Hybrid pko-based wind-solar-hydrogen storage and charging integrated charging station optimal operation method

Abstract

This invention discloses an optimized operation method for integrated wind-solar-hydrogen-storage-charging stations based on a hybrid PKO (Power Optimization Kinematics) algorithm, relating to the field of new energy industry technology. The method includes the following steps: constructing an integrated wind-solar-hydrogen-storage-charging station consisting of a photovoltaic power generation system, a wind power generation system, energy storage, an electrolyzer, a hydrogen storage tank, electric vehicle charging, and a hydrogen refueling unit for hydrogen fuel cell vehicles, with the minimum overall cost of the integrated power station as the objective function; constructing a PKO-ZOA hybrid optimization algorithm, composed of the PKO optimization algorithm and the ZOA optimization algorithm, to solve the objective function, thereby optimizing and controlling the operation of the constructed integrated wind-solar-hydrogen-storage-charging station. This invention improves the global search capability in the charging optimization problem of integrated photovoltaic-storage-charging stations, thereby finding a better charging optimization scheme under relevant constraints, providing new technical support for the development of the new energy industry.

Description

Technical Field

[0001] This invention relates to the field of new energy industry technology, and more specifically, to an optimized operation method for integrated wind-solar-hydrogen storage and charging stations based on hybrid PKO. Background Technology

[0002] With the rapid rise of the new energy vehicle industry, the construction of charging stations, which are closely related to it, has attracted much attention. As a core node in multi-energy coupling, charging stations play a unique and crucial role, enabling efficient utilization and coordinated management of various energy forms, especially in meeting the charging needs of new energy vehicles. Due to the current imperfections in charging infrastructure, traditional charging stations are relatively simple in form and cannot fully meet the growing charging demands of new energy vehicle users. Integrated photovoltaic-storage-charging stations combine photovoltaic power generation systems and energy storage systems. Through the complementarity of photovoltaic power generation and energy storage, they not only ensure the power supply needs of electric vehicles powered by photovoltaic power stations but also significantly improve the resource utilization rate of photovoltaic power stations, effectively solving various shortcomings of traditional photovoltaic charging stations. However, disorderly charging of new energy vehicles can lead to curtailment of solar power and even adverse effects on the power grid. Therefore, developing a strategy to guide orderly charging of electric vehicles is key to achieving optimal charging strategies for integrated photovoltaic-storage-charging stations. Solving the optimization problem presented in this paper, based on relevant constraints, is crucial to achieving optimal charging strategies for integrated photovoltaic-storage-charging stations. Currently, many studies use metaheuristic algorithms to solve the above problems. However, metaheuristic algorithms are prone to getting trapped in local optima. Therefore, there is an urgent need for a new technology to solve the problems of solar power curtailment and adverse effects on the power grid caused by disordered charging of new energy vehicles, as well as to address the shortcomings of metaheuristic algorithms in getting trapped in local optima and improve the global search capability of the algorithm in the charging optimization problem of integrated photovoltaic-storage-charging stations. Summary of the Invention

[0003] To address the aforementioned problems, this invention provides an optimized operation method for integrated wind-solar-hydrogen storage and charging stations based on a hybrid PKO, comprising the following steps: Construct an integrated wind-solar-hydrogen-storage-charging station consisting of a photovoltaic power generation system, a wind power generation system, energy storage, an electrolyzer, a hydrogen storage tank, an electric vehicle charging unit, and a hydrogen refueling unit for hydrogen fuel cell vehicles, with the goal of minimizing the overall cost of the integrated power station. A PKO-ZOA hybrid optimization algorithm, consisting of the PKO and ZOA optimization algorithms, is constructed to solve the objective function, thereby optimizing the operation and control of the constructed integrated wind-solar-hydrogen storage and charging station.

[0004] Preferably, when constructing the objective function, the objective function is constructed by considering equipment investment and operation and maintenance costs, electricity purchase costs from the grid, costs of curtailing wind and solar power, and carbon emission costs.

[0005] Preferably, when constructing the objective function, power balance constraints and unit output constraints are used as constraints of the objective function. The power balance constraints include: electricity balance constraints and hydrogen energy balance constraints; the unit output constraints include wind turbine output constraints, photovoltaic output constraints, electrolyzer operation output constraints, and hydrogen storage tank operation output constraints.

