A shared energy storage capacity optimization configuration method and device and electronic equipment

Abstract

The application provides a shared energy storage capacity optimization configuration method and device and electronic equipment. The method comprises the following steps: establishing a schedulable potential model of flexible resources, wherein the flexible resources comprise air conditioner virtual energy storage and electric vehicle battery swap station energy storage; establishing an electricity-hydrogen two-way variable working condition model of centralized reversible solid oxide cells; based on the two models, a shared energy storage double-layer capacity optimization configuration model is established, wherein the double-layer model comprises an upper-layer model with the maximum annual net income of a shared energy storage operator as a target and a lower-layer model with the minimum operation cost of each park user as a target; the double-layer model is solved, and the configuration strategy of the reversible solid oxide cells and the flexible resources and the operation scheme of each park are output. The application constructs a new shared energy storage mode of the centralized reversible solid oxide cells and the flexible resources, can reduce the overall investment cost of the energy storage system, and effectively improves the accuracy and economy of the capacity configuration.

Description

Technical Field

[0001] This invention relates to the field of power system energy storage optimization configuration and operation scheduling technology, and in particular to a method, device and electronic equipment for optimized configuration of shared energy storage capacity. Background Technology

[0002] With the increasing penetration rate of renewable energy, energy storage systems have become a key component in enhancing grid flexibility and the capacity to absorb renewable energy. Hydrogen energy storage technologies, represented by RSOC, have attracted much attention due to their advantages of high energy density, cleanliness, and long-term storage capabilities. At the same time, distributed flexible resources, represented by electric vehicle battery swapping stations and virtual energy storage for air conditioning, have also demonstrated enormous regulation potential due to their large scale and rapid response.

[0003] However, existing research often treats RSOC hydrogen energy storage and distributed flexible resources independently. On the one hand, research on the optimal configuration of RSOC often focuses on its own electro-hydrogen conversion efficiency and operating strategies, failing to fully utilize the more affordable distributed resources to supplement it, resulting in high system configuration costs. On the other hand, research on the scheduling of flexible resources such as electric vehicles and air conditioners is mostly focused on their short-term applications in demand response or frequency regulation, lacking a mechanism for unified planning and leasing operation of them as a long-term reliable standardized energy storage capacity.

[0004] Existing shared energy storage capacity configuration technologies generally employ a single type of energy storage, resulting in highly homogenized configuration schemes. While some existing technologies mention the potential of virtual energy storage in areas such as air conditioning and electric vehicles, they primarily treat these as auxiliary regulation methods, rather than considering them as configurable resources on par with physical energy storage. Furthermore, existing RSOC technologies mostly analyze their performance at the level of a single device or microgrid, failing to evaluate them within the macro-scale of regional power systems or integrated energy resource coordination.

[0005] Therefore, how to construct a two-layer collaborative planning framework that takes into account the refined RSOC model, the flexible resource regulation model, and the interests of multiple stakeholders, and systematically coordinate the allocation of centralized RSOC hydrogen energy storage and distributed flexible resources, remains a technical challenge that urgently needs to be solved in this field. Summary of the Invention

[0006] To address the aforementioned technical problems, this invention provides a method, apparatus, and electronic device for optimizing the configuration of shared energy storage capacity.

[0007] To achieve the above objectives, the present invention adopts the following technical solution: In a first aspect, the present invention provides a method for optimizing the configuration of shared energy storage capacity, comprising the following steps: Step S1: Establish a schedulable potential model for flexible resources, including virtual energy storage for air conditioning and energy storage for electric vehicle battery swapping stations. Step S2: Establish a bidirectional variable operating condition model of the centralized reversible solid oxide battery between electricity and hydrogen. Step S3: Based on the schedulable potential model of the flexible resources and the electric-hydrogen bidirectional variable operating condition model of the centralized reversible solid oxide battery, establish a shared energy storage dual-layer capacity optimization configuration model, which includes an upper-layer capacity optimization configuration model and a lower-layer multi-campus optimization operation model. Step S4: Solve the shared energy storage dual-layer capacity optimization configuration model, and output the configuration strategy of the reversible solid oxide battery and the flexible resources, as well as the operation plan of each park.

[0008] Furthermore, in step S1, establishing the virtual energy storage model for the air conditioner includes: The thermal dynamics of the building are described using a first-order equivalent thermal parameter model. Establish a mapping relationship between the adjustable power of the air conditioning load and the virtual energy storage capacity; By aggregating multiple virtual energy storage unit models of air conditioners using Minkowski methods, the adjustable power boundary and capacity boundary of the aggregated model are obtained.

[0009] Furthermore, in step S1, establishing the energy storage model for the electric vehicle battery swapping station includes: A battery swapping demand prediction model is established, which includes a driving behavior feature model, a battery swapping threshold feature model, and a spatiotemporal coupling model of battery swapping demand based on the driving behavior feature model and the battery swapping threshold feature model. Establish a battery classification cluster control model, and classify the batteries in the battery swapping station into uncontrollable, priority controllable, and standby controllable categories based on the battery state of charge; A charging and discharging power regulation model is established to optimize and regulate the charging and discharging power of the battery swapping station while meeting the battery swapping needs of electric vehicles.

