A wind-solar-hydrogen micro-grid master-slave game optimization scheduling method, system, device and product

Abstract

This invention discloses a master-slave game-theoretic optimization scheduling method, system, equipment, and products for wind-solar-hydrogen microgrids, relating to the fields of renewable energy microgrid optimization scheduling, water electrolysis hydrogen production, energy storage collaborative control, and electricity market trading. The method includes the following steps: S1, acquiring operational data, equipment data, and market price data of the wind-solar-hydrogen microgrid; S2, the microgrid operator as the upper-level leader, and various types of hydrogen production systems as lower-level followers; S3, constructing an upper-level dynamic internal electricity price optimization model for the microgrid operator; S4, constructing a lower-level electricity purchase and hydrogen production response model for various types of hydrogen production systems; S5, constructing a Stackelberg master-slave game model; S6, generating an optimized scheduling scheme for the wind-solar-hydrogen microgrid. This invention employs the aforementioned master-slave game-theoretic optimization scheduling method, system, equipment, and products for wind-solar-hydrogen microgrids to ensure safe equipment operation, improve the local consumption level of renewable energy, and enhance the economic viability of market-based operation of wind-solar-hydrogen microgrids.

Description

Technical Field

[0001] This invention relates to the fields of new energy microgrid optimization scheduling, water electrolysis hydrogen production, energy storage collaborative control, and electricity market trading, and in particular to a master-slave game optimization scheduling method, system, equipment, and product for wind-solar-hydrogen microgrids. Background Technology

[0002] As the penetration rate of renewable energy sources such as wind power and photovoltaics continues to increase in new power systems, the randomness, intermittency, and volatility of wind and solar power output can easily cause power supply and demand imbalances within microgrids, leading to wind and solar curtailment issues. Utilizing water electrolysis to produce hydrogen is an important technological path to improve the renewable energy absorption rate, reduce carbon emissions, and achieve long-term energy storage.

[0003] Existing wind-solar-hydrogen microgrid optimization dispatch schemes are mostly based on centralized dispatch, typically assuming that wind power generation, photovoltaic power generation, energy storage devices, and hydrogen production equipment belong to the same investment entity, and aiming to minimize the total system operating cost or maximize global benefits. However, in actual commercial microgrid operation, microgrid operators and hydrogen production operators often belong to different investment entities: microgrid operators want to increase internal electricity sales revenue and reduce curtailment, while hydrogen production operators want to reduce electricity purchase costs and ensure the economic viability of hydrogen production. Without a reasonable internal electricity price incentive mechanism, centralized dispatch instructions are difficult to match with the autonomous decision-making logic of each entity, easily leading to pricing conflicts and imbalances in the distribution of benefits.

[0004] Furthermore, existing research and software implementations often simplify hydrogen production systems into a single price-sensitive load, failing to fully reflect the physical differences between different types of electrolyzers. Alkaline electrolyzers offer advantages such as low investment costs and suitability for stable operation, but are constrained by minimum standby power requirements, slow start-up and shutdown times, and limited ramp-up capabilities. Proton exchange membrane electrolyzers offer fast response times and wide adjustment ranges, making them suitable for tracking high-frequency fluctuations in wind and solar power output, but their operation and maintenance costs are higher. Without embedding these differentiated physical constraints into the optimization model, it will be difficult to simultaneously ensure equipment safety, wind and solar power integration, and the main entity's profitability under market-based trading conditions.

[0005] Therefore, it is necessary to propose a master-slave game optimization scheduling method and software system for wind-solar-hydrogen microgrids that can simultaneously consider the interests of microgrid operators and multiple types of hydrogen production systems, dynamic internal electricity prices, differentiated operating characteristics of ALK / PEM, and energy storage safety boundaries. Summary of the Invention

[0006] The purpose of this invention is to provide a master-slave game-theoretic optimization scheduling method, system, equipment, and product for wind-solar-hydrogen microgrids, solving the problem mentioned in the background art that existing centralized wind-solar-hydrogen microgrid scheduling is difficult to balance the benefits of multiple stakeholders, dynamic internal pricing, and the physical constraints of multiple types of hydrogen production equipment.

[0007] To achieve the above objectives, this invention provides a master-slave game-theoretic optimal scheduling method for wind-solar-hydrogen microgrids, comprising the following steps: S1. Obtain operational data, equipment data, and market price data for the wind-solar-hydrogen microgrid; among which, operational data includes wind power output, photovoltaic power output, and dispatch periods; equipment data includes operational parameters of alkaline electrolyzers, proton exchange membrane electrolyzers, and energy storage batteries; market price data includes the on-grid electricity price and peak-valley time-of-use retail electricity price of the external power grid; S2. Microgrid operators, as the upper-level leaders, take the lead in releasing the internal electricity sales price for the whole day. Various types of hydrogen production systems, as lower-level followers, optimize their electricity purchase, hydrogen production, and energy storage strategies based on the internal electricity sales price and provide feedback on their electricity purchase needs to the microgrid operators. S3. Based on operational data and market price data, construct a dynamic internal electricity price optimization model for the upper layer of microgrid operators, with the goal of maximizing the net profit of electricity sales within the dispatch cycle of microgrid operators; S4. Based on equipment data and the internal electricity price output by the upper-level dynamic internal electricity price optimization model, construct a lower-level electricity purchase and hydrogen production response model for multiple types of hydrogen production systems, with the goal of minimizing the comprehensive operating cost of multiple types of hydrogen production systems within the scheduling cycle. S5. Couple the upper-level dynamic internal electricity price optimization model with the lower-level electricity purchase and hydrogen production response model to construct a Stackelberg master-slave game model, and use the model equivalent reconstruction method or iterative optimization method to solve the Stackelberg equilibrium result. S6. Generate an optimized dispatch scheme for the wind-solar-hydrogen microgrid based on the Stackelberg equilibrium results. The optimized dispatch scheme includes internal electricity sales price, internal electricity purchase power, hydrogen production power of alkaline electrolyzer, hydrogen production power of proton exchange membrane electrolyzer, charging and discharging power of energy storage battery, surplus electricity fed into the grid, and economic benefits for each stakeholder.

