Wind and solar hydrogen storage capacity configuration planning method considering wind and solar power output interval uncertainty

Abstract

The wind and light hydrogen storage capacity configuration planning method of wind and light output interval uncertainty of the application, through cooperation planning of wind and light hydrogen storage capacity, the planning mode guarantees that the cooperation benefits of wind power, photovoltaic and hydrogen production are not lower than the benefits of independent operation, realizes balanced consideration of multi-subject benefits, and realizes double optimization targets of system operation cost reduction and total benefit significant improvement through optimization of equipment capacity configuration and electricity trading strategy. In addition, the application not only effectively improves wind and light clean energy consumption efficiency and overall resource utilization efficiency, but also greatly improves the comprehensive economy of the wind and light hydrogen storage multi-energy system, and further verifies the practical application value and practical feasibility of the cooperation game theory in the field of wind and light hydrogen storage multi-subject energy system capacity configuration and optimal operation.

Description

Technical Field

[0001] This invention relates to the field of clean energy power generation technology, specifically to a method for planning the configuration of wind, solar, and hydrogen storage capacity under conditions of uncertainty in wind and solar power output ranges. Background Technology

[0002] With the nation's vigorous development of "dual-carbon" goals, clean energy power generation technologies such as wind power and photovoltaic power generation have entered a stage of rapid development. The installed capacity of photovoltaic panels and wind turbines is increasing year by year. However, due to the instability and strong volatility of new energy output, which is greatly affected by climate and the natural environment, energy storage technology is usually used to smooth out the fluctuations in wind and photovoltaic output, achieving the purpose of peak shaving and valley filling. Common energy storage methods include hydrogen energy storage, electrochemical energy storage, and mechanical energy storage. Hydrogen energy, as a clean energy source, has a wide range of applications and is currently one of the best choices in the field of energy storage.

[0003] Traditional research generally treats hydrogen storage systems as a single entity, neglecting the independent stakeholders of wind power, photovoltaic, and electro-hydrogen production systems, thus failing to consider the interests of each entity. There is an urgent need for a planning method for wind-solar-hydrogen storage capacity allocation to address the uncertainty of wind and solar power output ranges, enabling coordinated development among multiple stakeholders and achieving optimal overall benefits. Summary of the Invention

[0004] The wind-solar-hydrogen storage capacity configuration planning method, equipment, and storage medium proposed in this invention, which address the uncertainty of wind and solar power output ranges, can at least solve one of the technical problems in the background art.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: A method for planning the allocation of wind, solar, and hydrogen storage capacity under uncertain wind and solar power output ranges involves executing the following steps using computer equipment. S1: The power output scenarios of three modules, wind power, photovoltaic and hydrogen load, are simulated by Monte Carlo sampling. N scenarios are simulated for each module. Then, the simulated scenarios are clustered into K typical power output scenarios by K-means clustering method, and the probability of each scenario is analyzed. S2: Construct independent operation models for wind power generation, photovoltaic power generation, and hydrogen production, with the core objective of maximizing expected operating revenue under all typical scenarios. Combine the output data of K typical output scenarios obtained in S1 and the corresponding occurrence probability weighted calculation to construct the objective function. S3: Based on the objective function obtained in step S2, construct a Nash negotiation model for a hybrid system of wind, solar and hydrogen storage, and treat the participating entities as independent and rational individuals. Maximize the overall benefit of each participating entity through Nash negotiation. Based on the theory of cooperative game, construct the objective function again with the goal of maximizing the product of the improvement value of the cooperative benefits of each entity. S4: The Nash negotiation model of the wind, solar and hydrogen storage hybrid system constructed in S3 is equivalently transformed, and the Alternating Directional Multiplier Method (ADMM) is used to solve the sub-models in a distributed manner to obtain the distributed optimization model of each subject. After 50 updates and iterations, the optimal expected solution is obtained, thus obtaining the best configuration plan.

[0006] In another aspect, the present invention also discloses a computer-readable storage medium storing a computer program, which, when executed by a processor, causes the processor to perform the steps of the method described above.

[0007] In another aspect, the present invention also discloses a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor performs the steps of the method described above.

[0008] As can be seen from the above technical solution, the wind-solar-hydrogen storage capacity configuration planning method of the present invention addresses the uncertainty of wind and solar power output and hydrogen load. First, it accurately constructs typical power output scenarios and quantifies their probability characteristics by combining Monte Carlo sampling with K-means clustering. Then, it sequentially builds independent operation models for wind power, photovoltaic, and hydrogen production. Subsequently, it constructs a Nash negotiation model based on cooperative game theory and uses the alternating direction multiplier method to achieve distributed solution. This method protects the benefits of each entity while coordinating the planning of wind, solar, and hydrogen storage capacity. This planning approach ensures that the cooperative benefits of wind power, photovoltaics, and hydrogen production are no less than their independent operating benefits, achieving a balance of interests among multiple stakeholders. Furthermore, by optimizing the capacity configuration of each device and the electricity trading strategy, it achieves the dual optimization goals of reducing system operating costs and significantly increasing total revenue. This invention not only effectively improves the efficiency of wind and solar clean energy consumption and overall resource utilization, but also significantly enhances the comprehensive economics of the wind-solar-hydrogen-storage multi-energy system. It also fully verifies the practical application value and feasibility of cooperative game theory in the field of capacity configuration and optimized operation of wind-solar-hydrogen-storage multi-stakeholder energy systems. Attached Figure Description

