Microgrid climate adaptive scheduling method and system based on cross-season hydrogen energy storage

Abstract

The application discloses a kind of microgrid response climate adaptability scheduling method and system based on cross-season hydrogen energy, belong to microgrid integrated energy technical field.The method includes: obtaining microgrid full-time period basic data and extreme weather scenario data;Hydrogen heat cross-season hybrid energy storage operation framework is constructed;Definition equipment configuration and multi-energy scheduling optimization variable;Establish the system covering multi-energy balance, cross-season hydrogen storage dynamics and fuel cell cogeneration constraint;With minimum microgrid equal annual value comprehensive cost as target to construct mixed integer linear programming model and solve;Output hydrogen storage configuration and extreme cold period scheduling strategy.The application realizes the low carbon, economic and reliable operation of microgrid under extreme climate scenario by hydrogen energy long-period storage mechanism and cogeneration technology used in summer and winter, effectively responds to the dual crisis of electricity and heat under extreme cold wave.

Description

Technical Field

[0001] This invention belongs to the field of microgrid integrated energy technology, specifically relating to a microgrid climate-adaptive scheduling method and system based on cross-seasonal hydrogen energy storage. Background Technology

[0002] Currently, the high proportion of renewable energy connected to the grid in microgrids has become a core direction for energy structure transformation. The installed capacity of renewable energy sources such as wind power and photovoltaics in microgrids continues to increase, effectively promoting the low-carbon development of microgrids. However, the intensification of global climate change has led to frequent extreme weather events. Extreme scenarios such as continuous cold waves in winter and prolonged periods without wind and solar power have become major challenges to the stable energy supply of microgrids. Microgrids face severe pressure to ensure both electricity and heat supply, while also facing significant risks of carbon emission rebound. The technical deficiencies of existing microgrid energy storage and operation scheduling models are becoming increasingly apparent, and they are no longer suitable for the development needs of microgrids with a high proportion of renewable energy.

[0003] First, existing microgrid energy storage systems lack cross-seasonal energy transfer capabilities. Mainstream battery energy storage, limited by high cost and high self-discharge rate, can only smooth out intraday high-frequency power fluctuations, failing to address cross-seasonal energy supply and demand mismatches on longer timescales. The large amount of surplus electricity generated by wind and solar resources in summer and autumn cannot be effectively stored, leading to curtailment. Meanwhile, during the extreme cold of winter, wind and solar output plummets, and microgrids face severe energy shortages. The long-term energy dispatch needs of summer storage and winter use cannot be met, resulting in low energy utilization efficiency.

[0004] Secondly, microgrids rely on a single, highly carbon-intensive means of power supply under extreme weather conditions. In extremely cold winters with no wind or solar power, renewable energy output is almost zero, while heat load surges dramatically due to the sudden drop in temperature. Microgrids can only rely on the main grid to purchase electricity to meet their electricity and heat demands. On the one hand, the price of electricity purchased from the grid rises sharply under extreme weather conditions, leading to a surge in microgrid operating costs. On the other hand, the grid's peak-shaving power sources are mostly fossil fuels such as coal-fired power plants, and purchasing electricity from external sources will cause the microgrid's carbon footprint to increase dramatically, seriously deviating from the original intention of deep decarbonization of microgrids.

[0005] Furthermore, the coupling between carbon control mechanisms and extreme energy supply scenarios is severely insufficient. Existing carbon control studies mostly adopt complex tiered carbon trading mechanisms, which are computationally difficult, have poor operability, and do not consider the time-varying characteristics of the dynamic carbon emission factor of the power grid. They uniformly use fixed carbon emission factors to calculate the carbon emissions of purchased electricity, which cannot truly reflect the high carbon cost of purchasing electricity from the power grid under extreme weather conditions. Carbon tax penalties cannot be accurately implemented, and it is difficult to use economic means to force microgrids to reduce power purchases from the power grid and increase the proportion of green energy utilization.

[0006] In addition, existing microgrid dispatching focuses on optimizing a single power system and has not built a dispatching system that couples multiple energy sources such as electricity, hydrogen, and heat. It cannot realize the cascade utilization of energy. When electricity and heat loads surge in extreme weather, a single power dispatching system is difficult to meet the energy supply needs of both types of loads at the same time, and the problem of insufficient heat load supply is likely to occur.

[0007] In summary, there is an urgent need for a microgrid hybrid energy storage optimization configuration and operation scheduling technology that can achieve cross-seasonal energy transfer, construct an electric-hydrogen-thermal multi-energy coupling system, adapt to the dual supply needs of electric and thermal energy in extreme weather, and combine precise dynamic carbon control, in order to solve many of the shortcomings of existing technologies and promote the stable, low-carbon, and economical operation of high-proportion new energy microgrids. Summary of the Invention

[0008] The technical problem to be solved by this invention is to provide a climate-adaptive scheduling method and system for microgrids based on cross-seasonal hydrogen energy storage, which addresses the shortcomings of the prior art. By constructing a closed-loop architecture of electricity-hydrogen-heat mass flow for summer storage and winter use, the invention achieves the extreme cold climate adaptability and deep decarbonization of microgrids, thereby solving the technical problems of insufficient energy supply climate adaptability and poor long-term energy storage synergy of existing microgrids under extreme weather conditions.

[0009] The present invention adopts the following technical solution: A climate-adaptive dispatch method for microgrids based on cross-seasonal hydrogen energy storage includes the following steps: S1. Acquire microgrid basic data and extreme weather scenario data. The microgrid basic data includes regular electricity load demand data, heat load demand data, wind power output forecast data, photovoltaic power output forecast data, equipment capacity limit data, investment cost parameters, operation and maintenance cost parameters, efficiency parameters, capacity limit parameters, grid dynamic carbon emission factor sequence, and fixed carbon tax price. The extreme weather scenario data includes surge heat load data and regular electricity load data superimposed under the scenario of continuous extreme cold in winter and no wind and solar power. S2. Based on the microgrid basic data obtained in step S1, construct an electric-hydrogen-thermal cross-seasonal hybrid energy storage operation framework. The operation framework includes a short-term high-frequency response layer composed of battery energy storage and short-term thermal storage equipment, and a cross-seasonal low-carbon supply layer composed of electrolyzers, hydrogen storage tanks and fuel cells. S3. Define optimization variables for coverage device configuration and multi-energy scheduling, including capacity configuration variables, scheduling operation variables, and status variables; S4. Based on the operational framework of step S2 and the optimization variables of step S3, establish a constraint system covering multi-energy balance constraints, cross-seasonal hydrogen storage dynamic constraints, fuel cell cogeneration constraints, and dynamic carbon emission constraints. S5. With the goal of minimizing the annual comprehensive cost of microgrids, a mixed integer linear programming optimization model is constructed in conjunction with the constraint system of step S4, and the mixed integer linear programming optimization model is solved to obtain the hydrogen storage configuration parameters and the joint scheduling strategy of thermal power during the extreme cold period. S6. Based on the solution results of step S5, output the scheduling characteristic curve and the carbon tax sensitivity analysis results.

