A multi-region integrated energy system optimal scheduling method considering shared energy storage

Abstract

The present application belongs to the field of power system optimal dispatch, and particularly relates to a multi-regional integrated energy system optimal dispatch model considering shared energy storage. The present application solves the problems of high system operation cost and large system pollutant emission in the prior art. The present application analyzes the multi-regional integrated energy system considering shared energy storage, establishes a shared energy storage model of the system, a demand response model of each integrated energy system, and an energy conversion relationship between systems, and accordingly establishes a multi-regional integrated energy system optimal dispatch method considering shared energy storage with the minimum operation cost and pollutant emission as the target. The present application improves the demand side response capability and level of the multi-regional integrated energy system under the premise of meeting the safe operation of the multi-regional integrated energy system, improves the energy utilization rate and system economy of the system, and reduces the emission of pollutants.

Description

Technical Field

[0001] This invention belongs to the field of power system optimization and dispatch, specifically relating to a method for optimizing and dispatching a multi-regional integrated energy system that considers shared energy storage. Background Technology

[0002] With increasing global attention to environmental pollution and the energy crisis, traditional thermal power generation is facing severe challenges due to its low efficiency, high carbon emissions, and serious pollution. Energy has always played an indispensable role in the development of human society, and how to reduce the waste of energy resources to achieve sustainable social development has become a primary issue facing humanity.

[0003] To address current challenges and further improve energy efficiency, microgrids have been gradually evolving into an energy internet centered on integrated energy systems in recent years. The energy internet has become the latest direction in global energy development and a strategic priority for China's future energy development. Summary of the Invention

[0004] This invention provides an optimized scheduling method for multi-region integrated energy systems considering shared energy storage, solving the problems of high system operating costs and large system pollutant emissions in existing technologies. This invention optimizes the scheduling of system energy conversion equipment and shared energy storage systems, improving the demand-side response capability and level of the multi-region integrated energy system while ensuring safe operation, thereby increasing energy utilization and system economy, and reducing pollutant emissions.

[0005] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0006] An optimized scheduling method for a multi-regional integrated energy system considering shared energy storage includes: modeling the coupling relationship between various energy conversion devices within the integrated energy system and establishing a mathematical model of the shared energy storage system; connecting the mathematical model of the shared energy storage system with multiple regional integrated energy interconnection systems to establish a multi-regional integrated energy system model considering shared energy storage;

[0007] Based on the compensation cost for users responding to demand, establish demand-side response models for each region.

[0008] Based on the demand-side response model and the multi-region integrated energy system model considering shared energy storage, an optimal scheduling model and optimal scheduling strategy for the multi-region integrated energy system considering shared energy storage are established, and multi-objective collaborative optimization is performed. By using the multi-objective gray wolf algorithm to solve the optimal scheduling model of the multi-region integrated energy system considering shared energy storage, the optimal scheduling result of the multi-region integrated energy system considering shared energy storage is obtained.

[0009] The modeling of the coupling relationships between various energy conversion devices within the integrated energy system includes:

[0010] A mathematical model for the battery storage device is established based on the constraints of the stored energy and charge / discharge power of the battery:

[0011]

[0012]

[0013] E ce (t) represents the amount of electricity stored in the centralized energy storage system at time t. The electricity is obtained from the i-th region and the centralized energy storage system, respectively, and supplied to H. c η d These represent the charge and discharge efficiencies of the centralized energy storage system. (C) SES To share energy storage costs, η SES,t Energy storage leasing prices The electrical energy exchanged between the regional integrated energy system n and the shared energy storage system during time period t.

[0014] The demand-side response employs three methods: load reduction, load transfer, and load substitution. The allowable power load adjustment constraints under these three methods are as follows:

[0015]

[0016]

[0017]

[0018]

[0019] In the formula, C DR The compensation cost for users participating in DSM; α, β, and γ are the unit capacity compensation coefficients for load reduction, load transfer, and load replacement, respectively; P L (t) represents the load of region i in time period t before demand response was implemented; δ C ,δ M and δ T These are the proportions of demand-side load that can be reduced, transferred, and replaced, respectively.

