A microgrid optimal scheduling method considering hydrogen energy storage and building thermal inertia

Abstract

The application discloses a micro-grid optimal scheduling method considering hydrogen energy storage and building thermal inertia, and relates to the technical field of comprehensive energy system optimal scheduling, and the method comprises the following steps: constructing an electric-thermal-hydrogen multi-energy flow micro-grid system comprising a wind turbine generator, a photovoltaic power generation system, a micro gas turbine, an electric boiler, a heat storage tank, a hydrogen energy storage system and building thermal inertia; based on the electric-thermal-hydrogen multi-energy flow micro-grid system, taking the minimum total cost of the comprehensive operation of the micro-grid system on a typical day as the optimization target, and taking the electric power balance, the thermal power balance and the equipment output constraint as the constraint condition, a micro-grid optimal scheduling model considering hydrogen energy storage and building thermal inertia is constructed; and the micro-grid optimal scheduling model is solved to obtain the micro-grid optimal scheduling result considering hydrogen energy storage and building thermal inertia. The application fundamentally reduces the abandoned wind and light electric power, and significantly improves the local consumption level of new energy in the micro-grid.

Description

Technical Field

[0001] This invention relates to the field of integrated energy system optimization scheduling technology, and in particular to a microgrid optimization scheduling method that takes into account hydrogen energy storage and building thermal inertia. Background Technology

[0002] Microgrids, as a key form integrating distributed renewable energy sources such as wind and solar power, various types of energy storage, and flexible loads, can significantly improve the local energy consumption rate and the overall energy efficiency of the system. However, the strong randomness and volatility of high-proportion wind and solar power output often lead to severe "wind and solar curtailment" phenomena. At the same time, traditional microgrid dispatching mainly focuses on single-energy balance, neglecting the coupling and complementary potential of multiple energy flows such as thermal energy and energy storage within the system. Therefore, how to deeply explore the flexible regulation resources under the multi-energy system architecture and achieve economical, low-carbon, and efficient synergistic operation is a problem that the academic community urgently needs to solve.

[0003] Hydrogen storage systems (HSS) overcome the shortcomings of traditional energy storage systems with their advantages of high energy density, ultra-long storage and release cycles, zero carbon emissions, and multi-energy compatibility. Utilizing water electrolysis devices to produce hydrogen from surplus wind and solar power can overcome the limitations of immediate electricity consumption, reducing wind and solar curtailment at its source. Hydrogen energy can also be converted back into electricity and heat, deeply coupling multiple energy flows within the microgrid. This approach balances short-term power smoothing and cross-seasonal energy dispatch, effectively improving system operational flexibility, power supply reliability, and the level of local renewable energy consumption. It is a core component for optimizing the operation of microgrids with high renewable energy penetration rates.

[0004] However, existing research on the scheduling of hydrogen energy storage microgrids still has several limitations. On the one hand, in existing studies on the optimal scheduling of hydrogen energy storage microgrids, most literature provides a simplistic and one-sided functional model of the hydrogen energy storage system, often equating it only to a simple electrical energy storage and conversion device. The focus is on its power throughput and renewable energy absorption capacity, neglecting the waste heat continuously generated during the energy conversion process of core equipment such as electrolyzer hydrogen production and fuel cell power generation. This waste heat has significant recovery and utilization value, effectively replenishing the system's heat load and reducing heating energy consumption. However, this significantly reduces the overall energy utilization rate of multi-energy systems, making it difficult to achieve economical and efficient operation of the microgrid. On the other hand, existing models in the optimal scheduling of hydrogen energy storage microgrids have a relatively singular and limited dimension in exploring flexible thermal regulation resources. Scheduling optimization focuses only on traditional active thermal storage devices such as thermal storage tanks, relying on artificial energy storage devices to complete thermal energy storage and cross-time period shift regulation. Most studies, when constructing electricity-heat-hydrogen coupled scheduling models, have generally overlooked the high-quality passive thermal regulation resource of building thermal inertia. This resource possesses significant advantages such as zero operating cost, large thermal storage capacity, and strong flexible heat load transfer capability. Because the natural thermal buffering and heat storage characteristics of the building itself are not fully utilized, the heat load regulation space of multi-energy microgrids containing hydrogen storage is greatly compressed, resulting in insufficient overall system regulation flexibility and making it difficult to fully leverage the synergistic scheduling advantages and optimization potential of electricity, heat, and hydrogen multi-energy coupling. Summary of the Invention

[0005] The present invention aims to at least partially solve one of the technical problems in the related art.