[0008] Therefore, the present invention employs the above-mentioned master-slave game optimization scheduling method, system, equipment, and product for wind-solar-hydrogen microgrids, which has the following beneficial effects: (1) By setting a dynamic internal electricity price between the external grid electricity price and the external retail electricity price, the present invention breaks down the external electricity price barrier between microgrid operators and hydrogen production systems, thereby increasing the internal electricity sales revenue of microgrid operators and reducing the electricity purchase cost of hydrogen production systems.

[0009] (2) The present invention incorporates the stable operation characteristics, minimum standby power and ramp-up limitation characteristics of ALK electrolyzer, as well as the rapid start-up and wide power adjustment characteristics of PEM electrolyzer into the optimization model to achieve coordinated operation of different hydrogen production equipment that takes advantage of their strengths and avoids their weaknesses.

[0010] (3) This invention achieves cross-time period energy transfer and peak shaving and valley filling by using the safety boundary of the state of charge of the energy storage battery and the mutual exclusion constraint of charging and discharging, thereby further improving the local consumption capacity of wind and solar power.

[0011] (4) This invention transforms the non-convex bilayer master-slave game problem into a single-layer mixed integer linear programming model that can be stably solved by commercial solvers by using KKT conditions, Big-M method and strong duality theory, thereby improving the solution efficiency and feasibility.

[0012] (5) After the software implementation of this invention, parameters, game architecture, internal electricity price, microgrid power balance, multi-device response and economic benefit comparison can be displayed through a graphical interface, which is convenient for engineers to conduct scheduling analysis and decision verification.

[0013] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0014] Figure 1 This is a flowchart illustrating an embodiment of the master-slave game-theoretic optimization scheduling method, system, equipment, and product of a wind-solar-hydrogen microgrid according to the present invention. Figure 2 This is a schematic diagram of the integrated multi-type electrolyzer wind-solar-hydrogen microgrid system architecture, representing an embodiment of the wind-solar-hydrogen microgrid master-slave game optimization scheduling method, system, equipment, and product of the present invention. Figure 3 This is a schematic diagram of the master-slave game-theoretic interaction architecture between a microgrid operator and multiple types of hydrogen production systems, representing an embodiment of the master-slave game-theoretic optimization scheduling method, system, equipment, and product of a wind-solar-hydrogen microgrid according to the present invention. Figure 4 This is a schematic diagram comparing the internal optimized electricity price and the external grid electricity price under the Stackelberg equilibrium state in an embodiment of the master-slave game-theoretic optimal scheduling method, system, equipment and product of the wind-solar-hydrogen microgrid of the present invention. Figure 5 This is a schematic diagram of real-time power balance on the microgrid operator side under the master-slave game mode, representing an embodiment of the wind-solar-hydrogen microgrid master-slave game optimization scheduling method, system, equipment and product of the present invention. Figure 6 This is a schematic diagram of the collaborative power response of multiple devices on the hydrogen production system side under the master-slave game mode, representing an embodiment of the master-slave game optimization scheduling method, system, equipment and product of a wind-solar-hydrogen microgrid of the present invention. Figure 7 This is a schematic diagram comparing the economic benefits of various stakeholders under different scheduling modes of an embodiment of the master-slave game optimization scheduling method, system, equipment and product of a wind-solar-hydrogen microgrid of the present invention; Figure 8This is a schematic diagram of the Stackelberg master-slave game optimization scheduling software execution flow, representing an embodiment of the master-slave game optimization scheduling method, system, equipment, and product of a wind-solar-hydrogen microgrid according to the present invention. Detailed Implementation

[0015] The technical solution of the present invention will be further described below with reference to the accompanying drawings and embodiments.