[0009] Figure 1 This is a flowchart of the optimized configuration process of the present invention; Figure 2 This is the collaborative planning architecture of the wind-solar-hydrogen storage system of the present invention; Figure 3 These are the six wind power generation curves after clustering according to the present invention; Figure 4 These are the six photovoltaic power generation curves after clustering according to the present invention; Figure 5 These are the six electrical load power curves after clustering according to the present invention; Figure 6 This is the battery operating status curve of the present invention; Figure 7 This is the wind-solar-hydrogen main trading curve of the present invention; Figure 8 This is the total benefit value of the wind-solar-hydrogen storage system of the present invention; Figure 9 These are the power curves of the electrolytic cell and compressor of this invention. Detailed Implementation

[0010] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are some embodiments of the present invention, but not all embodiments.

[0011] Reference Figure 1 , Figure 2 A method for planning the allocation of wind-solar-hydrogen storage capacity under uncertain wind and solar power output ranges involves the following steps executed using computer equipment: S1: The power output scenarios of three modules, wind power, photovoltaic and hydrogen load, are simulated by Monte Carlo sampling. N scenarios are simulated for each module. Then, the simulated scenarios are clustered into K typical power output scenarios by K-means clustering method, and the probability of each scenario is analyzed. S2: Construct independent operation models for wind power generation, photovoltaic power generation, and hydrogen production, with the core objective of maximizing expected operating revenue under all typical scenarios. Combine the output data of K typical output scenarios obtained in S1 and the corresponding occurrence probability weighted calculation to construct the objective function. S3: Based on the objective function obtained in step S2, construct a Nash negotiation model for a hybrid system of wind, solar and hydrogen storage, and treat the participating entities as independent and rational individuals. Maximize the overall benefit of each participating entity through Nash negotiation. Based on the theory of cooperative game, construct the objective function again with the goal of maximizing the product of the improvement value of the cooperative benefits of each entity. S4: The Nash negotiation model of the wind, solar and hydrogen storage hybrid system constructed in S3 is equivalently transformed, and the Alternating Directional Multiplier Method (ADMM) is used to solve the sub-models in a distributed manner to obtain the distributed optimization model of each subject. After 50 updates and iterations, the optimal expected solution is obtained, thus obtaining the best configuration plan.

[0012] The following describes the specific implementation steps of the wind-solar-hydrogen storage capacity configuration planning method for situations with uncertainties in wind and solar power output ranges, with a simulation scenario of 1000 and a power output scenario of 6: I. The Monte Carlo sampling technique and K-means clustering method were used to analyze the probability of each scenario occurring: S1-1: Data Foundation and Distribution Modeling of Monte Carlo Sampling Techniques; Based on historical operating data of wind power, photovoltaic output, and hydrogen load in a wind-solar-hydrogen-storage system, the optimal probability distribution type for each output parameter is determined. Specifically, wind power output follows a two-parameter Weibull distribution, with the following probability density function:

[0013] In the formula, For wind speed, For shape parameters, Here is the scale parameter. Its output is:

[0014] In the formula, This refers to the rated capacity of the fan. The cut-in wind speed of the fan; Rated wind speed; To cut off the wind speed.

[0015] The photovoltaic power output distribution follows a Beta distribution, and its probability density function is:

[0016] In the formula, To standardize light intensity, , For shape parameters, This is a Beta function. Its output is:

[0017] In the formula, This refers to the rated capacity of the photovoltaic unit. This is the rated light intensity.

[0018] The hydrogen load follows a normal distribution, and its probability density function is:

[0019] In the formula, This represents the average hydrogen load (unit: kg / h). The standard deviation is calculated based on historical hydrogen production load data.

[0020] S1-2: Monte Carlo Sampling Scenario Generation and Constraint Satisfaction; Using the inverse transformation sampling method, 1000 sets of time-series scenario data are randomly generated based on the above probability distribution. Each set of data spans one day, and each time period includes specific values ​​of three key parameters: wind power output, photovoltaic power output, and hydrogen load, forming a multi-scenario set covering different power output fluctuation characteristics. The sampling process must strictly meet the actual operating constraints of each parameter, such as wind power output not exceeding the rated power limit; photovoltaic power output is limited by the sunshine period, with zero output at night; and hydrogen load must be within the user's hydrogen demand range.

[0021] S1-3: Definition of samples and distance metric for K-means clustering; using 1000 sampled scenes as clustering samples, each sample corresponds to a three-dimensional time-series vector of 96 time periods, as follows: .