[0006] The main objective of this invention is to provide a microgrid optimization scheduling method that takes into account hydrogen energy storage and building thermal inertia.

[0007] To achieve the above objectives, embodiments of the present invention propose a microgrid optimization scheduling method considering hydrogen energy storage and building thermal inertia, comprising: Mathematical models of wind turbine generators, photovoltaic power generation systems, micro gas turbines, electric boilers, thermal storage tanks, hydrogen energy storage systems, and building thermal inertia were constructed respectively, and an electric-thermal-hydrogen multi-energy flow microgrid system model was constructed based on each mathematical model; Based on the electric-thermal-hydrogen multi-energy flow microgrid system, a microgrid optimization scheduling model considering hydrogen energy storage and building thermal inertia is constructed with the goal of minimizing the typical daily comprehensive operating cost of the microgrid system and with constraints such as electric power balance, thermal power balance, and equipment output constraints. The microgrid optimal scheduling model is solved to obtain the optimal scheduling results of the microgrid considering hydrogen energy storage and building thermal inertia.

[0008] Optionally, during the operation of a wind turbine generator, energy undergoes a two-stage conversion process: from wind energy to mechanical energy, and then from mechanical energy to electrical energy. The output power of the wind turbine generator... The real-time wind speed is closely related to the location of the wind turbine hub, and the mathematical model is as follows:

[0009] In the formula: Represents instantaneous wind speed; This refers to the rated output power of the wind turbine generator set. , , These are the inlet velocity, rated velocity, and outlet velocity of the fan, respectively. Where the coefficient , The value can be:

[0010] In the formula: Represents instantaneous wind speed; This refers to the rated output power of the wind turbine generator set. , , These are the inlet wind speed, rated wind speed, and outlet wind speed of the fan, respectively.

[0011] Optionally, photovoltaic power generation systems utilize the photovoltaic effect to directly convert solar energy into electrical energy, with an output power of [missing information]. Mainly affected by solar irradiance and battery operating temperature, the mathematical model is as follows:

[0012] The relationship between the operating temperature of photovoltaic cells and ambient temperature and irradiance is as follows:

[0013] In the formula: Rated power of the photovoltaic system; Let be the actual light irradiance at time t; Standard light intensity; The power temperature coefficient; This refers to the operating temperature of the solar panel. =25℃ is the standard test temperature; The ambient temperature; This refers to the nominal operating battery temperature.

[0014] Optionally, the micro gas turbine achieves the simultaneous conversion of chemical energy into electrical and thermal energy through the combustion of natural gas. Its thermoelectric output characteristics and fuel consumption characteristics are described by a mathematical model as follows:

[0015] In the formula: Let t be the waste heat power that the micro gas turbine can recover at time t; Let t be the unit's output electrical power. Let be the real-time power generation efficiency of the unit under varying load at time t; The coefficient for non-recoverable heat loss, including unit exhaust and heat dissipation, is a constant. Optimize the scheduling time step for microgrids; This represents the volume of natural gas consumed by the unit during this scheduling period; It represents the lower heating value of natural gas.

[0016] Optionally, an electric boiler heats water into hot water or steam by consuming electricity, which is used to absorb surplus power from renewable energy sources, including wind and solar power, and to smooth out system power fluctuations. The mathematical model is as follows:

[0017] In the formula: Let t be the thermal power (kW) output by the electric boiler at time t. Let t be the electrical power (kW) consumed by the electric boiler at time t. This refers to the electrothermal efficiency of an electric boiler.

[0018] Optionally, the thermal storage tank, as a device for mitigating fluctuations in heat supply and demand, has the following discretized dynamic energy balance equation:

[0019]

[0020] In the formula: For storing heat; The self-dissipation rate; and These are the charging and discharging power, respectively. and This corresponds to the charge / discharge efficiency; and These are mutually exclusive 0-1 variables in the charging and discharging states.

