A method for coordinated planning of source, load and storage of multiple energy stations in a regional integrated energy system

Abstract

This invention relates to the field of integrated energy system planning, and more particularly to a method for coordinated planning of multiple energy stations, sources, loads, and storage in a regional integrated energy system. The proposed planning method first utilizes a CCP system to convert excess electricity into H2 via electricity-to-gas conversion and synthesize CH4 from CO2 captured by carbon capture and storage, then transports it to a natural gas pipeline. Second, it constructs the framework for coordinated planning of multiple energy stations, sources, loads, and storage in the regional integrated energy system. Then, it constructs the objective function and constraints of the planning framework considering both normal and fault operation scenarios. Third, it uses piecewise linearization and the Big-M method to linearize the nonlinear terms in the planning model. Finally, considering the impact of source-load uncertainty on the planning scheme, it employs a data-driven two-stage partial Bruker optimization algorithm and the CCG algorithm to solve the problem. This method can achieve a balance between the economy and reliability of the planning scheme, reduce carbon emissions, and simultaneously increase PV installed capacity.

Description

Technical Field

[0001] This invention relates to a method for coordinated planning of multiple energy stations, sources, loads, and storage in a regional integrated energy system. It is applicable to the optimization planning problem of regional integrated energy systems and belongs to the field of integrated energy systems. Background Technology

[0002] To alleviate the energy crisis and mitigate climate change, new power systems with high-proportion renewable energy integration and integrated energy systems with multi-energy coupling have been proposed. Traditional integrated energy system planning frameworks are no longer sufficient to support the construction of integrated energy stations. Specifically, on the source side, the uncertainty and volatility of massive distributed renewable energy output will affect the reliability of system operation, and existing planning frameworks and technologies cannot adequately address this issue. On the load side, with the widespread application of terminal electrification equipment, user-side load demand is diversifying, and the integration of diverse loads adds new burdens to the reliable operation of integrated energy systems. Since user-side demand response resources have a certain degree of flexibility, reasonable improvement measures can be taken to fully exploit the adjustability of flexible resources, which can greatly improve the reliability and economy of integrated energy systems, while simultaneously increasing renewable energy absorption capacity and reducing carbon emissions. However, existing regional integrated energy system planning frameworks cannot account for the impact of source-load uncertainty on the reliability and carbon emissions of planning schemes.

[0003] To address the aforementioned issues, scholars both domestically and internationally have conducted extensive research on the coordinated planning and optimized operation strategies of source-grid-load-storage systems for regional integrated energy systems. Some scholars have proposed using energy storage systems and demand response resources to improve system reliability and have utilized load-storage flexibility to reduce the impact of uncertainties in distributed power generation output. Meanwhile, some researchers have proposed using electricity-to-gas conversion and carbon capture devices to reduce the high carbon emissions of integrated energy systems. Analysis of these studies reveals that current research on the impact of integrated demand response and low-carbon emission facilities on the reliability and carbon emissions of regional integrated energy systems is largely limited to operation strategies, failing to fully consider the coordinated planning of distributed power generation, integrated demand response, multiple types of energy storage, and energy conversion facilities within integrated energy stations. Furthermore, current research has not adequately considered the impact of source-load uncertainty on the source-load-storage planning scheme of regional integrated energy systems, which is detrimental to reducing system construction costs. Therefore, based on existing research, this invention proposes a coordinated planning method for source-load-storage systems of multiple energy stations in regional integrated energy systems that considers source-load uncertainty and reliability, achieving the construction requirements for high reliability and low carbon emissions of multiple regional integrated energy stations. Summary of the Invention

[0004] In order to overcome the shortcomings and deficiencies of the existing technology, the purpose of this invention patent is to propose a multi-energy station source-load-storage collaborative planning method for a domain integrated energy system;

[0005] This invention provides a method for coordinated planning of multiple energy stations, sources, loads, and storage in a regional integrated energy system, comprising the following specific steps:

[0006] (1) Establish a regional integrated energy system with a multi-energy station energy supply architecture, including an energy supply network, energy exchange modules, energy storage modules, energy supply units, and end users; wherein the energy supply network includes the distribution network and the gas distribution network; the energy exchange modules include combined cooling, heating and power (CCHP) devices, carbon capture, power-to-gas conversion, electric boilers, gas boilers, electric refrigeration and other equipment; the energy storage modules include various types of energy storage systems, such as electric energy storage, thermal energy storage and cold energy storage; the energy supply units include PV; and the end users include rigid loads of electricity / cooling / heating and flexible loads, wherein the flexible loads are considered as loads that can be reduced and transferred.

[0007] (2) Construct a planning architecture for a collaborative planning method for multiple energy stations in a regional integrated energy system comprising distributed power generation (PV), multiple types of energy storage, and diverse loads. The planning architecture mainly includes four stages: parameter input, planning, operation, and output. The main parameters in the parameter input stage include model parameters such as energy supply network, energy exchange module, energy storage module, energy supply unit, and end users. The planning stage mainly plans the capacity of PV, integrated demand response for electricity / cooling / heating, multiple types of energy storage, and energy conversion equipment. The operation stage analyzes the planning schemes from the planning stage under normal and general fault scenarios to obtain the output and operating status of each device and the overall operating cost. The output stage obtains the optimal planning scheme for the energy station by comparing and analyzing the costs under different configuration schemes.

[0008] (3) Establish the objective function and constraints of the planning framework for the collaborative planning method of multi-energy station source-load-storage in regional integrated energy system. The objective function mainly includes three modules: construction cost, operating cost under normal scenario and reliability conversion cost under general failure scenario. The constraints include equality constraints and inequality constraints for safety optimization control.

[0009] (4) To improve the calculation speed, a piecewise linearization method is used to linearize the calculation of carbon emissions of the combined cooling, heating and power plant, and the Big-M method is used to linearize nonlinear terms such as reliability calculation, energy storage charging and discharging status, and energy coordination status between energy stations.

[0010] (5) The multi-energy station energy supply architecture, objective function and constraints of the regional integrated energy system are combined. Considering the impact of source load uncertainty on the planning scheme, based on historical data, the probability distribution curves and fuzzy sets of PV output and load characteristics are obtained by using the k-means clustering algorithm. The model is solved by the data-driven two-stage sub-Bruker optimization algorithm and CCG algorithm, and the best planning scheme is obtained.

[0011] The regional integrated energy system multi-energy station energy supply architecture, which includes an energy supply network, energy exchange module, energy storage module, energy supply unit, and end users, is established according to step (1), as follows:

[0012] (1) The multi-energy station energy supply architecture of the regional integrated energy system mainly consists of an energy supply network, regional integrated energy stations and end users. The energy supply network includes a distribution network and a gas distribution network, which provide electricity and natural gas to the energy stations. The energy stations purchase energy from the energy supply network and provide electricity, cooling and heating to end users through energy conversion equipment, serving as the energy hub of the regional integrated energy system. End users interact with the energy supply side of the energy stations by participating in demand response.

[0013] (2) The energy stations in the regional integrated energy system mainly consist of energy exchange modules, energy storage modules, and energy supply units. Among them, the energy exchange modules include combined cooling, heating, and power (CCHP) devices, carbon capture, electricity-to-gas conversion, electric boilers, gas boilers, and electric refrigeration equipment; the energy storage modules include multiple types of energy storage systems for electricity, cooling, and heating; and the energy supply units mainly include distributed power generation (PV). Integrated energy stations can exchange electrical energy through the power distribution network. When a system failure causes an energy station to be unable to meet load demand, it can be supported by energy from other energy stations to reduce energy shortages and improve the reliability of energy supply. Considering the significant losses in long-distance pipeline transmission of cooling and heating networks, each integrated energy station only supplies energy to cooling / heating users within its jurisdiction. That is, the region can form multi-source interconnection through pipelines, but it is not interconnected with the cooling / heating pipelines of other integrated energy station jurisdictions.

