Multi-stage planning method and system for regional integrated energy system under future extreme climate

Abstract

The application provides a multi-stage planning method and system for a regional comprehensive energy system under future extreme climate, future extreme weather prediction data is obtained according to historical extreme weather data, the comprehensive energy system is planned according to the obtained future extreme weather prediction data, the planning result is more reasonable, and the economy and safety of the system are increased; moreover, the growth of regional load is considered, a multi-stage planning model of the regional comprehensive energy system in different time periods is established, and the situation that device capacity redundancy or load demand cannot be met in the system operation process is solved; the carbon capture device and the electric-to-gas device are considered in the constraint condition, the carbon emission of the system is reduced; meanwhile, the demand response is considered, and the flexibility of the regional comprehensive energy system is increased.

Description

Technical Field

[0001] This invention belongs to the field of integrated energy planning technology, and in particular relates to a multi-stage planning method and system for regional integrated energy systems under future extreme climate conditions. 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] The intensification of global climate change and the frequent occurrence of extreme weather events have severely impacted the safe operation of regional integrated energy systems. Therefore, it is particularly important to consider this issue in the planning and design of future regional integrated energy systems. In addition, the time span of regional planning, construction, and operation is relatively long, and the total load of the region is showing an increasing trend. Traditional single-stage planning methods cannot meet load requirements or cause equipment capacity redundancy, thus failing to guarantee the economic efficiency and safety of the system.

[0004] Existing research has not considered the impact of future extreme weather events on the planning and design of regional integrated energy systems. Furthermore, the planning and design of regional integrated energy systems ignores changes in regional load and fails to account for changes in energy prices over time. In addition, existing research on regional integrated energy system planning largely focuses on the supply side, neglecting the demand side. Summary of the Invention

[0005] To overcome the shortcomings of the prior art, this invention provides a multi-stage planning method and system for regional integrated energy systems under future extreme climate conditions. It takes into account the impact of future extreme climate on system planning and design, and considers carbon capture equipment and power-to-gas equipment to reduce carbon emissions. In addition, it considers demand response to improve the flexibility and economy of system operation.

[0006] To achieve the above objectives, a first aspect of the present invention provides a multi-stage planning method for regional integrated energy systems under future extreme climate conditions, comprising:

[0007] The overall planning period is divided into different sub-planning phases based on the actual construction and usage sequence of buildings within the area;

[0008] Based on historical extreme weather data, future extreme weather forecast data for different time periods are obtained. Based on the future extreme weather forecast data for different time periods and the type and area of ​​buildings in the sub-planning stages of the different time periods, the total load of the planning area for the sub-planning stages of the different time periods is calculated.

[0009] An objective function is established with the goal of minimizing the total operating cost of the sub-planning phase of the integrated energy system within the region.

[0010] The constraints include system demand-side response constraints within the sub-planning phase, and power balance constraints for integrated energy equipment determined based on the total load of the planning area under future extreme climate conditions; the integrated energy equipment includes carbon capture equipment and power-to-gas conversion equipment.

[0011] The objective function is solved under the determined constraints to obtain the optimal planning results of the integrated energy system in different sub-planning stages within the region.

[0012] A second aspect of the present invention provides a multi-stage planning system for regional integrated energy systems under future extreme climate conditions, comprising:

[0013] Module division: The overall planning period is divided into different sub-planning phases based on the actual construction and usage sequence of buildings within the area;

[0014] Climate Prediction Module: Obtains future extreme weather forecast data for different time periods based on historical extreme weather data, and calculates the total load of the planning area for each sub-planning stage based on the future extreme weather forecast data for different time periods and the type and area of ​​buildings within the sub-planning stages of the different time periods;

[0015] Objective function establishment module: The objective function is established with the goal of minimizing the total operating cost of the sub-planning stage of the integrated energy system within the region;

[0016] Condition setting module: Sets constraints, including system demand-side response constraints within the sub-planning phase, and power balance constraints of integrated energy equipment determined based on the total load of the planning area under future extreme climate conditions; the integrated energy equipment includes carbon capture equipment and power-to-gas conversion equipment;

[0017] Planning and solving module: Solve the objective function under the determined constraints to obtain the optimal planning results of the integrated energy system in the region at different sub-planning stages.