[0021] Optionally, the hydrogen energy storage system consists of three core modules: water electrolysis for hydrogen production, hydrogen storage, and fuel cell power generation; among which: An alkaline electrolytic cell consists of a cell body, electrodes, and a diaphragm. Its mathematical model is as follows:

[0022] In the formula: Let t be the hydrogen production of the electrolyzer at time t; This represents the theoretical hydrogen production per unit of electricity. For the operating efficiency of the electrolytic cell; Let t be the electrical power input to the electrolytic cell. Let t be the recoverable heat power during the operation of the electrolytic cell at time t; The mathematical model for a fuel cell is as follows:

[0023] In the formula: Let t be the output electrical power of the PEMFC; Its overall power generation efficiency; Let t be the amount of hydrogen consumed at time t; Let t be the recoverable waste heat power during its power generation process; The mathematical model for the hydrogen storage tank is as follows:

[0024] In the formula: Let t be the amount of hydrogen stored in the hydrogen storage tank at time t; , These represent the hydrogen storage efficiency and hydrogen consumption efficiency of the hydrogen storage tank, respectively.

[0025] Optionally, for building thermal inertia, based on a first-order RC equivalent thermodynamic model, the dynamic evolution equation of indoor temperature is discretized as follows:

[0026]

[0027] In the formula: Let t be the indoor temperature. Outdoor ambient temperature; This refers to the actual heating power consumed by the building. Equivalent thermal resistance of the building; Equivalent heat capacity of the building; The building thermal time constant; The predicted average vote count is introduced as the benchmark for thermal comfort evaluation, and its calculation model is as follows:

[0028] in, The value is used to quantify the human body's thermal comfort level. , and These are, respectively, the average temperature of human skin, metabolic rate, and thermal resistance of clothing; The comfortable operating range of indoor temperature in buildings is determined according to the ISO 7730 international standard for thermal comfort, and the specific constraints are shown in the following formula:

[0029] in, This meets the ISO 7730 international minimum standard for thermal comfort. It meets the ISO 7730 international standard for thermal comfort.

[0030] Optional, typical daily integrated total operating cost Energy costs Operation and maintenance costs and carbon emissions trading costs It consists of three parts, and the expression is:

[0031] in, This includes the cost of purchasing electricity from the main grid, the cost of selling electricity, the cost of natural gas, and the penalties for curtailing wind and solar power. This includes the operation and maintenance costs of the micro gas turbine unit, thermal storage tank electric boiler, and hydrogen energy storage system. It includes penalties for carbon emissions from purchasing electricity from external grids and burning natural gas.

[0032] Optionally, the power balance can be expressed as:

[0033] in, , This refers to the actual wind and solar power absorbed. Basic electrical load; The heat power balance is expressed as:

[0034] in, For the corresponding equipment's heat generation capacity, Represents building heat load; Equipment output constraints include:

[0035]

[0036]

[0037]

[0038]

[0039]

[0040]

[0041]

[0042]

[0043]

[0044]

[0045]

[0046] in, This refers to the maximum thermal power of the corresponding equipment. This refers to the maximum electrical power of the corresponding device; This represents the maximum heat storage capacity of the thermal storage tank. This represents the maximum hydrogen storage capacity of the hydrogen storage tank.

[0047] The embodiments of the present invention have the following beneficial effects: (1) This invention introduces a hydrogen energy storage system consisting of an electrolyzer, a hydrogen storage tank and a fuel cell into the process of optimizing the scheduling of an electric-thermal-hydrogen multi-energy flow microgrid, and fully recovers the waste heat generated during the operation of the electrolyzer and fuel cell to participate in the system heating, effectively reducing the heating demand of the electric boiler and improving the overall energy utilization efficiency of the system.

[0048] (2) This invention introduces building thermal inertia resources. By establishing a building thermal inertia model and thermal comfort constraints, the building thermal load can be flexibly adjusted across time periods while ensuring user comfort. This enhances the system's thermal load adjustment capability and improves the microgrid's operational flexibility.