[0016] Unless otherwise defined, the technical or scientific terms used in this invention shall have the ordinary meaning understood by one of ordinary skill in the art to which this invention pertains. The terms "first," "second," and similar terms used in this invention do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed following the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are used only to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0017] Please see Figures 1-8 This invention provides a master-slave game-theoretic optimization scheduling method, system, equipment, and product for wind-solar-hydrogen microgrids. The method includes the following steps: S1. Acquire microgrid operation data, equipment operating boundaries, and market price data. Microgrid operation data includes wind power output, photovoltaic power output, and dispatch time scale. Equipment data includes: wind power installed capacity, photovoltaic installed capacity, rated operating power of alkaline electrolyzers, minimum standby power of alkaline electrolyzers, maximum single-step ramp rate of alkaline electrolyzers, rated operating power of proton exchange membrane electrolyzers, maximum single-step ramp rate of proton exchange membrane electrolyzers, rated capacity of energy storage batteries, maximum charge / discharge power of energy storage batteries, charge / discharge efficiency of energy storage batteries, minimum state of charge (SOC) limit of energy storage batteries, and maximum SOC limit of energy storage batteries. Market price data includes external grid-connected electricity price, external peak-valley time-of-use retail electricity price, hydrogen sales price, and equipment operation and maintenance cost coefficient.

[0018] S2. A sequential decision-making relationship based on a Stackelberg master-slave game is constructed, with the microgrid operator as the upper-level leader and various types of hydrogen production systems as lower-level followers. The microgrid operator first publishes its internal electricity sales price for the entire day. The various types of hydrogen production systems optimize their electricity purchase, hydrogen production, and energy storage strategies based on this internal price and report their internal electricity purchase needs back to the microgrid operator.

[0019] S3. Construct a dynamic internal electricity price optimization model for the microgrid operator's upper layer, with the objective of maximizing the microgrid operator's net profit from electricity sales. The upper-layer dynamic internal electricity price optimization model includes the objective function and constraints, as detailed below: The objective function is: ; in, This represents the net profit from electricity sales by microgrid operators during the dispatch cycle. for t Internal electricity price during the specified time period for t Internal power purchase capacity during the time period for t External grid connection price for the specified time period for t The remaining power available to the grid during a given period for t Operating and maintenance costs of wind turbines and photovoltaic equipment during certain periods This represents the time interval for scheduling.

[0020] The constraints include: Source-side physical power balance constraints: ; Linkage capacity constraints: ; Internal electricity price scissors constraint: ; Daily electricity price anti-monopoly constraints: ; in, and They are respectively t The actual available output of wind and solar power during the period For the curtailment of wind and solar power, This represents the maximum forward transmission power of the tie line. For external power grids t Peak-valley time-of-use retail electricity prices during different time periods T The scheduling period is [number].

[0021] S4. Construct a response model for power purchase and hydrogen production at the lower level of multiple types of hydrogen production systems, with the goal of minimizing the overall operating cost of multiple types of hydrogen production systems.

[0022] The lower-level power purchase and hydrogen production response model includes the objective function and constraints, as detailed below: The objective function is: ; in, The overall operating cost of various types of hydrogen production systems, For internal electricity purchase costs, To cover the operation and maintenance costs of hydrogen production equipment, Depreciation costs for energy storage charging and discharging. For the revenue from hydrogen sales, and These represent the operating power of the alkaline electrolyzer and the proton exchange membrane electrolyzer, respectively. and These are the charging power and discharging power of the energy storage battery, respectively. This represents the time interval for scheduling.

[0023] The constraints include: Internal power balance constraints of various types of hydrogen production systems: Operating constraints of alkaline electrolyzers: It shall not be less than the minimum standby power and not greater than the rated operating power, and shall meet the single-step ramp rate limit of the alkaline electrolytic cell; Operating constraints of proton exchange membrane electrolyzers: The power rating shall be no less than zero and no greater than the rated operating power, and shall meet the single-step ramp rate limit of the proton exchange membrane electrolyzer. Energy storage battery operation constraints: charging power and discharging power shall not exceed the maximum charging and discharging power, charging state and discharging state are mutually exclusive, and the state of charge evolves sequentially between the lower limit of state of charge and the upper limit of state of charge.

[0024] S5. Couple the upper-level and lower-level models into a Stackelberg master-slave game model, and use KKT conditions, Big-M method, and strong duality theory for equivalent reconstruction to obtain a single-level mixed-integer linear programming model. The specific steps are as follows: S51A, The response model of lower-level power purchase and hydrogen production is represented as a linear convex optimization problem; S52A. Construct the KKT conditions for the linear convex optimization problem and embed the KKT conditions as additional constraints into the upper-level dynamic internal electricity price optimization model. S53A, The Big-M method is used to linearize the complementary relaxation conditions; S54A: Based on strong duality theory, the bilinear product term of internal electricity sales price and internal electricity purchase power in the upper-level dynamic internal electricity price optimization model is replaced with a linear expression composed of original variables, dual variables and boundary parameters. S55A: The Stackelberg master-slave game model is equivalently reconstructed into a single-layer mixed integer linear programming model, and the optimization solver is called to perform global optimization.

[0025] Alternatively, the Stackelberg equilibrium can be obtained using an iterative solution method based on internal electricity prices. The specific steps are as follows: S51B: Initialize the internal electricity sales price to the midpoint between the external grid connection price and the external retail price; When the current internal electricity price is fixed, S52B solves the lower-level electricity purchase and hydrogen production response model to obtain the optimal internal electricity purchase power for multiple types of hydrogen production systems. When the optimal internal power purchase capacity is fixed, S53B solves the upper-level dynamic internal electricity price optimization model and updates the internal electricity sales price. S54B determines that Stackelberg equilibrium has been reached when the change in internal electricity price between two consecutive iterations is less than the preset convergence accuracy; otherwise, it continues to iterate.

