A method for integrated energy station-network collaborative planning considering dual uncertainty

Abstract

This invention discloses a method for collaborative planning of integrated energy stations and networks that considers dual uncertainties. This method considers both wind power uncertainty and the planner's risk preference, collaboratively planning integrated energy stations and energy transmission networks. This helps improve system reliability and its ability to withstand adverse scenarios. By defining the magnitude of a "risk preference parameter," the system's balance between economy and robustness can be controlled. This method balances both economy and robustness. By utilizing collaborative planning of integrated energy stations and energy transmission networks that considers dual uncertainties, the planning solution can be flexibly output as a comprehensive optimal solution that combines economy and robustness.

Description

Technical Field

[0001] The present invention belongs to the technical field of integrated energy system planning, and proposes an integrated energy station-network collaborative planning method that considers the dual uncertainties of new energy output and risk preference. Background Art

[0002] With the large-scale integration of distributed renewable energy into integrated energy systems (IES), IES planning faces a series of challenges, including complex models and fluctuating renewable energy output. IES planning is a common technical approach to effectively address these issues, but the solutions obtained by existing planning methods have certain limitations. Furthermore, IESs are not just independent entities; they are often interconnected with energy transmission networks, thus influencing the planning of interconnected IESs. By collaborating with energy transmission networks, IESs can achieve potential flexibility, thereby increasing renewable energy consumption, reducing network investment costs, and improving asset utilization. Summary of the Invention

[0003] To address the shortcomings of existing planning techniques, this paper proposes a dual-uncertainty integrated energy station-network collaborative planning method. This method not only considers the uncertainty of renewable energy output but also aims for comprehensive economic optimization while factoring in the uncertainty of the planner's decision-making risk preferences. This method balances both economy and robustness. By utilizing the dual-uncertainty-considered collaborative planning of integrated energy stations and energy transmission networks, the planning solution can be flexibly output as a comprehensive optimal solution that combines economy and robustness.

[0004] To achieve the above-mentioned purpose, a method for integrated energy station-network collaborative planning considering dual uncertainty is provided, comprising the following steps:

[0005] Step 1: Obtain wind power output data of wind turbines within one year, and use clustering algorithm to obtain typical output scenarios of wind turbines on six typical days: summer wind is strong, summer wind is weak, transition monsoon is strong, transition monsoon is weak, winter wind is strong, and winter wind is weak.

[0006] Step 2: Obtain typical daily load curves for the integrated energy station in summer, transition season, and winter during the planning year and relevant parameters for the integrated energy station-network planning, including line / pipeline transmission limits, line / pipeline investment costs, equipment conversion efficiency, equipment rated capacity, equipment investment costs, and equipment operating life;

[0007] Step 3: Establish an integrated energy station-network collaborative planning model considering dual uncertainties.

[0008] Based on actual conditions, different nodes are selected from a multi-node energy transmission network with different energy types to connect to the integrated energy station. The nodes of the energy transmission network can be equipped with power supplies of different energy types, and the integrated energy station can be equipped with energy conversion equipment and energy storage equipment of different capacities.

[0009] The uncertainty of wind power output is represented by intervals, where the predicted value of wind turbine output is the typical daily data of wind turbines obtained in step 1. A "risk preference parameter" is introduced to characterize the risk uncertainty of the planner, and an integrated energy station-network collaborative planning model is established through objective functions and constraints. The model takes the minimum total cost as the objective function. The total cost includes line investment cost, pipeline investment cost, integrated energy station equipment investment cost and system operation cost. Constraints include node energy balance constraints, generator / natural gas source constraints, line power / pipeline flow constraints, energy conservation constraints within the integrated energy station, upper and lower limit constraints of equipment output and energy storage equipment constraints;

[0010] Step 4: The integrated energy station-network collaborative planning model established in Step 3 is decomposed into a main problem and subproblems, which are solved iteratively using the column constraint generation (CCG) algorithm. The YALMIP optimization toolbox in MATLAB is used to call the GUROBI solver to solve the integrated energy station-network planning model. The solution results serve as the equipment capacity planning results for the integrated energy station and the expansion planning results for the energy transmission network, and the total cost is output under different "risk preference parameters."

[0011] Among them, the objective function expression of the integrated energy station-network collaborative planning model is:

[0012]

[0013] Where: is the annual value of the investment cost of the new line; is the annual value of the investment cost of the natural gas pipeline; is the annual value of the investment cost of the gas compressor; C eh is the annual value of the equipment investment cost in the integrated energy station; C gen and C gas are the annual operating costs of the generator and natural gas source, respectively.

