optimal solution. The present invention improves the whale optimization algorithm by introducing the Skew Tent chaotic sequence, nonlinear convergence factors in different time periods, and the Cauchy mutation strategy, and uses the improved whale optimization algorithm to study the scheduling of the hydrogen energy storage system. This method can solve high-dimensional problems, increase the quality and diversity of individual whales, and improve the algorithm's early search ability. It has superior performance in improving the ability to find the best solution and preventing it from falling into the local optimum, and accelerates the global convergence speed. The improved algorithm is used to solve the target model to optimize the overall performance of the system, give priority to the use of renewable energy, followed by hydrogen fuel cells, and make the system output a smoother supply curve according to demand. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1 The present invention provides a flowchart of a hydrogen energy storage system optimization method based on an improved whale algorithm in a preferred embodiment;

[0063] Figure 2 System operation architecture diagram of the hydrogen energy storage system optimization method based on the improved whale optimization algorithm. DETAILED DESCRIPTION

[0064] The following will describe the technical solutions in the embodiments of the present invention in detail in conjunction with the accompanying drawings in the embodiments of the present invention. The described embodiments are only part of the embodiments of the present invention.

[0065] The technical solution of the present invention to solve the above technical problems is:

[0066] The method of the present invention first determines the output model according to the application scenario of the hydrogen storage system, and optimizes the hydrogen energy storage system based on the improved whale optimization algorithm. The improved whale algorithm flow chart is as follows: Figure 1 As shown; then the system model structure diagram is as follows Figure 2 shown.

[0067] The model for determining the hydrogen energy storage system specifically includes: a wind power generation model, a photovoltaic power generation model, and a hydrogen fuel cell model; the energy storage model is: a hydrogen production model.

[0068] The wind power model is specifically:

[0069]

[0070] Where, ρ is the density of air; v is the undisturbed wind speed; R is the blade radius of the wind turbine; λ is the tip speed ratio, which is the ratio of the linear velocity of the tip of the wind rotor blade to the wind speed; β is the blade pitch angle; C is the blade tip speed ratio, which is the ratio of the linear velocity of the tip of the wind rotor blade to the wind speed; p Wind energy utilization coefficient; ω is the angular velocity of the rotor rotation; n ω is the speed of the fan.

[0071] Under certain conditions, the wind energy utilization coefficient of a wind turbine under certain wind speed conditions can be established based on the relationship between the original wind energy utilization coefficient of the wind turbine and the tip speed ratio.

[0072] The photoelectric model described:

[0073]

[0074] Where P PV Indicates the actual power generation of the photovoltaic unit; f PV Represents photovoltaic power generation efficiency; P PV,N Indicates the rated power generation of the photovoltaic unit; G t Indicates the actual light intensity; G ref represents the reference light intensity; α represents the temperature coefficient; T t Indicates the actual temperature of the photovoltaic unit; T ref Indicates the reference temperature of the PV system.

[0075] The hydrogen production model described:

[0076]

[0077] Where P DC are the DC power used for hydrogen production by electrolysis of water at time t, m H is the mass flow rate of hydrogen produced by water electrolysis at time t, η 1 is the electrolytic cell production efficiency, ρ 0 is the density of hydrogen under standard conditions, H LHV Hydrogen has a low calorific value.

[0078] The hydrogen fuel cell model:

[0079]

[0080] In the formula, m FC is the mass flow rate of hydrogen consumed by the hydrogen fuel cell at time t, P F is the power generation of the fuel cell at time t, η FC is the power generation efficiency of the fuel cell, H HHV Hydrogen has a high calorific value.

[0081] The objective function is the weighted comprehensive cost of equipment operation cost and environmental pollution control cost, which specifically includes:

[0082] Equipment operating cost, the objective function is:

[0083]

[0084] In the formula, C W , C PV , C D , C H , C F They are the costs of wind power, photovoltaic power, thermal power, hydrogen production, and fuel cells;

[0085] Wind power and photovoltaic costs:

[0086] C W =α 1 C W，T

[0087] C PV =α 2 C PV，T

[0088] In the formula, α 1 , α 2 are the number of wind power and photovoltaic units, C W，T is the unit wind power equipment operating cost, C PV，T It is the unit operating cost of photovoltaic equipment.

[0089] Thermal power cost:

[0090] C D =C T +C fuel

[0091]

[0092] In the formula, C T is the cost of thermal power generation equipment and its operating cost, C fuel is the fuel cost of the diesel generator, P D is the output power of the diesel generator, k 1 , k 2 , k 3 is the fuel cost factor.