[0026] S6. Generate an optimized scheduling scheme for the wind-solar-hydrogen microgrid based on the Stackelberg equilibrium results. Control the ALK electrolyzer to undertake stable hydrogen production power, control the PEM electrolyzer to respond quickly to wind and solar fluctuations, and control the energy storage battery to charge during low electricity price periods and discharge during high electricity price periods, so as to achieve interest coordination and wind and solar power consumption.

[0027] Example This embodiment provides a master-slave game-theoretic optimization scheduling method for wind-solar-hydrogen microgrids, such as... Figure 1 The process includes the following steps: obtaining the wind power output, photovoltaic power output, external grid-connected electricity price, external retail electricity price, and operating parameters of the ALK electrolyzer, PEM electrolyzer, and energy storage battery for the wind-solar-hydrogen microgrid; constructing an upper-level dynamic internal electricity price model with the microgrid operator as the upper-level leader and various types of hydrogen production systems as lower-level followers, aiming to maximize the microgrid operator's net profit from electricity sales, and a lower-level electricity purchase and hydrogen production response model aiming to minimize the comprehensive operating cost of the hydrogen production system; and limiting the internal electricity price to a range between the external grid-connected electricity price and the external retail electricity price. In addition, by combining wind and solar power balance, tie line capacity, ALK / PEM power upper and lower limits and ramping constraints, and energy storage state of charge constraints, a Stackelberg master-slave game optimization scheduling model is formed. The two-level model is equivalently reconstructed into a single-level mixed integer linear programming model through KKT conditions, Big-M method and strong duality theory, or the Stackelberg equilibrium is obtained by iterative solution through internal electricity price. Based on the equilibrium results, dynamic internal electricity price, internal power purchase, hydrogen production power, energy storage charging and discharging power and the income of each stakeholder are output.

[0028] This method is designed for application scenarios where microgrid operators and various types of hydrogen production systems belong to different investment entities. By establishing a coupling relationship between internal dynamic electricity prices and hydrogen production load response through Stackelberg master-slave game, microgrid operators can guide hydrogen production systems to absorb wind and solar power through price signals. At the same time, hydrogen production systems can optimize the coordinated operation of ALK electrolyzers, PEM electrolyzers and energy storage batteries based on internal electricity prices.

[0029] like Figure 2 As shown, the wind-solar-hydrogen microgrid includes wind power generation units, photovoltaic power generation units, energy storage battery units, ALK electrolyzers, PEM electrolyzers, hydrogen storage equipment, auxiliary loads, and an external grid interface. The renewable energy generated by the wind and photovoltaic power generation units first supplies the internal loads and hydrogen production loads of the microgrid. When wind and solar power output is surplus, a portion of the electricity can be converted into hydrogen via the ALK and PEM electrolyzers, and the remaining portion can be stored in the energy storage battery or sold to the external grid. When wind and solar power output is insufficient, the energy storage battery can discharge to compensate for the power demand on the hydrogen production side.

[0030] In this embodiment, the scheduling period is divided into T The set of discrete time periods is represented as follows: No. t The total output of wind and solar renewable energy during a given time period is expressed as follows: in, Indicates the first t Wind power generation capacity during different time periods Indicates the first t Photovoltaic power generation during the period Indicates the first t Total available wind and solar power during the time period. If wind and solar forecasting errors are considered, the wind and solar output can also be expressed as the sum of the forecast value and the error term: in, and These represent the day-ahead forecasts for wind power and solar power, respectively. and represents the prediction errors for wind power and solar power, respectively. The above expressions allow the system to be applied to deterministic day-ahead scheduling as well as extended to multi-scenario stochastic scheduling that considers uncertainties.

[0031] like Figure 2In the structure shown, the microgrid operator (MGO) acts as the supplier of wind and solar power and the internal price setter, while the multi-type hydrogen production system (HPS) acts as the demander of electricity and the price responder. The MGO and HPS establish a trading relationship through internal electricity prices and internal power sales, while the MGO and the external power grid establish an external trading relationship through grid-connected power and grid-connected electricity prices. Key symbols are explained in Table 1 below.

[0032] Table 1 Explanation of Main Symbols

[0033] like Figure 3 As shown, the relationship between the MGO and HPS constitutes a Stackelberg master-slave game. The MGO, as the leader, first publishes its internal dynamic electricity price. The HPS, as the follower, optimizes its electricity purchase, hydrogen production, and energy storage strategies after receiving the internal dynamic electricity price, and feeds back its internal electricity purchase volume as a response to the MGO. The MGO adjusts its profit-maximizing decisions based on the HPS response, thus forming a closed loop between price signals and load responses.

[0034] The Stackelberg master-slave game can be represented as the following bi-level optimization problem: in, The internal dynamic electricity price under game equilibrium. The internal electricity price vector established for MGO x For the set of operational decision variables of HPS, The optimal response of HPS under a given internal electricity price. For the feasible region of internal electricity pricing, For the feasible domain to operate the HPS device, Net profit from electricity sales for microgrid operators. To account for the comprehensive operating costs of various types of hydrogen production systems, The independent variable is used to make the objective function reach an extreme value.