[0014] (1) Line investment cost:

[0015]

[0016] Where: is a 0-1 variable indicating whether the line to be built l is to be built or not; is the investment cost of the new transmission line No. l; LC e is the set of transmission lines to be built; τl represents the operating life of the line l to be built; r is the discount rate; R is the residual value rate of fixed assets.

[0017] (2) Pipeline investment cost:

[0018]

[0019] Where: and They are 0-1 variables indicating whether the new pipeline and gas compressor are to be built or not; and They represent the investment costs of the newly built pipeline and gas compressor for the first l; LC g A collection of natural gas pipelines to be built.

[0020] (3) Investment cost of integrated energy station equipment:

[0021]

[0022] Where: N represents the total number of integrated energy stations; Ω∈{CHP, CERG, HP, HS, CS, WT} represents the set of equipment types in the integrated energy station; dev represents any type of equipment in the integrated energy station; N dev It is expressed as the total capacity of dev type equipment in the integrated energy station; represents the unit investment cost of the jth capacity of the dev type equipment in the i-th integrated energy station; It is a 0-1 variable indicating whether the j-th capacity of the dev type equipment of the i-th integrated energy station is to be built or not; It is represented as the upper limit of the j-th capacity of the dev type equipment of the integrated energy station; τ dev Expressed as the operating life of dev type equipment in an integrated energy station.

[0023] (4) Operating costs:

[0024]

[0025] Where: w s Indicates the total number of scenes; Indicates the probability of scene s appearing; BUS e 、BUS g represent the set of nodes connected to the energy transmission network, namely the generator and the natural gas source; and are expressed as the unit costs of the generator set and natural gas source connected to node b, respectively; and They represent the actual output of the generator set and natural gas source connected to node b at time t under scenario s.

[0026] Constraints include node energy balance constraints, generator / natural gas source constraints, line power / pipeline flow constraints, equipment output upper and lower limit constraints, energy storage equipment constraints, and energy balance constraints within the integrated energy station. Specific constraints are as follows:

[0027] (1) Node energy balance constraint:

[0028]

[0029]

[0030] Where: It represents the actual output of the wind turbine connected to node b at time t under scenario s; L1b and L2b represent the sets of lines or pipelines starting and ending at node b, respectively; and They are respectively represented as the power on the line and pipeline at time t under scenario s; if node b is connected to an integrated energy station, and They represent the electricity input and gas input of the integrated energy station at time t under scenario s. If the node is not connected to the integrated energy station, and They are respectively represented as the electric load and gas load at time t under scenario s.

[0031] (2) Generator / natural gas source constraints:

[0032]

[0033]

[0034]

[0035] Where: and They represent the lower and upper output limits of the generator set connected to node b respectively; and They represent the lower and upper output limits of the natural gas source connected to node b respectively; It is expressed as the maximum output ramp of the generator set connected to node b.

[0036] (3) Line power / pipeline flow constraints:

[0037]

[0038]

[0039]

[0040]

[0041] Where: and They are respectively represented as the upper limit of transmission power of transmission lines and the upper limit of flow of natural gas pipelines.

[0042] (4) Equipment output upper and lower limit constraints:

[0043]

[0044] Where: and They are respectively represented as the lower power limit and upper power limit of the j-th capacity of the dev type equipment of the i-th integrated energy station; It is expressed as the input power of the jth capacity of the dev type equipment of the i-th integrated energy station under scenario s at time t.

[0045] (5) Energy storage equipment constraints:

[0046]

[0047]

[0048]

[0049]

[0050]

[0051] Where: and They are respectively represented as the lower and upper limits of the charging and discharging power of the energy storage device with energy form k under the j-th capacity of the i-th integrated energy station (k=h represents a hot energy storage device, k=c represents a cold energy storage device); and They are respectively represented as the charge and discharge state variables of the energy storage device of the jth capacity in the i-th integrated energy station under scenario s at time t; and They are respectively represented as the lower and upper capacity limits of the energy storage equipment under the j-th capacity of the i-th integrated energy station; It is represented as the actual capacity of the energy storage device of the jth capacity in the i-th integrated energy station under scenario s at time t; It is represented as the initial capacity of the energy storage device of the jth capacity in the i-th integrated energy station under scenario s.