[0093] Hydrogen production cost:

[0094]

[0095] In the formula, C H is the hydrogen production cost, α is the number of electrolysis equipment, C E Unit equipment depreciation cost, C EA is the unit equipment cost, C WA Price of water consumed, E WA It is the amount of water required to produce each unit of hydrogen.

[0096] Hydrogen fuel cell power generation cost:

[0097]

[0098] Where: β is the number of fuel cell devices, C F is the unit power generation cost of the hydrogen fuel cell; C H，FC The unit cost of hydrogen production equipment is amortized.

[0099] Environmental cost, the objective function is:

[0100]

[0101] in is the sum of various governance costs, and PD(t) is the output power at time t.

[0102] Comprehensive cost, the objective function is:

[0103] min M=β 1 ·M 1 +β 2 ·M 2

[0104] In the formula, β1 and β 2 is the weight coefficient, indicating the relative optimization weight, 0<β 1 β 2 <1, β 1 +β 2 <1, calculate the appropriate comprehensive cost by changing the weight value.

[0105] Its electric power constraints are:

[0106] P Load (t) = P W (t)+P PV (t)+P D (t)+P F (t)

[0107] Where P Load (t) is the load power of the power grid at time t; P W (t) is wind power; P PV (t) is the photoelectric power; P D (t) is thermal power; P F (t) is the power of hydrogen fuel cell.

[0108] The constraints of the device itself are used as constraints:

[0109] The output constraint of the generator set is:

[0110]

[0111] Where P i (t) is the output power of the i-th controllable generator set at time t. are the upper and lower limits of the output power of the i-th controllable generator set respectively.

[0112] Generator set maximum ramp rate constraint:

[0113] P i (t)-P i (t-△t)≤p i △t

[0114] In the formula, p i is the maximum ramp rate of the ith controllable generator set. Δt is the running time increment.

[0115] Hydrogen fuel cell charging and discharging power constraints:

[0116]

[0117] In the formula, i represents charge and discharge, Indicates the upper and lower limits of charging and discharging power;

[0118] Hydrogen storage and output constraints:

[0119] 0≤Q E ≤Q S，max

[0120] In the formula, Q E is the amount of hydrogen consumed per unit output power; Q S，max is the oxygen storage capacity of the hydrogen storage device.

[0121] The whale optimization algorithm specifically includes:

[0122] First, randomly search for prey from the initial position, and the position change update formula is:

[0123] X(t+1)=X * (t)-A·D

[0124] The whale that finds the best position is called the optimal whale, and its position relationship with other whales is:

[0125] D=|C·X * (t)-X(t)|

[0126] Where t is the current number of iterations; X * (t) is the optimal whale position in the tth generation; X(t) is the individual whale position in the tth generation. A and C are coefficient vectors, which can be calculated by the following formula:

[0127] A=2ar 1 -a

[0128] C=2r 2

[0129] a=2-2t / t max

[0130] In the formula, r 1 and r 2 is a random number between [0, 1]; a is the convergence factor, which decreases from 2 to 0 as the number of iterations increases; t max is the maximum number of iterations.

[0131] Then, the prey is captured by the spiral mechanism, which is achieved by changing the convergence factor a, and the value range of A is between [-a, a]. The position update formula is:

[0132] X(t+1)=D′·e bl ·cos(2πl)+X * (t)l

[0133] D′=|X * (t)-X(t)|

[0134] Where D′ is the distance between the whale and the optimal whale; b is the spiral constant, which is l; l is a random number between [-1, 1]. Since the whale group preys using a spiral ascending mechanism and also adopts a shrinking and encircling strategy, the probability of choosing both methods is set to 50%, and the expression is:

[0135]

[0136] Where p is a random number between [0, 1].

[0137] Finally, a global search for prey is implemented to prevent falling into a local optimal solution. This method improves the optimization performance. The process is as follows:

[0138] D rand =|C·X rand (t)-X(t)|

[0139] X(t+1)=X rand (t)-A·D

[0140] Where, X rand (t) is the current position of a whale randomly selected from the population at generation t.

[0141] The improved whale algorithm not only inherits the existing characteristics, but also introduces chaotic sequences, nonlinear convergence factors in different time periods and Cauchy mutation strategies. This can effectively solve high-dimensional problems and improve the ability to find better solutions.

