A power and natural gas integrated energy distribution network system planning method

Abstract

The application discloses a kind of electricity and natural gas integrated energy distribution network system planning method, comprising the following specific steps: step 1: input the network of electricity and natural gas integrated energy distribution network system to be planned and the parameters of various types of facilities to be planned;Step 2: using historical data set to construct renewable energy and load scene, clustering is carried out using K-means algorithm;Step 3: according to the network and facility parameters provided in step 1 and the scene clustering result obtained in step 2, construct mixed hydrogen natural gas operation model, including electric power to gas facilities, mixed gas component, and natural gas consumption is modeled;Step 4: construct the double-layer planning model of wind turbine and soft switch investment construction planning;Step 5: using convex approximation and relaxation technique, the electricity and natural gas integrated energy distribution network system planning method disclosed by the application has the effect of obtaining the optimal planning strategy by solving through an iterative algorithm based on decomposition and reconstruction.

Description

Technical Field

[0001] This invention relates to the field of integrated energy planning systems, and more particularly to a planning method for an integrated energy distribution network system combining electricity and natural gas. Background Technology

[0002] To alleviate climate change and energy shortages, renewable energy generation has increased significantly in active distribution networks (ADNs) in recent years. Power-to-gas (P2G) is a technology that converts excess renewable energy into hydrogen and methane, considered an effective way to achieve high renewable energy penetration. Through P2G facilities, ADN and gas distribution networks (GDNs) are converted into an integrated system, namely the Integrated Electricity and Gas Distribution Network (IEGDS). Optimal planning of IEGDS has become an important topic in recent years, aiming to find the best strategy for planning IEGDS construction while meeting technical and economic constraints. Many experts and scholars have proposed efficient energy conversion technologies, energy storage technologies, and new business models to help achieve optimal IEGDS planning. However, in most existing studies, only one gas, such as hydrogen or methane, is injected into the GDN and mixed with natural gas. Theoretical analysis shows that natural gas mixed with hydrogen and methane, i.e., hydrogen-mixed natural gas (HCNG), is compatible with existing gas distribution network facilities and can improve economic efficiency. HCNG can serve as a substitute for natural gas, offering better environmental benefits. Since 2007, numerous IEGDS pilot projects operating under HCNG (High-Hydrogen Gas Generation) models have been conducted in Europe, such as the Loran Hydrogen Community (2007, Denmark), H2BER (2015, Germany), and GRHYD (2017, France). Therefore, injecting only one gas in future IEGDS projects is impractical and may lead to suboptimal planning results. Furthermore, existing IEGDS planning studies often use overly simplified HCNG models, such as neglecting gas safety constraints and treating methane and natural gas as a single gas.

[0003] To effectively integrate HCNGs into existing integrated energy systems, most current research focuses on the transmission network level, with only limited studies on the distribution network level. However, some of these studies neglect methane injection, while others employ difficult-to-solve nonlinear programming models. Another challenge is mitigating voltage deviations caused by the high proportion of renewable energy sources in IEGDS. Due to the coupling relationship between electricity and natural gas systems, the capacity of P2G facilities to absorb renewable energy is limited. Recently, the concept of flexible soft switching (SOP) has provided an effective solution for enhancing power flow control and reactive power compensation. Many studies have shown the potential value of SOP in improving ADN operating performance, particularly in voltage and reactive power control through voltage distribution optimization. However, most of these studies focus on power systems, neglecting the application value of SOP in IEGDS. Therefore, there is an urgent need for an IEGDS planning method that considers methane injection and to investigate the application value of SOP within it. Summary of the Invention

[0004] This invention discloses a planning method for an integrated energy distribution network system combining electricity and natural gas, aiming to solve the technical problem that the application value of Standard Operating Procedures (SOPs) in IEGDS has been neglected in research.