[0052] (6) Energy balance constraints within integrated energy stations:

[0053]

[0054]

[0055]

[0056] Where: and They are respectively represented as the total electricity, cooling and heating loads of the i-th integrated energy station at time t under scenario s; and They are respectively represented as the electricity input and gas input from the energy transmission network at the i-th integrated energy station at time t under scenario s; N chp 、N cerg 、N hp 、N hs and N cs They are respectively expressed as the total capacity of cogeneration units, compression refrigeration units, electric heat pump units, thermal energy storage equipment and cold energy storage equipment; and They are respectively represented as the electrical output power and thermal output power of the j-th capacity cogeneration unit of the i-th integrated energy station at time t under scenario s; and They are respectively represented as the input power and output power of the j-th capacity electric heat pump unit of the i-th integrated energy station at time t under scenario s; and They are respectively represented as the input power and output power of the j-th capacity compression refrigeration unit of the i-th integrated energy station at time t under scenario s;

[0057] The integrated energy station-network collaborative planning model is summarized as follows:

[0058]

[0059] Where: x and y represent planning variables and optimization variables, respectively; α and c represent the cost parameter column vectors corresponding to x and y in the objective function, respectively; D and K represent the coefficient matrices of inequality constraints and equality constraints related to optimization variables, respectively; F and G represent the coefficient matrices of inequality constraints related to planning variables and optimization variables, respectively; I wt is represented as the identity matrix; d, k, and h are constant column vectors.

[0060]

[0061] The actual output of a wind turbine can be described by the uncertainty interval:

[0062]

[0063] Where: It represents the predicted output value of the wind turbine connected to node b. The predicted output curve of the typical day scenario has been given in step 1. Indicates the maximum allowable fluctuation deviation of the wind turbine output.

[0064] Taking into account the risk preference of the planner, the “risk preference parameter” Γ is introduced wt , then the uncertainty interval of the wind turbine can be converted into:

[0065]

[0066] Where: is a binary variable. When the value is 1, it means that the uncertain variable of the corresponding period has reached the boundary; Γ wt It is an integer ranging from 0 to 24, indicating the total number of time periods during which wind power output reaches the minimum value of the fluctuation range within the scheduling period, and is used to satisfy planners to make adjustments based on their own preferences.

[0067] The integrated energy station-network collaborative planning model is decomposed into a main problem and sub-problems through CCG, where the main problem is as follows:

[0068]

[0069] Where: q represents the number of iterations currently being performed; y l It is represented as the feasible solution of the optimization variable of the main problem after the lth iteration; It represents the value of the wind turbine in the main problem after the lth iteration.

[0070] The decomposed sub-problems are as follows:

[0071]

[0072] Where: γ represents the dual variable within the feasible domain for the generator / natural gas source constraint, the first and second equations of the line power / pipeline flow constraint, and the first four equations of the energy storage device constraint; λ represents the dual variable within the feasible domain for the node energy balance constraint, the fifth equation of the energy storage device constraint, and the energy conservation constraint within the integrated energy station; ν represents the dual variable within the feasible domain for the third and fourth equations of the line power / pipeline flow constraint, the fourth equation of the energy storage device constraint, and the upper and lower limits of the device output; π represents the dual variable within the feasible domain for the predicted output of the wind turbine. Substituting the wind turbine output considering the "risk preference parameter" into the subproblem yields:

[0073]

[0074] Where: is the auxiliary variable introduced; is the upper bound of the dual variable π, which is a sufficiently large positive real number.

[0075] The beneficial effects of the present invention are as follows:

[0076] This method considers both the uncertainty of wind power and the planner's risk appetite, synergizing the planning of integrated energy stations with the energy transmission network. This improves system reliability and its ability to withstand adverse scenarios. By defining the size of the "risk appetite parameter," the balance between economy and robustness can be controlled. BRIEF DESCRIPTION OF THE DRAWINGS

[0077] Figure 1 This is the example diagram;

[0078] Figure 2 This is a typical daily output curve diagram of wind power;

[0079] Figure 3 This is a typical daily load curve. DETAILED DESCRIPTION

[0080] The technical solution of the present invention is further described below with reference to the accompanying drawings and embodiments;

[0081] A method for integrated energy station-network collaborative planning considering dual uncertainty includes the following steps:

[0082] Step 1: Obtain wind power output data of wind turbines within one year, and use clustering algorithm to obtain typical output scenarios of wind turbines on six typical days: summer wind is strong, summer wind is weak, transition monsoon is strong, transition monsoon is weak, winter wind is strong, and winter wind is weak.

[0083] Step 2: Obtain typical daily load curves for the integrated energy station in summer, transition season, and winter during the planning year and relevant parameters for the integrated energy station-network planning, including line / pipeline transmission limits, line / pipeline investment costs, equipment conversion efficiency, equipment rated capacity, equipment investment costs, and equipment operating life;

[0084] Step 3: Establish an integrated energy station-network collaborative planning model considering dual uncertainties.

[0085] Based on actual conditions, different nodes are selected from a multi-node energy transmission network with different energy types to connect to the integrated energy station. The nodes of the energy transmission network can be equipped with power supplies of different energy types, and the integrated energy station can be equipped with energy conversion equipment and energy storage equipment of different capacities.