[0049] (3) This invention establishes a multi-energy flow collaborative optimization scheduling model for electricity, heat and hydrogen that takes into account energy costs, operation and maintenance costs and carbon emission trading costs, and realizes the collaborative optimization operation of wind power generation, photovoltaic power generation, hydrogen energy storage system and building heat load, effectively reducing the overall operating cost of microgrid.

[0050] (4) This invention fully leverages the long-term energy storage capacity of the hydrogen energy storage system and the synergistic adjustment effect of building thermal inertia to form an economical operation strategy such as wind power heating and photovoltaic hydrogen production, thereby improving the local consumption capacity of renewable energy sources such as wind power and photovoltaics, reducing the phenomenon of wind and solar curtailment, and enhancing the utilization level of new energy in microgrids.

[0051] (5) This invention takes into account the system's economy, low carbon emissions and energy supply reliability, and realizes the complementary and synergistic utilization of electricity, heat and hydrogen. It can be widely applied in scenarios such as park microgrids, integrated energy systems and new power systems, and has good engineering application value and promotion prospects. Attached Figure Description

[0052] The above-described and additional aspects and advantages of the present invention will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, in which: Figure 1 A flowchart illustrating a microgrid optimization scheduling method considering hydrogen energy storage and building thermal inertia, provided as an embodiment of the present invention. Detailed Implementation

[0053] It should be noted that, unless otherwise specified, the embodiments and features described in the present invention can be combined with each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0054] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0055] The following describes, with reference to the accompanying drawings, a microgrid optimization scheduling method considering hydrogen energy storage and building thermal inertia proposed according to an embodiment of the present invention.

[0056] Example 1 This embodiment provides a microgrid optimization scheduling method that considers hydrogen energy storage and building thermal inertia. For example... Figure 1 As shown, the method includes the following steps: S1. Construct mathematical models for wind turbine generators, photovoltaic power generation systems, micro gas turbines, electric boilers, thermal storage tanks, hydrogen energy storage systems, and building thermal inertia, respectively, and construct an electric-thermal-hydrogen multi-energy flow microgrid system model based on each mathematical model.

[0057] The following sections will explain wind turbine generators, photovoltaic power generation systems, micro gas turbines, electric boilers, thermal storage tanks, hydrogen energy storage systems, and building thermal inertia.

[0058] 1) Wind power generation.

[0059] It is understandable that during the operation of a wind turbine, energy undergoes a two-stage conversion process: from wind energy to mechanical energy, and then from mechanical energy to electrical energy. The output power of the wind turbine... The real-time wind speed is closely related to the location of the wind turbine hub, and the mathematical model is as follows:

[0060] In the formula: Represents instantaneous wind speed; This refers to the rated output power of the wind turbine generator set. , , These are the inlet velocity, rated velocity, and outlet velocity of the fan, respectively. Where the coefficient , The value can be:

[0061] In the formula: Represents instantaneous wind speed; This refers to the rated output power of the wind turbine generator set. , , These are the inlet wind speed, rated wind speed, and outlet wind speed of the fan, respectively.

[0062] 2) Photovoltaic power generation.

[0063] In one embodiment of the present invention, the photovoltaic power generation system utilizes the photovoltaic effect to directly convert solar energy into electrical energy, and its output power is... Mainly affected by solar irradiance and battery operating temperature, the mathematical model is as follows:

[0064] The relationship between the operating temperature of photovoltaic cells and ambient temperature and irradiance is as follows:

[0065] In the formula: Rated power of the photovoltaic system; Let be the actual light irradiance at time t; Standard light intensity; The power temperature coefficient; This refers to the operating temperature of the solar panel. =25℃ is the standard test temperature; The ambient temperature; This refers to the nominal operating battery temperature, typically 45°C. 47℃, but this application does not specify a specific temperature.

[0066] 3) Micro gas turbine.

[0067] Micro turbines (MTs) are the core controllable distributed power source in microgrid combined cooling, heating, and power (CCHP) systems. They achieve the simultaneous conversion of chemical energy into electrical and thermal energy through the combustion of natural gas, resulting in a significantly higher overall energy efficiency than simple power generation. Their thermal and electrical output characteristics and fuel consumption characteristics can be described as follows:

[0068] In the formula: Let t be the waste heat power that the micro gas turbine can recover at time t; Let t be the unit's output electrical power. Let be the real-time power generation efficiency of the unit under varying load at time t; The coefficient for non-recoverable heat loss, including unit exhaust and heat dissipation, is a constant. Optimize the scheduling time step for microgrids; This represents the volume of natural gas consumed by the unit during this scheduling period; It represents the lower heating value of natural gas.