[0010] Preferably, the multi-energy balance constraints established in step S4 include electrical balance constraints, thermal balance constraints, and hydrogen energy balance constraints. The expression for the power balance constraint is as follows:

[0011] The expression for the thermodynamic balance constraint is:

[0012] The expression for the hydrogen energy balance constraint is:

[0013] in, Represents electrical power. Represents thermal power. Represents the mass flow rate of hydrogen. For time step index, Represents the power grid. Representing wind power, Represents photovoltaics, Represents discharge or heat release. It represents charging or heat storage. Represents an electrolytic cell. Representing fuel cells, Represents combined heat and power (CHP). Represents electric boilers. Represents load.

[0014] Preferably, the expression for the cross-seasonal dynamic constraint on hydrogen storage established in step S4 is:

[0015] in, and They are respectively Time and The hydrogen charge status of the hydrogen storage tank at all times. For hydrogen charging efficiency, For hydrogen release efficiency, For hydrogen charging mass flow rate, This represents the hydrogen release mass flow rate.

[0016] Preferably, the expression for the fuel cell cogeneration constraint established in step S4 is:

[0017] in, To generate heat power from waste heat recovery in fuel cells, For fuel cell power generation, The coefficient for waste heat recovery from cogeneration in fuel cells.

[0018] Preferably, the objective function expression of the mixed-integer linear programming optimization model constructed in step S5 is:

[0019] in, The system's equivalent annual comprehensive cost, The equivalent annual investment cost of each energy storage and conversion device. For annualized operation and maintenance costs, To calculate the annualized cost of purchased electricity, The cost of carbon tax penalties.

[0020] Preferably, carbon tax penalty costs for:

[0021] in, For the set fixed carbon tax price, for Dynamic carbon emission factors of the power grid at all times To purchase power from the main power grid.

[0022] Preferably, the extreme weather scenario data in step S1 is set to a scenario of 14 consecutive days of extreme cold in winter with no wind or solar power. Under this scenario, the predicted wind power output data and the predicted photovoltaic output data are both set to 0, and the heat load demand data increases by 50% compared to a normal winter day.

[0023] Preferably, the state variables in step S3 include the hydrogen charge state of the hydrogen storage tank, the charge state of the battery energy storage, and the thermal storage state of the short-term thermal storage device; the scheduling operation variables include the hydrogen production power of the electrolyzer, the power generation power of the fuel cell, the power grid interaction power, and the heating power of the electric boiler.

[0024] Preferably, in step S5, the mixed-integer linear programming optimization model is solved, specifically by calling a commercial optimization solver to iteratively calculate the model until the objective function converges or reaches the preset maximum number of iterations, thereby determining the hydrogen storage tank capacity in the hydrogen storage configuration parameters and the hourly output plan of each device in the combined thermal and power scheduling strategy during extremely cold periods.

[0025] Secondly, embodiments of the present invention provide a microgrid climate-adaptive scheduling system based on cross-seasonal hydrogen energy storage, comprising: The data acquisition module is used to acquire microgrid basic data and extreme weather scenario data. The microgrid basic data includes regular electricity load demand data, heat load demand data, wind power output forecast data, photovoltaic output forecast data, equipment capacity limit data, investment cost parameters, operation and maintenance cost parameters, efficiency parameters, capacity limitation parameters, grid dynamic carbon emission factor sequence, and fixed carbon tax price. The extreme weather scenario data includes surge heat load data and regular electricity load data superimposed under the scenario of continuous extreme cold in winter and no wind and solar power. The framework construction module is used to construct a cross-seasonal hybrid energy storage operation framework based on the acquired microgrid basic data. The operation framework includes a short-term high-frequency response layer composed of battery energy storage and short-term thermal storage equipment, and a cross-seasonal low-carbon supply layer composed of electrolyzers, hydrogen storage tanks and fuel cells. The variable definition module is used to define optimization variables for covering device configuration and multi-energy scheduling. The optimization variables include capacity configuration variables, scheduling operation variables, and status variables. The constraint establishment module is used to establish a constraint system based on the operating framework and the optimization variables, covering multi-energy balance constraints, cross-seasonal hydrogen storage dynamic constraints, fuel cell cogeneration constraints, and dynamic carbon emission constraints. The model solving module is used to construct a mixed integer linear programming optimization model with the constraint system as the objective of minimizing the annual comprehensive cost of microgrids, and solve the mixed integer linear programming optimization model to obtain hydrogen storage configuration parameters and thermal power joint scheduling strategy during extremely cold periods; The results output module is used to output scheduling characteristic curves and carbon tax sensitivity analysis results based on the solution results.

[0026] Thirdly, a computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the steps of the above-described microgrid climate-adaptive scheduling method based on cross-seasonal hydrogen energy storage.

[0027] Fourthly, embodiments of the present invention provide a computer-readable storage medium including a computer program, which, when executed by a processor, implements the steps of the above-described microgrid climate-adaptive scheduling method based on cross-seasonal hydrogen energy storage.

[0028] Fifthly, a chip includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the steps of the above-described microgrid climate-adaptive scheduling method based on cross-seasonal hydrogen energy storage.

[0029] In a sixth aspect, embodiments of the present invention provide an electronic device including a computer program, which, when executed by the electronic device, implements the steps of the above-described microgrid climate-adaptive scheduling method based on cross-seasonal hydrogen energy storage.

[0030] Compared with the prior art, the present invention has at least the following beneficial effects: A climate-adaptive scheduling method for microgrids based on cross-seasonal hydrogen energy storage is proposed. First, it acquires all-time basic data and extreme weather scenario data for the microgrid, covering core information such as conventional power and heat loads, wind and solar power output forecasts, equipment parameters, and dynamic carbon emission factors of the power grid. Then, it constructs a cross-seasonal hybrid energy storage operation framework for electricity, hydrogen, and heat, defining optimization variables covering equipment configuration and multi-energy scheduling, and establishing a constraint system including multi-energy balance, cross-seasonal hydrogen storage dynamics, and fuel cell cogeneration. A mixed-integer linear programming model is constructed and solved with the objective of minimizing the microgrid's annual comprehensive cost, ultimately outputting hydrogen storage configuration parameters and a joint scheduling strategy for cogeneration during extremely cold periods. By integrating all-time data and extreme scenarios, it achieves full lifecycle coordination from planning to operation.

[0031] Furthermore, the power balance constraint coordinates all power flows, including grid power purchase, wind and solar power output, and energy storage charging and discharging, ensuring real-time matching of power supply and demand. The thermal balance constraint integrates thermal resources such as combined heat and power, electric boilers, and thermal storage tanks, adapting to the surge in heat load demand under extreme weather conditions. The hydrogen energy balance constraint achieves full-process control of hydrogen production, storage, and utilization, ensuring the stable operation of hydrogen energy storage across seasons. These three types of constraints are interconnected and mutually supportive, forming a closed-loop balance of power, hydrogen, and thermal mass flows. This avoids power supply failures caused by imbalances in a single energy system and provides precise constraint boundaries for the optimization model, allowing the solved scheduling strategy to better fit the energy flow patterns of the microgrid's actual operation, thus improving the stability and accuracy of scheduling.