[0086] The uncertainty of wind power output is represented by intervals, where the predicted value of wind turbine output is the typical daily data of wind turbines obtained in step 1. A "risk preference parameter" is introduced to characterize the risk uncertainty of the planner, and an integrated energy station-network collaborative planning model is established through objective functions and constraints. The model takes the minimum total cost as the objective function. The total cost includes line investment cost, pipeline investment cost, integrated energy station equipment investment cost and system operation cost. Constraints include node energy balance constraints, generator / natural gas source constraints, line power / pipeline flow constraints, energy conservation constraints within the integrated energy station, upper and lower limit constraints of equipment output and energy storage equipment constraints;

[0087] Among them, the objective function expression of the integrated energy station-network collaborative planning model is:

[0088]

[0089] Where: is the annual value of the investment cost of the new line; is the annual value of the investment cost of the natural gas pipeline; is the annual value of the investment cost of the gas compressor; C eh is the annual value of the equipment investment cost in the integrated energy station; C gen and C gas are the annual operating costs of the generator and natural gas source, respectively.

[0090] (1) Line investment cost:

[0091]

[0092] Where: is a 0-1 variable indicating whether the line to be built l is to be built or not; is the investment cost of the new transmission line No. l; LC e is the set of transmission lines to be built; τ l represents the operating life of the line l to be built; r is the discount rate; R is the residual value rate of fixed assets.

[0093] (2) Pipeline investment cost:

[0094]

[0095] Where: and They are 0-1 variables indicating whether the new pipeline and gas compressor are to be built or not; and They represent the investment costs of the newly built pipeline and gas compressor for the first l; LC g A collection of natural gas pipelines to be built.

[0096] (3) Investment cost of integrated energy station equipment:

[0097]

[0098] Where: N represents the total number of integrated energy stations; Ω∈{CHP, CERG, HP, HS, CS, WT} represents the set of equipment types in the integrated energy station; dev represents any type of equipment in the integrated energy station; N dev It is expressed as the total capacity of dev type equipment in the integrated energy station; represents the unit investment cost of the jth capacity of the dev type equipment in the i-th integrated energy station; It is a 0-1 variable indicating whether the j-th capacity of the dev type equipment of the i-th integrated energy station is to be built or not; It is represented as the upper limit of the j-th capacity of the dev type equipment of the integrated energy station; τ dev Expressed as the operating life of dev type equipment in an integrated energy station.

[0099] (4) Operating costs:

[0100]

[0101] Where: w s Indicates the total number of scenes; Indicates the probability of scene s appearing; BUS e 、BUS g represent the set of nodes connected to the energy transmission network, namely the generator and the natural gas source; and are expressed as the unit costs of the generator set and natural gas source connected to node b, respectively; and They represent the actual output of the generator set and natural gas source connected to node b at time t under scenario s.

[0102] Constraints include node energy balance constraints, generator / natural gas source constraints, line power / pipeline flow constraints, equipment output upper and lower limit constraints, energy storage equipment constraints, and energy balance constraints within the integrated energy station. Specific constraints are as follows:

[0103] (1) Node energy balance constraint:

[0104]

[0105]

[0106] Where: It represents the actual output of the wind turbine connected to node b at time t under scenario s; L1b and L2b represent the sets of lines or pipelines starting and ending at node b, respectively; and They are respectively represented as the power on the line and pipeline at time t under scenario s; if node b is connected to an integrated energy station, and They represent the electricity input and gas input of the integrated energy station at time t under scenario s. If the node is not connected to the integrated energy station, and They are respectively represented as the electric load and gas load at time t under scenario s.

[0107] (2) Generator / natural gas source constraints:

[0108]

[0109]

[0110]

[0111] Where: and They represent the lower and upper output limits of the generator set connected to node b respectively; and They represent the lower and upper output limits of the natural gas source connected to node b respectively; It is expressed as the maximum output ramp of the generator set connected to node b.

[0112] (3) Line power / pipeline flow constraints:

[0113]

[0114]

[0115]

[0116]

[0117] Where: and They are respectively represented as the upper limit of transmission power of transmission lines and the upper limit of flow of natural gas pipelines.

[0118] (4) Equipment output upper and lower limit constraints:

[0119]

[0120] Where: and They are respectively represented as the lower power limit and upper power limit of the j-th capacity of the dev type equipment of the i-th integrated energy station; It is expressed as the input power of the jth capacity of the dev type equipment of the i-th integrated energy station under scenario s at time t.