[0018] A third aspect of the present invention provides a computer device comprising: a processor, a memory, and a bus, wherein the memory stores machine-readable instructions executable by the processor, and when the computer device is running, the processor communicates with the memory via the bus, and the machine-readable instructions, when executed by the processor, execute a multi-stage planning method for a regional integrated energy system under future extreme climate conditions.

[0019] A fourth aspect of the present invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, performs a multi-stage planning method for a regional integrated energy system under future extreme climate conditions.

[0020] The above one or more technical solutions have the following beneficial effects:

[0021] In this invention, future extreme weather forecasts are obtained based on historical extreme weather data. The integrated energy system is then planned based on these forecasts, resulting in more rational planning and increased system economy and security. Furthermore, the invention considers regional load growth and establishes a multi-stage planning model for the regional integrated energy system at different times, addressing potential issues such as equipment capacity redundancy or inability to meet load demands during system operation. Carbon capture equipment and power-to-gas conversion equipment are considered in the constraints, reducing system carbon emissions. Demand response is also taken into account, increasing the flexibility of the regional integrated energy system.

[0022] Advantages of additional aspects of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0023] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0024] Figure 1 This is a flowchart of the multi-stage planning method for a regional integrated energy system under future extreme climate conditions in Embodiment 1 of the present invention;

[0025] Figure 2 This is a schematic diagram illustrating the division of the regional integrated energy system planning stages in Embodiment 1 of the present invention;

[0026] Figure 3 This is a flowchart illustrating the generation of EPW meteorological files for future extreme climates in Embodiment 1 of the present invention;

[0027] Figure 4 This is a flowchart illustrating the process of selecting historical extreme weather years based on historical actual meteorological data in Embodiment 1 of the present invention.

[0028] Figure 5 This is a structural diagram of the regional integrated energy system in Embodiment 1 of the present invention;

[0029] Figure 6 This is a structural diagram of the multi-stage planning model for a regional integrated energy system in Embodiment 1 of the present invention. Detailed Implementation

[0030] It should be noted that the following detailed descriptions are exemplary and intended to provide further illustration of the invention. 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 invention pertains.

[0031] It should be noted that the terminology used herein is for the purpose of describing particular implementations only and is not intended to limit the exemplary implementations of the present invention.

[0032] Where there is no conflict, the embodiments and features in the embodiments of the present invention can be combined with each other.

[0033] Example 1

[0034] This embodiment discloses a multi-stage planning method for regional integrated energy systems under future extreme climate conditions, including:

[0035] The overall planning period is divided into different sub-planning phases based on the actual construction and usage sequence of buildings within the area;

[0036] Based on historical extreme weather data, future extreme weather forecast data for different time periods are obtained. Based on the future extreme weather forecast data for different time periods and the type and area of ​​buildings in the sub-planning stages of the different time periods, the total load of the planning area in the sub-planning stages of the different time periods is calculated.

[0037] An objective function is established with the goal of minimizing the total operating cost of the sub-planning phase of the integrated energy system within the region.

[0038] The constraints include system demand-side response constraints within the sub-planning phase, and power balance constraints for integrated energy equipment determined based on the total load of the planning area under future extreme climate conditions; the integrated energy equipment includes carbon capture equipment and power-to-gas conversion equipment.

[0039] The objective function is solved under the determined constraints to obtain the optimal planning results of the integrated energy system in different sub-planning stages within the region.

[0040] like Figure 1 As shown in this embodiment, the multi-stage planning method for regional integrated energy systems under future extreme climate conditions specifically includes:

[0041] Step 1: As Figure 2 The overall planning period is divided into N different planning stages based on the actual construction and usage sequence of buildings within the area.