[0035] In this embodiment, the set of operational decision variables for HPS can be further represented as follows: in, For the internal power consumption of HPS, and These are the energy storage charging and discharging power, respectively. and These are the start-up and shutdown state variables for the ALK electrolytic cell and the PEM electrolytic cell, respectively.

[0036] like Figure 3In the master-slave interaction architecture shown, the goal of MGO is not simply to increase the internal electricity price, but to seek the optimal balance between internal electricity sales revenue, external grid connection revenue, and wind and solar curtailment penalties, while meeting the upper and lower limits of the internal electricity price; the goal of HPS is not simply to reduce the amount of electricity purchased, but to seek the optimal balance between electricity purchase cost, hydrogen production revenue, equipment start-up and shutdown cost, and energy storage charging and discharging cost.

[0037] like Figure 4 As shown, the internally optimized electricity price lies between the external grid connection price and the external retail price. This constraint can be called the internal electricity price scissors constraint, which aims to simultaneously ensure the economic viability of MGO and HPS participating in internal transactions: if the internal electricity price is lower than the external grid connection price, MGO is more willing to sell electricity to the external grid; if the internal electricity price is higher than the external retail price, HPS is more willing to purchase electricity directly from the external grid. Therefore, the internal dynamic electricity price satisfies: in, This indicates the price at which MGO sells electricity to the external power grid. This represents the time-of-use retail electricity price that HPS purchases from the external grid. This indicates the internal electricity price that MGO publishes to HPS.

[0038] In this embodiment, to avoid frequent start-ups and shutdowns of hydrogen production equipment due to drastic fluctuations in electricity prices within adjacent time periods, a smoothing constraint on electricity prices can be further set: in, This represents the maximum permissible change in electricity price within adjacent scheduling periods.

[0039] The upper-level objective function of MGO is to maximize the net revenue within the scheduling cycle, specifically including internal electricity sales revenue, external grid-connected electricity sales revenue, wind and solar operation and maintenance costs, and wind and solar curtailment penalty costs, expressed as: in, For MGO net income, This refers to the internal power output of MGO to HPS. This refers to the electricity sold by MGO to the external power grid. For the operation and maintenance costs of wind and solar power generation, To incur the penalties for abandoning wind and solar power, The time interval is the scheduling period. The operation and maintenance cost of wind and solar power can be expressed as: The penalty cost of curtailing wind and solar power can be expressed as: in, and These represent the unit operation and maintenance costs of wind power and solar power, respectively. This represents the penalty coefficient for wind and solar power curtailment. and These represent the power curtailed from wind and the power curtailed from solar power, respectively.

[0040] The power balance constraint on the MGO side is: The above constraints indicate that wind and solar power generation is allocated in any given time period as internal power sales, external grid connection power, and curtailed wind and solar power. Each power variable also satisfies the following boundary constraints: When Figure 4 When the internally optimized electricity price drops during the midday peak solar power generation period, HPS's willingness to purchase electricity for hydrogen production increases, and MGO can reduce curtailment of solar power or grid connection at low prices; when the internally optimized electricity price rises during the evening peak period, HPS reduces electricity purchases, and MGO can prioritize the grid connection and sale of surplus electricity, thereby increasing MGO's net revenue.

[0041] like Figure 5 As shown, the power balance results on the MGO side reflect the time-series relationship between wind and solar power generation, internal electricity sales, and external grid-connected power. During periods of lower internal electricity prices, HPS increases its electricity purchases, leading to an increase in MGO internal electricity sales and a decrease in external grid-connected power; conversely, during periods of higher internal electricity prices, HPS decreases its electricity purchases, leading to an increase in MGO external grid-connected power.

[0042] To ensure that the power allocation results of MGO meet physical feasibility, in addition to power balance constraints, tie-line capacity constraints and curtailment rate constraints can also be set: in, This indicates the maximum allowable power supply for the MGO to connect to the external power grid. This indicates the wind and solar curtailment rate within the scheduling cycle. This indicates the upper limit of the permissible curtailment rate for wind and solar power. This curtailment rate constraint can further strengthen the orientation towards renewable energy consumption.

[0043] In this embodiment, if the MGO needs to prioritize power supply to the local load, the local load can be... Incorporating power balance constraints, we obtain: The above-described extensions enable the present invention to be applicable to various microgrid operation scenarios, including local fixed loads, flexible hydrogen production loads, and external grid connection interfaces.

[0044] like Figure 6As shown, the multi-device collaborative power response results on the HPS side demonstrate the collaborative operation patterns of the ALK electrolyzer, PEM electrolyzer, and energy storage battery under the influence of internal dynamic electricity prices. The ALK electrolyzer is characterized by low cost and suitability for stable operation, but with a slow ramp-up, making it suitable for handling stable base load hydrogen production power. The PEM electrolyzer features fast start-up and shutdown, a wide adjustment range, and strong ramp-up capability, making it suitable for handling fluctuating wind and solar power and rapid response power guided by price signals. The energy storage battery is used for cross-time energy shifting and peak shaving / valley filling.

