Micro-energy grid composite energy storage capacity configuration method, system, medium and equipment

Abstract

The invention discloses a micro-energy grid composite energy storage capacity configuration method, system, medium and equipment, and belongs to the field of comprehensive energy system optimizing.The method comprises the steps that a micro-energy grid system containing fused salt heat storage, water electrolysis hydrogen production, wind power, photovoltaic and energy storage batteries is constructed; wherein the power generation system supplies power to the load, the energy storage battery, the electric heating equipment and the hydrogen production system through the direct-current bus, the fused salt heat storage power conversion module is connected with the direct-current bus, and the hydrogen storage tank outputs hydrogen to external equipment; respectively constructing a wind power plant service life model, a photovoltaic service life model and an energy storage battery service life model; and all the models are integrated, a target function is constructed with maximization of daily comprehensive net income as a target to form a configuration model, iterative solution is performed in combination with equipment capacity, operation, electrical performance and system strategy constraints, a scheme is output, and energy storage capacity is configured. According to the invention, the problem of insufficient economy and multi-energy coupling characteristic of composite energy storage capacity configuration in a micro-energy network in an industrial park in the prior art can be solved.

Description

Technical Field

[0001] This invention belongs to the field of integrated energy system optimization and relates to a method, system, medium and equipment for configuring composite energy storage capacity in micro energy grids. Background Technology

[0002] As the global energy structure shifts towards a green and low-carbon model, industrial parks, as key areas of energy consumption and carbon emissions, are facing an increasingly urgent need for large-scale grid connection of distributed energy sources (such as wind power and photovoltaics). Microgrids, as a key technology integrating multiple energy forms, can effectively improve energy utilization efficiency and reduce carbon emissions by synergistically optimizing the flow of electricity, heat, hydrogen, and other energy sources, and have become an important direction for upgrading energy systems in industrial parks.

[0003] In existing technologies, energy storage configurations for industrial park microgrids often rely on a single type of energy storage (such as battery storage or thermal storage). While this can mitigate fluctuations in renewable energy output to some extent, it suffers from poor economic efficiency (e.g., short battery storage lifespan and high replacement costs) and insufficient consideration of multi-energy coupling characteristics (e.g., inadequate coordination of electric, thermal, and hydrogen energy storage units). Furthermore, traditional configuration methods often fail to account for the impact of energy storage lifespan degradation on long-term economics, leading to a disconnect between optimization results and actual operational needs, making it difficult to simultaneously meet the dual objectives of energy supply reliability and system economic operation. Summary of the Invention

[0004] This application provides a method, system, medium, and equipment for configuring composite energy storage capacity in microgrids, which can solve the problems of insufficient economy and multi-energy coupling characteristics in the configuration of composite energy storage capacity in industrial park microgrids in the prior art.

[0005] To achieve the above objectives, in a first aspect, the present invention provides a method for configuring composite energy storage capacity in a microgrid, comprising: A microgrid system is constructed, comprising: a molten salt thermal storage system, a water electrolysis hydrogen production system, a wind farm power generation system, a photovoltaic power generation system, and an energy storage battery. The wind farm power generation system and the photovoltaic power generation system are connected to the load equipment via a DC bus. The electrical energy output by the wind farm power generation system and the photovoltaic power generation system is distributed via the DC bus to the load equipment, the energy storage battery system, the electric heating equipment of the molten salt thermal storage system, and the water electrolysis hydrogen production system. The power conversion module of the molten salt thermal storage system is connected to the DC bus, and the hydrogen storage tank of the water electrolysis hydrogen production system outputs hydrogen to external equipment. Based on the nonlinear relationship between the actual output power of wind power generation equipment and wind speed, a wind farm power generation model is constructed. Based on the relationship between the actual output power of photovoltaic power generation equipment and light intensity and temperature, a photovoltaic power generation model is constructed. Based on the quantitative relationship between the operating conditions and lifespan of energy storage batteries, a lifespan model of energy storage batteries that takes into account actual operating conditions is constructed. By integrating the aforementioned wind farm power generation model, photovoltaic power generation model, and lifetime model, and constructing an objective function with the goal of maximizing the daily comprehensive net income, a configuration model is obtained. By combining preset equipment capacity constraints, equipment operation constraints, electrical performance constraints, and system strategy constraints, the configuration model is iteratively solved, and a configuration scheme is output to configure the energy storage capacity of the microgrid system.

[0006] Compared to existing technologies, the embodiments of this application have the following beneficial effects: By constructing a micro-energy grid system with a DC bus as its core, wind farms, photovoltaic power generation systems, load equipment, energy storage batteries, molten salt thermal energy storage systems (electric heating equipment), and water electrolysis hydrogen production systems form a centralized energy distribution network. This allows wind and solar energy to be directly distributed to loads or various energy storage units, avoiding the conversion losses of traditional AC grid connection. Simultaneously, it achieves efficient diversion of surplus electricity to thermal energy storage and hydrogen production (e.g., prioritizing load needs during periods of high wind and solar power generation, with excess electricity immediately allocated to electric heating equipment for thermal energy storage or to hydrogen production in the electrolyzer). The water electrolysis hydrogen production system outputs hydrogen to external equipment through hydrogen storage tanks, expanding energy utilization scenarios and increasing additional revenue. The closed-loop connection between the power conversion module and the DC bus upgrades the molten salt thermal energy storage system from a "one-way energy storage" unit to a "two-way regulation" unit. This allows for supplementary power supply during peak load periods, enhancing the system's peak-shaving capability. Combined with the rapid response characteristics of the energy storage battery, this forms a dynamic balance network of "electricity-heat-hydrogen" multi-energy complementarity, achieving deep coupling of electricity, heat, and hydrogen, and improving the capacity for renewable energy absorption. A configuration model considering the lifespan of the energy storage battery is constructed based on electrical parameters to maximize daily net revenue, linking economic efficiency with equipment lifespan and avoiding long-term cost spikes caused by short-term optimization. Iterative solutions output configuration schemes to ensure globally optimal capacity parameters, guaranteeing system power supply reliability. The overall solution, through a multi-energy collaborative architecture, lifespan-aware economic optimization, and solution process, systematically solves the problems of poor economic efficiency of single energy storage and insufficient multi-energy coupling, achieving a balance between the economy and reliability of composite energy storage capacity configuration.

[0007] In some embodiments of the first aspect of this application, the step of constructing a wind farm power generation model based on the nonlinear relationship between the actual output power of the wind power generation equipment and the wind speed includes: Based on the relationship between the cut-in wind speed, rated wind speed and cut-out wind speed of wind power generation equipment, wind speed ranges are defined. Based on the wind speed-power characteristics within each wind speed range, segmented power output rules are established, and the wind farm power generation model is constructed.

[0008] Compared with existing technologies, the above embodiments have the following beneficial effects: By dividing the wind speed range according to the cut-in wind speed, rated wind speed, and cut-out wind speed of the wind power generation equipment, the power output boundary conditions under different wind speeds are clarified; based on the wind speed-power characteristics within each range, segmented power output rules are established, enabling the wind power output model to accurately map actual wind speed changes (such as zero output at low wind speeds, full output at rated wind speeds, and cut-out shutdown at high wind speeds), thus improving the accuracy of wind power output prediction; this model provides reliable wind and solar power generation data input for subsequent capacity configuration, avoiding redundancy or insufficiency in energy storage configuration due to output prediction deviations, and enhancing the engineering practicality of the overall configuration scheme.

[0009] In some embodiments of the first aspect of this application, the step of constructing a photovoltaic power generation model based on the relationship between the actual output power of the photovoltaic power generation equipment and light intensity and temperature includes: Based on standard light intensity and standard temperature parameters, combined with actual light intensity and photovoltaic panel temperature, a power correction coefficient is calculated by using the ratio of light intensity and temperature correction term, establishing a mapping relationship between photovoltaic power generation and environmental parameters, thus obtaining the photovoltaic power generation model.

