Multi-energy complementary operation method of micro-grid containing solid electric heat storage and hydrogen energy storage combined heat and power supply

Abstract

The invention provides a multi-energy complementary operation method for a micro-grid containing solid electric heat storage and hydrogen energy storage combined heat and power. The method comprises the following steps: firstly, constructing a micro-grid multi-energy coupling system containing solid electric heat storage and hydrogen energy storage combined heat and power; secondly, a solid heat accumulator thermal stress loss and carbon transaction mechanism is introduced, a multi-time-scale collaborative optimization model comprising a day-ahead economic dispatching layer and an intra-day real-time correction layer is constructed, a cascade thermal power balance constraint based on a temperature alignment principle is established, and a temperature alignment judgment coefficient is introduced to prevent heat countercurrent; and finally, solving the collaborative optimization model by adopting an improved whale algorithm which introduces a dynamic lens imaging reverse learning and Cauchy variation dual disturbance mechanism to obtain an optimal power distribution strategy and an operation state of each unit of the system. Based on the solid electric heat storage and hydrogen energy combined heat and power cooperative technology, deep coupling and gradient utilization of electricity-heat-hydrogen multi-energy flow are achieved, the operation economical efficiency of the system is improved, and the adaptability of the system to wind and light fluctuation is enhanced.

Description

Technical Field

[0001] This invention relates to the field of microgrid operation control and integrated energy utilization technology, and in particular to a multi-energy complementary operation method for microgrids that includes solid-state electric thermal storage and hydrogen energy storage combined heat and power. Background Technology

[0002] As the penetration rate of renewable energy sources such as wind and solar power in microgrids continues to increase, the inherent randomness and volatility of wind and solar resources pose a huge challenge to the power balance of microgrids, which can easily lead to the loss of a large amount of wind and solar power or insufficient power supply.

[0003] To address these issues, existing microgrids typically regulate energy through the configuration of energy storage batteries or traditional electric boilers. However, single electrochemical energy storage is costly and difficult to achieve long-term energy storage; while traditional direct-heating electric boilers can absorb some wind and solar energy, they lack thermal storage capabilities; and although the more common hydrogen energy storage has the ability to store energy across seasons, most focus only on the electrical energy storage aspect of hydrogen energy storage, neglecting the large amount of heat energy generated during hydrogen electrolysis and fuel cell power generation, resulting in low system energy utilization efficiency.

[0004] Existing multi-energy complementary planning or operation methods often consider heat load and electrical load separately, lacking a mechanism for tiered utilization of heat sources of different grades. At the same time, at the optimization algorithm level, traditional algorithms are prone to getting stuck in local optima when dealing with high-dimensional nonlinear models with complex physical constraints, resulting in inaccurate scheduling strategies.

[0005] Therefore, to solve the above problems, a method is needed that can utilize the high-power time-shifting characteristics of solid-state electric thermal storage and the combined heat and power characteristics of hydrogen energy storage to achieve economical and low-carbon operation of microgrids through multi-timescale collaborative optimization. Summary of the Invention

[0006] This invention proposes a multi-energy complementary operation method for microgrids that incorporates solid-state electric thermal storage and hydrogen energy storage combined heat and power. It aims to solve the problems of insufficient power supply or power generation caused by the inherent randomness and volatility of wind and solar resources in wind and solar renewable energy microgrids, as well as poor operating economy and insufficient renewable energy absorption capacity due to complex optimization models.

[0007] This invention provides a multi-energy complementary operation method for a microgrid that incorporates solid-state electric thermal storage and hydrogen energy storage combined heat and power, the method comprising the following steps:

[0008] Step S1: Construct a microgrid multi-energy coupling system based on energy quality cascade matching, including solid-state electric thermal storage and hydrogen energy storage cogeneration, and establish a thermal energy cascade utilization topology based on energy quality, with solid-state electric thermal storage undertaking high-temperature main heating and hydrogen energy storage cogeneration recovering medium and low-temperature waste heat for auxiliary heating.

[0009] Step S2: Based on the microgrid multi-energy coupling system, construct a multi-timescale collaborative optimization model including a day-ahead economic dispatch layer and an intraday real-time correction layer; wherein, the day-ahead layer aims to minimize the daily operating cost of the system and introduces the thermal stress loss of solid thermal storage and carbon trading mechanism; the intraday layer is based on model predictive control rolling optimization, with the goal of eliminating source load prediction bias.

[0010] Step S3: Based on the physical characteristics of each unit in the system, establish a multi-energy flow balance model including heat-electricity-hydrogen and the constraints of the equipment operation safety boundary.

[0011] Step S4: The improved whale algorithm, which incorporates a dual perturbation mechanism of dynamic lens imaging back learning and Cauchy mutation, is used to solve the collaborative optimization model. By balancing global search and local exploitation capabilities through a nonlinear convergence factor, the optimal power allocation strategy and operating state of each unit in the system at different time scales are obtained.

[0012] Furthermore, the specific method for constructing a microgrid multi-energy coupling system based on energy quality cascade matching, including solid-state electric thermal storage and hydrogen energy storage cogeneration, and establishing a thermal energy cascade utilization topology based on energy quality, with solid-state electric thermal storage undertaking high-temperature main heating and hydrogen energy storage cogeneration recovering medium- and low-temperature waste heat for auxiliary heating, includes:

[0013] The microgrid multi-energy coupling system includes: a renewable energy power generation unit, a solid-state electric thermal storage unit, a hydrogen energy storage combined heat and power unit, and a comprehensive load unit;

[0014] S1-1: Determine the core equipment and energy quality layering architecture of the system;

[0015] The microgrid multi-energy coupling system is divided into three levels based on heating temperature and energy quality: The first level is the high-energy-quality main heating layer, with the core equipment being a solid-state electric thermal energy storage unit. This unit consists of a high-voltage resistance heating array and a high-temperature resistant solid thermal energy storage body, storing high-temperature thermal energy during periods of high wind and solar power generation and providing high-temperature water or steam above 85°C when released. The second level is the medium-energy-quality auxiliary heating layer, with the core equipment being the fuel cell module in the hydrogen energy storage cogeneration unit. This module generates medium-temperature waste heat while generating electricity through electrochemical processes, which is then connected to the water supply end of the heating network for secondary heating via a heat exchanger. The third level is the low-energy-quality preheating and recovery layer, with the core equipment being an electrolytic water electrolysis hydrogen production module and a solid thermal energy storage body heat dissipation system. The low-temperature waste heat generated during the operation of the electrolyzer is collected and fed back into the heating network. The water pipeline provides initial preheating of the heat network return water at around 45°C; the hydrogen storage tank can mitigate the discrepancy between the random fluctuations in new energy hydrogen production and the continuous power demand of fuel cells; the electrochemical battery pack utilizes its rapid response characteristics to collaboratively maintain instantaneous power balance within the microgrid; the integrated load unit serves as the system's demand boundary and flexible adjustment resource, including industrial production and residential equipment within the microgrid area; its operating mechanism is as follows: on the heat load side, it accurately distinguishes the tiered temperature demands of high-temperature steam / heating and domestic hot water, forming a rigid constraint on the tiered heating supply on the source side; on the electrical load side, it collects user load curves in real time and utilizes the shiftable / interruptible characteristics of flexible loads to participate in demand-side response, cooperating with the source and storage sides to achieve dynamic power coordination;

[0016] S1-2: Analyze the energy and mass flow relationships between each unit and construct a "electric-thermal-hydrogen" multi-energy flow network topology; among them, the multi-energy flow coupling network of the wind-solar solid-state electric thermal storage and hydrogen cogeneration system includes energy flow and mass flow, mainly involving real-time balance of electric power, cascade balance of thermal power and dynamic balance of hydrogen mass flow.

