Multi-time-scale collaborative optimization design method and system for integrated energy systems

Abstract

The present invention discloses a multi-time-scale collaborative optimization design method and system for an integrated energy system, comprising: obtaining relevant data of the integrated energy system; dividing different time-scale characteristics based on the problems requiring optimization design of the integrated energy system; the problems requiring optimization design include: structural design, equipment capacity configuration, and operation strategy optimization; establishing corresponding optimization design sub-models for each time scale; considering the time correlation and physical coupling between structural design, equipment capacity configuration, and operation strategy, constructing an interaction mechanism between each sub-problem; selecting a solution algorithm for each optimization design sub-model, combining the interaction mechanism, and performing hierarchical hierarchical operations on each optimization design sub-model to obtain the optimal integrated energy system design solution. The present invention decomposes complex optimization problems involving multiple time scales into sub-problems on each time scale, simplifying calculations.

Description

Technical Field

[0001] The present invention relates to the technical field of integrated energy system optimization, and in particular to a multi-time-scale collaborative optimization design method and system for an integrated energy system. 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 system (IES) is centered on renewable energy and distributed combined cooling, heating and power systems. Based on energy cascade utilization technology, it simultaneously completes power generation, cooling and heating, improves the renewable energy absorption rate and comprehensive energy utilization efficiency, and effectively reduces greenhouse gas emissions, helping to solve energy shortages and has great development potential.

[0004] System design is a prerequisite for the efficient operation of IES. However, there are time scale differences between different energy flows, between supply and demand sides, and between structure, capacity and operation in the IES system, making system design modeling and optimization very difficult.

[0005] Currently, most IES planning and design methods are to establish large-scale mixed integer programming models, characterizing time scale differences by setting a large number of constraints, which makes the models too complex. Alternatively, they establish multi-level functional (structure-capacity-operation) collaborative models based on functional differences. However, under this system, it is difficult to take all time scale differences into account, which may lead to inaccurate design solutions and make them difficult to apply in actual projects. Summary of the Invention

[0006] In order to solve the above problems, the present invention proposes a multi-time-scale collaborative optimization design method and system for an integrated energy system. Starting from the perspective of time scale differences, this method decomposes complex optimization problems containing multiple time scales into sub-problems on each time scale, and establishes a collaborative mechanism between sub-problems based on time correlation and physical coupling, and then constructs a hierarchical algorithm to solve and obtain the optimal design solution.

[0007] In some embodiments, the following technical solutions are adopted:

[0008] A multi-timescale collaborative optimization design method for an integrated energy system, comprising:

[0009] Obtain data on the integrated energy system equipment type, daily biogas production, electrical output of the CHP generator set and thermal output of the gas boiler, power generation efficiency of the CHP generator set and thermal efficiency of the gas boiler, rated power generation and waste heat recovery efficiency of the CHP generator set, grid interaction power, and output power, heating load, and cooling load of renewable energy sources;

[0010] Based on the problems that require optimization design of the integrated energy system, different time scale characteristics are divided; the problems that require optimization design include: structural design and equipment capacity configuration, and operation strategy optimization;

[0011] For each time scale, a corresponding optimization design sub-model is established;

[0012] Considering the temporal correlation and physical coupling between structural design, equipment capacity configuration, and operation strategy, an interaction mechanism between each sub-problem is constructed;

[0013] A solution algorithm is selected for each optimization design sub-model respectively, and combined with the interaction mechanism, hierarchical and hierarchical operations are performed on each optimization design sub-model to obtain the optimal integrated energy system design solution.

[0014] As an optional solution, based on the problems that require optimization design of the integrated energy system, different time scale characteristics are divided, including:

[0015] Structural design and equipment capacity configuration have annual scale characteristics; biogas distribution and electricity load response optimization have daily scale characteristics; and operational optimization of electric and thermal energy flows has hourly scale characteristics.

[0016] As an optional solution, for the annual scale characteristics of structural design and equipment capacity configuration, with the goal of minimizing the total annual cost of the system and considering the maximum capacity of each device, an annual scale structural design and equipment capacity configuration optimization design sub-model is established.

[0017] As an optional solution, the total annual cost of the system includes: initial investment costs, system maintenance costs and annual operating costs.