[0042] The phase division in this embodiment is based on the actual construction and commissioning situation. The type and number of buildings in each phase are determined according to the actual engineering plan of the area. For example, if the total planning period is 20 years, and a batch of buildings are put into use at the beginning, another batch of buildings are built five years later, and another batch of buildings are built fifteen years later, then the phase division is 5, 10, and 15 years.

[0043] Step 2: Generate EPW meteorological files for future extreme weather events;

[0044] like Figure 3 As shown, specifically, meteorological data for historical extreme weather years are selected based on actual historical meteorological data; then, combined with future weather monthly-scale prediction data generated by the global climate model released by CMIP6, the deformation method is used to generate future extreme climate meteorological data for different planning periods that can be used for building performance simulation; then, EPW meteorological files of future extreme climates that can be used by building performance simulation software are generated.

[0045] The meteorological data mainly includes: dry-bulb temperature, dew point temperature, relative humidity, solar radiation intensity, wind speed, and atmospheric pressure.

[0046] Specifically, the process of selecting historical extreme weather years based on actual historical meteorological data is as follows:

[0047] Dry-bulb temperature, dew point temperature, and solar radiation intensity are used as selection criteria, and different weights are assigned to them;

[0048] Calculate the monthly average value of each of the above parameters for each year and month over a historical period;

[0049] Calculate the cumulative annual average and standard deviation of each of the above parameters for each month over a historical period;

[0050] The monthly average values ​​calculated for the above parameters are standardized using z-score; the standardized results are then weighted and summed with their corresponding weights.

[0051] The extreme months were selected based on the weighted summation results to form historical extreme weather years.

[0052] Among them, deformation methods include: displacement, tension, and a combination of displacement and tension.

[0053] Displacement: x = x0 + Δx m

[0054] Stretching: x = a m x0

[0055] Combining displacement and tension: x = x0 + Δx m +a m [x0-(x0) m ]

[0056] In the formula, x represents hourly meteorological data for future extreme weather events; x0 represents hourly meteorological data for historical extreme weather years; (x0) m The average monthly meteorological data for month m of a historical extreme weather year; Δx m For the predicted changes in future meteorological data for month m; a m The stretching factor for future meteorological data in month m.

[0057] Step 3: Based on the actual building types to be constructed in the area, use EnergyPlus to create different typical buildings, import the EPW file of future extreme climates into EnergyPlus for simulation to obtain the load of each typical building; then, based on the building types and areas to be constructed in each planning stage, calculate the total regional load for each planning stage according to the following formula.

[0058]

[0059] In the formula, This represents the total regional load in the nth planning phase under future extreme climate conditions; n is the planning phase; d is the number of days; t is the time sequence number; k is the type of load, including cooling load, heating load, and electrical load. The load of a typical building of type b in the nth planning stage under future extreme climate conditions; A represents the area of ​​a typical building of type b in the nth planning phase; n,b denoted as the total area of ​​type b buildings within the area of ​​the nth planning phase; b represents the building type, including office buildings, commercial buildings, residential buildings, educational buildings, etc.

[0060] Step 4: Assess the current resource status within the region, determine the available resources and equipment types in the regional integrated energy system, and establish mathematical models for the corresponding equipment; then determine the changes in electricity prices, gas prices, carbon trading prices, and demand response compensation prices at different planning stages; such as Figure 5 As shown, common equipment types in regional integrated energy systems include photovoltaic (PV) systems, wind turbines (WT), gas turbines (GT), heat pumps (HP), electric chillers (EC), absorption chillers (AC), gas boilers (GB), electric boilers (EB), power-to-gas (P2G) systems, carbon capture systems (CCS), batteries (EES), thermal energy storage systems (HES), cold energy storage systems (CES), and gas storage systems (NGS).

[0061] (1) Photovoltaic system

[0062]

[0063] In the formula, P PV (t) represents the power generation of the photovoltaic system; X PV I(t) represents the rated capacity of the photovoltaic system; I(t) represents the solar radiation intensity under future extreme weather conditions; I STC Solar radiation intensity under standard test conditions; α T T is the temperature coefficient; T(t) is the dry-bulb temperature under future extreme climate conditions; T STC This refers to the dry-bulb temperature under standard test conditions.