[0142] The improved whale optimization algorithm specifically includes:

[0143] The chaotic sequence is used to initialize the population, so that the initial solution distribution is more uniform and the quality is improved. The expression is:

[0144]

[0145] In the formula, is a random number; Z∈[0,1], n is the number of whale populations; k is the current iteration number.

[0146] Combined with chaotic sequence The process of further generating the initial position sequence of individual whales in the search area is as follows:

[0147]

[0148] In the formula, Chaotic Sequence The maximum and minimum values ​​of .

[0149] Using nonlinear convergence factors at different time periods, the expression is:

[0150]

[0151] The Cauchy mutation strategy is added to prevent local optimality, and its function is as follows:

[0152]

[0153] The improved expression of individual whale position is:

[0154]

[0155] In the formula, The current optimal whale individual obtains a new position through Cauchy mutation; carchy(0,1) is the Cauchy operator.

[0156] The specific steps include:

[0157] (1) Let the number of whales be N, the dimension of each whale be D, and the maximum number of iterations of the algorithm be T. max , search space [ub,lb];

[0158] (2) Using the chaotic mapping strategy to initialize the population, according to the updated nonlinear convergence factor a, according to the calculated coefficient vectors A and C, l and p are randomly generated;

[0159] (3) Calculate the fitness value of individual whales and save the current optimal whale position. If the optimal whale position has not changed for 10 consecutive generations, perform a Cauchy mutation operation on the current optimal whale, recalculate the fitness of individual whales, and update the optimal whale position.

[0160] (4) If p ≥ 0.5, use bubble net to capture and update the position; if p < 0.5, continue to judge the coefficient vector |A|, if |A| < 1, surround the prey and update the position; if |A| > 1, search for prey globally and update the position;

[0161] (5) After the position update is completed, the fitness value is calculated again. If there is a better result, the current optimal solution is updated;

[0162] (6) Determine whether the algorithm meets the termination requirements. If so, output the optimal individual, indicating that the algorithm has found the optimal solution. Otherwise, return to step 3 and enter the next cycle.

[0163] The present invention uses an improved whale optimization algorithm to establish a hydrogen energy storage system model based on the improved whale optimization algorithm. The comprehensive goal of weighted equipment operating cost and environmental maintenance cost is taken as the goal. The larger the weight ratio, the more important it is at this time, and then the optimal economic benefit is solved. The local development ability and global exploration ability of individual whales in the early and late stages of the algorithm are balanced, and the Cauchy mutation strategy is introduced to enhance the ability of individual whales to jump out of the local optimum and accelerate the global convergence speed, so as to obtain a better system scheduling operation strategy, reduce the operating cost of the system, and play a guiding and reference role in the optimal scheduling of the hydrogen energy storage system.

[0164] The systems, devices, modules or units described in the above embodiments may be implemented by computer chips or entities, or by products with certain functions.

[0165] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0166] The above embodiments should be understood to be only used to illustrate the present invention and not to limit the protection scope of the present invention. After reading the contents of the present invention, technicians can make various changes or modifications to the present invention, and these equivalent changes and modifications also fall within the scope defined by the claims of the present invention.

Claims

1. A hydrogen energy storage system optimization method based on an improved whale optimization algorithm, characterized in that: The following steps are involved: Obtain a model for the hydrogen energy storage system, using the weighted comprehensive cost of equipment operation cost and environmental pollution control cost as the objective function; using electric power, upper and lower limits of each power generation unit, ramp rate of the generator set, hydrogen storage and output, and hydrogen fuel cell charging and discharging power constraints as constraints; The whale optimization algorithm is improved by introducing the Skew Tent chaotic sequence, nonlinear convergence factors in different time periods and the Cauchy mutation strategy. The improved whale optimization algorithm is used to solve the optimal solution of the hydrogen energy storage system and dispatch it according to the optimal solution.

2. The hydrogen energy storage system optimization method based on the improved whale optimization algorithm according to claim 1 is characterized in that: The models of the hydrogen energy storage system specifically include: a wind power generation model, a photovoltaic power generation model, and a hydrogen fuel cell model; the energy storage model is: a hydrogen production model.

3. The hydrogen energy storage system optimization method based on the improved whale optimization algorithm according to claim 1 is characterized in that: The objective function is the weighted comprehensive cost of equipment operation cost and environmental pollution control cost. The objective function is: min M=β1·M1+β2·M2 In the formula, β1 and β2 are weight coefficients, 0<β1β2<1, β1+β2<1. By changing the weight value, M1 is the equipment operation cost and M2 is the environmental governance cost.

