Hydrogen energy storage-containing high-speed service area source load storage optimization configuration method and device

Abstract

The invention belongs to the technical field of source and load storage optimal configuration, and particularly relates to a hydrogen energy storage-containing high-speed service area source and load storage optimal configuration method and device, and the method comprises the steps: constructing a high-speed service area architecture comprising a photovoltaic array, an energy storage battery, a hydrogen fuel cell, an electrolytic cell, a hydrogen storage tank and a load; constructing a storage battery model and a hydrogen energy storage system model; establishing a multi-service-area collaborative optimization model; establishing an upper-layer model and setting constraint conditions of the upper-layer model by taking minimization of the internal total operation cost and investment cost of the high-speed service area as a target function; establishing a lower-layer model and setting constraint conditions of the lower-layer model by taking the minimum single-day operation cost of the high-speed service area as an objective function; and taking the upper-layer model as a planning layer, taking the lower-layer model as an operation layer, and iteratively solving the double-layer model to obtain a high-speed service area source-load-storage optimal configuration scheme. Through collaborative optimization of the hydrogen energy storage system and the multiple service areas, the operation cost is remarkably reduced, and the reliability and economical efficiency of the high-speed service area are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of source-load-storage optimization configuration, and particularly relates to a method and device for optimizing the source-load-storage configuration of a high-speed service area with hydrogen energy storage. Background Art

[0002] With the development of renewable energy and the urgent need for optimizing the energy structure, the high-speed service area, as a new type of energy management method and distribution mode, has gradually received extensive attention. Current research mainly focuses on the coordinated cooperation among wind power, photovoltaic power, and energy storage batteries to optimize the capacity configuration of high-speed service areas, reduce loss costs, and improve the reliability and flexibility of the system. Although micro gas turbines are used as emergency backup equipment to improve power supply reliability, the emissions of polluting gases are contradictory to the vision of clean energy development. Therefore, research has begun to explore cleaner new energy power generation forms represented by hydrogen energy as backup equipment. The mutual cooperation between electric energy and hydrogen energy can effectively improve the energy utilization rate and power supply stability of high-speed service areas, and the electricity-hydrogen coupling system has gradually become a research hotspot.

[0003] Based on the electricity-hydrogen coupling system, existing research has proposed various optimization configuration methods, such as a two-layer configuration model based on carbon trading and maximum capacity optimization, an optimization method for a hydrogen energy storage grid-connected high-speed service area considering stepped carbon trading and demand response, and a capacity configuration method for hydrogen energy storage in a wind-hydrogen hybrid system for the uncertainty of wind farm output. In addition, research on multi-service area collaborative optimization has also made progress. By the power interaction between multiple high-speed service areas, the penetration rate of renewable energy has been increased, the flexibility and economy of the system have been enhanced, and the function of energy power mutual assistance between service areas has been given.

[0004] However, there are still some deficiencies in the existing technology. First, most electricity-hydrogen coupling models only focus on the hybrid energy storage operation architecture, and do not fully consider the operation and maintenance balance of hydrogen energy and the construction of a trading model between hydrogen energy and the outside world. Second, existing research mostly optimizes with the reliability and energy utilization rate of high-speed service areas as the goal, rarely analyzes the electricity scheduling situation at different times of a day in high-speed service areas, and lacks a comparative analysis of the energy scheduling situation in new energy high-speed service areas, making it difficult to fully reflect the improvement of flexibility by the electricity-hydrogen coupling system. Finally, most of the research on multi-service area collaborative optimization operation is limited to the interaction of electric energy and does not involve other forms of energy interaction, resulting in room for improvement in the flexibility and economy of high-speed service areas. Summary of the Invention

[0005] In view of the deficiencies in the above prior art, the object of the present invention is to provide a method and device for optimizing the configuration of source, load and storage in a high-speed service area with hydrogen energy storage. Through the collaborative optimization of the hydrogen energy storage system and multiple service areas, green economic operation, flexible scheduling and efficient energy complementarity are achieved, significantly reducing the operating cost and improving the reliability and economy of the high-speed service area.

[0006] To achieve the above object, the present invention provides a method for optimizing the configuration of source, load and storage in a high-speed service area with hydrogen energy storage, including the following steps: S1. Construct a high-speed service area architecture including a photovoltaic array, energy storage batteries, a hydrogen fuel cell, an electrolyzer, a hydrogen storage tank, and a load, where the batteries are the electrical energy storage system, and the hydrogen fuel cell, electrolyzer, and hydrogen storage tank are the hydrogen energy storage system; S2. Construct a battery model and a hydrogen energy storage system model; S3. Considering the power interaction of electrical energy and hydrogen energy between multiple high-speed service areas, establish a collaborative optimization model for multiple service areas; S4. Taking the minimum of the total operating cost and investment cost within the high-speed service area as the objective function, establish an upper-layer model and set its constraint conditions; S5. Taking the lowest single-day operating cost of the high-speed service area as the objective function, establish a lower-layer model and set its constraint conditions; S6. Taking the upper-layer model as the planning layer and the lower-layer model as the operation layer, iteratively solve the two-layer model to obtain an optimized configuration scheme for the source, load and storage in the high-speed service area.

[0007] As a preferred embodiment of the present invention, in S2, the battery model includes the battery storage at time t and the state of charge of the battery at time t , which is expressed as: (1); In the formula, is the battery storage at time t-1; is the power loss rate; is the charge and discharge rate of the battery; is the charging power of the battery at time t; is the time step; is the discharge power of the battery at time t; which is expressed as: (2); In the formula, is the battery capacity.

[0008] As a preferred embodiment of the present invention, in S2, the hydrogen energy storage system model includes: An electrolyzer model, which is expressed as: (3); Wherein, is the power consumption of the electrolyzer; is the output efficiency of the electrolyzer; is the hydrogen power output by the electrolyzer; The hydrogen fuel cell model is expressed as: (4); Wherein, is the output electric power of the hydrogen fuel cell; is the output efficiency of the hydrogen fuel cell; is the hydrogen power consumed by the hydrogen fuel cell; The hydrogen storage tank model is expressed as: (5); Wherein, , are the hydrogen storage amounts in the hydrogen storage tank at times t and t - 1 respectively; is the charging and discharging efficiency of the hydrogen storage tank; is the hydrogen power input into the hydrogen storage tank per unit time; is the hydrogen power input from the hydrogen storage tank into the hydrogen fuel cell per unit time; The output powers of the electrolyzer and the hydrogen fuel cell are limited by their capacities and the remaining capacity of the hydrogen storage tank, and are expressed as: (6); (7); Wherein, is the capacity of the electrolyzer; is the capacity of the hydrogen fuel cell; , are the upper and lower limits of the capacity of the hydrogen storage tank respectively; is the upper limit of the power consumption of the electrolyzer at time t; is the upper limit of the output electric power of the hydrogen fuel cell at time t; The electro - hydrogen coupling unit model: all the hydrogen generated by the electrolyzer is stored in the hydrogen storage tank. When the electric energy is insufficient to meet the load demand, the hydrogen energy is introduced into the hydrogen fuel cell for power generation; the hydrogen rejection amount is used for sale, and when the hydrogen energy is insufficient to meet the load demand, hydrogen energy is purchased from the outside to maintain the supply - demand balance, and is expressed as: (8); Wherein, is the hydrogen power output by the electrolyzer at time t; is the hydrogen power consumed by the hydrogen fuel cell at time t; , are the hydrogen power purchased from the outside per unit time and the hydrogen power sold to the outside at time t respectively.