[0010] Compared with existing technologies, the above embodiments have the following advantages: Based on standard light intensity and standard temperature parameters, combined with actual light intensity and photovoltaic panel temperature, the power correction coefficient is calculated through the ratio of light intensity and temperature correction term, establishing a dynamic mapping relationship between photovoltaic power generation and environmental parameters; this model can respond in real time to the impact of light fluctuations (such as light changes caused by cloud cover) and temperature drift (such as efficiency reduction caused by high summer temperatures) on photovoltaic output, improving the accuracy of photovoltaic output prediction; compared with the traditional simplified model that only considers light, this model is more in line with actual environmental conditions, providing more accurate new energy output data support for the configuration of composite energy storage capacity.

[0011] In some embodiments of the first aspect of this application, the step of constructing a lifespan model for the energy storage battery that takes into account actual operating conditions based on the quantitative relationship between the operating conditions and lifespan of the energy storage battery includes: Based on the power function relationship between the actual depth of discharge and the number of cycles when the energy storage battery switches between charging and discharging states, the charge-discharge cycles at different depths of discharge are converted into the equivalent number of cycles at the full discharge depth, and the daily equivalent number of cycles of the energy storage battery is calculated in combination with the daily optimization duration. Based on the number of cycles under the condition of full discharge depth of the energy storage battery, a quantitative relationship between the cycle life of the energy storage battery and the daily equivalent number of cycles is established, and coupled with the float charge life, a life model of the energy storage battery under actual working conditions is constructed.

[0012] Compared with existing technologies, the above embodiments have the following beneficial effects: By capturing the actual discharge depth during the switching of charging and discharging states, and based on its power function relationship with the number of cycles, the charging and discharging cycles of different discharge depths are uniformly converted into the equivalent number of cycles under the full discharge depth, realizing the quantification of life decay for different operating conditions such as "small-amplitude frequent charging and discharging" and "deep charging and discharging" in typical daily load fluctuations in the park; by combining the daily optimized duration (e.g., 24 hours) to calculate the daily equivalent number of cycles, the dynamic fluctuations of the daily load curve (e.g., changes in charging and discharging depth caused by peak-valley load differences) are converted into an accumulative life loss index, establishing a dynamic correlation between "load fluctuation - number of cycles - life decay"; based on the coupling of the number of cycles at the full discharge depth and the float charging life, an actual operating condition life model is constructed, enabling the life assessment to respond in real time to the dynamic characteristics of the park's daily load curve (e.g., the cycle mode of high load discharge during the day and low load charging at night), ultimately achieving a precise mapping of "life - operating condition" for typical daily load fluctuation characteristics, providing life parameter support that fits the actual operating conditions of the park for the configuration of composite energy storage capacity.

[0013] In some embodiments of the first aspect of this application, the construction of the objective function with the goal of maximizing the daily comprehensive net income includes: Calculate the daily net income based on revenue from electricity sales and hydrogen sales. The total cost is calculated based on investment cost, operation and maintenance cost, replacement cost, depreciation cost, and power deviation penalty cost; wherein, the power deviation penalty cost is calculated based on power deviation; the power deviation represents the sum of the absolute values ​​of the unmet load and the excess energy of the microgrid system; An objective function is constructed with the goal of maximizing the difference between the daily net income and the total cost.

[0014] Compared to existing technologies, the above embodiments have the following beneficial effects: Daily net revenue is calculated based on electricity and hydrogen sales revenue, comprehensively covering the economic value of multiple energy products; total cost includes investment, operation and maintenance, replacement, depreciation costs, and power deviation penalty costs (power deviation is the sum of the absolute values ​​of unmet load and excess energy), achieving synergistic optimization of short-term operating costs and long-term asset costs; an objective function is constructed with the goal of maximizing the difference between "daily net revenue and total cost," forcing the system to strictly control costs and energy waste while increasing revenue, improving load fulfillment rate (reducing unmet load) and energy utilization rate (reducing excess energy), ultimately achieving dual optimization of economic benefits and resource utilization efficiency.

[0015] In some embodiments of the first aspect of this application, the system strategy constraints include: power curtailment constraints of energy storage batteries, power curtailment constraints of molten salt thermal storage systems, and power curtailment constraints of water electrolysis hydrogen production systems. The constraint on the curtailment operation of the energy storage battery is expressed as follows: when the microgrid system is in a curtailment state, the energy storage battery is only allowed to charge; otherwise, it is only allowed to discharge. The power curtailment operation constraint of the molten salt thermal energy storage system is expressed as follows: when the microgrid system is in a power curtailment state, the molten salt thermal energy storage system is only allowed to start the electric heating equipment for thermal storage operation; otherwise, it is only allowed to start the power conversion module for power generation operation. The power curtailment operation constraint of the molten salt thermal storage system is expressed as follows: when the microgrid system is in a power curtailment state, the water electrolysis hydrogen production system is allowed to start hydrogen production operation; otherwise, hydrogen production operation is prohibited. The term "waste power" refers to a state where the total power generation of the microgrid system exceeds the total load demand.

[0016] Compared with existing technologies, the above embodiments have the following beneficial effects: In the state of power abandonment, the energy storage battery only charges, the molten salt thermal storage system only stores heat, and the water electrolysis hydrogen production system starts producing hydrogen. In the state of non-power abandonment, the operation is reversed. By distinguishing the states, the operating mode of each unit is precisely controlled, avoiding energy waste during power abandonment and maximizing the utilization of surplus electricity (thermal storage and hydrogen production). In the state of non-power abandonment, priority is given to ensuring the power supply to the load (discharging and generating electricity), thereby improving the renewable energy absorption rate and the system operation flexibility.

[0017] Secondly, the present invention also provides a micro-energy grid composite energy storage capacity configuration system, comprising: a system construction module, a model construction module, an integration module, and a solution module; The system construction module is used to construct a microgrid system. The microgrid system includes: a molten salt thermal storage system, a water electrolysis hydrogen production system, a wind farm power generation system, a photovoltaic power generation system, and an energy storage battery. The wind farm power generation system and the photovoltaic power generation system are connected to the load equipment via a DC bus. The electrical energy output from the wind farm power generation system and the photovoltaic power generation system is distributed via the DC bus to the load equipment, the energy storage battery system, the electric heating equipment of the molten salt thermal storage system, and the water electrolysis hydrogen production system. The power conversion module of the molten salt thermal storage system is connected to the DC bus, and the hydrogen storage tank of the water electrolysis hydrogen production system outputs hydrogen to external equipment. The model building module is used to build a wind farm power generation model based on the nonlinear relationship between the actual output power of wind power generation equipment and wind speed, to build a photovoltaic power generation model based on the relationship between the actual output power of photovoltaic power generation equipment and light intensity and temperature, and to build a life model of energy storage battery that takes into account the actual operating conditions based on the quantitative relationship between the operating conditions and lifespan of energy storage battery. The integration module is used to integrate the wind farm power generation model, photovoltaic power generation model and lifetime model, and construct an objective function with the goal of maximizing the daily comprehensive net income to obtain the configuration model; The solution module is used to iteratively solve the configuration model by combining preset equipment capacity constraints, equipment operation constraints, electrical performance constraints and system strategy constraints, output the configuration scheme and configure the energy storage capacity of the microgrid system.

[0018] Compared to existing technologies, the embodiments of this application have the following beneficial effects: By constructing a micro-energy grid system with a DC bus as its core, wind farms, photovoltaic power generation systems, load equipment, energy storage batteries, molten salt thermal energy storage systems (electric heating equipment), and water electrolysis hydrogen production systems form a centralized energy distribution network. This allows wind and solar energy to be directly distributed to loads or various energy storage units, avoiding the conversion losses of traditional AC grid connection. Simultaneously, it achieves efficient diversion of surplus electricity to thermal energy storage and hydrogen production (e.g., prioritizing load needs during peak wind and solar power generation, with excess electricity immediately allocated to electric heating equipment for thermal energy storage or to hydrogen production in the electrolyzer). The water electrolysis hydrogen production system outputs hydrogen to external equipment through hydrogen storage tanks, expanding energy utilization scenarios and increasing additional energy output. Revenue streams: The closed-loop connection between the power conversion module and the DC bus upgrades the molten salt thermal energy storage system from "one-way energy storage" to a "two-way regulation" unit, enabling supplementary power supply during peak load periods, enhancing the system's peak-shaving capability. Combined with the rapid response characteristics of the energy storage battery, this forms a dynamic balance network of "electricity-heat-hydrogen" multi-energy complementarity, achieving deep coupling of electricity, heat, and hydrogen, and improving the capacity for renewable energy absorption. A configuration model considering the lifespan of the energy storage battery is constructed based on electrical parameters with the goal of maximizing daily comprehensive net revenue, linking economic efficiency with equipment lifespan and avoiding long-term cost surges caused by short-term optimization. Iterative solutions are used to output configuration schemes, ensuring globally optimal capacity parameters and guaranteeing system power supply reliability. The overall solution, through a multi-energy collaborative architecture, lifespan-aware economic optimization, and solution process, systematically solves the problems of poor economic efficiency of single energy storage and insufficient multi-energy coupling, achieving a balance between the economy and reliability of composite energy storage capacity configuration.