[0069] 4) Electric boiler.

[0070] Electric boilers, as key equipment in coupled electric and thermal networks, heat water into hot water or steam by consuming electrical energy. This is used to absorb surplus power from renewable energy sources, including wind and solar power, and to smooth system power fluctuations. The mathematical model is as follows:

[0071] In the formula: Let t be the thermal power (kW) output by the electric boiler at time t. Let t be the electrical power (kW) consumed by the electric boiler at time t. This refers to the electro-thermal conversion efficiency of an electric boiler.

[0072] 5) Thermal Storage Tank (TES).

[0073] As a device used to mitigate fluctuations in heat supply and demand, the discretized dynamic energy balance equation of a thermal storage tank is as follows:

[0074]

[0075] In the formula: For storing heat; The self-dissipation rate; and These are the charging and discharging power, respectively. and This corresponds to the charge / discharge efficiency; and These are mutually exclusive 0-1 variables in the charging and discharging states.

[0076] 6) Hydrogen energy storage system.

[0077] A complete hydrogen energy storage system consists of three core modules: water electrolysis for hydrogen production, hydrogen storage, and fuel cell power generation. Compared with traditional electrochemical energy storage, hydrogen energy storage, with its unique advantages of large storage capacity and long-term storage, has become a key support for improving the renewable energy absorption rate and enhancing system resilience in independent microgrids: when wind and solar power output is surplus, the electrolyzer converts redundant electrical energy into hydrogen energy for large-scale storage; when renewable energy output is insufficient or encounters extreme operating conditions, the proton exchange membrane fuel cell (PEMFC) quickly starts generating electricity, effectively ensuring the reliability of power supply to the microgrid.

[0078] In the electro-hydrogen energy conversion process of hydrogen energy storage systems, the alkaline electrolyzer occupies a core position. It mainly consists of a tank, electrodes, and a diaphragm, and is widely used in hydrogen production, chlor-alkali industries, and non-ferrous metal smelting. Its mathematical model can be expressed as:

[0079] In the formula: Let t be the hydrogen production of the electrolyzer at time t; This represents the theoretical hydrogen production per unit of electricity. For the operating efficiency of the electrolytic cell; Let t be the electrical power input to the electrolytic cell. Let t be the recoverable heat power during the operation of the electrolyzer at time t.

[0080] A fuel cell is a highly efficient and clean power generation device that directly converts the chemical energy of hydrogen and oxygen into electrical and thermal energy. It boasts advantages such as zero carbon emissions, high energy conversion efficiency, and rapid response. In microgrids, it is often used as a core combined heat and power (CHP) device to ensure the reliability of the system's power supply. Its mathematical model is as follows:

[0081] In the formula: Let t be the output electrical power of the PEMFC; Its overall power generation efficiency; Let t be the amount of hydrogen consumed at time t; Let t be the amount of waste heat power that can be recovered during the power generation process.

[0082] Hydrogen storage tanks are the core equipment in hydrogen energy storage systems for storing high-pressure hydrogen. They are used to mitigate fluctuations in renewable energy and enable energy transfer in time and space. The technology is mature and relatively low-cost. Its mathematical model is as follows:

[0083] In the formula: Let t be the amount of hydrogen stored in the hydrogen storage tank at time t; , These represent the hydrogen storage efficiency and hydrogen consumption efficiency of the hydrogen storage tank, respectively.

[0084] 7) Building thermal inertia.

[0085] Building envelopes and indoor air exhibit significant thermal resistance-capacitance (RC) characteristics, enabling flexible shifts in heat load over time while ensuring human comfort. Based on a first-order RC equivalent thermodynamic model, the dynamic evolution equation of indoor temperature is discretized as follows:

[0086]

[0087] In the formula: Let t be the indoor temperature. Outdoor ambient temperature; This refers to the actual heating power consumed by the building. Equivalent thermal resistance of the building; Equivalent heat capacity of the building; This is the building thermal time constant.