[0032] Furthermore, by introducing hydrogen charging and discharging efficiency parameters and considering energy loss during hydrogen storage, the calculation of hydrogen state of charge is made more consistent with the actual equipment characteristics. Simultaneously, through continuous time series correlation, dynamic tracking of the hydrogen storage state over 8760 hours is achieved, ensuring the continuity and stability of hydrogen storage volume during cross-seasonal scheduling (summer storage, winter use). This avoids problems such as overcharging and over-discharging of hydrogen storage tanks, extending equipment lifespan. It also allows for more accurate calculation of hydrogen storage configuration parameters, enabling optimization of hydrogen storage tank capacity configuration based on hydrogen storage and usage demands at different times. While ensuring energy supply during extreme weather, this reduces investment costs for hydrogen storage equipment and improves the economics and reliability of cross-seasonal hydrogen energy storage.

[0033] Furthermore, by utilizing the waste heat recovery coefficient during power generation, the waste heat generated is utilized in a cascaded manner, significantly improving the energy efficiency of fuel cells. Compared to traditional single power generation equipment, the energy utilization rate is increased by more than 30%. In extremely cold winter scenarios with no wind or solar power, fuel cell power generation fills the power gap, while the waste heat generated is directly used for heating, significantly reducing the power consumption demand of electric boilers and reducing grid power purchases and carbon tax expenditures. At the same time, this quantitative constraint provides a clear thermoelectric conversion relationship for the optimization model, allowing the model to accurately optimize the output plan of fuel cells, achieve optimal scheduling of simultaneous power and heat compensation, and improve the energy supply guarantee capability of microgrids under extreme weather conditions.

[0034] Furthermore, the annualized investment cost spreads the one-time equipment investment over the entire life cycle, which is more in line with the actual economic accounting method; the annualized operation and maintenance cost takes into account the daily operation and maintenance expenses of each piece of equipment; the annualized external power purchase cost covers the grid power purchase cost under normal and extreme weather conditions; the carbon tax cost incorporates the economic cost of carbon emissions into the cost system; and the comprehensive coverage of the economic expenditure of microgrid operation allows the scheduling strategy and equipment configuration scheme solved by the model to achieve the optimal overall cost while ensuring the reliability and low carbon emissions of power supply.

[0035] Furthermore, a dynamic carbon emission factor that varies over time is introduced, taking into account the differences in carbon emissions from grid-purchased electricity at different times. For example, the carbon emission factor is higher during peak evening hours, which can truly reflect the high carbon cost of purchasing electricity under extreme weather conditions. At the same time, carbon emissions are converted into direct economic costs through a fixed carbon tax price, which is incorporated into the objective function of the optimization model, forming an economic incentive mechanism. This allows microgrid dispatch to fully consider carbon emission costs when making decisions, incentivizing the system to configure large-capacity green hydrogen energy storage, reducing grid-purchased electricity, avoiding carbon emission rebound under extreme scenarios, and simultaneously achieving linearized calculations for carbon control, simplifying the model solution difficulty.

[0036] Furthermore, it provides precise extreme operating condition boundaries for microgrid scheduling, allowing the model to specifically optimize hydrogen storage configuration and thermal power scheduling strategies to ensure reliable energy supply even under extreme scenarios. Simultaneously, incorporating a 50% surge in heat load into the model makes the configuration and scheduling of heat resources more targeted, avoiding insufficient heat supply during extreme weather conditions; setting wind and solar output to 0 forces the model to fully utilize the role of cross-seasonal hydrogen energy storage.

[0037] Furthermore, the state variables cover the hydrogen charge state of the hydrogen storage tank, the battery charge state, and the thermal storage state of the thermal storage tank, enabling full-dimensional tracking of the state of the three types of energy storage equipment and ensuring that the operating state of the energy storage equipment is always within a controllable range; the scheduling operation variables clarify the output variables of core equipment such as electrolyzers, fuel cells, power grids, and electric boilers, allowing the model to accurately optimize the hourly output plan of each device.

[0038] Furthermore, the commercial solver possesses highly efficient iterative computation capabilities, enabling it to quickly find the optimal hydrogen storage configuration parameters and hourly output plan for equipment under massive decision variables and constraints, achieving a solution efficiency improvement of over 50% compared to traditional algorithms. Through dual constraints of convergence conditions and the maximum number of iterations, it ensures the optimality of the solution while avoiding the inefficiency caused by infinite iterations.

[0039] It is understood that the beneficial effects of the second to sixth aspects mentioned above can be found in the relevant descriptions in the first aspect mentioned above, and will not be repeated here.

[0040] In summary, this invention solves the challenges of power supply and decarbonization of microgrids under extreme climates by constructing an electricity-hydrogen-heat cross-seasonal collaborative architecture. It achieves long-cycle energy transfer, efficient utilization of cogeneration, and optimal economic efficiency throughout the entire life cycle, significantly improving the climate adaptability and overall energy efficiency of microgrids.

[0041] 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

[0042] Figure 1 This is a graph showing the continuous variation of SOC in hydrogen energy storage across seasons; Figure 2 This is a graph showing the power dispatch curve of the microgrid during a 14-day extreme cold wave. Figure 3 This is a graph showing the microgrid thermal scheduling during a 14-day extreme cold wave. Figure 4 A sensitivity analysis diagram of carbon tax and hydrogen storage cost; Figure 5 A schematic diagram of a computer device provided in an embodiment of the present invention; Figure 6This is a block diagram of a chip according to an embodiment of the present invention; Figure 7 This is a schematic diagram of the microgrid system structure provided by the present invention; Figure 8 This is a flowchart of the present invention.

[0043] Among them, 60. Computer equipment; 61. Processor; 62. Memory; 63. Computer program; 600. Electronic device; 610. Processing unit; 620. Storage unit; 6201. Random access memory unit; 6202. Cache memory unit; 6203. Read-only memory unit; 6204. Program / utility; 6205. Program module; 630. Bus; 640. Display unit; 650. Input / output interface; 660. Network adapter; 700. External device. Detailed Implementation

[0044] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0045] In the description of this invention, it should be understood that the terms "comprising" and "including" indicate the presence of the described features, integrals, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0046] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0047] It should also be further understood that the term "and / or" as used in this specification and the appended claims refers to any combination and all possible combinations of one or more of the associated listed items, and includes such combinations. For example, A and / or B can represent three cases: A alone, A and B simultaneously, and B alone. Additionally, the character " / " in this invention generally indicates that the preceding and following objects have an "or" relationship.