[0019] In some embodiments of the second aspect of this application, the model building module includes: an interval division unit and a wind power model building unit; The interval division unit is used to divide the wind speed interval according to the relationship between the cut-in wind speed, rated wind speed and cut-out wind speed of the wind power generation equipment. The wind power model construction unit is used to establish segmented power output rules based on the wind speed-power characteristics in each wind speed range, and to construct the wind farm power generation model.

[0020] Compared with existing technologies, the above embodiments have the following beneficial effects: By dividing the wind speed range according to the cut-in wind speed, rated wind speed, and cut-out wind speed of the wind power generation equipment, the power output boundary conditions under different wind speeds are clarified; based on the wind speed-power characteristics within each range, segmented power output rules are established, enabling the wind power output model to accurately map actual wind speed changes (such as zero output at low wind speeds, full output at rated wind speeds, and cut-out shutdown at high wind speeds), thus improving the accuracy of wind power output prediction; this model provides reliable wind and solar power generation data input for subsequent capacity configuration, avoiding redundancy or insufficiency in energy storage configuration due to output prediction deviations, and enhancing the engineering practicality of the overall configuration scheme.

[0021] Thirdly, the present invention also provides a microgrid composite energy storage capacity configuration device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the computer program is loaded onto the processor, it implements the steps of any one of the microgrid composite energy storage capacity configuration methods of the present invention.

[0022] Fourthly, embodiments of this application also provide a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of any of the microgrid composite energy storage capacity configuration methods of the present invention. Attached Figure Description

[0023] Figure 1 This is a flowchart illustrating a method for configuring the composite energy storage capacity of a microgrid, as provided in some embodiments of the present invention.

[0024] Figure 2 This is a schematic diagram of a microgrid composite energy storage capacity configuration system provided in some embodiments of the present invention.

[0025] Figure 3 : This is a structural diagram of a microgrid composite energy storage capacity configuration device provided in some embodiments of the present invention.

[0026] Figure 4 This is a schematic diagram of the architecture of a microgrid system for an industrial park provided in some embodiments of the present invention.

[0027] Figure 5 : This is a schematic diagram of a dual-tank molten salt thermal storage system provided in some embodiments of the present invention.

[0028] Figure 6 This is a schematic diagram of a water electrolysis hydrogen production process provided in some embodiments of the present invention.

[0029] Figure 7 This is a flowchart of a method for solving a composite energy storage capacity configuration model, provided in some embodiments of the present invention.

[0030] Figure 8 This is a schematic diagram of the system power balance on a typical day, provided in some embodiments of the present invention. Detailed Implementation

[0031] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some 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 are within the scope of protection of the present invention.

[0032] Example 1: Please refer to Figure 1 To address the shortcomings in the economic efficiency and multi-energy coupling characteristics of composite energy storage capacity configuration in existing industrial park microgrids, an embodiment of the present invention provides a method for configuring composite energy storage capacity in microgrids, comprising steps S1 to S4: Step S1: Construct a microgrid system; wherein the microgrid system includes: a molten salt thermal storage system, a water electrolysis hydrogen production system, a wind farm power generation system, a photovoltaic power generation system, and an energy storage battery; the wind farm power generation system and the photovoltaic power generation system are connected to the load equipment via a DC bus, and the electrical energy output by the wind farm power generation system and the photovoltaic power generation system is distributed via the DC bus to the load equipment, the energy storage battery system, the electric heating equipment of the molten salt thermal storage system, and the water electrolysis hydrogen production system; the power conversion module of the molten salt thermal storage system is connected to the DC bus, and the hydrogen storage tank of the water electrolysis hydrogen production system outputs hydrogen to external equipment.

[0033] In specific implementation, refer to Figure 4 The diagram illustrates the architecture of a microgrid system for an industrial park. This system deeply integrates wind farms, photovoltaic power plants, energy storage batteries, molten salt thermal storage systems, and water electrolysis hydrogen production systems. The molten salt thermal storage system innovatively replaces the heliostat field of traditional solar thermal power plants with electric heating equipment. It converts surplus electricity from wind and solar power generation into high-temperature molten salt, which is stored in a dual-tank system. The heat-to-electricity conversion is then achieved via a Rankine cycle steam turbine. The combination of energy storage batteries and the molten salt thermal storage system offers greater flexibility in regulation, enabling it to meet smaller load demands. Therefore, the combination of cost-effective molten salt thermal storage systems and energy storage batteries can effectively address the mismatch between renewable energy generation and load. Furthermore, the water electrolysis hydrogen production system can convert renewable energy generation that cannot be scheduled or stored into commercial hydrogen, which can then be transported externally via pipelines or other transportation methods for profit, further contributing to the utilization of wind and solar resources.

[0034] Step S2: Based on the nonlinear relationship between the actual output power of wind power generation equipment and wind speed, construct a wind farm power generation model; based on the relationship between the actual output power of photovoltaic power generation equipment and light intensity and temperature, construct a photovoltaic power generation model; based on the quantitative relationship between the operating conditions and lifespan of energy storage batteries, construct a lifespan model of energy storage batteries that takes into account actual operating conditions.

[0035] Furthermore, step S2 can be implemented through the following preferred embodiments, including steps S21-S25, as detailed below: S21: Based on the relationship between the cut-in wind speed, rated wind speed and cut-out wind speed of wind power generation equipment, wind speed ranges are defined; S22: Based on the wind speed-power characteristics within each wind speed range, segmented power output rules are established to construct the wind farm power generation model.

[0036] In this preferred embodiment, wind speed intervals are defined based on the cut-in wind speed, rated wind speed, and cut-out wind speed of the wind power generation equipment, thus clarifying the power output boundary conditions under different wind speeds. Segmented power output rules are established based on the wind speed-power characteristics within each interval, enabling the wind power output model to accurately map actual wind speed changes (e.g., zero output at low wind speeds, full output at rated wind speeds, and cut-out shutdown at high wind speeds), improving the accuracy of wind power output prediction. This model provides reliable wind and solar power generation data input for subsequent capacity configuration, avoiding redundancy or insufficiency in energy storage configuration due to output prediction deviations, and enhancing the engineering practicality of the overall configuration scheme.

[0037] S23: Based on standard light intensity and standard temperature parameters, combined with actual light intensity and photovoltaic panel temperature, calculate the power correction coefficient through the ratio of light intensity and temperature correction term, establish the mapping relationship between photovoltaic power generation and environmental parameters, and obtain the photovoltaic power generation model.

[0038] In this preferred embodiment, based on standard irradiance and standard temperature parameters, and combined with actual irradiance and photovoltaic panel temperature, a power correction coefficient is calculated using the ratio of irradiance and temperature correction term, establishing a dynamic mapping relationship between photovoltaic power generation and environmental parameters. This model can respond in real time to the impact of irradiance fluctuations (such as changes in irradiance caused by cloud cover) and temperature drift (such as efficiency reduction caused by high summer temperatures) on photovoltaic output, improving the accuracy of photovoltaic output prediction. Compared with traditional simplified models that only consider irradiance, this model is more in line with actual environmental conditions, providing more accurate new energy output data support for the configuration of composite energy storage capacity.

