A method and system for designing and operating an integrated energy station based on distributed optimization

Abstract

The present invention belongs to the field of energy systems, and provides a method and system for designing and operating an integrated energy station based on distributed optimization. The method includes obtaining data required for energy station design and operation optimization to obtain the goal of optimized design and operation; establishing a super structure based on all optional equipment for optimizing system structure and equipment capacity; establishing a load rate variable matrix with the load rate of each device at each moment as the operation optimization variable; constructing a single energy station continuous operation optimization model based on the load rate variable matrix, combined with the goal of optimal design and operation of the energy station in a continuous period of time and constraints; based on the theory of space-time conversion, converting the single energy station continuous operation optimization model into a multi-energy station serial operation optimization model; using the distributed optimization algorithm to solve the super structure and the multi-energy station serial operation optimization model, obtaining the optimal design and operation plan of the multi-energy station, and then obtaining the optimal design and operation plan of the single energy station.

Description

Technical Field

[0001] The present invention belongs to the field of energy systems, and in particular relates to a design and operation method and system of an integrated energy station based on distributed optimization. Background Art

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

[0003] The integrated energy station is an energy system that fully utilizes local renewable energy to meet the various energy needs of users. The volatility and randomness of renewable energy and user loads result in the diversity of integrated energy station operation scenarios and increase the complexity of operating conditions. In particular, with the addition of energy storage equipment, the real-time matching of source and load in the integrated energy station is decoupled, and the operation plan is more flexible and changeable.

[0004] The operating performance of an integrated energy station directly affects its design scheme selection, operation scheduling plan, investment return analysis, and energy supply price setting. However, an integrated energy station contains a large number of equipment, has a long operation cycle, high operating costs, and difficult to build an operating environment. It is impossible to obtain complete data and accurate performance through experimental testing. Simulation evaluation has become the main way to obtain the performance of an integrated energy station. However, the simulation evaluation of operating performance faces a large number of continuous operation scenarios, which are complex and diverse, with many optimization variables, and it is difficult for global optimization to take into account both solution accuracy and calculation speed.

[0005] At present, most of the integrated energy station design and operation methods use centralized optimization algorithms for performance evaluation. The solution process is computationally intensive and time-consuming, and it is easy to fall into local optimality, resulting in inaccurate evaluation results and affecting the optimization results of design and operation. Summary of the invention

[0006] In order to solve the technical problems existing in the above-mentioned background technology, the present invention provides a design and operation method of an integrated energy station based on distributed optimization, which helps to improve the speed and accuracy of the performance evaluation of the integrated energy station, and provides support for the optimization of its structural design, capacity configuration, and energy scheduling.

[0007] In order to achieve the above object, the present invention adopts the following technical solution:

[0008] The first aspect of the present invention provides a method for designing and operating an integrated energy station based on distributed optimization.

[0009] A method for designing and operating an integrated energy station based on distributed optimization, comprising:

[0010] Obtain the data required for energy station design and operation optimization to achieve the goal of optimized design and operation;

[0011] Establish super structure based on all optional equipment for optimization of system structure and equipment capacity;

[0012] Taking the load rate of each device at each moment as the operation optimization variable, a load rate variable matrix is ​​established;

[0013] According to the load rate variable matrix, the continuous operation optimization model of a single energy station is constructed in combination with the objectives and constraints of the optimal design and operation of the energy station in a continuous period of time;

[0014] Based on the theory of time-space conversion, the continuous operation optimization model of a single energy station is converted into a series operation optimization model of multiple energy stations;

[0015] By adopting the distributed optimization algorithm, the superstructure and multi-energy station series operation optimization model is solved to obtain the optimal design and operation scheme of the multi-energy station, and then the optimal design and operation scheme of the single energy station is obtained.

[0016] The second aspect of the present invention provides a comprehensive energy station design and operation system based on distributed optimization.