[0009] As a preferred embodiment of the present invention, in step S3, the process of constructing the multi-service area collaborative optimization model is as follows: If the electric energy and hydrogen energy generated within a highway service area cannot meet the load demand, then energy needs to be purchased from other highway service areas; conversely, if the generated energy is greater than the load demand, then energy can be sold to other highway service areas. Define the number of highway service areas as m, where i and j are the indices of highway service areas and i≠j. Then, the multi-service area collaborative optimization power of highway service area i at time t satisfies the power balance constraint: (9); In the formula, represents the electric power purchased by highway service area i from highway service area j at time t, represents the electric power purchased by highway service area j from highway service area i at time t; represents the battery discharge power of highway service area i at time t; represents the power purchased by highway service area i from the main grid per unit time; represents the electric power output by the hydrogen fuel cell of highway service area i by burning hydrogen energy at time t; represents the predicted value of the photovoltaic output of highway service area i at time t; represents the predicted value of the wind turbine output of highway service area i at time t; represents the load demand power per unit time of highway service area i predicted using historical data for the load result; represents the power sold by highway service area i to the main grid per unit time; represents the power consumed by the electrolyzer of highway service area i at time t; represents the battery charging power of highway service area i at time t; Subtract the above and below of formula (8) and consider the power interaction between multiple highway service areas to obtain the hydrogen power balance constraint of highway service area i at time t: (10); In the formula, represents the hydrogen power purchased by highway service area i from highway service area j during time period t, represents the hydrogen power purchased by highway service area j from highway service area i during time period t; represents the hydrogen power output by the electrolyzer of highway service area i at time t; represents the hydrogen power input from the hydrogen storage tank to the hydrogen fuel cell of highway service area i per unit time at time t; represents the hydrogen power purchased by highway service area i from the outside per unit time; Denote the hydrogen power input into the hydrogen storage tank per unit time at high-speed service area \(i\) at time \(t\). Denote the hydrogen power consumed by the hydrogen fuel cell at high-speed service area \(i\) at time \(t\). Denote the hydrogen power sold to the outside world per unit time at high-speed service area \(i\).

[0010] As a preferred solution of the present invention, in the above-mentioned S4, the upper-layer model takes the configuration of each device capacity as the optimization variable. For high-speed service area \(i\), its objective function is expressed as: (11); In the formula, is the equivalent annual value investment cost of high-speed service area \(i\); is the system operation cost of high-speed service area \(i\); is the demand response cost inside high-speed service area \(i\); The calculation method of is: (13); In the formula, denotes the set of investment construction devices, including photovoltaic, wind turbine, battery, hydrogen storage tank, electrolyzer, and hydrogen fuel cell, and \(k\) is the index of the device; denotes the capital recovery factor; denotes the unit power investment cost of device \(k\); denotes the installed capacity of device \(k\) in high-speed service area \(i\); denotes the discount rate; denotes the operation life of device \(k\); The calculation method of is: In the formula, represents the set of four seasons, \(n\) is the index of the season; \(s\) is the index of the typical day; denotes the number of days of the typical day in season \(n\); denotes the power purchase and sale cost of high-speed service area \(i\); denotes the hydrogen purchase and sale cost of high-speed service area \(i\); denotes the internal operation cost of high-speed service area \(i\); For , , its calculation method is: (15); In the formula, denotes the electricity price at time \(t\), Represents the hydrogen price at time t, is the maintenance ratio coefficient; Represents the operation and maintenance cost coefficient of equipment k; Represents the operating power of equipment k in highway service area i; The calculation method of is: (16); (17); In the formula, , Represent the quadratic term and linear term compensation coefficients of industrial load reduction; Represents the reduction amount of industrial load; Represents the industrial load in highway service area i after participating in the load demand response at time t.

[0011] As a preferred solution of the present invention, the constraint conditions of the upper layer model are: (18); In the formula, , respectively represent the installed capacity configured by the battery in highway service area i and the maximum battery capacity that the highway service area can bear, Represents the lower limit of the battery storage in highway service area i, and takes the maximum value of the battery capacity obtained by the lower layer model in a day during model calculation; , respectively represent the installed capacity configured by the photovoltaic in highway service area i and the maximum photovoltaic capacity that the highway service area can bear, Represents the lower limit of the predicted photovoltaic output in highway service area i, and takes the maximum value of the photovoltaic output obtained by the lower layer model in a day during model calculation; , respectively represent the installed capacity configured by the electrolyzer in highway service area i and the maximum electrolyzer capacity that the highway service area can bear, Represents the lower limit of the power consumption of the electrolyzer in highway service area i, and takes the maximum value of the power consumption of the electrolyzer obtained by the lower layer model in a day during model calculation; , respectively represent the installed capacity configured by the hydrogen fuel cell in highway service area i and the maximum hydrogen fuel cell capacity that the highway service area can bear, Represents the lower limit of the output power of the hydrogen fuel cell in highway service area i, and takes the maximum value that the output power of the hydrogen fuel cell can reach obtained by the lower layer model in a day during model calculation; , respectively represent the installed capacity configured by the hydrogen storage tank in highway service area i and the maximum hydrogen storage tank capacity that the highway service area can bear, represents the lower limit of the hydrogen storage tank capacity of highway service area i, and in the model calculation, the maximum value reached by the hydrogen storage tank in a day obtained from the lower-layer model is taken; 、 respectively represent the installed capacity configured by the wind turbines in highway service area i and the maximum wind turbine capacity that the highway service area can withstand, represents the lower limit of the output of the wind turbines in highway service area i, and in the model calculation, the maximum output value of the wind turbines obtained from the lower-layer model is taken.

[0012] As a preferred solution of the present invention, in S5, the lower-layer model calculates the scenario with the lowest single-day operating cost according to the optimal capacity configuration obtained from the upper-layer model, and brings this single-day scenario into the constraint conditions of the upper-layer model again to complete the iteration of the upper and lower-layer models; for highway service area i, the objective function of the lower-layer model is expressed as: (19).