[0020] The multi-region integrated energy system model considering shared energy storage is as follows:

[0021]

[0022]

[0023]

[0024]

[0025] In the formula, and Let electrical energy, gas energy, and thermal energy be the inputs to the integrated energy system i at time t. and Let E represent the electrical load, gas load, and heat load of the i-th regional integrated energy system at time t; E is the centralized energy storage system matrix. Let t be the electrical energy obtained or supplied by the centralized energy storage system from the i-th regional integrated energy system. and These represent the demand response of the i-th regional integrated energy system at time t. and Let be the amount of electricity reduction, electricity transfer, and electricity substitution load in the i-th regional integrated energy system at time t; Let t be the gas substitution load of the i-th regional integrated energy system at time t.

[0026] The multi-region integrated energy system optimization scheduling model considering shared energy storage includes two sets of objective functions:

[0027] The objective function is to minimize the operating cost of a multi-regional integrated energy system.

[0028] The objective function is to minimize pollutant emissions from a multi-regional integrated energy system.

[0029] The multi-region integrated energy system optimization scheduling model considering shared energy storage also includes the following constraints: power balance constraint, heat balance constraint, gas balance constraint, upper and lower limits of power generation output constraint, and centralized energy storage system constraint.

[0030] The standard form of the multi-region integrated energy system optimization scheduling model considering shared energy storage is as follows:

[0031]

[0032] In the formula: minf i (x) is the objective function; g j (x) and h k (x) represent equality constraints and inequality constraints, respectively.

[0033] The process of the multi-region integrated energy system optimization and scheduling strategy considering shared energy storage includes:

[0034] S1. Input the wind and solar power output, heat source output, and electricity, heat, and gas load data for each region;

[0035] S2. Based on load data, and with the goals of minimizing total cost and pollutant emissions, conduct coordinated regulation between the integrated energy system and the shared energy storage system.

[0036] S3. Determine the relationship between energy demand and energy input in each region based on the results of coordinated regulation;

[0037] S4. By understanding the relationship between energy demand and energy input, obtain the optimal energy conversion within the integrated energy system, the optimal charging and discharging strategy of the shared energy storage system, and the coordinated control method between the integrated energy system and the shared energy storage.

[0038] S5. Based on S4, the optimal scheduling strategy for a multi-regional integrated energy system that takes into account shared energy storage is finally obtained.

[0039] The beneficial effects of this invention are as follows: By analyzing a multi-regional integrated energy system considering shared energy storage, a shared energy storage model of the system, demand response models of each integrated energy system, and energy conversion relationships between systems are established. Based on this, an optimal scheduling method for a multi-regional integrated energy system considering shared energy storage is established, aiming to minimize operating costs and pollutant emissions. The established method effectively reduces the system's environmental pollution, improves the energy coordination capability between multiple integrated energy systems and the shared energy storage system, and enhances the overall system's demand response performance. It provides relevant reference for addressing the resource scarcity and environmental pollution problems faced by power grid construction. Attached Figure Description

[0040] Figure 1 This is a diagram of a multi-regional integrated energy system considering shared energy storage, as described in this invention.

[0041] Figure 2 This is a diagram illustrating the optimized scheduling strategy for a multi-regional integrated energy system considering shared energy storage, as described in this invention.

[0042] Figure 3 This is a flowchart of the solution process for the multi-region integrated energy system optimization scheduling model considering shared energy storage in this invention. Detailed Implementation

[0043] The technical solution of the present invention will be further described below with reference to the accompanying drawings and specific embodiments:

[0044] Example 1

[0045] 1. Multi-regional integrated energy system with shared energy storage

[0046] The physical object of the mathematical model established in this invention is a shared energy storage system connected to multiple regional integrated energy interconnection systems via electrical interconnection lines, as shown in the schematic diagram below. Figure 1As shown, its main electricity, natural gas, and heat are supplied by connecting different energy sources through a regional external grid, and multiple integrated energy systems are interconnected with shared energy storage systems through electrical interconnection lines, forming a large-scale and complex integrated energy system cluster, which is defined as a multi-regional integrated energy interconnection system.