[0048] It should be understood that although terms such as first, second, third, etc., may be used in the embodiments of the present invention to describe the preset range, these preset ranges should not be limited to these terms. These terms are only used to distinguish the preset ranges from one another. For example, without departing from the scope of the embodiments of the present invention, the first preset range may also be referred to as the second preset range, and similarly, the second preset range may also be referred to as the first preset range.

[0049] Depending on the context, the word "if" as used here can be interpreted as "when," "when," "in response to determination," or "in response to detection." Similarly, depending on the context, the phrase "if determination" or "if detection (of the stated condition or event)" can be interpreted as "when determination," "in response to determination," "when detection (of the stated condition or event)," or "in response to detection (of the stated condition or event)."

[0050] The accompanying drawings illustrate various structural schematic diagrams according to embodiments disclosed in this invention. These drawings are not to scale, and some details have been enlarged for clarity, and some details may have been omitted. The shapes of the various regions and layers shown in the drawings, as well as their relative sizes and positional relationships, are merely exemplary and may deviate from reality due to manufacturing tolerances or technical limitations. Furthermore, those skilled in the art can design regions / layers with different shapes, sizes, and relative positions as needed.

[0051] This invention provides a climate-adaptive scheduling method for microgrids based on cross-seasonal hydrogen energy storage, constructing a multi-energy coupled operation framework encompassing wind and solar power generation, cross-seasonal hydrogen storage, and fuel cell cogeneration. It defines long- and short-term timescale operating variables and establishes a constraint system covering long- and short-cycle energy balance, hydrogen storage continuity, and dynamic carbon tax. A multi-timescale optimization model is constructed with the goal of minimizing the overall operating cost of the microgrid. The optimal hydrogen storage configuration and cogeneration scheduling strategy during extremely cold periods are determined by solving the model, and sensitivity analysis is used to assess the system's climate adaptation boundary. This invention achieves full-process synergy between cross-seasonal long-cycle energy storage and short-cycle emergency scheduling, significantly improving the energy supply reliability and low-carbon economy of microgrids under extreme weather conditions, and is suitable for the planning and optimized operation of high-proportion renewable energy microgrids.

[0052] Please see Figure 8 This invention discloses a climate-adaptive scheduling method for microgrids based on cross-seasonal hydrogen energy storage, comprising the following steps: S1. Acquire basic microgrid data and extreme weather scenario data; S101: Collects routine electricity and heat load demand data for the microgrid for 8760 hours throughout the year; S102. Obtain the annual power output forecast data and equipment capacity limit for wind power and photovoltaic power for 8760 hours. S103. Obtain the investment cost, operation and maintenance cost, efficiency and capacity limitation parameters of short-term energy storage (batteries, thermal storage tanks) and cross-seasonal energy storage (electrolyzers, hydrogen storage tanks, fuel cells); S104. Construct extreme weather scenario data: Overlay a scenario of 14 consecutive days of extreme cold and no wind and light in winter to obtain data on the surge in heat load and conventional electrical load under this scenario. S105. Obtain carbon-related parameters: time-varying dynamic carbon emission factor sequence of the power grid, and fixed carbon tax price.

[0053] S2. Construct a cross-seasonal hybrid energy storage framework for electricity, hydrogen, and heat; Construct a multi-energy complementary closed-loop architecture encompassing electricity, heat, and hydrogen, and define a two-tiered energy storage mechanism for short-term high-frequency response and cross-seasonal low-carbon supply assurance: (1) Short-term high-frequency response layer: Utilize battery energy storage and short-term heat storage equipment to smooth out intraday wind and solar fluctuations under normal weather conditions.

[0054] (2) Cross-seasonal low-carbon supply layer: utilizing electrolyzers (P2H), large-capacity hydrogen storage tanks, and fuel cells (FC). Hydrogen is produced during the summer and autumn when wind and solar power are abundant and stored in large-capacity hydrogen storage tanks (summer storage); during the extremely cold winter when there is no wind or solar power, the fuel cells are started to consume hydrogen to generate electricity, and the waste heat of the fuel cells is recovered and used directly for heating (winter use and cogeneration).

[0055] S3. Define optimization variables; Define decision variables covering device configuration and multi-energy scheduling, including: 1. Capacity configuration variables Battery capacity, thermal storage tank capacity, electrolyzer power, hydrogen storage tank capacity, fuel cell power.

[0056] 2. Scheduling runtime variables Power purchased by the power grid, wind and solar power absorption, battery charging and discharging power, thermal storage tank charging and discharging power, electrolyzer hydrogen production power, and fuel cell power generation and heat generation power at different times.

[0057] 3. State variables; Battery SOC, thermal storage tank SOC, and cross-seasonal hydrogen storage tank SOC (covering 8760h continuous operation).

[0058] S4. Establish a constraint system; (1) Multi-energy equilibrium constraint 1. Power balance:

[0059] Where t is the time step index; The actual wind power output (kW) at any given moment; Actual photovoltaic output (kW); Battery discharge power (kW); The power output of the fuel cell (kW); Power purchased from the main power grid (kW); For the conventional basic electrical load of the microgrid (kW); Battery charging power (kW); Power consumption (kW) for hydrogen production in electrolyzer; The power consumption of the electric boiler (kW).

[0060] 2. Thermal equilibrium:

[0061] in, The heat dissipation power of the thermal storage tank (kW); The heat recovery power (kW) of fuel cell waste heat generation; The heat output power of the electric boiler (kW); For microgrid heat load (including extreme weather surge load) (kW); The power (kW) for charging the thermal storage tank.

[0062] 3. Hydrogen energy balance:

[0063] in, The mass flow rate of hydrogen produced by the electrolyzer (kg / h); The hydrogen mass flow rate consumed by the fuel cell is (kg / h). Hydrogen mass flow rate (kg / h) for charging hydrogen storage tanks; Hydrogen discharge mass flow rate (kg / h) from hydrogen storage tank; (2) Dynamic constraints on hydrogen storage across seasons

[0064] in, and The hydrogen state (percentage) of the hydrogen storage tank at time t and time t-1. For hydrogen charging efficiency; For hydrogen release efficiency; The rated configuration capacity (kg) of the hydrogen storage tank.

[0065] (3) Constraints on fuel cell cogeneration

[0066] in, The waste heat recovery coefficient of fuel cell cogeneration (i.e., the ratio of heat generation to power generation).

[0067] (4) Dynamic carbon emission constraints The total carbon emissions of the system are calculated as follows:

[0068] in, The total annual carbon emissions of the microgrid (kg); Let t be the dynamic carbon emission factor of the power grid at time t (kg / kWh).

[0069] S5. With the goal of minimizing the annual comprehensive cost of microgrids, a mixed integer linear programming optimization model is constructed in conjunction with the constraint system of step S4, and the mixed integer linear programming optimization model is solved to obtain the hydrogen storage configuration parameters and the joint scheduling strategy of thermal power during the extreme cold period. To minimize the equivalent annual comprehensive cost, a mixed-integer linear programming (MILP) model is established:

[0070] in, The system's equivalent annual comprehensive cost; The equivalent annual investment cost of each energy storage and conversion device; Annualized operating and maintenance costs; This represents the annualized cost of purchased electricity. The cost of carbon tax penalties The fixed carbon tax price (RMB / kg) is set.

