Wind-solar hydrogen storage scheduling method based on sparrow search algorithm and related equipment

Abstract

The invention belongs to the technical field of wind and light hydrogen storage resource allocation, and discloses a wind and light hydrogen storage scheduling method based on a sparrow search algorithm and related equipment. The wind-solar hydrogen storage scheduling method based on the sparrow search algorithm comprises the steps of constructing a wind-solar hydrogen storage operation day-ahead profit model according to a scheduling target of a wind-solar hydrogen storage system, obtaining a profit maximization objective function through the wind-solar hydrogen storage operation day-ahead profit model, and solving the profit maximization objective function by adopting the sparrow search algorithm. Obtaining a globally optimal solution, and scheduling the wind and light hydrogen storage system according to the globally optimal solution; the method can overcome the defects that in the prior art, the optimization speed is reduced during optimization scheduling, the randomness of an optimization scheduling result is high, and local optimum is likely to happen.

Description

Technical Field

[0001] This invention relates to the field of wind, solar, and hydrogen storage resource allocation technology, specifically to a wind, solar, and hydrogen storage scheduling method and related equipment based on the sparrow search algorithm. Background Technology

[0002] With the increasing demand for energy conservation and emission reduction and the rise of clean energy, wind and solar power are playing an increasingly important role in my country's power system. However, wind-solar hybrid power generation systems are significantly affected by climate and the environment. The peak-valley characteristics of load, the randomness and volatility of wind and solar output lead to prominent problems such as wind and solar curtailment, low equipment utilization, and power supply reliability. To achieve interconnection between renewable energy power generation systems and the main power grid, and to fully utilize renewable energy power generation systems to enhance power system stability and meet the diverse needs of electricity users, research has proposed a microgrid system model that combines energy storage systems with renewable energy power generation systems. A microgrid is a miniature power network integrating distributed power sources, power conversion devices, energy storage devices, power loads, and monitoring systems. It possesses autonomous security and control management capabilities, enabling grid-connected operation and off-grid operation when necessary. The main technical challenge currently facing microgrids lies in achieving effective energy allocation and rational scheduling. The energy scheduling strategy of microgrids is crucial for achieving economical, efficient, stable, and reliable operation, and is also key to improving the adaptability and utilization rate of renewable energy.

[0003] However, existing research uses particle swarm optimization to solve economic scheduling problems. While it is easy to implement in solving optimal scheduling problems, it requires a large number of initial flocks of birds to search for the optimal result, which reduces the optimization speed. The optimization result is highly dependent on the initial value of the particles, and the calculated result is highly random and prone to getting trapped in local optima. Summary of the Invention

[0004] The purpose of this invention is to provide a wind-solar-hydrogen storage scheduling method and related equipment based on the sparrow search algorithm, so as to overcome the shortcomings of the existing technology in terms of reduced optimization speed, high randomness of optimization scheduling results and easy getting trapped in local optima.