[0014] (3) The end-user load of the regional integrated energy system is mainly divided into rigid load and flexible load. Rigid load is an unreducible load. When the system fails and the rigid load is short-supplyed, the user must be compensated for the energy shortage, which is the reliability discount cost. Flexible load is mainly composed of reduceable load and transferable load. Users can obtain certain economic compensation by participating in system scheduling according to their wishes.

[0015] Based on the planning framework of the regional integrated energy system multi-energy station collaborative planning method for distributed power generation (PV), multiple types of energy storage, and diverse loads constructed in step (2), the proposed module models within the energy station, such as energy exchange modules, energy storage modules, and integrated demand response of end users, are established:

[0016] (1) Model of a combined cooling, heating and power (CCHP) unit containing carbon capture and electricity-to-gas conversion:

[0017] Traditional combined cooling, heating and power (CCHP) units mainly consist of gas turbines, waste heat recovery devices, and absorption chillers. The mathematical model is as follows:

[0018]

[0019] in, This refers to the waste heat from the exhaust of a combined cooling, heating, and power (CCHP) unit, which is also the energy input into the waste heat recovery system. It is the electrical power output of the combined cooling, heating and power (CCHP) unit; The efficiency of the gas turbine is set at 75%. The heat dissipation coefficient of the combined cooling, heating and power (CCHP) unit is set at 15%. and These refer to the heating or cooling capacity provided by the waste heat from the gas turbine; The efficiency of flue gas waste heat recovery; and The heating coefficient or cooling coefficient of the bromide cooler is set to 1.2 and 0.95, respectively; T1 and T2 are environmental coefficients, set to 573.15K and 423.25K, respectively. This represents the amount of natural gas consumed by the combined cooling, heating, and power (CCHP) unit; L is the lower calorific value of natural gas, set at 32.97 kW·h / m³. 3 Δt represents the unit running time;

[0020] To reduce carbon emissions, traditional combined cooling, heating, and power (CCHP) units are being converted into CCHP systems incorporating electricity-to-gas conversion and carbon capture. The mathematical model is as follows:

[0021]

[0022] in, The electricity supplied to the energy station load or to the upper-level distribution network for the combined cooling, heating and power (CCHP) unit. The amount of electricity consumed by the power-to-gas conversion unit to produce gas. The amount of electricity consumed by the carbon capture device to capture CO2. The amount of CO2 generated by the combustion gas consumed in the combined cooling, heating and power (CCHP) unit. The heat power provided by the gas turbine to the waste heat recovery unit. These are the energy conversion efficiency and coefficient for electricity-to-gas conversion and carbon capture, respectively. and The CO2 coefficient generated by the combined cooling, heating and power (CCHP) unit;

[0023] (2) Models of electric boilers, electric refrigeration systems and gas boilers:

[0024]

[0025] in, The heat output of electric boilers and gas boilers, respectively. The cooling power generated by the electric chiller. The electrical power consumed by the electric boiler. The electrical power consumed by the electric chiller. This refers to the amount of natural gas consumed by the gas-fired boiler. This refers to the amount of CO2 produced by the gas-fired boiler. The energy conversion efficiencies are for electric boilers, electric chillers, and gas boilers, respectively. This is the coefficient for CO2 produced by the gas-fired boiler;

[0026] (3) Multiple types of energy storage models:

[0027] This invention mainly considers three types of energy storage systems: electric, cold, and hot. Since the charging and discharging characteristics of different types of energy storage are similar, a unified mathematical model is adopted here, as follows:

[0028]

[0029] Where n represents the type of electrical / cold / heat energy; These represent the charging and discharging states of the energy storage system for energy n. Indicates the charging status of the energy storage system. Indicates the discharge state of the energy storage system; These are the charging and discharging power of the energy storage system for energy n, respectively. Let be the minimum and maximum charging power of the energy storage system for energy n, respectively. These are the minimum and maximum discharge power of the energy storage system for energy n, respectively. These are the remaining capacity of the energy storage system n, the minimum remaining capacity, the maximum remaining capacity, the remaining capacity at the end of the scheduling period, and the remaining capacity at the beginning of the scheduling period, respectively. These are the charging and discharging efficiencies of the energy storage system (n), respectively.

[0030] (4) Integrated demand response model:

[0031] This demand response primarily considers transferable and reduceable loads. Transferable loads can adjust demand periods based on energy prices to reduce energy purchase costs. The model is as follows:

[0032]

[0033] in, This represents the proportion of transferable load to user i's total load; This represents the amount of transferable load for user i before and after the transfer at time t; Indicates the proportion of transferable load transferred in / out; This represents the total transferable load of load n at time t;

[0034] Reduceable loads refer to loads whose demand is not high and cannot be directly interrupted due to external factors, and are expressed as:

[0035]

[0036] in, This indicates the proportion of the total load that can be reduced relative to user i; This represents the amount of load that user i can reduce at time t; This indicates that user i is in a state of load reduction; This indicates that the load n can always be reduced by the amount of load at time t;

[0037] The objective function and constraints of the planning framework for the regional integrated energy system multi-energy station source-load-storage coordinated planning method described in step (3) specifically include:

[0038] (1) Objective function of planning architecture:

[0039] 1) Overall objective function of the planning architecture:

[0040] Planning layer costs include three modules: construction costs, fixed maintenance costs, and comprehensive demand response planning capacity costs. Operation layer costs include operating costs under normal scenarios and reliability costs for load reduction under fault scenarios. The functions are as follows:

[0041]

[0042] Among them, C INV For construction costs, Fixed maintenance costs for equipment, To comprehensively consider the cost of demand response capacity planning, C OPE For normal operating costs, C REL The reliability cost of load reduction under typical failure scenarios;

[0043] 2) Objective function of the planning layer:

[0044]

[0045] Among them, y k The lifecycle of device k is represented by a (years); d represents the annual discount rate. The construction cost per unit capacity of equipment k; Build capacity for equipment k; This refers to the fixed maintenance cost coefficient for the equipment. Planning cost per unit capacity for demand response to load n; Plan capacity for demand response to load n;

[0046] 3) Runtime layer objective function:

[0047] ① Objective function under normal runtime scenario:

[0048] Under normal circumstances, operating costs include the cost of buying and selling energy between the energy station and the upstream energy supply network, equipment operation and maintenance costs, comprehensive demand response power dispatch costs, and carbon emission costs, as follows:

[0049]

[0050] in, The cost of buying and selling electricity between energy stations and distribution networks; The cost of buying and selling natural gas at energy stations and natural gas networks; For equipment operation and maintenance costs; To comprehensively consider the power scheduling cost of demand response; For carbon emission costs; p s The probability represents a discrete scene, and its initial value can be obtained using the k-means clustering method. These are the electricity purchase and sale prices for energy stations and distribution networks, respectively. These refer to the electricity traded between energy stations and distribution networks; These are the gas prices for energy stations and natural gas networks, respectively. These refer to the gas trading volume at energy stations and through natural gas networks. The unit of operation and maintenance cost for equipment k varies; Output power to equipment k; The cost per unit power for demand response to load n; The required response power for the scheduled load n; Cost per unit of CO2 emission, expressed in yuan / ton; These are the CO2 emissions from combined cooling, heating and power (CCHP) units and gas-fired boilers, respectively, in tons. This refers to the amount of CO2 consumed during the electricity-to-gas conversion process. These represent the electrical power generated by the PV and combined cooling, heating and power (CCHP) units, respectively. For carbon quotas;