[0013] As a preferred solution of the present invention, the constraint conditions of the lower-layer model include: Upper and lower limit constraints of decision variables: (20); In the formula, represents the per-unit value of the predicted photovoltaic output data of highway service area i; represents the per-unit value of the predicted output data of the wind turbines in highway service area i; represents the hydrogen storage amount in the hydrogen storage tank of highway service area i at time t; 、 、 respectively represent the state of charge of the battery in highway service area i at time t and its minimum and maximum values; Hydrogen storage tank hydrogen charging and discharging power constraint: (21); (22); (23); In the formula, represents the hydrogen charging and discharging state variable of the hydrogen storage tank, which is a Boolean variable; represents the upper limit of the hydrogen charging and discharging power of highway service area i; represents the proportional coefficient of the hydrogen power upper limit and the hydrogen storage tank capacity; Battery charging and discharging constraint: (24); (25); (26); In the formula, is the state variable of the energy storage charge and discharge power, which is a Boolean variable; is the upper limit of the energy storage charge and discharge power of highway service area i; is the battery capacity of highway service area i; is the fixed proportional coefficient of the energy storage power upper limit and the capacity; Power interaction constraint between highway service area and distribution network: (27); In the formula, represents the state variable of the power purchase and sale of highway service area i, which is a Boolean variable; represents the upper limit of the interactive power of the connection line between highway service areas; Power interaction constraint between highway service area and the outside world for hydrogen: (28); In the formula, represents the state variable of the hydrogen purchase and sale power of highway service area i and the main grid at time t, which is a Boolean variable; represents the upper limit of the hydrogen power that can be imported by the hydrogen pipeline for the interaction between the highway service area and the outside world; Power interaction constraint between highway service areas: (29); In the formula, , are the upper limits of the interactive power that the connection line and the hydrogen pipeline between highway service areas can withstand; Demand response constraint: (30); In the formula, represents the maximum load reduction coefficient of industry.

[0014] As a preferred solution of the present invention, in the above-mentioned S6, based on Yalmip and CPLEX mathematical optimization solvers on the MATLAB platform, the model is solved, and the solving process is as follows: S6.1. Initialize and assign values to the equipment of the highway service area, which is expressed as: (31); In the formula, is the initial capacity configuration prediction value of the photovoltaic capacity of highway service area i; is the initial rated power configuration prediction value of the electrolyzer capacity of highway service area i; is the initial rated power configuration prediction value of the hydrogen fuel cell of highway service area i; is the initial capacity configuration prediction value of the battery capacity of highway service area i; is the predicted value of the initial capacity configuration of the hydrogen storage tank in highway service area i; S6.2. At the planning layer, determine the capacity of each device with the lowest internal cost of the highway service area as the goal, and send the result of capacity determination to the operation layer as the operation upper limit; S6.3. Input the capacity determination value of the planning layer and the predicted value of the photovoltaic output load demand, simulate the typical daily operation of the highway service area with the goal of maximizing the revenue of the highway service area, and return the result of the operation to the planning layer; S6.4. Return the optimization variables of the operation layer to the planning layer. The maximum value of the optimization variables of the operation layer is used as the minimum value of the constraints of the optimization variables of the planning layer. The planning layer determines the capacity again with the goal of minimizing the internal cost of the highway service area; S6.5. Repeat S6.2 - S6.4 for iterative calculation. The optimization goal of the planning layer for each time is denoted as The optimization goal of the operation layer for each time is denoted as When and are both satisfied, the iteration stops, where , respectively represent the convergence thresholds of the planning layer and the operation layer. w is the index of the iteration times. The solution result when the iteration stops is the final capacity configuration result.

[0015] The source-load-storage optimization configuration device of the highway service area with hydrogen energy storage includes a memory, a processor, and a computer program stored on the memory and capable of running on the processor. The above method is implemented by the processor executing the computer program.

[0016] The beneficial effects of the present invention are as follows: By adopting a hydrogen energy storage system to replace the traditional micro gas turbine, the present invention effectively reduces the dependence on fossil fuels, reduces fuel costs and environmental pollution. The hydrogen energy storage system uses renewable energy to electrolyze water to produce hydrogen, forming a closed-loop renewable energy system, greatly reducing the annual operation cost of the highway service area and realizing green economic operation.

[0017] The electro-hydrogen coupling model established by the present invention can flexibly dispatch electric energy and hydrogen energy, effectively solving the problem of load supply-demand imbalance in different time periods of the highway service area. When the sunlight is sufficient, the excess electric energy is used for hydrogen production and storage; when the sunlight is insufficient or the load demand is high, the stored hydrogen energy is converted into electric energy through a hydrogen fuel cell to meet the load demand, improving the energy utilization efficiency and the stability of the microgrid (highway service area architecture).

[0018] The multi-service area collaborative optimization model proposed by the present invention not only realizes the electro-hydrogen coupling and multi-utilization within a single highway service area, but also fully utilizes the flexibility and complementarity of highway service areas through the interaction of electric energy and hydrogen energy between multiple highway service areas. This collaborative optimization significantly improves the flexible complementary and mutual assistance ability of energy, further reduces the operating cost, and enhances the economy and reliability of the microgrid. Through the power interaction between multiple service areas, the electric energy and hydrogen energy purchased from the outside are reduced, and the independence and economy of the microgrid are enhanced. Description of the Drawings

[0019] Figure 1 is the process schematic diagram of the present invention; Figure 2 is the schematic diagram of the highway service area architecture in the present invention; Figure 3 is the schematic diagram of the framework of multi-service area collaborative optimization in the present invention; Figure 4 is the schematic diagram of the double-layer model in the present invention; Figure 5 is the typical daily power operation scenario diagram of Highway Service Area 1 in Scenario 4 during the verification process of the present invention; Figure 6 is the typical daily hydrogen energy operation scenario diagram of Highway Service Area 1 in Scenario 4 during the verification process of the present invention; Figure 7 is the typical daily power operation scenario diagram of Highway Service Area 1 in Scenario 5 during the verification process of the present invention; Figure 8 is the typical daily hydrogen energy operation scenario diagram of Highway Service Area 1 in Scenario 5 during the verification process of the present invention. Detailed Embodiments

[0020] The embodiments of the present invention will be further described below with reference to the drawings: Embodiment 1: As Figure 1 shown, the optimization configuration method for the source, load and storage of a highway service area with hydrogen energy storage includes the following steps: S1. Construct a highway service area architecture including a photovoltaic array, a storage battery, a hydrogen fuel cell, an electrolyzer, a hydrogen storage tank, and a load, where the storage battery is an electric energy storage system, and the hydrogen fuel cell, electrolyzer, and hydrogen storage tank are hydrogen energy storage systems; S2. Construct a storage battery model and a hydrogen energy storage system model; S3. Considering the power interaction of electric energy and hydrogen energy between multiple highway service areas, establish a multi-service area collaborative optimization model; S4. Taking the minimum of the total operating cost and investment cost within the highway service area as the objective function, establish an upper-layer model and set its constraint conditions; S5. Taking the minimum daily operating cost of the highway service area as the objective function, establish the lower-layer model and set its constraints; S6. Taking the upper-layer model as the planning layer and the lower-layer model as the operation layer, iteratively solve the bi-level model to obtain the optimal configuration scheme of the source-load-storage in the highway service area.

[0021] In the electrical energy storage system, when the power generation is greater than the load demand, the excess electricity is stored in the energy storage device (battery); if the load demand is greater than the power generation, the electricity is transferred from the energy storage device to supply the load demand. The energy storage device can effectively solve the problem of uneven chronological distribution of new energy output and effectively improve the economy of the internal operation of the highway service area.

[0022] The schematic diagram of the highway service area architecture is as Figure 2 shown.