[0018] As an optional solution, for the daily-scale characteristics of biogas distribution and electricity load response optimization, with the minimum daily operating cost as the optimization goal and the optimization results of annual-scale characteristics as constraints, a daily-scale biogas distribution and electricity load response optimization design sub-model is established.

[0019] As an optional solution, the daily-scale biogas distribution and electricity load response optimization design sub-model is specifically as follows:

[0020]

[0021] Among them, μ Bio and μ Grid are the carbon emission coefficients of biomass gas and power grid respectively; E CHP and Q GB are the electrical output of the combined heat and power generator set and the thermal output of the gas boiler, respectively; d represents the dth day; η CHP,d(t) is the power generation efficiency of CHP at time t on the dth day, η GB is the thermal efficiency of the gas boiler; E Grid and P Grid are the grid interaction power and electricity price respectively, C bf,d is the cost of biomass raw materials on the dth day, C ct is the unit carbon tax, b GB Indicates whether the gas boiler is configured, which is a binary variable, and Δt is the optimization time interval.

[0022] As an optional solution, for the hourly-scale characteristics of the operation optimization of the electric and thermal energy flow, an operation optimization design sub-model of the hourly-scale electric and thermal energy flow is established with the optimization results of the daily-scale characteristics as input, the real-time energy balance and equipment model as constraints, and minimizing the system carbon emissions as the optimization goal.

[0023] As an optional solution, the operation optimization design sub-model of the hourly-scale electrothermal energy flow is specifically as follows:

[0024]

[0025] Among them, μ Bio and μ Grid are the carbon emission coefficients of biomass gas and power grid respectively; CE d (t) is the carbon emission at time t on day d; C ct is the unit carbon tax, E CHP,d (t) is the electrical output of the CHP unit at time t on the dth day; η CHP,d (t) is the power generation efficiency of CHP at time t on day d, b GB Indicates whether the gas boiler is configured, which is a binary variable; Q GB,d (t) is the thermal efficiency of the gas boiler at time t on day d; η GB is the thermal efficiency of the gas boiler, μ Grid is the carbon emission coefficient of the power grid, is the purchased power, and Δt is the optimization time interval.

[0026] As an optional solution, consider the temporal correlation and physical coupling between structural design, equipment capacity configuration, and operation strategy, and construct an interaction mechanism between the sub-problems. Specifically,

[0027] Annual scale structural design and equipment capacity configuration optimization design sub-model, daily scale biogas distribution and electric load response optimization design sub-model, hourly scale electric and thermal energy flow operation optimization design sub-model

[0028] The equipment types and capacities optimized by the annual-scale structural design and equipment capacity configuration optimization design sub-model serve as constraints for the daily-scale biogas distribution and electricity load response optimization design sub-model. The optimization results of the daily-scale biogas distribution and electricity load response optimization design sub-model are used for the iterative calculation of the objective function of the annual-scale structural design and equipment capacity configuration optimization design sub-model.

[0029] The biogas distribution and electricity load curves optimized by the daily-scale biogas distribution and electricity load response optimization design sub-model are the input data of the hourly-scale electrothermal energy flow operation optimization design sub-model, which is used to calculate the capacity balance equation. The optimization results of the hourly-scale electrothermal energy flow operation optimization design sub-model constitute the target values of the daily-scale biogas distribution and electricity load response optimization design sub-model.

[0030] In other embodiments, the following technical solutions are adopted:

[0031] A multi-timescale collaborative optimization design system for an integrated energy system, comprising:

[0032] A data acquisition module is used to obtain the integrated energy system equipment type, daily biogas production, electrical output of the cogeneration generator set and thermal output of the gas boiler, power generation efficiency of the cogeneration generator set and thermal efficiency of the gas boiler, rated power generation and waste heat recovery efficiency of the cogeneration generator set, grid interaction power, output power of renewable energy, heating load, and cooling load data;

[0033] A scale feature division module is used to divide different time scale features based on the problems that require optimization design of the integrated energy system; the problems that require optimization design include: structural design and equipment capacity configuration, and operation strategy optimization;

[0034] The optimization model construction module is used to establish the corresponding optimization design sub-model for each time scale;

[0035] The optimization model solving module is used to consider the time correlation and physical coupling between structural design, equipment capacity configuration and operation strategy, and to establish an interaction mechanism between each sub-problem. It selects a solution algorithm for each optimization design sub-model and, combined with the interaction mechanism, performs hierarchical and hierarchical operations on each optimization design sub-model to obtain the optimal integrated energy system design solution.