[0010] Furthermore, the driving behavior feature model uses a normal distribution function to describe the randomness of the daily driving mileage of electric vehicle users; the battery swapping threshold feature model uses a normal distribution to describe the differences in the state of charge threshold of battery swapping demand; the battery swapping demand spatiotemporal coupling model establishes a battery swapping demand model by combining the relationship between driving mileage and power consumption, and uses the Monte Carlo method to randomly simulate user samples to output the number of battery swapping demands in each time period.

[0011] Furthermore, in step S3, establishing the electro-hydrogen bidirectional variable operating condition model of the centralized reversible solid oxide battery includes: establishing a model of the relationship between the mass of hydrogen participating in the reaction and the electric power of the reversible solid oxide battery based on the Butler-Volmer equation and Faraday's law. Establish the energy conversion equation of the reversible solid oxide battery in solid oxide fuel cell mode: ; in, P SOFC To output electrical power for solid oxide fuel cells, η SOFC For hydrogen fuel cell efficiency, L H2 This refers to the calorific value of hydrogen. The mass of hydrogen consumed in SOFC mode. This refers to the number of battery cells. F 2 It is a Faraday constant. For SOFC resistivity density, A For battery area, This refers to the molar mass of hydrogen gas. Establish the energy conversion equation of the reversible solid oxide battery in solid oxide electrolysis cell mode: ; in, P SOEC Input electrical power to the solid oxide electrolytic cell, η SOEC For the efficiency of the electrolytic cell, The mass of hydrogen produced in SOEC mode. This represents the SOEC resistivity density.

[0012] Furthermore, the upper-layer capacity optimization configuration model takes maximizing the annual net revenue of the shared energy storage operator as the objective function, and the decision variables include the configuration power limit and capacity limit of the reversible solid oxide battery, as well as the charging and discharging power of the flexibility resources in each time period; the lower-layer multi-park optimization operation model takes minimizing the operating cost of users in each park as the objective function.

[0013] Furthermore, the shared energy storage operator invests in and constructs the centralized reversible solid oxide battery, and leases the capacity resources of the air conditioning virtual energy storage and the electric vehicle battery swapping station energy storage; the annual net income of the upper-level capacity optimization configuration model is calculated by subtracting the annual fixed investment cost from the annual operating income, and the annual fixed investment cost includes the investment cost of the reversible solid oxide battery and the annual fixed lease cost of the air conditioning virtual energy storage and the electric vehicle battery swapping station energy storage.

[0014] Furthermore, solving the shared energy storage two-layer capacity optimization configuration model includes: A Lagrangian function is constructed for the lower-level multi-park optimized operation model, and the KKT conditions are derived. The complementary relaxation conditions in the KKT conditions are linearized using the Big M method. The linearized KKT conditions are added as constraints to the upper-level capacity optimization configuration model to form a single-level mathematical programming model. The solver is invoked to solve the single-layer mathematical programming model.

[0015] In a second aspect, the present invention provides a shared energy storage capacity optimization configuration device, comprising: The first modeling module is used to establish a schedulable potential model of flexible resources, including virtual energy storage for air conditioning and energy storage for electric vehicle battery swapping stations. The second modeling module is used to establish a bidirectional variable operating condition model of the centralized reversible solid oxide battery between electricity and hydrogen. The third modeling module is used to establish a shared energy storage two-layer capacity optimization configuration model based on the schedulable potential model of the flexible resources and the electric-hydrogen bidirectional variable operating condition model of the centralized reversible solid oxide battery. The solution module is used to solve the shared energy storage two-layer capacity optimization configuration model, and output the configuration strategy of the reversible solid oxide battery and the flexible resources, as well as the operation plan of each park.

[0016] Thirdly, the present invention provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the shared energy storage capacity optimization configuration method as described above.

[0017] The advantages and positive effects of this invention are as follows: 1. Explore and quantify the energy storage potential of flexible resources. This invention establishes a refined dispatchable potential model for virtual energy storage for air conditioning and energy storage for electric vehicle battery swapping stations. It incorporates air conditioning loads and electric vehicle battery swapping stations as virtual energy storage resources into a shared energy storage system for unified allocation, transforming "load" into "resource". This fully utilizes inexpensive distributed flexible resources and reduces the system's capacity dependence on centralized energy storage.

[0018] 2. Improve the accuracy of RSOC configuration. This invention establishes an RSOC electric-hydrogen bidirectional variable operating condition model based on the Butler-Volmer equation and Faraday's law, abandoning the traditional constant efficiency simplified model. It can accurately reflect the nonlinear influence of parameters such as current density, temperature, and voltage on efficiency under actual operating conditions, avoiding capacity redundancy and investment waste caused by model simplification.