[0051] ② Reliability-based cost of load reduction under normal fault conditions:

[0052] This patent selects the LOEE (Low Energy Expectation) as an indicator for calculating the reliability of energy stations, using the following formula:

[0053]

[0054] in, λ represents the expected energy deficit for load n at energy station j, expressed in MW·h / a. k For equipment failure rate k; ΔT k For device k, the duration of the failure. The formula for calculating the energy shortage in load n caused by a fault in equipment k at energy station j is as follows:

[0055]

[0056] in, This represents the load demand n in energy station j; The total demand response power of load n in energy station j; The power exchange between energy station j and the energy supply network; The power that generates n energy for device k*; Let n be the net charging and discharging power of the energy storage system n; The n-energy collaborative power between energy station j* and energy station j;

[0057] and The specific calculation formula is as follows:

[0058]

[0059] in, The coordinated state of energy station j providing n energy to energy station j* is represented by a value of 1, indicating that power flows from energy station j to energy station j*. The coordinated state of energy station j* providing n energy to energy station j is represented by a value of 1, indicating that power flows from energy station j* to energy station j. These represent the coordinated power of n energy sources between energy station j and energy station j*, respectively.

[0060] Under normal fault conditions, the reliability cost of load reduction is calculated as follows:

[0061]

[0062] in, The unit loss cost for load n at energy station j;

[0063] (2) Constraints on the planning framework, including equality constraints and inequality constraints:

[0064] 1) Planning-level constraints:

[0065] The planning layer constraints in the two-level planning model mainly include the maximum planning capacity constraints for distributed power generation (PV), energy conversion equipment, various types of energy storage systems, and comprehensive demand response in the energy station, as follows:

[0066]

[0067] in, These respectively represent the planned capacity of equipment, multiple types of energy storage systems, transferable loads, and loads that can be reduced; These represent the maximum planned capacity of equipment, multiple types of energy storage systems, transferable loads, and loads that can be reduced, respectively.

[0068] 2) Runtime layer constraints:

[0069] The constraints at the energy station operation level mainly include power balance constraints, equipment output constraints, multi-type energy storage operation constraints, integrated demand response scheduling power constraints, and power constraints for interaction between the energy station and the upstream energy supply network, as shown below:

[0070] ① Power balance constraints

[0071] Power balance constraints mainly include electrical, gas, cooling, and heating power balance constraints:

[0072]

[0073] ② Equipment output constraints:

[0074] The output power of the units and equipment is limited by capacity constraints:

[0075]

[0076] in, These represent the minimum and maximum proportions of the unit's output relative to the planned capacity, respectively. To provide power to equipment k in energy station j;

[0077] In addition to being constrained by planned capacity, various types of energy storage systems are also constrained by their own models;

[0078] ③Comprehensive demand response constraints:

[0079] In addition to being constrained by its own model operation, the overall demand response is also constrained by the planned capacity:

[0080]

[0081] in, These represent the minimum and maximum proportions of the unit's output relative to the planned capacity, respectively. To provide power to equipment k in energy station j;

[0082] ④ Constraints on the power exchange between the energy station and the upstream energy supply network:

[0083] The power exchange between the energy station and the upstream energy supply network is mainly constrained by the capacity of the power distribution lines and natural gas pipelines connected to the energy station.

[0084]

[0085] in, The maximum transmission capacity of the power distribution lines and natural gas pipelines connected to the energy station;

[0086] According to step (4), the calculation of carbon emissions from the combined cooling, heating and power (CCHP) unit is linearized using a piecewise linearization method. The Big-M method is used to linearize nonlinear terms such as reliability calculations, energy storage charging and discharging states, and energy coordination between energy stations. Specifically, this includes:

[0087] (1) The piecewise linearization method is used to linearize the calculation of carbon emissions from combined cooling, heating and power (CCHP) units:

[0088] Since the formula for calculating CO2 emissions from a combined cooling, heating, and power (CCHP) plant contains a quadratic nonlinear term, a piecewise linear block method is used to approximate the convex nonlinear function to improve calculation speed. This approximation is achieved through constraints, and the model is as follows:

[0089]

[0090]

[0091]

[0092] in, Let i be the number of segments * The coefficient;

[0093] (2) The Big-M method linearizes the calculations of energy storage charging and discharging states, energy coordination states between energy stations, and reliability:

[0094] The mathematical models established in steps (2) and (3), such as the charging and discharging states of multi-type energy storage systems, the power interaction states between energy stations and energy supply networks, and the energy coordination states between energy stations, all contain 0 / 1 variables, which increases the difficulty of solving the problem and the computation time. Therefore, the Big-M method is used to linearize the nonlinear constraints with 0 / 1 variables.

[0095] Taking the ESS charge / discharge state as an example, the multi-type energy storage model is transformed into the following linear constraints:

[0096]

[0097] Where M is an artificially defined maximum number;

[0098] Meanwhile, reliability calculation is also a non-linear function. When using the Big-M method for linear processing, a Boolean variable is first introduced. variable and in The linearization model constraints for reliability calculations are as follows:

[0099]

[0100] Among them, when This means that the energy n in the energy station meets the demand of the rigid load n, that is, the rigid load n is not reduced; otherwise, the rigid load n needs to be reduced.

[0101] The method for solving the model based on the data-driven two-stage split-bar optimization algorithm and CCG algorithm described in step (5) includes:

[0102] (1) Data-driven two-stage partial Bruker bar solution algorithm:

[0103]

[0104]

[0105] Wherein, the objective function represents the investment cost of the energy station, and the vector x represents the decision variables for the first stage; By s +Kξ s The vector y represents the runtime and reliability cost of scenario s. s p represents the decision variable for the second stage. s This represents the probability of the s-th discrete scenario occurring. ξ represents the initial probability of each discrete scenario; s The variable is an uncertain quantity, including PV output and load demand after demand response; Cx≤c represents the linear inequality constraint in the first stage, and Dy s ≤d、Ey s =e and F1ξ s +F2y s =f represents the equality and inequality constraints in the second stage;

[0106] Theoretically, the feasible region ψ to which the probability value of each discrete scenario belongs is arbitrary. However, to guarantee the actual probability distribution p... s To better reflect actual computational data and allow fluctuations within a reasonable range, 1-norm and ∞-norm were constructed as constraints to limit the probability distribution values ​​for each scenario;

[0107]

[0108] Among them, γ1 and γ ∞ Let represent the allowable deviation limits between the actual probability and the initial probability for each discrete scenario under the 1-norm and ∞-norm constraints, respectively. Here, it is assumed that the 1-norm and ∞-norm constraints satisfy the following confidence levels:

[0109]

[0110] Where α1 and α ∞ These are the confidence levels for the 1-norm and ∞-norm constraints, respectively, and their constraints can be equivalently represented as:

[0111]

[0112] Among them, the confidence level and allowable deviation established in this patent can limit the fluctuation range of the probability distribution, γ1 and γ ∞ It can be calculated using the following formula:

[0113]

[0114]

[0115] (2) CCG solution algorithm:

[0116] To facilitate the solution, the original problem is divided into a main problem MP and a subproblem SP, and solved using the CCG algorithm. The main problem is as follows:

[0117]

[0118]

[0119] Cx≤c

[0120]

[0121]

[0122]

[0123] Where r is the number of iterations;

[0124] SP aims to obtain the solution based on the optimization result x* of MP and find the worst probability distribution p. s The optimization results of SP are then provided to MP for further iterative calculations. The objective function of SP is as follows:

[0125]

[0126] Due to the discrete scenario probability value p in the subproblem s Since the variables in the second stage are independent of each other, a step-by-step iterative solution can be adopted. First, solve the inner minimum value problem in the subproblem, and then solve the outer problem in the subproblem, until the difference between the inner and outer solution results meets the error setting range, then stop updating and output the optimal value.