[0045] The objective function of HPS can be expressed as minimizing the overall operating cost: in, This represents the overall operating cost of HPS. This indicates the internal electrical power that HPS purchases from MGO. , and These represent the operating costs of ALK electrolyzers, PEM electrolyzers, and energy storage batteries, respectively. This represents the revenue from hydrogen production. Each item can be specifically represented as: The power balance constraint on the HPS side is: in, For energy storage discharge power, For energy storage charging power, This refers to the power consumption of the hydrogen production auxiliary equipment. When the power of the auxiliary equipment is proportional to the power of the electrolyzer, it can be expressed as: The constraints of the ALK electrolyzer include power upper and lower limits, ramping constraints, and start / stop logic constraints, specifically: To reflect ALK's suitability for stable operation and its unsuitability for frequent start-stop cycles, a start-stop limit can also be set: PEM electrolyzer constraints include power limits, rapid ramp-up constraints, and start / stop logic constraints, specifically: The coordinated power allocation between ALK and PEM can be represented by the following relationship: in, The total electrical power used for hydrogen production by HPS This refers to the stable base load power supplied by ALK. Figure 6 The power response results in the data reflect this collaborative logic: during low-price periods, ALK gradually increases and remains stable, PEM quickly starts to absorb fluctuations, and energy storage charges; during high-price periods, PEM quickly exits, ALK slowly drops to the hot standby range, and energy storage discharges to support the bottom.

[0046] The total hydrogen production of HPS is composed of the hydrogen production of ALK and PEM: When the hydrogen-to-electric conversion efficiency is used as the characterization method, the hydrogen production can be expressed as: When using the unit electricity consumption for hydrogen production as a characterization method, the amount of hydrogen produced can be expressed as: If there is a daily demand for hydrogen production, then the following constraints shall be set: The constraints for updating the state of charge of the energy storage battery are: Energy storage batteries also meet safety boundaries, charge / discharge power, and mutual exclusion constraints: in, and These represent the lower and upper limits of the safe state of charge for energy storage, respectively, where E_{bat} is the rated capacity of the energy storage. and These are the charging efficiency and the discharging efficiency, respectively. = This is used to ensure that the energy storage status is consistent at the beginning and end of the scheduling cycle, thereby improving the executability of continuous daily scheduling.

[0047] like Figure 7As shown, this embodiment compares the independent operation mode, centralized scheduling mode, and master-slave game mode. In the independent operation mode, there is no internal transaction between MGO and HPS; MGO mainly profits from external grid connection, while HPS mainly purchases electricity from the external grid to produce hydrogen. In the centralized scheduling mode, the system is scheduled by a unified scheduling center with the goal of maximizing global revenue, but the distribution of benefits between MGO and HPS is not fully reflected. In the master-slave game mode, MGO guides HPS response through internal electricity prices, and HPS makes optimal electricity purchases and hydrogen production based on price signals, thereby achieving benefit coordination.

[0048] The system's global benefit can be expressed as: HPS net income can be expressed as: in, For the revenue from hydrogen production during the scheduling cycle, This represents the total operating cost of HPS. The revenue from hydrogen production can be further expressed as: In this embodiment, to evaluate the improvement effect of the master-slave game mode compared to the independent operation mode, the master's profit improvement rate can be defined: In this context, the superscript "game" indicates a master-slave game mode, and "ind" indicates an independent running mode. (The rest of the text appears to be a continuation of the previous sentence and can be left as is.) Figure 7 The economic benefit comparison shown verifies that the present invention not only improves the overall economy of the system, but also improves the benefit distribution relationship between MGO and HPS.

[0049] like Figure 8 As shown, the execution flow of the master-slave game optimization scheduling software for wind-solar-hydrogen microgrids includes the following steps: S1. Parameter Loading. The software reads the predicted wind power, predicted photovoltaic power, external grid time-of-use retail electricity price, grid connection price, wind and solar curtailment penalty coefficient, rated power and minimum power of ALK electrolyzer, rated power of PEM electrolyzer, energy storage capacity, SOC upper and lower limits, and internal electricity price upper and lower limits.

[0050] S2. Construct the upper-level MGO dynamic pricing model. The software is based on formula F. MGO The corresponding objective function establishes an MGO revenue maximization model, and adds power balance constraints, internal electricity price scissor difference constraints, tie line capacity constraints, and wind and solar curtailment boundary constraints.

[0051] S3. Construct the lower-level HPS response model. The software follows formula C. HPS The corresponding objective function is used to establish an HPS cost minimization model, and ALK operation constraints, PEM operation constraints, energy storage SOC constraints, hydrogen production constraints, and HPS power balance constraints are added.

[0052] S4. Model Equivalent Transformation. Since the original model is a two-layer master-slave game model, the software transforms the lower-layer HPS optimization problem into an upper-layer model with additional constraints through KKT conditions, and uses the Big-M method to handle complementary relaxation constraints.

[0053] The lower-level model of HPS is abstracted as follows: Construct the Lagrangian function: The corresponding KKT conditions include: make =- (x), the complementary relaxation condition can be expressed as: Introducing binary variables With a sufficiently large positive constant M, the Big-M linearization constraint is: Therefore, the two-level model can be reconstructed into a single-level mixed-integer linear programming model: S5. Solution and Convergence Criteria. The software uses a mixed-integer programming solver to solve the single-layer model, obtaining the internal dynamic electricity price, MGO power allocation, and HPS multi-device response. If an iterative solution method is used, the following convergence criterion can be employed: Where k is the number of iterations. This is the convergence threshold.