[0019] 3. Achieving synergistic optimization of interests among multiple parties. This invention establishes a two-layer optimization model with shared energy storage operators at the top and integrated energy multi-park users at the bottom. The top layer optimizes RSOC configuration capacity and flexible resource charging and discharging power with the goal of maximizing the operator's annual net revenue, while the bottom layer optimizes shared energy storage dispatch strategies with the goal of minimizing the operating costs of each park. This approach balances the interests of investors and users, maximizing overall economic benefits.

[0020] 4. Leveraging the complementary characteristics of different resources. This invention coordinates and optimizes the configuration of RSOC, air conditioning virtual energy storage, and electric vehicle battery swapping station energy storage, leveraging the complementary advantages of different energy storage methods in terms of time scale and regulation characteristics. This avoids the overuse or idleness of a single energy storage resource, and improves the equipment utilization rate and operational reliability of the entire shared energy storage system.

[0021] 5. Provides precise quantitative basis. This invention outputs configuration schemes for RSOC and flexibility resources, as well as operation schemes for each park, through a two-layer optimization model, providing precise quantitative basis and feasible implementation schemes for the planning and operation of shared energy storage power stations in actual engineering projects. Attached Figure Description

[0022] Figure 1 This is a flowchart illustrating the overall method of the present invention; Figure 2 This is a flowchart illustrating the establishment of the flexible resource schedulable potential model in this invention; Figure 3 This is a flowchart illustrating the establishment of the RSOC electric-hydrogen bidirectional variable operating condition model in this invention.

[0023] 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 a part of the embodiments of the present invention, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention. Detailed Implementation

[0024] 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 a part of the embodiments of the present invention, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0025] Please see Figure 1 This invention provides a method for optimizing the configuration of shared energy storage capacity, comprising the following implementation steps: S1: Establish a model of the potential for flexible resource scheduling; S2: Establish a bidirectional variable operating condition model for the centralized RSOC (electricity-hydrogen exchange). S3: Based on the flexible resource dispatchable potential model and the centralized RSOC electric-hydrogen bidirectional variable operating condition model, establish a shared energy storage two-layer capacity optimization configuration model. S4: Solve the shared energy storage two-layer capacity optimization configuration model, and output the configuration strategy of RSOC and flexibility resources, as well as the operation plan of each park; Figure 2 Provide a flowchart for step S1; The step S1, which establishes a flexible resource schedulable potential model, includes: S11: Establish a virtual energy storage model for air conditioning; S12: Establish an energy storage model for electric vehicle battery swapping stations; The establishment of virtual energy storage for air conditioning in S11 includes: using a first-order equivalent thermal parameter model to describe the thermal dynamics of the building, constructing a mapping relationship between the adjustable power of the air conditioning load and the virtual energy storage capacity, and aggregating the data through Minkowski sums to obtain the adjustable power and capacity boundaries of the aggregated model. The establishment of energy storage for electric vehicle battery swapping stations in S12 includes: a battery swapping demand forecasting module, a battery classification and cluster control module, and a charging and discharging power control module; the battery swapping demand forecasting module includes: S121: Driving behavior feature modeling; S122: Battery swapping threshold feature modeling; S123: Establish a spatiotemporal coupling model of battery swapping demand based on S121-S122; The driving behavior feature modeling includes: based on historical data analysis, using a normal distribution function to describe the randomness of the daily driving mileage of electric vehicle users, where the probability density function of the daily driving mileage S is: ; in, σ b The standard deviation is taken as 0.88; μ bThe mean is taken as 3.2; The battery swapping threshold feature modeling includes: using a normal distribution to describe the SOC threshold difference of battery swapping demand, where the threshold follows a normal distribution: ; in, This represents the average battery swapping threshold. Its standard deviation; The spatiotemporal coupling model of battery swapping demand includes: establishing a battery swapping demand model by combining the relationship between driving mileage and power consumption; and starting from the user's initial SOC value. SOC start Drive to the battery swapping threshold The amount of electricity consumed is converted into the equivalent driving range. S O : ;