[0127] This patent proposes a multi-energy station source-load-storage collaborative planning method for regional integrated energy systems. This method primarily plans the capacity of source-side PV, load-side integrated demand response (ED / C / H), multi-type energy storage (ED / C / H), and energy conversion equipment within the energy station. Simultaneously, considering the impact of source-load uncertainty on energy station planning schemes, a data-driven two-stage sub-Bruker solution method is proposed. To improve computational speed, a piecewise linearization method is used to linearize the carbon emissions of combined cooling, heating, and power (CCHP) units, and the Big-M method is employed for reliability calculations. The proposed planning method fully utilizes the flexible adjustment capabilities of integrated demand response and multi-type energy storage systems, as well as energy coordination strategies among multiple energy stations, to improve the reliability and economy of regional integrated energy stations, increase PV installed capacity, and reduce carbon emissions from energy stations through electricity-to-gas conversion and carbon capture devices. Attached Figure Description

[0128] To more clearly illustrate the technical solution of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0129] Figure 1 This is a flowchart illustrating a multi-energy station source-load-storage coordinated planning method for a regional integrated energy system proposed in this embodiment.

[0130] Figure 2 This is an energy station architecture diagram of a multi-energy station source-load-storage collaborative planning method for a regional integrated energy system proposed in this embodiment;

[0131] Figure 3 This is a schematic diagram of the planning architecture for the multi-energy station source-load-storage collaborative planning method for regional integrated energy systems proposed in this embodiment;

[0132] Figure 4 This is a graph showing the PV output and multi-variable load demand characteristics of the area to be planned in this embodiment, obtained using the k-means clustering method. Detailed Implementation

[0133] To make the structure and advantages of the present invention clearer, the structure of the present invention will be further described below with reference to the accompanying drawings.

[0134] like Figure 1 As shown, a method for coordinated planning of source, load, and storage for multiple energy stations in a regional integrated energy system includes the following steps:

[0135] (1) Establish a regional integrated energy system with a multi-energy station energy supply architecture, including an energy supply network, energy exchange modules, energy storage modules, energy supply units, and end users; wherein the energy supply network includes the distribution network and the gas distribution network; the energy exchange modules include combined cooling, heating and power (CCHP) devices, carbon capture, power-to-gas conversion, electric boilers, gas boilers, electric refrigeration and other equipment; the energy storage modules include various types of energy storage systems, such as electric energy storage, thermal energy storage and cold energy storage; the energy supply units include PV; and the end users include rigid loads of electricity / cooling / heating and flexible loads, wherein the flexible loads are considered as loads that can be reduced and transferred.

[0136] (2) Construct a planning architecture for a collaborative planning method for multiple energy stations in a regional integrated energy system comprising distributed power generation (PV), multiple types of energy storage, and diverse loads. The planning architecture mainly includes four stages: parameter input, planning, operation, and output. The main parameters in the parameter input stage include model parameters such as energy supply network, energy exchange module, energy storage module, energy supply unit, and end users. The planning stage mainly plans the capacity of PV, integrated demand response for electricity / cooling / heating, multiple types of energy storage, and energy conversion equipment. The operation stage analyzes the planning schemes from the planning stage under normal and general fault scenarios to obtain the output and operating status of each device and the overall operating cost. The output stage obtains the optimal planning scheme for the energy station by comparing and analyzing the costs under different configuration schemes.

[0137] (3) Establish the objective function and constraints of the planning framework for the collaborative planning method of multi-energy station source-load-storage in regional integrated energy system. The objective function mainly includes three modules: construction cost, operating cost under normal scenario and reliability conversion cost under general failure scenario. The constraints include equality constraints and inequality constraints for safety optimization control.

[0138] (4) To improve the calculation speed, a piecewise linearization method is used to linearize the calculation of carbon emissions of the combined cooling, heating and power plant, and the Big-M method is used to linearize nonlinear terms such as reliability calculation, energy storage charging and discharging status, and energy coordination status between energy stations.

[0139] (5) The multi-energy station energy supply architecture, objective function and constraints of the regional integrated energy system are combined. Considering the impact of source load uncertainty on the planning scheme, based on historical data, the probability distribution curves and fuzzy sets of PV output and load characteristics are obtained by using the k-means clustering algorithm. The model is solved by the data-driven two-stage sub-Bruker optimization algorithm and CCG algorithm, and the best planning scheme is obtained.

[0140] I. The regional integrated energy system multi-energy station operation architecture established in step (1), including energy supply network, energy exchange module, energy storage module, energy supply unit and end user, such as Figure 2 As shown, the details are as follows:

[0141] (1) The multi-energy station energy supply architecture of the regional integrated energy system mainly consists of an energy supply network, regional integrated energy stations and end users. The energy supply network includes a distribution network and a gas distribution network, which provide electricity and natural gas to the energy stations. The energy stations purchase energy from the energy supply network and provide electricity, cooling and heating to end users through energy conversion equipment, serving as the energy hub of the regional integrated energy system. End users interact with the energy supply side of the energy stations by participating in demand response.

[0142] (2) The energy stations in the regional integrated energy system mainly consist of energy exchange modules, energy storage modules, and energy supply units. Among them, the energy exchange modules include combined cooling, heating, and power (CCHP) devices, carbon capture, electricity-to-gas conversion, electric boilers, gas boilers, and electric refrigeration equipment; the energy storage modules include multiple types of energy storage systems for electricity, cooling, and heating; and the energy supply units mainly include distributed power generation (PV). Integrated energy stations can exchange electrical energy through the power distribution network. When a system failure causes an energy station to be unable to meet load demand, it can be supported by energy from other energy stations to reduce energy shortages and improve the reliability of energy supply. Considering the significant losses in long-distance pipeline transmission of cooling and heating networks, each integrated energy station only supplies energy to cooling / heating users within its jurisdiction. That is, the region can form multi-source interconnection through pipelines, but it is not interconnected with the cooling / heating pipelines of other integrated energy station jurisdictions.

[0143] (3) The end-user load of the regional integrated energy system is mainly divided into rigid load and flexible load. Rigid load is an unreducible load. When the system fails and the rigid load is short-supplyed, the user must be compensated for the energy shortage, which is the reliability discount cost. Flexible load is mainly composed of reduceable load and transferable load. Users can obtain certain economic compensation by participating in system scheduling according to their wishes.