[0005] To achieve the above objectives, the technical solution adopted by the present invention is as follows: In a first aspect, the present invention provides a wind-solar-storage hydrogen scheduling method based on the sparrow search algorithm, comprising the following steps: Based on the scheduling objectives of the wind-solar-hydrogen storage system, a day-ahead revenue model for wind-solar-hydrogen storage operation is constructed, and the revenue maximization objective function is obtained through the day-ahead revenue model for wind-solar-hydrogen storage operation. The Sparrow Search algorithm is used to solve the objective function of maximizing profit, and the global optimal solution is obtained. Scheduling of the wind-solar-hydrogen storage system based on the global optimal solution; Furthermore, the wind-solar-hydrogen storage system includes a wind power generation system, a photovoltaic power generation system, a hydrogen production system, an electrochemical energy storage system, and a power grid; The wind power generation system, photovoltaic power generation system, hydrogen production system, and energy storage system are all connected to the power grid. Furthermore, the scheduling objectives include energy balance constraints of the wind-solar-hydrogen storage system, upper and lower limits constraints of wind power generation, hydrogen production rate constraints, wind and solar power curtailment constraints, and energy storage operation status constraints. Furthermore, the formula for the energy balance constraint of the wind-solar-hydrogen storage system includes: ; In the formula, Indicates wind power generation capacity; Indicates photovoltaic power generation capacity; Indicates the discharge power of the energy storage system; Indicates the power purchased; Indicates hydrogen production capacity; Indicates the charging power of the energy storage system; Indicates the power output; The formulas for the upper and lower limits of wind power generation constraints include: ; In the formula, This indicates the upper limit of wind power generation; This indicates the lower limit of wind power generation. Indicates the upper limit of photovoltaic power generation; Indicates the lower limit of photovoltaic power generation; This indicates the upper limit of the hydrogen production system's power output; Indicates the lower limit power of the hydrogen production system; This indicates the upper limit of the charging power of the energy storage system; This indicates the lower limit of charging power for the energy storage system; This indicates the upper limit of the discharge power of the energy storage system; Indicates the lower limit power of the energy storage system's discharge; Furthermore, the formula for the hydrogen production rate constraint includes: ; In the formula, Indicates the hydrogen production power-up rate; This indicates the upper limit of the hydrogen production power rate; Indicates the rate of decrease in hydrogen production power; This indicates the upper limit of the hydrogen production power reduction rate; The formula for the constraint on wind and solar power curtailment includes: ; In the formula, Indicates the amount of wind power curtailed; Indicates the power of light discarded; Indicates the upper limit of wind curtailment power; Indicates wind power generation capacity; Indicates photovoltaic power generation capacity; The formulas for the energy storage operation state constraints include: ; In the formula, Indicates the upper limit of the energy storage system; Indicates the lower limit of the energy of the energy storage system; This indicates the initial state of the energy storage system; Indicates the end state of the energy storage system; Furthermore, the formula for maximizing the objective function of the day-ahead profit model for wind, solar, and hydrogen storage includes: ; ; ; ; ; In the formula, This represents the objective function for maximizing profits; This represents the total daily operating cost; Indicates operating and maintenance costs; This indicates the penalty cost for abandoning wind and solar power. This indicates the cost of hydrogen production and sales; This indicates the cost of buying and selling electricity; T Indicates the operating cycle; t Indicates time; This represents the unit operation and maintenance cost coefficient of the wind turbine; This represents the unit operation and maintenance cost coefficient for photovoltaic systems. This represents the unit operation and maintenance cost coefficient of an energy storage system; Indicates wind power generation capacity; Indicates photovoltaic power generation capacity; Indicates the charging power of the energy storage system; Indicates the discharge power of the energy storage system; Indicates the wind curtailment penalty coefficient; Indicates the light-wasting penalty coefficient; Indicates the amount of wind curtailed; Indicates the amount of light discarded; This indicates the unit cost of hydrogen production using an electrolyzer; Indicates hydrogen production capacity; This represents the unit coefficient for hydrogen sales price; Indicates hydrogen sales capacity; This represents the electricity purchase cost coefficient; Indicates the power purchased; This represents the electricity sales revenue coefficient; Indicates the power output; Furthermore, the global optimal solution includes the optimal output distribution curves for energy storage output, hydrogen production and storage, grid energy interaction, and wind and solar power output; The scheduling of the wind-solar-hydrogen storage system based on the global optimal solution specifically includes: Based on the energy storage output, hydrogen production and storage, grid energy interaction, and the optimal output allocation curve of wind and solar power, determine the optimal power consumption plan for wind power generation, photovoltaic power generation, and hydrogen production, calculate the amount of electricity that the system needs to buy or sell, and the revenue or expenditure of the wind, solar, and hydrogen storage system.

[0006] Secondly, this invention provides a wind-solar-hydrogen storage scheduling system based on the sparrow search algorithm, comprising: The module for constructing the day-ahead revenue model for wind, solar and hydrogen storage operation and obtaining the revenue maximization objective function is used to construct the day-ahead revenue model for wind, solar and hydrogen storage operation based on the scheduling objectives of the wind, solar and hydrogen storage system, and obtain the revenue maximization objective function through the day-ahead revenue model for wind, solar and hydrogen storage operation. The global optimal solution acquisition module is used to solve the profit maximization objective function using the sparrow search algorithm to obtain the global optimal solution. The wind-solar-hydrogen storage system scheduling module is used to schedule the wind-solar-hydrogen storage system based on the global optimal solution.

[0007] Thirdly, the present invention provides a computer device including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the above-described method.

[0008] Fourthly, the present invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the above-described method.

[0009] The above technical solution has the following advantages or beneficial effects: Firstly, this invention provides a wind-solar-storage-hydrogen scheduling method based on the sparrow search algorithm. The core guiding principle of this method is maximizing revenue. It comprehensively considers various factors in the wind-solar-storage-hydrogen system, including power generation costs of various energy sources, charging and discharging losses of energy storage equipment, hydrogen production and storage costs, and electricity market transaction revenue. This accurately constructs a revenue assessment system before the system's operational date. Using this as the objective function ensures that scheduling decisions always aim to improve overall economic efficiency. Regarding optimization speed, the sparrow search algorithm possesses a highly efficient search mechanism. By simulating the foraging and anti-predation behavior of sparrow flocks, it can quickly conduct global exploration and local development in the solution space. Compared to traditional methods, this algorithm can rapidly locate regions where optimal solutions may exist, significantly shortening the optimization time and significantly improving the efficiency of optimization during scheduling. The hydrogen storage system can make scheduling decisions more promptly based on real-time data. Regarding the randomness of optimizing scheduling results, the sparrow search algorithm exhibits stable convergence. The position update rules for individual sparrows and the setting of warning values ​​in the algorithm give the search process a certain directionality and regularity, avoiding fluctuations in results caused by random searches and ensuring that each optimized scheduling yields relatively reliable and stable results. To avoid getting trapped in local optima, the algorithm introduces a role-switching mechanism between discoverers and participants. Discoverers are responsible for searching areas rich in food, while participants follow the discoverers to forage. Simultaneously, some sparrows will conduct random searches. This dynamic role switching and diversified search strategies effectively enhance the algorithm's ability to escape local optima, thereby finding the global optimum and maximizing the benefits of the wind-solar-hydrogen storage system, improving the overall system's operational efficiency and stability.