[0026] This mileage can also be expressed as the actual cumulative mileage traveled from the start to the battery swapping time t: ; in, t n The moment when the vehicle needs a battery swap; The percentage of daily mileage traveled by a vehicle at different times is expressed as a statistical result. Using the Monte Carlo method, a large-scale user sample is randomly simulated. The theoretical battery swapping time is calculated using the above formula, and the number of vehicles generating battery swapping demand in each time period is statistically analyzed to output the number of electric vehicle battery swapping demands. N swap (t) , which serves as the basic input for the shared energy storage two-layer capacity optimization configuration model; The battery classification cluster control module includes: classifying the batteries in the battery swapping station into uncontrollable, priority control, and standby control categories based on the battery SOC value; the uncontrollable category includes batteries that meet the following conditions: or The priority control class includes: battery meets... The backup control class includes: batteries that meet... ;in, Let be the state of charge of the i-th battery at time t. SOC max It is at its maximum state of charge; The charging and discharging power regulation module includes: when the battery SOC of the battery swapping station is above 95%, it is considered to be in a fully charged state and can be used for battery swapping. Under the premise of meeting the battery swapping needs of electric vehicles, the battery swapping station can be used as an energy storage system for charging and discharging power regulation. The charging and discharging power of the electric vehicle battery swapping station is optimized according to the shared energy storage dual-layer capacity optimization configuration model. Figure 3 Provide a flowchart for step S2; Step S2 establishes a centralized RSOC electric-hydrogen bidirectional variable operating condition model, including: S21: Based on the Butler-Volmer equation and Faraday's law, establish a model for the relationship between the mass of hydrogen participating in the reaction and the power of the RSOC. S22: Establishing the energy conversion equation for a reversible solid oxide fuel cell model: ; in, P SOFC To output electrical power for solid oxide fuel cells, η SOFC For hydrogen fuel cell efficiency, L H2 This refers to the calorific value of hydrogen. The mass of hydrogen consumed in SOFC mode. F represents the number of solar cells. 2 It is a Faraday constant. For SOFC resistivity density, A For battery area, This refers to the molar mass of hydrogen gas. S23: Establish the energy conversion equation for the reversible solid oxide electrolysis cell model: ; in, P SOEC Input electrical power to the solid oxide electrolytic cell, η SOEC For the efficiency of the electrolytic cell, The mass of hydrogen produced in SOEC mode. SOEC resistivity density; Step S3 establishes a two-layer capacity optimization configuration model for shared energy storage, including: establishing an upper-layer capacity optimization configuration model with the goal of maximizing the annual net revenue of shared energy storage operators and a lower-layer multi-park optimization operation model with the goal of minimizing the operating costs of each user. Specifically, the establishment of the air conditioning virtual energy storage model in step S11 includes: The mathematical expression for the dispatchable potential model of virtual energy storage for air conditioning is as follows: ; in, α represents the remaining electricity of the air conditioner's virtual energy storage during time period t; FTCLs and γ FTCLs The conversion coefficients for the air conditioning virtual energy storage model; and These represent the charging and discharging power of the air conditioner's virtual energy storage during time period t; and These are the upper limits of the virtual energy storage charging and discharging power for air conditioners; and These represent the virtual energy storage charging and discharging states of the air conditioner. This is the upper limit of the virtual energy storage capacity for air conditioners; The relevant parameter calculation formulas are as follows: ; in: air conditioners r Relevant conversion parameters for virtual energy storage unit model; R r,a and C r,a air conditioners r The equivalent heat capacity and thermal resistance of the room; δ r For air conditioning r Operating temperature dead zone; cop r For air conditioning r The energy efficiency ratio; and air conditioners r The upper limit of charging and discharging power of a virtual energy storage unit model; and air conditioners r Rated power and baseline power; N FTCL This represents the total number of FTCLs. Let t be the energy storage capacity of the virtual energy storage unit model of the air conditioner at time t; and These represent the charging and discharging power of the virtual energy storage unit model of the air conditioner at time t.

[0027] Specifically, step S1 includes: The charging and discharging power regulation module in step S12 includes the following constraints: When battery i is not participating in the battery swapping service at time t, its State of Charge (SOC) changes with the charging and discharging process, which can be described by a discrete-time dynamic model: ; in, CAP This refers to the rated capacity of the battery. Let be the charging power of battery i at time t; Let be the discharge power of battery i at time t; η EV,c The charging power of the battery; η EV,d The discharge efficiency of the battery; The duration of the discrete scheduling period; This represents the initial SOC of battery i when it is switched to the station at time t; This is a binary variable used to indicate whether battery i undergoes a battery swap at time t. This indicates that battery i was used to exchange with the vehicle battery at time t; This indicates that the battery was not replaced at that moment. The battery swapping station should guarantee that each vehicle has a fully charged battery at every time there is demand; therefore, the constraint is: ; in, N to This refers to all battery packs within the battery swapping station; Since the battery swapping operation requires the removed battery to be fully charged, the model needs to ensure that if battery i is swapped at time t, its state of charge (SOC) must be above 95% at that time. The corresponding constraint is: ; in, This represents the maximum permissible state of charge of the battery. Upper limit constraints on the charging and discharging power of electric vehicle batteries: ; in, and These are the upper limits of charging and discharging power for electric vehicle batteries; Battery SOC is limited by physical capacity and must meet the following requirements: ; in, SOC EV,min Indicates the minimum state of charge of the battery; The overall battery charging and discharging power of the battery swapping station is: ; in, P BESS,c (t) and P BESS,d (t) These refer to the overall charging and discharging power of the battery swapping station; Total charging power of battery swapping station P BESS,c (t) and total discharge power PBESS,d (t) Satisfying complementary constraints: ; The above non-convex constraints are linearized using the Big M method, introducing integer 0-1 variables. : ; Where M is a sufficiently large constant.