[0121] (5) Energy storage equipment constraints:

[0122]

[0123]

[0124]

[0125]

[0126]

[0127] Where: and They are respectively represented as the lower and upper limits of the charging and discharging power of the energy storage device with energy form k under the j-th capacity of the i-th integrated energy station (k=h represents a hot energy storage device, k=c represents a cold energy storage device); and They are respectively represented as the charge and discharge state variables of the energy storage device of the jth capacity in the i-th integrated energy station under scenario s at time t; and They are respectively represented as the lower and upper capacity limits of the energy storage equipment under the j-th capacity of the i-th integrated energy station; It is represented as the actual capacity of the energy storage device of the jth capacity in the i-th integrated energy station under scenario s at time t; It is represented as the initial capacity of the energy storage device of the jth capacity in the i-th integrated energy station under scenario s.

[0128] (6) Energy balance constraints within integrated energy stations:

[0129]

[0130]

[0131]

[0132] Where: and They are respectively represented as the total electricity, cooling and heating loads of the i-th integrated energy station at time t under scenario s; and They are respectively represented as the electricity input and gas input from the energy transmission network at the i-th integrated energy station at time t under scenario s; N chp 、Ncerg 、N hp 、N hs and N cs They are respectively expressed as the total capacity of cogeneration units, compression refrigeration units, electric heat pump units, thermal energy storage equipment and cold energy storage equipment; and They are respectively represented as the electrical output power and thermal output power of the j-th capacity cogeneration unit of the i-th integrated energy station at time t under scenario s; and They are respectively represented as the input power and output power of the j-th capacity electric heat pump unit of the i-th integrated energy station at time t under scenario s; and They are respectively represented as the input power and output power of the j-th capacity compression refrigeration unit of the i-th integrated energy station at time t under scenario s;

[0133] The integrated energy station-network collaborative planning model is summarized as follows:

[0134]

[0135] Where: x and y represent planning variables and optimization variables, respectively; α and c represent the cost parameter column vectors corresponding to x and y in the objective function, respectively; D and K represent the coefficient matrices of inequality constraints and equality constraints related to optimization variables, respectively; F and G represent the coefficient matrices of inequality constraints related to planning variables and optimization variables, respectively; I wt is represented as the identity matrix; d, k, and h are constant column vectors.

[0136]

[0137] The actual output of a wind turbine can be described by the uncertainty interval:

[0138]

[0139] Where: It represents the predicted output value of the wind turbine connected to node b. The predicted output curve of the typical day scenario has been given in step 1. Indicates the maximum allowable fluctuation deviation of the wind turbine output.

[0140] Taking into account the risk preference of the planner, the “risk preference parameter” Γ is introduced wt , then the uncertainty interval of the wind turbine can be converted into:

[0141]

[0142] Where: is a binary variable. When the value is 1, it means that the uncertain variable of the corresponding period has reached the boundary; Γ wt It is an integer ranging from 0 to 24, indicating the total number of time periods during which wind power output reaches the minimum value of the fluctuation range within the scheduling period, and is used to satisfy planners to make adjustments based on their own preferences.

[0143] The integrated energy station-network collaborative planning model is decomposed into a main problem and sub-problems through CCG, where the main problem is as follows:

[0144]

[0145] Where: q represents the number of iterations currently being performed; y l It is represented as the feasible solution of the optimization variable of the main problem after the lth iteration; It represents the value of the wind turbine in the main problem after the lth iteration.

[0146] The decomposed sub-problems are as follows:

[0147]

[0148] Where: γ represents the dual variable within the feasible domain for the generator / natural gas source constraint, the first and second equations of the line power / pipeline flow constraint, and the first four equations of the energy storage device constraint; λ represents the dual variable within the feasible domain for the node energy balance constraint, the fifth equation of the energy storage device constraint, and the energy conservation constraint within the integrated energy station; ν represents the dual variable within the feasible domain for the third and fourth equations of the line power / pipeline flow constraint, the fourth equation of the energy storage device constraint, and the upper and lower limits of the device output; π represents the dual variable within the feasible domain for the predicted output of the wind turbine. Substituting the wind turbine output considering the "risk preference parameter" into the subproblem yields:

[0149]

[0150] Where: is the auxiliary variable introduced; is the upper bound of the dual variable π, which is a sufficiently large positive real number.

[0151] Step 4: The integrated energy station-network collaborative planning model established in Step 3 is decomposed into a main problem and subproblems, which are solved iteratively using the column constraint generation (CCG) algorithm. The YALMIP optimization toolbox in MATLAB is used to call the GUROBI solver to solve the integrated energy station-network planning model. The solution results serve as the equipment capacity planning results for the integrated energy station and the expansion planning results for the energy transmission network, and the total cost is output under different "risk preference parameters."