[0088] Considering the ambiguity in human perception of ambient temperature, minor disturbances in the indoor thermal environment will not significantly affect user comfort. Leveraging this characteristic, the indoor temperature boundary can be flexibly relaxed within acceptable limits to guide a flexible response to heat load, which can significantly improve the wind power absorption capacity of microgrids. To accurately quantify the user comfort adjustment margin, this application introduces the Predicted Mean Vote (PMV) as a benchmark for thermal comfort evaluation. This index comprehensively considers multiple factors such as indoor air temperature, human metabolic rate, and clothing thermal resistance, and its calculation model is as follows:

[0089] in, The value is used to quantify the human body's thermal comfort level. , and These are average human skin temperature, metabolic rate, and thermal resistance of clothing, respectively.

[0090] Furthermore, the comfortable operating range of indoor temperature in buildings is determined according to the ISO 7730 international thermal comfort standard, with specific constraints as shown in the following formula:

[0091] in, This meets the ISO 7730 international minimum standard for thermal comfort. It meets the ISO 7730 international standard for thermal comfort.

[0092] S2. Based on the electric-thermal-hydrogen multi-energy flow microgrid system, with the goal of minimizing the typical daily comprehensive operating cost of the microgrid system, and with constraints such as electric power balance, thermal power balance, and equipment output constraints, a microgrid optimization scheduling model considering hydrogen energy storage and building thermal inertia is constructed.

[0093] In this embodiment of the invention, considering the system's economy and low carbon footprint, the optimization objective is to minimize the typical daily total operating cost of the microgrid system. Total cost Energy costs Operation and maintenance costs and carbon emissions trading costs It consists of three parts:

[0094] in, This includes the cost of purchasing electricity from the main grid, the cost of selling electricity, the cost of natural gas, and the penalties for curtailing wind and solar power. This includes the operation and maintenance costs of the micro gas turbine unit, thermal storage tank electric boiler, and hydrogen energy storage system. It includes penalties for carbon emissions from purchasing electricity from external grids and burning natural gas.

[0095] Specifically The calculation formula is:

[0096]

[0097] in, The scheduling period; , Let be the electricity purchase price and the electricity sales price at time t, respectively. , For the purchase and sale of electrical power; For natural gas prices; This is the energy curtailment penalty coefficient; Let t be the total amount of wind and solar power that has not been utilized at time t.

[0098] The calculation formula is:

[0099] in, This represents the unit power operation and maintenance cost coefficient for the corresponding device i.

[0100] The calculation formula is:

[0101] in, Carbon is the unit price for carbon trading; , The components are carbon emission factors from grid power supply and natural gas combustion, respectively.

[0102] In one embodiment of the present invention, the constraints include: (1) Power balance.

[0103] The system satisfies the real-time power balance of "source-grid-load-storage":

[0104] in, , This refers to the actual wind and solar power absorbed. It is the basic electrical load.

[0105] (2) Thermal power balance.

[0106] The heat power balance is expressed as:

[0107] in, For the corresponding equipment's heat generation capacity, This represents the building's heat load.

[0108] (3) Equipment output constraints.

[0109] Equipment output constraints include:

[0110]

[0111]

[0112]

[0113]

[0114]

[0115]

[0116]

[0117]

[0118]

[0119]

[0120]

[0121] in, This refers to the maximum thermal power of the corresponding equipment. This refers to the maximum electrical power of the corresponding device; This represents the maximum heat storage capacity of the thermal storage tank. This represents the maximum hydrogen storage capacity of the hydrogen storage tank.

[0122] S3. Solve the microgrid optimization scheduling model to obtain the microgrid optimization scheduling results considering hydrogen energy storage and building thermal inertia.

[0123] After completing the microgrid system modeling, objective function construction, and constraint establishment, the established microgrid optimal scheduling model is solved to obtain the microgrid optimal scheduling results considering hydrogen energy storage and building thermal inertia.