[0017] A comprehensive energy station design and operation system based on distributed optimization, including:

[0018] A data acquisition module is configured to: acquire data required for energy station design and operation optimization to obtain a goal of optimized design and operation;

[0019] A structure selection module, which is configured to: establish a super structure based on all optional equipment for optimization of system structure and equipment capacity;

[0020] The variable identification module is configured to: establish a load rate variable matrix by taking the load rate of each device at each moment as the operation optimization variable;

[0021] A model building module is configured to: build a single energy station continuous operation optimization model according to a load rate variable matrix, combined with the objectives and constraints of the optimal design and operation of the energy station in a continuous period of time;

[0022] A model conversion module is configured to: based on the time-space conversion theory, convert the single energy station continuous operation optimization model into a multi-energy station series operation optimization model;

[0023] The output module is configured to: adopt and run a distributed optimization algorithm to solve the superstructure and multi-energy station series operation optimization model, obtain the optimal design and operation plan of the multi-energy station, and then obtain the optimal design and operation plan of the single energy station.

[0024] A third aspect of the present invention provides a computer-readable storage medium.

[0025] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps in the method for designing and operating an integrated energy station based on distributed optimization as described in the first aspect above.

[0026] A fourth aspect of the present invention provides a computer device.

[0027] A computer device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the steps in the method for designing and operating an integrated energy station based on distributed optimization as described in the first aspect above are implemented.

[0028] Compared with the prior art, the present invention has the following beneficial effects:

[0029] The distributed evaluation method of the present invention helps to improve the rapidity and accuracy of the performance evaluation of the integrated energy station, and provides support for the optimization of its structural design, capacity configuration, and energy scheduling.

[0030] The present invention combines multi-agent, distributed optimization and time-space conversion theory, regards the entire integrated energy station as an intelligent agent, and then converts the operation simulation problem of the entire integrated energy station in multiple continuous scenarios into a collaborative operation simulation problem of multiple intelligent agents with adjacent connections and independent scenarios. It also uses a distributed optimization algorithm to optimize the overall operation of the multi-agent system, obtains the operation plan of each intelligent agent, and obtains performance evaluation data, thereby realizing efficient and accurate simulation evaluation of the performance of the integrated energy station.

[0031] The present invention realizes long-term high-resolution optimization operation of energy stations based on a distributed gradient descent algorithm, evaluates operation performance, and realizes optimal design of energy stations. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] The accompanying drawings in the specification, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0033] Figure 1 is a flow chart of a method for designing and operating an integrated energy station based on distributed optimization, shown in Embodiment 1 of the present invention;

[0034] Figure 2 It is a block diagram of the continuous operation of a single hydrogen-containing integrated energy station at multiple times shown in the first embodiment of the present invention;

[0035] Figure 3 It is a block diagram of the coordinated operation of multiple hydrogen-containing integrated energy stations at a single moment shown in the first embodiment of the present invention. DETAILED DESCRIPTION

[0036] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0037] It should be noted that the following detailed descriptions are all illustrative and intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present invention belongs.

[0038] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprising" and / or "including" are used in this specification, it indicates the presence of features, steps, operations, devices, components and / or combinations thereof.

[0039] It should be noted that the flowcharts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the methods and systems according to various embodiments of the present disclosure. It should be noted that each box in the flowchart or block diagram can represent a module, a program segment, or a part of a code, and the module, program segment, or a part of a code may include one or more executable instructions for implementing the logical functions specified in each embodiment. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in an order different from that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, or they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the flowchart and / or block diagram, and the combination of boxes in the flowchart and / or block diagram can be implemented using a dedicated hardware-based system that performs a specified function or operation, or can be implemented using a combination of dedicated hardware and computer instructions.

[0040] Embodiment 1

[0041] like Figure 1As shown, this embodiment provides a method for designing and operating an integrated energy station based on distributed optimization. This embodiment uses the method applied to a server as an example for illustration. It is understandable that the method can also be applied to a terminal, and can also be applied to a system including a terminal and a server, and is implemented through the interaction between the terminal and the server. The server can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network servers, cloud communications, middleware services, domain name services, security services CDN, and big data and artificial intelligence platforms. The terminal can be a smart phone, a tablet computer, a laptop computer, a desktop computer, a smart speaker, a smart watch, etc., but is not limited thereto. The terminal and the server can be directly or indirectly connected via wired or wireless communication, which is not limited in this application. In this embodiment, the method includes the following steps:

[0042] Obtain the data required for energy station design and operation optimization to achieve the goal of optimized design and operation;

[0043] Establish super structure based on all optional equipment for optimization of system structure and equipment capacity;

[0044] Taking the load rate of each device at each moment as the operation optimization variable, a load rate variable matrix is ​​established;

[0045] According to the load rate variable matrix, the continuous operation optimization model of a single energy station is constructed in combination with the objectives and constraints of the optimal design and operation of the energy station in a continuous period of time;

[0046] Based on the theory of time-space conversion, the continuous operation optimization model of a single energy station is converted into a series operation optimization model of multiple energy stations;

[0047] By adopting the distributed optimization algorithm, the superstructure and multi-energy station series operation optimization model is solved to obtain the optimal design and operation scheme of the multi-energy station, and then the optimal design and operation scheme of the single energy station is obtained.