[0005] To achieve the above objectives, the present invention adopts the following technical solution:

[0006] A planning method for an integrated energy distribution network system combining electricity and natural gas includes the following specific steps:

[0007] Step 1: Input the network parameters of the integrated electricity and natural gas energy distribution network system to be planned, as well as the parameters of various facilities to be planned;

[0008] Step 2: Construct renewable energy and load scenarios using historical datasets, and perform clustering using the K-means algorithm;

[0009] Step 3: Based on the network and facility parameters provided in Step 1 and the scenario clustering results obtained in Step 2, construct a hydrogen-to-natural gas operation model, including modeling the power-to-gas facility, the mixed gas composition, and natural gas consumption;

[0010] Step 4: Construct a two-layer planning model for the investment and construction planning of wind turbine generators and soft switches;

[0011] Step 5: Using convex approximation and relaxation techniques, the nonlinear model in Step 4 is transformed into an easily solvable two-layer mixed integer second-order cone model;

[0012] Step 6: Solve using an iterative algorithm based on reconstruction decomposition;

[0013] Step 7: Output the planning and execution results.

[0014] By considering the injection of hydrogen and methane in the operation of hydrogen-natural gas mixtures, a more practical and tractable two-layer mixed integer second-order cone optimization model is proposed for planning wind turbines and flexible soft switches in an integrated energy system of electricity and natural gas considering the operation of hydrogen-natural gas mixtures with hydrogen and methane injection. The optimal planning strategy is obtained by solving the model through an iterative algorithm based on decomposition and reconstruction.

[0015] In a preferred embodiment, the network and facility parameters in step 1 are: power distribution network parameters, gas distribution network parameters, electricity-to-gas facility parameters, SOP parameters, and wind turbine parameters;

[0016] The specific implementation method of step 3 is as follows:

[0017] Step 8: Constructing a mixed hydrogen natural gas operation model: This model consists of four parts: mixed hydrogen natural gas source, mixed hydrogen natural gas mixing, mixed hydrogen natural gas distribution, and mixed hydrogen natural gas consumption. The mixed hydrogen natural gas source includes upstream natural gas supply, as well as hydrogen and methane produced by the power-to-gas conversion facility. These are mixed with natural gas to form mixed hydrogen natural gas, which is then transported to users through the natural gas distribution network. For ease of explanation, the gas is centrally mixed at the natural gas inlet valve station. However, the method of this invention can also be used to model distributed mixing methods where the gas is injected at other nodes of the natural gas pipeline network.

[0018] Step 9: Model the power-to-gas conversion facility: The power-to-gas conversion facility includes an electrolyzer and a methanation reactor, wherein the operating constraints of the electrolyzer are shown in the following formula:

[0019] , ,

[0020] in the formula Indicates the use of electricity to gas conversion facilities In scenario s, the volumetric flow rate of hydrogen generated by the electrolyzer through water electrolysis; Indicates electricity-to-gas conversion facility The efficiency of hydrogen production in the electrolyzer; This represents the active power consumed by the electrolyzer at grid node j and scenario s. This indicates the maximum active power that the electrolytic cell is allowed to consume; This represents a set constraint that stores all data related to the electricity-to-gas conversion facility. The numbers of the connected power grid nodes; s is the index of the electricity-to-gas conversion facility, indicating which electricity-to-gas conversion facility it is; s is the index of the operating scenario, indicating which operating scenario it is. Indicates the maximum allowable power consumption of the electrolytic cell;

[0021] The output of the methanation reactor is shown in the following formula:

[0022] ,

[0023] in the formula Indicates the use of electricity to gas conversion facilities Under scenario s, the volumetric flow rate of methane output from the methanation reactor; Indicates the use of electricity to gas conversion facilities Conversion efficiency of the methanation reactor; Indicates the use of electricity to gas conversion facilities The volumetric flow rate of hydrogen entering the methanation reactor under scenario s; This indicates the higher heating value of methane;

[0024] The gas volume in the electro-gas conversion facility satisfies the formula shown below:

[0025] ,

[0026] in the formula Indicates the use of electricity to gas conversion facilities The volume of hydrogen directly injected into the natural gas network under scenario s;

[0027] Step 10: Modeling the hydrogen-natural gas mixture: Under the same pressure, according to Dalton's law, the volume of the hydrogen-natural gas mixture is equal to the sum of the volumes of each component, as shown in the following formula:

