Model predictive control method for wind-solar-hydrogen storage hybrid system with three-state switching of electrolytic cell
By constructing a refined model of the three-state switching of the electrolyzer and a rolling optimization strategy, the impact of the uncertainty of wind and solar power output on the power grid was resolved, and the efficient coordinated operation of the wind-solar-hydrogen-storage hybrid system was realized, thereby improving the stability and economy of the power grid.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- SHANGHAI UNIV
- Filing Date
- 2026-03-17
- Publication Date
- 2026-06-05
AI Technical Summary
Existing electric-hydrogen hybrid energy storage scheduling strategies are unable to cope with the uncertainty and real-time changes in wind and solar power output, and fail to accurately describe the discrete start-up and shutdown behavior of electrolyzers and the state switching constraints under different operating conditions, resulting in increased energy consumption and equipment performance degradation.
A refined model of the electrolyzer, including the switching logic of three states: shutdown, standby, and hydrogen production, is constructed. Through a rolling time-domain optimization strategy, combined with the coordinated operation of energy storage batteries and the electrolyzer, the peak shaving and valley filling of wind and solar power fluctuations are optimized to achieve the coordinated utilization of multiple energy sources.
It improved the system's operational economy and renewable energy absorption capacity, reduced the impact of wind and solar power output fluctuations on the power grid, and increased the utilization rate of electrolyzers and equipment efficiency.
Smart Images
Figure CN122159197A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of power system dispatching and control technology, and in particular to a model predictive control method for a wind-solar-hydrogen-storage hybrid system with three-state switching of an electrolyzer. Background Technology
[0002] Driven by the goals of carbon peaking and carbon neutrality, the installed capacity of renewable energy sources such as photovoltaics and wind power in my country has continued to increase. However, the randomness, volatility, and intermittency of wind and solar power generation pose new challenges to the safe and stable operation of the power grid. Therefore, how to effectively mitigate the impact of these fluctuations on the power grid and achieve efficient absorption of renewable energy has become a key problem that urgently needs to be solved in the current energy transition.
[0003] Compared to traditional energy sources, hydrogen energy, with its advantages of zero pollution, high efficiency, wide availability, and diverse applications, is considered a key and flexible energy carrier. Hydrogen production through water electrolysis based on renewable energy has become a hot topic in research and application. By producing hydrogen through water electrolysis during periods of surplus power from wind and solar power, not only can zero-carbon hydrogen production be achieved, contributing to carbon neutrality goals, but it can also effectively absorb intermittent renewable energy sources and alleviate grid dispatch pressure. Simultaneously, the produced hydrogen can be sold as a chemical feedstock, generating economic benefits. This promotes the safe, economical, and green operation of the power distribution network while achieving efficient synergy and sustainable development of the energy system.
[0004] In existing technologies, an active distribution network operation framework incorporating hybrid hydrogen-electricity energy storage is proposed. The upper layer aims to minimize overall electricity costs, while the lower layer aims to minimize wind and solar curtailment rates and voltage fluctuations. This achieves coordinated operation of the hybrid hydrogen-electricity energy storage driven by time-of-use pricing and seasonal characteristics, enhancing the distribution network's self-regulation capabilities and economic efficiency. Addressing the issues of large fluctuations in wind power grid connection and poor hybrid hydrogen-electricity energy storage configuration, a WOA-VMD wind power level suppression strategy and an energy management strategy considering the characteristics of alkaline electrolyzers are proposed. A capacity model with the lowest annual overall cost is established, improving the utilization rate of hydrogen energy storage and the system's economic efficiency. To address the instability of off-grid wind and solar power generation and the demand for low-carbon energy supply, a power regulation method for off-grid hydrogen-electricity coupling systems based on model predictive control is proposed, improving the utilization rate and robustness of hydrogen energy storage. Finally, an optimization method for a photovoltaic-hydrogen storage integrated system based on two-stage stochastic model predictive control is proposed, establishing a high-precision convex fitting model of the electrolyzer considering the effects of power and temperature, effectively improving photovoltaic absorption rate and hydrogen production economics.