[0048] Among them, the data required for energy station design and operation optimization are obtained, including the energy demand (cold, heat, electricity) of users served by the energy station, the renewable energy resources (wind, light) available to the energy station, the performance parameters of the equipment, and the price emission coefficient of the power grid.

[0049] 1) Data collection and classification. The data required for optimal design and operation include local renewable energy, customer energy demand, external grid electricity prices, and equipment types, capacities, and efficiency curves. Indicators and goals for optimal design and operation are also derived.

[0050] 2) Equipment and structure modeling. The equipment model includes energy input, energy output, equipment capacity and efficiency curve. For optimal operation problems, the system structure and equipment capacity have been determined and can be described directly. For optimal design problems, a super structure is established based on all optional equipment for optimization of system structure and equipment capacity.

[0051] 3) Define variables and constraints. For optimal operation problems, the load rate of each device at each time is used as the operation optimization variable, and a load rate variable matrix is ​​established. The number of rows in the matrix is ​​the number of devices, and the number of columns is the number of operation times. The load rate variable matrix should satisfy the constraints of the energy balance equation. For optimization design problems, the capacity of each device in the superstructure is also an optimization variable.

[0052] 4) According to the connection mode of the equipment in the energy station structure and the collated data, a single energy station continuous operation optimization model with constraints such as multi-energy flow conversion, transmission, and storage is established.

[0053] 5) Model conversion: based on the theory of time-space conversion, the continuous operation optimization model of a single energy station is converted into a serial operation optimization model of multiple energy stations.

[0054] 6) Set the algorithm. Based on the running optimization model and distributed optimization theory, set the optimization variables, optimization objectives, constraints, convergence conditions, etc. of the distributed optimization algorithm.

[0055] 7) Simulation solution, using distributed optimization algorithm to solve the super structure and multi-energy station series operation optimization model, to obtain the optimal design and operation plan of the multi-energy station, and then to obtain the optimal design and operation plan of the single energy station.

[0056] For hydrogen-containing integrated energy stations, which have the advantage of cross-seasonal energy storage, their operating performance must be optimized over a long period. This case compares the distributed operation optimization method with the centralized operation optimization method. The selected energy station structure is as follows: Figure 2 shown.

[0057] This energy station uses renewable energy as its main energy source, and meets the user's electricity, heat and cooling loads through energy conversion of 6 devices. The energy station only replenishes the current energy required from the power grid when its own energy is exhausted. Each operating moment of the energy station involves 6 optimization variables, corresponding to the load rate of 6 devices, so as to adjust the power consumption of hydrogen production and storage equipment, the power generation and heating of fuel cells, the charging and discharging power of energy storage batteries, the charging and discharging power of hot water storage tanks, the cooling power of absorption chillers, and the cooling and heating power of ground source heat pumps.

[0058] When the centralized operation optimization method is used, the optimization objectives and constraints of this energy station in continuous operation at 1752 moments can be expressed as:

[0059] After using the distributed operation optimization method of this embodiment, the optimization problem of the single energy station running continuously at 1752 moments can be converted into the optimization problem of 1752 connected energy stations running in coordination at a single moment. Figure 3 As shown, after the conversion, the energy storage devices of each energy station are replaced by energy transmission pipelines, and the energy transmission in the pipelines is unidirectional, that is, an energy station can only obtain energy from the energy station in front of it, and can only provide the remaining energy in its own energy storage device to the energy station behind it.

[0060] The optimization objectives and constraints for the coordinated operation of these 1752 energy stations at a single moment can be expressed as:

[0061]

[0062]

[0063] Where n represents the total number of energy stations, Y i =[ht i fc i et i tt i ac i hp i ] T represents the load rate variable matrix of the i-th energy station, fc, ac and hp represent the load rate variables of the fuel cell, absorption chiller and ground source heat pump respectively, ht, et and tt represent the load rate change variables of the hydrogen energy transmission pipeline, electric energy transmission pipeline and thermal energy transmission pipeline respectively, η represents the efficiency of each energy transmission pipeline, and its value is equal to 1 minus the self-discharge rate of the energy storage device.