[0028] ,

[0029] in the formula This represents the total volumetric flow rate of the hydrogen-mixed natural gas formed after mixing, under scenario s. This represents the volume of natural gas purchased by gas valve station m from upstream in scenario s; This indicates the sum of the corresponding quantities for each electricity-to-gas conversion facility; This means calculating and summing all nodes belonging to the natural gas valve station set one by one;

[0030] The volume fraction of hydrogen is expressed as follows:

[0031] ,

[0032] in the formula Indicates in the scene Below, the volume percentage of hydrogen in the hydrogen-mixed natural gas;

[0033] The volume fraction of methane is expressed as follows:

[0034] ,

[0035] in the formula Indicates in the scene Below is the volume percentage of artificially injected methane in the hydrogen-mixed natural gas;

[0036] The volume fraction of all gaseous components must satisfy the constraint condition shown in the following equation:

[0037] ,

[0038] in the formula It is a gas type index, representing the different components in a gas mixture; Indicates the volume fraction of each component;

[0039] The high calorific value (HHV) of hydrogen-mixed natural gas can be estimated using the following formula:

[0040] ,

[0041] in the formula This indicates the higher calorific value of the hydrogen-mixed natural gas in scenario s; Represents pure component gas High calorific value;

[0042] The Warber index (WI) is shown in the following formula:

[0043] ,

[0044] in the formula This represents the Wobbe index of hydrogen-mixed natural gas in scenario s;

[0045] In the formula The specific gravity of the hydrogen-mixed natural gas is expressed by the following formula:

[0046] ,

[0047] in the formula Represents pure component gas The relative density;

[0048] Through simulation, the feasible operational region of the hydrogen-natural gas mixture according to the 12T standard in my country's GB / T13611-2018 standard can be approximated by a quadrilateral region concerning the feasible volume fraction as follows:

[0049] ;

[0050] in the formula Indicates the volume fraction of natural gas;

[0051] Step 11: Modeling the consumption of mixed hydrogen and natural gas: The gas load at each node is modeled as a linear combination of volumetric load and energy load, which can be calculated using the following formula:

[0052] ,

[0053] in the formula This represents the actual volumetric flow rate of mixed hydrogen natural gas consumed by the user at gas network node m in scenario s. This indicates the proportion of energy-type loads in the total load within the gas network; This represents the relative gas load level in scenario s; This represents the baseline demand for volumetric loads at node m. This represents the baseline energy demand of the energy-type load at node m of the gas grid under scenario s; the specific method of step 4 is as follows: the two-level planning model for the investment and construction of wind turbines and flexible soft switches consists of an upper-level problem and a set of lower-level problems, wherein the upper-level problem aims to minimize the annual investment and operating costs, and the lower-level problems reflect the operating performance of the integrated energy system under various typical scenarios based on the investment decisions of the upper-level problem;

[0054] The specific implementation steps of step 4 are as follows:

[0055] Step 12: The upper-level problem is to plan the location and capacity of wind turbines and flexible switches, and minimize the annualized investment and operating costs of the system. The objective function is as follows:

[0056] ,

[0057] in the formula This represents the total annual cost of the integrated energy system; This represents the annual investment cost for soft switching equipment. This represents the annual investment cost of wind turbines. This represents the total annual cost for the distribution network (adn) to purchase electricity from the upstream power grid. This represents the total annual cost for the gas distribution network (DGN) to purchase natural gas from upstream suppliers.

[0058] The annual investment costs of WT and SOP are defined as follows:

[0059] , ,

[0060] in the formula The discount rate represents the opportunity cost and risk of capital; y represents the lifespan of the equipment. This indicates the annual investment cost of a wind turbine (WT). This represents the capacity of the wind turbine installed at node i; This indicates the annual investment cost of the voltage source converter (VSC). This indicates the apparent power capacity of the voltage source converter;

[0061] The annual electricity purchase cost is defined as follows:

[0062] ,

[0063] in the formula This represents the number of operating hours per year for scenario s; Indicates electricity price; This represents the power purchased by the substation in scenario s.

[0064] The annual natural gas purchase cost is defined as follows:

[0065] ,

[0066] in the formula Indicates the price of natural gas; This represents the volume of natural gas purchased from valve station m in scenario s.