[0022] To eliminate the influence of differences in the dimensions of different physical quantities on the clustering results, standardized Euclidean distance is used as a measure of scene similarity, and the calculation formula is as follows:

[0023] In the formula, For the first The first sample The values ​​of each dimension , are the mean and standard deviation of the i-th dimension, respectively.

[0024] S1-4: Iterative process and convergence criteria of K-means clustering; the number of clusters is set to 6, and the specific steps of the clustering iteration are as follows: (1) Initialization: Randomly select 6 samples from 1000 samples as the initial cluster centers. .

[0025] (2) Sample allocation: Calculate the standardized Euclidean distance between each sample and the 6 cluster centers, and assign the sample to the category corresponding to the nearest cluster center.

[0026] (3) Center update: Calculate the mean vector of each class of samples as the new cluster center.

[0027] (4) Convergence criterion: If the update magnitude of all cluster centers satisfies ( If a preset convergence threshold is set, the iteration stops; otherwise, the sample allocation and center update steps are repeated.

[0028] S1-5: Typical scenarios and their occurrence probabilities are determined. After clustering convergence, the time-series vectors corresponding to the six final cluster centers are obtained, which are the six typical power output scenarios. Each scenario contains a set of representative wind power, photovoltaic, and hydrogen load time-series data. The proportion of the number of samples in each cluster to the total number of samples is used as the occurrence probability of the corresponding typical scenario.

[0029] II. The mathematical models for the constructed wind power, photovoltaic, and electro-hydrogen production systems are as follows: S2-1: Wind power generation main operation model; the wind power main body takes maximizing its own operating income as the core objective, and the trading partners include the power grid and the hydrogen production entity. The calculation of income and cost needs to be combined with the probability distribution of typical output scenarios.

[0030] The total revenue of a wind power plant is equal to the revenue from selling electricity to the grid. Revenue from selling electricity to hydrogen production entities The sum, minus wind power operation and maintenance costs Grid transfer fees for electricity trading ,Right now:

[0031] The key constraints for wind power are as follows: Based on the time-series power allocation mechanism, the actual output of wind power needs to be rationally allocated in both directions—"selling electricity to the grid" and "selling electricity to the hydrogen production entity"—and the power sold in either direction must meet the upper limit constraint, while strictly adhering to the principle of energy conservation. The formula is shown below:

[0032]

[0033] In the formula, for The amount of electricity sold by the wind farm to the grid at any given time, in kWh; for Electricity sold from wind power plants to hydrogen production plants at any given time, in kWh; In terms of revenue accounting, the revenue from selling electricity to the grid is calculated based on the product of the fixed on-grid electricity price and the electricity sold in each time period, while the revenue from selling electricity to the hydrogen production entity is calculated based on the transaction price determined through bilateral negotiations. Both types of revenue need to be weighted and summed by combining the probability weights of six typical scenarios with the power output in the corresponding time period to accurately represent the expected revenue level under uncertainty conditions. In terms of cost accounting, the operation and maintenance cost is calculated by multiplying the operation and maintenance coefficient per unit of power generation by the actual power generation, while the grid access fee is quantified by an accounting model represented by a quadratic function. This model includes a power square term and a linear term, which can effectively represent the nonlinear correlation between grid access cost and the power sold to the hydrogen production entity.

[0034] S2-2: Photovoltaic power plant operation model; the trading partners of photovoltaic power plants include the power grid and the hydrogen production entity, and the operating revenue of photovoltaic power plants includes the revenue from selling electricity to the power grid. and the revenue from selling electricity to hydrogen production entities Operating costs include the maintenance costs of the photovoltaic power plant. and internet access fee The benefits of photovoltaic power generation The maximization model can be represented as:

[0035] The key constraints are as follows: Due to the time-series characteristics of sunlight, the photovoltaic output during periods without sunlight is forced to be zero. The actual output during periods of sunlight must be allocated through a power distribution mechanism in two directions: "grid-based electricity sales" and "electricity sales from hydrogen production entities." Furthermore, the electricity sold in either direction must not exceed the actual photovoltaic output value under the corresponding typical scenario. Simultaneously, the actual photovoltaic output during periods of sunlight must meet the energy conservation requirement. The formula is shown below:

[0036]

[0037] In the formula, for The amount of electricity sold by the photovoltaic power station to the grid at any given time, in kWh; for The electricity sold by the photovoltaic power station to the hydrogen production entity at any given time is expressed in kWh; the revenue accounting and cost accounting are similar to the operation model of the wind power entity.

[0038] S2-3: Operational Model of Hydrogen Production System Based on Electricity; As the core consumer of wind and solar power, the main hydrogen production system comprises an electrolyzer, hydrogen compressor, hydrogen storage tank, and electric energy storage system. Its operational objective is to maximize revenue, which essentially involves minimizing overall operating costs by optimizing power purchase strategies and equipment operating conditions. The objective function can be expressed as:

[0039] In the formula, The cost of purchasing electricity from the grid. Operation and maintenance costs, including the operation and maintenance costs of energy storage and electrolyzers. , These are the electricity purchase fees paid to the wind power company and the photovoltaic company, respectively.