[0036] Compared with the prior art, the present invention has the following beneficial effects:

[0037] (1) This invention innovatively proposes a multi-time-scale coordinated integrated energy system optimization design method, which decomposes the complex optimization problem containing multiple time scales into sub-problems on each time scale, and establishes a coordination mechanism between sub-problems based on time correlation and physical coupling, and then constructs a hierarchical algorithm to solve and obtain the optimal design solution.

[0038] (2) The multi-scale and multi-time-scale collaborative optimization design method of the present invention can effectively solve the multiple time-scale differences between different energy flows, between supply and demand, and between structure, capacity and operation of the integrated energy system, and realize the coordinated optimization design among various systems.

[0039] Other features and advantages of additional aspects of the present invention will be given in part in the following description and in part will be obvious from the following description or learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 Schematic diagram of a wind-solar-biomass integrated energy system in an embodiment of the present invention;

[0041] Figure 2 A schematic diagram of multi-time scale characteristics optimized for system design in an embodiment of the present invention;

[0042] Figure 3 4 is a flowchart of a hierarchical algorithm in an embodiment of the present invention. DETAILED DESCRIPTION

[0043] It should be noted that the following detailed description is illustrative and is intended to provide further explanation of the present application. Unless otherwise specified, all technical and scientific terms used in the present invention have the same meaning as commonly understood by those skilled in the art to which the present application belongs.

[0044] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present application. 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 "comprise" and / or "include" are used in this specification, they indicate the presence of features, steps, operations, devices, components and / or combinations thereof.

[0045] Example 1

[0046] In one or more embodiments, a multi-time-scale collaborative optimization design method for an integrated energy system is disclosed, specifically comprising the following process:

[0047] (1) Obtain data on the equipment type of the integrated energy system, daily biogas production, electrical output of the cogeneration generator set and thermal output of the gas boiler, power generation efficiency of the cogeneration generator set and thermal efficiency of the gas boiler, rated power generation and waste heat recovery efficiency of the cogeneration generator set, grid interaction power, output power of renewable energy, heating load and cooling load data of the building throughout the year, investment cost and efficiency of each equipment, electricity price, natural gas price, and carbon emission coefficient of natural gas and grid.

[0048] (2) Based on the problems that require optimization design of the integrated energy system, different time scale characteristics are divided; the problems that require optimization design include: structural design and equipment capacity configuration, and operation strategy optimization;

[0049] (3) For each time scale, establish the corresponding optimization design sub-model;

[0050] (4) Considering the temporal correlation and physical coupling between structural design, equipment capacity configuration, and operation strategy, an interaction mechanism between each sub-problem is constructed;

[0051] (5) A solution algorithm is selected for each optimization design sub-model, and combined with the interaction mechanism, hierarchical operations are performed on each optimization design sub-model to obtain the optimal integrated energy system design solution.

[0052] This embodiment uses Figure 1 The optimized design of the wind-solar-biomass integrated energy system shown is used as an example for explanation. Of course, the optimized design of other types of integrated energy systems can also be applied.

[0053] The system's optimization design issues include system structure design, equipment capacity configuration, and operation strategy optimization. Structural design refers to the selection of energy conversion equipment, which in this system is manifested as whether to choose to use electric boilers, electric refrigerators, absorption refrigerators, or gas boilers.

[0054] The specific steps include:

[0055] Step 1: Analyze the multi-timescale characteristics of the system design problem.

[0056] like Figure 2 As shown, structural design and capacity configuration are long-term investment decision-making issues. Usually, the entire life cycle of the equipment is considered, and the annual comprehensive cost is calculated and decisions are made with one year as the optimization cycle. Therefore, structural design and capacity configuration have annual scale characteristics.

[0057] Operational optimization is an energy supply and demand matching problem. Since biogas is limited by the daily biogas production in practical applications, the biogas distribution optimization has daily constraints. At the same time, on the demand side, the demand response optimization of the electricity load is also constrained by the daily total load. Therefore, the biogas distribution and electricity load response optimization have daily scale characteristics.