[0067] The binary expansion of the capacity limits of the voltage source converter and WT in SOP is shown below:

[0068] ,

[0069] ,

[0070] ,

[0071] ;

[0072] in the formula Represents the 0-1 decision variables for SOP installation; Indicates the maximum capacity allowed to be installed under the SOP; This represents the 0-1 decision variables for WT installation; Indicates the maximum capacity that WT allows to be installed; This indicates the minimum capacity that VSC allows to be installed; Indicates the first The step size of VSC capacity in each SOP; The number of binary variables representing the VSC capacity; Indicates the first The k-th 0-1 decision variable in the binary expansion of the VSC capacity in each SOP; Indicates the minimum capacity that WT allows to be installed; This represents the step size of the i-th WT capacity; The number of binary variables representing the capacity of WT; This represents the k-th 0-1 decision variable in the binary expansion of the WT capacity at node i; This represents the collection of all wind turbines.

[0073] Step 13: The lower-level problem utilizes a linear weighted sum method to minimize both network loss and voltage deviation. The objective function for the lower-level problem is shown in the following equation:

[0074] ,

[0075] in the formula This represents the overall operational performance index of the lower-level problem; This represents the annual line loss of the distribution network; This indicates the annual operating loss of the SOP; This represents the integral of the annual voltage deviation; Indicates the multi-objective weighting coefficients;

[0076] The definitions of network loss, SOP loss, and voltage deviation are as follows:

[0077] ,

[0078] ,

[0079] ;

[0080] in the formula This represents the number of annual operating hours for scenario s; This represents the square of the current flowing through line ij in scenario s; This represents the resistance value of line ij; This indicates that it belongs to SOP. The active power loss of the VSC located at node i in scenario s; Indicates connection to SOP The nodes are accumulated; This represents the actual voltage value of node i in scenario s; Indicates the reference voltage; This represents the sum of all power grid nodes; This represents the set of all nodes.

[0081] Step 14: SOP Operation Modeling The steady-state operation model of SOP running under PQ-VdcQ control mode is described as follows:

[0082] Power balance:

[0083] ,

[0084] in the formula This indicates that it belongs to SOP. The active power injected into the grid by the VSC located at node i in scenario s; This indicates the active power consumed by the VSC during operation;

[0085] VSC loss:

[0086] ,

[0087] in the formula SOP The power loss ratio factor of the VSC at node i; Represents the absolute value of the transmitted power;

[0088] SOP capacity limitations:

[0089] ,

[0090] in the formula This represents the reactive power injected into the grid by the VSC in scenario s; Indicates the rated capacity of the VSC; The 2-norm of a vector;

[0091] VSC reactive power limit:

[0092] ;

[0093] in the formula Indicates the reactive power coefficient;

[0094] Step 15: Active Distribution Network Operation Modeling: The operation constraints of the active distribution network are modeled using a second-order cone relaxation DistFlow model, as described below:

[0095] ,

[0096] in the formula Represents a node In the scene The net injected active power, with positive values ​​indicating injection nodes and negative values ​​indicating outflow from nodes. Indicates from node Flow to all downstream nodes The sum of the active power of the branches. This indicates that there are nodes on all the nodes. Inflow node The active power of the branch, minus the active power loss of that branch. .in branch road The active power at the head end, The square of the branch current, For branch resistance, Represents all nodes A set of.

[0097] ,

[0098] in the formula Represents a node In the scene Net injected reactive power; Indicates from node The sum of reactive power flowing downstream; This represents the reactive power flowing in from upstream minus reactive power losses. ,in For branch circuit reactors.

[0099] ,

[0100] in the formula Represents a node and nodes The square of the voltage amplitude; This represents the square of the branch impedance magnitude; Indicates the square of the branch current; Indicates a branch The active and reactive power at the head end; This represents the set of all branches.

[0101] ,

[0102] ,

[0103] in the formula Represents a node The active power output of the wind turbine generator; Indicates if node If it is a substation node, add the active power injected by the substation; otherwise, add 0. This indicates that all SOPs are connected to the node. The sum of active power injected by the VSC on the device; Representing a scene The relative active load level; Represents a node The baseline active load; Represents a node The active power of the smaller electrolytic cell.