[0017] The wind-solar multi-energy complementary system primarily utilizes electrical energy. The electricity generated by the renewable energy generation unit is prioritized for supplying the electrical load in the integrated load unit and charging the electrochemical batteries in the integrated energy storage unit. When the system detects a surplus of wind and solar power, the surplus electricity is allocated according to a priority strategy to the solid-state electrothermal storage unit for high-power electrothermal conversion and storage, and to the electrolyzer module in the hydrogen energy storage cogeneration unit for electro-hydrogen conversion. When wind and solar power generation is insufficient, the fuel cell module in the hydrogen energy storage cogeneration unit and the electrochemical batteries in the integrated energy storage unit provide discharge supplementation, or electrical energy is purchased from the external grid to maintain the power balance of the DC / AC buses. The water electrolysis hydrogen production module uses surplus electricity to produce hydrogen, which is then compressed and stored in a high-pressure hydrogen storage tank. The hydrogen storage tank is used to decouple the strong coupling between hydrogen production and consumption. During periods of renewable energy scarcity, hydrogen flows from the storage tank to the fuel cell module, and through an electrochemical reaction, it feeds back electricity to the grid via an inverter, completing the "electricity-hydrogen-electricity" energy cycle.

[0018] Furthermore, step S2 describes constructing a multi-timescale collaborative optimization model based on the microgrid multi-energy coupling system, comprising a day-ahead economic dispatch layer and an intraday real-time correction layer. The day-ahead layer aims to minimize the system's daily operating cost and incorporates solid thermal storage thermal stress loss and a carbon trading mechanism. The intraday layer utilizes model predictive control rolling optimization to eliminate source-load prediction biases. Specific methods include:

[0019] S2-1 establishes a two-layer collaborative optimization architecture of "day-ahead and intraday", constructs a two-layer optimization framework covering the day-ahead economic scheduling layer and the intraday real-time correction layer, and realizes cross-period collaboration between long-term global planning and short-term dynamic adjustment.

[0020] The upper layer is the day-ahead economic dispatch layer, with a dispatch cycle of 24 hours and a time step of 1 hour. Based on short-term forecast data of wind and solar power generation and load demand, it makes decisions on the start-up and shutdown status and baseline output plan of each unit at all times of the day, and solves the problem of supply and demand balance and economic allocation of electricity and heat in the long-term dimension.

[0021] The lower layer is the intraday real-time correction layer, with a rolling window of 1 hour and a time step of 15 minutes. Based on the day-ahead scheduling plan, and based on ultra-short-term forecast data and real-time status feedback, the model predictive control mechanism is used to perform rolling correction on the output of solid-state electric thermal storage and hydrogen energy storage cogeneration, eliminating the power deviation caused by random fluctuations on both the source and load sides.

[0022] S2-2 Establish a mathematical model for bidirectional collaborative optimization objectives;

[0023] Currently, the economic dispatch layer primarily considers the system's operating economic costs and environmental benefits, with its optimization objective being to minimize the microgrid's total daily operating cost. This cost consists of operation and maintenance costs, environmental remediation costs, and penalties for wind and solar power curtailment. The objective function is constructed as follows:

[0024]

[0025]

[0026] In the formula, The number of time periods in the scheduling cycle is set to 24 hours. The system operation and maintenance costs, covering the operating wear and tear costs of wind and solar turbines, electrolyzers, fuel cells, and auxiliary equipment, include a penalty for thermal stress loss in solid-state electric thermal storage. For equipment The unit maintenance coefficient, The thermal stress life loss coefficient caused by drastic temperature changes in the heat storage body; To mitigate environmental governance costs, a carbon trading mechanism should be introduced to balance the relationship between purchased energy and the clean energy available within the system. For carbon trading prices, These are the carbon emission factors for electricity purchased from the grid; The penalty cost for abandoning renewable energy is used to incentivize the system to maximize the utilization of wind and solar resources. For the first The device in the Output power during the time period; For the first Power purchased from the main power grid during a given period; For energy storage systems in the first Input power during a given time period.

[0027] The optimization objective of the intraday real-time correction layer is to minimize the sum of squares of the deviations between the actual output of each controllable unit and the planned value from the previous day. To ensure the stability of system operation:

[0028]

[0029] In the formula, To optimize window length for scrolling, and These are the interpolations of real-time power during the day and planned power for the day ahead, respectively. These are weighting coefficients, corresponding to the adjustment priorities of the solid-state electric thermal storage unit and the hydrogen cogeneration unit, respectively. This is the starting time period for the current rolling optimization; To optimize the time period index within the scrolling window; For solid-state electric thermal storage units in the first Real-time output during the day; For solid-state electric thermal storage units in the first The planned output for the current period; For hydrogen cogeneration units in the first Real-time output during the day; For hydrogen cogeneration units in the first The daytime plan for the period will be implemented.

[0030] Furthermore, the specific method for establishing a multi-energy flow balance model including heat-electricity-hydrogen and constraint conditions for equipment operation safety boundaries based on the physical characteristics of each unit in the system, as described in step S3, includes:

[0031] Dynamic characteristic model of solid-state electric thermal energy storage unit: Introducing temperature-loss coupling mechanism;

[0032] Considering the heat radiation loss of solid heat storage bodies in the high-temperature range, a self-heating coefficient that dynamically varies with the heat storage state is established; simultaneously, considering the difference in heat transfer capacity of gas-water heat exchangers under different temperature differences, a heat transfer efficiency correction factor is introduced:

[0033]

[0034] In the formula, for The amount of heat stored in the heat storage body at all times; This represents the maximum designed heat storage capacity of the solid heat storage body. This is the basic self-heating loss coefficient at room temperature; The input electric heating power; This refers to the output heating power; For electrothermal conversion efficiency; This is the rated heat exchange efficiency of the gas-water heat exchanger. This is the coefficient for enhancing high-temperature heat loss; This is the heat exchange temperature difference correction factor; This is the time interval between two adjacent scheduling periods.

[0035] Establish operating models for electrolyzers and fuel cells that take into account thermoelectric coupling characteristics;

[0036] Electrolyzer Operation Model: Considering the strong randomness of renewable energy input in microgrids, and the fact that electrolyzers operate under conditions of severe power fluctuations for extended periods, a dynamic nonlinear efficiency model is established, incorporating a dynamic fluctuation penalty factor. :

[0037]

[0038] In the formula, This refers to the operating temperature of the electrolytic cell. Current density; The fitting coefficients are the Faraday efficiency coefficients. As a dynamic fluctuation penalty factor; The rate of change of the input power to the electrolytic cell; and These are the volatility sensitivity coefficient and the nonlinear exponent, respectively.

[0039] Fuel cell operating model:

[0040]

[0041] In the formula, This refers to the load voltage of a single battery cell; It is the thermodynamic open-circuit voltage; These are the activation polarization, ohmic polarization, and concentration polarization overpotentials, respectively, and are all output currents. Highly nonlinear function; This is the fuel cell stack health status coefficient; For fuel utilization rate, Hydrogen has a high calorific value;

[0042] Power balance constraint: As the core driving constraint of the system, it requires that the power output, energy storage throughput and load demand remain in balance at any time.

[0043]

[0044] In the formula, for The output power of the wind turbine generator at any given time; for The output power of the photovoltaic generator set at any given time; for The output power of the fuel cell at all times; for The discharge power of the battery at all times; This refers to the interaction power between the microgrid and the external power grid. for The load demand power of the microgrid at any given time; for The heat storage input power of the solid-state electric thermal storage unit at all times; for The power consumption of the electrolytic cell at all times; for The charging power of the battery at all times;

[0045] Cascaded heat power balance constraint:

[0046]

[0047] In the formula, To meet heat load requirements; High-temperature base charge heat source for solid-state electric thermal storage; for The heat generated during the operation of the electrolytic cell; The heat utilization efficiency coefficient of the electrolytic cell; for The heat generated during the operation of the fuel cell; This is the thermal efficiency coefficient of the fuel cell. The temperature-based discrimination coefficient;

[0048] Hydrogen mass flow balance constraints:

[0049]

[0050] In the formula, The mass of hydrogen stored in the hydrogen storage tank, The hydrogen production rate of the electrolyzer. To ensure the hydrogen consumption rate of fuel cells and to guarantee the mass conservation of hydrogen during its production, storage and consumption processes;

[0051] Establish temperature boundary constraints for cascaded waste heat utilization: By comparing the outlet fluid temperature of the hydrogen energy storage cogeneration and the return water temperature of the heating network, a 0-1 state variable is introduced to control waste heat access and prevent heat backflow; Establish non-negative thermal power constraints for solid-state electric thermal storage units: When the waste heat recovered by the hydrogen energy storage cogeneration exceeds the heat load demand, the heat release power of the solid-state electric thermal storage unit is forced to be zero, and the excess heat is consumed through the heat dissipation device; Establish equipment start-up and shutdown state switching constraints: Set the minimum continuous operating time and minimum continuous shutdown time of the solid-state electric thermal storage unit to limit frequent equipment operation.