[0040] To achieve this goal, the timing and coordination constraints of the core equipment must be met: the electrolyzer, as the core unit of hydrogen production, must meet both the maximum power constraint and the power ramp-up constraint during operation.

[0041] In the formula: The maximum input electrical power of the electrolytic cell is kW; The maximum ramp / ramp power of the electrolytic cell is kW.

[0042] Hydrogen storage tanks are used to store compressed hydrogen gas. The internal pressure of the tank can indirectly indicate the amount of hydrogen stored. The internal pressure of the hydrogen storage tank must meet the following requirements:

[0043]

[0044] In the formula: for The internal pressure of the hydrogen storage tank at any given time, in bars; The internal temperature of the hydrogen storage tank, in K; The molar mass of hydrogen is expressed in kg / mol. for The amount of hydrogen compressed by the compressor at any given time, in kg / h; express Hydrogen load demand at any given time, kg / h; , These represent the minimum and maximum gas pressures of the hydrogen storage tank, respectively.

[0045] The operation of an energy storage system needs to meet constraints such as dynamic changes in energy storage and charging / discharging power. The operating model can be represented as follows:

[0046] In the formula: express Storing energy at all times; , These represent the minimum and maximum energy storage capacity of the energy storage system, respectively, in kW·h; , They represent Real-time charging and discharging power, kW; , These represent the maximum charging power and the maximum discharging power, respectively, in kW.

[0047] III. Constructing a Nash Negotiation Model for a Hybrid System of Wind, Solar, and Hydrogen Storage: S3-1: Establishing Negotiation Parties and Retained Gains; The Nash negotiation model considers wind power entities, photovoltaic entities, and hydrogen production system entities as independent and rational decision-making participants. Each entity's initial goal is to maximize its own interests. The premise of cooperative game is to ensure that the cooperative gains of each entity are not lower than the independent operating gains (i.e., retained gains). Retained gains are defined as the maximum operating gains when each entity does not participate in cooperation, derived from the independent optimization model of each entity in step S2: the retained gains of the wind power entity are the maximum gains when it only sells electricity to the grid and does not supply electricity to the hydrogen production system; the retained gains of the photovoltaic entity are the maximum gains when it only sells electricity to the grid and does not supply electricity to the hydrogen production system; the retained gains of the hydrogen production system are the maximum gains when it only purchases electricity from the grid and does not receive power from wind and solar power (reflected as a negative number of the comprehensive cost), that is:

[0048] In the formula, as the main body The revenue function for independent operation needs to be calculated using a probability-weighted approach based on the six typical scenarios determined in step S1 to ensure that the impact of uncertainties in wind and solar power output and hydrogen load is reflected.

[0049] S3-2: Establishing the negotiation objective function; Based on cooperative game theory, the core objective of the Nash negotiation model is to maximize the product of the increased benefits of cooperation among all parties, achieving an equilibrium between "individual rationality" and "alliance rationality." This means determining the electricity trading strategy between wind / solar and hydrogen production entities through negotiation, maximizing the product of the increased benefits for each party. The objective function is expressed as:

[0050] In the formula, as the main negotiating body The benefits; The point at which negotiations break down represents the benefits that the participating entities benefit from before the negotiations begin; it also represents the participating entities in the negotiations. The goal of Nash negotiations is to maximize the benefits gained through cooperation for all participating parties.

[0051] S3-3: Set model constraints; the negotiation model must meet system operation constraints and benefit distribution constraints to ensure the feasibility and rationality of the cooperation strategy: First, subject operation constraints, the operation of each subject must follow the mathematical model constraints of each subject established in step S2; second, non-negative benefit increase constraints, to ensure that each subject benefits from cooperation; third, system power balance constraints, to ensure energy conservation.

[0052] S3-4: Establishing the core characteristics of the Nash negotiation model; this model satisfies the Markov property extension characteristic, that is... The negotiation decisions (power and price in electricity trading) and the corresponding benefits at any given moment depend solely on... The typical scenario states (wind and solar power output, hydrogen load) at any given time are independent of the current revenue levels of each entity and are unrelated to the historical negotiation process. Furthermore, since step S1 has quantified the uncertainties of wind and solar power output and hydrogen load into six typical scenarios and probabilities, the revenue calculation in the negotiation model must be based on scenario probability weighting to ensure that the results reflect the expected revenue under uncertainty conditions. In addition, the model is essentially a non-convex nonlinear programming problem; there are no other strategies that can increase the revenue of one entity without decreasing the revenue of another.