[0010] Furthermore, wind and solar power generation complement each other, hydrogen production and electrochemical energy storage work in synergy, and all are connected to the power grid, which enhances the system's ability to accept and convert multiple energy sources, improves the flexibility of energy utilization, ensures power supply stability, and provides a solid hardware foundation for scheduling based on the sparrow search algorithm.

[0011] Furthermore, energy balance constraints ensure stable system operation, various upper and lower limits and rate constraints guarantee equipment safety, wind and solar curtailment constraints improve energy utilization, and energy storage operation constraints optimize charging and discharging strategies. Together, these factors contribute to the precise scheduling of the Sparrow Search algorithm, enabling efficient synergy between wind, solar, and hydrogen storage to maximize benefits.

[0012] Furthermore, the energy balance constraint formula for the wind-solar-hydrogen storage system precisely defines the input-output relationship of various power types in the system, ensuring a dynamic balance among wind power generation, photovoltaic power generation, energy storage system charging and discharging power, electricity purchase and sale power, and hydrogen production power. This effectively avoids system fluctuations or even collapses caused by power mismatch, laying a solid foundation for the stable operation of the wind-solar-hydrogen storage system and ensuring the continuity and reliability of power supply. A series of formulas, such as the upper and lower limit constraints for wind power generation, set clear operating boundaries for each device within the system. The upper and lower limit constraints for wind and photovoltaic power generation can prevent damage to power generation equipment due to overload or underload operation, extending equipment lifespan and reducing maintenance costs. Hydrogen production... System upper and lower limit constraints ensure that the hydrogen production process operates within a safe and efficient parameter range, avoiding low hydrogen production efficiency or safety hazards. Energy storage system charge and discharge upper and lower limit constraints help to rationally control the charging and discharging rhythm of energy storage devices, preventing irreversible damage to batteries from overcharging and over-discharging, and improving the reliability and cycle life of the energy storage system. Under the premise of satisfying these constraints, the algorithm seeks the scheduling scheme that maximizes benefits, making scheduling decisions more scientific and reasonable. This not only fully taps the potential of wind, solar, and hydrogen storage systems, achieving efficient utilization of multiple energy sources, but also effectively reduces operating costs, improves the economic benefits of the entire system, and promotes the development of the wind, solar, and hydrogen storage industry towards intelligence and efficiency.

[0013] Furthermore, the hydrogen production rate constraint formula clarifies the upper limit of the hydrogen production power increase and decrease rate, effectively preventing the hydrogen production system from being subjected to excessive stress due to rapid power changes, preventing equipment damage and performance degradation, and extending the service life of the hydrogen production equipment. At the same time, reasonable rate constraints can ensure the smooth operation of the hydrogen production process, improve hydrogen production efficiency and product quality, and ensure the safe and stable operation of the hydrogen production system, providing important support for the reliable operation of wind, solar and hydrogen storage systems. The wind and solar curtailment power constraint formula limits the upper limit of wind and solar curtailment power, prompting the system to absorb as much wind and solar power as possible, which helps to reduce the waste of renewable energy, improve energy utilization efficiency, give full play to the role of wind, solar and hydrogen storage systems in energy transition, and promote the large-scale application of clean energy. The energy storage operation state constraint formula specifies the upper and lower limits of the energy of the energy storage system as well as the initial and final states, which can reasonably control the charging and discharging process of the energy storage system, avoid overcharging and over-discharging to damage the energy storage equipment, optimize the operation strategy of the energy storage system, and enhance the stability and adaptability of the entire wind, solar and hydrogen storage system in the face of power fluctuations.

[0014] Furthermore, from operation and maintenance costs to the penalty costs for wind and solar curtailment, and then to the costs of hydrogen production, sales, electricity purchase, and sales, every aspect of system operation is meticulously considered. This avoids scheduling decision deviations caused by inadequate cost-benefit considerations, providing a solid data foundation for maximizing system benefits. By clarifying the quantitative relationship between various costs and benefits, the formula can guide the system to rationally utilize wind and solar resources and reduce wind and solar curtailment. The setting of penalty costs for wind and solar curtailment prompts the system to prioritize the consumption of renewable energy, improving energy utilization efficiency. At the same time, the consideration of hydrogen production, sales, and electricity purchase and sales costs helps optimize the system's energy conversion and trading strategies under different operating conditions, achieving synergistic complementarity among multiple energy sources. The above formula provides a clear and specific optimization objective for the sparrow search algorithm, enabling it to quickly and accurately search for the global optimum in a complex solution space based on the objective function. This effectively avoids getting trapped in local optima, improves the efficiency and accuracy of scheduling optimization, and ultimately enhances the overall economic benefits and operational stability of the wind-solar-hydrogen storage system.