[0023] In S2, the battery model includes the battery storage at time t and the state of charge of the battery at time t , which is expressed as: (1); In the formula, is the battery storage at time t-1; is the power loss rate; is the charge and discharge rate of the battery; is the charging power of the battery at time t; is the time step; is the discharging power of the battery at time t; which is expressed as: (2); In the formula, is the battery capacity.

[0024] The electrolyzer-hydrogen storage tank-hydrogen fuel cell model has the same function as the battery energy storage. During the period with sufficient sunlight, the excess electrical energy is used for hydrogen production by the electrolyzer, and the produced hydrogen is stored in the hydrogen storage tank; in the case of insufficient sunlight, the hydrogen fuel cell uses the hydrogen in the hydrogen storage tank as fuel for power generation to meet the load demand.

[0025] In S2, the hydrogen energy storage system model includes: The electrolyzer can electrolyze water into hydrogen and oxygen, which is a device that can convert electrical energy into hydrogen energy. The electrolyzer model is expressed as: (3); In the formula, is the power consumption of the electrolyzer; is the output efficiency of the electrolyzer; is the hydrogen power output from the electrolyzer; The hydrogen fuel cell is a proton exchange membrane fuel cell that uses hydrogen and oxygen as fuels to convert chemical energy into electrical energy for storage. The hydrogen fuel cell model is expressed as: (4); In the formula, is the output electric power of the hydrogen fuel cell; is the output efficiency of the hydrogen fuel cell; is the hydrogen power consumed by the hydrogen fuel cell; The hydrogen storage tank is used to collect and store hydrogen and supply hydrogen to the hydrogen fuel cell when the load demand is large. The hydrogen storage tank has better safety and lower energy loss compared to the storage battery. Among them, the hydrogen storage amount in the hydrogen storage tank at each moment is equal to the hydrogen storage amount at the previous moment plus the hydrogen change amount at this moment. The hydrogen storage tank model is expressed as: (5); In the formula, , are the hydrogen storage amounts in the hydrogen storage tank at times t and t - 1 respectively; is the charging and discharging efficiency of the hydrogen storage tank; is the hydrogen power input into the hydrogen storage tank per unit time; is the hydrogen power input from the hydrogen storage tank into the hydrogen fuel cell per unit time; The output powers of the electrolyzer and the hydrogen fuel cell are limited by their capacities and the remaining capacity of the hydrogen storage tank, and are expressed as: (6); (7); In the formula, is the capacity of the electrolyzer; is the capacity of the hydrogen fuel cell; , are the upper and lower limits of the capacity of the hydrogen storage tank respectively; is the upper limit of the power consumption of the electrolyzer at time t; is the upper limit of the output electric power of the hydrogen fuel cell at time t; In this embodiment, , , is the capacity of the hydrogen storage tank.

[0026] For the electro - hydrogen coupling unit model, all the hydrogen generated by the electrolyzer is stored in the hydrogen storage tank. When the electric energy is not enough to meet the load demand, the hydrogen energy is introduced into the hydrogen fuel cell for power generation; the hydrogen rejection amount is used for sale, and when the hydrogen energy is not enough to meet the load demand, hydrogen energy is purchased from the outside to maintain the supply - demand balance, and is expressed as: (8); In the formula, is the hydrogen power output by the electrolyzer at time t; is the hydrogen power consumed by the hydrogen fuel cell at time t; , are respectively the hydrogen power purchased from the outside and the hydrogen power sold to the outside per unit time at time t.

[0027] Through the electro-hydrogen coupling unit model, the battery model and the hydrogen energy storage system model can form an electro-hydrogen coupling model.

[0028] As Figure 3 shown, in the multi-service area collaborative optimization model, not only can the electro-hydrogen coupling multi-utilization in a single high-speed service area be realized, but also the electricity and hydrogen interaction between different high-speed service areas can be achieved, making full use of the flexible characteristics of the high-speed service area and improving the energy flexible complementary and mutual assistance ability of the high-speed service area. Figure 3 In, MG is the microgrid, which is the high-speed service area architecture in this embodiment, and z is the number of microgrids involved.

[0029] In S3, the construction process of the multi-service area collaborative optimization model is as follows: If the electric energy and hydrogen energy generated inside the high-speed service area cannot meet the load demand, energy needs to be purchased from other high-speed service areas; on the contrary, if the generated energy is greater than the load demand, energy can be sold to other high-speed service areas with larger load demands. Define the number of high-speed service areas as m, i and j are the indexes of high-speed service areas and i≠j, then the multi-service area collaborative optimization power of high-speed service area i at time t satisfies the power balance constraint: (9); In the formula, represents the electric power purchased by high-speed service area i from high-speed service area j at time t, represents the electric power purchased by high-speed service area j from high-speed service area i at time t; represents the battery discharge power of high-speed service area i at time t; represents the power purchased by high-speed service area i from the main grid per unit time; represents the electric power output by the hydrogen fuel cell of high-speed service area i by burning hydrogen energy at time t; represents the predicted value of the photovoltaic output of high-speed service area i at time t; represents the predicted value of the wind turbine output of high-speed service area i at time t; represents the load demand power per unit time of high-speed service area i predicted by using historical data for the load result; represents the power sold by high-speed service area i to the main grid per unit time; represents the power consumption of the electrolyzer of high-speed service area i at time t; represents the battery charging power of highway service area \(i\) at time \(t\); Taking the difference between the upper and lower parts of formula (8) and considering the power interaction between multiple highway service areas, the hydrogen power balance constraint of highway service area \(i\) at time \(t\) is obtained: (10); In the formula, represents the hydrogen power purchased by highway service area \(i\) from highway service area \(j\) during time period \(t\), represents the hydrogen power purchased by highway service area \(j\) from highway service area \(i\) during time period \(t\); represents the hydrogen power output by the electrolyzer of highway service area \(i\) at time \(t\); represents the hydrogen power input from the hydrogen storage tank to the hydrogen fuel cell per unit time of highway service area \(i\) at time \(t\); represents the hydrogen power purchased from the outside by highway service area \(i\) per unit time; represents the hydrogen power input into the hydrogen storage tank per unit time of highway service area \(i\) at time \(t\); represents the hydrogen power consumed by the hydrogen fuel cell of highway service area \(i\) at time \(t\); represents the hydrogen power sold to the outside by highway service area \(i\) per unit time.

[0030] As Figure 4 shown, the upper-layer model is the planning layer, with the minimum of the total operation cost and investment cost within the highway service area as the objective function, the capacity configuration of each device as the optimization variable, and the lower limit of the variable constraint as the variable, which is the maximum value for the lower-layer operation. The lower-layer model is the operation layer, with the minimum of the single-day operation cost of the highway service area as the goal. Substitute the optimal installed capacity in the upper-layer model into the typical daily operation of the lower layer, and obtain the optimal typical daily operation data through calculation. Then substitute its maximum value into the upper-layer model, and complete the calculation through multiple interactions and iterations between the upper and lower layers.