[0053] S3-5: Establish the profit distribution logic; to achieve a fair distribution of cooperative profits, a value function is introduced to quantify the cooperative contributions of each entity. The value function is calculated based on a weighted average of scenario probabilities and profit enhancement values, namely:

[0054] In the formula, as the main body The expected increase in returns, as the main body In the Retained revenue in various scenarios.

[0055] The Nash negotiation solution maximizes the Nash product, thus maximizing the benefits for each agent. The goal is to achieve a balance and avoid a mismatch between the contribution and benefits of any one entity. Simultaneously, the distribution of benefits must be directly linked to the electricity trading strategy: the increased benefits for wind and solar power entities come from the additional revenue generated from selling electricity to the hydrogen production system; the increased benefits for hydrogen production entities come from the cost savings of replacing grid-purchased electricity with wind and solar power. The three entities achieve revenue linkage through negotiated trading prices and volumes, ensuring the physical rationality and economic fairness of the distribution results.

[0056] IV. The specific details of the model equivalent transformation and problem solution are as follows: S4-1: Model Equivalent Transformation; For the non-convex nonlinear Nash negotiation model constructed in step S3 (the objective is to maximize the product of the cooperative benefits of each entity, with constraints including operational constraints and non-negative benefit increase constraints for each entity), in order to achieve distributed solution and protect the privacy of each entity, an equivalent transformation is performed based on the ADMM principle. That is, by introducing Lagrange multipliers and penalty factors, an augmented Lagrange function is constructed, which transforms the coupling constraint of power and price in electricity trading into a distributed iterative optimization objective. This objective covers both the goal of maximizing the overall benefits of the wind-solar-hydrogen alliance and the fair distribution of benefits among each entity. The expression of the augmented Lagrange function is as follows:

[0057] In the formula, , , The revenue from cooperation among entities involved in hydrogen production via wind, solar, and electricity is respectively. , The expected electricity purchase volume for the main body of hydrogen production by electricity; , The expected electricity sales volume for wind and solar power projects; , For Lagrange multipliers; , As a penalty factor; , , , To retain revenue for each entity, T is the number of time periods into which a day is divided, which is 24; T represents the set of time points, t∈T=[1,2,…,24].

[0058] Problem-solving approach: A distributed solution framework of "local optimization by the subject + iterative update by the coordination center" is adopted. Each subject only interacts with expected power and electricity price data, without sharing local costs, output curves, or other private information. The specific optimization logic is as follows: (1) Hydrogen production entity: based on the expected electricity sales volume uploaded by wind and solar power entities. , Based on its own equipment constraints (electrolyzer power, hydrogen storage tank pressure, etc.), the expected electricity purchase volume is optimized. , Electricity purchased from the power grid To ensure power balance; (2) Wind power main body: Based on the expected purchase volume uploaded by the hydrogen production main body. In conjunction with wind power output constraints, optimize the electricity sold to the grid. Compared with expected electricity sales To maximize one's own benefits; (3) Photovoltaic main body: Similarly, based on optimization and And satisfy the timing constraint that the output is 0 at night; (4) Coordination Center: Based on the power deviation of interactions among the entities, update the Lagrange multipliers:

[0059] At the same time, the electricity trading price is determined in coordination with the electricity trading volume formula. , This ensures that the increase in benefits for all stakeholders is balanced.

[0060] S4-3: Key Parameters and Convergence Criteria; To ensure the convergence and optimization accuracy of the solution, core parameters need to be preset and the iteration termination condition needs to be clearly defined: (1) Key parameter: maximum number of iterations Power deviation convergence threshold Electricity price deviation convergence threshold Power coordination penalty factor Electricity price coordination penalty factor Initial values ​​of Lagrange multipliers Expected initial value of electricity sales Initial value of electricity trading price .

[0061] (2) Convergence condition: Determine the convergence status of the algorithm. If the iteration termination condition is met:

[0062] If the iteration terminates, it returns to the initial calculation and repeats until the convergence condition or the maximum number of iterations is met.

[0063] S4-4: Feasibility Verification of the Solution; After convergence, the validity of the solution needs to be verified from both physical and economic constraints. Physical Constraint Verification: Wind power output must satisfy the following: the total power sold to the grid and to the hydrogen production entity must be non-negative and not exceed the actual wind power output during the corresponding time period; simultaneously, the change in wind power output between adjacent time periods must not exceed the maximum ramp rate. Photovoltaic output must satisfy the following: output is 0 during nighttime; during daytime, the total power sold to the grid and to the hydrogen production entity must be non-negative and not exceed the actual photovoltaic output during the corresponding time period. Hydrogen production equipment must satisfy the following: the power consumption of the electrolyzer is within the range of 0 to the rated maximum power; the internal pressure of the hydrogen storage tank must be maintained between the minimum and maximum allowable pressure; and the energy storage system must not simultaneously perform charging and discharging operations during the same time period. Economic Constraint Verification: the cooperative benefits of each entity must satisfy the following: , , To ensure that the benefits of cooperation are no less than those of independent operation, the final output is a wind, solar, hydrogen, and storage capacity configuration and operation strategy that meets all constraints.