[0144] II. Based on step (2), construct the planning framework for a multi-energy station collaborative planning method for a regional integrated energy system comprising distributed power generation (PV), multiple types of energy storage, and diverse loads, as follows: Figure 3 As shown, the proposed models for each module within the energy station, such as the energy exchange module, energy storage module, and integrated demand response for end users, are established:

[0145] (1) Model of a combined cooling, heating and power (CCHP) unit containing carbon capture and electricity-to-gas conversion:

[0146] Traditional combined cooling, heating and power (CCHP) units mainly consist of gas turbines, waste heat recovery devices, and absorption chillers. The mathematical model is as follows:

[0147]

[0148] in, This refers to the waste heat from the exhaust of a combined cooling, heating, and power (CCHP) unit, which is also the energy input into the waste heat recovery system. It is the electrical power output of the combined cooling, heating and power (CCHP) unit; The efficiency of the gas turbine is set at 75%. The heat dissipation coefficient of the combined cooling, heating and power (CCHP) unit is set at 15%. and These refer to the heating or cooling capacity provided by the waste heat from the gas turbine; The efficiency of flue gas waste heat recovery; and The heating coefficient or cooling coefficient of the bromide cooler is set to 1.2 and 0.95, respectively; T1 and T2 are environmental coefficients, set to 573.15K and 423.25K, respectively. This represents the amount of natural gas consumed by the combined cooling, heating, and power (CCHP) unit; L is the lower calorific value of natural gas, set at 32.97 kW·h / m³. 3 Δt represents the unit running time;

[0149] To reduce carbon emissions, traditional combined cooling, heating, and power (CCHP) units are being converted into CCHP systems incorporating electricity-to-gas conversion and carbon capture. The mathematical model is as follows:

[0150]

[0151] in, The electricity supplied to the energy station load or to the upper-level distribution network for the combined cooling, heating and power (CCHP) unit. The amount of electricity consumed by the power-to-gas conversion unit to produce gas. The amount of electricity consumed by the carbon capture device to capture CO2. The amount of CO2 generated by the combustion gas consumed in the combined cooling, heating and power (CCHP) unit. The heat power provided by the gas turbine to the waste heat recovery unit. These are the energy conversion efficiency and coefficient for electricity-to-gas conversion and carbon capture, respectively. and The CO2 coefficient generated by the combined cooling, heating and power (CCHP) unit;

[0152] (2) Models of electric boilers, electric refrigeration systems and gas boilers:

[0153]

[0154] in, The heat output of electric boilers and gas boilers, respectively. The cooling power generated by the electric chiller. The electrical power consumed by the electric boiler. The electrical power consumed by the electric chiller. This refers to the amount of natural gas consumed by the gas-fired boiler. This refers to the amount of CO2 produced by the gas-fired boiler. The energy conversion efficiencies are for electric boilers, electric chillers, and gas boilers, respectively. This is the coefficient for CO2 produced by the gas-fired boiler;

[0155] (3) Multiple types of energy storage models:

[0156] This invention mainly considers three types of energy storage systems: electric, cold, and hot. Since the charging and discharging characteristics of different types of energy storage are similar, a unified mathematical model is adopted here, as follows:

[0157]

[0158] Where n represents the type of electrical / cold / heat energy; These represent the charging and discharging states of the energy storage system for energy n. Indicates the charging status of the energy storage system. Indicates the discharge state of the energy storage system; These are the charging and discharging power of the energy storage system for energy n, respectively. Let be the minimum and maximum charging power of the energy storage system for energy n, respectively. These are the minimum and maximum discharge power of the energy storage system for energy n, respectively. These are the remaining capacity of the energy storage system n, the minimum remaining capacity, the maximum remaining capacity, the remaining capacity at the end of the scheduling period, and the remaining capacity at the beginning of the scheduling period, respectively. These are the charging and discharging efficiencies of the energy storage system (n), respectively.

[0159] (4) Integrated demand response model:

[0160] This demand response primarily considers transferable and reduceable loads. Transferable loads can adjust demand periods based on energy prices to reduce energy purchase costs. The model is as follows:

[0161]

[0162] in, This represents the proportion of transferable load to user i's total load; This represents the amount of transferable load for user i before and after the transfer at time t; Indicates the proportion of transferable load transferred in / out; This represents the total transferable load of load n at time t;

[0163] Reduceable loads refer to loads whose demand is not high and cannot be directly interrupted due to external factors, and are expressed as:

[0164]

[0165] in, This indicates the proportion of the total load that can be reduced relative to user i; This represents the amount of load that user i can reduce at time t; This indicates that user i is in a state of load reduction; This indicates that the load n can always be reduced by the amount of load at time t;

[0166] III. Based on the objective function and constraints of the planning framework for the regional integrated energy system multi-energy station source-load-storage coordinated planning method described in step (3), such as Figure 3 As shown, it specifically includes:

[0167] (1) Objective function of planning architecture:

[0168] 1) Overall objective function of the bilevel programming model:

[0169] Planning layer costs include three modules: construction costs, fixed maintenance costs, and comprehensive demand response planning capacity costs. Operation layer costs include operating costs under normal scenarios and reliability costs for load reduction under fault scenarios. The functions are as follows:

[0170]

[0171] Among them, C INV For construction costs, Fixed maintenance costs for equipment, To comprehensively consider the cost of demand response capacity planning, C OPE For normal operating costs, C REL The reliability cost of load reduction under typical failure scenarios;

[0172] 2) Objective function of the planning layer:

[0173]

[0174] Among them, y k The lifecycle of device k is represented by a (years); d represents the annual discount rate. The construction cost per unit capacity of equipment k; Build capacity for equipment k; This refers to the fixed maintenance cost coefficient for the equipment. Planning cost per unit capacity for demand response to load n; Plan capacity for demand response to load n;

[0175] 3) Runtime layer objective function:

[0176] ① Operating costs under normal circumstances:

[0177] Under normal circumstances, operating costs include the cost of buying and selling energy between the energy station and the upstream energy supply network, equipment operation and maintenance costs, comprehensive demand response power dispatch costs, and carbon emission costs, as follows:

[0178]

[0179] in, The cost of buying and selling electricity between energy stations and distribution networks; The cost of buying and selling natural gas at energy stations and natural gas networks; For equipment operation and maintenance costs; To comprehensively consider the power scheduling cost of demand response; For carbon emission costs; p s The probability of a discrete scene can be initially obtained using the k-means clustering method, such as... Figure 4 As shown; These are the electricity purchase and sale prices for energy stations and distribution networks, respectively. These refer to the electricity traded between energy stations and distribution networks; These are the gas prices for energy stations and natural gas networks, respectively. These refer to the gas trading volume at energy stations and through natural gas networks. The unit of operation and maintenance cost for equipment k varies; Output power to equipment k; The cost per unit power for demand response to load n; The required response power for the scheduled load n; Cost per unit of CO2 emission, expressed in yuan / ton; These are the CO2 emissions from combined cooling, heating and power (CCHP) units and gas-fired boilers, respectively, in tons. The amount of CO2 consumed by the electro-gas conversion device; These represent the electrical power generated by the PV and combined cooling, heating and power (CCHP) units, respectively. For carbon quotas;

[0180] ② Reliability-based cost of load reduction under normal fault conditions:

[0181] This patent selects the LOEE (Low Energy Expectation) as an indicator for calculating the reliability of energy stations, using the following formula:

[0182]

[0183] in, λ represents the expected energy deficit for load n at energy station j, expressed in MW·h / a. kFor equipment failure rate k; ΔT k For device k, the duration of the failure. The formula for calculating the energy shortage in load n caused by a fault in equipment k at energy station j is as follows:

[0184]

[0185] in, This represents the load demand n in energy station j; The total demand response power of load n in energy station j; The power exchange between energy station j and the energy supply network; The power that generates n energy for device k*; Let n be the net charging and discharging power of the energy storage system n; The n-energy collaborative power between energy station j* and energy station j;

[0186] and The specific calculation formula is as follows:

[0187]

[0188] in, The coordinated state of energy station j providing n energy to energy station j* is represented by a value of 1, indicating that power flows from energy station j to energy station j*. The coordinated state of energy station j* providing n energy to energy station j is represented by a value of 1, indicating that power flows from energy station j* to energy station j. These represent the coordinated power of n energy sources between energy station j and energy station j*, respectively.

