A double-layer optimization method and system considering collaborative operation of multiple integrated energy stations

Abstract

The present application belongs to the technical field of comprehensive energy optimal allocation, and particularly relates to a double-layer optimization method and system considering collaborative operation of multiple comprehensive energy stations, comprising: obtaining a structure of a regional comprehensive energy system, multiple comprehensive energy stations being arranged in the regional comprehensive energy system; constructing a double-layer optimization model of the comprehensive energy system according to the obtained structure; solving the constructed double-layer optimization model to obtain an optimal energy interaction and energy station operation scheme of the comprehensive energy system, and realizing collaborative operation of the multiple comprehensive energy stations in the region. The present application adopts double-layer nested circulation iteration, finally obtains the optimal energy interaction and energy station operation plan of the system, realizes collaborative operation of the comprehensive energy stations in the region, promotes real-time supply-demand balance of the energy-using buildings in the region, and further improves the economy of the system.

Description

Technical Field

[0001] This invention belongs to the field of integrated energy optimization and allocation technology, specifically relating to a two-layer optimization method and system that takes into account the coordinated operation of multiple integrated energy stations. Background Technology

[0002] The statements in this section are merely background information related to the present invention and do not necessarily constitute prior art.

[0003] Developing renewable energy and improving the overall energy utilization rate are important means to achieve efficient, green, and low-carbon energy use. Integrated energy systems utilize technologies such as energy production, conversion, and storage to convert renewable and traditional energy sources into electricity, cooling, and heat within a certain region, promoting the consumption of renewable energy and improving the overall energy utilization rate. Operational optimization is a crucial means to achieve economical, efficient, and low-carbon operation of integrated energy systems. However, regional-level integrated energy systems consist of multiple small integrated energy stations, resulting in a highly complex structure, multi-energy flow coupling, and random and variable source and load characteristics. The coordinated operation of multiple integrated energy stations is particularly important, making system operation optimization extremely difficult.

[0004] According to the inventor, there are relatively mature methods for optimizing the independent operation of a single energy station, but there is less research on optimizing the operation of a regional integrated energy system that takes into account the coordination of multiple energy stations. Summary of the Invention

[0005] To address the aforementioned issues, this invention proposes a two-layer optimization method and system that considers the coordinated operation of multiple integrated energy stations. By employing a two-layer optimization iterative cycle, the optimal system operation plan is obtained, achieving the best matching of sources and loads in the integrated energy station, thereby improving the economic reliability of the system.

[0006] According to some embodiments, the first solution of the present invention provides a two-layer optimization method that takes into account the coordinated operation of multiple integrated energy stations, and adopts the following technical solution:

[0007] A two-level optimization method considering the coordinated operation of multiple integrated energy stations includes:

[0008] The structure of a regional integrated energy system is obtained, wherein the regional integrated energy system contains multiple integrated energy stations;

[0009] Based on the obtained structure, a two-layer optimization model of the integrated energy system is constructed;

[0010] Solving the constructed two-layer optimization model yields the optimal energy interaction and energy station operation scheme of the integrated energy system, enabling the coordinated operation of multiple integrated energy stations within the region.

[0011] As a further technical limitation, the multiple integrated energy stations within the regional integrated energy system have different structures; the integrated energy station includes at least wind turbines, photovoltaic power stations, and energy storage equipment.

[0012] As a further technical limitation, the two-layer optimization model includes an upper-layer optimization model and a lower-layer optimization model; the upper-layer optimization model is a collaborative scheduling layer, and the lower-layer optimization model is an optimized operation layer.

[0013] Furthermore, the upper-level optimization model aims at global economic optimization, optimizes energy interaction between different integrated energy stations, and uses the optimized data as input to the lower-level optimization model.

[0014] Furthermore, the lower-level optimization model aims to minimize the operating cost of the integrated energy station. Based on the input optimized energy interaction between different integrated energy stations, it optimizes the equipment output and energy storage status within the integrated energy station to obtain the operating plan with the lowest operating cost.

[0015] As a further technical limitation, a nested loop iterative solution is used in the process of solving the constructed bi-level optimization model; the upper optimization model in the bi-level optimization model is a hybrid linear programming model, which is solved using the differential evolution algorithm; the lower optimization model in the bi-level optimization model is a linear model, which is solved using a linear optimization algorithm.

[0016] Furthermore, in the solution process of the lower-level optimization model in the two-level optimization model, the algorithm is nested within the algorithm of the upper-level optimization model and iterated cyclically until the optimal solution is obtained.