[0064] (2) Wind turbine

[0065]

[0066] In the formula, P WT (t) represents the power generation of the wind turbine; X WT v is the rated capacity of the wind turbine; v(t) is the wind speed under future extreme weather conditions; v ci v r v co These are the cut-in wind speed, rated wind speed, and cut-out wind speed, respectively.

[0067] (3) Gas turbine

[0068]

[0069]

[0070] In the formula, P GT (t), These represent the power generation and heating capacity of the gas turbine, respectively; V GT (t) represents the amount of natural gas consumed by the gas turbine; η GT LHV is the power generation efficiency of a gas turbine. gas The lower heating value of natural gas; η l This represents the energy loss coefficient of the gas turbine.

[0071] (4) Heat pump

[0072]

[0073]

[0074] In the formula, These are the heating and cooling capacities of the heat pump, respectively; P HP (t) represents the electrical power consumed by the heat pump; COP h COP c These are the coefficients of performance for heating and cooling, respectively.

[0075] (5) Electric refrigeration unit

[0076]

[0077] In the formula, The cooling capacity of the electric chiller; COP EC P is the coefficient of performance for an electric chiller. EC (t) represents the power consumption of the electric chiller.

[0078] (6) Absorption chiller

[0079]

[0080] In the formula, The cooling capacity of an absorption chiller; COP AC The coefficient of performance (COP) of an absorption chiller; This refers to the heat consumption power of the electric chiller.

[0081] (7) Gas-fired boiler

[0082]

[0083] In the formula, η is the heating capacity of the gas-fired boiler. GB V represents the heating efficiency of a gas-fired boiler. GB (t) represents the natural gas consumption of the gas-fired boiler.

[0084] (8) Electric boiler

[0085]

[0086] In the formula, η is the heating capacity of the electric boiler. EB The heating efficiency of the electric boiler; P EB (t) represents the power consumption of the electric boiler.

[0087] (9) Carbon capture equipment

[0088] Carbon capture equipment can recover carbon dioxide generated by equipment such as gas turbines or boilers.

[0089] P CCS (t)=η CCS M CCS (t)

[0090] In the formula, M CCS (t) represents the amount of carbon dioxide captured by the carbon capture device; P CCS (t) represents the power consumption of the carbon capture device; η CCS The amount of electricity consumed by a carbon capture device to capture carbon dioxide.

[0091] (10) Electric-to-gas conversion equipment

[0092] The power-to-gas (PTO) equipment can use electricity to electrolyze water to produce hydrogen, and then use carbon dioxide captured from carbon capture equipment to react with hydrogen to produce methane, which can be used by the system as natural gas.

[0093] M CCS (t)=ζP P2G (t)

[0094] V P2G (t)=η P2G P P2G (t)

[0095] In the formula, P P2G (t) represents the power consumption of the electro-gas conversion equipment; ζ is the calculation coefficient for carbon dioxide; V P2G (t) represents the amount of natural gas produced by the power-to-gas conversion equipment; η P2G The efficiency of power-to-gas conversion equipment for producing natural gas;

[0096] Step 5: Establish a multi-stage planning model, determine the decision variables, objective function, and constraints of the planning. The structure of this model is as follows: Figure 6 As shown.

[0097] Step 5-1: Decision variables include planning variables and operational variables. Planning variables include the planned capacity of each device. Operational variables include the hourly operating power of each device, the hourly interaction power with the power grid, and the gas purchase power from the gas grid.

[0098] Step 5-2: Planning with the objective of minimizing total cost, the objective function is total cost C. tot Including equipment investment cost C inv Carbon trading costs C car Purchase and sale energy costs C pse Demand response cost C idr Equipment maintenance cost C mai .