[0189] Under normal fault conditions, the reliability cost of load reduction is calculated as follows:

[0190]

[0191] in, The unit loss cost for load n at energy station j;

[0192] (2) Constraints on the planning framework, including equality constraints and inequality constraints:

[0193] 1) Planning-level constraints:

[0194] The planning layer constraints in the two-level planning model mainly include the maximum planning capacity constraints for distributed power generation (PV), energy conversion equipment, various types of energy storage systems, and comprehensive demand response in the energy station, as follows:

[0195]

[0196] in, These respectively represent the planned capacity of equipment, multiple types of energy storage systems, transferable loads, and loads that can be reduced; These represent the maximum planned capacity of equipment, multiple types of energy storage systems, transferable loads, and loads that can be reduced, respectively.

[0197] 2) Runtime layer constraints:

[0198] The constraints at the energy station operation level mainly include power balance constraints, equipment output constraints, multi-type energy storage operation constraints, integrated demand response scheduling power constraints, and power constraints for interaction between the energy station and the upstream energy supply network, as shown below:

[0199] ① Power balance constraints:

[0200] Power balance constraints mainly include electrical, gas, cooling, and heating power balance constraints:

[0201]

[0202] ② Equipment output constraints:

[0203] The output power of the equipment is limited by capacity constraints:

[0204]

[0205] in, These represent the minimum and maximum proportions of the unit's output relative to the planned capacity, respectively. To provide power to equipment k in energy station j;

[0206] In addition to being constrained by planned capacity, various types of energy storage systems are also constrained by their own models;

[0207] ③Comprehensive demand response constraints:

[0208] In addition to being constrained by its own model operation, the overall demand response is also constrained by the planned capacity:

[0209]

[0210] in, These represent the minimum and maximum proportions of the unit's output relative to the planned capacity, respectively. To provide power to equipment k in energy station j;

[0211] ④ Constraints on the power exchange between the energy station and the upstream energy supply network:

[0212] The power exchange between the energy station and the upstream energy supply network is mainly constrained by the capacity of the power distribution lines and natural gas pipelines connected to the energy station.

[0213]

[0214] in, The maximum transmission capacity of the power distribution lines and natural gas pipelines connected to the energy station;

[0215] IV. In step (4), the calculation of carbon emissions from the combined cooling, heating, and power (CCHP) unit is linearized using a piecewise linearization method. The Big-M method is used to linearize nonlinear terms such as reliability calculations, energy storage charging and discharging states, and energy coordination states between energy stations. Specifically, the linearization method includes:

[0216] (1) The piecewise linearization method is used to linearize the calculation of carbon emissions from combined cooling, heating and power (CCHP) units:

[0217] Since the CO2 emission calculation formula of CCHP contains a quadratic nonlinear term, a piecewise linear block method is used to approximate the convex nonlinear function to improve the calculation speed. The model is obtained by applying constraints, as follows:

[0218]

[0219]

[0220]

[0221] in, Let i be the number of segments * The coefficient.

[0222] (2) The Big-M method linearizes the calculations of energy storage charging and discharging states, energy coordination states between energy stations, and reliability:

[0223] The mathematical models established in step (2), such as the charging and discharging states of multi-type energy storage systems, the power interaction states between energy stations and energy supply networks, and the energy coordination states between energy stations, all contain 0 / 1 variables, which increases the difficulty of solving the problem and the computation time. Therefore, the Big-M method is used to linearize the nonlinear constraints with 0 / 1 variables.

[0224] Taking the ESS charge / discharge state as an example, the multi-type energy storage model is transformed into the following linear constraints:

[0225]

[0226] Here, M is an artificially defined maximum number.

[0227] Meanwhile, reliability calculation is also a non-linear function. When using the Big-M method for linear processing, a Boolean variable is first introduced. variable and in The linearization model constraints for reliability calculations are as follows:

[0228]

[0229] Among them, when This means that the energy n in the energy station meets the demand of the rigid load n, that is, the rigid load n is not reduced; otherwise, the rigid load n needs to be reduced.

[0230] V. The data-driven two-stage split-bar optimization algorithm and CCG algorithm described in step (5) specifically include:

[0231] (1) Data-driven two-stage partial Bruker bar solution algorithm:

[0232]

[0233]

[0234] Wherein, the objective function represents the investment cost of the energy station, and the vector x represents the decision variables for the first stage; By s +Kξ s The vector y represents the runtime and reliability cost of scenario s. s p represents the decision variable for the second stage. s This represents the probability of the s-th discrete scenario occurring. ξ represents the initial probability of each discrete scenario; s The variable is an uncertain quantity, including PV output and load demand after demand response; Cx≤c represents the linear inequality constraint in the first stage, and Dy s ≤d、Ey s =e and F1ξ s +F2y s =f represents the equality and inequality constraints in the second stage.

[0235] Theoretically, the feasible region ψ to which the probability value of each discrete scenario belongs is arbitrary. However, to guarantee the actual probability distribution p... s To better reflect actual computational data and allow fluctuations within a reasonable range, 1-norm and ∞-norm were constructed as constraints to limit the probability distribution values ​​for each scenario.

[0236]

[0237] Among them, γ1 and γ ∞ Let represent the allowable deviation limits between the actual probability and the initial probability for each discrete scenario under the 1-norm and ∞-norm constraints, respectively. Here, it is assumed that the 1-norm and ∞-norm constraints satisfy the following confidence levels:

[0238]

[0239] Where α1 and α∞ These are the confidence levels for the 1-norm and ∞-norm constraints, respectively, and their constraints can be equivalently represented as:

[0240]

[0241] Among them, the confidence level and allowable deviation established in this patent can limit the fluctuation range of the probability distribution, γ1 and γ ∞ It can be calculated using the following formula:

[0242]

[0243]

[0244] (2) CCG solution algorithm and process:

[0245] To facilitate solving, the original problem is divided into a master problem (MP) and sub-problems (SP), and solved using the CCG algorithm. The master problem is as follows:

[0246]

[0247]

[0248] Cx≤c

[0249]

[0250]

[0251]

[0252] Where r is the number of iterations.

[0253] SP aims to obtain the solution based on the optimization result x* of MP and find the worst probability distribution p. s The optimization results of SP are then provided to MP for further iterative calculations. The objective function of SP is as follows:

[0254]

[0255] Due to the discrete scenario probability value p in the subproblem s Since the variables in the second stage are independent of each other, a step-by-step iterative solution can be adopted. First, solve the inner minimum value problem in the subproblem, and then solve the outer problem in the subproblem, until the difference between the inner and outer solution results meets the error setting range, then stop updating and output the optimal value.

[0256] Combination Figure 1 , Figure 2 , Figure 3With specific embodiments, the beneficial effects of the proposed method for coordinated planning of multiple energy stations, source, load, and storage in urban integrated energy systems, addressing source-load uncertainty and reliability, are explained in detail below:

[0257] (1) Overview of the Implementation Examples:

[0258] Taking the planning of a multi-energy integrated energy system in a northern city as an example, the energy stations to be planned have multiple energy inputs (including electricity and gas) and multiple energy outputs (including electric heating and cooling loads). The planning area includes three energy stations (ES1 to ES3) to be planned. The physical structure and equipment composition of the energy stations are as follows: Figure 2 As shown. ES1, ES2, and ES3 supply energy to the residential, commercial, and industrial areas, respectively. The loads of these three areas account for 30%, 30%, and 40% of the total load, respectively. The proportions of movable loads for users in these three areas are 10%, 20%, and 30%, respectively, resulting in load reductions of 10%, 12%, and 15%. The penalty costs for electricity / heating / cooling loads are as follows:

[0259] (2) Overall solution process of the regional integrated energy system multi-energy station source-load-storage collaborative planning model based on the embodiment:

[0260] Based on the two-level planning model and planning method for energy stations in the aforementioned regional integrated energy system based on historical data, steps (1) to (5) are as follows:

[0261] 1) Based on historical data, the k-means clustering method is used to obtain the characteristic curves of photovoltaic power output and multi-load demand under different scenarios, such as... Figure 4 As shown;

[0262] 2) Define the following eight planning scenarios;

[0263] ①Scenario 1: Not considering multiple types of energy storage, comprehensive demand response, or P2G and CCS planning;

[0264] ②Scenario 2: Considering multiple types of energy storage, but not comprehensive demand response, P2G and CCS planning;

[0265] ③Scenario 3: Ignoring multiple types of energy storage, focusing on comprehensive demand response, and excluding P2G and CCS planning;

[0266] ④Scenario 4: Considering multiple types of energy storage and comprehensive demand response, but not P2G and CCS planning;

[0267] ⑤Scenario 5: Without considering multiple types of energy storage, without considering comprehensive demand response, but considering P2G and CCS planning;

[0268] ⑥Scenario 6: Consider multiple types of energy storage, but not comprehensive demand response; consider P2G and CCS planning.