[0031] The upper-layer optimization is the capacity configuration optimization, with the minimum of the equivalent annual value investment cost and annual maintenance cost of the highway service area as the goal, and adding the capacity configuration in the highway service area to the typical day of the highway service area operation. The decision variables are the configuration capacities of the photovoltaic array, battery, electrolyzer, hydrogen storage tank, and hydrogen fuel cell.

[0032] In S4, the upper-layer model takes the capacity configuration of each device as the optimization variable. For highway service area \(i\), its objective function is expressed as: (11); In the formula, is the equivalent annual value investment cost of highway service area \(i\); is the system operation cost of highway service area \(i\); is the demand response cost within highway service area \(i\); The calculation method is as follows: (12); (13); In the formula, represents the set of investment construction equipment, including photovoltaic, wind turbines, batteries, hydrogen storage tanks, electrolyzers, and hydrogen fuel cells. k is the index of the equipment; represents the capital recovery factor; represents the unit power investment cost of equipment k; represents the installed capacity of equipment k in highway service area i; represents the discount rate; represents the operation life of equipment k; includes the investment cost per unit capacity of energy storage , the investment cost per unit power of photovoltaic power generation , the investment cost per unit power of wind turbines , the investment cost per unit power of electrolyzers , the investment cost per unit capacity of hydrogen storage tanks , the investment cost per unit power of hydrogen fuel cells .

[0033] includes the operation life of batteries , the operation life of photovoltaic , the operation life of electrolyzers , the operation life of hydrogen storage tanks , the operation life of hydrogen fuel cells .

[0034] The calculation method is as follows: (14); In the formula, represents the set of four seasons. n is the index of the season; s is the index of the typical day; represents the number of days in the typical day under season n; represents the electricity purchase and sale cost of highway service area i; represents the hydrogen purchase and sale cost of highway service area i; represents the internal operation cost of highway service area i; For, , , Its calculation method is as follows: (15); In the formula, represents the electricity price at time t, represents the hydrogen price at time t, is the maintenance ratio coefficient; represents the operation and maintenance cost coefficient of equipment k; represents the operating power of equipment k in highway service area i; includes the operation and maintenance cost coefficient of the storage battery, the operation and maintenance cost coefficient of the photovoltaic, the operation and maintenance cost coefficient of the electrolyzer, the operation and maintenance cost coefficient of the hydrogen fuel cell, the operation and maintenance cost coefficient of the hydrogen storage tank.

[0035] The calculation method of is: (16); (17); In the formula, , represent the quadratic term and linear term compensation coefficients of industrial load reduction; represents the reduction amount of industrial load; represents the industrial load after highway service area i participates in load demand response at time t.

[0036] The constraint conditions of the upper-layer model are: (18); In the formula, , respectively represent the installed capacity configured by the storage battery in highway service area i and the maximum storage battery capacity that the highway service area can bear, represents the lower limit of the storage amount of the storage battery in highway service area i, and the maximum value of the storage battery capacity obtained by the lower-layer model in a day is taken in the model calculation; , respectively represent the installed capacity configured by the photovoltaic in highway service area i and the maximum photovoltaic capacity that the highway service area can bear, represents the lower limit of the predicted photovoltaic output value in highway service area i, and the maximum value of the photovoltaic output obtained by the lower-layer model in a day is taken in the model calculation; , respectively represent the installed capacity configured by the electrolyzer in highway service area i and the maximum electrolyzer capacity that the highway service area can bear, represents the lower limit of the power consumption of the electrolyzer in highway service area i, and the maximum value of the power consumption of the electrolyzer obtained by the lower-layer model in a day is taken in the model calculation; , respectively represent the installed capacity of the hydrogen fuel cell configured in the high-speed service area i and the maximum hydrogen fuel cell capacity that the high-speed service area can withstand. represents the lower limit of the output power of the hydrogen fuel cell in the high-speed service area i, and in the model calculation, it takes the maximum value that the output power of the hydrogen fuel cell can reach in a day obtained from the lower-layer model. 、 respectively represent the installed capacity of the hydrogen storage tank configured in the high-speed service area i and the maximum hydrogen storage tank capacity that the high-speed service area can withstand. represents the lower limit of the hydrogen storage tank capacity in the high-speed service area i, and in the model calculation, it takes the maximum value of the hydrogen storage tank reached in a day obtained from the lower-layer model. 、 respectively represent the installed capacity of the wind turbine configured in the high-speed service area i and the maximum wind turbine capacity that the high-speed service area can withstand. represents the lower limit of the wind turbine output in the high-speed service area i, and in the model calculation, it takes the maximum value of the wind turbine output obtained from the lower-layer model.

[0037] In S5, the lower-layer model calculates the scenario with the lowest daily operating cost according to the optimal capacity configuration obtained from the upper-layer model, and brings this daily scenario into the constraint conditions of the upper-layer model again to complete the iteration of the upper and lower-layer models; for the high-speed service area i, the objective function of the lower-layer model is expressed as: (19).

[0038] The constraint conditions of the lower-layer model include: Decision variable upper and lower limit constraints: (20); In the formula, represents the per-unit value of the predicted photovoltaic output data of the high-speed service area i; represents the per-unit value of the predicted wind turbine output data of the high-speed service area i; represents the hydrogen storage in the hydrogen storage tank of the high-speed service area i at time t; 、 、 respectively represent the state of charge of the battery in the high-speed service area i at time t and its minimum and maximum values; in this embodiment, 、 are set to 0.1 and 0.9.

[0039] Hydrogen storage tank hydrogen charging and discharging power constraints: (21); (22); (23); In the formula, represents the state variable of hydrogen charging and discharging of the hydrogen storage tank, which is a Boolean variable; represents the upper limit of hydrogen charging and discharging power of the high-speed service area i; represents the proportionality coefficient of the hydrogen power upper limit and the hydrogen storage tank capacity; Battery charging and discharging constraint: (24); (25); (26); In the formula, is the state variable of the energy storage charging and discharging power, which is a Boolean variable; is the upper limit of the energy storage charging and discharging power of the high-speed service area i; is the battery capacity of the high-speed service area i; is the fixed proportionality coefficient of the energy storage power upper limit and the capacity; Interactive power constraint between the high-speed service area and the power distribution network: (27); In the formula, represents the state variable of power purchase and sale of the high-speed service area i, which is a Boolean variable; represents the upper limit of the interactive power of the connecting line between high-speed service areas; Interactive hydrogen power constraint between the high-speed service area and the outside world: (28); In the formula, represents the state variable of hydrogen power purchase and sale between the high-speed service area i and the main grid at time t, which is a Boolean variable; represents the upper limit of the hydrogen power that can be imported by the hydrogen pipeline for the interaction between the high-speed service area and the outside world; Interactive power constraint between high-speed service areas: (29); In the formula, , are the upper limits of the interactive power that the connecting line and the hydrogen pipeline between high-speed service areas can withstand; Demand response constraint: (30); In the formula, represents the maximum load reduction coefficient of industry.