[0124] In this embodiment, since the established optimal scheduling model includes an objective function and various operational constraints, it can be solved using existing mature mathematical optimization methods. For example, mixed integer linear programming (MILP) can be used, combined with commercial or open-source optimization solvers to complete the model solution, obtaining the optimal operating strategies for equipment such as wind power generation, photovoltaic power generation, micro gas turbines, electric boilers, electrolyzers, fuel cells, thermal storage tanks, hydrogen storage tanks, and power interaction with the main grid during each scheduling period. At the same time, the optimal adjustment scheme for building heat load is obtained, thereby achieving the optimal scheduling objective of minimizing the overall operating cost of the microgrid system.

[0125] During the model solving process, each device participates in joint optimization according to the pre-established mathematical model and operating constraints. Under the conditions of ensuring the balance of electric power, the balance of thermal power, the satisfaction of equipment operating constraints and building thermal comfort constraints, the long-term energy storage capacity, waste heat recovery capacity and flexible adjustment capacity of building thermal inertia of the hydrogen energy storage system are fully utilized to achieve efficient consumption of renewable energy sources such as wind power and photovoltaics, improve the overall energy utilization efficiency of the system, and reduce the overall operating cost of the system and the curtailment rate of wind and solar power.

[0126] It should be noted that the above-described optimization solution method is only a preferred embodiment of the present invention. Those skilled in the art can select other solution algorithms or platforms suitable for the optimization model of the present invention according to actual application needs, such as different types of mathematical programming algorithms, intelligent optimization algorithms, or other solution methods that can achieve the optimization of the objective function of the present invention. As long as they do not depart from the technical concept of the present invention, they should all fall within the protection scope of the present invention.

[0127] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

[0128] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0129] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this invention, "a plurality of" means at least two, such as two, three, etc., unless otherwise explicitly specified.

Claims

1. A microgrid optimal scheduling method considering hydrogen energy storage and building thermal inertia, characterized in that, include: Mathematical models of wind turbine generators, photovoltaic power generation systems, micro gas turbines, electric boilers, thermal storage tanks, hydrogen energy storage systems, and building thermal inertia were constructed respectively, and an electric-thermal-hydrogen multi-energy flow microgrid system model was constructed based on each mathematical model; Based on the electric-thermal-hydrogen multi-energy flow microgrid system, a microgrid optimization scheduling model considering hydrogen energy storage and building thermal inertia is constructed with the goal of minimizing the typical daily comprehensive operating cost of the microgrid system and with constraints such as electric power balance, thermal power balance, and equipment output constraints. The microgrid optimal scheduling model is solved to obtain the optimal scheduling results of the microgrid considering hydrogen energy storage and building thermal inertia.

2. The method according to claim 1, characterized in that, During the operation of a wind turbine generator, energy undergoes a two-stage conversion process: from wind energy to mechanical energy, and then from mechanical energy to electrical energy. The output power of the wind turbine generator... The real-time wind speed is closely related to the location of the wind turbine hub, and the mathematical model is as follows: In the formula: Represents instantaneous wind speed; This refers to the rated output power of the wind turbine generator set. , , These are the inlet velocity, rated velocity, and outlet velocity of the fan, respectively. Where the coefficient , The value can be: In the formula: Represents instantaneous wind speed; This refers to the rated output power of the wind turbine generator set. , , These are the inlet wind speed, rated wind speed, and outlet wind speed of the fan, respectively.

3. The method according to claim 1, characterized in that, Photovoltaic power generation systems utilize the photovoltaic effect to directly convert solar energy into electrical energy, and their output power... Mainly affected by solar irradiance and battery operating temperature, the mathematical model is as follows: The relationship between the operating temperature of photovoltaic cells and ambient temperature and irradiance is as follows: In the formula: Rated power of the photovoltaic system; Let be the actual light irradiance at time t; Standard light intensity; The power temperature coefficient; This refers to the operating temperature of the solar panel. =25℃ is the standard test temperature; Ambient temperature; This refers to the nominal operating battery temperature.

4. The method according to claim 1, characterized in that, Micro gas turbines achieve the simultaneous conversion of chemical energy into electrical and thermal energy through the combustion of natural gas. The mathematical model describing their thermoelectric output characteristics and fuel consumption characteristics is as follows: In the formula: Let t be the waste heat power that the micro gas turbine can recover at time t; Let t be the unit's output electrical power. Let be the real-time power generation efficiency of the unit under varying load at time t; The coefficient for non-recoverable heat loss, including unit exhaust and heat dissipation, is a constant. Optimize the scheduling time step for microgrids; This represents the volume of natural gas consumed by the unit during this scheduling period; It represents the lower heating value of natural gas.