[0054] S6. Results Display. The software outputs and displays the game theory architecture model diagram, the internal electricity price comparison diagram, the power balance diagram on the MGO side, the multi-device collaborative power response diagram on the HPS side, and the economic benefit comparison diagram of multiple stakeholders.

[0055] In this embodiment, both the wind power installed capacity and the photovoltaic installed capacity are set at 200kW, and the feed-in tariff is set at 0.25. / kWh, the penalty coefficient for wind and solar curtailment is set at 0.171. / kWh, with peak, flat, and valley electricity prices from the external grid set at 0.9 respectively. / kWh, 0.6 / kWh and 0.4 / kWh. The rated power of ALK is set to 600kW, the minimum stable power is set to 120kW, and the ramp rate is set to 150kW / h; the rated power of PEM is set to 400kW, and the ramp rate is set to 400kW / h; the energy storage capacity is set to 1200kWh, and the SOC operating range is set to 0.2 to 0.9, as shown in Table 2 below.

[0056] Table 2 Exemplary Simulation Parameters

[0057] Based on the above parameters, the master-slave game scheduling can achieve the following operational effects: When the photovoltaic power generation is high at noon and the internal electricity price is low, HPS increases internal power purchases, ALK gradually increases and maintains stable output, PEM responds quickly to wind and solar power fluctuations, and the energy storage battery is charged; when the evening peak is high and the internal electricity price is high, HPS reduces internal power purchases, PEM quickly reduces power or shuts down, ALK gradually decreases due to ramp-up constraints, and the energy storage battery discharges to compensate for part of the hydrogen production load.

[0058] Compared to the independent operation mode, the master-slave game mode can improve the revenue from electricity sales within the MGO and the net revenue from hydrogen production in HPS; compared to the centralized dispatch mode, the master-slave game mode can approach the global optimum of the system while retaining the autonomous decision-making power of each entity, thus avoiding the problem of uneven distribution of benefits under unified dispatch.

[0059] In this embodiment, the payoffs of each party in the master-slave game model can satisfy the following relationship: in, This represents the overall system benefit under centralized scheduling mode. This relationship illustrates that the present invention can balance the coordination of market-oriented interests with the physical operational constraints of equipment, making it suitable for day-ahead optimized scheduling and software-based deployment of wind-solar-hydrogen microgrids.

[0060] This embodiment also provides a computer device, which includes a processor, a memory, an input / output interface, and a communication interface. The memory is used to store wind and solar power output data, electricity price data, equipment parameters, and computer programs; the processor is used to execute the computer programs to implement the master-slave game optimization scheduling method for wind-solar-hydrogen microgrids of the present invention.

[0061] This embodiment also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements all or part of the steps of the method of the present invention. The computer-readable storage medium includes read-only memory, random access memory, flash memory, magnetic disk, optical disk, and other media capable of storing program code.

[0062] This embodiment also provides a computer program product, which includes a computer program that, when executed by a processor, implements all or part of the steps of the method of the present invention.

[0063] Therefore, the present invention adopts the above-mentioned master-slave game optimization scheduling method, system, equipment and products for wind-solar-hydrogen microgrids, which can guide ALK electrolyzers to undertake stable hydrogen production loads, PEM electrolyzers to respond quickly to wind and solar fluctuations, and energy storage batteries to transfer energy across time periods. Under the premise of ensuring the safe operation of equipment, it realizes the coordination of interests between microgrid operators and hydrogen production systems, improves the local consumption level of renewable energy and the economic efficiency of market-oriented operation of wind-solar-hydrogen microgrids.