[0269] ⑦ Scenario 7: Without considering multiple types of energy storage, consider comprehensive demand response, and include P2G and CCS planning;

[0270] ⑧Scenario 8: Consider multiple types of energy storage, comprehensive demand response, and P2G and CCS planning;

[0271] 3) Comparative analysis of planning schemes and economic feasibility:

[0272] The configuration results and costs of the devices in each scenario are shown in Table 1. The data in the table shows that among the eight typical scenarios, scenario 1 has the highest total cost, followed by scenarios 2 and 3 with decreasing costs, and scenario 4 with the lowest total cost. Among scenarios 5 to 8, scenario 5 has the highest total cost, followed by scenarios 6 and 7 with decreasing costs, and scenario 8 with the lowest total cost. Comparing scenarios 1 to 4 and 5 to 8, the latter has a lower total cost and a lower reliability cost compared to the former.

[0273] As shown in Table 1, among scenarios 1-4, scenario 1 has the highest CO2 cost, followed by scenarios 2 and 3 with decreasing costs, and scenario 4 has the lowest CO2 cost. Among scenarios 5-8, scenario 5 has the highest CO2 cost, followed by scenarios 6 and 7 with decreasing costs, and scenario 8 has the lowest CO2 cost. Comparing scenarios 1-4 and 5-8, the latter has a lower CO2 cost than the former.

[0274] Similarly, when comparing PV absorption capacity under different scenarios, scenario 8 has the highest PV absorption capacity.

[0275] Table 1

[0276]

[0277]

[0278] As can be seen from the above figures and tables, the energy station planning method that considers the synergy of "source, load, and storage" as well as CCS and P2G facilities can effectively reduce economic costs, increase photovoltaic installed capacity, reduce CO2 emissions, achieve a balance between the economic efficiency and robustness of the planning scheme, avoid economic waste caused by unreasonable planning, improve equipment utilization efficiency, and provide effective support for the design of subsequent energy station planning schemes.

[0279] The above description is merely an embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for coordinated planning of source, load, and storage at multiple energy stations in a regional integrated energy system, characterized in that, The specific steps include the following: (1) Establish a multi-energy station energy supply architecture for a regional integrated energy system that includes an energy supply network, energy exchange module, energy storage module, energy supply unit and end users; The energy supply network includes distribution networks and gas distribution networks; the energy exchange modules include combined cooling, heating and power (CCHP) units, carbon capture, power-to-gas conversion, electric boilers, gas boilers, and electric refrigeration equipment; the energy storage modules include various types of energy storage systems composed of electric energy storage, thermal energy storage, and cold energy storage; the energy supply units include photovoltaic (PV); and the end users include rigid electric / cooling / heating loads and flexible loads, where flexible loads are considered as loads that can be reduced or transferred. (2) Construct a planning architecture for a collaborative planning method for multiple energy stations in a regional integrated energy system with distributed power generation (PV), multiple types of energy storage, and diverse loads. The planning architecture mainly includes four stages: parameter input stage, planning stage, operation stage, and output stage. The main parameters in the parameter input stage include energy supply network, energy exchange module, energy storage module, energy supply unit, and end-user model parameters. The planning stage mainly plans the capacity of PV, integrated demand response for electricity / cooling / heating, multiple types of energy storage, and energy conversion equipment. The operation stage analyzes the planning scheme in the planning stage under normal and general fault scenarios to obtain the output and operating status of each device and the overall operating cost. The output stage obtains the optimal planning scheme for the energy station by comparing and analyzing the costs under different configuration schemes. (3) Establish the objective function and constraints of the planning framework for the multi-energy station source-load-storage collaborative planning method of regional integrated energy system. The objective function mainly includes three modules: construction cost, operating cost under normal scenario and reliability conversion cost under general failure scenario. The constraints include equality constraints and inequality constraints for safety optimization control. (4) To improve the calculation speed, a piecewise linearization method is used to linearize the calculation of carbon emissions of the combined cooling, heating and power plant, and the Big-M method is used to linearize the nonlinear terms of reliability calculation, energy storage charging and discharging status, and energy coordination status between energy stations. (5) Combine the multi-energy station energy supply architecture, objective function and constraints of the regional integrated energy system, consider the impact of source load uncertainty on the planning scheme, and use the k-means clustering algorithm to obtain the probability distribution curves and fuzzy sets of PV output and load characteristics based on historical data. Use the data-driven two-stage sub-Bruker optimization algorithm and CCG algorithm to solve the model and obtain the best planning scheme.

2. The method for coordinated planning of multiple energy stations, source, load, and storage in a regional integrated energy system according to claim 1, characterized in that, The established regional integrated energy system multi-energy station energy supply architecture, comprising an energy supply network, energy exchange modules, energy storage modules, energy supply units, and end users, is detailed below: (1) The multi-energy station energy supply architecture of the regional integrated energy system mainly consists of an energy supply network, regional integrated energy stations and end users. The energy supply network includes a distribution network and a gas distribution network, which provide electricity and natural gas to the energy stations. The energy stations purchase energy from the energy supply network and provide electricity, cooling and heating to end users through energy conversion equipment, serving as the energy hub of the regional integrated energy system. End users interact with the energy supply side of the energy stations by participating in demand response. (2) The energy station in the regional integrated energy system is mainly composed of energy exchange module, energy storage module and energy supply unit. Among them, the energy exchange module includes combined cooling, heating and power (CCHP) device, carbon capture, electric to gas conversion, electric boiler, gas boiler and electric refrigeration equipment; the energy storage module includes multiple types of energy storage systems for electricity, cooling and heat; the energy supply unit mainly includes distributed power generation (PV); the integrated energy stations interact with each other through the power distribution network. When the system fails and the energy station cannot meet the load demand, the energy support of other energy stations reduces the energy shortage loss, thereby improving the energy supply reliability of the energy station; considering that the cooling and heating networks have large losses in long-distance pipeline transmission, each integrated energy station only supplies energy to the cooling / heating users in its jurisdiction, that is, the region forms a multi-source interconnection through pipelines, and does not interconnect with the cooling / heating pipelines in the jurisdiction of other integrated energy stations. (3) The end-user load of the regional integrated energy system is mainly divided into rigid load and flexible load. Rigid load is an unreducible load. When the system fails and the rigid load is short-supplyed, the user needs to be compensated for the energy shortage, which is the reliability discount cost. Flexible load is mainly composed of reduceable load and transferable load. Users can obtain certain economic compensation by participating in system scheduling according to their wishes.