[0047] (1) Shared energy storage system

[0048] Figure 1 In China, shared energy storage power station operators leverage their financial advantages to establish large-scale shared energy storage power stations among user groups. These stations are then uniformly operated and managed, providing shared energy storage services to multiple integrated energy systems. Shared energy storage services allow users to utilize the energy storage devices of shared energy storage power stations to meet their internal charging and discharging needs, without time or capacity limitations. Users pay service fees to the energy storage power station operators in exchange for these shared energy storage services. These service fees are the charges users pay to the shared energy storage power station for charging and discharging.

[0049] Centralized energy storage devices utilize batteries for charging and discharging; therefore, a mathematical model is established for the battery storage device based on constraints such as the energy storage capacity and charging / discharging power of the batteries.

[0050]

[0051]

[0052] E ce (t) represents the amount of electricity stored in the centralized energy storage system at time t. H represents the power acquisition and supply for the i-th region and the centralized energy storage system, respectively. c η d These represent the charge and discharge efficiencies of the centralized energy storage system. (C) SES To share energy storage costs, η SES,t Energy storage leasing prices The electrical energy exchanged between the regional integrated energy system n and the shared energy storage system during time period t.

[0053] (2) Demand-side response model

[0054] In regional integrated energy systems, the role of demand-side response is mainly reflected in three aspects: ① tapping into and utilizing resilient loads and enhancing load controllability; ② improving the load curve profile, smoothing load fluctuations, and improving the system's safety and economy; ③ delaying the pressure of peak load growth and delaying investment demand for power generation and transmission capacity.

[0055] Demand-side response (DSM) can be implemented through three methods: load reduction, load shifting, and load substitution. In various integrated energy systems, users can adopt these methods. The mathematical descriptions of these three DSM methods are given below. Based on the power load adjustment amounts under these three methods, the compensation costs for users participating in demand-side response are determined. According to the actual needs of the system, constraints are imposed on the allowable power load adjustment amounts under the three DSM methods:

[0056]

[0057]

[0058]

[0059]

[0060] In the formula, C DR The compensation cost for users participating in DSM; α, β, and γ are the unit capacity compensation coefficients for load reduction, load transfer, and load replacement, respectively. Where: P L (t) represents the load of region i in time period t before demand response was implemented, δ C ,δ M and δ T These are the proportional coefficients for demand-side loads that can be reduced, transferred, and replaced, respectively, and are determined by the structure and characteristics of the specific load.

[0061] (3) Multi-region integrated energy system model considering shared energy storage

[0062] The structure of each regional integrated energy system in MRIEIS is as follows: Figure 3 As shown, the system comprises a substation, gas turbine, gas boiler, P2G (Power-to-Gas), electric chiller, heat-absorbing chiller, and air conditioning. This integrated energy system distributes and aggregates the input electrical, natural gas, and thermal energy through an energy manager before providing electricity, gas, heat, and cooling energy to users. Addressing the energy conversion and coupling issues between different energy forms in multi-regional energy interconnection systems, this invention introduces six time-varying scheduling factors to improve the demand-side response capability of the multi-regional energy interconnection system: air conditioning cooling and heating scheduling factor α, electric chiller cooling capacity proportion factor ε, gas turbine power generation proportion factor β, and gas boiler heat generation proportion factor... The P2G equipment's gas production ratio factor λ and the heat absorption and cooling capacity ratio factor γ of the heat-absorbing refrigerator are used to determine the energy coupling matrix C of the integrated energy system studied in this invention, based on the above analysis.

[0063]

[0064] In the formula, η P2G , η GT and η GB These are the energy conversion efficiencies of electricity-to-gas equipment, air conditioning heating energy, gas turbines, and gas boilers.

[0065] Based on the above model, an MRIEIS energy conversion model considering shared energy storage systems and integrated energy systems is established.