[0040] In S6, the model is solved based on Yalmip and CPLEX mathematical optimization solvers on the MATLAB platform. The solving process is as follows: S6.1. Initialize and assign values to the high-speed service area equipment, which is expressed as: (31); wherein is the predicted value of the initial capacity configuration of the photovoltaic capacity of the highway service area i; is the predicted value of the initial rated power configuration of the electrolyzer capacity of the highway service area i; is the predicted value of the initial rated power configuration of the hydrogen fuel cell of the highway service area i; is the predicted value of the initial capacity configuration of the battery capacity of the highway service area i; is the predicted value of the initial capacity configuration of the hydrogen storage tank capacity of the highway service area i; S6.2. At the planning level, the capacities of each device are determined with the goal of minimizing the internal cost of the highway service area, and the results of the capacity determination are sent to the operation level as the operation upper limit; S6.3. Input the capacity determination values of the planning level and the predicted values of the photovoltaic output load demand, and simulate the operation of the typical day of the highway service area with the goal of maximizing the revenue of the highway service area, and return the operation results to the planning level; S6.4. Return the optimization variables of the operation level to the planning level. The maximum value of the optimization variables of the operation level is used as the minimum value of the constraints of the optimization variables of the planning level. The planning level determines the capacity again with the goal of minimizing the internal cost of the highway service area; S6.5. Repeat S6.2 - S6.4 for iterative calculation. The optimization goal of the planning level each time is denoted as and the optimization goal of the operation level each time is denoted as When and are both satisfied, the iteration stops, where , represent the convergence thresholds of the planning level and the operation level respectively, w is the index of the iteration times, and the solution result when the iteration stops is the final capacity configuration result.

[0041] The verification process is as follows: Select the load and light data of a certain area as the input data. The parameter settings are shown in Table 1. Set to 1h.

[0042] Table 1 Parameter configuration

[0043] Consider five scenarios. Among them, Scenario 1 is the optimal configuration for the independent operation of multiple service areas based on photovoltaic and energy storage. Scenario 2 is the optimal configuration for the independent operation of multiple service areas considering the addition of a traditional micro gas turbine under the condition of Scenario 1. Scenario 3 is the optimal configuration for the high-speed service area considering the interaction between the traditional micro gas turbine and multiple service areas under the condition of Scenario 2. Scenario 4 is the hydrogen-containing multi-service area photovoltaic energy storage optimization configuration that replaces the traditional gas turbine in Scenario 3 with a hydrogen energy storage system. Scenario 5 adds the coordinated operation between multiple service areas on the basis of Scenario 4, and constructs a two-layer optimization configuration model for multiple service areas with hydrogen energy storage.

[0044] Configuration results and operation results of the high-speed service area: Set the number of high-speed service areas to 3. According to the optimal configuration method mentioned in this embodiment, the configuration scheme and operation result values of the high-speed service area are finally obtained, as shown in Table 2.

[0045] Table 2 Configuration results of the high-speed service area in Scenario 5

[0046] The comparison of the operation results of High-speed Service Area 1 under five scenarios is shown in Table 3.

[0047] Table 3 Operation results of the high-speed service area under five scenarios

[0048] Analysis shows that by comparing Scenario 1, Scenario 2, and Scenario 4, it can be seen that although the micro gas turbine can meet the load demand of the microgrid when the light is insufficient at night, the micro gas turbine consumes a large amount of fuel, and the resulting fuel cost greatly increases the operation cost of the microgrid. The investment cost of the hydrogen energy storage system is relatively high, but the system consumes renewable resources, and its operation cost is much lower than that of Scenario 2. Thus, it can be obtained that the hydrogen energy storage system is more green and economical than the traditional gas turbine system. By comparing Scenario 4 and Scenario 5, it can be seen that the operation cost of Scenario 5 is lower than that of Scenario 4. In the case of insufficient electric energy in a single microgrid, Scenario 5 realizes the coordinated operation between multiple microgrids through the buying and selling of electric energy and hydrogen energy between multiple microgrids. Thus, it can be seen that the coordinated operation of multiple microgrids can further improve the economy of the microgrid. Hydrogen energy is a green and efficient energy compared with the micro gas turbine. Hydrogen energy does not need to consider the fuel cost in the gas turbine, but produces hydrogen through the electrolysis of water by renewable energy to form a closed-loop renewable energy system, which greatly reduces the annual operation cost of the high-speed service area. Scenario 5 adds the interaction of electric energy and hydrogen energy between high-speed service areas on the basis of Scenario 4. By comparison, it can be obtained that for the reasonable configuration and stable operation of the high-speed service area, the multi-service area hydrogen energy storage optimization interaction system considered in Scenario 5 has a better effect than the traditional high-speed service area optimization configuration.

[0049] Typical daily simulation scenario of the high-speed service area: Taking the operation scenario of a typical day in spring at the high-speed service area 1 in Scenario 4 as an example, analyze the photovoltaic output, electrolyzer, hydrogen fuel cell, battery, and load output in each period of the electric-hydrogen coupling system.

[0050] Through analysis, it can be seen that the optimization configuration model proposed in this embodiment can effectively allocate power. When there is sufficient sunlight in the high-speed service area 1 from 11:00 to 14:00, the photovoltaic output is relatively high. The output of the hydrogen fuel cell decreases significantly during the period from 10:00 to 13:00, and the output of the electrolyzer reaches its peak during this period. It can be seen that during the daytime with sufficient sunlight, the photovoltaic output is higher than the load demand, and the abandoned light is used for hydrogen production by the electrolyzer. Utilizing the characteristic that the hydrogen storage tank can store hydrogen for a long time, the produced hydrogen is stored in the hydrogen storage tank to generate electricity by combustion during the period when the power is insufficient to meet the load demand, increasing the internal revenue of the high-speed service area. When the photovoltaic output is insufficient to meet the load demand during the night, the hydrogen energy in the hydrogen storage tank, the hydrogen energy produced by the electrolyzer during this period, and the hydrogen energy purchased from the outside will be used to generate electricity through the hydrogen fuel cell to meet the load demand. Therefore, the complementary characteristics of the electric energy storage system and the hydrogen energy storage system greatly reduce the abandoned light, improve the energy utilization rate, and enhance the stability of the high-speed service area system.

[0051] Taking the typical day in spring at the high-speed service area 1 in Scenario 5 as an example, compare Figure 5 、 Figure 6 Analyze the photovoltaic output, electrolyzer, hydrogen fuel cell output, and electrical energy interaction in each period of multiple service areas with hydrogen energy storage optimization.

[0052] Through Figure 7 and Figure 8 and Figure 5 and Figure 6 Through comparative analysis, it can be seen that the output of the electrolyzer and the hydrogen fuel cell fluctuates more significantly and has a relatively higher output compared to Scenario 4. It can be seen that on the basis of Scenario 4, Scenario 5 needs to carry out collaborative optimization among multiple service areas. While meeting the internal load demand of the high-speed service area, it is necessary to consider the load demand of other high-speed service areas to achieve the balance of the multi-service area system and reduce the purchased electrical energy and hydrogen energy from the outside.

[0053] Embodiment 2: An optimized configuration device for the source-load-storage of a high-speed service area with hydrogen energy storage, including a memory, a processor, and a computer program stored on the memory and capable of running on the processor. The method in Embodiment 1 is implemented by the processor executing the computer program.