[0104] ,

[0105] formula This represents the reactive power injected into the substation; This represents the reactive power injected into the VSC at the SOP. This represents the baseline reactive load of the node.

[0106] ,

[0107] in the formula Represents the square of the current in the branch; This represents the square of the maximum allowable current in the branch.

[0108] ,

[0109] in the formula Represents a node The square of the voltage; This represents the lower and upper limits of the square of the voltage.

[0110] ,

[0111] in the formula Representing a scene The number of hours allowed to continue; Representing a scene The total active power output of all wind turbines; Representing a scene The sum of active power injected by all substations; This indicates the set threshold for wind power penetration rate.

[0112] ;

[0113] in the formula Represents a node Wind turbine in the scene Those who have made meritorious contributions; Representing a scene The wind power intensity coefficient; This indicates the installed capacity of the node fan.

[0114] Step 16: Natural Gas Distribution Network Operation Modeling: The steady-state Weymouth natural gas network model is shown below:

[0115] ,

[0116] in the formula This represents the node-gas valve station association matrix. It is used to allocate the injection volume of gas valve stations (GVS) to the corresponding nodes, and the matrix elements represent the connection relationship between the nodes and GVSs. Represents a node Total HCNG injection volume at the site; Represents a node In the scene The actual gas demand; Indicates from node Flow to all downstream nodes The sum of gas flow rates; Indicates from all upstream nodes Inflow node The sum of gas flow rates.

[0117] ,

[0118] in the formula Indicates pipeline The Wiemos constant; Represents a node In the scene The absolute pressure of the gas; Represents a node In the scene The absolute pressure of the gas; Indicates pipeline In the scene Gas flow rate; This represents the collection of all gas pipelines.

[0119] ,

[0120] in the formula Represents a node In the scene The gas pressure; Represents a node Minimum allowable pressure; Represents a node Maximum allowable pressure; This represents the set of all gas nodes.

[0121] ,

[0122] in the formula Indicates pipeline In the scene Gas flow rate; Indicates the minimum allowable flow rate of the pipeline; This indicates the maximum allowable flow rate of the pipeline.

[0123] ;

[0124] in the formula This indicates that the gas valve station (GVS) is at the node. The volumetric flow rate of natural gas injected at the point represents the amount of natural gas entering the gas distribution network from the upstream gas source; This indicates the minimum injection flow allowed by GVS; Indicates the maximum injection traffic allowed by GVS; This represents the set of all nodes that have gas valve stations installed.

[0125] The specific method of step 5 is as follows: the second-layer model is converted into a mixed-integer second-order cone programming model by using the following approximation or convex relaxation techniques;

[0126] The specific implementation steps of step 5 are as follows:

[0127] Step 17: Bilinear terms in the hydrogen-natural gas mixing model: Discretize the bilinear product terms in the equation using the binary expansion method, and use the bilinear product terms as the basis for the discretization. For example, the volume fraction of hydrogen can be converted into the following form:

[0128] ,

[0129] ,

[0130] ,

[0131] ,

[0132] ,

[0133] in, It's the step length. It is the number of segments. It is the total volumetric flow rate of the HCNG in the scenario. For segmented indicator variables, As an auxiliary variable, M is a very large number;

[0134] Step 18: Hydrogen-to-Natural Gas Consumption Model: The 1 / HHVs term in the hydrogen-to-natural gas consumption model can be approximated by the McCormick envelope, as shown below:

[0135] ,

[0136] ,

[0137] ,

[0138] ,

[0139] ,

[0140] ,

[0141] in, This represents an auxiliary continuous variable introduced in the scenario, defined as the reciprocal of the high calorific value of HCNG; Represents a node The actual gas demand in the scenario; This indicates the proportion of energy-intensive loads to the total load; Represents a node The benchmark volumetric gas load; Represents a node The energy-type gas load demand in the scenario. In the above formula... The range is arrive .