[0071] S6. Based on the solution results of step S5, output the scheduling characteristic curve and the carbon tax sensitivity analysis results.

[0072] Output the optimal configuration scheme and scheduling curves, including: the SOC change curve of the hydrogen storage tank across seasons, the electrothermal synergy supply curve in extreme cold scenarios, and the carbon tax sensitivity analysis results.

[0073] In another embodiment of the present invention, a microgrid climate-adaptive scheduling system based on cross-seasonal hydrogen energy storage is provided. This system can be used to implement the above-mentioned microgrid climate-adaptive scheduling method based on cross-seasonal hydrogen energy storage. Specifically, the microgrid climate-adaptive scheduling system based on cross-seasonal hydrogen energy storage includes a data acquisition module, a framework construction module, a variable definition module, a constraint establishment module, a model solving module, and a result output module.

[0074] The data acquisition module is used to acquire microgrid basic data and extreme weather scenario data. The microgrid basic data includes regular electricity load demand data, heat load demand data, wind power output forecast data, photovoltaic output forecast data, equipment capacity limit data, investment cost parameters, operation and maintenance cost parameters, efficiency parameters, capacity limit parameters, grid dynamic carbon emission factor sequence, and fixed carbon tax price. The extreme weather scenario data includes surge heat load data and regular electricity load data superimposed under the scenario of continuous extreme cold in winter and no wind and solar power. The framework construction module is used to construct a cross-seasonal hybrid energy storage operation framework based on the acquired microgrid basic data. The operation framework includes a short-term high-frequency response layer composed of battery energy storage and short-term thermal storage equipment, and a cross-seasonal low-carbon supply layer composed of electrolyzers, hydrogen storage tanks and fuel cells. The variable definition module is used to define optimization variables for covering device configuration and multi-energy scheduling. The optimization variables include capacity configuration variables, scheduling operation variables, and status variables. The constraint establishment module is used to establish a constraint system based on the operating framework and the optimization variables, covering multi-energy balance constraints, cross-seasonal hydrogen storage dynamic constraints, fuel cell cogeneration constraints, and dynamic carbon emission constraints. The model solving module is used to construct a mixed integer linear programming optimization model with the constraint system as the objective of minimizing the annual comprehensive cost of microgrids, and solve the mixed integer linear programming optimization model to obtain hydrogen storage configuration parameters and thermal power joint scheduling strategy during extremely cold periods; The results output module is used to output scheduling characteristic curves and carbon tax sensitivity analysis results based on the solution results.

[0075] This invention provides a terminal device comprising a processor and a memory. The memory stores a computer program, which includes program instructions. The processor executes the program instructions stored in the computer storage medium. The processor may be a Central Processing Unit (CPU), or other general-purpose processors, graphics processing units (GPUs), tensor processing units (TPUs), digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing and control core of the terminal, suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions to achieve corresponding method flows or corresponding functions. The processor described in this embodiment can be used in the operation of a microgrid climate-adaptive scheduling method based on cross-seasonal hydrogen energy storage, including: Acquire basic microgrid data and extreme weather scenario data. The basic microgrid data includes all-time data on conventional electricity load demand, heat load demand, wind power output forecast, photovoltaic power output forecast, equipment capacity limits, investment cost parameters, operation and maintenance cost parameters, efficiency parameters, capacity limitation parameters, grid dynamic carbon emission factor sequences, and fixed carbon tax prices. The extreme weather scenario data includes surge heat load data and conventional electricity load data overlaid under continuous extreme cold winter conditions with no wind or solar power. Based on the acquired basic microgrid data, construct an electric-hydrogen-thermal hybrid energy storage operation framework. This framework includes a short-term high-frequency response system composed of battery energy storage and short-term thermal storage devices. The system comprises a layer consisting of electrolyzers, hydrogen storage tanks, and fuel cells, and a cross-seasonal low-carbon supply layer. Optimization variables covering equipment configuration and multi-energy scheduling are defined, including capacity configuration variables, scheduling operation variables, and state variables. Based on the operational framework and optimization variables, a constraint system is established covering multi-energy balance constraints, cross-seasonal hydrogen storage dynamic constraints, fuel cell cogeneration constraints, and dynamic carbon emission constraints. With the objective of minimizing the annual comprehensive cost of microgrids, a mixed-integer linear programming optimization model is constructed in conjunction with the constraint system. The mixed-integer linear programming optimization model is solved to obtain hydrogen storage configuration parameters and a cogeneration scheduling strategy for extremely cold periods. Based on the solution results, scheduling characteristic curves and carbon tax sensitivity analysis results are output.

[0076] Please see Figure 5 The terminal device is a computer device. In this embodiment, the computer device 60 includes a processor 61, a memory 62, and a computer program 63 stored in the memory 62 and executable on the processor 61. When executed by the processor 61, the computer program 63 implements the microgrid climate-adaptive scheduling method based on cross-seasonal hydrogen energy storage described in this embodiment. To avoid repetition, these details are not elaborated here. Alternatively, when executed by the processor 61, the computer program 63 implements the functions of each model / unit in the microgrid climate-adaptive scheduling system based on cross-seasonal hydrogen energy storage described in this embodiment. To avoid repetition, these details are not elaborated here.

[0077] Computer device 60 can be a desktop computer, laptop, handheld computer, cloud server, or other computing device. Computer device 60 may include, but is not limited to, a processor 61 and a memory 62. Those skilled in the art will understand that... Figure 5 This is merely an example of computer device 60 and does not constitute a limitation on computer device 60. It may include more or fewer components than shown, or combine certain components, or different components. For example, computer device may also include input / output devices, network access devices, buses, etc.

[0078] The processor 61 may be a Central Processing Unit (CPU), or other general-purpose processors, graphics processing units (GPUs), tensor processing units (TPUs), digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0079] The memory 62 can be an internal storage unit of the computer device 60, such as a hard disk or memory of the computer device 60. The memory 62 can also be an external storage device of the computer device 60, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc. equipped on the computer device 60.

[0080] Furthermore, the memory 62 may include both internal storage units of the computer device 60 and external storage devices. The memory 62 is used to store computer programs and other programs and data required by the computer device. The memory 62 can also be used to temporarily store data that has been output or will be output.

[0081] Please see Figure 6 The terminal device is an electronic device 600, which is manifested in the form of a general-purpose computing device. The components of the electronic device may include, but are not limited to: at least one processing unit 610, at least one storage unit 620, a bus 630 connecting different platform components (including storage unit 620 and processing unit 610), a display unit 640, etc.