[0064] As one or more implementation modes, the optimal operation plan of the multi-energy station includes: when the cost is lowest, the load rate of the fuel cell in the multi-energy station, the load rate of the absorption chiller, the load rate of the ground source heat pump, the load rate of the hydrogen energy transmission pipeline, the load rate of the electric energy transmission pipeline and the load rate of the heat energy transmission pipeline.

[0065] As one or more implementation modes, the optimal operation plan of the single energy station includes: when the cost is lowest, the variable vector corresponding to the load rate of the hydrogen production and storage equipment, the load rate of the fuel cell, the load rate of the energy storage battery, the load rate of the hot water storage tank, the load rate of the absorption chiller and the load rate of the ground source heat pump in the single energy station.

[0066] Specifically, the main steps of the distributed operation optimization method of the integrated energy station in this embodiment are as follows:

[0067] Step 1: Data collation, including energy resources, user load, energy price, weather, equipment type capacity, equipment full-condition efficiency curve, operation goals, etc.

[0068] Step 2: Energy station modeling. According to the connection method of the equipment in the energy station structure and the sorted data, a single energy station continuous operation optimization model with constraints such as multi-energy flow conversion, transmission, and storage is established.

[0069] Step 3: Model conversion: Based on the theory of time-space conversion, the single energy station continuous operation optimization model is converted into a multi-energy station serial operation optimization model.

[0070] Step 4: Set the algorithm. Based on the running optimization model and distributed optimization theory, set the optimization variables, optimization objectives, constraints, convergence conditions, etc. of the distributed optimization algorithm.

[0071] Step 5: Simulate and solve, by running the distributed optimization algorithm, solve the operation plan of multiple energy stations, and then obtain the operation plan and operation performance of a single energy station.

[0072] In step 2, the optimization model of continuous operation of a single energy station can be expressed as:

[0073] min J(X)

[0074]

[0075] In the formula, represents the matrix composed of all optimization variables in the continuous operation optimization model of a single energy station, n represents the number of moments in the model, m represents the number of optimization variables involved in each operating moment, J represents the optimization objective, L and U represent the lower and upper limits of the matrix X value, A and b represent linear inequality constraints, Aeq and beq represent linear equality constraints, C represents nonlinear inequality constraints, and Ceq represents nonlinear equality constraints.

[0076] In step 3, the optimization model of multi-energy station series operation can be expressed as:

[0077]

[0078]

[0079] Where Y i =[x 1,i … x m,i ] T represents the optimization variable of the i-th energy station in the multi-energy station series operation optimization model, J i represents the optimization objective of the i-th energy station, D represents the nonlinear inequality constraint within the energy station, Deq represents the nonlinear equality constraint within the energy station, E represents the nonlinear inequality constraint between energy stations, and Eeq represents the nonlinear equality constraint between energy stations.

[0080] In step 4, the optimization algorithm uses a distributed gradient descent optimization method, which can be expressed as:

[0081]

[0082]

[0083]

[0084] Z i =Y i (t)+h·K i

[0085]

[0086] Where G represents the gradient function in the continuous operation optimization model of a single energy station, G i =[g 1,i … g m,i ] T represents the gradient component at the i-th moment, K i represents the gradient function of the i-th energy station in the multi-energy station series operation optimization model, X(t) and Y i (t) represents the variable matrix at the tth optimization, Z i represents the variable matrix after gradient descent, and h represents the learning rate.

[0087] The integrated energy station design method in this embodiment adds optimization of capacity configuration and structural design on the basis of the distributed operation optimization method. The main steps are as follows:

[0088] Data collation is consistent with the distributed operation optimization method.

[0089] Structural design, according to the design requirements of the energy station, all available optional equipment is divided into groups for connection to describe all feasible structural solutions, and the superstructure method is used to optimize the design of the equipment type and equipment capacity of the energy station.

[0090] Energy station modeling, consistent with distributed operation optimization methods.

[0091] Model transformation, consistent with the distributed operation optimization method.