[0039] S24: Based on the power function relationship between the actual depth of discharge and the number of cycles when the energy storage battery switches between charging and discharging states, the charge and discharge cycles at different depths of discharge are converted into the equivalent number of cycles at the full discharge depth, and the daily equivalent number of cycles of the energy storage battery is calculated in combination with the daily optimization duration. S25: Based on the number of cycles under the condition of full discharge depth of the energy storage battery, establish a quantitative relationship between the cycle life of the energy storage battery and the daily equivalent number of cycles, and couple it with the float charge life to construct a life model of the energy storage battery under actual working conditions.

[0040] In this preferred embodiment, by capturing the actual discharge depth during charge / discharge state switching and based on its power function relationship with the number of cycles, charge / discharge cycles with different discharge depths are uniformly converted into equivalent cycle counts under full discharge depth. This achieves the quantification of lifetime degradation for different operating conditions such as "small-amplitude frequent charge / discharge" and "deep charge / discharge" in typical daily load fluctuations in the park. By combining the daily optimized duration (e.g., 24 hours) to calculate the daily equivalent cycle count, the dynamic fluctuations of the daily load curve (e.g., changes in charge / discharge depth caused by peak-valley load differences) are transformed into an accumulative lifetime loss index, establishing a dynamic correlation between "load fluctuation - cycle count - lifetime degradation". Based on the coupling of full discharge depth cycle count and float charging lifetime, an actual operating condition lifetime model is constructed, enabling lifetime assessment to respond in real time to the dynamic characteristics of the park's daily load curve (e.g., the cycle pattern of high load discharge during the day and low load charging at night). Ultimately, this achieves accurate "lifetime-operating condition" mapping for typical daily load fluctuation characteristics, providing lifetime parameter support for the configuration of composite energy storage capacity that fits the actual operating conditions of the park.

[0041] In practical implementation, the configuration model corresponding to the microgrid system contains multiple sub-models of internal systems, among which: The mathematical model of wind power generation equipment in a wind farm power generation system is as follows: ; in, This indicates the actual output power of the wind power generation equipment; This indicates the rated power of the wind power generation equipment; Indicates the current wind speed; This indicates the rated wind speed of the wind power generation equipment; and These represent the cut-in wind speed and cut-out wind speed of the wind power generation equipment, respectively. and Two coefficients are used to describe the relationship between the output power of wind power generation equipment and wind speed.

[0042] The mathematical model of photovoltaic power generation equipment in a photovoltaic power generation system is as follows: , Among them, the actual output power of photovoltaic power generation equipment It can be obtained through the landing factor This coefficient is defined as the ratio of the actual output power of a photovoltaic power generation device to its rated output power. This indicates the rated power of the photovoltaic power generation equipment under standard test conditions; Indicates the actual light intensity; Indicates the light intensity under standard conditions; This indicates the temperature coefficient of photovoltaic power generation equipment; This indicates the actual temperature of the photovoltaic panel; This indicates the temperature of the photovoltaic panel under standard conditions, typically 25°C.

[0043] like Figure 5 The diagram shows a schematic of a dual-tank molten salt thermal storage system. This system comprises an electric heating device, a low-temperature molten salt tank, a high-temperature molten salt tank, and a power conversion module. Molten salt thermal storage generally involves two processes: heat storage and heat release. During heat storage, the electric heating device converts electrical energy into thermal energy for storage. During heat release, the power conversion module converts thermal energy back into electrical energy. The mathematical model of the molten salt thermal storage system is not considered here; only its operational constraints during operation are taken into account.

[0044] like Figure 6 The diagram illustrates a water electrolysis hydrogen production process. The system consists of an electrolyzer and a hydrogen storage tank. This system uses residual electricity to drive an electrochemical reaction, causing water molecules to dissociate at the electrode interface, producing hydrogen and oxygen. The generated hydrogen is transported to a subsequent processing unit via a dedicated pipeline. After gas-liquid separation, purification, and other processes, it is ultimately stored in the hydrogen storage tank as high-purity hydrogen. The hydrogen in the storage tank can then be transferred to external equipment for sale to generate revenue. Again, the mathematical model of the water electrolysis hydrogen production system is not considered; only its operational constraints during operation are taken into account.

[0045] Next, we construct the lifespan model for the energy storage battery. To optimize the cycle life of the energy storage battery, we use a power function fitting method to quantify the number of cycles. Based on this quantification result, we establish a cycle count limitation mechanism to effectively control the degradation process of the energy storage battery's lifespan, as shown below: ; Among them, during the life cycle of the energy storage battery, Indicates the total number of cycles of the energy storage battery; This indicates the number of cycles under conditions of full discharge of the energy storage battery; Indicates the actual depth of discharge of the energy storage battery; The degradation coefficient represents the lifespan of an energy storage battery.

[0046] During the charging and discharging process of an energy storage battery, when the system switches from a discharging state to a charging state, a complete charge-discharge cycle is formed. At this time, the depth of discharge in the cycle is determined based on the depth of discharge value at the previous moment. ;in, for The depth of discharge of the energy storage battery at any given time; This represents the cyclic state variable of the energy storage battery at time t, with a value of 1 indicating the completion of one cycle; express The state of charge of the energy storage battery at all times.

[0047] Based on the degradation process of energy storage battery life, Actual depth of discharge at time The corresponding number of cycles converted to the equivalent number of cycles at the depth of full discharge : ; Next, the daily equivalent cycle count of the battery can be derived from the equivalent cycle count at the full discharge depth of the energy storage battery. : ;in, To optimize the daily duration, you can use 24 hours. For time step.

[0048] Then the cycle life of the energy storage battery can be calculated. for: ; Ultimately, based on the cycle life and float life of energy storage batteries... The lifespan of energy storage batteries under actual operating conditions can be derived. : .

[0049] Step S3: Integrate the wind farm power generation model, photovoltaic power generation model, and lifetime model, and construct an objective function with the goal of maximizing the daily comprehensive net income to obtain the configuration model.

[0050] Furthermore, the objective function can be implemented through the following preferred embodiments, including steps S31-S33: S31: Calculate the daily net income based on electricity sales revenue and hydrogen sales revenue; S32: Calculate the total cost based on investment cost, operation and maintenance cost, replacement cost, depreciation cost, and power deviation penalty cost; wherein, the power deviation penalty cost is calculated based on power deviation; the power deviation represents the sum of the absolute values ​​of the unmet load and the excess energy of the microgrid system; S33: Construct an objective function with the goal of maximizing the difference between the daily comprehensive net income and total cost.

[0051] In this preferred implementation example, the daily net income is calculated based on electricity sales revenue and hydrogen sales revenue, comprehensively covering the economic value of multiple energy products; the total cost includes investment, operation and maintenance, replacement, depreciation costs, and power deviation penalty costs (power deviation is the sum of the absolute values ​​of unmet load and excess energy), achieving synergistic optimization of short-term operating costs and long-term asset costs; an objective function is constructed with the goal of maximizing the difference between "daily net income and total cost", forcing the system to strictly control costs and energy waste while improving income, improving load fulfillment rate (reducing unmet load) and energy utilization rate (reducing excess energy), and ultimately achieving dual optimization of economic benefits and resource utilization efficiency.

[0052] In practical implementation, the objective function is specifically expressed as follows: ; in, This indicates the system's daily net profit. This indicates the system's daily overall revenue; , , and These correspond to the system's daily investment cost, daily maintenance cost, daily replacement cost, and daily depreciation cost, respectively. A positive value indicates that the system is economically feasible; conversely, a negative value indicates that the system is unprofitable and not economically feasible.

[0053] The formula for calculating daily comprehensive returns is as follows: in, The electricity price; The price of hydrogen; Price penalty for power deviation; for The actual load on the timekeeping system; for The amount of hydrogen delivered by the time-of-flight system; This is the power deviation, and its value is related to the unmet load and excess energy.

[0054] The actual load of the time-based system can be expressed as: in, for The output power of the wind farm at any given time; for The output power of the photovoltaic power generation system at all times and They are respectively The charging and discharging power of the energy storage battery at all times; for The output heat power of the electric heating equipment at any given time; for Power generation capacity of the power module at any given time; for The hydrogen production capacity of water electrolysis at any given time.