[0064] V. Example Description This case study focuses on a multi-entity energy system encompassing wind, solar, hydrogen, and storage, comprising three independent stakeholders: wind farms, solar power plants, and an electro-hydrogen production system (including an electrolyzer, compressor, and electro-hydrogen production system). The test environment is constructed based on historical operating data to create uncertain scenarios: Monte Carlo sampling is used to generate 1000 sets of time-series data for wind power output, solar power output, and hydrogen load. Then, K-means clustering is used to reduce these 1000 scenarios to six typical scenarios.

[0065] VI. Analysis of Actual Control Behavior This example uses a simulation platform built in the MATLAB environment. Combining the K-means clustering scenario generation module and the ADMM distributed solution module, six typical scenarios are generated after K-means clustering, such as... Figures 3 to 5 As shown in Table 1, the simulation of multi-entity collaborative operation and capacity configuration involves a core control flow of scenario and parameter initialization, distributed iteration, convergence, and device coordination. The parameter settings during the algorithm iteration process are shown in Table 1.

[0066] Table 1 System Parameters

[0067] VII. Case Comparison and Analysis After solving the algorithm, the capacity configuration of each module is determined, under which the benefits of each entity are maximized. The specific capacity configuration of each module is shown in Table 2.

[0068] Table 2. Comparison of the capacity and economy of each entity before and after cooperative game theory

[0069] As shown in Table 2, the total capacity of each entity is different. Therefore, the benefits for each entity and the entire alliance differ before and after cooperation. By comparing the data in the table, it can be concluded that by adjusting the capacity configuration of wind turbines, photovoltaics, and electrolyzers, the benefits after cooperation are significantly improved compared to before cooperation, and operating costs are reduced by about 10%, achieving a dual optimization of reduced operating costs and increased total benefits. This indicates that the cooperation model of wind-solar-hydrogen storage systems effectively coordinates the capacity and operating strategies of each device, improving resource utilization efficiency and significantly enhancing economics, thus verifying the application value of cooperative game theory in multi-entity energy systems.

[0070] like Figures 6 to 9 As shown, the cooperative operation characteristics of the wind-solar-hydrogen storage system are demonstrated from four aspects: energy storage regulation, electricity trading, algorithm convergence, and equipment coordination. It presents the dynamic adjustment process of energy storage within 24 hours; demonstrates the flexibility of energy storage in multi-entity systems; achieves cost-optimal electricity trading; verifies the convergence characteristics of the ADMM algorithm, indicating that the ADMM distributed algorithm can efficiently find the optimal benefit point of the wind-solar alliance in a short time, demonstrating the practicality and efficiency of the algorithm in multi-entity optimization; and showcases the coordinated operation of the electro-hydrogen equipment, a typical manifestation of the electro-hydrogen system's adaptation to wind and solar power output characteristics.

[0071] In another aspect, the present invention also discloses a computer-readable storage medium storing a computer program, which, when executed by a processor, causes the processor to perform the steps of the method described above.

[0072] In another aspect, the present invention also discloses a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor performs the steps of the method described above.

[0073] In another embodiment provided in this application, a computer program product containing instructions is also provided, which, when run on a computer, causes the computer to execute the wind-solar-hydrogen storage capacity configuration planning method for any wind and solar power output range uncertainty in the above embodiments.

[0074] It is understood that the systems, devices, and storage media provided in the embodiments of the present invention correspond to the methods provided in the embodiments of the present invention, and the explanations, examples, and beneficial effects of the relevant content can be referred to the corresponding parts of the above methods.

[0075] In the above embodiments, implementation can be achieved entirely or partially through software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented entirely or partially as a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid state disk (SSD)).

[0076] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0077] The various embodiments in this specification are described in a related manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the system embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0078] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. 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 of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for planning the configuration of wind-solar-hydrogen storage capacity under uncertain wind and solar power output ranges, characterized in that, Perform the following steps using a computer device. S1: The power output scenarios of three modules, wind power, photovoltaic and hydrogen load, are simulated by Monte Carlo sampling. N scenarios are simulated for each module. Then, the simulated scenarios are clustered into K typical power output scenarios by K-means clustering method, and the probability of each scenario is analyzed. S2: Construct independent operation models for wind power generation, photovoltaic power generation, and hydrogen production, with the core objective of maximizing expected operating revenue under all typical scenarios. Combine the output data of K typical output scenarios obtained in S1 and the corresponding occurrence probability weighted calculation to construct the objective function. S3: Based on the objective function obtained in step S2, construct a Nash negotiation model for a hybrid system of wind, solar and hydrogen storage, and treat the participating entities as independent and rational individuals. Maximize the overall benefit of each participating entity through Nash negotiation. Based on the theory of cooperative game, construct the objective function again with the goal of maximizing the product of the improvement value of the cooperative benefits of each entity. S4: The Nash negotiation model of the wind, solar and hydrogen storage hybrid system constructed in S3 is equivalently transformed, and the Alternating Directional Multiplier Method (ADMM) is used to solve the sub-models in a distributed manner to obtain the distributed optimization model of each subject. After 50 updates and iterations, the optimal expected solution is obtained, thus obtaining the best configuration plan.