[0017] According to some embodiments, a second aspect of the present invention provides a two-layer optimization system that takes into account the coordinated operation of multiple integrated energy stations, employing the following technical solution:

[0018] A two-tier optimization system considering the coordinated operation of multiple integrated energy stations includes:

[0019] An acquisition module is configured to acquire the structure of a regional integrated energy system, which includes multiple integrated energy stations.

[0020] The modeling module is configured to construct a two-layer optimization model of the integrated energy system based on the acquired structure.

[0021] The optimization module is configured to solve the constructed two-layer optimization model to obtain the optimal energy interaction and energy station operation scheme of the integrated energy system, and realize the coordinated operation of multiple integrated energy stations in the region.

[0022] According to some embodiments, a third aspect of the present invention provides a computer-readable storage medium, employing the following technical solution:

[0023] A computer-readable storage medium having a program stored thereon, which, when executed by a processor, implements the steps of the two-layer optimization method for the coordinated operation of multiple integrated energy stations as described in the first aspect of the present invention.

[0024] According to some embodiments, the fourth aspect of the present invention provides an electronic device, which adopts the following technical solution:

[0025] An electronic device includes a memory, a processor, and a program stored in the memory and executable on the processor. When the processor executes the program, it implements the steps in the two-layer optimization method for the coordinated operation of multiple integrated energy stations as described in the first aspect of the present invention.

[0026] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0027] This embodiment proposes a two-layer optimization strategy that considers the coordinated operation of multiple integrated energy stations. The upper layer is a coordination and scheduling layer, which aims for optimal economic efficiency and uses a differential evolution algorithm to optimize the energy interaction plan between the integrated energy stations. The optimized data is then used as input for the lower layer optimization. The lower layer is an operation optimization layer for each energy station, which aims to minimize operating costs. It optimizes the output and energy storage status of each device within the station and outputs the operating costs to the upper layer optimization. Through a two-layer nested iterative loop, the optimal energy interaction and energy station operation plan of the system are finally obtained. This achieves the coordinated operation of the integrated energy stations in the region, promotes real-time supply and demand balance among energy-consuming buildings in the region, and further improves the system's economic efficiency. Attached Figure Description

[0028] The accompanying drawings, which form part of this embodiment, are used to provide a further understanding of this embodiment. The illustrative embodiments and their descriptions are used to explain this embodiment and do not constitute an improper limitation of this embodiment.

[0029] Figure 1 This is a flowchart of a two-layer optimization method for the coordinated operation of multiple integrated energy stations in Embodiment 1 of the present invention;

[0030] Figure 2 This is a schematic diagram of the regional integrated energy system structure in Embodiment 1 of the present invention;

[0031] Figure 3 This is a schematic diagram of the integrated energy station structure in Embodiment 1 of the present invention;

[0032] Figure 4 This is a schematic diagram of the two-layer optimization logic relationship in Embodiment 1 of the present invention;

[0033] Figure 5 This is a structural block diagram of a two-layer optimization system that considers the coordinated operation of multiple integrated energy stations in Embodiment 2 of the present invention. Detailed Implementation

[0034] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0035] It should be noted that the following detailed descriptions are exemplary and intended to provide further explanation of this application. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.

[0036] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0037] Where there is no conflict, the embodiments and features in the embodiments of the present invention can be combined with each other.

[0038] Example 1

[0039] Embodiment 1 of this invention introduces a two-layer optimization method that takes into account the coordinated operation of multiple integrated energy stations.

[0040] The two-layer optimization method for the coordinated operation of multiple integrated energy stations introduced in this embodiment differs from the current operation optimization method for single integrated energy systems. For regional integrated energy systems, it considers the energy interaction between multiple integrated energy stations. In order to achieve the optimal coordinated operation of multiple energy stations, a two-layer optimization method is proposed. The first layer optimizes the energy interaction between each energy station and passes the optimization result to the second layer as input condition to optimize the operation plan of each energy station. The result is then returned to the first layer for iterative optimization until the optimal operation scheme of the entire system is obtained. This achieves the lowest operating cost for each energy station while optimizing the overall system economy.

[0041] like Figure 1 The illustrated two-level optimization method considering the coordinated operation of multiple integrated energy stations includes:

[0042] The structure of a regional integrated energy system is obtained, wherein the regional integrated energy system contains multiple integrated energy stations;

[0043] Based on the obtained structure, a two-layer optimization model of the integrated energy system is constructed;

[0044] Solving the constructed two-layer optimization model yields the optimal energy interaction and energy station operation scheme of the integrated energy system, enabling the coordinated operation of multiple integrated energy stations within the region.