[0055] The formula for calculating daily investment cost is as follows: in, The discount rate of the system; This refers to the system's operational lifespan; here These respectively represent photovoltaic power plants, wind farms, molten salt thermal storage systems, energy storage batteries, and water electrolysis hydrogen production systems. The specific formula for calculating the investment cost of the corresponding system is as follows: in, and These represent the installed capacity of photovoltaic power plants and wind farms, respectively. and These represent the unit capacity cost of photovoltaic power plants and wind farms, respectively. and These represent the rated power of the electric heating equipment and the power conversion module, respectively. Indicates the rated thermal storage capacity of the molten salt thermal storage tank; and These represent the unit power cost of the electric heating equipment and the power conversion module, respectively. This indicates the unit capacity cost of a molten salt thermal storage tank. and These represent the rated power and rated capacity of the energy storage battery, respectively. and These represent the unit power cost and unit capacity cost of energy storage batteries, respectively. and These represent the rated power and rated capacity of the water electrolysis hydrogen production system, respectively. and These represent the unit power cost and unit capacity cost of the water electrolysis hydrogen production system, respectively.

[0056] Daily maintenance cost of the system Daily replacement cost Daily depreciation cost The calculation formula is as follows: in, This is the operation and maintenance cost coefficient; Total number of battery replacements; When calculating the number of times, round up to the nearest integer. The first of the energy storage batteries Replacement; This refers to the actual lifespan of the energy storage battery. This is the residual value coefficient.

[0057] Power deviation The specific calculation method is as follows: in, for The load demand of the microgrid system at any time.

[0058] Step S4: Combining the preset equipment capacity constraints, equipment operation constraints, electrical performance constraints and system strategy constraints, iteratively solve the configuration model, output the configuration scheme, and configure the energy storage capacity of the microgrid system.

[0059] Furthermore, the system strategy constraints include: constraints on the curtailment of energy storage batteries, constraints on the curtailment of molten salt thermal storage systems, and constraints on the curtailment of water electrolysis hydrogen production systems. The constraint on the curtailment operation of the energy storage battery is expressed as follows: when the microgrid system is in a curtailment state, the energy storage battery is only allowed to charge; otherwise, it is only allowed to discharge. The power curtailment operation constraint of the molten salt thermal energy storage system is expressed as follows: when the microgrid system is in a power curtailment state, the molten salt thermal energy storage system is only allowed to start the electric heating equipment for thermal storage operation; otherwise, it is only allowed to start the power conversion module for power generation operation. The power curtailment operation constraint of the molten salt thermal storage system is expressed as follows: when the microgrid system is in a power curtailment state, the water electrolysis hydrogen production system is allowed to start hydrogen production operation; otherwise, hydrogen production operation is prohibited. The term "waste power" refers to a state where the total power generation of the microgrid system exceeds the total load demand.

[0060] In this preferred embodiment, under the condition of power curtailment, the energy storage battery is only charged, the molten salt thermal storage system is only used for thermal storage, and the water electrolysis hydrogen production system starts to produce hydrogen. Under the condition of non-power curtailment, the operation is reversed. By distinguishing the states, the operating modes of each unit are precisely controlled, which avoids energy waste during power curtailment and maximizes the utilization of surplus electricity (thermal storage and hydrogen production). Under the condition of non-power curtailment, priority is given to ensuring the power supply to the load (discharging and power generation), thereby improving the renewable energy absorption rate and the system operation flexibility.

[0061] Furthermore, the molten salt thermal storage system also includes a molten salt thermal storage tank; the water electrolysis hydrogen production system includes an electrolyzer and a hydrogen storage tank. The equipment capacity constraints include: molten salt thermal storage tank capacity constraints and hydrogen storage tank capacity constraints; The equipment operation constraints include: the charging and discharging power constraints, charging and discharging state constraints, and state of charge constraints of the energy storage battery; the operation state constraints of the molten salt thermal storage system; the output constraints of the electric heating equipment; the output constraints, start-up and shutdown time constraints, and ramp-up constraints of the steam turbine in the power conversion module; and the hydrogen production capacity constraints, power operation range constraints, and hydrogen storage tank capacity balance constraints of the electrolyzer. The electrical performance constraints include the output constraints of wind farm power generation systems and photovoltaic power generation systems.

[0062] In practical implementation, the charging and discharging power constraints of the energy storage battery are expressed as follows: in, This represents the maximum charge and discharge power of the energy storage battery.

[0063] The state of charge / discharge constraints of the energy storage battery are expressed as follows: in, This represents the state variable of the energy storage battery during the charging process. A value of 1 indicates that the energy storage battery is in the charging process. This represents the state variable of the energy storage battery during the discharge process. A value of 1 indicates that the energy storage battery is in the discharge process. This represents the charging state variable of the energy storage battery; a value of 1 indicates that the energy storage battery is charging. This represents the discharge state variable of the energy storage battery; a value of 1 indicates that the energy storage battery is discharging.

[0064] The state-of-charge constraints of energy storage batteries are expressed as follows: in, and These represent the lower and upper limits of the state of charge of the energy storage battery, respectively. express The state of charge of the energy storage battery at all times; and These represent the charging efficiency and discharging efficiency of the energy storage battery, respectively.

[0065] The output constraints of the electric heating equipment are expressed as follows: in, This variable represents the operating status of the electric heating equipment. A value of 1 indicates that the electric heating equipment is running.

[0066] The capacity constraint of the molten salt thermal storage tank is expressed as follows: in, and These represent the lower and upper limits of the capacity of the molten salt thermal storage tank, respectively. Indicates molten salt thermal storage tank Heat storage at all times; and These represent the heat storage efficiency and heat release efficiency of the molten salt thermal storage tank, respectively.

[0067] The turbine output constraint in the power conversion module is expressed as follows: in, This indicates the operating status of the steam turbine; a value of 1 indicates that the steam turbine is running. and These represent the minimum and maximum technical output coefficients of the steam turbine, respectively.

[0068] The start-up and shutdown time constraints and ramp-up constraints of the steam turbine in the power module are expressed as follows: in, , These represent the shutdown time and startup time of the steam turbine at time t, respectively. , These are the shortest shutdown time and shortest startup time for the steam turbine, respectively. and These are the turbine's maximum upward climbing power and maximum downward climbing power, respectively.

[0069] The operating state constraints of the molten salt thermal energy storage system are as follows: .

[0070] The hydrogen production capacity constraints of the electrolyzer are expressed as follows: in, For the electrolytic cell in Hydrogen production rate at any given time. For hydrogen production efficiency.

[0071] The power operating range constraint of the electrolytic cell is expressed as follows: in, This indicates the working status of the electrolytic cell; a value of 1 indicates that the electrolytic cell is working. and These represent the minimum and maximum technical output coefficients of the electrolytic cell, respectively.

[0072] The capacity of the hydrogen storage device is expressed as follows: ;in, express The amount of hydrogen stored in the hydrogen storage device at any given time.

[0073] The capacity constraint of the hydrogen storage device is expressed as follows: in, Indicates that the hydrogen storage device is in The amount of hydrogen being transported outwards at all times.

[0074] The output constraints of photovoltaic and wind power systems are specifically expressed as follows: in, and These represent the power generation of wind power and photovoltaic power at time t, respectively. and Wind power and solar power respectively Normalized theoretical power generation at any given time; and These represent the installed capacity of wind power and solar power, respectively.

[0075] In addition, there are system policy constraints, which are represented as follows under the power curtailment state: , in, This is a power curtailment state variable; a value of 1 indicates that the system is in a power curtailment state. It is an infinitesimal positive number.