[0066]

[0067]

[0068]

[0069]

[0070] In the formula, and Let t be the electrical energy, gas energy, and thermal energy input into the integrated energy system i at time t. and Let represent the electrical load, gas load, and heat load of the i-th regional integrated energy system at time t. Let E be the centralized energy storage system matrix. Let t be the amount of electricity that the centralized energy storage system obtains or supplies from the i-th regional integrated energy system. and These represent the demand response of the i-th regional integrated energy system at time t. and Let t represent the amount of electricity reduction, transfer, and substitution load in the i-th regional integrated energy system at time t. Let t be the gas substitution load of the i-th regional integrated energy system at time t.

[0071] 2. Optimal scheduling model for multi-regional integrated energy systems considering shared energy storage

[0072] In MRIEIS, a multi-objective collaborative optimization of centralized energy storage systems and demand response is considered. The daily study period is divided into 24 time periods, and two optimization objectives are considered: total system operating cost and pollutant emissions during energy conversion. Simultaneously, considering electricity, gas, and heat balance constraints, as well as other constraints, a multi-MRIEIS multi-objective optimization model is established.

[0073] (1) Objective function

[0074] 1) Objective function 1: Minimize the operating cost of a multi-regional integrated energy system

[0075] The first objective function aims to minimize the operating costs of the MRIEIS, which consist of three parts: the operating costs of the regional integrated energy system, the centralized energy storage system, and the demand response costs.

[0076]

[0077] In the formula, C COST Let be the total operating cost of the MRIEIS system, and i be the number of regional integrated energy systems. Let be the power generation cost of gas turbine i at time t. Let t be the heat production cost of gas-fired boiler i. and The costs of purchasing electricity, gas, and heat for the integrated energy system at time t are respectively: Let i be the heat production cost of air conditioner i at time t. Let C be the gas production cost of P2G device i at time t. E This refers to the operating cost of a centralized energy storage system. Let i be the electricity generated by the gas turbine at time t. Let be the heat production power of the gas-fired boiler and the air conditioner i at time t, respectively. Let be the power consumption of P2G device i at time t. The power purchased by the integrated energy system at time t represents the power of electricity, gas, and heat purchased.

[0078] 2) Objective function 2: Minimize pollutant emissions from the multi-regional integrated energy system

[0079]

[0080] In the formula, and These represent the emissions of carbides, sulfides, and nitrogen oxides from gas turbine i at time t. and These represent the emissions of carbides, sulfides, and nitrogen oxides from gas-fired boiler i at time t. and These represent the emissions of carbides, sulfides, and nitrides from heat source i at time t.

[0081] (2) Constraints

[0082] 1) Electrical balance constraint

[0083]

[0084] In the formula, and These are the electricity consumptions of air conditioners, P2G devices, and load i after demand response. The power generation of gas turbine i. To supply or obtain electricity for centralized energy storage systems, This refers to the exchange of electrical energy between the integrated energy system i and the power grid.

[0085] 2) Thermal equilibrium constraint

[0086]

[0087] In the formula, The heat output of integrated energy system i The heat consumption of load i after the demand-side response. The heat energy exchanged between the integrated energy system i and the heat source.

[0088] 3) Gas balance constraint

[0089]

[0090] In the formula, and These are gas-fired boilers, gas turbines, and the gas consumption after passing through demand-side response load i. The integrated energy system i exchanges gas energy with the gas source.

[0091] 4) Upper and lower limits of power generation output constraints

[0092]

[0093] In the formula, and These represent the upper and lower limits of the electrical energy exchanged between the integrated energy system i and the power grid.

[0094] 5) Constraints of centralized energy storage systems

[0095] Due to technical limitations, centralized energy storage systems face the following constraints:

[0096]

[0097]

[0098]

[0099] In the formula, This is the upper limit for the centralized energy storage system to obtain electricity from the i-th regional integrated energy system; The upper limit of the electricity supplied by the centralized energy storage system to the i-th regional integrated energy system; u e To centrally store energy and supply it to external sources, the variable of obtaining electricity is as follows: when u e When u = 1, the centralized energy storage system obtains electrical energy from the outside; when u = 1, the centralized energy storage system obtains electrical energy from the outside. e When the value is 0, the centralized energy storage system supplies electricity to the outside. These represent the upper and lower limits of the storage capacity of a centralized energy storage system.