[0152] use Figure 1The example shown performs planning analysis, resulting in equipment planning for an integrated energy station and expansion planning for an electric-gas energy network. Planning costs are also output for different "risk preference parameters." Both the electric power network and the natural gas network consist of six nodes, connected by seven transmission lines and five natural gas pipelines, forming the integrated energy station-network topology. The candidate transmission line and natural gas pipeline sets are all existing corridors. Nodes 1, 4, and 5 connect to integrated energy stations 1, 2, and 3, respectively. Nodes 2, 3, and 6 connect to centralized grid-connected wind turbines 1, 2, and 3, respectively. Nodes 3 and 6 connect to natural gas sources 1 and 2, respectively. Nodes 1, 2, and 6 connect to generator sets 1, 2, and 3, respectively.

[0153] Figure 2 This is a typical daily output curve diagram of wind power; Figure 3 This is a typical daily load curve.

[0154] Example

[0155] The present invention establishes a comprehensive energy station-network coordinated planning model that considers dual uncertainty. The specific process is as follows:

[0156] 1. Parameter initialization: Given a set of wind power forecast values as the initial worst-case scenario, set the lower bound LB = -∞, the upper bound UB = +∞, and the number of iterations k = 1;

[0157] 2. Based on the set of wind power forecast values obtained in the first step Solve the main problem to obtain the optimal solution The objective function value of the main problem is used as the new lower bound

[0158] 3. The solution obtained above Substitute into the subproblem and solve the objective function value of the subproblem and uncertain variables in the corresponding scenarios The value of Update the upper bound of the model

[0159] 4. Given the convergence accuracy σ, if UB-LB ≤ σ, stop the iteration and return the optimal solution and Otherwise, increase the variable and the corresponding constraints. Let k = k + 1 and jump to until convergence;

[0160] This invention aims at the integrated energy station-network collaborative planning model considering dual uncertainty. Compared with the traditional robust optimization method, it proposes a "risk preference parameter" that can flexibly take into account the economy and robustness of the planning scheme, and therefore has better engineering practical value.

[0161] Simulation Results

[0162] In order to analyze the impact of risk preference factors on the planning model, the following scenarios are set for comparative analysis. Scenario S1: Use the typical daily scenario value of wind power for planning analysis; Scenario S2: Based on Scenario 1, set Γ wt =6, and conduct planning analysis for the model; Scenario S3: Based on Scenario 1, set Γ wt =12, and conduct planning analysis for the model; Scenario S4: Based on Scenario 1, set Γ wt =18, and conduct planning analysis for the model; Scenario S5: Based on Scenario 1, set Γ wt The model was used for planning analysis. This paper set the fluctuation deviation of wind turbines to 15% of the predicted value. Based on the wind power uncertainty interval, 100 scenarios were randomly generated and the proportion of operational scenarios in the planning results under different scenarios was calculated. The planning cost results of the model under different scenarios are shown in Table 1.

[0163] Table 1 Comparison of configuration costs in different scenarios

[0164]

[0165] First, the percentage of operational scenarios shows that scenario S5 outperforms S4, which in turn outperforms S3, which in turn outperforms S2, and S2 outperforms S1. This demonstrates that, after factoring in risk appetite, planners can make autonomous adjustments based on economic and robustness requirements. In terms of investment costs, scenarios S3, S4, and S5 are similar, with a slight increase compared to scenarios S1 and S2. In terms of operating costs, as the "risk appetite parameter" increases, operating costs gradually increase.

[0166] Regarding the planning and configuration of internal equipment in the integrated energy station, the results of the EH1 internal equipment configuration for all scenarios are shown in Table 2. Due to the upper limits of CS and HS configurations and the operational characteristics of CHP, scenarios S1-S5 all configured the highest-capacity HS and EHP to meet the high heat load demand. In contrast, scenarios S1 and S2 increase the CHP capacity and reduce the capacity of the less economical CERG compared to the other scenarios. Planners can flexibly configure EH internal equipment capacity based on risk preference parameters to reduce overall costs.

[0167] Table 2 Comparison of EH1 configuration results in different scenarios

[0168]

[0169] In terms of multi-energy network planning, the transmission line configuration results for different scenarios are shown in Table 3, and the natural gas pipeline and gas compressor configuration results for different scenarios are shown in Table 4.

[0170] Table 3 Comparison of line configuration results in different scenarios

[0171]

[0172] Table 4 Comparison of pipeline and gas compressor configuration results in different scenarios

[0173]

[0174] Tables 3 and 4 show that the transmission line configuration results for each scenario consistently lead to the construction of L4 and L5. Comparing scenarios S2 and S1, and S4 and S3, it can be seen that an increase in the "risk preference parameter" leads to a decrease in wind turbine output, necessitating the addition of the natural gas pipeline P2 and gas compressor Com2. Combined with Table 2, it can be seen that scenarios S3 and S4 increase CHP capacity. Therefore, at the expense of adding certain pipelines and compressors, increasing the capacity of EH internal equipment can meet the EH's multi-energy load requirements and improve the economic efficiency of the planning scheme.