[0052] Furthermore, the improved whale algorithm described in step S4, which incorporates a dual perturbation mechanism of dynamic lens imaging back learning and Cauchy mutation, is used to solve the collaborative optimization model. The specific method for obtaining the optimal power allocation strategy and operating state of each unit in the system at different time scales by balancing global search and local exploitation capabilities through a nonlinear convergence factor includes:

[0053] Decision variables This represents the individual position vector of the algorithm in the search space, i.e., the microgrid's position vector throughout the entire scheduling cycle. The set of control instructions for all controllable units within;

[0054] Assuming the population size is , No. The decision vector of an individual is defined as:

[0055]

[0056] In the formula, Electric heating input power including solid-state electric thermal storage With heat energy release output power ; Includes hydrogen production power of the electrolyzer throughout the entire time period With fuel cell power generation ; Charging power including the battery With discharge power Used to maintain the instantaneous power balance of the DC bus; Includes the interaction power between the microgrid and the external power grid. ; Temperature discrimination coefficient for the entire time period ;

[0057] The initial population is generated using real-number encoding and Sobol low-bias sequences. Leveraging their uniform distribution within the hypercube, this replaces pseudo-random initialization, enabling the initial solution to more evenly cover the feasible domain of solid-state thermal energy storage and hydrogen energy dispatch. The formula is as follows:

[0058]

[0059] In the formula, For the first Boundaries of dimensional decision variables, The Sobol quasi-random sequence values ​​are used; nonlinear dynamic parameter adjustment is employed; individual fitness is calculated, and the global optimal solution is recorded. ; through nonlinear time-varying convergence factor and adaptive inertia weights The dynamic balancing algorithm's global exploration and local exploitation capabilities; with the number of iterations... The increase, It exhibits nonlinear decay. It increases non-linearly; in the global exploration phase, through a stochastic learning strategy, solutions that are better than the current individual in the population are randomly selected for differential guidance to correct the scheduling instructions of solid-state electric thermal storage; in the local development phase, a spiral bubble net predation mechanism is executed to approximate the current global optimal solution with a logarithmic spiral path;

[0060] A dynamic lens imaging back-learning mechanism is introduced; after each iteration update, the current global best individual is... Perform dynamic lens imaging back-learning, using the reference boundary as the lens center, to generate a back-mapping solution for the current optimal solution. To broaden the search scope:

[0061]

[0062] In the formula, , Decision variables The upper and lower boundaries; The lens scaling factor is adjusted non-linearly with the number of iterations; calculation If the fitness is better than If it is directly replaced, then the deterministic geometric law will escape the local extremum trap;

[0063] Apply a Cauchy variation "long-tail" perturbation; if the lens imaging mechanism fails to update the optimal solution and the algorithm stagnates, i.e., continuously... If the optimal value is not improved, then Cauchy mutation is applied to the optimal individual:

[0064]

[0065] In the formula, The term is a standard Cauchy distribution random perturbation term with intensity control. Its function is to apply a random change with "long-tail characteristics" to the optimal individual, so that it can escape local extrema when it is stagnant.

[0066] Leveraging the Cauchy distribution's significantly higher probability of generating large-span random numbers compared to the ordinary normal distribution, this algorithm exhibits the ability to "jump large distances" when the algorithm is not effectively updated. This effectively avoids minimizing local costs in microgrid scheduling, ensuring that the obtained solid-state thermal energy storage and hydrogen energy coordinated scheduling scheme is the globally optimal solution. The algorithm takes as input wind and solar renewable energy output forecast data, electrical load and heat load demand categorized by energy quality, and dynamic model parameters. After solving using the improved whale algorithm, it outputs the optimal operating strategy set for the microgrid system.

[0067]

[0068] In the formula, Corresponding to the above decision variables Optimize the optimal value after convergence; Including real-time heat storage of solid heat storage materials Hydrogen storage mass in hydrogen storage tank State of charge of the battery ; This includes the lowest total daily operating cost of the output system during the scheduling period. Or minimum power deviation sum of squares .

[0069] Compared with the prior art, the present invention has the following advantages:

[0070] The positive effects of this invention are as follows:

[0071] 1. This invention constructs a unified electric-thermal-hydrogen multi-energy flow coupling mechanism model, which solves the problem of imprecise modeling of combined heat and power in the prior art, and provides solid theoretical support for the refined management of microgrids.

[0072] 2. The two-layer collaborative optimization framework of this invention achieves the unity of planning and real-time performance. It ensures the lowest global cost through the day-ahead economic scheduling layer and eliminates prediction deviations through the intraday real-time correction layer, ensuring that the deviation of the optimal scheduling scheme is minimized.

[0073] 3. By introducing carbon trading mechanisms and operation and maintenance costs into the objective function, a quantifiable economic operation path is provided for the low-carbon transformation of microgrids.

[0074] 4. This invention enhances the absorption capacity of renewable energy and effectively reduces the curtailment rate of wind and solar power; realizes energy cascade utilization and improves the system's energy utilization rate; enhances the flexibility of system operation and realizes energy transfer across time scales; ensures the service life of solid-state electric thermal storage equipment and improves the safety of system operation.

[0075] Based on the implementation methods provided in the above aspects, this application can be further combined to provide more implementation methods. Attached Figure Description

[0076] The above and other objects, features, and advantages of exemplary embodiments of the present invention will become readily apparent upon reading the following detailed description with reference to the accompanying drawings. In the drawings, several embodiments of the invention are illustrated by way of example and not limitation, with the same or corresponding reference numerals denoteing the same or corresponding parts, wherein:

[0077] Figure 1 This is a patent flowchart of the multi-energy complementary operation method of microgrid containing solid-state electric thermal storage and hydrogen energy storage combined heat and power of the present invention;

[0078] Figure 2 This is a structural diagram of the microgrid system of the present invention;

[0079] Figure 3 This is a flowchart of the improved lens imaging dual-perturbation whale algorithm solution of the present invention. Detailed Implementation

[0080] The exemplary embodiments disclosed in this application will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of this application are shown in the drawings, it should be understood that this application can be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of this application and to fully convey the scope of this application to those skilled in the art. Unless otherwise specified, the technical means used in the embodiments are conventional means well known to those skilled in the art.

[0081] Combination Figure 1 As shown, an optional embodiment of the present invention provides a multi-energy complementary operation method for a microgrid containing solid-state electric thermal storage and hydrogen energy storage combined heat and power. The specific steps of the method include:

[0082] S1. Construct a multi-energy coupled microgrid system structure based on energy quality cascade matching, including solid-state electric thermal storage and hydrogen energy storage. The system includes: a renewable energy generation unit, a solid-state electric thermal storage unit, a hydrogen energy storage cogeneration unit, and a comprehensive load unit, such as... Figure 2 As shown, based on the energy grade, a three-level thermal energy cascade utilization topology is established, consisting of solid-state electric thermal storage providing high-temperature main heating, fuel cells providing medium-temperature auxiliary heating, and waste heat from electrolyzers providing low-temperature preheating and return water, to achieve efficient absorption of clean energy and cascade utilization of thermal energy.