[0092] The algorithm is set to adopt a two-layer optimization method. The outer layer optimizes the design scheme, and the outer layer algorithm is a centralized optimization algorithm; the inner layer optimizes the operation scheme, and the inner layer algorithm is consistent with the distributed operation optimization method.

[0093] Simulation solution, by running a two-layer optimization algorithm, solves the design solution and its operating performance.

[0094] Embodiment 2

[0095] This embodiment provides a comprehensive energy station design and operation system based on distributed optimization.

[0096] A comprehensive energy station design and operation system based on distributed optimization, including:

[0097] A data acquisition module is configured to: acquire data required for energy station design and operation optimization to obtain a goal of optimized design and operation;

[0098] A structure selection module, which is configured to: establish a super structure based on all optional equipment for optimization of system structure and equipment capacity;

[0099] The variable identification module is configured to: establish a load rate variable matrix by taking the load rate of each device at each moment as the operation optimization variable;

[0100] A model building module is configured to: build a single energy station continuous operation optimization model according to a load rate variable matrix, combined with the objectives and constraints of the optimal design and operation of the energy station in a continuous period of time;

[0101] A model conversion module is configured to: based on the time-space conversion theory, convert the single energy station continuous operation optimization model into a multi-energy station series operation optimization model;

[0102] The output module is configured to: adopt and run a distributed optimization algorithm to solve the superstructure and multi-energy station series operation optimization model, obtain the optimal design and operation plan of the multi-energy station, and then obtain the optimal design and operation plan of the single energy station.

[0103] It should be noted that the above data acquisition module, structure selection module, variable identification module, model construction module, model conversion module and output module are the same as the examples and application scenarios implemented by the steps in Embodiment 1, but are not limited to the contents disclosed in Embodiment 1. It should be noted that the above modules as part of the system can be executed in a computer system such as a set of computer executable instructions.

[0104] Embodiment 3

[0105] This embodiment provides a computer-readable storage medium having a computer program stored thereon. When the program is executed by a processor, the steps in the method for designing and operating an integrated energy station based on distributed optimization as described in the first embodiment above are implemented.

[0106] Embodiment 4

[0107] This embodiment provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the steps in the method for designing and operating an integrated energy station based on distributed optimization as described in the first embodiment above are implemented.

[0108] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of hardware embodiments, software embodiments, or embodiments combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage and optical storage, etc.) containing computer-usable program code.

[0109] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0110] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0111] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0112] A person skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program, and the program can be stored in a computer-readable storage medium, and when the program is executed, it can include the processes of the embodiments of the above-mentioned methods. The storage medium can be a disk, an optical disk, a read-only memory (ROM) or a random access memory (RAM), etc.

[0113] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for designing and operating an integrated energy station based on distributed optimization, characterized in that: include: Obtain the data required for energy station design and operation optimization to achieve the goal of optimized design and operation; Establish super structure based on all optional equipment for optimization of system structure and equipment capacity; Taking the load rate of each device at each moment as the operation optimization variable, a load rate variable matrix is ​​established; According to the load rate variable matrix, the continuous operation optimization model of a single energy station is constructed in combination with the objectives and constraints of the optimal design and operation of the energy station in a continuous period of time; Based on the theory of time-space conversion, the continuous operation optimization model of a single energy station is converted into a series operation optimization model of multiple energy stations; The distributed optimization algorithm is used to solve the superstructure and multi-energy station series operation optimization model, and the optimal design and operation scheme of the multi-energy station is obtained, and then the optimal design and operation scheme of the single energy station is obtained; The single energy station continuous operation optimization model is: min J(X)=(P(HS)-P(FC)+P(ES)+P(HP)+EL)×p Where X = [HS T FC T ES T TS T AC T HP T ] T represents the load rate variable matrix, the number of its rows is equal to the number of load rate variables, the number of its columns is equal to the number of operating moments, HS, FC, ES, TS, AC and HP represent the variable vectors of the load rates of hydrogen production and storage equipment, fuel cells, energy storage batteries, hot water storage tanks, absorption chillers and ground source heat pumps, respectively. When the variable value is greater than 0, it represents energy storage charging or heat pump heating, and when the variable value is less than 0, it represents energy storage discharge or heat pump cooling. P represents the power generation and consumption function of each device, H represents the heating and consumption function of each device, Q represents the cooling function of each device, p represents the power selling price vector of the power grid, EL, HL and CL represent the user's electric load, heat load and cooling load, respectively. The conversion of a single energy station continuous operation optimization model into a multi-energy station serial operation optimization model specifically includes: converting the operation objectives and constraints of a specified energy station at several moments in a continuous period of time into the operation objectives and constraints of several energy stations at a certain moment.