[0099] C tot =C inv +C pse +C mai +C car +C idr

[0100]

[0101]

[0102]

[0103]

[0104]

[0105] In the formula, N is the total number of planning stages; c i X represents the purchase price per unit capacity of the i-th type of equipment; i,n Let be the planned capacity of the i-th type of equipment; r be the discount rate; y be the planned capacity of the i-th type of equipment. n Y represents the initial year of the nth planning phase; Y represents the total number of years in the planning period; N represents the initial year of the nth planning phase. * This indicates the planning stage in year y; These represent the electricity purchase price, electricity sales price, and gas purchase price, respectively; y represents the number of years; P ele,y,d (t), P sel,y,d (t) represent the system's purchased power and sold power, respectively; V gas,y,d (t) represents the system's gas purchase volume; c m,i Maintenance cost per unit output power of equipment i; P i,y,d (t) represents the output power of device i; For carbon trading prices; ω e ω h φ1 and φ2 are the carbon emission allowances per unit of electricity and per unit of heat, respectively; ψ is the conversion coefficient for converting electricity generation into heat supply; φ1 and φ2 are the carbon emission coefficients for coal-fired and gas-fired power units, respectively; P GT,y,d (t) represents the power generation capacity of the gas turbine; This refers to the heating capacity of the gas turbine. M is the heating power of the gas-fired boiler at time t; CCS,y,d (t) represents the amount of carbon dioxide captured by the carbon capture device; These are the compensation prices for load reduction and load cut, respectively; τ down τ cut These represent the percentages of load reduction and load cut, respectively. The total regional load at time t on day d of year y under future extreme climate conditions is given, and the hourly load is equal every year within the same planning phase.

[0106] Step 5-3: Constraints include demand response constraints, energy balance constraints, equipment capacity constraints, equipment operating power constraints, equipment operating ramp constraints, and energy storage equipment operating constraints.

[0107] (a) Demand response constraints

[0108]

[0109]

[0110] τ down ≤τ down,max

[0111] τ up ≤τ up,max

[0112] τ cut ≤τ cut,max

[0113] In the formula, For loads under extreme weather conditions after demand response; τ up τ represents the percentage increase in load; down,max ;τ up,max ;τcut,max These are the limits for the percentage reduction, increase, and cut of load, respectively.

[0114] (b) Energy balance constraints

[0115] The hourly electricity, heat, cooling and natural gas outputs are kept in balance each year during each planning phase.

[0116] Power balance:

[0117]

[0118] In the formula, P PV,y,d (t) represents the photovoltaic power generation; P WT,y,d (t) represents the power generation capacity of the wind turbine; P GT,y,d This refers to the power generation capacity of the gas turbine. This refers to the battery's discharge power. For future electricity load under extreme weather conditions after demand response; P HP,y,d (t) represents the power consumption of the heat pump; P EC,y,d (t) represents the power consumption of the electric chiller; P EB,y,d (t) represents the power consumption of the electric heat pump; P CCS,y,d (t) represents the power consumption of the carbon capture device; P P2G,y,d (t) represents the power consumption of the electro-gas conversion equipment; The charging power of the battery.

[0119] Thermal power balance:

[0120]

[0121] In the formula, This refers to the heating capacity of the heat pump; This refers to the heating capacity of the boiler. This refers to the heating capacity of the gas turbine. This refers to the heating capacity of the electric boiler. The heat release power of the thermal storage device; This refers to the heat load under extreme weather conditions after demand response; This refers to the heat consumption power of the absorption chiller; This refers to the thermal storage capacity of the thermal storage equipment.

[0122] Cold power balance:

[0123]

[0124] In the formula, This refers to the cooling capacity of the heat pump; This refers to the cooling capacity of the absorption chiller. The cooling capacity of the electric chiller; The cooling capacity of the cold storage equipment; This refers to the cooling load under extreme weather conditions after demand response; This refers to the cooling capacity of the cold storage equipment.