Claims

1. A method for optimizing the configuration of source, load and storage in a high-speed service area with hydrogen energy storage, characterized in that It includes the following steps: S1. Construct a high-speed service area architecture including a photovoltaic array, an energy storage battery, a hydrogen fuel cell, an electrolyzer, a hydrogen storage tank, and a load, where the battery is an electrical energy storage system, and the hydrogen fuel cell, electrolyzer, and hydrogen storage tank are hydrogen energy storage systems; S2. Construct a battery model and a hydrogen energy storage system model; S3. Considering the power interaction of electric energy and hydrogen energy between multiple high-speed service areas, establish a multi-service area collaborative optimization model; S4. Taking the minimum of the total operating cost and investment cost within the high-speed service area as the objective function, establish an upper-layer model and set its constraint conditions; S5. Taking the lowest single-day operating cost of the high-speed service area as the objective function, establish a lower-layer model and set its constraint conditions; S6. Taking the upper-layer model as the planning layer and the lower-layer model as the operation layer, iteratively solve the two-layer model to obtain an optimized configuration plan for the source, load, and storage in the high-speed service area.

2. The method for optimizing the configuration of source, load and storage in a high-speed service area with hydrogen energy storage according to claim 1, wherein In the above-mentioned S2, the battery model includes the battery storage at time t and the state of charge of the battery at time t , which is expressed as: （1）； Wherein, is the storage capacity of the battery at time t-1; is the power loss rate; is the charge and discharge rate of the battery; is the charging power of the battery at time t; is the time step; is the discharging power of the battery at time t; Expressed as: （2）； In the formula, is the battery capacity.

3. The method for optimizing the configuration of power generation, load, and energy storage with hydrogen energy storage in a high-speed service area according to claim 2, wherein In the above-mentioned S2, the hydrogen energy storage system model includes: An electrolyzer model, expressed as: （3）； Wherein, is the power consumption of the electrolyzer; is the output efficiency of the electrolyzer; is the hydrogen power output by the electrolyzer; A hydrogen fuel cell model, expressed as: （4）； In the formula, is the output electric power of the hydrogen fuel cell; is the output efficiency of the hydrogen fuel cell; is the hydrogen power consumed by the hydrogen fuel cell; A hydrogen storage tank model, expressed as: （5）； In the formula, , are the hydrogen storage amounts in the hydrogen storage tank at times t and t - 1, respectively; is the charging and discharging efficiency of the hydrogen storage tank; is the hydrogen power input into the hydrogen storage tank per unit time; is the hydrogen power input from the hydrogen storage tank into the hydrogen fuel cell per unit time; The output power of the electrolyzer and the hydrogen fuel cell is limited by their capacities and the remaining capacity of the hydrogen storage tank, expressed as: （6）； （7）； In the formula, is the capacity of the electrolyzer; is the capacity of the hydrogen fuel cell; , are respectively the upper and lower limits of the capacity of the hydrogen storage tank; is the upper limit of the power consumption of the electrolyzer at time t; is the upper limit of the output electric power of the hydrogen fuel cell at time t; An electric-hydrogen coupling unit model. All the hydrogen generated by the electrolyzer is stored in the hydrogen storage tank. When the electric energy is not enough to meet the load demand, the hydrogen energy is introduced into the hydrogen fuel cell for power generation; the hydrogen rejection amount is used for sale, and when the hydrogen energy is not enough to meet the load demand, hydrogen energy is purchased from the outside to maintain the supply-demand balance, expressed as: （8）； Wherein, is the hydrogen power output by the electrolytic cell at time t; is the hydrogen power consumed by the hydrogen fuel cell at time t; , are respectively the hydrogen power purchased from the outside and the hydrogen power sold to the outside per unit time at time t.

4. The optimized configuration method for source, load and storage in a high-speed service area with hydrogen energy storage according to claim 3, characterized in that In the above-mentioned S3, the construction process of the multi-service area collaborative optimization model is as follows: If the electric energy and hydrogen energy generated within the high-speed service area cannot meet the load demand, energy needs to be purchased from other high-speed service areas; on the contrary, if the generated energy is greater than the load demand, energy can be sold to other high-speed service areas; Define the number of high-speed service areas as m, i and j are the indexes of high-speed service areas and i≠j, then the multi-service area collaborative optimization power of high-speed service area i at time t satisfies the power balance constraint: （9）； Wherein, represents the electric power purchased by highway service area i from highway service area j at time t; represents the electric power purchased by highway service area j from highway service area i at time t; represents the battery discharge power of highway service area i at time t; represents the power purchased by highway service area i from the main grid per unit time; represents the electric power output by the hydrogen fuel cell of highway service area i by burning hydrogen energy at time t; represents the predicted value of the photovoltaic output of highway service area i at time t; represents the predicted value of the wind turbine output of highway service area i at time t; represents the load demand power per unit time of highway service area i for predicting the load result using historical data; represents the power sold by highway service area i to the main grid per unit time; represents the power consumption of the electrolyzer of highway service area i at time t; represents the battery charging power of highway service area i at time t; Subtract the above and below of formula (8) and consider the power interaction between multiple high-speed service areas to obtain the hydrogen power balance constraint of high-speed service area i at time t: （10）； In the formula, represents the hydrogen power purchased by highway service area i from highway service area j during period t, represents the hydrogen power purchased by highway service area j from highway service area i during period t; represents the hydrogen power output by the electrolyzer in highway service area i at time t; represents the hydrogen power input from the hydrogen storage tank to the hydrogen fuel cell per unit time in highway service area i at time t; represents the hydrogen power purchased from the outside per unit time by highway service area i; represents the hydrogen power input into the hydrogen storage tank per unit time in highway service area i at time t; represents the hydrogen power consumed by the hydrogen fuel cell in highway service area i at time t; represents the hydrogen power sold to the outside per unit time by highway service area i.

5. The method for optimizing the configuration of source, load and storage in a high-speed service area with hydrogen energy storage according to claim 4, wherein, In S4 described above, the upper-layer model uses the device capacity configuration of each device as the optimization variable. For the high-speed service area i, its objective function is expressed as: （11）； Wherein, is the equivalent annual value investment cost of highway service area i; is the system operation cost of highway service area i; is the demand response cost within highway service area i; The calculation method is as follows: （12）； （13）； In the formula, represents the set of investment and construction equipment, including photovoltaic, wind turbines, storage batteries, hydrogen storage tanks, electrolyzers, and hydrogen fuel cells, where k is the index of the equipment; represents the capital recovery factor; represents the unit power investment cost of equipment k; represents the installed capacity of equipment k in the high-speed service area i; represents the discount rate; represents the operating life of equipment k; The calculation method is as follows: （14）； In the formula, represents the set of four seasons, n is the index of the season; s is the index of the typical day; represents the number of days of the typical day in season n; represents the cost of electricity purchase and sale of highway service area i; represents the cost of hydrogen purchase and sale of highway service area i; represents the operating cost within highway service area i; For , , its calculation method is as follows: （15）； In the formula, represents the electricity price at time t, represents the hydrogen price at time t, is the maintenance ratio coefficient; represents the operation and maintenance cost coefficient of device k; represents the operating power of device k in highway service area i; The calculation method is as follows: （16）； （17）； Wherein, , represent the quadratic and linear compensation coefficients for industrial load reduction; represents the reduction amount of industrial load; represents the industrial load of the high-speed service area i after participating in the load demand response at time t.