[0058] The operational optimization of electric and thermal energy flows requires real-time matching of supply and demand, is constrained by energy flow balance, and has hourly scale characteristics.

[0059] Step 2: Establish a multi-timescale collaborative optimization model.

[0060] (1) The first level is the annual scale structural design and capacity configuration model. This model takes economy as its goal and aims to minimize the total annual cost of the system, including initial investment cost (TIC), system maintenance cost (TMC) and annual operation cost (TOC). The optimization objectives are:

[0061] Min(TIC+TMC+TOC) (1)

[0062] The decision variables X1 of the IES optimal planning model include the choice of equipment (b EB ,b EC ,b AC ,b GB ), and the capacity of each device (N PV ,N WT ,N Bio ,N grid ,N CHP ,N HS ,N EB ,N EC ,N AC ,N GB ); where b EB ,b EC ,b AC ,b GB Respectively indicate whether electric boiler, electric refrigerator, absorption refrigerator and gas boiler are configured; N PV ,N WT ,N Bio ,N grid ,N CHP ,N HS ,N EB ,N EC ,N AC ,N GB They represent photovoltaic capacity, wind turbine capacity, daily biogas production (used to calculate the size of the biogas pool), maximum grid interaction, generator set capacity, energy storage equipment capacity, electric boiler capacity, electric chiller capacity, absorption chiller capacity and gas boiler capacity respectively.

[0063] Therefore, the calculation formula for each cost is:

[0064] TIC=N i IC i +b k N k IC k ,i∈[PV,WT,CHP,HS,Bio],k∈[EB,EC,AC,GB] (2)

[0065] TMC=N i MC i +b k N k MC k ,i∈[PV,WT,CHP,HS,Bio],k∈[EB,EC,AC,GB] (3)

[0066] Among them, IC i is the annual investment cost of each equipment, MC i The total annual operating cost is calculated by adding up the daily operating costs:

[0067]

[0068] Among them, C bf,d is the daily cost of biomass raw materials, which is determined by the daily biogas production; C grid,d CE is the daily grid interaction cost; d is the daily carbon emissions; C ct For unit carbon tax.

[0069] Constraints:

[0070]

[0071] (2) The second level is the daily scale biogas distribution and electricity load response optimization model. This model accepts the optimization results of the annual scale model as constraints and takes the lowest daily operating cost as the optimization goal:

[0072]

[0073] Among them, μ Bio and μ Grid are the carbon emission coefficients of biomass gas and power grid respectively; E CHP and Q GB are the electrical output of the combined heat and power generator set and the thermal output of the gas boiler, respectively; d represents the dth day; η CHP,d (t) is the power generation efficiency of CHP at time t on the dth day, η GB is the thermal efficiency of the gas boiler; E Grid and P Gridare the grid interaction power and electricity price respectively, C bf,d is the cost of biomass raw materials on the dth day, C ct is the unit carbon tax, b GB Indicates whether the gas boiler is configured, which is a binary variable, and Δt is the optimization time interval.

[0074] The decision variables X2 for daily biogas distribution and electricity load response optimization include [E CHP,d (1),…, E CHP,d (24),E GB,d (1),…,E GB,d (24), E load,tran,d (1),…,E load,tran,d (twenty four)].

[0075] Among them, E CHP,d (1),…,E CHP,d (24) represents the output plan of the CHP unit for one day (1-24 hours), E GB,d (1),…,E GB,d (24) represents the output plan of the gas boiler for one day (1-24 hours), E load,tran,d (1),…,E load,tran,d (24) represents the one-day (1-24h) scheduling plan of the electric load.