[0125] Natural gas balance:

[0126]

[0127] In the formula, V GB,y,d (t) represents the amount of natural gas consumed by the gas-fired boiler; V GT,y,d (t) represents the amount of natural gas consumed by the gas turbine; The amount of natural gas released from the gas storage facility; V gas,y,d (t) represents the amount of natural gas purchased from the gas network; V P2G,y,d (t) represents the amount of natural gas produced by the power-to-gas conversion equipment; The amount of natural gas stored in the gas storage facility.

[0128] (c) Equipment capacity constraints

[0129]

[0130] In the formula, X i,n X represents the planned capacity of device i in the nth planning phase; i,min X i,max These represent the minimum and maximum planned capacity of the equipment, respectively.

[0131] (d) Output power constraints of equipment operation

[0132]

[0133] (e) Climbing constraints for equipment operation

[0134]

[0135] In the formula, θ i Let be the ratio of the ramping power of device i to the device capacity.

[0136] (f) Operational constraints of energy storage devices

[0137]

[0138]

[0139]

[0140]

[0141]

[0142]

[0143] In the formula, S represents the type of energy storage device, including batteries, thermal storage devices, cold storage devices, and gas storage devices; These represent the energy storage capacities of the energy storage devices at time t+1 and time t, respectively; a ES The self-loss rate of the energy storage device; These refer to the charging efficiency and discharging efficiency of energy storage devices, respectively. These refer to the charging power and discharging power of the energy storage device, respectively. These represent the energy storage capacities of the energy storage devices at 1 hour and 24 hours of the day, respectively; S min S max These represent the minimum and maximum charging states of the energy storage device, respectively. These represent the charging rate and the energy storage rate of the energy storage device, respectively; X S,n Let S be the planned capacity of energy storage device S in the nth planning phase; It is a binary variable representing the charging and discharging state.

[0144] Step 6: Use the Gurobi solver to solve the multi-stage programming model to obtain the final solution.

[0145] Example 2

[0146] The purpose of this embodiment is to provide a multi-stage planning system for regional integrated energy systems under future extreme climate conditions, including:

[0147] Module division: The overall planning period is divided into different sub-planning phases based on the actual construction and usage sequence of buildings within the area;

[0148] Climate Prediction Module: Obtains future extreme weather forecast data for different time periods based on historical extreme weather data, and calculates the total load of the planning area for each sub-planning stage based on the future extreme weather forecast data for different time periods and the type and area of ​​buildings within the sub-planning stages for each time period;

[0149] Objective function establishment module: The objective function is established with the goal of minimizing the total operating cost of the sub-planning stage of the integrated energy system within the region;

[0150] Condition setting module: Sets constraints, including system demand-side response constraints within the sub-planning phase, and power balance constraints for integrated energy equipment determined based on the total regional load under future extreme climate conditions; the integrated energy equipment includes carbon capture equipment and power-to-gas conversion equipment;

[0151] Planning and solving module: Solve the objective function under the determined constraints to obtain the optimal planning results of the integrated energy system in the region at different sub-planning stages.

[0152] Example 3

[0153] The purpose of this embodiment is to provide a computing device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the above-described method.

[0154] Example 4

[0155] The purpose of this embodiment is to provide a computer-readable storage medium.

[0156] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, performs the steps of the above method.

[0157] The steps and methods involved in the apparatuses of Embodiments 2, 3, and 4 above correspond to those in Embodiment 1. For specific implementation details, please refer to the relevant description section of Embodiment 1. The term "computer-readable storage medium" should be understood as a single medium or multiple media including one or more instruction sets; it should also be understood as including any medium capable of storing, encoding, or carrying an instruction set for execution by a processor and enabling the processor to perform any of the methods in this invention.

[0158] Those skilled in the art will understand that the modules or steps of the present invention described above can be implemented using general-purpose computer devices. Optionally, they can be implemented using computer-executable program code, thereby allowing them to be stored in a storage device for execution by a computer device, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. The present invention is not limited to any particular combination of hardware and software.

[0159] While the specific embodiments of the present invention have been described above in conjunction with the accompanying drawings, this 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 without creative effort based on the technical solutions of the present invention are still within the scope of protection of the present invention.