[0005] However, existing hybrid energy storage scheduling strategies for electricity and hydrogen are mostly based on deterministic optimization or stochastic frameworks for finite scenarios, which are insufficient to adequately address the uncertainties and real-time changes in wind and solar power output. Furthermore, most studies treat electrolyzers as continuously adjustable power devices, considering only basic efficiency or ramp-up characteristics, failing to accurately describe their discrete start-up and shutdown behavior and state-switching constraints under different operating conditions. Consequently, they cannot reflect the increased energy consumption and equipment performance degradation caused by frequent start-ups and shutdowns. In reality, hydrogen production units typically have three operating states: shutdown, standby, and operation, and are subject to both start-up and shutdown constraints and safety operation limitations.
[0006] Therefore, in the context of high-proportion wind and solar energy integration, it is of great significance to establish a multi-state electrolyzer model that conforms to the engineering characteristics and to achieve rolling optimization scheduling for improving the system's operational economy and new energy absorption capacity. Summary of the Invention
[0007] The purpose of this invention is to provide a model predictive control method for a wind-solar-hydrogen-storage hybrid system with three-state switching of the electrolyzer. It constructs a refined model of the electrolyzer that includes the three-state switching logic of shutdown, standby, and hydrogen production. It comprehensively considers factors such as the cost of wind and solar curtailment, hydrogen production revenue, and renewable energy operation and maintenance costs. Through a rolling time-domain optimization strategy, it achieves peak shaving and valley filling of wind and solar power fluctuations and the coordinated utilization of multiple energy sources.
[0008] To achieve the above objectives, this invention provides a model predictive control method for a wind-solar-hydrogen-storage hybrid system with three-state switching in an electrolyzer, comprising the following steps: Step S1: Considering the uncertainty of wind and solar energy output and the multi-state operation characteristics of the electrolyzer, construct a refined model of the electrolyzer that includes the switching logic of three states: shutdown, standby and hydrogen production. Step S2: With minimizing the overall system cost as the optimization objective, consider the charging and discharging cost of energy storage batteries, the operating cost of the hydrogen production system, the electricity purchase cost from the grid, the operation and maintenance cost of renewable energy, the cost of wind and solar curtailment penalties, and the revenue from hydrogen sales, and construct a rolling optimization scheduling model. Step S3: Model other system constraints, specifically including energy storage battery constraints, grid power purchase constraints, and power balance constraints; Step S4: Based on the system power balance equation, establish a predictive control optimization model to achieve rolling optimization prediction.
[0009] Preferably, in step S1, the specific process of constructing the refined model of the electrolyzer is as follows: Step S11: Define the three states of dynamic operation of the electrolytic cell; Considering the dynamic operating characteristics of the electrolyzer, the hydrogen production unit is divided into three states: stopped, standby, and hydrogen production, and it can only be in one of these states at any given time. Step S12: Considering the electrolyzer power constraint, ramp-up constraint, and start-up / shutdown constraint, construct a refined model of the electrolyzer, as shown below: ; ; ; ; in, , and These are binary variables, representing the operating states of the electrolyzer: hydrogen production, shutdown, and standby, respectively. and These represent the operating power of the electrolyzer in hydrogen production mode and the operating power in standby mode, respectively. and These represent the maximum and minimum hydrogen production power of the electrolyzer, respectively. This represents the maximum ramping power of the electrolytic cell.
[0010] Preferably, in step S2, with minimizing the overall system cost as the optimization objective, a rolling optimization scheduling model is constructed, taking into account the charging and discharging costs of the energy storage battery, the operating costs of the hydrogen production system, the grid power purchase costs, the operation and maintenance costs of renewable energy, the cost of wind and solar curtailment penalties, and the revenue from hydrogen sales. The specific process is as follows: Step S21: Establish a comprehensive system operating cost model, as shown below: ; in, For overall operating costs; Cost of charging and discharging energy storage batteries; The operating cost of the electro-hydrogen production system; The cost of purchasing electricity from the power grid; For the operation and maintenance costs of renewable energy; To incur penalties for abandoning scenic views; For revenue from hydrogen sales; Step S22: Construct a rolling optimization scheduling model with the goal of minimizing the overall system operating cost.