[0045] like Figure 2 and 3 The regional integrated energy system and energy station structure shown are not fixed in number and are determined by the actual energy consumption scenario. Furthermore, the structure of each energy station is different. An integrated energy station typically consists of wind turbines, photovoltaic panels, generator sets, absorption chillers, electric chillers, boilers, energy storage, cold storage, and thermal storage equipment. Electrical load is supplied by wind turbines, photovoltaic panels, internal combustion generator sets, and the upstream power grid; cooling load is met by heat pumps, absorption chillers, and cold storage equipment; and heat load is met by heat pumps, waste heat from generator sets, boilers, and thermal storage equipment. In a regional integrated energy system, each energy station must not only meet the electrical, cooling, and heating loads of nearby buildings but also consider energy interaction and coordinated operation with neighboring energy stations to maintain real-time supply and demand balance across the entire energy consumption area and improve the system's operational economy.

[0046] Taking integrated energy station i as an example, its power balance relationship is as follows:

[0047]

[0048] Among them, E load For electrical load; E pv Photovoltaic power; E wp E represents wind power. pgu E represents the output power of the generator set. es This refers to the charging and discharging power of the battery, specifically during discharge (E). es >0), E during charging es <0); E grid To exchange power with the grid, at the time of electricity purchase (E grid >0), when selling electricity (E grid <0); E ec The electric chiller consumes electrical power; λe i,j Let λe represent the energy interaction state between integrated energy station i and integrated energy station j, which is a 0-1 variable. It is 1 when there is energy interaction and 0 when there is no energy interaction. When i = j, λe i,j =0; E i,j For the electrical interaction power between integrated energy station i and integrated energy station j, the input time (E) i,j >0), output (E) i,j <0).

[0049] Total gas consumption F of the system at time t gas for:

[0050]

[0051] Among them, H gb For the output heat power of the gas-fired boiler; η pgu and η gb These represent the power generation efficiency of the generator set and the heating efficiency of the boiler at time t, respectively.

[0052] The thermal energy balance relationship is:

[0053]

[0054] Among them, H load For heat load; H hr For waste heat recovery power; H ac H represents the input heat power of the absorption chiller. hs For the input / output power of the hot water storage tank, the output power is (H) hs >0), input (H) hs <0). λh i,j Let λh represent the thermal energy interaction state between integrated energy station i and integrated energy station j, a 0-1 variable. λh is 1 when there is energy interaction and 0 when there is no energy interaction. When i = j, λh i,j =0; H i,j For the heat exchange power between integrated energy station i and integrated energy station j, the input time (H) i,j >0), output (H) i,j <0).

[0055] The residual heat H of the generator set at time t hr for:

[0056]

[0057] Where, η hr Let be the waste heat recovery efficiency of the generator set at time t.

[0058] The cold energy balance relationship is:

[0059]

[0060] Among them, C load For cooling load; C ac Cooling power of absorption refrigeration mechanism; C ec C represents the cooling capacity of the electric chiller. cs For the input / output power of the cold storage device, when outputting (C) cs >0), input (C) cs <0). n is the number of integrated energy stations; λc i,j Let λc represent the cold energy interaction state between integrated energy station i and integrated energy station j, a 0-1 variable. λc is 1 when there is energy interaction and 0 when there is no energy interaction. When i = j, λc i,j =0; C i,jFor the cold interaction power between integrated energy station i and integrated energy station j, the input time is (C) i,j >0), output (C) i,j <0). Output power C of the absorption chiller ac for:

[0061] C ac (t)=H ac (t)COP ac (6)

[0062] Among them, COP ac This refers to the energy efficiency ratio of an absorption chiller.

[0063] The input electrical power E of the electric chiller at time t ec for:

[0064]

[0065] Among them, COP ec This refers to the energy efficiency ratio of an electric refrigeration unit.

[0066] For the three types of energy storage devices—electric, cold, and heat—there are:

[0067]

[0068]

[0069] Among them, Q sta (t+1) and Q sta (t) represents the energy storage state of the energy storage (electrical, cooling, and heating) device at time t+1 and time t, respectively; η s For the efficiency of energy storage devices; N i Q is the rated capacity of energy storage device i; s E represents es H hs and C cs .