[0006] Preferably, when constructing the PKO-ZOA hybrid optimization algorithm, a dynamic population is generated by fusing the ZOA matrix and the PKO fluctuation factor during the algorithm initialization phase.

[0007] Preferably, when constructing the PKO-ZOA hybrid optimization algorithm, during the adaptive exploration phase, when the population diversity is high, the ZOA pioneer zebra is used as a guide, and the PKO wing beat frequency factor is introduced to achieve dynamic adjustment of the step size with the iteration progress: large step size in the early stage to quickly expand the search space, and small step size in the later stage to focus on high-quality areas.

[0008] Preferably, when constructing the PKO-ZOA hybrid optimization algorithm, during the adaptive exploration phase, when the population diversity is low, the adaptive step size of the PKO wingbeat frequency and the zigzag characteristic of ZOA are combined to enhance the global scanning capability in high-dimensional space.

[0009] Preferably, when constructing the PKO-ZOA hybrid optimization algorithm, during the dive and symbiosis mechanism development stage, the original PKO dive model is replaced by ZOA pioneer zebras to improve development accuracy by utilizing the local guidance capabilities of ZOA elite individuals, thereby achieving dive development.

[0010] Preferably, when constructing the PKO-ZOA hybrid optimization algorithm, during the dive and symbiosis mechanism development stage, the PKO symbiosis probability is optimized by introducing the ZOA iteration progress factor, the PKO symbiosis mechanism is preserved, the perturbation range is optimized, local optima are avoided, and the symbiosis escape development is completed.

[0011] Preferably, when constructing the PKO-ZOA hybrid optimization algorithm, the accuracy of the solution is guaranteed by the greedy selection of ZOA, and the elite retention strategy of PKO is incorporated to adapt to engineering constraints.

[0012] Preferably, when constructing the PKO-ZOA hybrid optimization algorithm, a dual threshold update combining ZOA greedy selection and PKO elite retention is adopted.

[0013] The present invention discloses the following technical effects: The PKO-ZOA hybrid optimization algorithm designed in this invention can effectively search in a multi-constraint space through its own optimization mechanism. Compared with some traditional algorithms (such as simple genetic algorithms which are prone to premature convergence and particle swarm algorithms which have slow convergence in the later stages), it is more suitable for this complex multi-constraint comprehensive cost optimization scenario and accurately discovers the optimal solution in the feasible region. This invention can more effectively reduce the cost of wind and solar curtailment, while coordinating equipment investment, electricity purchase and other costs to achieve a better overall cost. The PKO-ZOA hybrid optimization algorithm designed in this invention can both escape local optima through global search (such as avoiding local optimal cost traps such as over-reliance on grid power purchase and unreasonable equipment configuration) and finely optimize local links in the later stage (such as precisely controlling the charging and discharging power of energy storage and the pressure of hydrogen storage tanks). Compared with some algorithms that have an imbalance between global and local search (such as difficulty in finding the global optimum with only local fine-tuning, or low efficiency of blind global search), it can converge to a better comprehensive cost solution more quickly. Attached Figure Description

[0014] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0015] Figure 1 This is the integrated wind-solar-hydrogen storage and charging power station described in this invention; Figure 2 This is a flowchart of the EPKO algorithm described in this invention. Detailed Implementation

[0016] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. The components of the embodiments of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely represents selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0017] like Figures 1-2 As shown, this invention provides an optimized operation method for integrated wind-solar-hydrogen storage and charging stations based on a hybrid PKO, specifically including the following: 1. Architecture design of integrated wind-solar-hydrogen storage and charging stations: Integrated charging station for wind, solar, hydrogen storage and charging, such as Figure 1As shown, this charging station comprises a photovoltaic power generation system, a wind power generation system, energy storage, an electrolyzer, a hydrogen storage tank, electric vehicle charging facilities, and hydrogen refueling stations. It can simultaneously provide electric vehicle charging and hydrogen refueling services, effectively utilizing and converting both electricity and hydrogen energy to meet complex energy demands. Renewable energy sources are the primary power source for the charging station, prioritizing the load demand of electric vehicles and powering the electrolyzer for hydrogen production. Excess electricity can be stored in the energy storage system. The power grid provides backup power to ensure load demand. Under the influence of direct current, the electrolyzer decomposes water into hydrogen through an electrochemical reaction, which is then stored in the hydrogen storage tank. The hydrogen storage tank supplies the stored hydrogen to hydrogen fuel cell vehicles, providing them with refueling services.