Claims

1. A method for integrated energy station-network collaborative planning considering dual uncertainty, characterized in that: The following steps are involved: Step 1: Obtain wind power output data of wind turbines within a year, and use a clustering algorithm to obtain typical output scenarios of wind turbines on six typical days: high summer wind, low summer wind, high transition monsoon, low transition monsoon, high winter wind, and low winter wind. Step 2: Obtain typical daily load curves for the integrated energy station in summer, transition season, and winter during the planning year and relevant parameters for the integrated energy station-network planning, including line / pipeline transmission limits, line / pipeline investment costs, equipment conversion efficiency, equipment rated capacity, equipment investment costs, and equipment operating life; Step 3: Establish an integrated energy station-network collaborative planning model considering dual uncertainties; According to actual conditions, different nodes are selected from a multi-node energy transmission network with different energy types to connect to the integrated energy station. Power supplies of different energy types can be configured on the nodes of the energy transmission network, and energy conversion equipment and energy storage equipment of different capacities can be configured in the integrated energy station. The uncertainty of wind power output is represented by intervals based on the predicted output of wind turbines, where the predicted output of wind turbines is the typical daily data of wind turbines obtained in step 1. A "risk preference parameter" is introduced to characterize the planner's risk uncertainty. An integrated energy station-network collaborative planning model is established through objective functions and constraints. The model uses the minimum total cost as the objective function. The total cost includes the line investment cost, pipeline investment cost, integrated energy station equipment investment cost, and system operating cost. The constraints include node energy balance constraints, generator / natural gas source constraints, line power / pipeline flow constraints, energy conservation constraints within the integrated energy station, upper and lower limits of equipment output constraints, and energy storage equipment constraints. Step 4: Decompose the integrated energy station-network collaborative planning model established in Step 3 into a main problem and subproblems, which are solved iteratively using a column constraint generation algorithm. The YALMIP optimization toolbox in MATLAB is used to call the GUROBI solver to solve the integrated energy station-network planning model. The solution results are used as the equipment capacity planning results for the integrated energy station and the expansion planning results for the energy transmission network, and the total cost under different "risk preference parameters" is output.

2. The method for integrated energy station-network collaborative planning considering dual uncertainty according to claim 1, characterized in that: The objective function expression of the integrated energy station-network collaborative planning model is: ； Where: is the annual value of the investment cost of the new line; is the annual value of the investment cost of the natural gas pipeline; is the annual value of the investment cost of the gas compressor; The annual value of the investment cost of equipment in the integrated energy station; and are the annual operating costs of the generator and natural gas source, respectively; (1) Line investment cost: ； Where: For lines to be built A 0-1 variable indicating whether to be built or not; For the Investment cost of new transmission lines; is a collection of transmission lines to be built; Indicates lines to be built operating life; is the discount rate; is the residual value rate of fixed assets; (2) Pipeline investment cost: ； Where: and Respectively expressed as A 0-1 variable indicating whether a new pipeline and gas compressor are to be built; and Respectively expressed as Investment costs for new pipelines and gas compressors; A collection of natural gas pipelines to be built; (3) Investment cost of integrated energy station equipment: ； Where: Expressed as the total number of integrated energy stations; Represented as a collection of equipment types within a comprehensive energy station; Represents any type of equipment in an integrated energy station; Indicates that it is within the integrated energy station Total capacity of equipment of type; Indicates the Integrated energy stations Type of equipment Unit investment cost of each capacity; Expressed as Integrated energy stations Type of equipment A 0-1 variable indicating whether the capacity is to be built; Represented as a comprehensive energy station Type of equipment The upper limit of the capacity; Represented as a comprehensive energy station The operating life of the type of equipment; (4) Operating costs: ； Where: Indicates the total number of scenes; Representation scene Probability of occurrence; 、 represent the set of nodes connected to the energy transmission network, namely the generator and the natural gas source; and Represented as connected at nodes Unit costs of the generator sets and natural gas sources on the and Represented as connected at nodes The generator set and natural gas source in the scene Down Actual output at the moment.