[0015] Furthermore, the global optimal solution encompasses the optimal allocation curves for various aspects, including energy storage, hydrogen production and storage, grid interaction, and wind and solar power output. Based on this, system scheduling can achieve precise coordination between wind and solar power generation and energy storage, hydrogen production, and other related processes. By determining the optimal power generation and consumption plans, as well as the volume of electricity trading, the system's revenue or expenditure can be accurately calculated, helping to minimize costs and maximize revenue while meeting system operational needs. The scheduling method based on the global optimal solution fully considers the operating status and constraints of each part of the system, enabling advance planning of strategies to cope with different operating conditions. This effectively addresses the uncertainties of wind and solar power generation, reduces the impact of power fluctuations on the system, and enhances the stability and reliability of the entire wind-solar-hydrogen storage system.

[0016] Secondly, this invention provides a wind-solar-storage-hydrogen scheduling system based on the sparrow search algorithm. The day-ahead benefit model of the wind-solar-storage-hydrogen system comprehensively considers various factors such as power generation costs, energy storage losses, hydrogen production costs, and electricity trading revenue and expenditure during the operation of the system, providing a basis for accurately evaluating system benefits. Based on the benefit maximization objective function obtained from this model, the direction of scheduling optimization is clarified, namely, to maximize the overall system benefit as the ultimate goal, making scheduling decisions targeted and avoiding resource waste and low efficiency caused by blind scheduling. The sparrow search algorithm is the key tool for achieving the goal, with powerful global search capabilities. By simulating the foraging and vigilance behavior of sparrows, it efficiently explores the solution space. Compared with traditional algorithms, it can quickly locate the optimal solution of the benefit maximization objective function, greatly improving the optimization speed. At the same time, its unique search mechanism avoids getting trapped in local optima, reduces the randomness of the optimized scheduling results, and ensures that each scheduling obtains a solution close to the global optimum, thereby significantly improving the operational efficiency and stability of the wind-solar-storage-hydrogen system.

[0017] Thirdly, the present invention provides a computer device that, through a processor executing a specific computer program, can efficiently implement the steps of the method of the present invention. When performing data processing tasks, the computer device can accurately perform numerical calculations and logical judgments, avoiding errors caused by human factors. At the same time, since the computer program has high stability and reliability, it can ensure the accuracy and consistency of the data processing results.

[0018] Fourthly, the present invention provides a computer-readable storage medium. By programming the steps of the method of the present invention into a computer program and storing it on the computer-readable storage medium, users can easily load these programs onto any compatible computer device and execute them without rewriting or converting the code, which greatly improves the convenience and flexibility of program execution. Attached Figure Description

[0019] Figure 1 This is a flowchart illustrating the wind-solar-storage hydrogen scheduling method based on the sparrow search algorithm of the present invention. Figure 2 This is a flowchart illustrating the sparrow search algorithm of the present invention; Figure 3 This is a schematic diagram of the structure of the computer device of the present invention. Detailed Implementation