[0083] Specifically, S1.1 defines the core equipment and energy quality hierarchical architecture of the system: The microgrid multi-energy coupling system is divided into three levels based on heating temperature and energy quality: The first level is the high-energy-quality main heating layer, with the core equipment being a solid-state electric thermal energy storage unit. This unit consists of a high-voltage resistance heating array and a high-temperature resistant solid thermal energy storage body, storing high-temperature thermal energy during periods of high wind and solar power generation, and providing high-temperature water or steam above 85°C when released; The second level is the medium-energy-quality auxiliary heating layer, with the core equipment being the fuel cell module in the hydrogen energy storage cogeneration unit. This module generates medium-temperature waste heat while generating electricity through electrochemical processes, which is then connected to the heating network water supply end for secondary heating via a heat exchanger; The third level is the low-energy-quality preheating and recovery layer, with the core equipment being the water electrolysis hydrogen production module and the solid thermal energy storage heat dissipation system. The low-temperature waste heat generated by the operation of the electrolyzer is channeled into the heating network return water pipeline to perform primary preheating of the heating network return water at around 45°C. Hydrogen storage tanks can mitigate the discrepancy between the random fluctuations in new energy hydrogen production and the continuous power demand of fuel cells; electrochemical battery packs, with their rapid response characteristics, collaboratively maintain instantaneous power balance within the microgrid. Integrated load units, serving as the system's demand boundary and flexible regulation resource, include industrial production and residential equipment within the microgrid area. Their operating mechanism involves: on the heat load side, precisely distinguishing the tiered temperature demands of high-temperature steam / heating and domestic hot water, forming a rigid constraint on tiered heating supply from the source side; on the electrical load side, real-time acquisition of user load curves, and utilizing the shiftable / interruptible characteristics of flexible loads to participate in demand-side response, coordinating with the source and storage sides to achieve dynamic power synergy. This unit transforms traditional passive energy consumption into active system regulation resources, effectively improving the microgrid's dynamic balance capability and operational economy under complex supply and demand conditions.

[0084] S1.2 Analyzes the energy and mass flow relationships between each unit and constructs a "electric-thermal-hydrogen" multi-energy flow network topology. Among them, the multi-energy flow coupling network of the wind-solar solid-state electric thermal storage and hydrogen cogeneration system includes energy flow and mass flow, mainly involving real-time balance of electric power, cascade balance of thermal power, and dynamic balance of hydrogen mass flow.

[0085] The wind-solar multi-energy complementary system primarily utilizes electrical energy. The electricity generated by the renewable energy generation unit is prioritized for supplying the electrical load in the integrated load unit and charging the electrochemical batteries in the integrated energy storage unit. When the system detects a surplus of wind and solar power, the surplus electricity is allocated according to a priority strategy to the solid-state electrothermal storage unit for high-power electrothermal conversion and storage, and to the electrolyzer module in the hydrogen energy storage cogeneration unit for electro-hydrogen conversion. When wind and solar power generation is insufficient, the fuel cell module in the hydrogen energy storage cogeneration unit and the electrochemical batteries in the integrated energy storage unit provide supplementary discharge, or electricity is purchased from the external grid to maintain the power balance of the DC / AC buses. The water electrolysis hydrogen production module uses surplus electricity to produce hydrogen, which is then compressed and stored in a high-pressure hydrogen storage tank. The hydrogen storage tank decouples the strong coupling between hydrogen production and consumption. During periods of renewable energy scarcity, hydrogen flows from the storage tank to the fuel cell module, where it undergoes an electrochemical reaction and is fed back to the grid via an inverter, completing the "electricity-hydrogen-electricity" energy cycle.

[0086] Solid-state electric thermal energy storage unit serves as the main heat source, directly supplying the main water supply line to the heat load demand side using the high-temperature thermal energy it stores. Hydrogen energy storage combined heat and power unit serves as an auxiliary heat source, collecting the low-temperature waste heat generated by the operation of the electrolyzer and the medium-temperature waste heat generated by the operation of the fuel cell into the return water pipeline of the heating network for preheating, thereby reducing the temperature rise pressure of the solid-state electric thermal energy storage unit. All heat sources work together to ensure the constant temperature and continuous supply of heat load in the integrated load unit.

[0087] S2. Establish the nonlinear dynamic mathematical model of the system structure and each unit, and construct a multi-timescale collaborative optimization model including a day-ahead economic scheduling layer and an intraday real-time correction layer. The day-ahead layer introduces the thermal stress loss of solid thermal storage and a carbon trading mechanism, aiming to minimize the system's daily operating cost for long-scale planning; the intraday layer uses model predictive control rolling optimization to perform short-scale correction with the goal of eliminating source-load prediction bias, achieving intertemporal coupling between long and short timescales.

[0088] Specifically, S2.1 establishes a two-layer collaborative optimization architecture of "day-ahead and intraday," constructing a two-layer optimization framework encompassing a day-ahead economic dispatch layer and an intraday real-time correction layer, achieving cross-period collaboration between long-term global planning and short-term dynamic adjustment. The upper layer is the day-ahead economic dispatch layer, with a 24-hour dispatch cycle and a time step of 1 hour. Based on short-term forecast data of wind and solar power generation and load demand, it determines the start-up and shutdown status and baseline output plan of each unit at each time period of the day, solving the supply and demand balance and economic allocation problem of electricity and heat energy in the long-term dimension. The lower layer is the intraday real-time correction layer, with a 1-hour rolling window and a time step of 15 minutes. Based on the day-ahead dispatch plan, and based on ultra-short-term forecast data and real-time status feedback, it uses a model predictive control mechanism to perform rolling correction on the output of solid-state electric thermal storage and hydrogen energy storage combined heat and power, eliminating power deviations caused by random fluctuations on both the source and load sides.

[0089] S2.2 Establishing a mathematical model for bidirectional collaborative optimization objectives

[0090] Currently, the economic dispatch layer primarily considers the system's operating economic costs and environmental benefits, with its optimization objective being to minimize the microgrid's total daily operating cost. This cost consists of operation and maintenance costs, environmental remediation costs, and penalties for wind and solar power curtailment. The objective function is constructed as follows:

[0091]

[0092] In the formula, The number of time periods in the scheduling cycle is set to 24 hours. The system operation and maintenance costs, covering the operating wear and tear costs of wind and solar turbines, electrolyzers, fuel cells, and auxiliary equipment, include a penalty for thermal stress loss in solid-state electric thermal storage. For equipment The unit maintenance coefficient, The thermal stress life loss coefficient caused by drastic temperature changes in the heat storage body; To mitigate environmental governance costs, a carbon trading mechanism should be introduced to balance the relationship between purchased energy and the clean energy available within the system. For carbon trading prices, These are the carbon emission factors for electricity purchased from the grid; The penalty cost for abandoning renewable energy is used to incentivize the system to maximize the utilization of wind and solar resources.

[0093] The optimization objective of the intraday real-time correction layer is to minimize the sum of squares of the deviations between the actual output of each controllable unit and the planned value from the previous day. To ensure the stability of system operation:

[0094]

[0095] In the formula, To optimize window length for scrolling, and These are the interpolations of real-time power during the day and planned power for the day ahead, respectively. These are weighting coefficients, corresponding to the adjustment priorities of the solid-state electric thermal storage unit and the hydrogen cogeneration unit, respectively.

[0096] S3. Based on the physical characteristics of each unit in the system, establish a multi-energy flow balance model including heat, electricity, and hydrogen, as well as constraints on the safety boundaries of equipment operation. Specifically, construct a tiered heat power balance constraint based on the temperature matching principle, introduce a temperature matching discrimination coefficient to prevent heat backflow, and combine this with the dynamic balance of hydrogen mass flow to establish the feasible region for the system's multi-mass energy flow operation.