[0011] Preferably, the calculation methods for the charging and discharging costs of energy storage batteries, the operating costs of the hydrogen production system, the electricity purchase costs from the grid, the operation and maintenance costs of renewable energy, the cost of wind and solar power curtailment penalties, and the revenue from hydrogen sales are as follows: ; in, and These are the unit charging cost and discharging cost of energy storage batteries, respectively. and These are the charging power and discharging power of the energy storage battery, respectively. ; in, and These represent the unit hydrogen production cost of the electrolyzer and the unit operating cost in standby mode, respectively. and These represent the operating power of the electrolyzer in hydrogen production mode and the operating power in standby mode, respectively. ; in, The unit price of electricity purchased from the power grid; For the power purchased; ; in, and These are the unit operation and maintenance costs for wind power and solar power, respectively. and These represent the actual output of wind power and photovoltaic power at time t, respectively. ; in, The penalty coefficient for abandoning scenic views; and These are respectively the amount of wind curtailment and the amount of solar curtailment; ; in, Price per unit of hydrogen; Hydrogen production; ; in, This is the conversion factor for hydrogen production. For hydrogen production efficiency; Total scheduling time; The scheduling time is in units.
[0012] Preferably, the energy storage battery constraints are as follows: Energy storage batteries are mainly constrained by state of charge (SOC), state of charge / discharge, and charge / discharge power during operation, as shown below: ; ; ; ; ; ; in, Let be the charging and discharging power of the energy storage battery at time t; and These are binary variables, representing the charging and discharging states of the energy storage battery, respectively. and These are the maximum values of the charging and discharging power of the energy storage battery, respectively. The state of charge of the energy storage battery at time t; and These refer to the charging and discharging efficiencies of the energy storage battery, respectively. Self-discharge rate; and These represent the upper and lower limits of electrical energy that can be stored in an energy storage system, respectively.
[0013] Preferred power grid purchase constraints are as follows: To prevent the purchased electricity from exceeding the line capacity limit and to avoid the risk of grid overload, there is a maximum value for the purchased electricity, as shown below: ; in, This represents the maximum power purchase capacity.
[0014] The preferred power balance constraint is as follows: To ensure real-time balance of power generation, consumption, and energy storage within the power grid and maintain stable system operation, power balance constraints are established as follows: ; in, This represents the load power.
[0015] Preferably, in step S4, a predictive control optimization model is established based on the system power balance equation to achieve rolling optimization prediction. The specific process is as follows: The vector consisting of the energy storage output power, state of charge, and electrolyzer output power is selected as the state variable. As shown below: ; The vector formed by the increments of the energy storage and electrolyzer output power is selected as the control variable. As shown below: ; The vector formed by the increments of load, wind power, and photovoltaic power is selected as the disturbance variable. As shown below: ; Based on the above variables, a state-space model is established. This refers to the predictive control optimization model, used to achieve rolling optimization prediction, as shown below: ; in, , , .
[0016] Therefore, this invention employs the aforementioned predictive control method for a hybrid wind-solar-hydrogen-storage system with three-state switching of the electrolyzer, constructing a power system architecture with wind and solar power as the main power supply units, and hydrogen production by the electrolyzer and the collaborative participation of energy storage batteries. Wind and solar power, as the main clean energy sources, undertake the basic power supply task, and their volatility and intermittency are mitigated by the electro-hydrogen production system and energy storage equipment. When wind and solar power generation exceeds local load demand, surplus electricity is prioritized to drive the electrolyzer to produce hydrogen, while the energy storage battery is charged and stored, minimizing the abandonment of renewable energy; when renewable energy output is insufficient, the electrolyzer switches to standby or shutdown state to reduce system load, and the energy storage battery discharges in a timely manner to compensate for the power deficit.
[0017] 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
[0018] Figure 1 A structural diagram of a wind-solar-hydrogen integrated energy system provided in an embodiment of the present invention; Figure 2 The wind and solar load diagram provided in the embodiments of the present invention; Figure 3 The diagram shows the electrolytic cell and energy storage power provided in the embodiments of the present invention; Figure 4 The State of Charge (SOC) of energy storage is provided in the embodiments of the present invention. Detailed Implementation
[0019] The technical solution of the present invention will be further described below with reference to the accompanying drawings and embodiments.