[0020] The present invention will be further described in detail below with reference to specific embodiments. These descriptions are for explanation purposes only and are not intended to limit the scope of the invention. To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention. It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0021] In one possible embodiment of the invention, see [link to relevant documentation]. Figure 1 This paper presents a wind-solar-hydrogen storage scheduling method based on the sparrow search algorithm, which includes the following steps: Step 1: Based on the scheduling objectives of the wind-solar-hydrogen storage system, construct a day-ahead revenue model for wind-solar-hydrogen storage operation to obtain the revenue maximization objective function. The wind-solar-hydrogen storage system includes a wind power generation system, a photovoltaic power generation system, a hydrogen production system, an electrochemical energy storage system, and the power grid. The wind power generation system is connected to the power grid through AC / DC and DC / AC converters, while the photovoltaic power generation system, hydrogen production system, and electrochemical energy storage system are connected to the power grid through DC / AC inverters, respectively. In one possible embodiment of the present invention, the availability of each energy source is determined based on real-time data of the wind-solar-hydrogen storage system, which specifically includes wind power generation, photovoltaic power generation, and hydrogen production electricity consumption, such as parameters like wind power, solar intensity, and hydrogen production power; market electricity price data is also included to calculate the cost of buying and selling electricity. In one possible embodiment of the present invention, during actual power supply, clean energy sources such as wind and solar power are preferentially used for discharge. If the aforementioned discharge amount cannot meet the DC power supply demand, energy storage facilities need to be mobilized for power supply. If none of the above power supply methods can meet the required power, electricity needs to be purchased from the grid. For example, during the midday period, the sunlight intensity is sufficient, the photovoltaic power generation system has a high output power, and the renewable energy power generation is relatively abundant. In addition to maintaining the normal operation of the microgrid system, the system also performs battery energy storage, hydrogen production and storage, and electricity sales. During the night, since only the wind power generation system outputs power, the power is low, and renewable energy power generation alone cannot maintain the operation. The system maintains the normal operation of the microgrid system through battery discharge and grid power purchase. The final scheduling objective is to maximize the comprehensive benefits of the microgrid system while considering the scheduling objective. Therefore, it is necessary to establish a day-ahead benefit model for the integrated operation of wind, solar, and hydrogen storage systems, with a benefit maximization objective function. In one possible embodiment of the present invention, the formula for the energy balance constraint of the wind-solar-hydrogen storage system includes: ; In the formula, Indicates the power generation capacity of the wind turbine; Indicates photovoltaic power generation capacity; Indicates the discharge power of the energy storage system; Indicates the power purchased; Indicates hydrogen production capacity; Indicates the charging power of the energy storage system; Indicates the power output; The formulas for the upper and lower limits of wind power generation constraints include: ; In the formula, This indicates the upper limit of wind power generation; This indicates the lower limit of wind power generation. Indicates the upper limit of photovoltaic power generation; Indicates the lower limit of photovoltaic power generation; This indicates the upper limit of the hydrogen production system's power output; Indicates the lower limit power of the hydrogen production system; This indicates the upper limit of the charging (absorbing electrical energy) power of the energy storage system; This indicates the lower limit of the charging (energy absorption) power of the energy storage system; This indicates the upper limit of the discharge (release of electrical energy) power of the energy storage system; This indicates the lower limit of the discharge (energy release) power of the energy storage system; The formulas for constraining the hydrogen production rate include: ; In the formula, Indicates the hydrogen production power-up rate; This indicates the upper limit of the hydrogen production power rate; Indicates the rate of decrease in hydrogen production power; This indicates the upper limit of the hydrogen production power reduction rate; The formulas for constraining wind and solar power curtailment include: ; In the formula, Indicates the amount of wind power curtailed; Indicates the power of light discarded; Indicates the upper limit of wind curtailment power; Indicates wind power generation capacity; Indicates photovoltaic power generation capacity; The formulas for energy storage operation state constraints include: ; In the formula, Indicates the upper limit of the energy storage system; Indicates the lower limit of the energy of the energy storage system; This indicates the initial state of the energy storage system; Indicates the end state of the energy storage system; By setting constraints on the parameters in the day-ahead revenue model for wind, solar, and hydrogen storage, the stable operation of the microgrid system can be guaranteed, ensuring safety and improving economic efficiency, thereby optimizing the microgrid dispatch strategy. In one possible embodiment of the invention, by establishing a day-ahead revenue model (i.e., objective function) for wind, solar, and hydrogen storage operations, the complex microgrid scheduling optimization problem is transformed into a problem of solving this model. The goal is to maximize the overall economic benefit of the microgrid, thereby obtaining the output allocation relationship of each unit module. The total daily operating cost of the system is then considered. Optimization targets include operation and maintenance costs. The cost of curtailing wind and solar power Costs of hydrogen production and sales and the cost of buying and selling electricity The following formula is the objective function for maximizing revenue obtained through the day-ahead revenue model of wind, solar, and hydrogen storage operation: ; In the formula, This represents the objective function for maximizing profits; This represents the total daily operating cost; Indicates operating and maintenance costs; This indicates the penalty cost for abandoning wind and solar power. This indicates the cost of hydrogen production and sales; This indicates the cost of buying and selling electricity; (1) Operation and maintenance costs

[0022] The operation and maintenance costs of wind-solar-hydrogen storage systems (also known as wind-solar-hydrogen storage microgrid systems) mainly include the daily operation and maintenance costs of wind power, photovoltaics, and energy storage systems.

[0023] ; In the formula, T Indicates the operating cycle; t Indicates time; This represents the unit operation and maintenance cost coefficient of the wind turbine; This represents the unit operation and maintenance cost coefficient for photovoltaic systems. This represents the unit operation and maintenance cost coefficient of an energy storage system; Indicates wind power generation capacity; Indicates photovoltaic power generation capacity; Indicates the charging power of the energy storage system; Indicates the discharge power of the energy storage system; (2) Penalty costs for wind and solar power curtailment

[0024] Curtailment of wind and solar power penalties It can be represented by the following mathematical expression: ; In the formula, Indicates the wind curtailment penalty coefficient; Indicates the light-wasting penalty coefficient; Indicates the amount of wind curtailed; Indicates the amount of light discarded; (3) Costs of hydrogen production and sales

[0025] Building hydrogen production facilities and selling hydrogen to users can be a revenue stream for wind, solar, and hydrogen storage systems, with costs associated with both hydrogen production and sales. It can be represented by the following mathematical expression: ; In the formula, This indicates the unit cost of hydrogen production using an electrolyzer; Indicates hydrogen production capacity; This represents the unit coefficient for hydrogen sales price; Indicates hydrogen sales capacity; (4) Costs of buying and selling electricity

[0026] ; In the formula, This represents the electricity purchase cost coefficient; Indicates the power purchased; This represents the electricity sales revenue coefficient; This indicates the power output.

[0027] Step 2: The Sparrow Search algorithm is used to solve the objective function of maximizing revenue to obtain the global optimal solution. The global optimal solution includes the optimal output allocation curves for energy storage output, hydrogen production and storage, grid energy interaction, and wind and solar power output.