[0097] Dynamic characteristic model of solid-state electric thermal storage unit: Introducing a temperature-loss coupling mechanism. Considering the heat radiation loss of the solid thermal storage body in the high-temperature range, a self-heating coefficient that dynamically changes with the thermal storage state is established; simultaneously, considering the difference in heat transfer capacity of the gas-water heat exchanger under different temperature differences, a heat transfer efficiency correction factor is introduced.

[0098]

[0099] In the formula, for The amount of heat stored in the heat storage body at all times; This represents the maximum designed heat storage capacity of the solid heat storage body. This is the basic self-heating loss coefficient at room temperature; The input electric heating power; This refers to the output heating power; For electrothermal conversion efficiency; This is the rated heat exchange efficiency of the gas-water heat exchanger. This is the coefficient for enhancing high-temperature heat loss; This is the heat exchange temperature difference correction factor.

[0100] Establish operating models for electrolyzers and fuel cells that take into account thermoelectric coupling characteristics.

[0101] Electrolyzer Operation Model: Considering the strong randomness of renewable energy input in microgrids, and the fact that electrolyzers operate under conditions of severe power fluctuations for extended periods, a dynamic nonlinear efficiency model is established, incorporating a dynamic fluctuation penalty factor. :

[0102]

[0103] In the formula, This refers to the operating temperature of the electrolytic cell. Current density; The fitting coefficients are the Faraday efficiency coefficients. As a dynamic fluctuation penalty factor; The rate of change of the input power to the electrolytic cell; and These are the volatility sensitivity coefficient and the nonlinear exponent, respectively.

[0104] Fuel cell operating model:

[0105]

[0106] in, This refers to the load voltage of a single battery cell; It is the thermodynamic open-circuit voltage; These are the activation polarization, ohmic polarization, and concentration polarization overpotentials, respectively, and are all output currents. Highly nonlinear function; This is the fuel cell stack health status coefficient; For fuel utilization rate, It has a high calorific value for hydrogen.

[0107] Power balance constraint: As the core driving constraint of the system, it requires that the power output, energy storage throughput and load demand remain in balance at any time.

[0108]

[0109] In the formula, for The output power of the wind turbine generator at any given time; for The output power of the photovoltaic generator set at any given time; for The output power of the fuel cell at all times; for The discharge power of the battery at all times; This refers to the interaction power between the microgrid and the external power grid. for The load demand power of the microgrid at any given time; for The heat storage input power of the solid-state electric thermal storage unit at all times; for The power consumption of the electrolytic cell at all times; for The charging power of the battery at all times.

[0110] Cascaded heat power balance constraint:

[0111]

[0112] In the formula, To meet heat load requirements; High-temperature base charge heat source for solid-state electric thermal storage; for The heat generated during the operation of the electrolytic cell; The heat utilization efficiency coefficient of the electrolytic cell; for The heat generated during the operation of the fuel cell; This is the thermal efficiency coefficient of the fuel cell. Temperature discrimination coefficient

[0113] Hydrogen mass flow balance constraints:

[0114]

[0115] In the formula, The mass of hydrogen stored in the hydrogen storage tank, The hydrogen production rate of the electrolyzer. To ensure the hydrogen consumption rate of fuel cells and maintain the mass conservation of hydrogen during production, storage, and consumption.

[0116] Establish temperature boundary constraints for cascaded waste heat utilization: By comparing the outlet fluid temperature of the hydrogen energy storage cogeneration system with the return water temperature of the heating network, a 0-1 state variable is introduced to control waste heat access and prevent heat backflow. Establish non-negative thermal power constraints for solid-state electric thermal storage units: When the waste heat recovered by the hydrogen energy storage cogeneration system exceeds the heat load demand, the heat release power of the solid-state electric thermal storage unit is forced to zero, and the excess heat is consumed through the heat dissipation device. Establish equipment start-up and shutdown state switching constraints: Set the minimum continuous operating time and minimum continuous shutdown time of the solid-state electric thermal storage unit to limit frequent equipment operation.

[0117] The collaborative optimization model established in this invention is subject to strict constraints imposed by the aforementioned series of physical and operational constraints. These constraints establish safety boundaries for the independent operation of the solid-state electric thermal storage and hydrogen cogeneration units, enabling collaborative optimization of the microgrid multi-energy complementary system. By understanding the microgrid system containing solid-state electric thermal storage and hydrogen energy as a coupled network of energy flow and hydrogen mass flow, the real-time balance and dynamic optimal allocation of the system's multi-mass energy flow at different time scales are ensured.

[0118] S4. An improved whale algorithm, incorporating a dual perturbation mechanism of dynamic lens imaging reverse learning and Cauchy mutation, is used to solve the collaborative optimization model. By balancing global search and local exploitation capabilities through a nonlinear convergence factor, the optimal power allocation strategy and operating state of each unit in the system at different time scales are obtained.

[0119] Specifically, decision variables This represents the individual position vector of the algorithm in the search space, i.e., the microgrid's position vector throughout the entire scheduling cycle. The set of control instructions for all controllable units within the population. Assume the population size is... , No. The decision vector of an individual is defined as:

[0120]

[0121] In the formula, Electric heating input power including solid-state electric thermal storage With heat energy release output power ; Includes hydrogen production power of the electrolyzer throughout the entire time period With fuel cell power generation ; Charging power including the battery With discharge power Used to maintain the instantaneous power balance of the DC bus; Includes the interaction power between the microgrid and the external power grid. ; Temperature discrimination coefficient for the entire time period .

[0122] The initial population is generated using real-number encoding and Sobol low-bias sequences. Leveraging their uniform distribution within the hypercube, this replaces pseudo-random initialization, enabling the initial solution to more evenly cover the feasible domain of solid-state thermal energy storage and hydrogen energy dispatch. The formula is as follows:

[0123]

[0124] In the formula, For the first Boundaries of dimensional decision variables, This is a Sobol quasi-random sequence value.

[0125] Nonlinear dynamic parameter adjustment. Calculate individual fitness and record the global optimal solution. Through nonlinear time-varying convergence factors and adaptive inertia weights The dynamic balancing algorithm's global exploration and local exploitation capabilities. With the number of iterations... The increase, It exhibits nonlinear decay. It increases non-linearly. In the global exploration phase, a stochastic learning strategy is used to randomly select solutions in the population that are better than the current individual for differential guidance, thereby correcting the scheduling instructions for solid-state electric thermal storage. In the local development phase, a spiral bubble net predation mechanism is executed to approximate the current global optimum with a logarithmic spiral path.

[0126] A dynamic lens imaging back-learning mechanism is introduced. After each iteration update, the current globally optimal individual is... Perform dynamic lens imaging back-learning, using the reference boundary as the lens center, to generate a back-mapping solution for the current optimal solution. To broaden the search scope:

[0127]

[0128] In the formula, This is the lens scaling factor, which adjusts non-linearly with the number of iterations. Calculation If the fitness is better than If the value is directly replaced, then the deterministic geometric law can be used to escape the local extremum trap.

[0129] Apply a Cauchy variation "long-tail" perturbation. If the lens imaging mechanism fails to update the optimal solution and the algorithm stagnates, i.e., continuously... If the optimal value is not improved, then Cauchy mutation is applied to the optimal individual:

[0130]

[0131] Leveraging the Cauchy distribution's significantly higher probability of generating large-span random numbers compared to the ordinary normal distribution, the algorithm exhibits the ability to "jump large distances" when the algorithm is not effectively updated. This effectively avoids minimizing local costs in microgrid scheduling, ensuring that the obtained solid-state thermal energy storage and hydrogen energy coordinated scheduling scheme is the globally optimal solution. The algorithm takes as input wind and solar renewable energy output forecast data, electrical load and heat load demand categorized by energy quality, and dynamic model parameters. After solving using an improved whale algorithm, it outputs the optimal operating strategy set for the microgrid system.

[0132]

[0133] In the formula, Corresponding to the above decision variables Optimize the optimal value after convergence; Including real-time heat storage of solid heat storage materials Hydrogen storage mass in hydrogen storage tank State of charge of the battery ; This includes the lowest total daily operating cost of the output system during the scheduling period. Or minimum power deviation sum of squares .