[0142] Step 19: The Weymouth steady-state natural gas flow can be relaxed using a second-order cone form, as shown in the following equation:

[0143] ,

[0144] To make the relaxation tighter, a penalty term is added to the objective function of the lower-level problem, as shown in the following equation:

[0145] ,

[0146] In this formula, This represents a smaller penalty coefficient;

[0147] The specific method for step 6 is as follows: An algorithm based on reconstruction decomposition is used to solve the bilevel programming problem, wherein the compact form of the bilevel programming model is as follows:

[0148] , where n represents the number of second-order cone constraints;

[0149] The bi-level programming problem mentioned in step 6 can be decomposed into a main problem and two sub-problems. The main problem is shown in the following equation:

[0150] ,

[0151] In this formula, K represents the current iteration number of the main problem. These are the dual variables of the constraints during the k-th iteration. and These are the two dual variables of the i-th second-order cone constraint in the k-th iteration. The bilinear terms in the model can be handled using the big-M method.

[0152] The first subproblem is represented as:

[0153] ;

[0154] The second subproblem addresses the multiple solutions to the first subproblem, yielding a solution that optimizes the MP objective function. The second subproblem is expressed as:

[0155] ,

[0156] In the formula Let represent the optimal solution of SP2. Substitute the values ​​into MP to solve the problem, and iterate through MP and the two SPs until convergence.

[0157] By minimizing the annual investment and operating costs, power loss, and voltage deviation of the integrated power and natural gas energy distribution network system, the techno-economic efficiency of the integrated power and natural gas energy distribution network operation under mixed hydrogen and natural gas operating conditions is improved.

[0158] The planning method for integrated energy distribution network system of electricity and natural gas provided by the present invention has the technical effect of obtaining the optimal planning strategy by solving the problem through an iterative algorithm based on decomposition and reconstruction. Attached Figure Description

[0159] Figure 1 This is a flowchart of a planning method for an integrated energy distribution network system combining electricity and natural gas, as proposed in this invention.

[0160] Figure 2 This is a calculation example system diagram of a planning method for an integrated energy distribution network system combining electricity and natural gas proposed in this invention.

[0161] Figure 3 This is a daily average wind power intensity diagram for a planning method of integrated energy distribution network system of electricity and natural gas proposed in this invention.

[0162] Figure 4 This is a natural gas load level diagram for a planning method of integrated energy distribution network system of electricity and natural gas proposed in this invention.

[0163] Figure 5 This is a power load level diagram for a planning method of integrated energy distribution network system of electricity and natural gas proposed in this invention.

[0164] Figure 6 This is a monthly average wind power intensity diagram for a planning method of integrated energy distribution network system of electricity and natural gas proposed in this invention.

[0165] Figure 7 This is a diagram illustrating the operational framework of hydrogen-natural gas in a planning method for an integrated energy distribution network system combining electricity and natural gas, as proposed in this invention.

[0166] Figure 8 This is a simulation result diagram of the HCNG operating domain based on the 12T gas standard for a planning method of integrated energy distribution network system of electricity and natural gas proposed in this invention.

[0167] Figure 9 This is a typical structural diagram of the SOP (Standard Operating Procedure) for a planning method of integrated electricity and natural gas energy distribution network proposed in this invention.

[0168] Figure 10 This is a comparison chart of the P2G facility absorption power and the system supply power during the testing of the integrated energy distribution network planning method for electricity and natural gas proposed in this invention.

[0169] Figure 11 This is a voltage distribution diagram under extreme conditions during the testing of the integrated energy distribution network planning method for electricity and natural gas proposed in this invention.

[0170] Figure 12 This is a schematic diagram of the composition of an HCNG in a high-wind-power scenario during the testing of a planning method for an integrated energy distribution network system of electricity and natural gas proposed in this invention.

[0171] Figure 13 This is a schematic diagram of the gas operating point in a high-wind-power scenario during the testing of the integrated energy distribution network planning method for electricity and natural gas proposed in this invention.

[0172] Figure 14 This is a component diagram of HCNG in IEGDS under different energy load ratios in a high-wind-power scenario during the testing of an integrated energy distribution network planning method for electricity and natural gas proposed in this invention.

[0173] Figure 15 This is a gas pressure distribution diagram under different energy load ratios during the testing of a planning method for an integrated energy distribution network system of electricity and natural gas proposed in this invention.