[0100] 3. Optimization and scheduling strategies for multi-regional integrated energy systems considering shared energy storage

[0101] The optimized scheduling strategy process for multi-region integrated energy systems in this invention is as follows: Figure 2 As shown.

[0102] (1) Obtain the total heat source, gas source output forecast, wind and solar power output, and current energy demand in each region of the interconnected system. With the goal of optimizing the total operating cost of the MRIEIS system and optimizing pollutant emissions during energy conversion, optimize the energy scheduling of the MRIEIS system.

[0103] (2) Given the demand for electricity, gas and heat load of a certain region’s integrated energy system at time t, if the total energy supply capacity of the integrated energy system in this region is greater than its load demand, then it is considered that the region can provide a certain amount of electricity to other integrated energy systems and shared energy storage systems. According to the energy regulation needs of other integrated energy systems, energy coordination scheduling between regions can be carried out.

[0104] (3) Given the demand for electricity, gas and heat load in a certain area at time t, when there is a difference between the total energy supply capacity of the integrated energy system in this area and the total load, the energy supply and demand of the difference can send an energy adjustment request to other integrated energy systems or shared energy storage systems to carry out inter-regional energy coordinated scheduling.

[0105] (4) Considering the multi-regional energy interconnection system itself, under the premise of ensuring its own load demand, when the power consumption of the main grid reaches its peak, the multi-regional integrated energy interconnection system reduces the amount of electricity obtained from the main grid, and supplements the power shortage and heat shortage caused by the reduction in power consumption through shared energy storage and gas energy conversion. This plays a role in peak shaving.

[0106] (5) Considering the economic efficiency of the multi-regional energy interconnection system itself, under the premise of ensuring its own load demand, when the grid electricity price is in a valley at time t, the system increases the use of electricity: the shared energy storage system stores electricity to supply the system's electricity demand during other peak electricity price periods, and each integrated energy system increases the demand for electricity to reduce the demand for gas and heat energy. The multi-regional integrated energy system generally exhibits cluster demand-side response characteristics.

[0107] 4. Solve the multi-region integrated energy optimization scheduling model considering shared energy storage.

[0108] The MRIEIS system optimization scheduling model established in this invention is a multi-objective nonlinear programming problem, and its standard form is:

[0109]

[0110] This invention solves the aforementioned multi-objective nonlinear programming problem using the multi-objective gray wolf algorithm. The specific solution process is as follows: Figure 3 .

[0111] Example 2

[0112] This embodiment provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the multi-region integrated energy system optimization scheduling model considering shared energy storage provided in Embodiment 1 of the present invention.

[0113] Example 3

[0114] This embodiment provides a computer-readable storage medium storing a computer program, characterized in that the program, when executed by a processor, implements the multi-region integrated energy system optimization scheduling model considering shared energy storage provided in Embodiment 1 of the present invention.

[0115] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0116] The solutions in this application embodiment can be implemented using various computer languages, such as the object-oriented programming language Java and the interpreted scripting language JavaScript.

[0117] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0118] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1The function specified in one or more boxes.

[0119] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0120] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