3. The method for coordinated planning of multiple energy stations, source, load, and storage in a regional integrated energy system according to claim 1, characterized in that, The energy station includes carbon capture and combined cooling, heating and power (CCHP) units that convert electricity to gas, as well as the establishment of a comprehensive demand response model: (1) Model of a combined cooling, heating and power (CCHP) unit containing carbon capture and power-to-gas conversion: ； in, The electrical power output of the combined cooling, heating and power (CCHP) unit. The electricity supplied to the energy station load or to the upper-level distribution network for the combined cooling, heating and power (CCHP) unit. The amount of electricity consumed by the power-to-gas conversion unit to produce gas. The amount of electricity consumed by the carbon capture device to capture CO2. The amount of CO2 generated by the combustion gas consumed in the combined cooling, heating and power (CCHP) unit. The heat power provided by the gas turbine to the waste heat recovery unit. , , These are the energy conversion efficiency and coefficient for electricity-to-gas conversion and carbon capture, respectively. , and The CO2 coefficient generated by the combined cooling, heating and power (CCHP) unit; (2) Integrated demand response model: This demand response primarily considers transferable and reduceable loads, where transferable loads adjust demand periods based on energy prices to reduce energy purchase costs. The model is as follows: ； in, This represents the proportion of transferable load to user i's total load; / This represents the amount of transferable load for user i before and after the transfer at time t; / This indicates the proportion of load that user i can transfer in / out; This represents the total transferable load of load n at time t; Reduceable loads refer to loads whose demand is not high and cannot be directly interrupted due to external factors, and are expressed as: ； in, This indicates the proportion of the total load that can be reduced relative to user i's total load; , This represents the amount of load that user i can reduce at time t; This indicates that user i is in a state of load reduction; This indicates that the load n can always be reduced by the amount of load at time t; Among the various equipment models of energy station source, load and storage in the proposed regional integrated energy system, the cogeneration unit, carbon capture, power-to-gas, electric boiler, gas boiler and electric refrigeration equipment in the energy exchange module, as well as the energy supply network and energy supply unit are existing mature models.

4. The method for coordinated planning of multiple energy stations, source, load, and storage in a regional integrated energy system according to claim 1, characterized in that, The objective function and constraints of the planning architecture in collaborative planning methods include: (1) Objective function of planning architecture: 1) Overall objective function of the planning architecture: Planning layer costs include three modules: construction costs, fixed maintenance costs, and comprehensive demand response planning capacity costs. Operation layer costs include operating costs under normal scenarios and reliability costs for load reduction under fault scenarios. The functions are as follows: ； in, For construction costs, Fixed maintenance costs for equipment, To comprehensively consider the cost of demand response capacity planning, For normal operating costs, The reliability cost of load reduction under typical failure scenarios; 2) Objective function of the planning layer: ； Among them, y k d represents the lifecycle of device k in years; d represents the annual discount rate. The construction cost per unit capacity of equipment k; Build capacity for equipment k; This refers to the fixed maintenance cost coefficient for the equipment. Planning cost per unit capacity for demand response to load n; Plan capacity for demand response to load n; 3) Objective function of the runtime layer: ① Objective function under normal runtime scenario: Under normal circumstances, operating costs include the cost of buying and selling energy between the energy station and the upstream energy supply network, equipment operation and maintenance costs, comprehensive demand response power dispatch costs, and carbon emission costs, as follows: ； in, The cost of buying and selling electricity between energy stations and distribution networks; The cost of buying and selling natural gas at energy stations and natural gas networks; For equipment operation and maintenance costs; To comprehensively consider the power scheduling cost of demand response; Cost of carbon emissions; The probability represents a discrete scene, and its initial value is obtained by the k-means clustering method. / These are the electricity purchase and sale prices for energy stations and distribution networks, respectively. / These refer to the electricity traded between energy stations and distribution networks; / These are the gas prices for energy stations and natural gas networks, respectively. / These refer to the gas trading volume at energy stations and through natural gas networks. The unit of operation and maintenance cost for equipment k varies; Output power to equipment k; The cost per unit power for demand response to load n; The required response power for the scheduled load n; Cost per unit of CO2 emission, expressed in yuan / ton; , These are the CO2 emissions from combined cooling, heating and power (CCHP) units and gas-fired boilers, respectively, in tons. This refers to the amount of CO2 consumed during the electricity-to-gas conversion process. , These represent the electrical power generated by the PV and combined cooling, heating and power (CCHP) units, respectively. For carbon quotas; ② Reliability-based cost of load reduction under normal fault conditions: The LOEE (Low Energy Expectation) is chosen as the indicator for calculating the reliability of the energy station, using the following formula: ； in, The energy deficit expectation of load n at energy station j is expressed in MW·h / a. For equipment failure rate k; For device k, the duration of the failure. The formula for calculating the energy shortage in load n caused by a fault in equipment k at energy station j is as follows: ； in, This represents the load demand n in energy station j; The total demand response power of load n in energy station j; The power exchange between energy station j and the energy supply network; The power that generates n energy for device k*; Let n be the net charging and discharging power of the energy storage system n; The n-energy collaborative power between energy station j* and energy station j; , and The specific calculation formula is as follows: ； in, The coordinated state of energy station j providing n energy to energy station j* is represented by a value of 1, indicating that power flows from energy station j to energy station j*. The coordinated state of energy station j* providing n energy to energy station j is represented by a value of 1, indicating that power flows from energy station j* to energy station j. , These represent the coordinated power of n energy sources between energy station j and energy station j*, respectively. Under normal fault conditions, the reliability cost of load reduction is calculated as follows: ； in, The unit loss cost for load n at energy station j; (2) Constraints on the planning framework, including equality constraints and inequality constraints: 1) Planning layer constraints: The planning layer constraints in the two-level planning model mainly include distributed power generation (PV), energy conversion equipment, multiple types of energy storage systems, and the maximum planning capacity constraint for comprehensive demand response in the energy station; 2) Runtime layer constraints: The constraints at the energy station operation level mainly include power balance constraints, equipment output constraints, multi-type energy storage operation constraints, comprehensive demand response scheduling power constraints, and power constraints for interaction between the energy station and the upper-level energy supply network.

5. The method for coordinated planning of multiple energy stations, source, load, and storage in a regional integrated energy system according to claim 1, characterized in that, The linear processing method includes: (1) The piecewise linearization method is used to linearize the calculation of carbon emissions from combined cooling, heating and power (CCHP) units: Since the formula for calculating CO2 emissions from a combined cooling, heating, and power (CCHP) plant contains a quadratic nonlinear term, a piecewise linear block method is used to approximate the convex nonlinear function to improve calculation speed. This approximation is achieved through constraints, and the model is as follows: ； in, Let i be the number of segments * The coefficient; (2) The Big-M method linearizes the reliability calculation: For the mathematical model established in step (1), since the reliability calculation is a nonlinear function, the Big-M method is used to linearize it. The processing procedure is as follows: First, we introduce Boolean variables. ,variable and ,in: ； The linearization model constraints for reliability calculations are as follows: ； Among them, when This means that the energy n in the energy station meets the demand of the rigid load n, that is, the rigid load n is not reduced; otherwise, the rigid load n needs to be reduced.

6. An apparatus for a multi-energy station source-load-storage coordinated planning method in a regional integrated energy system, characterized in that, It includes a memory and a processor, the memory storing a program that runs on the processor, and the processor executing the steps of the regional integrated energy system multi-energy station source-load-storage coordinated planning method according to any one of claims 1-5 when running the program.

7. A computer-readable storage medium storing computer instructions thereon, characterized in that, When the computer instructions are executed, they perform the steps of the regional integrated energy system multi-energy station source-load-storage collaborative planning method as described in any one of claims 1-5.

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