[0070] like Figure 4 The diagram illustrates the two-layer optimization logic. The upper layer is the collaborative scheduling layer, which aims for optimal global economy by using an evolutionary algorithm to optimize the energy interaction plan between integrated energy stations. The optimized data is then used as input for the lower layer optimization. The lower layer is the operation optimization layer for each integrated energy station, which aims to minimize the operating costs of each station by optimizing equipment output and energy storage status. The operating costs are then output to the upper layer optimization. The two-layer optimization iterates repeatedly to ultimately obtain the optimal system operation plan, achieving the best source-load matching and further improving the system's economy.

[0071] Upper-level optimization model

[0072] The upper-level optimization model is a multi-energy station collaborative scheduling layer. A coordinated optimization model is established with the goal of achieving global economic optimization, obtaining the optimal energy interaction data between each energy station. Global economic optimization is used as the optimization objective function, i.e.:

[0073]

[0074] Among them, cost i The operating cost of integrated energy station i is also the target of lower-level operation optimization. The optimization variables include the interaction states and interaction power of electrical, cooling, and heating energy between energy stations at each time step:

[0075] {λe i,j (t),λh i,j (t),λc i,j (t),E i,j (t),H i,j (t),C i,j (t)},i,j∈[1,N],t∈[1,24](11)

[0076] Constraints:

[0077] λe i,j (t),λh i,j (t),λc i,j (t)∈{0,1} (12)

[0078] E min ≤E i,j (t)≤E max (13)

[0079] H min ≤H i,j (t)≤H max (14)

[0080] C min ≤C i,j (t)≤C max (15)

[0081] Among them, E min E max H min H max C min C max These are the upper and lower limits of energy interaction (electricity, heat, and cold) between different energy stations.

[0082] Lower-level optimization model

[0083] The lower-level optimization model is the operation optimization layer for each energy station. This layer aims to minimize operating costs and obtain the optimal operation plan for each energy station. The lower-level optimization model must meet the constraints of power, cooling, and heating energy balance and equipment capacity.

[0084] The objective function is to minimize the operating cost, i.e.:

[0085]

[0086] Among them, P grid (t) represents the grid interaction price at time t, which is positive when purchasing electricity and negative when selling electricity; P gas This refers to the price of natural gas.

[0087] The variables optimized by the lower-level optimization model include the output plan of each device at each time step: {E pgu,i (1),…,E pgu,i (24),H gb,i (1),…,H gb,i (24),C ac,i (1),…,C ac,i (24),C ec,i (1),…,C ec,i (24),E es,i (1)…,E es,i (24),H hs,i (1),…,H hs,i (24),C cs,i (1),…,C cs,i (24)}; Given that the new energy power generation and load data are the input data, E in the objective function grid,i (t) can be obtained from the electric balance equation (1), while F gas,i (t) can be obtained from the gas consumption formula (2), and the optimization function needs to satisfy the constraints of formulas (3)-(9). The variables involved in these formulas are the optimization variables.

[0088] In the process of solving the model, the upper-level optimization model is a mixed integer programming model, which is solved using the differential evolution algorithm; the lower-level model is a linear model, which can be solved using the linear optimization algorithm. Specifically, the upper-level algorithm first calculates the energy interaction plan between each energy station for electricity, cooling, and heating. This plan is then passed to the lower-level algorithm, and the energy demand data of each energy station is calculated together with the load data. The operation plan of each energy station is calculated, and the obtained operation cost is returned to the upper level for iterative calculation. The above process is repeated until the optimal solution is obtained.

[0089] The upper-level optimization model consists of (10)-(15), which is a mixed-integer programming problem and is solved using the differential evolution algorithm; the lower-level optimization model can be solved using the linear optimization algorithm. The specific two-level solution algorithm is as follows:

[0090] Step 1: System initialization. Input source payload data, differential evolution algorithm, and device parameters.

[0091] Step 2: Population initialization using the differential evolution algorithm. Randomly generate N individuals, each representing a plan for the interaction of electrical, cold, and heat energy between various energy stations;

[0092] Step 3: Calculate the fitness of individuals in the current population. Pass the plan of each individual to the lower level, use the linear programming algorithm to optimize the equipment output plan of each energy station under the energy interaction plan, and calculate the operating cost of each energy station using formula (16). Return to the upper level and use formula (10) to plan the fitness of individuals.

[0093] Step 4: Determine if the current population meets the termination requirements. If the conditions are met, the program ends; otherwise, continue the iterative operation of the progress algorithm.