[0018] 1.1 Hydrogen Production Model: Hydrogen fuel cells produce only water and heat during power generation, making them a sustainable and clean energy source. Water electrolysis for hydrogen production utilizes renewable energy sources to electrolyze water, resulting in near-zero carbon emissions. This method fully leverages abandoned wind, solar, and hydropower resources, significantly reducing production costs. It is a crucial technological link in achieving "green hydrogen" production and a key area for investment in the hydrogen energy sector. The principle of water electrolysis for hydrogen production is simple, and the hydrogen and oxygen produced are of high purity with no pollutant emissions. The core equipment of an electrolysis hydrogen production system is the electrolyzer.

[0019] (1) Electrolytic cell: Hydrogen production for hydrogen fuel cell vehicles is accomplished through an electrolyzer, and the produced hydrogen is stored in a high-pressure hydrogen tank to meet the hydrogen requirements of the vehicle.

[0020] (1); In the formula, yes Hydrogen production capacity (kg) of the electrolyzer at any given time; It is the conversion efficiency of the electrolytic cell; It is the power consumption of the electrolytic cell (kW); It has a low calorific value due to the combustion of hydrogen.

[0021] (2) Compressor: Its main function is to compress the hydrogen produced by the electrolyzer to a suitable pressure for storage in a hydrogen storage tank.

[0022] (2); In the formula, R is the adiabatic index of hydrogen, with a value of 1.4; R is the gas constant. The temperature of the hydrogen entering the compressor; for Molar flow rate of hydrogen at any given time (mol / s); for Constant compressor inlet hydrogen pressure (Pa); for Constantly monitor the hydrogen pressure at the compressor outlet. It refers to the compressor's efficiency.

[0023] (3) Hydrogen storage tank: The amount of hydrogen stored in the hydrogen storage tank at any given time is: (3); In the formula, for The amount of hydrogen in the storage tank at any given time (kg). , They are respectively The amount of hydrogen entering and exiting the hydrogen storage tank at any given time.

[0024] 1.2 Electric Vehicles: The remaining battery power of an electric vehicle is typically expressed using the state of charge (SCC). The charge capacity of a plug-in electric vehicle is: (4); (5); (6); (7); In the formula, In order to be in The charge capacity of plug-in electric vehicles at all times; For electric vehicles The charging power at any given moment; For electric vehicle charging efficiency; This is the maximum capacity of the battery. For the first The charging time of the vehicle; For the first The state of charge of the vehicle when it leaves; For the first The initial state of charge of the vehicle; and These are the minimum and maximum values ​​of the electric vehicle's state of charge, respectively. For the first The vehicle's battery rated capacity; For the first The charging power of the vehicle. The maximum charging power for electric vehicles.

[0025] 1.3 Energy Storage System: Energy storage systems can store electricity when renewable energy generation is in surplus and discharge it when renewable energy generation is insufficient. The amount of electricity stored in the battery within a time-of-use energy storage system can be expressed as: (8); (9); (10); (11); (12); (13); In the formula, for The amount of electricity stored in the energy storage batteries at the time station; and They are The charging and discharging power of the energy storage battery in the real-time battery swapping system; and These refer to the charging and discharging efficiency of the battery in the battery swapping system. and These are the minimum and maximum values ​​of the battery's state of charge, respectively. , These are the maximum charging and discharging power of the battery swapping system, respectively. This represents the maximum depth of discharge.

[0026] 1.4 Photovoltaic power generation system: Photovoltaic cells are not constant-power sources; their output power is closely related to light intensity and temperature. Increased light intensity leads to increased output power, and increased temperature leads to increased output power. For ease of modeling and calculation, the output of a photovoltaic power generation system can be considered to depend only on light intensity and temperature. The output power of a photovoltaic power generation system is: (14); (15); In the formula, for The output power of the photovoltaic system at any given time; This refers to the rated output power of photovoltaic power under standard conditions. The power generation efficiency of the photovoltaic system at standard temperature; It is the temperature coefficient; The temperature of the photovoltaic panel; This is the reference temperature for photovoltaic panels under standard conditions, typically 25°C. ; for The actual intensity of sunlight at any given moment; The solar radiation intensity under standard conditions is typically 1000 W / m². Ambient temperature; This refers to the temperature of the photovoltaic panel under standard conditions.