[0076] Constraints:

[0077] The following requirements must be met for the CHP unit model, gas boiler model, and electric load response model:

[0078] η CHP,rated =27.07(N bpgu ) 0.0563 (7)

[0079]

[0080]

[0081] 0≤E CHP (t)≤N CHP (10)

[0082] 0≤Q GB (t)≤N GB (11)

[0083]

[0084] E load (t) = E load,old (t)-E load,tran (t) (13)

[0085] -αEload,old (t)≤E load,tran (t)≤αE load,old (t) (14)

[0086]

[0087] Among them, E load is the electrical load, E load,old is the original electrical load, E load,tran For the movable electric load (load removal: E conload >0; Load transfer: E conload <0),η CHP,rated , η rh are the rated power generation capacity and waste heat recovery efficiency of the CHP unit, Q rh is the waste heat recovery power. Without considering the load that can be reduced, the load should remain constant throughout the day, α is the proportion of the shiftable load to the total load; the time interval Δt is 1h. CHP,d (t), η CHP,d (t) represent the electrical output and power generation efficiency of the generator set at time t on the dth day, respectively.

[0088] (3) The third level is the hourly scale electricity and heat supply and demand matching model. This model uses the daily scale results as input and is constrained by the real-time energy balance and equipment model. It aims to minimize the system carbon emissions. The optimization goal is:

[0089]

[0090] Among them, μ Bio and μ Grid are the carbon emission coefficients of biomass gas and power grid respectively; CE d (t) is the carbon emission at time t on day d; C ct is the unit carbon tax, E CHP,d (t) is the electrical output of the CHP unit at time t on the dth day; η CHP,d (t) is the power generation efficiency of CHP at time t on day d, b GB Indicates whether the gas boiler is configured, which is a binary variable; Q GB,d (t) is the thermal efficiency of the gas boiler at time t on day d; η GB is the thermal efficiency of the gas boiler, μ Grid is the carbon emission coefficient of the power grid, is the purchased power, and Δt is the optimization time interval.

[0091] The decision variables at this level are the real-time output plans of electrical and thermal equipment, including:

[0092] [E grid (t),Q HS (t),QEB (t),Q EC (t),Q AC (t)], where the parameters represent the grid interaction power, energy storage power, electric boiler heating power, electric chiller cooling power and absorption chiller cooling power, respectively.

[0093] Constraints:

[0094] Real-time balance of electricity, heat and cooling energy flows must be met.

[0095]

[0096]

[0097] Q c,load (t) = b EC Q EC (t)+b AC Q AC (t) (27)

[0098] Where, E PV Output electric power for photovoltaic power generation system; E WT Output electric power to the wind power generation system; E CHP Output power for the combined power supply system; Q h,load is the heat load; Q EB is the heat output of the electric boiler; Q HS is the input / output power of the heat storage device, when output (Q HS >0), input (Q HS <0); Q c,load is the cooling load; Q AC and Q EC are the cooling outputs of the absorption chiller and the electric chiller respectively; COP AC and COP EC are the refrigeration coefficients of absorption refrigerator and electric refrigerator respectively.

[0099] Step 3: Establish an interaction mechanism between models. The equipment types and capacities optimized by the annual-scale model serve as constraints for the daily-scale model. Conversely, the daily-scale optimization results are used to iteratively calculate the annual-scale model's objective function. The biogas distribution and electricity load curves optimized by the daily-scale model serve as input data for the hourly-scale model, used to calculate the capacity balance equation. Conversely, the hourly-scale optimization results form the target value for the daily-scale model.

[0100] Step 4: Construct a hierarchical algorithm. Figure 3The annual scale model is a mixed integer programming problem, which can be solved by using a heuristic algorithm (genetic algorithm, GA); the daily scale model is a nonlinear optimization problem, which can be solved by using a nonlinear programming method; the hourly scale model is solved by using a linear programming method. Then, a hierarchical hierarchical algorithm (such as Figure 3 ), solve the multi-time-scale collaborative optimization model to obtain the optimal design solution.

[0101] Because multi-time-scale optimization problems involving a large number of decision variables and constraints are extremely complex, they are difficult to solve using a single mathematical programming or intelligent algorithm. This embodiment decomposes this complex optimization problem into sub-problems at different time scales, so that each sub-problem has only decision variables and constraints at a single time scale, making it easier to solve. At the same time, the temporal correlation and physical coupling are utilized to establish a collaborative mechanism between sub-problems, facilitating iterative calculations. By selecting a targeted solution algorithm for each sub-problem and constructing a hierarchical, hierarchical algorithm, a global optimal solution to the problem can be obtained.