Claims

1. A multi-stage planning method for regional integrated energy systems under future extreme climate conditions, characterized by: include: The overall planning period is divided into different sub-planning phases based on the actual construction and usage sequence of buildings within the area; Predictions of future extreme weather events for different time periods are obtained based on historical extreme weather data. Historical extreme year meteorological data were selected based on actual historical meteorological data, specifically: Different weights were assigned to each parameter: dry-bulb temperature, dew point temperature, and solar radiation intensity. Calculate the monthly average value of each parameter for each year and each month over a historical period; Calculate the cumulative annual average and standard deviation of each of the above parameters for each month over a historical period; The monthly average values ​​of the above parameters are standardized using z-score. The standardized results of each parameter are summed with their corresponding weights using a weighted average. Meteorological data for extreme months were selected based on the weighted summation results to form historical extreme year meteorological data; The total load of the planning area in the sub-planning stage of each time period is calculated based on the future extreme weather forecast data for different time periods and the type and area of ​​buildings in the sub-planning stage of each time period. An objective function is established with the goal of minimizing the total operating cost of the sub-planning phase of the integrated energy system within the region. The constraints include system demand-side response constraints within the sub-planning phase, and power balance constraints for integrated energy equipment determined based on the total load of the planning area under future extreme climate conditions; the integrated energy equipment includes carbon capture equipment and power-to-gas conversion equipment. The power balance constraints include electrical power balance constraints, thermal power balance constraints, cooling power balance constraints, and natural gas balance constraints; wherein, the electrical power balance constraints satisfy the following: the sum of the power generation of photovoltaics, the power generation of wind turbines, the power generation of gas turbines, the discharge power of batteries, and the power purchased by the system is equal to the sum of the future electrical load under extreme weather conditions after demand response, the power sold by the system, the power consumption of heat pumps, the power consumption of electric chillers, the power consumption of electric heat pumps, the power consumption of carbon capture equipment, the power consumption of electric-to-gas equipment, and the charging power of batteries; The heat power balance constraint satisfies the following: the sum of the heating power of the heat pump, the heating power of the boiler, the heating power of the gas turbine, the heating power of the electric boiler, and the heat release power of the thermal storage equipment is equal to the sum of the heat load under extreme climate conditions after demand response, the heat consumption power of the absorption chiller, and the heat storage power of the thermal storage equipment. The cooling power balance constraint satisfies the following: the sum of the cooling power of the heat pump, the cooling power of the absorption chiller, the cooling power of the electric chiller, and the cooling power of the cold storage equipment is equal to the sum of the cooling load under future extreme climate conditions and the cold storage power of the cold storage equipment after demand response. The natural gas balance constraint satisfies the following: the sum of the amount of natural gas consumed by the gas boiler, the amount of natural gas consumed by the gas turbine, and the amount of natural gas released by the gas storage equipment is equal to the sum of the amount of natural gas purchased from the gas network, the amount of natural gas produced by the power-to-gas conversion equipment, and the amount of natural gas stored in the gas storage equipment. The objective function is solved under the determined constraints to obtain the optimal planning results of the integrated energy system in different sub-planning stages within the region.

2. The multi-stage planning method for regional integrated energy systems under future extreme climate conditions as described in claim 1, characterized in that, Also includes: Based on selected historical extreme year meteorological data and future weather monthly-scale prediction data generated by global climate models, the deformation method is used to generate future extreme weather data for different time periods of sub-planning stages that can be used for building performance simulation.

3. The multi-stage planning method for regional integrated energy systems under future extreme climate conditions as described in claim 1, characterized in that, The total operating cost of the sub-planning phase includes equipment investment cost, carbon trading cost, energy purchase and sale cost, demand response cost, and equipment maintenance cost.

4. The multi-stage planning method for regional integrated energy systems under future extreme climate conditions as described in claim 1, characterized in that, The constraints also include equipment capacity constraints, equipment operating power constraints, equipment operating ramp constraints, and energy storage equipment operating constraints.