[0134] Example

[0135] 1. Construction of a multi-energy coupling system for microgrids;

[0136] A microgrid multi-energy coupled system incorporating solid-state electric thermal storage and hydrogen energy storage cogeneration consists of renewable energy generation units, solid-state electric thermal storage units, hydrogen energy storage cogeneration units, and integrated load units, such as... Figure 2 As shown, based on the energy grade, a three-level thermal energy cascade utilization topology is established, consisting of solid-state electric thermal storage for high-temperature main heating, fuel cells for medium-temperature auxiliary heating, and waste heat from electrolyzers for low-temperature preheating and return water, to achieve efficient absorption of clean energy and cascade utilization of thermal energy.

[0137] 2. Multi-energy complementary and coordinated operation method;

[0138] By preprocessing collected wind and solar power output forecast data, load demand data, and electricity / carbon price data, a two-tiered collaborative optimization model of "day-ahead and intraday" is constructed and solved using an improved algorithm. Specific steps include: selecting historical data from typical days or seasons to generate a 1-hour day-ahead forecast sequence and a 15-minute intraday ultra-short-term forecast sequence; establishing a day-ahead economic dispatch function with the objective of minimizing the total daily operating cost of the system. The cost model covers equipment operation and maintenance costs, grid interaction costs, and carbon trading costs. Constraints include: electricity / heat power balance, equipment start-up and ramp-up constraints, and energy storage SOC constraints. Intraday real-time correction establishes a rolling optimization model based on model predictive control. The objective function is to minimize the sum of squares of the deviations between the actual output of each controllable unit and the day-ahead planned value, focusing on correcting power errors caused by random fluctuations in wind and solar power.

[0139] Energy flow balance constraints: Establish a cascade heating balance equation based on temperature matching, i.e.

[0140]

[0141] Furthermore, to ensure the feasibility of the tiered heating strategy, a temperature matching determination mechanism and waste heat absorption boundary conditions are introduced:

[0142] Considering that the fluid outlet temperature of hydrogen energy storage combined heat and power (CHP) may be lower than the return water temperature of the heating network during the initial startup or low-power operation phase, forced heat exchange could lead to heat backflow. Therefore, a temperature matching discrimination coefficient is introduced. Assume the system has a temperature monitoring point to collect the outlet temperature of the hydrogen energy storage cogeneration heat recovery loop in real time. With heating network return water temperature .

[0143] When satisfied At that time, it was determined that the conditions for waste heat recovery were met, so that... Open the heat exchange valve; otherwise, make Fluid bypass is either directly connected or discharged via a radiator. Among them, This is the minimum effective heat exchange temperature difference set.

[0144] Considering the special operating conditions during grid peak shaving, the waste heat generated by hydrogen energy storage combined heat and power may exceed the current heat load demand. To prevent the model from calculating the output power of the solid-state electric thermal storage unit. When the value is negative, a corrected constraint is applied to the stage heat balance equation:

[0145]

[0146] In the formula, This boundary condition ensures that the solid-state electric thermal storage unit automatically stops releasing heat when there is excess waste heat, and the excess heat is dissipated through auxiliary heat dissipation devices, thereby ensuring energy conservation.

[0147] Equipment physical constraints: A variable operating condition nonlinear efficiency model for the electrolyzer and fuel cell is introduced to accurately describe the coupling relationship between load rate and heat / hydrogen production; a dynamic thermal balance equation for the solid thermal storage device is introduced to consider self-heating loss.

[0148] 3. Improve the algorithm's solution process;

[0149] For the constructed high-dimensional, nonlinear two-layer collaborative optimization model, an improved adaptive algorithm is used for solving it, such as... Figure 3 As shown, this algorithm overcomes the shortcomings of traditional algorithms that are prone to getting trapped in local optima, ensuring the global optimality of the solid-state electric thermal storage and hydrogen energy dispatch strategy. The specific execution steps are as follows:

[0150] Step 1: Variable Encoding and Population Initialization

[0151] A real-number encoding method is used to map the charging and discharging heat power of solid-state thermal energy storage units, the hydrogen production power of electrolyzers, the power generation power of fuel cells, and the energy storage state variables into individual decision variable vectors for each scheduling period. The traditional pseudo-random initialization method is abandoned, and a low-biased Sobol sequence is introduced to generate the initial population. Utilizing the uniform distribution of the Sobol sequence within the hypercube, the initial solution can uniformly cover the feasible region of microgrid scheduling, providing a high-quality search starting point for the algorithm.

[0152] Step 2: Dynamic update of nonlinear parameters

[0153] During the iterative optimization process, the fitness value of each individual in the population is calculated, which is the total daily operating cost of the system, and the current global optimal solution is recorded. .

[0154] Introducing a nonlinear time-varying convergence factor and adaptive inertia weights As the number of iterations increases The increase, It exhibits nonlinear decay. The step size increases non-linearly. This mechanism allows the algorithm to have a larger search step size in the early stages of iteration for global exploration, and a smaller step size in the later stages of iteration for local refinement.

[0155] Step 3: Execute the hybrid search strategy

[0156] Based on the convergence factor The algorithm automatically switches search modes based on the value of the input. During the global exploration phase, a stochastic learning strategy is introduced, randomly selecting solutions from the population that are superior to the current individual for differential guidance, replacing blind random walks. During the local development phase, a spiral bubble net predation mechanism is implemented, using a logarithmic spiral path to approximate the current global optimum, simulating whale predation behavior for high-precision optimization.

[0157] Step 4: Dual anti-precocious puberty disturbance mechanism

[0158] To address the problem of multi-peak functions easily getting trapped in local extrema, a dual perturbation is introduced.

[0159] Deterministic perturbation: for the current global optimal solution Perform dynamic lens imaging back-end learning. Using the search boundary as a lens, calculate its back-mapped solution. .like Its fitness is better than If the result is not found, it is directly replaced. This mechanism utilizes the principles of geometric optics to expand the search breadth based on deterministic patterns.

[0160] Random perturbation: If the deterministic perturbation fails to update the optimal solution and the algorithm is continuous If generation stagnates, Cauchy mutation is applied to the optimal individual. Utilizing the long-tail characteristic of the Cauchy distribution, large-step mutations are generated, forcing the algorithm to escape the local convergence region.

[0161] Step 5: Constraint Handling and Policy Output

[0162] Boundary checks are performed on the updated population, and individuals that violate physical constraints are corrected using a dynamic penalty function method. The above steps are repeated until the maximum number of iterations is reached, ultimately outputting the optimal power allocation strategy for each microgrid unit on both day-ahead and intraday scales.

[0163] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A multi-energy complementary operation method for a microgrid incorporating solid-state electric thermal storage and hydrogen energy storage combined heat and power, characterized in that, The method includes the following steps: Step S1: Construct a microgrid multi-energy coupling system based on energy quality cascade matching, including solid-state electric thermal storage and hydrogen energy storage cogeneration, and establish a thermal energy cascade utilization topology based on energy quality, with solid-state electric thermal storage undertaking high-temperature main heating and hydrogen energy storage cogeneration recovering medium and low-temperature waste heat for auxiliary heating. Step S2: Based on the microgrid multi-energy coupling system, construct a multi-timescale collaborative optimization model including a day-ahead economic dispatch layer and an intraday real-time correction layer; wherein, the day-ahead layer aims to minimize the daily operating cost of the system and introduces the thermal stress loss of solid thermal storage and carbon trading mechanism; the intraday layer is based on model predictive control rolling optimization, with the goal of eliminating source load prediction bias. Step S3: Based on the physical characteristics of each unit in the system, establish a multi-energy flow balance model including heat-electricity-hydrogen and the constraints of the equipment operation safety boundary. Step S4: The improved whale algorithm, which incorporates a dual perturbation mechanism of dynamic lens imaging back learning and Cauchy mutation, is used to solve the collaborative optimization model. By balancing global search and local exploitation capabilities through a nonlinear convergence factor, the optimal power allocation strategy and operating state of each unit in the system at different time scales are obtained.