[0027] 1.5 Wind turbine units: (16); In the formula: This refers to the actual wind speed; This refers to the rated power of the wind turbine generator; , and These are the rated wind speed and the cut-in wind speed, respectively.

[0028] 2. Optimize the model design: 2.1 Objective Function: Based on the above integrated wind-solar-hydrogen storage-charging station model, mathematical models and objective functions for the costs of each component unit are established. Under the condition of meeting the charging demand within the station, the optimization objective is designed as follows: Taking the minimum overall cost of integrated power plants as the objective function, and considering equipment investment and operation and maintenance costs, electricity purchase costs from the grid, costs of curtailment of new energy sources such as wind and solar power, and carbon emission costs, the objective function is set as shown in equation (17): (17); In the formula, The overall cost of an integrated power plant; The total cost of equipment includes equipment investment cost and operation and maintenance cost; Cost of purchasing electricity from the grid; Costs associated with curtailing wind and solar power; Cost of carbon emissions.

[0029] The total cost of the equipment is: (18); In the formula, This represents the total cost of photovoltaic power generation equipment; The total cost of the wind turbine; This represents the total cost of the energy storage system. This represents the total equipment cost of the hydrogen storage system.

[0030] (19); (20); (twenty one); (twenty two); In the formula, For return on investment; The service life of the equipment; This refers to the unit investment cost coefficient for photovoltaic equipment. This refers to the number of operating hours per year. and These are the unit investment cost coefficients for the electrolyzer and the hydrogen storage tank, respectively. This is the maximum power of the electrolytic cell; This is the maximum capacity of the hydrogen storage tank. This represents the unit investment cost coefficient for the battery swapping system. This represents the maximum power of the battery swapping system. This is the unit investment cost coefficient for wind turbine generators; , , and These are the operation and maintenance costs for photovoltaic equipment, hydrogen storage equipment, energy storage equipment, and wind power generation equipment, respectively.

[0031] The cost of purchasing electricity from the grid is: (twenty three); In the formula for The price of electricity purchased from the power grid at all times; for Power purchased from the power grid at all times.

[0032] Costs of curtailing wind and solar power: (twenty four); In the formula and These are the curtailment cost coefficients for photovoltaic power generation systems and wind curtailment cost coefficients for wind turbine units, respectively. and They are respectively The predicted and actual output of photovoltaic power generation at any given time; and They are respectively The predicted and actual output of wind turbine units at all times.

[0033] Carbon emission costs: (25); In the formula: For carbon price; Carbon emissions per unit of electricity generated from power purchased from the grid (kg / kWh).

[0034] 2.2 Power balance constraints: Power balance constraints: (26); Hydrogen energy balance constraints: (27); In the formula, for The hydrogen demand of hydrogen fuel cell vehicles.

[0035] 2.3 Unit output constraints: Wind turbine output constraints: (28); In the formula: This represents the upper limit of the wind turbine's power generation capacity.

[0036] Photovoltaic output constraints: (29); In the formula: yes The upper limit of the output power of the photovoltaic unit at any given time.

[0037] Electrolytic cell operating output constraints: (30); In the formula: , These are the lower and upper limits of the power consumption of the electrolytic cell, respectively.

[0038] Hydrogen storage tank operating output constraints: (31); In the formula: , These are the lower and upper limits of the hydrogen storage tank capacity, respectively.

[0039] 3. Design of the solution method for the model: 3.1 PKO Optimization Algorithm: PKO (Kingfisher Optimization Algorithm) is a metaheuristic optimization algorithm that simulates the foraging behavior of kingfishers. It solves single-objective problems by modeling four behaviors: perching, hovering, diving, and symbiosis. The core process is as follows: (1) Population initialization: The initial population is generated using a random uniform method, as shown in the formula: (32); In the formula, For the individual's position; and For the maximum and minimum search range of the problem; It is a random number within the range [0, 1].