[0028] Step 3: Schedule the wind-solar-hydrogen storage system based on the global optimal solution; In one possible embodiment of the invention, see [link to relevant documentation]. Figure 2The Sparrow Search Algorithm (SSA) is a novel swarm intelligence algorithm that simulates the foraging and anti-predation behaviors of sparrows. It boasts good optimization accuracy, convergence speed, and both local exploitation and global optimization capabilities. Based on their different tasks in foraging and defense, sparrows can be categorized into three types: discoverers, followers, and scouts. Discoverers act as leaders, guiding the entire flock in a specific direction to find food, playing a leading role in the population's behavior. They have a relatively large search range, typically 10%–20% of the population, and consist of the best-performing sparrows in each iteration. To improve global search and local exploitation capabilities, a sine function is used for iterative optimization, and the position update formula is adjusted using the golden ratio. This not only enhances the detailed search of local areas but also ensures the algorithm maintains excellent optimization capabilities globally, achieving a good balance. The position update formula for discoverers is expressed as: ; In the formula, in the formula, j =1, 2, 3, ... , d, where d represents the dimension of the variable to be optimized; n Indicates the current iteration number; i Indicates the first i An individual, that is, the location of an individual; Indicates the updated location of the discoverer; R 1 represents a random variable in the range [0, 2π], used to determine the distance an individual moves in the next iteration; R 2 represents a random variable in the range [0, π], used to determine the direction of movement of an individual in the next iteration; ST Indicates a safe value; x 1. x 2 represents two parts using the golden ratio. The coefficients obtained through calculation are as follows: , This helps to narrow down the search scope and precisely guide individuals to approach the optimal solution; The position representing the optimal fitness of an individual; Q represents a random number generated from a normal distribution. L This represents a 1×d matrix with all elements being 1; Once the number of discoverers is determined, other individuals will become followers, and their number in proportion to the number of discoverers is fixed. Followers are influenced by the discoverers when searching for the optimal solution. The formula for updating their positions is expressed as: ; In the formula, Indicates that in the iteration to the thn The position of the individual with the worst fitness value at the next time; Indicates that in the iteration to the th n The current location of the individual with the best fitness performance after +1 iteration; A This represents a 1 x d matrix with elements randomly set to 1 or -1. ; m Indicates population size; Scouts play a crucial role in detecting danger within sparrow populations, typically comprising 10% to 20% of the total population. This responsibility is not borne by a single type of individual but is shared by both discoverers and followers. The formula for scouts updating their positions is expressed as: ; In the formula, A random number representing the standard normal distribution form, used to control the step size; K This represents a random number, taking a value in the range [-1, 1]. This represents the fitness value of the individual during the optimization process; and These represent the fitness values ​​of the best and worst positioned individuals, respectively. It represents the smallest constant that prevents the denominator from being zero.

[0029] In one possible embodiment of the invention, scheduling specifically includes: dividing the electricity bill within a day into three time periods based on the actual local electricity demand: peak period, off-peak period, and flat period, to more accurately reflect the supply and demand relationship in the electricity market. To better optimize the scheduling of the wind-solar-storage-hydrogen microgrid system, a day can be divided into 288 scheduling periods, each lasting 5 minutes, to schedule the output of each distributed system, enabling it to operate more rationally within a typical daily cycle. Based on known predicted wind and solar power output, and using constraints such as upper and lower limits for wind power generation, energy balance constraints for the hydrogen storage system, and constraints on energy storage operation status, the objective function is to maximize the comprehensive benefits of microgrid operation. A sparrow search algorithm is used to solve the objective function of the microgrid, obtaining the optimal allocation curves for hydrogen production, hydrogen delivery, hydrogen storage, grid energy interaction, and energy storage output every 5 minutes for the microgrid system over 24 hours. The final revenue curve is then plotted, and the total revenue is calculated. For example, based on the dispatch results, electricity is purchased in the early morning when electricity prices are low for hydrogen production and supply, and a small amount is used for energy storage to improve the efficiency of hydrogen supply when wind and solar power output is low; at noon, the main consideration is to sell the surplus electricity after hydrogen production and store the excess to prepare for the hydrogen supply requirements at night; at night, it is necessary to purchase electricity from the grid to maintain hydrogen balance, and when electricity prices are high, hydrogen can be produced and supplied using energy storage modules and hydrogen storage tanks to improve hydrogen supply efficiency and system economic benefits.

[0030] In one possible embodiment of the present invention, the scheduling can yield the following results: an optimized scheduling scheme for wind power generation, photovoltaic power generation, and hydrogen production electricity consumption: determining the optimal wind power generation, photovoltaic power generation, and hydrogen production electricity consumption plan based on real-time data and optimization algorithms; a plan for buying and selling electricity: calculating the amount of electricity the system needs to buy or sell based on market prices and electricity demand; and the revenue or expenditure costs of the wind, solar, and hydrogen storage system.

[0031] In one embodiment of the present invention, a wind-solar-storage-hydrogen scheduling system based on the sparrow search algorithm is provided, comprising: The module for constructing the day-ahead revenue model for wind, solar and hydrogen storage operation and obtaining the revenue maximization objective function is used to construct the day-ahead revenue model for wind, solar and hydrogen storage operation based on the scheduling objectives of the wind, solar and hydrogen storage system, and obtain the revenue maximization objective function through the day-ahead revenue model for wind, solar and hydrogen storage operation. The global optimal solution acquisition module is used to solve the profit maximization objective function using the sparrow search algorithm to obtain the global optimal solution. The wind-solar-hydrogen storage system scheduling module is used to schedule the wind-solar-hydrogen storage system based on the global optimal solution.