5. The multi-stage planning method for regional integrated energy systems under future extreme climate conditions as described in claim 2, characterized in that, Based on the actual building types in the region, different typical buildings are created using EnergyPlus. EPW files of future extreme weather data are imported into EnergyPlus for load simulation. The total regional load for each sub-planning phase is calculated based on the building types and areas constructed in each sub-planning phase.

6. A multi-stage planning system for regional integrated energy systems under future extreme climate conditions, characterized in that: include: Module division: The overall planning period is divided into different sub-planning phases based on the actual construction and usage sequence of buildings within the area; Climate Prediction Module: Obtains future extreme weather forecasts for different time periods based on historical extreme weather data; Historical extreme year meteorological data were selected based on actual historical meteorological data, specifically: Different weights were assigned to each parameter: dry-bulb temperature, dew point temperature, and solar radiation intensity. Calculate the monthly average value of each parameter for each year and each month over a historical period; Calculate the cumulative annual average and standard deviation of each of the above parameters for each month over a historical period; The monthly average values ​​of the above parameters are standardized using z-score. The standardized results of each parameter are summed with their corresponding weights using a weighted average. Meteorological data for extreme months were selected based on the weighted summation results to form historical extreme year meteorological data; The total load of the planning area for each sub-planning stage is calculated based on the future extreme weather forecast data for different time periods and the types and areas of buildings within the sub-planning stages for each time period. Objective function establishment module: The objective function is established with the goal of minimizing the total operating cost of the sub-planning stage of the integrated energy system within the region; Condition setting module: Sets constraints, including system demand-side response constraints within the sub-planning phase, and power balance constraints of integrated energy equipment determined based on the total load of the planning area under future extreme climate conditions; the integrated energy equipment includes carbon capture equipment and power-to-gas conversion equipment; The power balance constraints include electrical power balance constraints, thermal power balance constraints, cooling power balance constraints, and natural gas balance constraints; wherein, the electrical power balance constraints satisfy the following: the sum of the power generation of photovoltaics, the power generation of wind turbines, the power generation of gas turbines, the discharge power of batteries, and the power purchased by the system is equal to the sum of the future electrical load under extreme weather conditions after demand response, the power sold by the system, the power consumption of heat pumps, the power consumption of electric chillers, the power consumption of electric heat pumps, the power consumption of carbon capture equipment, the power consumption of electric-to-gas equipment, and the charging power of batteries; The heat power balance constraint satisfies the following: the sum of the heating power of the heat pump, the heating power of the boiler, the heating power of the gas turbine, the heating power of the electric boiler, and the heat release power of the thermal storage equipment is equal to the sum of the heat load under extreme climate conditions after demand response, the heat consumption power of the absorption chiller, and the heat storage power of the thermal storage equipment. The cooling power balance constraint satisfies the following: the sum of the cooling power of the heat pump, the cooling power of the absorption chiller, the cooling power of the electric chiller, and the cooling power of the cold storage equipment is equal to the sum of the cooling load under future extreme climate conditions and the cold storage power of the cold storage equipment after demand response. The natural gas balance constraint satisfies the following: the sum of the amount of natural gas consumed by the gas boiler, the amount of natural gas consumed by the gas turbine, and the amount of natural gas released by the gas storage equipment is equal to the sum of the amount of natural gas purchased from the gas network, the amount of natural gas produced by the power-to-gas conversion equipment, and the amount of natural gas stored in the gas storage equipment. Planning and solving module: Solve the objective function under the determined constraints to obtain the optimal planning results of the integrated energy system in the region at different sub-planning stages.

7. A computer device, characterized in that, include: The computer device includes a processor, a memory, and a bus. The memory stores machine-readable instructions executable by the processor. When the computer device is running, the processor communicates with the memory via the bus. When the machine-readable instructions are executed by the processor, they perform the steps of the multi-stage planning method for regional integrated energy systems under future extreme climate conditions as described in any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, performs the steps of the multi-stage planning method for regional integrated energy systems under future extreme climate conditions as described in any one of claims 1 to 5.

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