[0040] (2) Exploration process (Rand < 0.8). The mathematical model of these behaviors is shown in formula (18): (33); (34); In the formula, Individual The position at time t+1; It is a control parameter; This is usually related to some characteristics of the algorithm or random factors; individual j is randomly selected from the population and is different from individual j. Individuals. Among them: is a value from a random normal distribution; D is the dimension of the problem.

[0041] Habitat strategy (Rand > 0.5): (35); In the formula, is the maximum number of iterations; BF is a constant with a value of 8.

[0042] Hovering strategy (Rand < 0.5): (36); In the formula, No. The fitness value of each individual; It is the fitness value randomly selected in equation (18).

[0043] (3) Dive strategy ): (37); (38); (39); (40); In the formula: For the first Individuals The position at that moment; Dive strength parameters are related to individual fitness. This is a decay factor that decreases with each iteration. This is an intermediate variable, combining the current individual position with the optimal position; for The optimal position of an individual in the population at any given time.

[0044] (4) Symbiotic stage: Simulate the symbiotic hunting between kingfishers and otters to avoid local optima; (41); (42); In the formula: and yes Two individuals are randomly selected from the population at any given time; and Both are constants, with values ​​of 0.5 and 0.

[0045] 3.2 ZOA Optimization Algorithm: The Zebra Optimization Algorithm (ZOA) is a metaheuristic algorithm that simulates the foraging and predation defense behaviors of zebras. Its core involves solving the optimization problem in three steps: Initialization phase: Randomly generate the locations of N zebras, each zebra Given an m-dimensional vector, the population matrix is ​​represented as: (43); In the formula: X is A matrix, where N is the population size (number of zebras) and m is the number of decision variables. Indicates the first The first zebra Each zebra has a decision variable value. Calculate the objective function value F for each zebra and select the vanguard zebra PZ (the zebra with the best objective function value in the current population).

[0046] (44); Stage 1: Foraging behavior: Simulated foraging behavior: (45); A random number in the interval [0, 1] Let j be the j-th dimension position of the vanguard zebra (the current optimal solution); , .

[0047] Greedy choice to update : (46); For the new position The objective function value is used to retain only the better solution.

[0048] Phase 2: Anti-predation defense: New location calculation: (47); In the formula: A random number in the interval [0, 1]; , This represents the current iteration number. This represents the maximum number of iterations. For the random "attacked zebra" Dimensional position.

[0049] Greedy Choice Update: (48); For the new position The objective function value is used to retain only the better solution.

[0050] 3.3, such as Figure 2 As shown, the PKO-ZOA hybrid optimization algorithm: When using PKO (Kingfisher Optimization Algorithm) or ZOA (Zebra Optimization Algorithm) alone, both have significant limitations in the overall cost optimization scenario of integrated wind-solar-hydrogen storage-charging stations. Therefore, a hybrid design is needed to achieve "defect compensation and advantage superposition". Based on the principle of "complementary advantages and defect offsetting", a "three-stage collaborative framework" is constructed.

[0051] Initialization Phase: Dynamic Population Generation (Integration of ZOA Matrix and PKO Volatility Factor): When ZOA is initialized alone, it uses a purely random matrix, which can easily lead to "local clustering" (e.g., some individuals are concentrated in a certain region of the variable's value range), resulting in insufficient initial diversity. While PKO generates the population randomly, it lacks a periodic dispersion mechanism. Therefore, a dynamic fluctuation factor for PKO is introduced. The later population locations exhibit a "periodic dispersion" distribution, preserving the fundamental diversity of the ZOA random matrix while preventing individual clustering through fluctuations, thus providing a more comprehensive initial search foundation for subsequent exploration and development. The formula for generating the initial population after fusion is: (49); No. The first individual Dimensional position, , ; The population size is 30; and The first Upper and lower bounds of dimensional variables. The PKO dynamic fluctuation factor causes the initial population to disperse periodically, avoiding aggregation.