[0064] 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 preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the technical solutions of the present invention, and these modifications or equivalent substitutions cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A wind-solar-hydrogen microgrid master-slave game optimization scheduling method, characterized in that, Includes the following steps: S1. Obtain operational data, equipment data, and market price data for the wind-solar-hydrogen microgrid; among which, operational data includes wind power output, photovoltaic power output, and dispatch periods; equipment data includes operational parameters of alkaline electrolyzers, proton exchange membrane electrolyzers, and energy storage batteries; market price data includes the on-grid electricity price and peak-valley time-of-use retail electricity price of the external power grid; S2. Microgrid operators, as the upper-level leaders, take the lead in releasing the internal electricity sales price for the whole day. Various types of hydrogen production systems, as lower-level followers, optimize their electricity purchase, hydrogen production, and energy storage strategies based on the internal electricity sales price and provide feedback on their electricity purchase needs to the microgrid operators. S3. Based on operational data and market price data, construct a dynamic internal electricity price optimization model for the upper layer of microgrid operators, with the goal of maximizing the net profit of electricity sales within the dispatch cycle of microgrid operators; S4. Based on equipment data and the internal electricity price output by the upper-level dynamic internal electricity price optimization model, construct a lower-level electricity purchase and hydrogen production response model for multiple types of hydrogen production systems, with the goal of minimizing the comprehensive operating cost of multiple types of hydrogen production systems within the scheduling cycle. S5. Couple the upper-level dynamic internal electricity price optimization model with the lower-level electricity purchase and hydrogen production response model to construct a Stackelberg master-slave game model, and use the model equivalent reconstruction method or iterative optimization method to solve the Stackelberg equilibrium result. S6. Generate an optimized dispatch scheme for the wind-solar-hydrogen microgrid based on the Stackelberg equilibrium results. The optimized dispatch scheme includes internal electricity sales price, internal electricity purchase power, hydrogen production power of alkaline electrolyzer, hydrogen production power of proton exchange membrane electrolyzer, charging and discharging power of energy storage battery, surplus electricity fed into the grid, and economic benefits for each stakeholder. 2.The wind-solar-hydrogen microgrid master-slave game optimization scheduling method according to claim 1, characterized in that: The objective function of the upper-level dynamic internal electricity price optimization model in S3 is the net profit of electricity sales within the microgrid operator's scheduling cycle. The net profit of electricity sales is composed of the internal electricity sales price, the internal electricity purchase power, the external grid-connected electricity price, the surplus electricity grid-connected power, the operation and maintenance costs of wind turbine units, and the operation and maintenance costs of photovoltaic equipment. 3.The wind-solar-hydrogen microgrid master-slave game optimization scheduling method according to claim 2, characterized in that: The constraints of the upper-level dynamic internal electricity price optimization model in S3 include source-side physical power balance constraints, tie-line interaction capacity constraints, internal electricity price scissor difference constraints, and daily average electricity price anti-monopoly constraints. 4.The wind-solar-hydrogen microgrid master-slave game optimization scheduling method according to claim 1, characterized in that: The objective function of the lower-level power purchase and hydrogen production response model in S4 is the comprehensive operating cost of multiple types of hydrogen production systems within the scheduling cycle. The comprehensive operating cost consists of internal power purchase cost, hydrogen production equipment operation and maintenance cost, energy storage charging and discharging depreciation cost, and hydrogen sales revenue.

5. The master-slave game optimization scheduling method for a wind-solar-hydrogen microgrid according to claim 4, characterized in that: The constraints of the lower-level power purchase and hydrogen production response model in S4 include internal power balance constraints of multiple types of hydrogen production systems, operational constraints of alkaline electrolyzers, operational constraints of proton exchange membrane electrolyzers, and operational constraints of energy storage batteries.

6. The master-slave game optimization scheduling method for a wind-solar-hydrogen microgrid according to claim 1, characterized in that, The specific steps for solving the Stackelberg equilibrium result using the model equivalent reconstruction method in S5 are as follows: S51A: The lower-level power purchase and hydrogen production response model is transformed into a linear convex optimization problem; S52A. Construct the KKT conditions for the linear convex optimization problem and embed the KKT conditions as additional constraints into the upper-level dynamic internal electricity price optimization model. S53A. The complementary relaxation conditions in the KKT conditions are linearized using the Big-M method. S54A. Based on strong duality theory, the bilinear product term of internal electricity sales price and internal electricity purchase power in the upper-level dynamic internal electricity price optimization model is replaced to obtain a linear expression consisting of the original variable, dual variable and boundary parameter. S55A: The Stackelberg master-slave game model is equivalently reconstructed into a single-layer mixed integer linear programming model. The optimization solver is called to perform global optimization and obtain the Stackelberg equilibrium result.

7. The master-slave game optimization scheduling method for a wind-solar-hydrogen microgrid according to claim 1, characterized in that, The specific steps for solving the Stackelberg equilibrium result using the iterative optimization method in S5 are as follows: S51B: Initialize the internal electricity sales price to the midpoint between the external grid connection price and the external retail price; S52B: When the current internal electricity price is fixed, solve the lower-level electricity purchase and hydrogen production response model to obtain the optimal internal electricity purchase power for multiple types of hydrogen production systems. S53B. When the optimal internal power purchase capacity is fixed, solve the upper-level dynamic internal electricity price optimization model and update the internal electricity sales price. S54B: Determine whether the change in the internal electricity price between two adjacent iterations is less than the preset convergence accuracy. If so, determine that the iteration has converged and obtain the Stackelberg equilibrium result; otherwise, repeat the iteration update until the convergence condition is met.

8. A system applying the master-slave game optimization scheduling method for wind-solar-hydrogen microgrids according to any one of claims 1-7, characterized in that, include: The parameter acquisition module is used to acquire operating data, equipment data, and market price data. The upper-layer model building module is used to build the upper-layer dynamic internal electricity price optimization model for microgrid operators; The lower-level model building module is used to build lower-level power purchase and hydrogen production response models for various types of hydrogen production systems. The game solving module is used to solve the Stackelberg master-slave game model and output the Stackelberg equilibrium result. The collaborative scheduling module is used to generate collaborative operation strategies for alkaline electrolyzers, proton exchange membrane electrolyzers, and energy storage batteries based on the Stackelberg equilibrium results. The results display module is used to display the game architecture model, internal electricity price comparison results, microgrid operator power balance results, power response results of multiple types of hydrogen production systems, and economic benefit comparison results of multiple stakeholders.

9. A computer device, characterized in that: The computer device includes a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, it implements the master-slave game optimization scheduling method for wind-solar-hydrogen microgrids as described in any one of claims 1-7.

10. A computer program product, characterized in that: The computer program product includes a computer program that, when executed by a processor, implements the master-slave game optimization scheduling method for wind-solar-hydrogen microgrids as described in any one of claims 1-7.

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