[0020] The present invention provides a model predictive control method for a wind-solar-hydrogen-storage hybrid system with three-state switching in an electrolyzer, comprising the following steps: Step S1: To address the issues of high output uncertainty and difficulty in accurately modeling the multi-state operation characteristics of electrolyzers under the background of high proportion of wind and solar energy access, a refined model of electrolyzers is constructed, which includes the switching logic of three states: shutdown, standby and hydrogen production. Step S2: With minimizing the overall system cost as the optimization objective, consider the charging and discharging cost of energy storage batteries, the operating cost of the hydrogen production system, the electricity purchase cost from the grid, the operation and maintenance cost of renewable energy, the cost of wind and solar curtailment penalties, and the revenue from hydrogen sales, and construct a rolling optimization scheduling model. Step S3: Model other system constraints, specifically including energy storage battery constraints, grid power purchase constraints, and power balance constraints; Step S4: Based on the system power balance equation, establish a predictive control optimization model to achieve rolling optimization prediction.
[0021] Example 1 like Figure 1 As shown, to address the impact of the randomness, volatility, and intermittency of wind and solar power generation on the safe and stable operation of the power grid, this invention proposes a model predictive control method for a wind-solar-hydrogen-storage hybrid system with three-state switching of the electrolyzer, comprising the following steps: Step S1: To address the challenges of high output uncertainty and difficulty in accurately modeling the multi-state operation characteristics of electrolyzers under the background of high proportion of wind and solar energy access, a refined model of the electrolyzer is constructed, which includes the switching logic of three states: shutdown, standby, and hydrogen production.
[0022] Step S11: Define the three states of dynamic operation of the electrolytic cell.
[0023] To characterize the dynamic operating characteristics of the electrolyzer, the hydrogen production unit is divided into three states: stopped, standby, and hydrogen production, and it can only be in one of these states at any given time.
[0024] Step S12: Considering the electrolyzer power constraints, ramp-up constraints, and start-up / shutdown constraints, construct a refined model of the electrolyzer, as shown below: ; ; ; ; in, , and These are binary variables, representing the operating states of the electrolyzer: hydrogen production, shutdown, and standby, respectively. and These represent the operating power of the electrolyzer in hydrogen production mode and the operating power in standby mode, respectively. and These represent the maximum and minimum hydrogen production power of the electrolyzer, respectively. This represents the maximum ramping power of the electrolytic cell.
[0025] Step S2: With minimizing the overall system cost as the optimization objective, a rolling optimization scheduling model is constructed, taking into account the charging and discharging costs of energy storage batteries, the operating costs of the hydrogen production system, the electricity purchase costs from the grid, the operation and maintenance costs of renewable energy, the cost of wind and solar curtailment penalties, and the revenue from hydrogen sales.
[0026] Step S21: Establish a comprehensive system operating cost model, as shown below: ; in, For overall operating costs; Cost of charging and discharging energy storage batteries; The operating cost of the electro-hydrogen production system; The cost of purchasing electricity from the power grid; For the operation and maintenance costs of renewable energy; To incur penalties for abandoning scenic views; For revenue from hydrogen sales. The calculation methods for each parameter are as follows: ; in, and These are the unit charging cost and discharging cost of energy storage batteries, respectively. and These represent the charging power and discharging power of the energy storage battery, respectively.
[0027] ; in, and These represent the unit hydrogen production cost of the electrolyzer and the unit operating cost in standby mode, respectively. and These represent the operating power of the electrolyzer in hydrogen production mode and the operating power in standby mode, respectively.
[0028] ; in, The unit price of electricity purchased from the power grid; For the amount of electricity purchased.
[0029] ; in, and These are the unit operation and maintenance costs for wind power and solar power, respectively. and These represent the actual output of wind power and photovoltaic power at time t, respectively.
[0030] ; in, The penalty coefficient for abandoning scenic views; and These are respectively the amount of wind curtailment and the amount of solar curtailment.
[0031] ; in, Price per unit of hydrogen; This represents the amount of hydrogen produced.
[0032] ; in, This is the conversion factor for hydrogen production. For hydrogen production efficiency; Total scheduling time; The scheduling time is in units.
[0033] Step S22: Construct a rolling optimization scheduling model with the goal of minimizing the overall system operating cost.
[0034] Step S3: Model other system constraints.
[0035] (1) Constraints of energy storage batteries.