[0052] After initialization, the fitness vector is calculated to determine the "Pioneer Zebra (PZ)". (50); in, The fitness function; Phase 1: Adaptive Exploration The ZOA exploration phase relies on a 50% probability of randomly switching defense strategies, resulting in low efficiency in high-dimensional space searches; the PKO (Peacekeeper KO) habitat / hovering strategy lacks dynamic adaptation to population diversity. This phase aims to address the problems of "blind high-dimensional exploration with PKO and singular exploration direction with ZOA" by implementing adaptive exploration through a "diversity threshold switching mode," specifically designed as follows: (51); In the formula, For the first Maintaining the population centroid , For initial diversity.

[0053] (High diversity): When population diversity is high, ZOA (zoomorphic ovipositor) lead zebras can avoid the blindness of PKO (pheasant-knockout) exploration. However, when ZOA leads alone, the fixed stride length can easily lead to "overly coarse or overly fine exploration." Introducing the PKO wingbeat frequency factor... Subsequently, the step size is dynamically adjusted according to the iteration progress—large step sizes in the early stages rapidly expand the search space, while small step sizes in the later stages focus on high-quality areas. This retains the guiding nature of ZOA while solving the problem of fixed ZOA step sizes, adapting to the exploration needs of high-dimensional variables in charging stations. The position update formula is: (52); in, ; ; ;when hour, .

[0054] (Low diversity): By combining the adaptive step size of PKO flapping frequency and the zigzag characteristics of ZOA, the global scanning capability in high-dimensional space is enhanced, addressing the shortcomings of ZOA in high-dimensional exploration and the weaknesses of PKO in local development. (53); In the formula ; ;when hour, ; Iteration progress factor; The random factor is a normal distribution of PKO, which enhances the randomness of the exploration.

[0055] Phase 2: Precision Development (Dive and Symbiotic Mechanisms): Dive development: The original PKO dive model relies on global optima. This is replaced with the ZOA Pioneer Zebra (PZ), which leverages the local guidance capabilities of elite ZOA individuals to improve development accuracy and address the insufficient local convergence stability of PKO. The formula is: (54); (55); (56); (57); Among them, HA (Hunting Ability): integrates ZOA fitness ratio to enhance the targeting of development; This is the PKO iteration decay factor to ensure convergence in later development stages; ZOA-PKO hybrid guide point, balancing development precision and randomness.

[0056] Symbiotic escape: The iteration progress factor of ZOA is introduced to optimize the PKO co-existence probability, retain the PKO co-existence mechanism, optimize the perturbation range, and avoid local optima. Equation (59) makes the co-existence probability decrease with iteration, maintain high exploration (high PE) in the early stage and enhance development (low PE) in the later stage, while retaining the population interaction characteristics of the PKO co-existence mechanism, and solving the premature convergence problem of more than 60% decrease in diversity in the later stage of ZOA iteration.

[0057] (58); (59); In the formula ; ; and Individuals from two randomly selected populations; For predation efficiency.

[0058] Population renewal and termination: ZOA's greedy selection guarantees solution accuracy, while PKO's elite retention strategy adapts to engineering constraints, improving the algorithm's practicality in real-world engineering problems. A dual-threshold update approach combining ZOA's greedy selection and PKO's elite retention is employed. (60).

[0059] This invention utilizes the PKO-ZOA hybrid optimization algorithm to optimize the overall cost of integrated wind-solar-hydrogen storage-charging power plants. The solution includes the algorithm's logic for solving multiple cost objective functions and its adaptation strategy in multi-equipment collaboration scenarios within the power plant.

[0060] This invention combines the "pioneer guidance" of ZOA with the "wingbeat frequency" of PKO to solve the problem of high-dimensional space exploration efficiency; and embeds the PKO symbiosis mechanism into ZOA population updates to solve the problem of ZOA premature maturation.

[0061] In summary, this invention addresses the inefficiency of high-dimensional exploration in ZOA by introducing PKO flapping frequency to dynamically adjust the step size and combining it with a ZOA diversity threshold switching exploration mode; it replaces the global optimum with a ZOA pioneer zebra and inherits the boundary control of ZOA engineering variables to improve development accuracy and constraint adaptability; it uses a ZOA iteration factor to dynamically adjust the PKO co-occurrence probability, maintaining diversity in two stages and mitigating premature convergence; and it uses a PKO decay factor to control development intensity and integrates ZOA to defend against disturbances, balancing speed and accuracy.