[0032] See Figure 3 In one embodiment of the present invention, a computer device is provided, comprising a processor and a memory. The memory stores a computer program, which includes program instructions. The processor executes the program instructions stored in the computer storage medium. The processor may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing and control core of the terminal, and is suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions in the computer storage medium to realize a corresponding method flow or corresponding function. The processor described in this embodiment of the present invention can be used for the operation of a wind-solar-hydrogen storage scheduling method based on the sparrow search algorithm.

[0033] In one embodiment of the present invention, a computer-readable storage medium is provided, specifically a computer-readable storage medium (Memory), which is a memory device in a computer device used to store programs and data. It is understood that the computer-readable storage medium here can include both the built-in storage medium in the computer device and extended storage media supported by the computer device. The computer-readable storage medium provides storage space that stores the terminal's operating system; and the storage space also stores one or more instructions suitable for loading and execution by a processor. These instructions can be one or more computer programs (including program code). It should be noted that the computer-readable storage medium here can be a high-speed RAM memory or a non-volatile memory, such as at least one disk storage device. The processor can load and execute one or more instructions stored in the computer-readable storage medium to implement the corresponding steps of the wind-solar-storage-hydrogen scheduling method based on the sparrow search algorithm in the embodiment.

[0034] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0035] 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 should 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.

[0036] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0037] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0038] This invention uses the profit maximization objective function as its core guiding principle. It comprehensively considers various factors in wind-solar-hydrogen storage systems, including power generation costs of various energy sources, charging and discharging losses of energy storage equipment, hydrogen production and storage costs, and electricity market transaction revenue. This accurately constructs a profit assessment system before the system's operational date. Using this as the objective function ensures that scheduling decisions always aim to improve overall economic efficiency. Regarding optimization speed, the sparrow search algorithm possesses an efficient search mechanism. By simulating the foraging and anti-predation behavior of sparrows, it can quickly explore the global and local solutions in the solution space. Compared to traditional methods, this algorithm can rapidly locate regions where optimal solutions may exist, significantly shortening the optimization time and greatly improving the efficiency of optimization during scheduling. This allows wind-solar-hydrogen storage systems to make scheduling decisions more promptly based on real-time data. Addressing the randomness of optimization scheduling results, the sparrow search algorithm exhibits stable convergence. The position update rules for individual sparrows and the setting of warning values ​​in the algorithm give the search process a certain direction and regularity, avoiding fluctuations in results caused by random searches and ensuring that each optimization scheduling yields optimal results. The algorithm achieves relatively reliable and stable results. To avoid getting trapped in local optima, it introduces a role-switching mechanism between discoverers and participants. Discoverers search for food-rich areas, while participants follow them in their foraging. Some sparrows also engage in random searches. This dynamic role-switching and diverse search strategies effectively enhance the algorithm's ability to escape local optima, thereby finding the global optimum and maximizing the benefits of the wind-solar-hydrogen storage system, improving the overall system's operational efficiency and stability. Furthermore, this invention helps the wind-solar-hydrogen storage microgrid system fully utilize renewable energy by rationally allocating energy from wind power generation, photovoltaic power generation, and hydrogen production, improving energy efficiency and reducing energy waste. Simultaneously, by optimizing system operation scheduling, it reduces energy costs, improves system stability and reliability, and maximizes system benefits and economics. It can more effectively utilize energy from wind power generation, photovoltaic power generation, and hydrogen production, reducing energy waste. By optimizing planned electricity purchases and sales, it can buy or sell electricity at appropriate times, thereby reducing energy costs. Through optimized scheduling, it balances energy supply and demand, enabling the system to operate more stably and improving power supply reliability.

[0039] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A wind-solar-storage hydrogen scheduling method based on the sparrow search algorithm, characterized in that, Includes the following steps: Based on the scheduling objectives of the wind-solar-hydrogen storage system, a day-ahead revenue model for wind-solar-hydrogen storage operation is constructed, and the revenue maximization objective function is obtained through the day-ahead revenue model for wind-solar-hydrogen storage operation. The Sparrow Search algorithm is used to solve the objective function of maximizing profit, and the global optimal solution is obtained. The wind-solar-hydrogen storage system is scheduled based on the global optimal solution.

2. The wind-solar-storage hydrogen scheduling method based on the sparrow search algorithm according to claim 1, characterized in that, The wind-solar-hydrogen storage system includes a wind power generation system, a photovoltaic power generation system, a hydrogen production system, an electrochemical energy storage system, and a power grid; The wind power generation system, photovoltaic power generation system, hydrogen production system, and energy storage system are all connected to the power grid.