[0174] Figure 16 This diagram illustrates the model error during testing of the integrated energy distribution network planning method for electricity and natural gas proposed in this invention.

[0175] Figure 17 This is a table of technical specifications for the facilities to be planned during the testing of the integrated energy distribution network planning method for electricity and natural gas proposed in this invention.

[0176] Figure 18 This diagram shows the parameter settings of the planning model during the testing of the integrated energy distribution network planning method for electricity and natural gas proposed in this invention.

[0177] Figure 19 This is a table of gas parameters used in the testing of a planning method for an integrated energy distribution network system combining electricity and natural gas proposed in this invention.

[0178] Figure 20This is a clustering scenario diagram of load and wind power output levels during the testing of a planning method for an integrated energy distribution network system of electricity and natural gas proposed in this invention.

[0179] Figure 21 This invention presents an iterative solution algorithm based on reconstruction decomposition for a planning method of integrated energy distribution network system of electricity and natural gas.

[0180] Figure 22 This diagram illustrates different scenarios during testing of the integrated energy distribution network planning method for electricity and natural gas proposed in this invention.

[0181] Figure 23 This diagram shows the test planning results of the upper-level problem under four different scenarios for the planning method of the integrated energy distribution network system of electricity and natural gas proposed in this invention.

[0182] Figure 24 These are the test planning results for the lower-level problems under four scenarios of the planning method for an integrated energy distribution network system of electricity and natural gas proposed in this invention.

[0183] Figure 25 This diagram shows the test planning results under different energy type load ratios for the integrated energy distribution network planning method of electricity and natural gas proposed in this invention. Detailed Implementation

[0184] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0185] The planning method for an integrated energy distribution network system of electricity and natural gas disclosed in this invention is mainly applied to the scenario of integrated energy planning.

[0186] Reference Figures 1-25 A planning method for an integrated energy distribution network system combining electricity and natural gas includes the following specific steps:

[0187] Step 1: Input the network parameters of the integrated energy distribution network system to be planned, including the parameters of various facilities to be planned. The integrated energy distribution network test system consists of an IEEE 33-node active distribution network system and a 10-node natural gas distribution network system, including 3 P2G facilities, 7 WT planning locations, and 5 SOP planning locations, such as... Figure 17 As shown, the technical specifications of the equipment to be planned are given, and other parameter settings in the planning model are as follows. Figure 18 As shown, Figure 19 Parameters for different gases are given, with the threshold for WPPL set to 0.5 to achieve high wind power penetration;

[0188] Step 2: Construct renewable energy and load scenarios using historical datasets, and perform clustering using the K-means algorithm. The specific method in Step 2 is as follows: Electricity load and wind load conform to the standardized distribution of the dataset used in the step. It is assumed that gas load follows the standardized curve of the Irish Energy Regulatory Commission dataset. The annual distribution of load related to peak loads is as follows: Figure 3 As shown, Figure 3 The monthly and daily average wind power related to capacity are displayed, and 12 clustering scenarios are obtained using the K-means algorithm. Figure 20 The document provides the power load level, gas load level, wind intensity coefficient, and related operating hours for 12 different scenarios.

[0189] Step 3: Based on the network and facility parameters provided in Step 1 and the scenario clustering results obtained in Step 2, construct a hydrogen-to-natural gas operation model, including modeling the power-to-gas facility, the mixed gas composition, and natural gas consumption;

[0190] Step 4: Construct a two-layer planning model for the investment and construction planning of wind turbine generators and soft switches;

[0191] Step 5: Using convex approximation and relaxation techniques, the nonlinear model in Step 4 is transformed into an easily solvable two-layer mixed integer second-order cone model;

[0192] Step 6: Solve using an iterative algorithm based on reconstruction decomposition;