[0076] Regarding the operation and management strategy of energy storage batteries, given the energy conversion loss characteristics during the charging and discharging process, to prevent improper discharge behavior during the curtailment of renewable energy, when renewable energy curtailment occurs in the power system, the battery can only perform charging operations, and discharging operations are prohibited. Conversely, when the curtailment state variable becomes zero, the battery is only allowed to discharge, and charging behavior is restricted. Therefore, the curtailment operation constraints for energy storage batteries are expressed as follows: For molten salt thermal energy storage systems, if the power curtailment state variable is 1, it indicates that the system is in a power curtailment state. In this case, the electric heating equipment will start operating, while the power module will stop working. Conversely, when the power curtailment state variable is 0, it indicates that the system is not experiencing power curtailment; that is, when no power curtailment occurs, the electric heating equipment does not operate, but the power module does. By implementing this strategy, efficiency losses in the multiple energy conversion process of electricity-heat-electricity can be significantly reduced, thereby effectively improving the overall energy conversion efficiency. The power curtailment operation constraint is expressed as follows: For a water electrolysis hydrogen production system, the electrolyzer can only be put into operation when the power curtailment state variable is 1, i.e., in a power curtailment state; conversely, if the power curtailment state variable is 0, the electrolyzer must be shut down. The following power curtailment operation constraints must be followed for the electrolyzer's operation control strategy: When iteratively solving the configuration model, such as Figure 7 The flowchart shown illustrates a method for solving a composite energy storage capacity configuration model. First, given the installed capacity of wind and solar power, various parameters for the energy storage battery, molten salt thermal storage system, and water electrolysis hydrogen production system are set. Then, load forecast data and renewable energy output data of the industrial park's microgrid are obtained. With the objective function of maximizing the system's daily net comprehensive benefit, and considering the constraints of each system, a configuration model is established. Finally, the Gurobi solver is used to solve for the optimal capacity configuration scheme and the magnitude of the daily net comprehensive benefit.

[0077] In one embodiment, load data and renewable energy output data for a typical day are used as the basis, with a 24-hour time window and a resolution of 1 hour. The economic parameters of the wind-solar system, the molten salt thermal storage system, the energy storage battery parameters, and the water electrolysis hydrogen production system parameters are shown in Tables 1-4. Table 1. Economic parameters of wind-solar systems Table 2 Parameters of Molten Salt Thermal Storage System Table 3 Energy Storage Battery Parameters Table 4 Parameters of the water electrolysis hydrogen production system Based on the above parameter data, load forecast data, and renewable energy output data, optimization solutions were performed. Table 5 below shows the optimization results for the composite energy storage capacity configuration. Figure 8 The diagram shows a typical daily system power balance. The optimization results provide the optimal power and capacity configurations for the energy storage battery, molten salt thermal storage system, and water electrolysis hydrogen production system, achieving a high return of 300,800 yuan, thus verifying the effectiveness of the solution described in this invention.

[0078] Table 5 Optimization Results of Composite Energy Storage Capacity Configuration In summary, compared with the prior art, the above embodiments of this application have the following beneficial effects: By constructing a micro-energy grid system with a DC bus as the core, wind farms, photovoltaic power generation systems, load equipment, energy storage batteries, molten salt thermal storage systems (electric heating equipment), and water electrolysis hydrogen production systems form a centralized energy distribution network, enabling wind and solar energy to be directly distributed to loads or various energy storage units, avoiding the conversion losses of traditional AC grid connection, and simultaneously achieving efficient diversion of surplus electricity to thermal storage and hydrogen production (e.g., prioritizing load needs during peak wind and solar power generation, with surplus electricity immediately allocated to electric heating equipment for thermal storage or electrolysis for hydrogen production); the water electrolysis hydrogen production system outputs hydrogen to external equipment through hydrogen storage tanks, expanding energy utilization scenarios and increasing the amount of energy used. External revenue streams; the closed-loop connection between the power conversion module and the DC bus upgrades the molten salt thermal energy storage system from "one-way energy storage" to a "two-way regulation" unit, enabling supplementary power supply during peak load periods, enhancing the system's peak-shaving capability. Combined with the rapid response characteristics of the energy storage battery, this forms a dynamic balance network of "electricity-heat-hydrogen" multi-energy complementarity, achieving deep coupling of electricity, heat, and hydrogen, and improving the capacity for renewable energy absorption. A configuration model considering the lifespan of the energy storage battery is constructed based on electrical parameters with the goal of maximizing daily comprehensive net revenue, linking economic efficiency with equipment lifespan and avoiding long-term cost surges caused by short-term optimization. Iterative solutions are used to output configuration schemes, ensuring globally optimal capacity parameters and guaranteeing system power supply reliability. The overall solution, through a multi-energy collaborative architecture, lifespan-aware economic optimization, and solution process, systematically solves the problems of poor economic efficiency of single energy storage and insufficient multi-energy coupling, achieving a balance between the economy and reliability of composite energy storage capacity configuration.

[0079] Example 2: Please refer to Figure 2 Based on the same inventive concept, the present invention discloses a micro-energy grid composite energy storage capacity configuration system, comprising: a system construction module M1, a model construction module M2, an integration module M3, and a solution module M4; The system construction module M1 is used to construct a micro-energy grid system. This micro-energy grid system includes: a molten salt thermal storage system, a water electrolysis hydrogen production system, a wind farm power generation system, a photovoltaic power generation system, and an energy storage battery. The wind farm power generation system and the photovoltaic power generation system are connected to the load equipment via a DC bus. The electrical energy output from the wind farm power generation system and the photovoltaic power generation system is distributed via the DC bus to the load equipment, the energy storage battery system, the electric heating equipment of the molten salt thermal storage system, and the water electrolysis hydrogen production system. The power conversion module of the molten salt thermal storage system is connected to the DC bus, and the hydrogen storage tank of the water electrolysis hydrogen production system outputs hydrogen to external equipment.

[0080] The model building module M2 is used to build a wind farm power generation model based on the nonlinear relationship between the actual output power of wind power generation equipment and wind speed, to build a photovoltaic power generation model based on the relationship between the actual output power of photovoltaic power generation equipment and light intensity and temperature, and to build a lifespan model of energy storage batteries that takes into account actual operating conditions based on the quantitative relationship between the operating conditions and lifespan of energy storage batteries.

[0081] Furthermore, the model building module M2 includes: an interval division unit and a wind power model building unit; The interval division unit is used to divide the wind speed interval according to the relationship between the cut-in wind speed, rated wind speed and cut-out wind speed of the wind power generation equipment. The wind power model construction unit is used to establish segmented power output rules based on the wind speed-power characteristics in each wind speed range, and to construct the wind farm power generation model.

[0082] In this preferred embodiment, wind speed intervals are defined based on the cut-in wind speed, rated wind speed, and cut-out wind speed of the wind power generation equipment, thus clarifying the power output boundary conditions under different wind speeds. Segmented power output rules are established based on the wind speed-power characteristics within each interval, enabling the wind power output model to accurately map actual wind speed changes (e.g., zero output at low wind speeds, full output at rated wind speeds, and cut-out shutdown at high wind speeds), improving the accuracy of wind power output prediction. This model provides reliable wind and solar power generation data input for subsequent capacity configuration, avoiding redundancy or insufficiency in energy storage configuration due to output prediction deviations, and enhancing the engineering practicality of the overall configuration scheme.

[0083] Furthermore, the model building module M2 also includes: a mapping relationship building unit; The mapping relationship construction unit is used to establish a mapping relationship between photovoltaic power generation and environmental parameters based on standard light intensity and standard temperature parameters, combined with actual light intensity and photovoltaic panel temperature, by calculating the power correction coefficient through the ratio of light intensity and temperature correction term, and thus obtaining the photovoltaic power generation model.

[0084] In this preferred embodiment, based on standard irradiance and standard temperature parameters, and combined with actual irradiance and photovoltaic panel temperature, a power correction coefficient is calculated using the ratio of irradiance and temperature correction term, establishing a dynamic mapping relationship between photovoltaic power generation and environmental parameters. This model can respond in real time to the impact of irradiance fluctuations (such as changes in irradiance caused by cloud cover) and temperature drift (such as efficiency reduction caused by high summer temperatures) on photovoltaic output, improving the accuracy of photovoltaic output prediction. Compared with traditional simplified models that only consider irradiance, this model is more in line with actual environmental conditions, providing more accurate new energy output data support for the configuration of composite energy storage capacity.

[0085] Furthermore, the model building module M2 also includes: a daily equivalent cycle count calculation unit and a coupling construction unit; The daily equivalent cycle count calculation unit is used to convert the charge-discharge cycle at different discharge depths into the equivalent cycle count at the full discharge depth based on the power function relationship between the actual discharge depth and the cycle count when the energy storage battery switches between charge and discharge states, and to calculate the daily equivalent cycle count of the energy storage battery in combination with the daily optimization duration. The coupling construction unit is used to establish a quantitative relationship between the cycle life of the energy storage battery and the daily equivalent cycle number based on the number of cycles under the condition of full discharge depth of the energy storage battery, and to couple the float charge life to construct a life model of the energy storage battery under actual working conditions.