[0082] The storage unit stores program code, which can be executed by the processing unit 610 to perform the steps described in the method section of this specification according to various exemplary embodiments of the present invention. For example, the processing unit 610 can perform actions such as... Figure 1 The steps are shown in the figure.

[0083] Storage unit 620 may include a readable medium in the form of a volatile storage unit, such as random access memory (RAM) 6201 and / or cache memory 6202, and may further include a read-only memory (ROM) 6203.

[0084] Storage unit 620 may also include a program / utility 6204 having a set (at least one) program module 6205, such program module 6205 including but not limited to: operating system, one or more application programs, other program modules and program data, each or some combination of these examples may include an implementation of a network environment.

[0085] Bus 630 can represent one or more of several types of bus structures, including a memory cell bus or memory cell controller, a peripheral bus, a graphics acceleration port, a processing unit, or a local bus using any of the multiple bus structures.

[0086] Electronic device 600 can also communicate with one or more external devices 700 (e.g., keyboard, pointing device, Bluetooth device, etc.), and with one or more devices that enable a user to interact with electronic device 600, and / or with any device that enables electronic device 600 to communicate with one or more other computing devices (e.g., router, modem). This communication can be performed via input / output interface 650. Furthermore, electronic device 600 can also communicate with one or more networks (e.g., local area network, wide area network, and / or public network, such as the Internet) via network adapter 660. Network adapter 660 can communicate with other modules of electronic device 600 via bus 630. It should be understood that, although not shown in the figures, other hardware and / or software modules can be used in conjunction with electronic device 600, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage platforms.

[0087] This invention also provides a storage medium, specifically a computer-readable storage medium, which is a memory device in a terminal device for storing programs and data. It is understood that the computer-readable storage medium here can include both built-in storage media in the terminal device and extended storage media supported by the terminal device; it can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. The computer-readable storage medium provides storage space that stores the terminal's operating system. Furthermore, the storage space also stores one or more instructions suitable for loading and execution by a processor, which can be one or more computer programs (including program code). More specific examples of the computer-readable storage medium include: an electrical connection with one or more wires, a portable disk, a hard disk, random access memory, read-only memory, erasable programmable read-only memory, optical fiber, portable compact disk read-only memory, optical storage device, magnetic storage device, or any suitable combination thereof.

[0088] Computer-readable storage media also include data signals propagated in baseband or as part of a carrier wave, carrying readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A readable storage medium can also be any readable medium other than a readable storage medium that can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the readable storage medium can be transmitted using any suitable medium, including but not limited to wireless, wired, optical fiber, radio frequency, etc., or any suitable combination thereof.

[0089] Program code for performing the operations of this invention can be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java and C++, and conventional procedural programming languages ​​such as C or similar languages. The program code can execute entirely on the user's computing device, partially on the user's computing device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0090] One or more instructions stored in the computer-readable storage medium can be loaded and executed by the processor to implement the corresponding steps of the microgrid climate-adaptive scheduling method based on cross-seasonal hydrogen energy storage in the above embodiments; one or more instructions in the computer-readable storage medium are loaded and executed by the processor to perform the following steps: Acquire basic microgrid data and extreme weather scenario data. The basic microgrid data includes all-time data on conventional electricity load demand, heat load demand, wind power output forecast, photovoltaic power output forecast, equipment capacity limits, investment cost parameters, operation and maintenance cost parameters, efficiency parameters, capacity limitation parameters, grid dynamic carbon emission factor sequences, and fixed carbon tax prices. The extreme weather scenario data includes surge heat load data and conventional electricity load data overlaid under continuous extreme cold winter conditions with no wind or solar power. Based on the acquired basic microgrid data, construct an electric-hydrogen-thermal hybrid energy storage operation framework. This framework includes a short-term high-frequency response system composed of battery energy storage and short-term thermal storage devices. The system comprises a layer consisting of electrolyzers, hydrogen storage tanks, and fuel cells, and a cross-seasonal low-carbon supply layer. Optimization variables covering equipment configuration and multi-energy scheduling are defined, including capacity configuration variables, scheduling operation variables, and state variables. Based on the operational framework and optimization variables, a constraint system is established covering multi-energy balance constraints, cross-seasonal hydrogen storage dynamic constraints, fuel cell cogeneration constraints, and dynamic carbon emission constraints. With the objective of minimizing the annual comprehensive cost of microgrids, a mixed-integer linear programming optimization model is constructed in conjunction with the constraint system. The mixed-integer linear programming optimization model is solved to obtain hydrogen storage configuration parameters and a cogeneration scheduling strategy for extremely cold periods. Based on the solution results, scheduling characteristic curves and carbon tax sensitivity analysis results are output.

[0091] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0092] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0093] Please see Figure 7 , Figure 7 A schematic diagram of a microgrid system structure provided in an embodiment of the present invention is shown. The system includes a wind power generation unit, a photovoltaic power generation unit, a photoelectrolyzer (P2H), a large-capacity inter-seasonal hydrogen storage tank, a fuel cell (FC) cogeneration unit, short-term battery energy storage, a thermal storage tank, and an electric boiler. Each unit works collaboratively through an energy management system to form a closed-loop energy flow of electricity, hydrogen, and heat, with summer storage and winter use. The power grid serves as backup support, and the heating network is connected to the cold / heat loads.

[0094] Example 1 Basic data and scenario organization We statistically analyzed hourly electricity and heat load and wind and solar power output forecasts for the microgrid over 8760 hours throughout the year. We specifically extracted a 336-hour (14-day) period during winter as an extreme cold wave scenario with no wind or solar power. Under this scenario, wind and solar power output was set to 0, and heat load surged by 50% compared to a typical winter day. We also simultaneously input the grid dynamic carbon emission factor sequence for 8760 hours throughout the year.

[0095] Example 2 Energy balance between electricity, hydrogen, and heat across seasons and implementation of combined heat and power (CHP) During the summer and autumn seasons (such as January to October), the output of wind and solar power exceeds the electric heating load, and the system prioritizes filling the batteries and thermal storage tanks; the surplus electricity drives the electrolyzer to produce hydrogen, and the SOC of the hydrogen storage tank rises slowly.

[0096] Please see Figure 1 , Figure 1The graph shows the continuous change in the State of Charge (SOC) of hydrogen storage across seasons. The horizontal axis represents the time series of 8760 hours throughout the year, and the vertical axis represents the percentage of hydrogen charge in the storage tanks. The curve shows an overall trend of "slow rise in summer and autumn, and a sharp drop during the extreme cold period in winter." The red shaded area represents a scenario with 14 consecutive days of extreme cold and no wind or solar power in winter. During this period, the hydrogen charge in the storage tanks drops rapidly from 95% to 20%, which intuitively reflects the hydrogen energy storage scheduling process of "storing in summer and using in winter" across seasons and verifies the energy support role of hydrogen storage tanks in extreme scenarios.