[0102] Example 2

[0103] In one or more embodiments, a multi-timescale collaborative optimization design system for an integrated energy system is disclosed, comprising:

[0104] A data acquisition module is used to obtain the integrated energy system equipment type, daily biogas production, electrical output of the cogeneration generator set and thermal output of the gas boiler, power generation efficiency of the cogeneration generator set and thermal efficiency of the gas boiler, rated power generation and waste heat recovery efficiency of the cogeneration generator set, grid interaction power, output power of renewable energy, heating load, and cooling load data;

[0105] A scale feature division module is used to divide different time scale features based on the problems that require optimization design of the integrated energy system; the problems that require optimization design include: structural design and equipment capacity configuration, and operation strategy optimization;

[0106] The optimization model construction module is used to establish the corresponding optimization design sub-model for each time scale;

[0107] The optimization model solving module is used to consider the time correlation and physical coupling between structural design, equipment capacity configuration and operation strategy, and to establish an interaction mechanism between each sub-problem. It selects a solution algorithm for each optimization design sub-model and, combined with the interaction mechanism, performs hierarchical and hierarchical operations on each optimization design sub-model to obtain the optimal integrated energy system design solution.

[0108] It should be noted that the specific implementation of each of the above modules has been described in Example 1 and will not be described in detail here.

[0109] Although the above describes the specific embodiments of the present invention in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art on the basis of the technical solution of the present invention without any creative work are still within the scope of protection of the present invention.

Claims

1. A multi-time-scale collaborative optimization design method for an integrated energy system, characterized by: include: Obtain data on the integrated energy system equipment type, daily biogas production, electrical output of the CHP generator set and thermal output of the gas boiler, power generation efficiency of the CHP generator set and thermal efficiency of the gas boiler, rated power generation and waste heat recovery efficiency of the CHP generator set, grid interaction power, and output power, heating load, and cooling load of renewable energy sources; Based on the problem of optimizing the design of integrated energy systems, different time scale characteristics are divided; The problems requiring optimization design include: structural design and equipment capacity configuration, and operation strategy optimization; different time scale characteristics include: structural design and equipment capacity configuration have annual scale characteristics; biogas distribution and electricity load response optimization have daily scale characteristics; and operation optimization of electric and thermal energy flow has hourly scale characteristics; For each time scale, a corresponding optimization design sub-model is established. For daily scale characteristics, a daily scale biogas distribution and electricity load response optimization design sub-model is established, specifically: Min For hourly scale characteristics, an operation optimization design sub-model of hourly scale electric and thermal energy flow is established, specifically: Min in, and are the carbon emission coefficients of biomass gas and power grid respectively; E CHP and Q GB are the electrical output of the CHP generator set and the thermal output of the gas boiler, respectively, and d represents the dth day; is the power generation efficiency of CHP at time t on the dth day, η GB is the thermal efficiency of the gas boiler; E Grid and P Grid are the grid interaction power and electricity price respectively, is the cost of biomass raw materials for the dth day, is the unit carbon tax, Indicates whether the gas boiler is configured, which is a binary variable. To optimize the time interval; is the carbon emissions at time t on day d; is the electrical output of the CHP unit at time t on day d; Indicates whether the gas boiler is configured, which is a binary variable; is the thermal efficiency of the gas boiler at time t on day d; The power of purchased electricity; Considering the temporal correlation and physical coupling between structural design, equipment capacity configuration, and operation strategy, an interaction mechanism between each sub-problem is constructed; A solution algorithm is selected for each optimization design sub-model respectively, and combined with the interaction mechanism, hierarchical and hierarchical operations are performed on each optimization design sub-model to obtain the optimal integrated energy system design solution.

2. The multi-time-scale collaborative optimization design method for an integrated energy system according to claim 1, characterized in that: For the annual scale characteristics of structural design and equipment capacity configuration, with the goal of minimizing the total annual cost of the system and considering the maximum capacity of each equipment, an annual scale structural design and equipment capacity configuration optimization sub-model is established.

3. The multi-time-scale collaborative optimization design method for an integrated energy system according to claim 2, characterized in that: The total annual cost of the system includes: initial investment costs, system maintenance costs and annual operating costs.