[0086] In this preferred embodiment, by capturing the actual discharge depth during charge / discharge state switching and based on its power function relationship with the number of cycles, charge / discharge cycles with different discharge depths are uniformly converted into equivalent cycle counts under full discharge depth. This achieves the quantification of lifetime degradation for different operating conditions such as "small-amplitude frequent charge / discharge" and "deep charge / discharge" in typical daily load fluctuations in the park. By combining the daily optimized duration (e.g., 24 hours) to calculate the daily equivalent cycle count, the dynamic fluctuations of the daily load curve (e.g., changes in charge / discharge depth caused by peak-valley load differences) are transformed into an accumulative lifetime loss index, establishing a dynamic correlation between "load fluctuation - cycle count - lifetime degradation". Based on the coupling of full discharge depth cycle count and float charging lifetime, an actual operating condition lifetime model is constructed, enabling lifetime assessment to respond in real time to the dynamic characteristics of the park's daily load curve (e.g., the cycle pattern of high load discharge during the day and low load charging at night). Ultimately, this achieves accurate "lifetime-operating condition" mapping for typical daily load fluctuation characteristics, providing lifetime parameter support for the configuration of composite energy storage capacity that fits the actual operating conditions of the park.

[0087] The integration module M3 is used to integrate the wind farm power generation model, photovoltaic power generation model and lifetime model, and construct an objective function with the goal of maximizing the daily comprehensive net income to obtain the configuration model.

[0088] Furthermore, the integration module M3 includes: a revenue calculation unit, a cost calculation unit, and an objective function construction unit; The revenue calculation unit is used to calculate the daily net revenue based on electricity sales revenue and hydrogen sales revenue. The cost calculation unit is used to calculate the total cost based on investment cost, operation and maintenance cost, replacement cost, depreciation cost, and power deviation penalty cost; wherein, the power deviation penalty cost is calculated based on power deviation; the power deviation represents the sum of the absolute values ​​of the unmet load and the excess energy of the microgrid system; The objective function construction unit is used to construct an objective function with the objective of maximizing the difference between the daily comprehensive net income and the total cost.

[0089] In this preferred implementation example, the daily net income is calculated based on electricity sales revenue and hydrogen sales revenue, comprehensively covering the economic value of multiple energy products; the total cost includes investment, operation and maintenance, replacement, depreciation costs, and power deviation penalty costs (power deviation is the sum of the absolute values ​​of unmet load and excess energy), achieving synergistic optimization of short-term operating costs and long-term asset costs; an objective function is constructed with the goal of maximizing the difference between "daily net income and total cost", forcing the system to strictly control costs and energy waste while improving income, improving load fulfillment rate (reducing unmet load) and energy utilization rate (reducing excess energy), and ultimately achieving dual optimization of economic benefits and resource utilization efficiency.

[0090] The solution module M4 is used to iteratively solve the configuration model by combining preset equipment capacity constraints, equipment operation constraints, electrical performance constraints and system strategy constraints, output the configuration scheme and configure the energy storage capacity of the microgrid system.

[0091] Furthermore, the system strategy constraints include: constraints on the curtailment of energy storage batteries, constraints on the curtailment of molten salt thermal storage systems, and constraints on the curtailment of water electrolysis hydrogen production systems. The constraint on the curtailment operation of the energy storage battery is expressed as follows: when the microgrid system is in a curtailment state, the energy storage battery is only allowed to charge; otherwise, it is only allowed to discharge. The power curtailment operation constraint of the molten salt thermal energy storage system is expressed as follows: when the microgrid system is in a power curtailment state, the molten salt thermal energy storage system is only allowed to start the electric heating equipment for thermal storage operation; otherwise, it is only allowed to start the power conversion module for power generation operation. The power curtailment operation constraint of the molten salt thermal storage system is expressed as follows: when the microgrid system is in a power curtailment state, the water electrolysis hydrogen production system is allowed to start hydrogen production operation; otherwise, hydrogen production operation is prohibited. The term "waste power" refers to a state where the total power generation of the microgrid system exceeds the total load demand.

[0092] In this preferred embodiment, under the condition of power curtailment, the energy storage battery is only charged, the molten salt thermal storage system is only used for thermal storage, and the water electrolysis hydrogen production system starts to produce hydrogen. Under the condition of non-power curtailment, the operation is reversed. By distinguishing the states, the operating modes of each unit are precisely controlled, which avoids energy waste during power curtailment and maximizes the utilization of surplus electricity (thermal storage and hydrogen production). Under the condition of non-power curtailment, priority is given to ensuring the power supply to the load (discharging and power generation), thereby improving the renewable energy absorption rate and the system operation flexibility.

[0093] In summary, compared with existing technologies, the embodiments of this application have the following beneficial effects: By constructing a micro-energy grid system with a DC bus as its core, wind farms, photovoltaic power generation systems, load equipment, energy storage batteries, molten salt thermal energy storage systems (electric heating equipment), and water electrolysis hydrogen production systems form a centralized energy distribution network. This allows wind and solar energy to be directly distributed to loads or various energy storage units, avoiding the conversion losses of traditional AC grid connection. Simultaneously, it achieves efficient diversion of surplus electricity to thermal energy storage and hydrogen production (e.g., prioritizing load needs during periods of high wind and solar power generation, with excess electricity immediately allocated to electric heating equipment for thermal energy storage or to the electrolyzer for hydrogen production). The water electrolysis hydrogen production system outputs hydrogen to external equipment through hydrogen storage tanks, expanding energy utilization scenarios and increasing additional energy output. Revenue streams: The closed-loop connection between the power conversion module and the DC bus upgrades the molten salt thermal energy storage system from "one-way energy storage" to a "two-way regulation" unit, enabling supplementary power supply during peak load periods, enhancing the system's peak-shaving capability. Combined with the rapid response characteristics of the energy storage battery, this forms a dynamic balance network of "electricity-heat-hydrogen" multi-energy complementarity, achieving deep coupling of electricity, heat, and hydrogen, and improving the capacity for renewable energy absorption. A configuration model considering the lifespan of the energy storage battery is constructed based on electrical parameters with the goal of maximizing daily comprehensive net revenue, linking economic efficiency with equipment lifespan and avoiding long-term cost surges caused by short-term optimization. Iterative solutions are used to output configuration schemes, ensuring globally optimal capacity parameters and guaranteeing system power supply reliability. The overall solution, through a multi-energy collaborative architecture, lifespan-aware economic optimization, and solution process, systematically solves the problems of poor economic efficiency of single energy storage and insufficient multi-energy coupling, achieving a balance between the economy and reliability of composite energy storage capacity configuration.

[0094] Example 3: Figure 3 A structural diagram of a microgrid composite energy storage capacity configuration device according to this application is presented. For example... Figure 3 As shown, the microgrid composite energy storage capacity configuration device may include: processor N1, memory N2, data interface N3, and communication bus N4.

[0095] Wherein: processor N1, memory N2, and data interface N3 communicate with each other through communication bus N4; data interface N3 is used for data communication with other devices such as input devices or output devices; processor N1 is used to execute program N5, which can specifically execute the relevant steps in any of the above embodiments of the micro-energy grid composite energy storage capacity configuration method.

[0096] Specifically, program N5 may include program code, which includes computer-executable instructions.

[0097] The processor N1 may be a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this application. The microgrid composite energy storage capacity configuration device includes one or more processors, which may be processors of the same type, such as one or more CPUs, or processors of different types, such as one or more CPUs and one or more ASICs.

[0098] Memory N2 is used to store program N5. Memory N2 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0099] The algorithms or displays provided herein are not inherently related to any particular computer, virtual system, or other device. Furthermore, the embodiments in this application are not directed to any particular programming language.

[0100] Example 4: This invention also provides a computer-readable storage medium storing at least one executable instruction. When the executable instruction is run on a microgrid composite energy storage capacity configuration device / system, it causes the microgrid composite energy storage capacity configuration device / system to perform a microgrid composite energy storage capacity configuration method in any of the above method embodiments.