[0097] During extreme cold waves, the batteries are rapidly depleted, and the system activates the fuel cells. The fuel cells consume hydrogen from the hydrogen storage tank to generate electricity to fill the power gap, while the large amount of waste heat generated is directly injected into the heating network, significantly reducing the power consumption of the electric boiler and achieving simultaneous power and heat compensation.

[0098] Please see Figure 2 , Figure 2 This is a power dispatch curve for the microgrid during a 14-day extreme cold wave. The horizontal axis represents the 336-hour time series of the extreme scenario, and the vertical axis represents the power generation / purchase power of each device. The curves show the changing trends of fuel cell power generation, grid power purchase, and battery discharge power. It is evident that the fuel cell maintains its rated power generation throughout the extreme scenario, with grid power purchase only providing a small supplement. The battery discharges to zero within 8 hours, clearly demonstrating the core role of the fuel cell as a power supply source in extreme scenarios. Please refer to [link / reference]. Figure 3 , Figure 3 The graph shows the microgrid heat dispatch curves during a 14-day extreme cold wave. The horizontal axis represents the 336-hour time series of the extreme scenario, and the vertical axis represents the heat generation power of each device. The curves show the heat generation power of fuel cell waste heat recovery, the heat release power of the heat storage tank, and the supplementary heating power of the electric boiler. Among them, the heat generation power of fuel cell waste heat recovery accounts for more than 50% of the total heat load, and the electric boiler only needs a small amount of supplementary heating, which intuitively verifies the effective support of fuel cell cogeneration for the surge in heat load.

[0099] Example 3 Dynamic carbon factor and fixed carbon tax calculation In the optimization model, the carbon reduction mechanism is linearized: the electricity purchased from the power grid every hour is monitored in real time, multiplied by the grid's dynamic carbon emission factor for that hour (the carbon factor is usually higher during evening peak hours), and the total annual carbon emissions are accumulated. The total carbon emissions are then multiplied by a fixed carbon tax and added as a penalty cost to the overall objective function.

[0100] Example 4 Optimization Solution and Sensitivity Analysis A commercial solver was used to solve the above mixed-integer linear programming (MILP) model. Compared with the basic scenario with only batteries, the electro-hydrogen-thermal cross-seasonal mode of this invention has a lower total cost after taking carbon tax into account. By conducting sensitivity analysis on the unit investment cost of hydrogen storage and the carbon tax price, three decision regions can be identified: Region A (relying on the grid for backup), Region B (battery + medium-capacity hydrogen storage hybrid operation), and Region C (large-capacity hydrogen storage is absolutely dominant, achieving complete decarbonization autonomy).

[0101] Please see Figure 4 , Figure 4 This is a sensitivity analysis diagram of carbon tax and hydrogen storage costs. The horizontal axis represents the carbon tax price (RMB / kg), and the vertical axis represents the unit investment cost of hydrogen storage (RMB / kg). The diagram divides the plane into three decision-making regions, A, B, and C, using contour lines and regional division lines. Each region is marked with the corresponding range of hydrogen storage configuration ratios. The contour lines also represent the comprehensive operating costs of microgrids under different operating conditions. Based on the actual carbon tax policy and the cost of hydrogen storage equipment, the optimal hydrogen storage configuration strategy can be directly queried from the diagram, providing a quantitative decision-making basis for the practical engineering application of microgrids.

[0102] The present invention has the following advantages: (1) Strong cross-seasonal extreme cold supply capacity: Breaking through the time limitation of traditional battery energy storage, it uses large-capacity hydrogen storage to achieve summer storage and winter use; in particular, it utilizes the characteristics of fuel cell cogeneration to perfectly offset the dual crisis of "no wind and solar power shortage" and "surge in heating load and heat shortage" under extreme cold weather.

[0103] (2) Deep decarbonization and precise carbon footprint management: The introduction of dynamic grid carbon emission factors and fixed carbon tax truly reflects the high carbon cost of purchasing electricity from the grid under extreme weather conditions. Economic penalties force the system to configure large-capacity green hydrogen energy storage, thus avoiding carbon emission rebound under extreme scenarios.

[0104] (3) Excellent overall economic performance: By using the tiered utilization of energy storage in both short and long cycles (battery tubes for daily use, hydrogen tubes for seasonal use), the extremely expensive high-carbon electricity costs and carbon taxes under extreme weather conditions are eliminated, and the overall cost over the entire life cycle is better than the traditional battery + purchased high-carbon electricity model.

[0105] In summary, this invention presents a microgrid climate-adaptive scheduling method and system based on cross-seasonal hydrogen energy storage. During the summer and autumn seasons with surplus wind and solar power, hydrogen is produced through water electrolysis using surplus wind and solar power, converting electrical energy into hydrogen for long-term storage. During the extreme winter season when wind and solar power are scarce, fuel cells convert the hydrogen back into electrical and thermal energy. This "summer storage, winter use" mechanism overcomes the physical limitations of traditional batteries, such as short storage time and high self-discharge, achieving large-scale energy transfer over time and fundamentally resolving the structural contradiction between energy supply and demand in microgrids during the summer and winter seasons. Furthermore, by utilizing the combined heat and power (CHP) characteristics of fuel cells, a synergistic power supply defense line is constructed. In severe conditions where extreme cold waves cause a 50% surge in heat load and there is no wind or solar power output, the system activates fuel cells, providing not only scarce electricity but also directly meeting the surged heating demand by recovering waste heat. This "heat-driven power generation, heat-power synergy" operation mode effectively mitigates the dual crises of "power shortages due to lack of wind and solar power" and "heat shortages due to extreme cold," significantly improving the microgrid's survivability and power supply reliability under extreme weather conditions. The scheduling model incorporates a time-varying dynamic carbon emission factor from the power grid and a fixed carbon tax mechanism. This allows the model to accurately identify the "high carbon cost" of purchasing electricity from the grid under extreme weather conditions and, through an economic penalty mechanism, forces the system to prioritize the use of internal green hydrogen energy storage rather than purchasing high-carbon grid power. This mechanism design internalizes carbon costs as a scheduling driver, achieving a balance between low-carbon and economical operation of the microgrid. Compared to traditional models, this invention significantly reduces the carbon emission intensity throughout its entire lifecycle while ensuring power supply during extreme weather events, demonstrating significant environmental and economic benefits.

[0106] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0107] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0108] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed in this invention can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0109] In the embodiments provided by this invention, it should be understood that the disclosed devices / terminals and methods can be implemented in other ways. For example, the device / terminal embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0110] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0111] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0112] If the integrated module / unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random-access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.

[0113] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus, and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0114] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0115] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0116] The above content is only for illustrating the technical concept of the present invention and should not be construed as limiting the scope of protection of the present invention. Any modifications made to the technical solution based on the technical concept proposed in this invention shall fall within the scope of protection of the claims of this invention.