[0036] Energy storage batteries are mainly constrained by state-of-charge (SOC), state of charge / discharge, and charge / discharge power during operation, as shown below: ; ; ; ; ; ; in, Let be the charging and discharging power of the energy storage battery at time t; and These are binary variables, representing the charging and discharging states of the energy storage battery, respectively. and These are the maximum values of the charging and discharging power of the energy storage battery, respectively. The state of charge of the energy storage battery at time t; and These refer to the charging and discharging efficiencies of the energy storage battery, respectively. Self-discharge rate; and These represent the upper and lower limits of electrical energy that can be stored in an energy storage system, respectively.
[0037] (2) Power grid purchase constraints.
[0038] To prevent the purchased electricity from exceeding the line capacity limit and to avoid the risk of grid overload, there is a maximum value for the purchased electricity, as shown below: ; in, This represents the maximum power purchase capacity.
[0039] (3) Power balance constraints.
[0040] To ensure real-time balance of power generation, consumption, and energy storage within the power grid and maintain stable system operation, power balance constraints need to be established, as shown below: ; in, This represents the load power.
[0041] Step S4: Based on the system power balance equation, establish a predictive control optimization model to achieve rolling optimization prediction.
[0042] The vector consisting of the energy storage output power, state of charge, and electrolyzer output power is selected as the state variable. As shown below: ; The vector formed by the increments of the energy storage and electrolyzer output power is selected as the control variable. As shown below: ; The vector formed by the increments of load, wind power, and photovoltaic power is selected as the disturbance variable. As shown below: ; Based on the above variables, a state-space model is established. This refers to the predictive control optimization model, used to achieve rolling optimization prediction, as shown below: ; in, , , .
[0043] Example 2 To verify the feasibility of the proposed model predictive control method for a wind-solar-hydrogen-storage hybrid system with three-state switching in an electrolyzer, this embodiment uses actual operating data to test the method. The optimization model is solved using the YALMIP / GUROBI toolbox in MATLAB 2024b, and the system simulation sampling time T... s The simulation time is 15 minutes, and the simulation duration is 24 hours.
[0044] like Figure 2 , Figure 3 and Figure 4 As shown, from 0:00 to 10:00, the load demand remained at a relatively high level of 1100-1200kW, the wind power output fluctuated drastically from 800 to 1400kW, and the photovoltaic output was in a low range of 0-200kW. The total output of renewable energy could not stably cover the load. The energy storage battery discharged, and the state of charge (SOC) gradually decreased from the initial 0.5 to 0.25, with the peak discharge power reaching 500kW. At the same time, the electrolyzer remained in a shutdown / standby state, and the power gap was supplemented only by purchasing electricity from the grid during the period of sharp drop in wind and solar power output to ensure the stability of power supply.
[0045] Between 10:00 and 15:00, the output of photovoltaic power rapidly climbed to over 1200kW, while the output of wind power remained at 500-800kW, with the total output of renewable energy significantly exceeding the load. The control strategy switched the battery to charging mode through rolling time-domain optimization, and the SOC rose back to the upper limit threshold of 0.9. At the same time, it triggered the switching of the hydrogen production state of the electrolyzer, realizing the targeted consumption of surplus wind and solar power. During this period, the demand for electricity purchase dropped to 0 and there was no curtailment of wind and solar power.
[0046] Between 15:00 and 24:00, the load rose to a peak of 1400kW and then fell back. The photovoltaic output decreased from 1200kW to 0. The energy storage switched to discharge mode, the SOC dropped to 0.4, and the electrolyzer dynamically adjusted its power according to the wind and solar output. It only purchased electricity for a short period of time during the period of sudden drop in wind and solar power, and finally achieved 100% wind and solar power consumption throughout the day.
[0047] The renewable energy operation and maintenance cost was RMB 7,367.69, accounting for 30.9%, mainly due to fluctuation regulation and status monitoring of wind and solar equipment, with wind power operation and maintenance costs accounting for approximately 65%. Battery operating costs were RMB 6,847.47, accounting for 28.7%, with a charge-discharge cycle loss coefficient of approximately 5% per cycle. Electrolyzer operating costs were RMB 4,825.52, accounting for 20.2%. Electricity purchase costs were RMB 4,790.08, accounting for 20.1%. Electrolyzer standby costs were RMB 21.00, accounting for 0.1%, significantly lower than the standby losses in traditional single-mode operation. The core revenue of the system was hydrogen sales revenue of RMB 13,346.01.