4. The multi-time-scale collaborative optimization design method for an integrated energy system according to claim 1, characterized in that: For the daily scale characteristics of biogas distribution and electricity load response optimization, taking the minimum daily operating cost as the optimization goal and the optimization results of annual scale characteristics as the constraints, a daily scale biogas distribution and electricity load response optimization design sub-model is established.

5. The multi-time-scale collaborative optimization design method for an integrated energy system according to claim 1, characterized in that: For the hourly-scale characteristics of the operation optimization of the electric and thermal energy flow, an operation optimization design sub-model of the hourly-scale electric and thermal energy flow is established, taking the optimization results of the daily-scale characteristics as input, the real-time energy balance and equipment model as constraints, and minimizing the system carbon emissions as the optimization goal.

6. The multi-time-scale collaborative optimization design method for an integrated energy system according to claim 1, characterized in that: Considering the temporal correlation and physical coupling between structural design, equipment capacity configuration, and operation strategy, an interaction mechanism between each sub-problem is constructed, specifically: Annual scale structural design and equipment capacity configuration optimization design sub-model, daily scale biogas distribution and electric load response optimization design sub-model, hourly scale electric and thermal energy flow operation optimization design sub-model The equipment types and capacities optimized by the annual-scale structural design and equipment capacity configuration optimization design sub-model serve as constraints for the daily-scale biogas distribution and electricity load response optimization design sub-model. The optimization results of the daily-scale biogas distribution and electricity load response optimization design sub-model are used for the iterative calculation of the objective function of the annual-scale structural design and equipment capacity configuration optimization design sub-model. The biogas distribution and electricity load curves optimized by the daily-scale biogas distribution and electricity load response optimization design sub-model are the input data of the hourly-scale electrothermal energy flow operation optimization design sub-model, which is used to calculate the capacity balance equation. The optimization results of the hourly-scale electrothermal energy flow operation optimization design sub-model constitute the target values of the daily-scale biogas distribution and electricity load response optimization design sub-model.

7. A multi-time-scale collaborative optimization design system for an integrated energy system, characterized by: include: A data acquisition module is used to obtain the integrated energy system equipment type, daily biogas production, electrical output of the cogeneration generator set and thermal output of the gas boiler, power generation efficiency of the cogeneration generator set and thermal efficiency of the gas boiler, rated power generation and waste heat recovery efficiency of the cogeneration generator set, grid interaction power, output power of renewable energy, heating load, and cooling load data; The scale feature division module is used to divide different time scale features based on the problem of optimizing the design of the integrated energy system; The problems requiring optimization design include: structural design and equipment capacity configuration, and operation strategy optimization; different time scale characteristics include: structural design and equipment capacity configuration have annual scale characteristics; biogas distribution and electricity load response optimization have daily scale characteristics; and operation optimization of electric and thermal energy flow has hourly scale characteristics; The optimization model construction module is used to establish the corresponding optimization design sub-model for each time scale. For daily scale characteristics, the daily scale biogas distribution and electricity load response optimization design sub-models are established. Specifically: Min For hourly scale characteristics, an operation optimization design sub-model of hourly scale electric and thermal energy flow is established, specifically: Min in, and are the carbon emission coefficients of biomass gas and power grid respectively; E CHP and Q GB are the electrical output of the CHP generator set and the thermal output of the gas boiler, respectively, and d represents the dth day; is the power generation efficiency of CHP at time t on the dth day, η GB is the thermal efficiency of the gas boiler; E Grid and P Grid are the grid interaction power and electricity price respectively, is the cost of biomass raw materials for the dth day, is the unit carbon tax, Indicates whether the gas boiler is configured, which is a binary variable. To optimize the time interval; is the carbon emissions at time t on day d; is the electrical output of the CHP unit at time t on day d; Indicates whether the gas boiler is configured, which is a binary variable; is the thermal efficiency of the gas boiler at time t on day d; The power of purchased electricity; The optimization model solving module is used to consider the time correlation and physical coupling between structural design, equipment capacity configuration and operation strategy, and to establish an interaction mechanism between each sub-problem. It selects a solution algorithm for each optimization design sub-model and, combined with the interaction mechanism, performs hierarchical and hierarchical operations on each optimization design sub-model to obtain the optimal integrated energy system design solution.

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