[0094] Step 5: Generate a new population through steps such as crossover and mutation selection, and continue with step 3. Perform iterative optimization until the termination condition is met.

[0095] Step 6: Obtain the optimal solution.

[0096] This embodiment uses a double-layer nested loop iteration to finally obtain the optimal energy interaction and energy station operation plan of the system, realizes the coordinated operation of various integrated energy stations in the region, promotes the real-time supply and demand balance of various energy-consuming buildings in the region, and further improves the economic efficiency of the system.

[0097] Example 2

[0098] Embodiment 2 of the present invention introduces a two-layer optimization system that takes into account the coordinated operation of multiple integrated energy stations.

[0099] like Figure 5 The illustrated two-layer optimization system, which considers the coordinated operation of multiple integrated energy stations, includes:

[0100] An acquisition module is configured to acquire the structure of a regional integrated energy system, which includes multiple integrated energy stations.

[0101] The modeling module is configured to construct a two-layer optimization model of the integrated energy system based on the acquired structure.

[0102] The optimization module is configured to solve the constructed two-layer optimization model to obtain the optimal energy interaction and energy station operation scheme of the integrated energy system, and realize the coordinated operation of multiple integrated energy stations in the region.

[0103] The detailed steps are the same as those of the two-layer optimization method for the coordinated operation of multiple integrated energy stations provided in Example 1, and will not be repeated here.

[0104] Example 3

[0105] Embodiment 3 of the present invention provides a computer-readable storage medium.

[0106] A computer-readable storage medium having a program stored thereon, which, when executed by a processor, implements the steps of the two-layer optimization method for the coordinated operation of multiple integrated energy stations as described in Embodiment 1 of the present invention.

[0107] The detailed steps are the same as those of the two-layer optimization method for the coordinated operation of multiple integrated energy stations provided in Example 1, and will not be repeated here.

[0108] Example 4

[0109] Embodiment 4 of the present invention provides an electronic device.

[0110] An electronic device includes a memory, a processor, and a program stored in the memory and executable on the processor. When the processor executes the program, it implements the steps in the two-layer optimization method for the coordinated operation of multiple integrated energy stations as described in Embodiment 1 of the present invention.

[0111] The detailed steps are the same as those of the two-layer optimization method for the coordinated operation of multiple integrated energy stations provided in Example 1, and will not be repeated here.

[0112] The above description is merely a preferred embodiment of this practice and is not intended to limit the scope of this practice. Various modifications and variations can be made to this practice by those skilled in the art. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of this practice should be included within the protection scope of this practice.