2. The method for designing and operating an integrated energy station based on distributed optimization according to claim 1, characterized in that: The number of energy stations in the multi-energy station serial operation optimization model is the same as the number of moments in the single energy station continuous operation optimization model.

3. The method for designing and operating an integrated energy station based on distributed optimization according to claim 1, characterized in that: The optimization model for the serial operation of multiple energy stations is: Where n represents the total number of energy stations, Y i =[ht i fc i et i tt i ac i hp i ] T represents the load rate variable matrix of the i-th energy station, fc, ac and hp represent the load rate variables of the fuel cell, absorption chiller and ground source heat pump respectively, ht, et and tt represent the load rate change variables of the hydrogen energy transmission pipeline, electric energy transmission pipeline and thermal energy transmission pipeline respectively, η represents the efficiency of each energy transmission pipeline, and its value is equal to 1 minus the self-discharge rate of the energy storage device.

4. The method for designing and operating an integrated energy station based on distributed optimization according to claim 1, characterized in that: The optimal operation plan of the multi-energy station includes: when the cost is lowest, the load rate of the fuel cell in each energy station, the load rate of the absorption refrigerator, the load rate of the ground source heat pump, the load rate of the hydrogen energy transmission pipeline, the load rate of the electric energy transmission pipeline and the load rate of the heat energy transmission pipeline.

5. The method for designing and operating an integrated energy station based on distributed optimization according to claim 1, characterized in that: The optimal operation plan of the single energy station includes: when the cost is lowest, the variable vector of the load rate of the hydrogen production and storage equipment, the load rate of the fuel cell, the load rate of the energy storage battery, the load rate of the hot water storage tank, the load rate of the absorption chiller and the load rate of the ground source heat pump at each operating moment.

6. A comprehensive energy station design and operation system based on distributed optimization, characterized in that: include: A data acquisition module is configured to: acquire data required for energy station design and operation optimization to obtain a goal of optimized design and operation; A structure selection module, which is configured to: establish a super structure based on all optional equipment for optimization of system structure and equipment capacity; The variable identification module is configured to: establish a load rate variable matrix by taking the load rate of each device at each moment as the operation optimization variable; A model building module is configured to: build a single energy station continuous operation optimization model according to a load rate variable matrix, combined with the objectives and constraints of the optimal design and operation of the energy station in a continuous period of time; A model conversion module is configured to: based on the time-space conversion theory, convert the single energy station continuous operation optimization model into a multi-energy station series operation optimization model; The output module is configured to: adopt and run a distributed optimization algorithm to solve the superstructure and multi-energy station series operation optimization model, obtain the optimal design and operation plan of the multi-energy station, and then obtain the optimal design and operation plan of the single energy station; The single energy station continuous operation optimization model is: min J(X)=(P(HS)-P(FC)+P(ES)+P(HP)+EL)×p Where X = [HS T FC T ES T TS T AC T HP T ] T represents the load rate variable matrix, the number of its rows is equal to the number of load rate variables, the number of its columns is equal to the number of operating moments, HS, FC, ES, TS, AC and HP represent the variable vectors of the load rates of hydrogen production and storage equipment, fuel cells, energy storage batteries, hot water storage tanks, absorption chillers and ground source heat pumps, respectively. When the variable value is greater than 0, it represents energy storage charging or heat pump heating, and when the variable value is less than 0, it represents energy storage discharge or heat pump cooling. P represents the power generation and consumption function of each device, H represents the heating and consumption function of each device, Q represents the cooling function of each device, p represents the power selling price vector of the power grid, EL, HL and CL represent the user's electric load, heat load and cooling load, respectively. The conversion of a single energy station continuous operation optimization model into a multi-energy station serial operation optimization model specifically includes: converting the operation objectives and constraints of a specified energy station at several moments in a continuous period of time into the operation objectives and constraints of several energy stations at a certain moment.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps in the integrated energy station design and operation method based on distributed optimization as described in any one of claims 1 to 5 are implemented.

8. A computer 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, the steps in the integrated energy station design and operation method based on distributed optimization as described in any one of claims 1-5 are implemented.

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