Claims

1. A climate-adaptive dispatch method for microgrids based on cross-seasonal hydrogen energy storage, characterized in that, Includes the following steps: S1. Acquire microgrid basic data and extreme weather scenario data. The microgrid basic data includes regular electricity load demand data, heat load demand data, wind power output forecast data, photovoltaic power output forecast data, equipment capacity limit data, investment cost parameters, operation and maintenance cost parameters, efficiency parameters, capacity limit parameters, grid dynamic carbon emission factor sequence, and fixed carbon tax price. The extreme weather scenario data includes surge heat load data and regular electricity load data superimposed under the scenario of continuous extreme cold in winter and no wind and solar power. S2. Based on the microgrid basic data obtained in step S1, construct an electric-hydrogen-thermal cross-seasonal hybrid energy storage operation framework. The operation framework includes a short-term high-frequency response layer composed of battery energy storage and short-term thermal storage equipment, and a cross-seasonal low-carbon supply layer composed of electrolyzers, hydrogen storage tanks and fuel cells. S3. Define optimization variables for coverage device configuration and multi-energy scheduling, including capacity configuration variables, scheduling operation variables, and status variables; S4. Based on the operational framework of step S2 and the optimization variables of step S3, establish a constraint system covering multi-energy balance constraints, cross-seasonal hydrogen storage dynamic constraints, fuel cell cogeneration constraints, and dynamic carbon emission constraints. S5. With the goal of minimizing the annual comprehensive cost of microgrids, a mixed integer linear programming optimization model is constructed in conjunction with the constraint system of step S4, and the mixed integer linear programming optimization model is solved to obtain the hydrogen storage configuration parameters and the joint scheduling strategy of thermal power during the extreme cold period. S6. Based on the solution results of step S5, output the scheduling characteristic curve and the carbon tax sensitivity analysis results.

2. The microgrid climate-adaptive scheduling method based on cross-seasonal hydrogen energy storage according to claim 1, characterized in that, The multi-energy balance constraints established in step S4 include power balance constraints, thermodynamic balance constraints, and hydrogen energy balance constraints. The expression for the power balance constraint is as follows: The expression for the thermodynamic balance constraint is: The expression for the hydrogen energy balance constraint is: in, Represents electrical power. Represents thermal power. Represents the hydrogen mass flow rate. For time step index, Represents the power grid. Representing wind power, Represents photovoltaics, Represents discharge or heat release. It represents charging or heat storage. Represents an electrolytic cell. Representing fuel cells, Represents combined heat and power (CHP). Represents electric boilers. Represents load.

3. The microgrid climate-adaptive scheduling method based on cross-seasonal hydrogen energy storage according to claim 1, characterized in that, The expression for the cross-seasonal dynamic constraint for hydrogen storage established in step S4 is as follows: in, and They are respectively Time and The hydrogen charge status of the hydrogen storage tank at all times. For hydrogen charging efficiency, For hydrogen release efficiency, For hydrogen charging mass flow rate, This represents the hydrogen release mass flow rate.

4. The microgrid climate-adaptive scheduling method based on cross-seasonal hydrogen energy storage according to claim 1, characterized in that, The expression for the fuel cell cogeneration constraint established in step 4 is as follows: in, To generate heat power from waste heat recovery in fuel cells, For fuel cell power generation, The coefficient for waste heat recovery from cogeneration in fuel cells.

5. The microgrid climate-adaptive scheduling method based on cross-seasonal hydrogen energy storage according to claim 1, characterized in that, The objective function expression of the mixed-integer linear programming optimization model constructed in step 5 is: in, The system's equivalent annual comprehensive cost, The equivalent annual investment cost of each energy storage and conversion device. For annualized operation and maintenance costs, To calculate the annualized cost of purchased electricity, The cost of carbon tax penalties.

6. The microgrid climate-adaptive scheduling method based on cross-seasonal hydrogen energy storage according to claim 5, characterized in that, Carbon tax penalty costs for: in, For the set fixed carbon tax price, for Dynamic carbon emission factors of the power grid at all times To purchase power from the main power grid.

7. The microgrid climate-adaptive scheduling method based on cross-seasonal hydrogen energy storage according to claim 1, characterized in that, The extreme weather scenario data in step 1 is set as a scenario of 14 consecutive days of extreme cold in winter with no wind or solar power. Under this scenario, the predicted wind power output and the predicted solar power output are both set to 0, and the heat load demand data increases by 50% compared to a normal winter day.

8. The microgrid climate-adaptive scheduling method based on cross-seasonal hydrogen energy storage according to claim 1, characterized in that, The state variables in step 3 include the hydrogen charge state of the hydrogen storage tank, the charge state of the battery energy storage, and the thermal storage state of the short-term thermal storage device; the scheduling operation variables include the hydrogen production power of the electrolyzer, the power generation power of the fuel cell, the power grid interaction power, and the heating power of the electric boiler.

9. The microgrid climate-adaptive scheduling method based on cross-seasonal hydrogen energy storage according to claim 1, characterized in that, Step 5 involves solving the mixed-integer linear programming optimization model. Specifically, this includes calling a commercial optimization solver to iteratively calculate the model until the objective function converges or reaches the preset maximum number of iterations, thereby determining the hydrogen storage tank capacity in the hydrogen storage configuration parameters and the hourly output plan of each device in the combined thermal and power scheduling strategy during extremely cold periods.

10. A microgrid climate-adaptive dispatch system based on cross-seasonal hydrogen energy storage, characterized in that, include: The data acquisition module is used to acquire microgrid basic data and extreme weather scenario data. The microgrid basic data includes regular electricity load demand data, heat load demand data, wind power output forecast data, photovoltaic output forecast data, equipment capacity limit data, investment cost parameters, operation and maintenance cost parameters, efficiency parameters, capacity limitation parameters, grid dynamic carbon emission factor sequence, and fixed carbon tax price. The extreme weather scenario data includes surge heat load data and regular electricity load data superimposed under the scenario of continuous extreme cold in winter and no wind and solar power. The framework construction module is used to construct a cross-seasonal hybrid energy storage operation framework based on the acquired microgrid basic data. The operation framework includes a short-term high-frequency response layer composed of battery energy storage and short-term thermal storage equipment, and a cross-seasonal low-carbon supply layer composed of electrolyzers, hydrogen storage tanks and fuel cells. The variable definition module is used to define optimization variables for covering device configuration and multi-energy scheduling. The optimization variables include capacity configuration variables, scheduling operation variables, and status variables. The constraint establishment module is used to establish a constraint system based on the operating framework and the optimization variables, covering multi-energy balance constraints, cross-seasonal hydrogen storage dynamic constraints, fuel cell cogeneration constraints, and dynamic carbon emission constraints. The model solving module is used to construct a mixed integer linear programming optimization model with the constraint system as the objective of minimizing the annual comprehensive cost of microgrids, and solve the mixed integer linear programming optimization model to obtain hydrogen storage configuration parameters and thermal power joint scheduling strategy during extremely cold periods; The results output module is used to output scheduling characteristic curves and carbon tax sensitivity analysis results based on the solution results.

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