[0028] Specifically, step S21 includes: RSOC has two operating modes: solid oxide electrolyzer and solid oxide fuel cell, which can be switched bidirectionally according to energy demand. Its electro-hydrogen conversion relationship is based on the Butler-Volmer equation and Faraday's law, and the specific model is as follows: ; Among them, E Nernst E is the Nernst voltage; cell This refers to the external voltage of the fuel cell stack. These are activation overvoltage, concentration overvoltage, and ohmic overvoltage, respectively. ρ is the Gibbs free energy of the fuel gas; F is the Faraday constant; R It is the ideal gas constant; T The reaction temperature; These are the partial pressures of hydrogen, water vapor, and oxygen, respectively; J is the current density. This refers to the hydrogen flow rate per unit area. P r Output electrical power to RSOC; n A is the number of RSOC chips; I is the RSOC area; U is the RSOC current; U is the RSOC voltage. n e The number of hydrogen atoms, with a value of 2; Fitting power density ω with current density J Satisfying the quadratic relation: ; Where ω is the RSOC power density; α and β The coefficients of the quadratic function for fitting the curve; Combining the above relationships, the RSOC output power P r The relationship between the mass of hydrogen participating in the reaction and the mass of hydrogen gas is as follows: ; Among them, M H2 The molar mass of hydrogen gas is... A This refers to the battery area.

[0029] Specifically, step S3 includes: The shared energy storage system includes three types of resources: centralized RSOC, air conditioning virtual energy storage, and electric vehicle battery swapping stations. The method is implemented by the shared energy storage operator, which mainly involves investing in and constructing centralized RSOC, and secondarily leasing air conditioning virtual energy storage and electric vehicle battery swapping station resources. The objective of the upper-level model is: ; in, R SES For the annual net income of shared energy storage operators; and These represent the annual fixed investment cost and annual operating revenue of a shared energy storage operator, respectively; x1 and x2 are decision variables. The annual fixed investment cost is: ; in, The investment cost of a centralized RSOC; and The annual fixed lease costs are for virtual energy storage for air conditioning and energy storage for electric vehicle battery swapping stations, respectively; decision variable x1 is the upper limit of the configured power of a centralized RSOC. With capacity limit ; The configuration cost of a centralized RSOC is: ; Among them, c inv c is the initial investment cost at an equivalent annual value. op Annual operating and maintenance costs; ; Where L is the isochronous value conversion factor; The lifespan of the energy storage device; d c is the discount rate; pinv and c einv These represent the unit power and unit capacity construction costs of a centralized RSOC, respectively; c op The annualized operation and maintenance cost per unit power of a centralized RSOC; The annual fixed lease cost for flexible resources is: ; in, c FTCL and c EVCS These are the average annual fixed lease cost coefficients per unit capacity for air conditioning virtual energy storage and electric vehicle battery swapping station energy storage, respectively. and The upper limit of the rental capacity for virtual energy storage for air conditioners and energy storage for electric vehicle battery swapping stations; Annual operating revenue is: ; in, N USES The total number of users with energy storage charging and discharging needs; and These are the charging and discharging power of user i after purchasing the right to use energy storage; and These represent the electricity prices at time t for users selling and purchasing electricity from the shared energy storage operator; Let t be the unit price for leasing the right to use energy storage; decision variables X 2 includes the charging and discharging power of centralized RSOC, air conditioning virtual energy storage, and electric vehicle charging station energy storage during time period t; SOEC is the reversible solid oxide electrolyzer model; SOFC is the reversible solid oxide fuel cell model; FTCLs is the air conditioning virtual energy storage model; EVCS is the electric vehicle battery swapping station model; The upper-level power balance constraint is: ; The objective of the lower-level model is: ; in, For user i's annual energy cost; The cost of interaction between user i and the upstream distribution network; User interaction costs with the upstream distribution network: ; in, and Let be the power purchased by user i from the grid and the power generated at time t, respectively. and These represent the grid purchase and sale prices of electricity at time t; The lower-level power balance constraint is: ; in: and These refer to the wind power and solar power output of the lower-level user parks, respectively. To meet the electricity load requirements of each user park in the lower level.