Claims

1. A two-layer optimization method considering the coordinated operation of multiple integrated energy stations, characterized in that, include: The structure of a regional integrated energy system is obtained, wherein the regional integrated energy system contains multiple integrated energy stations; Based on the obtained structure, a two-layer optimization model of the integrated energy system is constructed; Solving the constructed two-level optimization model yields the optimal energy interaction and energy station operation scheme of the integrated energy system, enabling the coordinated operation of multiple integrated energy stations within the region; Among them, integrated energy station i The electrical energy balance relationship is ;in, E load For electrical load; E pv Photovoltaic power; E wp Wind power output; E pgu This refers to the output power of the generator set. E es This refers to the charging and discharging power of the battery. E grid For power exchange with the power grid; E ec The electric chiller consumes electrical power; λe i,j Integrated energy station i Integrated Energy Station j The energy interaction state is a 0-1 variable, with a value of 1 when there is energy interaction and 0 when there is no energy interaction. i = j hour, λ e i,j =0; E i,j Integrated energy station i Integrated Energy Station j The electrical interaction power; t Total gas consumption of the system at any time F gas for ;in, H gb This is to output heat power for the gas-fired boiler; η pgu and η gb They are respectively t The power generation efficiency of the generator set and the heating efficiency of the boiler at all times; The thermal energy balance relationship is ;in, H load For heat load; H hr This refers to the power of waste heat recovery. H ac The input heat power of the absorption chiller; H hs Input / output power for the hot water storage tank; λh i,j Integrated energy station i Integrated Energy Station j The thermal energy interaction state is a 0-1 variable, with a value of 1 when there is energy interaction and 0 when there is no energy interaction. i = j hour, λh i,j =0; H i,j Integrated energy station i Integrated Energy Station j Thermal interaction power; generator set t Residual heat at all times H hr for ;in, η hr for t Waste heat recovery efficiency of the generator set at any given time; The cold energy balance relationship is ;in, C load For cooling load; C ac The refrigeration power of the absorption refrigeration mechanism; C ec The cooling capacity of the electric chiller; C cs Input / output power for the cold storage device; n The number of integrated energy stations; λc i,j Integrated energy station i Integrated Energy Station j The cold energy interaction state is a 0-1 variable, with 1 when there is energy interaction and 0 when there is no energy interaction. i = j hour, λc i,j =0; C i,j Integrated energy station i Integrated Energy Station j Cold interaction power; Output power of absorption chiller C ac for ;in, COP ac The energy efficiency ratio of an absorption chiller; t Input power of the instantaneous refrigerator E ec for ;in, COP ec The energy efficiency ratio of an electric refrigeration unit; For the three types of energy storage devices: electric, cold, and hot, i.e. ; ;in, Q sta ( t +1) and Q sta ( t These are energy storage devices. t +1 time and t The energy storage status at any given moment; η s For the efficiency of energy storage devices; N i For energy storage devices i Rated capacity; Q s express E es , H hs and C cs ; The two-layer optimization model includes an upper-layer optimization model and a lower-layer optimization model; the upper-layer optimization model is a collaborative scheduling layer, and the lower-layer optimization model is an optimized operation layer. The upper-level optimization model aims at global economic optimization, optimizes energy interaction between different integrated energy stations, and uses the optimized data as input to the lower-level optimization model. The lower-level optimization model aims to minimize the operating cost of the integrated energy station. Based on the input optimized energy interaction between different integrated energy stations, it optimizes the equipment output and energy storage status within the integrated energy station to obtain the operating plan with the lowest operating cost. The upper-level optimization model is a multi-energy station collaborative scheduling layer. It establishes a coordinated optimization model with the goal of achieving global economic optimization, obtaining the optimal energy interaction data between each energy station. The global economic optimization is used as the objective function, i.e., min... ;in, cost i Integrated energy station i The operating cost is also the target of lower-level operation optimization; The optimization variables include the interaction state and interaction power of electrical, cooling, and heating energy between energy stations at each time step. ; The constraints are ； ； ； ； in, E min ，E max； H min ，H max； C min ，C max The upper and lower limits of energy interaction between different energy stations; The lower-level optimization model is the operation optimization layer for each energy station. The optimization objective is to obtain the optimal operation plan for each energy station by minimizing the operating cost. The lower-level optimization model must meet the constraints of power, cooling and heating energy balance and equipment capacity. The objective function is to minimize the operating cost, i.e., min ;in, P grid ( t )for t The grid interaction price is positive when purchasing electricity and negative when selling electricity; P gas For gas prices; The variables optimized by the lower-level optimization model include the output plan of each device at each time step; given that the input data are renewable energy generation and load data, the objective function contains... E grid,i ( t This is derived from the electrical balance equation, and F gas,i ( t The result can be obtained from the gas consumption formula, while the optimization function needs to satisfy the constraints. In solving the constructed bi-level optimization model, a bi-level nested iterative loop is used. The upper-level optimization model in the bi-level optimization model is a hybrid linear programming model, which is solved using the differential evolution algorithm. The lower-level optimization model in the bi-level optimization model is a linear model, which is solved using a linear optimization algorithm.

2. The two-layer optimization method for the coordinated operation of multiple integrated energy stations as described in claim 1, characterized in that, The regional integrated energy system has multiple integrated energy stations with different structures; each integrated energy station includes at least wind turbines, photovoltaic power stations, and energy storage equipment.

3. A two-layer optimization system considering the coordinated operation of multiple integrated energy stations, employing the two-layer optimization method considering the coordinated operation of multiple integrated energy stations as described in any one of claims 1-2, characterized in that, include: An acquisition module is configured to acquire the structure of a regional integrated energy system, which includes multiple integrated energy stations. The modeling module is configured to construct a two-layer optimization model of the integrated energy system based on the acquired structure. The optimization module is configured to solve the constructed two-layer optimization model to obtain the optimal energy interaction and energy station operation scheme of the integrated energy system, and realize the coordinated operation of multiple integrated energy stations in the region.

4. 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 steps of the two-layer optimization method as described in any one of claims 1-2, which takes into account the coordinated operation of multiple integrated energy stations.

5. 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 program, it implements the steps of the two-layer optimization method that considers the coordinated operation of multiple integrated energy stations as described in any one of claims 1-2.

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