2. The multi-energy complementary operation method of a microgrid containing solid-state electric thermal storage and hydrogen energy storage combined heat and power as described in claim 1, characterized in that, The specific method for constructing a microgrid multi-energy coupling system based on energy quality cascade matching, including solid-state electric thermal storage and hydrogen energy storage cogeneration, and establishing a thermal energy cascade utilization topology based on energy quality, with solid-state electric thermal storage providing high-temperature main heating and hydrogen energy storage cogeneration recovering medium- and low-temperature waste heat for auxiliary heating, includes: The microgrid multi-energy coupling system includes: a renewable energy power generation unit, a solid-state electric thermal storage unit, a hydrogen energy storage combined heat and power unit, and a comprehensive load unit; S1-1: Determine the core equipment and energy quality layering architecture of the system; The microgrid multi-energy coupling system is divided into three levels based on heating temperature and energy quality: The first level is the high-energy-quality main heating layer, with the core equipment being a solid-state electric thermal energy storage unit. This unit consists of a high-voltage resistance heating array and a high-temperature resistant solid thermal energy storage body, storing high-temperature thermal energy during periods of high wind and solar power generation and providing high-temperature water or steam above 85°C when released. The second level is the medium-energy-quality auxiliary heating layer, with the core equipment being the fuel cell module in the hydrogen energy storage cogeneration unit. This module generates medium-temperature waste heat while generating electricity through electrochemical processes, which is then connected to the water supply end of the heating network for secondary heating via a heat exchanger. The third level is the low-energy-quality preheating and recovery layer, with the core equipment being an electrolytic water electrolysis hydrogen production module and a solid thermal energy storage body heat dissipation system. The low-temperature waste heat generated during the operation of the electrolyzer is collected and fed back into the heating network. The water pipeline provides initial preheating of the heat network return water at around 45°C; the hydrogen storage tank can mitigate the discrepancy between the random fluctuations in new energy hydrogen production and the continuous power demand of fuel cells; the electrochemical battery pack utilizes its rapid response characteristics to collaboratively maintain instantaneous power balance within the microgrid; the integrated load unit serves as the system's demand boundary and flexible adjustment resource, including industrial production and residential equipment within the microgrid area; its operating mechanism is as follows: on the heat load side, it accurately distinguishes the tiered temperature demands of high-temperature steam / heating and domestic hot water, forming a rigid constraint on the tiered heating supply on the source side; on the electrical load side, it collects user load curves in real time and utilizes the shiftable / interruptible characteristics of flexible loads to participate in demand-side response, cooperating with the source and storage sides to achieve dynamic power coordination; S1-2: Analyze the energy and mass flow relationships between each unit and construct the "electric-thermal-hydrogen" multi-energy flow network topology; among them, the multi-energy flow coupling network of the wind-solar solid-state electric thermal storage and hydrogen cogeneration system includes energy flow and mass flow, mainly involving real-time balance of electric power, cascade balance of thermal power and dynamic balance of hydrogen mass flow. The wind-solar multi-energy complementary system primarily utilizes electrical energy. The electricity generated by the renewable energy generation unit is prioritized for supplying the electrical load in the integrated load unit and charging the electrochemical batteries in the integrated energy storage unit. When the system detects a surplus of wind and solar power, the surplus electricity is allocated according to a priority strategy to the solid-state electrothermal storage unit for high-power electrothermal conversion and storage, and to the electrolyzer module in the hydrogen energy storage cogeneration unit for electro-hydrogen conversion. When wind and solar power generation is insufficient, the fuel cell module in the hydrogen energy storage cogeneration unit and the electrochemical batteries in the integrated energy storage unit provide discharge supplementation, or electrical energy is purchased from the external grid to maintain the power balance of the DC / AC buses. The water electrolysis hydrogen production module uses surplus electricity to produce hydrogen, which is then compressed and stored in a high-pressure hydrogen storage tank. The hydrogen storage tank is used to decouple the strong coupling between hydrogen production and consumption. During periods of renewable energy scarcity, hydrogen flows from the storage tank to the fuel cell module, and through an electrochemical reaction, it feeds back electricity to the grid via an inverter, completing the "electricity-hydrogen-electricity" energy cycle.

3. The multi-energy complementary operation method of a microgrid containing solid-state electric thermal storage and hydrogen energy storage combined heat and power as described in claim 1, characterized in that, Step S2 describes constructing a multi-timescale collaborative optimization model based on the microgrid multi-energy coupling system, comprising a day-ahead economic dispatch layer and an intraday real-time correction layer. The day-ahead layer aims to minimize the system's daily operating cost and incorporates solid thermal storage thermal stress loss and a carbon trading mechanism. The intraday layer utilizes model predictive control rolling optimization to eliminate source-load prediction biases. Specific methods include: S2-1 establishes a "day-to-day" two-layer collaborative optimization architecture, constructs a two-layer optimization framework covering the day-to-day economic scheduling layer and the intraday real-time correction layer, and realizes cross-period collaboration between long-term global planning and short-term dynamic adjustment. The upper layer is the day-ahead economic dispatch layer, with a dispatch cycle of 24 hours and a time step of 1 hour. Based on short-term forecast data of wind and solar power generation and load demand, it makes decisions on the start-up and shutdown status and baseline output plan of each unit at all times of the day, and solves the problem of supply and demand balance and economic allocation of electricity and heat in the long-term dimension. The lower layer is the intraday real-time correction layer, with a rolling window of 1 hour and a time step of 15 minutes. Based on the day-ahead scheduling plan, and based on ultra-short-term forecast data and real-time status feedback, the model predictive control mechanism is used to perform rolling correction on the output of solid-state electric thermal storage and hydrogen energy storage cogeneration, eliminating the power deviation caused by random fluctuations on both the source and load sides. S2-2 Establish a mathematical model for bidirectional collaborative optimization objectives; Currently, the economic dispatch layer primarily considers the system's operating economic costs and environmental benefits, with its optimization objective being to minimize the microgrid's total daily operating cost. This cost consists of operation and maintenance costs, environmental remediation costs, and penalties for wind and solar power curtailment. The objective function is constructed as follows: ； ； In the formula, The number of time periods in the scheduling cycle is set to 24 hours. The system operation and maintenance costs, covering the operating wear and tear costs of wind and solar turbines, electrolyzers, fuel cells, and auxiliary equipment, include a penalty for thermal stress loss in solid-state electric thermal storage. For equipment The unit maintenance coefficient, The thermal stress life loss coefficient caused by drastic temperature changes in the heat storage body; To mitigate environmental governance costs, a carbon trading mechanism should be introduced to balance the relationship between purchased energy and the clean energy available within the system. For carbon trading prices, These are the carbon emission factors for electricity purchased from the grid; The penalty cost for abandoning renewable energy is used to incentivize the system to maximize the utilization of wind and solar resources. For the first The device in the Output power during the time period; For the first Power purchased from the main power grid during a given period; For energy storage systems in the first Input power during the time period; The optimization objective of the intraday real-time correction layer is to minimize the sum of squares of the deviations between the actual output of each controllable unit and the planned value from the previous day. To ensure the stability of system operation: ； In the formula, To optimize window length for scrolling, and These are the interpolations of real-time power during the day and planned power for the day ahead, respectively. These are weighting coefficients, corresponding to the adjustment priorities of the solid-state electric thermal storage unit and the hydrogen cogeneration unit, respectively. This is the starting time period for the current rolling optimization; To optimize the time period index within the scrolling window; For solid-state electric thermal storage units in the first Real-time output during the day; For solid-state electric thermal storage units in the first The planned output for the current period; For hydrogen cogeneration units in the first Real-time output during the day; For hydrogen cogeneration units in the first The daytime plan for the period will be implemented.