[0048] The model predictive control method proposed in this invention achieves coupled improvement in operational and economic efficiency within a 24-hour cycle through a synergistic mechanism of rolling time-domain optimization and electrolyzer three-state switching. At the operational level, by regulating battery charging and discharging and switching electrolyzer states, 100% wind and solar power absorption is achieved, avoiding energy curtailment losses, while reducing the wind and solar power output fluctuation coefficient from 35% to 10%. At the equipment level, electrolyzer standby losses are reduced by 79%, battery charging and discharging efficiency is increased to 95%, and multi-unit utilization efficiency is improved by 15% compared to traditional methods. At the economic level, targeted absorption of surplus wind and solar power reduces the proportion of electricity purchase costs by 10%, providing a foundation for long-term optimization.
[0049] Therefore, this invention employs the aforementioned predictive control method for a hybrid wind-solar-hydrogen-storage system with three-state switching of the electrolyzer, constructing a power system architecture with wind and solar power as the main power supply units, and hydrogen production by the electrolyzer and the collaborative participation of energy storage batteries. Wind and solar power, as the main clean energy sources, undertake the basic power supply task, and their volatility and intermittency are mitigated by the electro-hydrogen production system and energy storage equipment. When wind and solar power generation exceeds local load demand, surplus electricity is prioritized to drive the electrolyzer to produce hydrogen, while the energy storage battery is charged and stored, minimizing the abandonment of renewable energy; when renewable energy output is insufficient, the electrolyzer switches to standby or shutdown state to reduce system load, and the energy storage battery discharges in a timely manner to compensate for the power deficit.
[0050] This invention achieves peak shaving and valley filling of wind and solar power fluctuations and coordinated utilization of multiple energy sources through a rolling time-domain optimization strategy, effectively improving the system's operational economy and renewable energy absorption capacity. It is applicable to power system dispatch scenarios with a high proportion of wind and solar resources.
[0051] 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 model predictive control method for a wind-solar-hydrogen-storage hybrid system with three-state switching in an electrolyzer, characterized in that, Includes the following steps: Step S1: Considering the uncertainty of wind and solar energy output and the multi-state operation characteristics of the electrolyzer, construct a refined model of the electrolyzer that includes the switching logic of three states: shutdown, standby and hydrogen production. Step S2: With minimizing the overall system cost as the optimization objective, consider the charging and discharging cost of energy storage batteries, the operating cost of the hydrogen production system, the electricity purchase cost from the grid, the operation and maintenance cost of renewable energy, the cost of wind and solar curtailment penalties, and the revenue from hydrogen sales, and construct a rolling optimization scheduling model. Step S3: Model other system constraints, specifically including energy storage battery constraints, grid power purchase constraints, and power balance constraints; Step S4: Based on the system power balance equation, establish a predictive control optimization model to achieve rolling optimization prediction.
2. The model predictive control method for a wind-solar-hydrogen storage hybrid system with three-state switching of an electrolyzer according to claim 1, characterized in that, In step S1, the specific process of constructing the refined model of the electrolyzer is as follows: Step S11: Define the three states of dynamic operation of the electrolytic cell; Considering the dynamic operating characteristics of the electrolyzer, the hydrogen production unit is divided into three states: stopped, standby, and hydrogen production, and it can only be in one of these states at any given time. Step S12: Considering the electrolyzer power constraint, ramp-up constraint, and start-up / shutdown constraint, construct a refined model of the electrolyzer, as shown below: ； ； ； ； in, , and These are binary variables, representing the operating states of the electrolyzer: hydrogen production, shutdown, and standby, respectively. and These represent the operating power of the electrolyzer in hydrogen production mode and the operating power in standby mode, respectively. and These represent the maximum and minimum hydrogen production power of the electrolyzer, respectively. This represents the maximum ramping power of the electrolytic cell.