[0062] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0063] In the description of this invention, it should be understood that the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0064] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. An optimized operation method for integrated wind-solar-hydrogen storage and charging stations based on hybrid PKO, characterized in that, Includes the following steps: Construct an integrated wind-solar-hydrogen-storage-charging station consisting of a photovoltaic power generation system, a wind power generation system, energy storage, an electrolyzer, a hydrogen storage tank, an electric vehicle charging unit, and a hydrogen refueling unit for hydrogen fuel cell vehicles, with the goal of minimizing the overall cost of the integrated power station. A PKO-ZOA hybrid optimization algorithm, consisting of the PKO optimization algorithm and the ZOA optimization algorithm, is constructed to solve the objective function, so as to optimize and control the operation of the constructed integrated wind-solar-hydrogen storage and charging station.

2. The optimized operation method for integrated wind-solar-hydrogen storage and charging stations based on hybrid PKO according to claim 1, characterized in that: When constructing the objective function, the objective function is constructed by considering equipment investment and operation and maintenance costs, electricity purchase costs from the grid, costs of curtailing wind and solar power, and carbon emission costs.

3. The optimized operation method for integrated wind-solar-hydrogen storage and charging stations based on hybrid PKO according to claim 2, characterized in that: When constructing the objective function, power balance constraints and unit output constraints are used as constraints of the objective function. The power balance constraints include electricity balance constraints and hydrogen energy balance constraints. The unit output constraints include wind turbine output constraints, photovoltaic output constraints, electrolyzer operation output constraints, and hydrogen storage tank operation output constraints.

4. The optimized operation method for integrated wind-solar-hydrogen storage and charging stations based on hybrid PKO according to claim 3, characterized in that: When constructing the PKO-ZOA hybrid optimization algorithm, a dynamic population is generated by fusing the ZOA matrix and the PKO fluctuation factor during the algorithm initialization phase.

5. The optimized operation method for integrated wind-solar-hydrogen storage and charging stations based on hybrid PKO according to claim 4, characterized in that: When constructing the PKO-ZOA hybrid optimization algorithm, during the adaptive exploration phase, when the population diversity is high, the ZOA pioneer zebra is used as a guide, and the PKO wing beat frequency factor is introduced to achieve dynamic adjustment of the step size with the iteration progress: large step size in the early stage to quickly expand the search space, and small step size in the later stage to focus on high-quality areas.

6. The optimized operation method for integrated wind-solar-hydrogen storage and charging stations based on hybrid PKO according to claim 5, characterized in that: When constructing the PKO-ZOA hybrid optimization algorithm, during the adaptive exploration phase, when the population diversity is low, the adaptive step size of the PKO wingbeat frequency and the zigzag characteristic of ZOA are combined to enhance the global scanning capability in high-dimensional space.

7. The optimized operation method for integrated wind-solar-hydrogen storage and charging stations based on hybrid PKO according to claim 6, characterized in that: When constructing the PKO-ZOA hybrid optimization algorithm, during the development phase of the dive and symbiosis mechanism, the original PKO dive model is replaced by ZOA pioneer zebras to improve development accuracy by utilizing the local guidance capabilities of ZOA elite individuals, thus achieving dive development.

8. The optimized operation method for integrated wind-solar-hydrogen storage and charging stations based on hybrid PKO according to claim 7, characterized in that: When constructing the PKO-ZOA hybrid optimization algorithm, during the development phase of the dive and symbiosis mechanism, the PKO symbiosis probability is optimized by introducing the iteration progress factor of ZOA, the PKO symbiosis mechanism is preserved, the perturbation range is optimized, local optima are avoided, and the symbiosis escape development is completed.

9. The optimized operation method for integrated wind-solar-hydrogen storage and charging stations based on hybrid PKO according to claim 8, characterized in that: When constructing the PKO-ZOA hybrid optimization algorithm, the greedy selection of ZOA is used to ensure the accuracy of the solution, while the elite retention strategy of PKO is incorporated to adapt to engineering constraints.

10. The optimized operation method for integrated wind-solar-hydrogen storage and charging stations based on hybrid PKO according to claim 9, characterized in that: When constructing the PKO-ZOA hybrid optimization algorithm, a dual threshold update combining ZOA greedy selection and PKO elite retention is adopted.

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