[0030] Specifically, step S4 includes the following steps: 1. Initialize system parameters, including: RSOC technical parameters, air conditioning virtual energy storage parameters, electric vehicle battery swapping station parameters, new energy output curves and load curves for each park, and time-of-use electricity prices; 2. Initialize the parameters of the two-layer optimization model, including: the investment cost coefficient and leasing cost coefficient of the upper-layer shared energy storage operator, the upper and lower limits of the power purchase and sale of each park in the lower layer, and the large M parameter and convergence threshold required for KKT conversion; 3. The computer uses the MATLAB platform and calls the YALMIP toolbox to build the upper-level shared energy storage optimization model and the lower-level optimization operation model for each park. 4. Optimize the operation model of each park at the lower level, construct the Lagrangian function and derive the KKT conditions, including stability conditions, complementary relaxation conditions, original feasibility conditions and dual feasibility conditions; 5. The Big M method is used to linearize the complementary relaxation conditions, and binary variables are introduced to transform the nonlinear complementary relationship into a linear inequality constraint; 6. Add the transformed lower-level KKT conditions as constraints to the upper-level optimization model to form a single-level mathematical programming model with equilibrium constraints; 7. Use the Gurobi solver to solve the above MPEC model and obtain the optimization results, including: the upper limit of RSOC's optimal configuration power and capacity, the charging and discharging power of shared energy storage operators' air conditioning virtual energy storage and electric vehicle swapping stations at different times, and the power purchase and sale plans of each park at different times; Specifically, to fully explain the above steps, three industrial park users and one shared energy storage operator were selected for case analysis. Through simulation analysis of three typical schemes, the planning effects of different strategies were studied, as described in detail below: Option 1: Only a centralized RSOC hydrogen energy storage system is configured, without considering virtual energy storage for air conditioning and electric vehicle battery swapping stations. The RSOC adopts a traditional simplified model, which assumes that the RSOC is set to a constant energy conversion efficiency in both electrolysis and fuel cell modes, with SOEC efficiency of 85% and SOFC efficiency of 55%, ignoring the impact of operating parameters such as voltage, current, and temperature on efficiency.

[0031] Option 2: Only a centralized RSOC hydrogen energy storage system is configured, without considering air conditioning virtual energy storage and electric vehicle battery swapping stations. The RSOC adopts the centralized RSOC electric-hydrogen bidirectional variable operating condition model established in step S2.

[0032] Option 3: Based on Option 2, introduce the air conditioning virtual energy storage model and electric vehicle battery swapping station model established in step S1.

[0033] ; As shown in Table 1, Scheme 1, limited by a simplified model, ignores the nonlinear effects of parameters such as current density, temperature, and voltage on efficiency during actual operation. It fails to accurately reflect the efficiency variation characteristics of the RSOC under actual operating conditions, resulting in a conservative configuration and significant capacity redundancy and wasted investment, leading to the lowest return. Scheme 2, employing a refined RSOC electric-hydrogen bidirectional variable operating condition model, reduces the configured capacity by 1.3MW. The refined model more realistically reflects the nonlinear efficiency characteristics of the RSOC in electrolysis and fuel cell modes, avoiding over-configuration and increasing operational benefits. Scheme 3, further incorporating virtual energy storage for air conditioning and a battery swapping station model for electric vehicles, significantly reduces the RSOC configured power to 4.8MW, despite increased costs due to leasing flexible resources, resulting in the highest operational benefits. This fully demonstrates the substitution effect of multi-resource collaborative configuration.

[0034] It should be noted that the parts not described in detail in the above embodiments are all prior art.

[0035] Example 1: Overall Method Flow like Figure 1 As shown, the shared energy storage capacity optimization configuration method integrating RSOC and flexible resources provided by the present invention includes the following steps: S1: Establish a schedulable potential model for flexible resources, including a virtual energy storage model for air conditioning and an energy storage model for electric vehicle battery swapping stations.

[0036] S2: Establish a centralized RSOC model for bidirectional variable operating conditions of electricity and hydrogen.

[0037] S3: Based on the model established in steps S1 and S2, establish a shared energy storage two-layer capacity optimization configuration model.

[0038] S4: Solve the shared energy storage two-layer capacity optimization configuration model, and output the configuration strategy of RSOC and flexibility resources, as well as the operation plan of each park.

[0039] Example 2: Establishment of a model for the schedulable potential of flexible resources like Figure 2 As shown, the specific process for establishing the flexible resource schedulable potential model in step S1 is as follows: S11: Establish a virtual energy storage model for air conditioning. A first-order equivalent thermal parameter model is used to describe the building's thermal dynamics. The mapping relationship between the adjustable power of the air conditioning load and the virtual energy storage capacity is constructed, and the adjustable power and capacity boundaries of the aggregated model are obtained through Minkowski summation.

[0040] S12: Establish an energy storage model for electric vehicle battery swapping stations, including a battery swapping demand prediction module, a battery classification and cluster control module, and a charging and discharging power control module.

[0041] In the battery swapping demand forecasting module, a normal distribution is used to describe the randomness of daily mileage (mean 3.2, standard deviation 0.88), and a normal distribution is used to describe the differences in battery swapping thresholds. A Monte Carlo method is used to perform random simulations on a large-scale user sample, outputting the battery swapping demand for each time period. In the battery classification and cluster control module, batteries in the battery swapping station are classified into uncontrollable, priority control, and standby control categories based on battery SOC. In the charge / discharge power control module, the dynamic change model of battery SOC and related constraints are as described in the aforementioned formulas.