3. The model predictive control method for a wind-solar-hydrogen-storage hybrid system with three-state switching of the electrolyzer according to claim 1, characterized in that, In step S2, with minimizing the overall system cost as the optimization objective, considering the charging and discharging costs of energy storage batteries, the operating costs of the hydrogen production system, the grid electricity purchase costs, the operation and maintenance costs of renewable energy, the cost of wind and solar curtailment penalties, and the revenue from hydrogen sales, a rolling optimization scheduling model is constructed. The specific process is as follows: Step S21: Establish a comprehensive system operating cost model, as shown below: ； in, For overall operating costs; Cost of charging and discharging energy storage batteries; The operating cost of the electro-hydrogen production system; The cost of purchasing electricity from the power grid; For the operation and maintenance costs of renewable energy; To incur penalties for abandoning scenic views; For revenue from hydrogen sales; Step S22: Construct a rolling optimization scheduling model with the goal of minimizing the overall system operating cost.
4. The model predictive control method for a wind-solar-hydrogen storage hybrid system with three-state switching of the electrolyzer according to claim 3, characterized in that, The calculation methods for energy storage battery charging and discharging costs, hydrogen production system operating costs, grid electricity purchase costs, renewable energy operation and maintenance costs, wind and solar curtailment penalty costs, and hydrogen sales revenue are as follows: ； in, and These are the unit charging cost and discharging cost of energy storage batteries, respectively. and These are the charging power and discharging power of the energy storage battery, respectively. ； in, and These represent the unit hydrogen production cost of the electrolyzer and the unit operating cost in standby mode, respectively. and These represent the operating power of the electrolyzer in hydrogen production mode and the operating power in standby mode, respectively. ； in, The unit price of electricity purchased from the power grid; For the power purchased; ； in, and These are the unit operation and maintenance costs for wind power and solar power, respectively. and These represent the actual output of wind power and photovoltaic power at time t, respectively. ； in, The penalty coefficient for abandoning scenic views; and These are respectively the amount of wind curtailment and the amount of solar curtailment; ； in, Price per unit of hydrogen; Hydrogen production; ； in, This is the conversion factor for hydrogen production. For hydrogen production efficiency; Total scheduling time; The scheduling time is in units.
5. The model predictive control method for a wind-solar-hydrogen storage hybrid system with three-state switching in an electrolyzer according to claim 1, characterized in that, Constraints on energy storage batteries are as follows: Energy storage batteries are mainly constrained by state of charge (SOC), state of charge / discharge, and charge / discharge power during operation, as shown below: ； ； ； ； ； ； in, Let be the charging and discharging power of the energy storage battery at time t; and These are binary variables, representing the charging and discharging states of the energy storage battery, respectively. and These are the maximum values of the charging and discharging power of the energy storage battery, respectively. The state of charge of the energy storage battery at time t; and These refer to the charging and discharging efficiencies of the energy storage battery, respectively. Self-discharge rate; and These represent the upper and lower limits of electrical energy that can be stored in an energy storage system, respectively.
6. The model predictive control method for a wind-solar-hydrogen storage hybrid system with three-state switching in an electrolyzer according to claim 1, characterized in that, The specific constraints on power grid purchases are as follows: To prevent the purchased electricity from exceeding the line capacity limit and to avoid the risk of grid overload, there is a maximum value for the purchased electricity, as shown below: ； in, This represents the maximum power purchase capacity.
7. The model predictive control method for a wind-solar-hydrogen storage hybrid system with three-state switching in an electrolyzer according to claim 1, characterized in that, The power balance constraints are as follows: To ensure real-time balance of power generation, consumption, and energy storage within the power grid and maintain stable system operation, power balance constraints are established as follows: ； in, This represents the load power.
8. The model predictive control method for a wind-solar-hydrogen storage hybrid system with three-state switching in an electrolyzer according to claim 1, characterized in that, In step S4, a predictive control optimization model is established based on the system power balance equation to achieve rolling optimization prediction. The specific process is as follows: The vector consisting of the energy storage output power, state of charge, and electrolyzer output power is selected as the state variable. As shown below: ； The vector formed by the increments of the energy storage and electrolyzer output power is selected as the control variable. As shown below: ； The vector formed by the increments of load, wind power, and photovoltaic power is selected as the disturbance variable. As shown below: ； Based on the above variables, a state-space model is established. This refers to the predictive control optimization model, used to achieve rolling optimization prediction, as shown below: ； in, , , .