2. The wind-solar-hydrogen storage capacity configuration planning method for wind and solar power output range uncertainty according to claim 1, characterized in that, Step S1 includes: S1-1. Based on the historical operating data of wind power, photovoltaic power output and hydrogen load in the wind-solar-hydrogen storage system, determine the optimal probability distribution type of each output parameter; S1-2: Using the inverse transformation sampling method, N time-series scenario data are randomly generated based on the above optimal probability distribution. The time span of each data set is 1 day. Each time period contains the specific values ​​of three key parameters: wind power output, photovoltaic power output, and hydrogen load, forming a multi-scenario set covering different power output fluctuation characteristics. S1-3: Using the above N sampled scenarios as clustering samples, each sample corresponds to a three-dimensional time-series vector of 96 time periods, and the standardized Euclidean distance is used as the scene similarity metric to eliminate the influence of differences in the dimensions of different physical quantities on the clustering results. S1-4: Set the number of clusters to K and perform clustering iterations; S1-5: After clustering iteration, convergence is obtained to obtain the time series vectors corresponding to the 6 final cluster centers. The proportion of the number of samples in each cluster to the total number of samples is counted as the probability of occurrence of the corresponding typical scenario.

3. The wind-solar-hydrogen storage capacity configuration planning method for wind and solar power output range uncertainty according to claim 2, characterized in that, The optimal probabilities of each output parameter in step S1-1 are: Among them, the wind power output follows a two-parameter Weibull distribution, and its probability density function is: In the formula, For wind speed, For shape parameters, The scale parameter is: Its output is: In the formula, This refers to the rated capacity of the fan. The cut-in wind speed of the fan; Rated wind speed; To cut off the wind speed; The photovoltaic power output distribution follows a Beta distribution, and its probability density function is: In the formula, To standardize light intensity, , For shape parameters, It is a Beta function; its output is: In the formula, This refers to the rated capacity of the photovoltaic unit. Rated light intensity; The hydrogen load follows a normal distribution, and its probability density function is: In the formula, This represents the average hydrogen load. The standard deviation is calculated based on historical hydrogen production load data.

4. The wind-solar-hydrogen storage capacity configuration planning method for wind and solar power output range uncertainty according to claim 1, characterized in that, The main operation model of wind power generation in step S2 is modeled as follows: The total revenue of a wind power plant is equal to the revenue from selling electricity to the grid. Revenue from selling electricity to hydrogen production entities The sum, minus wind power operation and maintenance costs Grid transfer fees for electricity trading ,Right now: The key constraints for wind power are as follows: Based on the time-series power allocation mechanism, the actual output of wind power needs to be reasonably allocated in both directions of "selling electricity to the grid" and "selling electricity to the hydrogen production entity," and the power sold in either direction must meet the upper limit constraint, as shown in the following formula: In the formula, for The amount of electricity that a wind farm sells to the grid at any given time; for The amount of electricity sold from wind power plants to hydrogen production plants at any given time.

5. The wind-solar-hydrogen storage capacity configuration planning method for wind and solar power output range uncertainty according to claim 1, characterized in that, The photovoltaic main operation model is modeled in step S2 as follows: The revenue from operating a photovoltaic power plant includes the revenue from selling electricity to the grid. and the revenue from selling electricity to hydrogen production entities Operating costs include the maintenance costs of the photovoltaic power plant. and internet access fee Benefits of photovoltaic power generation The maximization model can be represented as: The key constraints are as follows: Due to the time-series characteristics of sunlight, the photovoltaic output during periods without sunlight is forced to be zero. The actual output during periods with sunlight is allocated in two directions—"grid power sales" and "power sales from hydrogen production"—through a power distribution mechanism. Simultaneously, the actual photovoltaic output during periods with sunlight must meet the energy conservation requirement. The formula is shown below: In the formula, for The amount of electricity that a photovoltaic power station sells to the grid at any given moment; for The amount of electricity sold by the photovoltaic power plant to the hydrogen production entity.