[0042] Example 3: Establishment of RSOC Electric-Hydrogen Bidirectional Variable Operating Condition Model like Figure 3 As shown, the specific process for establishing the centralized RSOC's electric-hydrogen bidirectional variable operating condition model in step S2 is as follows: S21: Based on the Butler-Volmer equation and Faraday's law, establish a model for the relationship between the mass of hydrogen participating in the reaction and the power of the RSOC.

[0043] S22: Establish the energy conversion equations for the SOFC mode.

[0044] S23: Establish the energy conversion equations for the SOEC model.

[0045] Example 4: Establishment and Solution of a Shared Energy Storage Two-Layer Capacity Optimization Configuration Model In step S3, the shared energy storage system includes three types of resources: centralized RSOC, air conditioning virtual energy storage, and electric vehicle battery swapping stations. Shared energy storage operators primarily invest in and construct centralized RSOCs, supplemented by leasing air conditioning virtual energy storage and electric vehicle battery swapping station resources.

[0046] In step S4, the specific process for solving the two-layer optimization model is as follows: (1) Initialize system parameters and model parameters; (2) Construct the Lagrangian function for the lower-level model and derive the KKT conditions; (3) The complementary relaxation conditions are linearized using the big M method; (4) Add the transformed KKT conditions as constraints to the upper-level model to form a single-layer MPEC model; (5) Call the Gurobi solver to solve the MPEC model.

[0047] The advantages and positive effects of this invention are as follows: 1. Explore and quantify the energy storage potential of flexible resources. This invention establishes a refined dispatchable potential model for virtual energy storage for air conditioning and energy storage for electric vehicle battery swapping stations. It incorporates air conditioning loads and electric vehicle battery swapping stations as virtual energy storage resources into a shared energy storage system for unified allocation, transforming "load" into "resource". This fully utilizes inexpensive distributed flexible resources and reduces the system's capacity dependence on centralized energy storage.

[0048] 2. Improve the accuracy of RSOC configuration. This invention establishes an RSOC electric-hydrogen bidirectional variable operating condition model based on the Butler-Volmer equation and Faraday's law, abandoning the traditional constant efficiency simplified model. It can accurately reflect the nonlinear influence of parameters such as current density, temperature, and voltage on efficiency under actual operating conditions, avoiding capacity redundancy and investment waste caused by model simplification.

[0049] 3. Achieving synergistic optimization of interests among multiple parties. This invention establishes a two-layer optimization model with shared energy storage operators at the top and integrated energy multi-park users at the bottom. The top layer optimizes RSOC configuration capacity and flexible resource charging and discharging power with the goal of maximizing the operator's annual net revenue, while the bottom layer optimizes shared energy storage dispatch strategies with the goal of minimizing the operating costs of each park. This approach balances the interests of investors and users, maximizing overall economic benefits.

[0050] 4. Leveraging the complementary characteristics of different resources. This invention coordinates and optimizes the configuration of RSOC (providing large-scale, cross-seasonal stable energy storage capacity), air conditioning virtual energy storage (providing fast and precise power regulation services), and electric vehicle battery swapping station energy storage. This leverages the complementary advantages of different energy storage methods in terms of time scale and regulation characteristics, avoiding overuse or idleness of single energy storage resources, and improving the equipment utilization rate and operational reliability of the entire shared energy storage system.

[0051] 5. Provides precise quantitative basis. This invention outputs configuration schemes for RSOC and flexible resources, as well as operation schemes for each park, through a two-layer optimization model, providing precise quantitative basis and feasible implementation schemes for the planning and operation of shared energy storage power stations in actual engineering projects.

[0052] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any other way. Any person skilled in the art may make changes or equivalent variations to the disclosed technical content and apply them to other fields. However, any simple modifications, equivalent variations, and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention. For those skilled in the art, the specific meaning of the above terms in the present invention can be understood through specific circumstances.

Claims

1. A method for optimizing the configuration of shared energy storage capacity, characterized in that: Includes the following steps: Step S1: Establish a schedulable potential model for flexible resources, including virtual energy storage for air conditioning and energy storage for electric vehicle battery swapping stations. Step S2: Establish a bidirectional variable operating condition model of the centralized reversible solid oxide battery between electricity and hydrogen. Step S3: Based on the schedulable potential model of the flexible resources and the electric-hydrogen bidirectional variable operating condition model of the centralized reversible solid oxide battery, establish a shared energy storage dual-layer capacity optimization configuration model, which includes an upper-layer capacity optimization configuration model and a lower-layer multi-campus optimization operation model. Step S4: Solve the shared energy storage dual-layer capacity optimization configuration model, and output the configuration strategy of the reversible solid oxide battery and the flexible resources, as well as the operation plan of each park.

2. The method for optimizing the configuration of shared energy storage capacity according to claim 1, characterized in that: In step S1, establishing the virtual energy storage model for the air conditioner includes: The thermal dynamics of the building are described using a first-order equivalent thermal parameter model. Establish a mapping relationship between the adjustable power of the air conditioning load and the virtual energy storage capacity; By aggregating multiple virtual energy storage unit models of air conditioners using Minkowski methods, the adjustable power boundary and capacity boundary of the aggregated model are obtained.