3. The method for integrated energy station-network collaborative planning considering dual uncertainty according to claim 2, characterized in that: Constraints include node energy balance constraints, generator / natural gas source constraints, line power / pipeline flow constraints, equipment output upper and lower limit constraints, energy storage equipment constraints, and energy balance constraints within the integrated energy station. The specific constraints are as follows: (1) Node energy balance constraint: ； Where: Represented as connected at the node Wind turbines on the scene Down Actual output at any moment; and Represented as nodes A collection of lines or pipelines with a starting point and an end point; and Represented as scenes Down The power on the line and pipeline at the moment; if the node If it is connected to a comprehensive energy station and Represented as scenes Down The power input and gas input of the integrated energy station at this moment. If the node is not connected to the integrated energy station, and Represented as scenes Down Electricity load and gas load at the moment; (2) Generator / natural gas source constraints: ； ； ； Where: and Represented as connected at nodes The lower and upper output limits of the generator sets above; and Represented as connected at nodes The lower and upper output limits of the natural gas source; Represented as connected at the node The maximum output of the generator set on the climbing; (3) Line power / pipeline flow constraints: ； ； ； ； Where: and They are respectively represented as the upper limit of transmission power of transmission lines and the upper limit of flow of natural gas pipelines; (4) Upper and lower limits of equipment output: ； Where: and Respectively expressed as Integrated energy stations Type of equipment The lower and upper power limits of each capacity; Represented as a scene Next Integrated energy stations Type of equipment Capacity Input power at the moment; (5) Energy storage equipment constraints: ； ； ； ； ； Where: 、 、 and Respectively expressed as The first integrated energy station The energy form under this capacity is The lower and upper limits of the charging and discharging power of the energy storage equipment, Represented as a thermal energy storage device, Represents a cold energy storage device; and Represented as scenes Next Integrated energy station Energy storage devices of various capacities The charge and discharge state variables at each moment; and Respectively expressed as The first integrated energy station The lower and upper capacity limits of energy storage equipment under certain capacities; Represented as a scene Next Integrated energy station Energy storage devices of various capacities Actual capacity at the moment; Represented as a scene Next Integrated energy station The initial capacity of the energy storage device of the capacity; (6) Energy balance constraints within integrated energy stations: ； ； ； Where: 、 and Represented as scenes Next Integrated energy stations Total electricity, cooling and heating loads at any given moment; For the scene Next Integrated energy stations Electrical input from the energy transmission network at all times; 、 、 、 and They are respectively expressed as the total capacity of cogeneration units, compression refrigeration units, electric heat pump units, thermal energy storage equipment and cold energy storage equipment; and Represented as scenes Next Integrated energy station The capacity of the combined heat and power unit is Electrical output power and thermal output power at the moment; and Represented as scenes Next Integrated energy station Electric heat pump units of various capacities Input power and output power at the moment; and Represented as scenes Next Integrated energy station Compression refrigeration units of various capacities The input power and output power at the moment.

4. The method for integrated energy station-network collaborative planning considering dual uncertainty according to claim 3 is characterized in that: The integrated energy station-network collaborative planning model is summarized as follows: ； Where: and are respectively represented as planning variables and optimization variables, and Respectively expressed as the corresponding and Column vector of cost parameters; and are respectively expressed as the coefficient matrices of the inequality constraints and equality constraints related to the optimization variables; and are the coefficient matrices of the inequality constraints related to the planning variables and the optimization variables respectively; Represented as the identity matrix; 、 and is a constant column vector; ； The actual output of a wind turbine can be described by the uncertainty interval: ； Where: Represented as connected at the node The predicted output value of the wind turbine on the ,the predicted output curve of the typical day scenario has been given in step 1; Indicates the maximum fluctuation deviation allowed for the wind turbine output; Taking into account the risk preference of the planner, the "risk preference parameter" is introduced , then the uncertainty interval of the wind turbine can be converted into: ； Where: It is a binary variable. When the value is 1, it means that the uncertain variable in the corresponding period has reached the boundary. It is an integer ranging from 0 to 24, indicating the total number of periods during which wind power output reaches the minimum value of the fluctuation range within the scheduling period. It is used to satisfy the planner's adjustment based on his or her own preferences. The integrated energy station-network collaborative planning model is decomposed into a main problem and sub-problems through CCG, where the main problem is as follows: ； Where: Indicates the number of iterations currently in progress; Expressed as The feasible solution of the optimization variables of the main problem after iterations; Expressed as The value of the wind turbine in the main problem after the iteration; The decomposed sub-problems are as follows: ； Where: The dual variables of the first and second equations of the generator / natural gas source constraint, the line power / pipeline flow constraint, and the first four equations of the energy storage device constraint within the feasible region are expressed as follows; It is expressed as the dual variables of the node energy balance constraint, the fifth formula of the energy storage device and the energy conservation constraint in the integrated energy station in the feasible region; Expressed as the dual variables of the third and fourth equations of the line power / pipeline flow constraint, the fourth equation of the energy storage device constraint, and the upper and lower limits of the device output in the feasible region; It is represented as the dual variable of the wind turbine generator set’s predicted output within the feasible region; Substituting the wind turbine output considering the "risk preference parameter" into the sub-problem, we can obtain: ； Where: is the auxiliary variable introduced; is the dual variable The upper bound of is a sufficiently large positive real number.

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