6. The wind-solar-hydrogen storage capacity configuration planning method for wind and solar power output range uncertainty according to claim 1, characterized in that, The operational model for the main electro-hydrogen production process in step S2 is modeled as follows: The main equipment for electrolytic hydrogen production includes an electrolyzer, a hydrogen compressor, a hydrogen storage tank, and an electrical energy storage system. Its operational objective is to maximize revenue by minimizing overall operating costs through optimizing electricity purchase strategies and equipment operating conditions. The objective function is expressed as: In the formula, The cost of purchasing electricity from the grid. Operation and maintenance costs, including the operation and maintenance costs of energy storage and electrolyzers. , These are the electricity purchase fees paid to the wind power company and the photovoltaic company, respectively. Electrolytic cells must meet both maximum power constraints and power ramp-up constraints during operation: In the formula: This represents the maximum input electrical power of the electrolytic cell; This represents the maximum ramp / ramp power of the electrolytic cell; The internal pressure of the hydrogen storage tank must meet the following requirements: In the formula: for Constantly monitor the internal pressure of the hydrogen storage tank; This refers to the internal temperature of the hydrogen storage tank. This refers to the molar mass of hydrogen gas. for The compressor compresses hydrogen at all times; express Hydrogen load demand at any given time; , These represent the minimum and maximum gas pressures of the hydrogen storage tank, respectively. The operating model of an energy storage system can be represented as: In the formula: express Storing energy at all times; , These represent the minimum and maximum stored energy of the energy storage system, respectively. , They represent Real-time charging and discharging power, kW; , These represent the maximum charging power and the maximum discharging power, respectively.

7. The wind-solar-hydrogen storage capacity configuration planning method for wind and solar power output range uncertainty according to claim 1, characterized in that, Step S3 includes: S3-1: The maximum operating benefit is obtained through the independent optimization model of each subject in step S2. Then, using the Nash negotiation model, each subject is considered an independent and rational decision-making participant, with each subject's initial goal being to maximize its own interests. This ensures that the cooperative benefit of each subject is not less than the maximum operating benefit, which is expressed by the formula: In the formula, as the main body The payoff function during stand-alone operation is solved by combining the probability weighted calculation of the six typical scenarios determined in step S1. S3-2: Based on the maximum operational benefit obtained above, establish the negotiation objective function, the expression of which is: In the formula, as the main negotiating body The benefits; The point at which negotiations break down represents the benefits that the participating entities benefit from before the negotiations begin; it also represents the participating entities in the negotiations. The goal of Nash negotiations is to maximize the benefits gained through cooperation for all participating parties. S3-3: Set constraints on the Nash negotiation model; S3-4: The core characteristics of establishing the Nash negotiation model are: satisfying the Markov property extension feature, and the payoff calculation of the Nash negotiation model needs to be based on scenario probability weighting; S3-5: Establishing the allocation logic: This involves quantifying the collaborative contributions of each entity by introducing a value function. The value function is calculated based on a weighted average of scenario probability and profit increase, i.e.: In the formula, as the main body The expected increase in returns, as the main body In the Retained revenue in various scenarios.

8. The wind-solar-hydrogen storage capacity configuration planning method for wind and solar power output range uncertainty according to claim 1, characterized in that, Step S4 includes: S4-1: By introducing Lagrange multipliers and penalty factors, an augmented Lagrange function is constructed to transform the coupling constraint between power trading and trading price in electricity trading into a distributed iterative optimization objective; The expression for the augmented Lagrange function is: In the formula, , , The revenue from cooperation among entities involved in hydrogen production via wind, solar, and electricity is respectively. , The expected electricity purchase volume for the main body of hydrogen production by electricity; , The expected electricity sales volume for wind and solar power projects; , For Lagrange multipliers; , As a penalty factor; , , , To retain revenue for each entity, T is the number of time periods into which a day is divided, which is 24; T represents the set of time points, t∈T=[1,2,…,24]; S4-2: A distributed solution framework of "local optimization of the subject + iterative update of the coordination center" is adopted, and the revenue of each subject is optimized by combining the electricity trading amount formula; S4-3: Define the iteration termination condition by presetting core parameters; S4-4: Finally, the validity of the solution is verified after convergence from both physical and economic constraints.

9. The wind-solar-hydrogen storage capacity configuration planning method for wind and solar power output range uncertainty according to claim 8, characterized in that, The optimization logic in step S4-2 is as follows: (1) Expected electricity sales based on wind and solar power uploads , Based on its own equipment constraints, the company optimized its expected electricity purchase volume. , Electricity purchased from the power grid ; (2) Expected purchase volume based on data uploaded by the hydrogen production entity In conjunction with wind power output constraints, optimize the electricity sold to the grid. Compared with expected electricity sales To maximize one's own benefits; (3) Similarly, for the photovoltaic main body, based on optimization and This satisfies the timing constraint that the output is zero at night; (4) Update the Lagrange multipliers based on the power deviation of the interactions between the agents: At the same time, the electricity trading price is determined in coordination with the electricity trading volume formula. , .

10. The wind-solar-hydrogen storage capacity configuration planning method for wind and solar power output range uncertainty according to claim 8, characterized in that, In step S4-3, the core parameters and the termination convergence condition are as follows: (1) The core parameters should include at least the maximum number of iterations. Power deviation convergence threshold Electricity price deviation convergence threshold Power coordination penalty factor Electricity price coordination penalty factor Initial values ​​of Lagrange multipliers Expected initial value of electricity sales Initial value of electricity trading price ; (2) The expression for calculating the termination convergence condition is: Iterate until the termination convergence condition or the maximum number of iterations is met.

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