3. The method for optimizing the configuration of shared energy storage capacity according to claim 1, characterized in that: In step S1, establishing the energy storage model for the electric vehicle battery swapping station includes: A battery swapping demand prediction model is established, which includes a driving behavior feature model, a battery swapping threshold feature model, and a spatiotemporal coupling model of battery swapping demand based on the driving behavior feature model and the battery swapping threshold feature model. Establish a battery classification cluster control model, and classify the batteries in the battery swapping station into uncontrollable, priority controllable, and standby controllable categories based on the battery state of charge; A charging and discharging power regulation model is established to optimize and regulate the charging and discharging power of the battery swapping station while meeting the battery swapping needs of electric vehicles.

4. The method for optimizing the configuration of shared energy storage capacity according to claim 3, characterized in that: The driving behavior feature model uses a normal distribution function to describe the randomness of daily driving mileage for electric vehicle users; the battery swapping threshold feature model uses a normal distribution to describe the differences in the state of charge threshold of battery swapping demand; the battery swapping demand spatiotemporal coupling model establishes a battery swapping demand model by combining the relationship between driving mileage and power consumption, and uses the Monte Carlo method to randomly simulate user samples to output the number of battery swapping demands in each time period.

5. The method for optimizing the configuration of shared energy storage capacity according to claim 4, characterized in that: In step S3, establishing the electro-hydrogen bidirectional variable operating condition model of the centralized reversible solid oxide battery includes: establishing a model of the relationship between the mass of hydrogen participating in the reaction and the electric power of the reversible solid oxide battery based on the Butler-Volmer equation and Faraday's law. Establish the energy conversion equation of the reversible solid oxide battery in solid oxide fuel cell mode: ； in, P SOFC To output electrical power for solid oxide fuel cells, η SOFC For hydrogen fuel cell efficiency, L H2 This refers to the calorific value of hydrogen. The mass of hydrogen consumed in SOFC mode. This refers to the number of battery cells. F 2 It is a Faraday constant. For SOFC resistivity density, A For battery area, This refers to the molar mass of hydrogen gas. Establish the energy conversion equation of the reversible solid oxide battery in solid oxide electrolysis cell mode: ； in, P SOEC Input electrical power to the solid oxide electrolytic cell, η SOEC For the efficiency of the electrolytic cell, The mass of hydrogen produced in SOEC mode. This represents the SOEC resistivity density.

6. The method for optimizing the configuration of shared energy storage capacity according to claim 5, characterized in that: The upper-level capacity optimization configuration model takes maximizing the annual net revenue of the shared energy storage operator as its objective function, and the decision variables include the configuration power limit and capacity limit of the reversible solid oxide battery, as well as the charging and discharging power of the flexibility resources in each time period; the lower-level multi-park optimization operation model takes minimizing the operating cost of users in each park as its objective function.

7. The method for optimizing the configuration of shared energy storage capacity according to claim 6, characterized in that: The shared energy storage operator invests in and constructs the centralized reversible solid oxide battery, and leases the capacity resources of the air conditioning virtual energy storage and the electric vehicle battery swapping station energy storage. The annual net income of the upper-level capacity optimization configuration model is calculated by subtracting the annual fixed investment cost from the annual operating income. The annual fixed investment cost includes the investment cost of the reversible solid oxide battery and the annual fixed lease cost of the air conditioning virtual energy storage and the electric vehicle battery swapping station energy storage.

8. The method for optimizing the configuration of shared energy storage capacity according to claim 7, characterized in that: Solving the shared energy storage two-layer capacity optimization configuration model includes: A Lagrangian function is constructed for the lower-level multi-park optimized operation model, and the KKT conditions are derived. The complementary relaxation conditions in the KKT conditions are linearized using the Big M method. The linearized KKT conditions are added as constraints to the upper-level capacity optimization configuration model to form a single-level mathematical programming model. The solver is invoked to solve the single-layer mathematical programming model.

9. A shared energy storage capacity optimization configuration device, characterized in that, include: The first modeling module is used to establish a schedulable potential model of flexible resources, including virtual energy storage for air conditioning and energy storage for electric vehicle battery swapping stations. The second modeling module is used to establish a bidirectional variable operating condition model of the centralized reversible solid oxide battery between electricity and hydrogen. The third modeling module is used to establish a shared energy storage two-layer capacity optimization configuration model based on the schedulable potential model of the flexible resources and the electric-hydrogen bidirectional variable operating condition model of the centralized reversible solid oxide battery. The solution module is used to solve the shared energy storage two-layer capacity optimization configuration model, and output the configuration strategy of the reversible solid oxide battery and the flexible resources, as well as the operation plan of each park.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the shared energy storage capacity optimization configuration method as described in any one of claims 1 to 8.

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