[0101] Numerous specific details are set forth in the specification provided herein. However, it will be understood that embodiments of this application may be practiced without these specific details. Similarly, for the purpose of simplification and aiding understanding of one or more aspects of the invention, in the above description of exemplary embodiments of this application, various features of the embodiments are sometimes grouped together in a single embodiment, figure, or description thereof. The claims, which follow the detailed description, are hereby expressly incorporated into that detailed description, wherein each claim itself is a separate embodiment of this application.

[0102] Those skilled in the art will understand that the modules in the device of the embodiment can be adaptively changed and placed in one or more devices different from that embodiment. Modules, units, or components in the embodiment can be combined into a single module, unit, or component, and further, they can be divided into multiple sub-modules, sub-units, or sub-components, except that at least some of such features and / or processes or units are mutually exclusive.

Claims

1. A method for configuring composite energy storage capacity in a microgrid, characterized in that, include: A microgrid system is constructed, comprising: a molten salt thermal storage system, a water electrolysis hydrogen production system, a wind farm power generation system, a photovoltaic power generation system, and an energy storage battery. The wind farm power generation system and the photovoltaic power generation system are connected to the load equipment via a DC bus. The electrical energy output by the wind farm power generation system and the photovoltaic power generation system is distributed via the DC bus to the load equipment, the energy storage battery system, the electric heating equipment of the molten salt thermal storage system, and the water electrolysis hydrogen production system. The power conversion module of the molten salt thermal storage system is connected to the DC bus, and the hydrogen storage tank of the water electrolysis hydrogen production system outputs hydrogen to external equipment. Based on the nonlinear relationship between the actual output power of wind power generation equipment and wind speed, a wind farm power generation model is constructed. Based on the relationship between the actual output power of photovoltaic power generation equipment and light intensity and temperature, a photovoltaic power generation model is constructed. Based on the quantitative relationship between the operating conditions and lifespan of energy storage batteries, a lifespan model of energy storage batteries that takes into account actual operating conditions is constructed. By integrating the aforementioned wind farm power generation model, photovoltaic power generation model, and lifetime model, and constructing an objective function with the goal of maximizing the daily comprehensive net income, a configuration model is obtained. By combining preset equipment capacity constraints, equipment operation constraints, electrical performance constraints, and system strategy constraints, the configuration model is iteratively solved, and a configuration scheme is output to configure the energy storage capacity of the microgrid system.

2. The method for configuring composite energy storage capacity in a microgrid as described in claim 1, characterized in that, The construction of a wind farm power generation model based on the nonlinear relationship between the actual output power of wind power generation equipment and wind speed includes: Based on the relationship between the cut-in wind speed, rated wind speed and cut-out wind speed of wind power generation equipment, wind speed ranges are defined. Based on the wind speed-power characteristics within each wind speed range, segmented power output rules are established, and the wind farm power generation model is constructed.

3. The method for configuring composite energy storage capacity in a microgrid as described in claim 1, characterized in that, The photovoltaic power generation model is constructed based on the relationship between the actual output power of the photovoltaic power generation equipment and the light intensity and temperature, including: Based on standard light intensity and standard temperature parameters, combined with actual light intensity and photovoltaic panel temperature, a power correction coefficient is calculated by using the ratio of light intensity and temperature correction term, establishing a mapping relationship between photovoltaic power generation and environmental parameters, thus obtaining the photovoltaic power generation model.

4. The method for configuring composite energy storage capacity in a microgrid as described in claim 1, characterized in that, The process of constructing a lifespan model for energy storage batteries that takes into account actual operating conditions, based on the quantitative relationship between operating conditions and lifespan, includes: Based on the power function relationship between the actual depth of discharge and the number of cycles when the energy storage battery switches between charging and discharging states, the charge-discharge cycles at different depths of discharge are converted into the equivalent number of cycles at the full discharge depth, and the daily equivalent number of cycles of the energy storage battery is calculated in combination with the daily optimization duration. Based on the number of cycles under the condition of full discharge depth of the energy storage battery, a quantitative relationship between the cycle life of the energy storage battery and the daily equivalent number of cycles is established, and coupled with the float charge life, a life model of the energy storage battery under actual working conditions is constructed.

5. The method for configuring composite energy storage capacity in a microgrid as described in claim 1, characterized in that, The objective function constructed with the goal of maximizing the daily comprehensive net return includes: Calculate the daily net income based on revenue from electricity sales and hydrogen sales. The total cost is calculated based on investment cost, operation and maintenance cost, replacement cost, depreciation cost, and power deviation penalty cost; wherein, the power deviation penalty cost is calculated based on power deviation; the power deviation represents the sum of the absolute values ​​of the unmet load and the excess energy of the microgrid system; An objective function is constructed with the goal of maximizing the difference between the daily net income and the total cost.

6. The method for configuring composite energy storage capacity in a microgrid as described in claim 1, characterized in that, The system strategy constraints include: the power curtailment constraints of the energy storage battery, the power curtailment constraints of the molten salt thermal storage system, and the power curtailment constraints of the water electrolysis hydrogen production system. The constraint on the curtailment operation of the energy storage battery is expressed as follows: when the microgrid system is in a curtailment state, the energy storage battery is only allowed to charge; otherwise, it is only allowed to discharge. The power curtailment operation constraint of the molten salt thermal energy storage system is expressed as follows: when the microgrid system is in a power curtailment state, the molten salt thermal energy storage system is only allowed to start the electric heating equipment for thermal storage operation; otherwise, it is only allowed to start the power conversion module for power generation operation. The power curtailment operation constraint of the molten salt thermal storage system is expressed as follows: when the microgrid system is in a power curtailment state, the water electrolysis hydrogen production system is allowed to start hydrogen production operation; otherwise, hydrogen production operation is prohibited. The term "waste power" refers to a state where the total power generation of the microgrid system exceeds the total load demand.

7. A microgrid composite energy storage capacity configuration system, characterized in that, include: The system consists of a construction module, a model construction module, an integration module, and a solution module. The system construction module is used to construct a microgrid system. The microgrid system includes: a molten salt thermal storage system, a water electrolysis hydrogen production system, a wind farm power generation system, a photovoltaic power generation system, and an energy storage battery. The wind farm power generation system and the photovoltaic power generation system are connected to the load equipment via a DC bus. The electrical energy output from the wind farm power generation system and the photovoltaic power generation system is distributed via the DC bus to the load equipment, the energy storage battery system, the electric heating equipment of the molten salt thermal storage system, and the water electrolysis hydrogen production system. The power conversion module of the molten salt thermal storage system is connected to the DC bus, and the hydrogen storage tank of the water electrolysis hydrogen production system outputs hydrogen to external equipment. The model building module is used to build a wind farm power generation model based on the nonlinear relationship between the actual output power of wind power generation equipment and wind speed, to build a photovoltaic power generation model based on the relationship between the actual output power of photovoltaic power generation equipment and light intensity and temperature, and to build a life model of energy storage battery that takes into account the actual operating conditions based on the quantitative relationship between the operating conditions and lifespan of energy storage battery. The integration module is used to integrate the wind farm power generation model, photovoltaic power generation model and lifetime model, and construct an objective function with the goal of maximizing the daily comprehensive net income to obtain the configuration model; The solution module is used to iteratively solve the configuration model by combining preset equipment capacity constraints, equipment operation constraints, electrical performance constraints and system strategy constraints, output the configuration scheme and configure the energy storage capacity of the microgrid system.

8. A microgrid composite energy storage capacity configuration system as described in claim 7, characterized in that, The model building module includes: an interval division unit and a wind power model building unit; The interval division unit is used to divide the wind speed interval according to the relationship between the cut-in wind speed, rated wind speed and cut-out wind speed of the wind power generation equipment. The wind power model construction unit is used to establish segmented power output rules based on the wind speed-power characteristics in each wind speed range, and to construct the wind farm power generation model.

9. A microgrid composite energy storage capacity configuration device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the computer program is loaded into the processor, it implements the steps of the microgrid composite energy storage capacity configuration method according to any one of claims 1-6.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the microgrid composite energy storage capacity configuration method according to any one of claims 1-6.

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