4. The multi-energy complementary operation method of a microgrid containing solid-state electric thermal storage and hydrogen energy storage combined heat and power as described in claim 1, characterized in that, The specific method for establishing a multi-energy flow balance model including heat-electricity-hydrogen and constraint conditions for equipment operation safety boundaries based on the physical characteristics of each unit in the system, as described in step S3, includes: Dynamic characteristic model of solid-state electric thermal energy storage unit: Introducing temperature-loss coupling mechanism; Considering the heat radiation loss of solid heat storage bodies in the high-temperature range, a self-heating coefficient that dynamically varies with the heat storage state is established; simultaneously, considering the difference in heat transfer capacity of gas-water heat exchangers under different temperature differences, a heat transfer efficiency correction factor is introduced: ； In the formula, for The amount of heat stored in the heat storage body at all times; This represents the maximum designed heat storage capacity of the solid heat storage body. This is the basic self-heating loss coefficient at room temperature; The input electric heating power; This refers to the output heating power; For electrothermal conversion efficiency; This is the rated heat exchange efficiency of the gas-water heat exchanger. This is the coefficient for enhancing high-temperature heat loss; This is the heat exchange temperature difference correction factor; The time interval between two adjacent scheduling periods; Establish operating models for electrolyzers and fuel cells that take into account thermoelectric coupling characteristics; Electrolyzer Operation Model: Considering the strong randomness of renewable energy input in microgrids, and the fact that electrolyzers operate under conditions of severe power fluctuations for extended periods, a dynamic nonlinear efficiency model is established, incorporating a dynamic fluctuation penalty factor. : ； In the formula, This refers to the operating temperature of the electrolytic cell. Current density; The fitting coefficients are the Faraday efficiency coefficients. As a dynamic fluctuation penalty factor; The rate of change of the input power to the electrolytic cell; and These are the volatility sensitivity coefficient and the nonlinear exponent, respectively. Fuel cell operating model: ； In the formula, This refers to the load voltage of a single battery cell; It is the thermodynamic open-circuit voltage; These are the activation polarization, ohmic polarization, and concentration polarization overpotentials, respectively, and are all output currents. Highly nonlinear function; This is the fuel cell stack health status coefficient; For fuel utilization rate, Hydrogen has a high calorific value; Power balance constraint: As the core driving constraint of the system, it requires that the power output, energy storage throughput and load demand remain in balance at any time. ； In the formula, for The output power of the wind turbine generator at any given time; for The output power of the photovoltaic generator set at any given time; for The output power of the fuel cell at all times; for The discharge power of the battery at all times; This refers to the interaction power between the microgrid and the external power grid. for The load demand power of the microgrid at any given time; for The heat storage input power of the solid-state electric thermal storage unit at all times; for The power consumption of the electrolytic cell at all times; for The charging power of the battery at all times; Cascaded heat power balance constraint: ； In the formula, To meet heat load requirements; High-temperature base charge heat source for solid-state electric thermal storage; for The heat generated during the operation of the electrolytic cell; The heat utilization efficiency coefficient of the electrolytic cell; for The heat generated during the operation of the fuel cell; This is the thermal efficiency coefficient of the fuel cell. The temperature-based discrimination coefficient; Hydrogen mass flow balance constraints: ； In the formula, The mass of hydrogen stored in the hydrogen storage tank. The hydrogen production rate of the electrolyzer. To ensure the hydrogen consumption rate of fuel cells and to guarantee the mass conservation of hydrogen during its production, storage and consumption processes; Establish temperature boundary constraints for cascaded waste heat utilization: By comparing the outlet fluid temperature of the hydrogen energy storage cogeneration and the return water temperature of the heating network, a 0-1 state variable is introduced to control waste heat access and prevent heat backflow; Establish non-negative thermal power constraints for solid-state electric thermal storage units: When the waste heat recovered by the hydrogen energy storage cogeneration exceeds the heat load demand, the heat release power of the solid-state electric thermal storage unit is forced to be zero, and the excess heat is consumed through the heat dissipation device; Establish equipment start-up and shutdown state switching constraints: Set the minimum continuous operating time and minimum continuous shutdown time of the solid-state electric thermal storage unit to limit frequent equipment operation.

5. A multi-energy complementary operation method for a microgrid containing solid-state electric thermal storage and hydrogen energy storage combined heat and power as described in claim 1, characterized in that, The improved whale algorithm, which incorporates a dual perturbation mechanism of dynamic lens imaging back learning and Cauchy mutation, described in step S4, is used to solve the collaborative optimization model. The specific method for obtaining the optimal power allocation strategy and operating state of each unit in the system at different time scales by balancing global search and local exploitation capabilities through a nonlinear convergence factor includes: Decision variables This represents the individual position vector of the algorithm in the search space, i.e., the microgrid's position vector throughout the entire scheduling cycle. The set of control instructions for all controllable units within; Assuming the population size is , No. The decision vector of an individual is defined as: ； In the formula, Electric heating input power including solid-state electric thermal storage With heat energy release output power ; Includes hydrogen production power of electrolyzers throughout the day With fuel cell power generation ; Charging power including the battery With discharge power Used to maintain the instantaneous power balance of the DC bus; Includes the interaction power between the microgrid and the external power grid. ; Temperature discrimination coefficient for the entire time period ; The initial population is generated using real-number encoding and Sobol low-bias sequences. Leveraging their uniform distribution within the hypercube, this replaces pseudo-random initialization, enabling the initial solution to more evenly cover the feasible domain of solid-state thermal energy storage and hydrogen energy dispatch. The formula is as follows: ； In the formula, For the first Boundaries of dimensional decision variables, The Sobol quasi-random sequence values ​​are used; nonlinear dynamic parameter adjustment is employed; individual fitness is calculated, and the global optimal solution is recorded. ; through nonlinear time-varying convergence factor and adaptive inertia weights The dynamic balancing algorithm's global exploration and local exploitation capabilities; with the number of iterations... The increase, It exhibits nonlinear decay. It increases non-linearly; in the global exploration phase, through a stochastic learning strategy, solutions that are better than the current individual in the population are randomly selected for differential guidance to correct the scheduling instructions of solid-state electric thermal storage; in the local development phase, a spiral bubble net predation mechanism is executed to approximate the current global optimal solution with a logarithmic spiral path; A dynamic lens imaging back-learning mechanism is introduced; after each iteration update, the current global best individual is... Perform dynamic lens imaging back-learning, using the reference boundary as the lens center, to generate a back-mapping solution for the current optimal solution. To broaden the search scope: ； In the formula, , Decision variables The upper and lower boundaries; The lens scaling factor is adjusted non-linearly with the number of iterations; calculation If the fitness is better than If it is directly replaced, then the deterministic geometric law will escape the local extremum trap; Apply a Cauchy mutation "long-tail" perturbation; if the lens imaging mechanism fails to update the optimal solution and the algorithm stagnates, i.e., continuously... If the optimal value is not improved, then Cauchy mutation is applied to the optimal individual: ； In the formula, The term is a standard Cauchy distribution random perturbation term with strength control. Its function is to apply a random change with "long-tail characteristics" to the optimal individual, so that it can escape local extrema when it is stagnant. Leveraging the Cauchy distribution's significantly higher probability of generating large-span random numbers compared to the ordinary normal distribution, this algorithm exhibits the ability to "jump between large ranges" when the algorithm is not effectively updated. This effectively avoids minimizing local costs in microgrid scheduling, ensuring that the obtained solid-state thermal energy storage and hydrogen energy coordinated scheduling scheme is the globally optimal solution. The algorithm takes as input wind and solar renewable energy output forecast data, electrical load and heat load demand categorized by energy quality, and dynamic model parameters. After solving using the improved whale algorithm, it outputs the optimal operating strategy set for the microgrid system. ； In the formula, Corresponding to the above decision variables Optimize the optimal value after convergence; Including real-time heat storage of solid heat storage materials Hydrogen storage quality in hydrogen storage tanks State of charge of the battery ; This includes the lowest total daily operating cost of the output system during the scheduling period. Or minimum power deviation sum of squares .

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