[0121] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

Claims

1. A method for optimizing the scheduling of a multi-regional integrated energy system considering shared energy storage, characterized in that, include: Model the coupling relationships between various energy conversion devices within the integrated energy system and establish a mathematical model for the shared energy storage system; By connecting the mathematical model of the shared energy storage system with multiple regional integrated energy interconnection systems, a multi-regional integrated energy system model considering shared energy storage is established. Based on the compensation cost for users responding to demand, establish demand-side response models for each region. Based on the demand-side response model and the multi-region integrated energy system model considering shared energy storage, an optimal scheduling model and optimal scheduling strategy for the multi-region integrated energy system considering shared energy storage are established, and multi-objective collaborative optimization is performed. By using the multi-objective gray wolf algorithm to solve the optimal scheduling model of the multi-region integrated energy system considering shared energy storage, the optimal scheduling result of the multi-region integrated energy system considering shared energy storage is obtained. The modeling of the coupling relationships between various energy conversion devices within the integrated energy system includes: A mathematical model for the battery storage device is established based on the constraints of the stored energy and charge / discharge power of the battery: (1) (2) Let t be the amount of electricity stored in the centralized energy storage system at time t. These represent the power acquisition of the i-th region and the centralized energy storage system, respectively. , These refer to the charging and discharging efficiency of the centralized energy storage system. To share energy storage costs, Energy storage leasing prices , These represent the electrical energy exchanged between the regional integrated energy system n and the shared energy storage system during time period t. The multi-region integrated energy system model considering shared energy storage is as follows: (8) (9) (10) (11) In the formula, , and for Constant input into the integrated energy system Electrical energy, gas energy, and thermal energy , and for Time of the first The electrical load, gas load, and heat load of a regional integrated energy system; For a centralized energy storage system matrix, for Real-time centralized energy storage system from the first Electricity obtained or supplied by a regional integrated energy system; and They are respectively Time of the first Demand response for electricity and gas in a regional integrated energy system; , and for Time of the first Electricity reduction, electricity transfer, and electricity substitution loads in a regional integrated energy system; for Time of the first Gas substitution load of a regional integrated energy system.

2. The method for optimizing the scheduling of a multi-regional integrated energy system considering shared energy storage as described in claim 1, characterized in that: The demand-side response employs three methods: load reduction, load transfer, and load substitution. The allowable power load adjustment constraints under these three methods are as follows: (3) (4) (5) (6) In the formula, To compensate users who participate in DSM for costs; , and These are the unit capacity compensation coefficients for load reduction, load transfer, and load replacement, respectively. For areas where demand response has not been implemented The load during time period t, , and These are the proportions of demand-side load that can be reduced, transferred, and replaced, respectively.

3. The method for optimizing the scheduling of a multi-regional integrated energy system considering shared energy storage as described in claim 1, characterized in that: The multi-region integrated energy system optimization scheduling model considering shared energy storage includes two sets of objective functions: The objective function is to minimize the operating cost of a multi-regional integrated energy system. The objective function is to minimize pollutant emissions from a multi-regional integrated energy system.

4. The method for optimizing the scheduling of a multi-regional integrated energy system considering shared energy storage as described in claim 3, characterized in that: The multi-region integrated energy system optimization scheduling model considering shared energy storage also includes the following constraints: power balance constraint, heat balance constraint, gas balance constraint, upper and lower limits of power generation output constraint, and centralized energy storage system constraint.

5. The method for optimizing the scheduling of a multi-regional integrated energy system considering shared energy storage as described in claim 4, characterized in that: The standard form of the multi-region integrated energy system optimization scheduling model considering shared energy storage is as follows: (21) In the formula: The objective function is... and These are equality constraints and inequality constraints, respectively.

6. The method for optimizing the scheduling of a multi-regional integrated energy system considering shared energy storage as described in claim 1, characterized in that: The process of the multi-region integrated energy system optimization and scheduling strategy considering shared energy storage includes: S1. Input the wind and solar power output, heat source output, and electricity, heat, and gas load data for each region; S2. Based on load data, and with the goals of minimizing total cost and pollutant emissions, conduct coordinated regulation between the integrated energy system and the shared energy storage system. S3. Determine the relationship between energy demand and energy input in each region based on the results of coordinated regulation; S4. By understanding the relationship between energy demand and energy input, obtain the optimal energy conversion within the integrated energy system, the optimal charging and discharging strategy of the shared energy storage system, and the coordinated control method between the integrated energy system and the shared energy storage. S5. Based on the results of S4, the optimal scheduling strategy for a multi-regional integrated energy system considering shared energy storage is finally obtained.

7. 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 multi-region integrated energy system optimization scheduling model that considers shared energy storage as described in any one of claims 1 to 6.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the multi-region integrated energy system optimization scheduling model that considers shared energy storage as described in any one of claims 1 to 6.

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