6. The method for optimizing the configuration of source, load and storage in a high-speed service area with hydrogen energy storage according to claim 5, wherein The constraint conditions of the upper-layer model are: （18）； In the formula, and respectively represent the installed capacity configured for the battery of highway service area i and the maximum battery capacity that the highway service area can withstand. represents the lower limit of the battery storage of highway service area i, and in the model calculation, the maximum value of the battery capacity obtained by the lower-level model in a day is taken. and respectively represent the installed capacity configured for the photovoltaic of highway service area i and the maximum photovoltaic capacity that the highway service area can withstand. represents the lower limit of the predicted photovoltaic output of highway service area i, and in the model calculation, the maximum value of the photovoltaic output obtained by the lower-level model in a day is taken. and respectively represent the installed capacity configured for the electrolyzer of highway service area i and the maximum electrolyzer capacity that the highway service area can withstand. represents the lower limit of the power consumption of the electrolyzer in highway service area i, and in the model calculation, the maximum value of the power consumption of the electrolyzer obtained by the lower-level model in a day is taken. and respectively represent the installed capacity configured for the hydrogen fuel cell of highway service area i and the maximum hydrogen fuel cell capacity that the highway service area can withstand. represents the lower limit of the output power of the hydrogen fuel cell in highway service area i, and in the model calculation, the maximum value that the output power of the hydrogen fuel cell can reach obtained by the lower-level model in a day is taken. and respectively represent the installed capacity configured for the hydrogen storage tank of highway service area i and the maximum hydrogen storage tank capacity that the highway service area can withstand. represents the lower limit of the hydrogen storage tank capacity of highway service area i, and in the model calculation, the maximum value reached by the hydrogen storage tank obtained by the lower-level model in a day is taken. and respectively represent the installed capacity configured for the wind turbine of highway service area i and the maximum wind turbine capacity that the highway service area can withstand. represents the lower limit of the wind turbine output of highway service area i, and in the model calculation, the maximum value of the wind turbine output obtained by the lower-level model is taken.

7. The method for optimizing the configuration of source, load and storage in a high-speed service area with hydrogen energy storage according to claim 6, characterized in that, In the above-mentioned S5, the lower-layer model calculates the scenario with the lowest daily operation cost according to the optimal capacity configuration obtained from the upper-layer model, and then brings this daily scenario into the constraint conditions of the upper-layer model again to complete the iteration of the upper and lower-layer models; for the highway service area i, the objective function of the lower-layer model is expressed as: （19）。 8. The method for optimizing the configuration of power generation, load and energy storage in a high-speed service area with hydrogen energy storage according to claim 7, characterized in that The constraint conditions of the lower-layer model include: Upper and lower limit constraints of decision variables: （20）； In the formula, represents the per-unit value of the predicted photovoltaic output data of the highway service area i; represents the per-unit value of the predicted wind turbine output data of the highway service area i; represents the hydrogen storage volume of the hydrogen storage tank of the highway service area i at time t; , , respectively represent the state of charge of the battery of the highway service area i at time t and its minimum and maximum values; Hydrogen charging and discharging power constraints of the hydrogen storage tank: （21）； （22）； （23）； In the formula, represents the state variable of hydrogen charging and discharging of the hydrogen storage tank, which is a Boolean variable; represents the upper limit of hydrogen charging and discharging power of the high-speed service area i; represents the proportionality coefficient of the hydrogen power upper limit and the hydrogen storage tank capacity; Battery charging and discharging constraints: （24）； （25）； （26）； In the formula, is the state variable of the energy storage charge and discharge power, which is a Boolean variable; is the upper limit of the energy storage charge and discharge power of highway service area i; is the battery capacity of highway service area i; is the fixed proportionality coefficient of the energy storage power upper limit and the capacity; Power interaction constraints between the high-speed service area and the distribution network: （27）； wherein, represents the power purchase and sale status variable of highway service area i, which is a Boolean variable; represents the upper limit of the interactive power of the connection lines between highway service areas; Hydrogen power interaction constraints between the high-speed service area and the outside: （28）； In the formula, represents the state variable of the hydrogen purchase and sale power of the high-speed service area i and the main network at time t, which is a Boolean variable; represents the upper limit of the hydrogen import power that the hydrogen pipeline for the interaction between the high-speed service area and the outside world can import. Power interaction constraints between high-speed service areas: （29）； In the formula, , are the upper limits of the interactive power that the connection lines between high-speed service areas and the hydrogen pipelines can withstand; Demand response constraints: （30）； In the formula, represents the maximum load reduction factor of the industry.

9. The method for optimizing the configuration of source, load and storage in a high-speed service area with hydrogen energy storage according to claim 8, wherein In the above-mentioned S6, based on the Yalmip and CPLEX mathematical optimization solvers on the MATLAB platform, the model is solved, and the solution process is as follows: S6.

1. Initialize and assign values to the high-speed service area equipment, expressed as: （31）； In the formula, is the predicted value of the initial capacity configuration of the photovoltaic capacity of highway service area i; is the predicted value of the initial rated power configuration of the electrolyzer capacity of highway service area i; is the predicted value of the initial rated power configuration of the hydrogen fuel cell of highway service area i; is the predicted value of the initial capacity configuration of the battery capacity of highway service area i; is the predicted value of the initial capacity configuration of the hydrogen storage tank capacity of highway service area i; S6.

2. At the planning layer, size the equipment with the lowest cost within the high-speed service area as the goal, and send the sizing results to the operation layer as the operation upper limit; S6.

3. Input the sizing values of the planning layer and the predicted values of photovoltaic output and load demand, simulate the typical daily operation of the high-speed service area with the goal of maximizing the revenue of the high-speed service area, and return the operation results to the planning layer; S6.

4. Return the optimized variables at the operation layer to the planning layer. The maximum value of the optimized variables at the operation layer is used as the minimum value of the constraints for the optimized variables at the planning layer. The planning layer then determines the capacity again with the goal of minimizing the internal cost of the highway service area. S6.

5. Repeat S6.2 - S6.4 for iterative operations. The optimization objective of each iteration in the planning layer is denoted as , and the optimization objective of each iteration in the operation layer is denoted as . When and are both satisfied, the iteration stops, where , represent the convergence thresholds of the planning layer and the operation layer respectively. w is the index of the iteration number, and the solution result when the iteration stops is the final capacity configuration result.

10. High-speed service area source-load-storage optimization configuration device for hydrogen-containing energy storage, characterized in that: It includes a memory, a processor, and a computer program stored on the memory and capable of running on the processor. The method described in any one of claims 1-9 is implemented by the processor executing the computer program.

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