3. The wind-solar-storage hydrogen scheduling method based on the sparrow search algorithm according to claim 1, characterized in that, The scheduling objectives include energy balance constraints for wind-solar-hydrogen storage systems, upper and lower limits constraints for wind power generation, hydrogen production rate constraints, wind and solar power curtailment constraints, and energy storage operation status constraints.

4. The wind-solar-storage hydrogen scheduling method based on the sparrow search algorithm according to claim 3, characterized in that, The formula for the energy balance constraint of the wind-solar-hydrogen storage system includes: ； In the formula, Indicates wind power generation capacity; Indicates photovoltaic power generation capacity; Indicates the discharge power of the energy storage system; Indicates the power purchased; Indicates hydrogen production capacity; Indicates the charging power of the energy storage system; Indicates the power output; The formulas for the upper and lower limits of wind power generation constraints include: ； In the formula, This indicates the upper limit of wind power generation; This indicates the lower limit of wind power generation. Indicates the upper limit of photovoltaic power generation; Indicates the lower limit of photovoltaic power generation; This indicates the upper limit of the hydrogen production system's power output; Indicates the lower limit power of the hydrogen production system; This indicates the upper limit of the charging power of the energy storage system; This indicates the lower limit of charging power for the energy storage system; This indicates the upper limit of the discharge power of the energy storage system; This indicates the lower limit of the discharge power of the energy storage system.

5. A wind-solar-storage hydrogen scheduling method based on the sparrow search algorithm according to claim 3, characterized in that, The formula for the hydrogen production rate constraint includes: ； In the formula, Indicates the hydrogen production power-up rate; This indicates the upper limit of the hydrogen production power rate; Indicates the rate of decrease in hydrogen production power; This indicates the upper limit of the hydrogen production power reduction rate; The formula for the constraint on wind and solar power curtailment includes: ； In the formula, Indicates the amount of wind power curtailed; Indicates the power of light discarded; Indicates the upper limit of wind curtailment power; Indicates wind power generation capacity; Indicates photovoltaic power generation capacity; The formulas for the energy storage operation state constraints include: ； In the formula, Indicates the upper limit of the energy storage system; Indicates the lower limit of the energy of the energy storage system; This indicates the initial state of the energy storage system; This indicates the end state of the energy storage system.

6. The wind-solar-storage hydrogen scheduling method based on the sparrow search algorithm according to claim 1, characterized in that, The formula for maximizing the objective function of the day-ahead profit model for wind, solar, and hydrogen storage operation includes: ； ； ； ； ； In the formula, This represents the objective function for maximizing profits; This represents the total daily operating cost; Indicates operating and maintenance costs; This indicates the penalty cost for abandoning wind and solar power. This indicates the cost of hydrogen production and sales; This indicates the cost of buying and selling electricity; T Indicates the operating cycle; t Indicates time; This represents the unit operation and maintenance cost coefficient of the wind turbine; This represents the unit operation and maintenance cost coefficient for photovoltaic systems. This represents the unit operation and maintenance cost coefficient of an energy storage system; Indicates wind power generation capacity; Indicates photovoltaic power generation capacity; Indicates the charging power of the energy storage system; Indicates the discharge power of the energy storage system; Indicates the wind curtailment penalty coefficient; Indicates the light-wasting penalty coefficient; Indicates the amount of wind curtailed; Indicates the amount of light discarded; This indicates the unit cost of hydrogen production using an electrolyzer; Indicates hydrogen production capacity; This represents the unit coefficient for hydrogen sales price; Indicates hydrogen sales capacity; This represents the electricity purchase cost coefficient; Indicates the power purchased; This represents the electricity sales revenue coefficient; This indicates the power output.

7. The wind-solar-storage hydrogen scheduling method based on the sparrow search algorithm according to claim 1, characterized in that, The global optimal solution includes the optimal output distribution curves for energy storage output, hydrogen production and storage, grid energy interaction, and wind and solar power output. The scheduling of the wind-solar-hydrogen storage system based on the global optimal solution specifically includes: Based on the energy storage output, hydrogen production and storage, grid energy interaction, and the optimal output allocation curve of wind and solar power, determine the optimal power consumption plan for wind power generation, photovoltaic power generation, and hydrogen production, calculate the amount of electricity that the system needs to buy or sell, and the revenue or expenditure of the wind, solar, and hydrogen storage system.

8. A wind-solar-hydrogen storage scheduling system based on the sparrow search algorithm, characterized in that, The wind-solar-storage-hydrogen scheduling method based on the sparrow search algorithm according to any one of claims 1-7 includes: The module for constructing the day-ahead revenue model for wind, solar and hydrogen storage operation and obtaining the revenue maximization objective function is used to construct the day-ahead revenue model for wind, solar and hydrogen storage operation based on the scheduling objectives of the wind, solar and hydrogen storage system, and obtain the revenue maximization objective function through the day-ahead revenue model for wind, solar and hydrogen storage operation. The global optimal solution acquisition module is used to solve the profit maximization objective function using the sparrow search algorithm to obtain the global optimal solution. The wind-solar-hydrogen storage system scheduling module is used to schedule the wind-solar-hydrogen storage system based on the global optimal solution.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method as described in any one of claims 1-7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1-7.

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