4. The hydrogen energy storage system optimization method based on the improved whale optimization algorithm according to claim 3 is characterized in that: The equipment operation cost, the objective function is: In the formula, C W , C PV , C D , C H , C F They are the costs of wind power, photovoltaic power, thermal power, hydrogen production, and fuel cells at time t; Environmental cost, the objective function is: In the formula is the sum of various governance costs, P D (t) is the output power at time t. D,en It is the pollution cost of thermal power generation.

5. The hydrogen energy storage system optimization method based on the improved whale optimization algorithm according to claim 1 is characterized in that: The electric power constraints are: P Load (t)=P W (t)+P PV (t)+P D (t)+P F (t) Where P Load (t) is the load power of the power grid at time t; P W (t) is wind power; P PV (t) is the photoelectric power; P D (t) is thermal power; P F (t) is the power of hydrogen fuel cell.

6. The hydrogen energy storage system optimization method based on the improved whale optimization algorithm according to claim 1 is characterized in that: The constraints of the device itself are used as constraints: The output constraints of each generator set are: Where P i (t) is the output power of the ith controllable generator set at time t, are the upper and lower limits of the output power of the i-th controllable generator set respectively; Generator set maximum ramp rate constraint: P i (t)-P i (t-Δt)≤p i Δt Where p i is the maximum ramp rate of each controllable generator set i; Δt is the running time increment; Hydrogen fuel cell charging and discharging power constraints: In the formula, i represents charge and discharge, Indicates the upper and lower limits of charging and discharging power; The hydrogen storage and output constraints are: 0≤Q E ≤Q S，max Where: Q E is the amount of hydrogen consumed per unit output power; Q S，max is the oxygen storage capacity of the hydrogen storage device.

7. The hydrogen energy storage system optimization method based on the improved whale optimization algorithm according to claim 1 is characterized in that: The whale optimization algorithm specifically includes: (1) Initialize various parameters in the whale population and randomly generate l and p; (2) Find the best individual whale and retain the best whale position by calculating the fitness of individual whales; (3) If p ≥ 0.5, use bubble net to catch prey; otherwise, jump directly to the next step; (4) If the coefficient vector |A|>1, use global search to capture the prey; if |A|<1, directly surround the prey; and update the position; (5) Recalculate the fitness value. If it is better than before, it is the current optimal solution. Determine whether the current end condition is met. If it is met, output it. Otherwise, return to the second step and continue the cycle; The improved optimized whale algorithm introduces chaotic sequences, nonlinear convergence factors in different time periods and Cauchy mutation strategies.

8. The hydrogen energy storage system optimization method based on the improved whale optimization algorithm according to claim 7 is characterized in that: The improved whale optimization algorithm introduces chaotic sequences, nonlinear convergence factors in different time periods and Cauchy mutation strategies, specifically including: The chaotic sequence is used to initialize the population, so that the initial solution distribution is more uniform. The expression is: In the formula, is a random number; Z∈[0,1], n is the number of whale populations; k is the current iteration number, combined with the chaotic sequence Using nonlinear convergence factors at different time periods, the expression is: The Cauchy mutation strategy is added to prevent local optimality, and its function is as follows:

9. The hydrogen energy storage system optimization method based on the improved whale optimization algorithm according to claim 7 is characterized in that: The specific steps to improve the whale optimization algorithm include: (1) Let the number of whales be N, the dimension of each whale be D, and the maximum number of iterations of the algorithm be T. max , search space [ub,lb]; (2) Using the chaotic mapping strategy to initialize the population, according to the updated nonlinear convergence factor a, according to the calculated coefficient vectors A and C, l and p are randomly generated; (3) Calculate the fitness value of individual whales and save the current optimal whale position. If the optimal whale position has not changed for 10 consecutive generations, perform a Cauchy mutation operation on the current optimal whale, recalculate the fitness of individual whales, and update the optimal whale position. (4) If p ≥ 0.5, use bubble net to capture and update the position; if p < 0.5, continue to judge the coefficient vector |A|, if |A| < 1, surround the prey and update the position; if |A| > 1, search for prey globally and update the position; (5) After the position update is completed, the fitness value is calculated again. If there is a better result, the current optimal solution is updated; (6) Determine whether the algorithm meets the termination requirements. If so, output the optimal individual, indicating that the algorithm has found the optimal solution. Otherwise, return to the third step and enter the next cycle.

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