5. The method according to claim 1, characterized in that, Electric boilers heat water into hot water or steam by consuming electricity. This is used to absorb excess power from renewable energy sources, including wind and solar power, and to smooth system power fluctuations. The mathematical model is as follows: In the formula: Let t be the thermal power (kW) output by the electric boiler at time t. Let t be the electrical power (kW) consumed by the electric boiler at time t. This refers to the electro-thermal conversion efficiency of an electric boiler.

6. The method according to claim 1, characterized in that, As a device used to mitigate fluctuations in heat supply and demand, the discretized dynamic energy balance equation of a thermal storage tank is as follows: In the formula: For storing heat; The self-dissipation rate; and These are the charging and discharging power, respectively. and This corresponds to the charge / discharge efficiency; and These are mutually exclusive 0-1 variables in the charging and discharging states.

7. The method according to claim 1, characterized in that, The hydrogen energy storage system consists of three core modules: water electrolysis for hydrogen production, hydrogen storage, and fuel cell power generation; among which: An alkaline electrolytic cell consists of a cell body, electrodes, and a diaphragm. Its mathematical model is as follows: In the formula: Let t be the hydrogen production of the electrolyzer at time t; This represents the theoretical hydrogen production per unit of electricity. For the operating efficiency of the electrolytic cell; Let t be the electrical power input to the electrolytic cell. Let t be the recoverable heat power during the operation of the electrolytic cell at time t; The mathematical model for a fuel cell is as follows: In the formula: Let t be the output electrical power of the PEMFC; Its overall power generation efficiency; Let t be the amount of hydrogen consumed at time t; Let t be the recoverable waste heat power during its power generation process; The mathematical model for the hydrogen storage tank is as follows: In the formula: Let t be the amount of hydrogen stored in the hydrogen storage tank at time t; , These represent the hydrogen storage efficiency and hydrogen consumption efficiency of the hydrogen storage tank, respectively.

8. The method according to claim 1, characterized in that, For building thermal inertia, based on the first-order RC equivalent thermodynamic model, the discretized equation for the dynamic evolution of indoor temperature is expressed as: In the formula: Let t be the indoor temperature. Outdoor ambient temperature; This refers to the actual heating power consumed by the building. Equivalent thermal resistance of the building; Equivalent heat capacity of the building; The building thermal time constant; The predicted average vote count is introduced as the benchmark for thermal comfort evaluation, and its calculation model is as follows: in, The value is used to quantify the human body's thermal comfort level. , and These are, respectively, the average temperature of human skin, metabolic rate, and thermal resistance of clothing; The comfortable operating range of indoor temperature in buildings is determined according to the ISO 7730 international standard for thermal comfort, and the specific constraints are shown in the following formula: in, This meets the ISO 7730 international minimum standard for thermal comfort. It meets the ISO 7730 international standard for thermal comfort.

9. The method according to claim 1, characterized in that, Typical daily comprehensive operating cost Energy costs Operation and maintenance costs and carbon emissions trading costs It consists of three parts, and the expression is: in, This includes the cost of purchasing electricity from the main grid, the cost of selling electricity, the cost of natural gas, and the penalties for curtailing wind and solar power. This includes the operation and maintenance costs of the micro gas turbine unit, thermal storage tank electric boiler, and hydrogen energy storage system. It includes penalties for carbon emissions from purchasing electricity from external grids and burning natural gas.

10. The method according to claim 1, characterized in that, Electric power balance is expressed as: in, , This refers to the actual wind and solar power absorbed. Basic electrical load; The heat power balance is expressed as: in, For the corresponding equipment's heat generation capacity, Represents building heat load; Equipment output constraints include: in, This refers to the maximum thermal power of the corresponding equipment. This refers to the maximum electrical power of the corresponding device; This represents the maximum heat storage capacity of the thermal storage tank. This represents the maximum hydrogen storage capacity of the hydrogen storage tank.

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