[0193] Step 7: Output planning and operation results. The specific method for Step 7 is as follows: To verify the effectiveness of flexible soft switching and methane injection in improving economic and technical performance, Figure 22 Four different scenarios were designed, in which the energy load ratio was set to 0.5. Figure 23 The planning results for the upper-level problem are provided under four scenarios. It can be seen that scenario 1 has the highest annual I&O cost, while scenario 4 has the lowest. In scenario 4, the costs of electricity and natural gas procurement are significantly reduced, achieving the maximum installed capacity of the wind turbine. Figure 24 The planning results for the lower-level problem are shown. Comparing Case 1 and Case 2, or Case 2 and Case 4, it can be found that SOP can significantly reduce line loss and voltage deviation in IEGD. Furthermore, it can be seen that Case 4 has the highest wind power penetration rate among the four cases, achieving a proper balance between high wind power penetration rate and operational performance. Figure 10 This shows the power absorption capacity of the P2G facility and the power provided by the system under four different scenarios, from... Figure 10 As can be seen from this, P2G facilities are only... In relatively large-scale scenarios, the system is more likely to violate safety constraints in areas with high wind power generation. Therefore, P2G facilities will convert excess wind power into hydrogen or methane. In scenario 4, the power consumption of P2G facilities accounts for the highest proportion of the total load, especially in scenario 11. At its highest level, in scenario 4, the P2G facility consumed 49.27% ​​of the total power supply. In contrast, this decreased to 8.70%, 13.26%, and 29.34% in scenarios 1, 2, and 3, respectively. Figure 11 The voltage distribution under two extreme conditions is shown, from Figure 11 It can be seen that the minimum voltage is highest in case 4. Figure 8 In the case of 3, the voltage distribution is similar to that in 4. Although the installed capacity of WT increases significantly in 4, the maximum voltage deviation in 4 is only about 0.002 pu higher than that in 3. This is acceptable for voltage deviation under ultra-high wind power penetration.

[0194] Reference Figure 2 and Figure 3 In a preferred embodiment, the network and facility parameters in step 1 are: power distribution network parameters, gas distribution network parameters, electricity-to-gas facility parameters, SOP parameters, and wind turbine parameters.

[0195] Reference Figure 4 , Figure 5 , Figure 7 and Figure 8 In a preferred embodiment, step 3 is implemented as follows:

[0196] Step 8: Constructing a mixed hydrogen natural gas operation model: This model consists of four parts: mixed hydrogen natural gas source, mixed hydrogen natural gas mixing, mixed hydrogen natural gas distribution, and mixed hydrogen natural gas consumption. The mixed hydrogen natural gas source includes upstream natural gas supply, as well as hydrogen and methane produced by the power-to-gas conversion facility. These are mixed with natural gas to form mixed hydrogen natural gas, which is then transported to users through the natural gas distribution network. For ease of explanation, the gas is centrally mixed at the natural gas inlet valve station. However, the method of this invention can also be used to model distributed mixing methods where the gas is injected at other nodes of the natural gas pipeline network.

[0197] Step 9: Model the power-to-gas conversion facility: The power-to-gas conversion facility includes an electrolyzer and a methanation reactor, wherein the operating constraints of the electrolyzer are shown in the following formula:

[0198] , ,

[0199] The output of the methanation reactor is shown in the following formula:

[0200] ,

[0201] The gas volume in the electro-gas conversion facility satisfies the formula shown below:

[0202] ;

[0203] Step 10: Modeling the hydrogen-natural gas mixture: Under the same pressure, according to Dalton's law, the volume of the hydrogen-natural gas mixture is equal to the sum of the volumes of each component, as shown in the following formula:

[0204] ,

[0205] The volume fraction of hydrogen is expressed as follows:

[0206] ,

[0207] The volume fraction of methane is expressed as follows:

[0208] ,

[0209] The volume fraction of all gaseous components must satisfy the constraint condition shown in the following equation:

[0210] ,

[0211] The high calorific value (HHV) of hydrogen-mixed natural gas can be estimated using the following formula:

[0212] ,

[0213] The Warber index (WI) is shown in the following formula:

[0214] ,

[0215] In the formula The specific gravity of the hydrogen-mixed natural gas is expressed by the following formula:

[0216] ,

[0217] Through simulation, the feasible operational region of the hydrogen-natural gas mixture according to the 12T standard in my country's GB / T13611-2018 standard can be approximated by a quadrilateral region concerning the feasible volume fraction as follows:

[0218] ;

[0219] Step 11: Modeling the consumption of mixed hydrogen and natural gas: The gas load at each node is modeled as a linear combination of volumetric load and energy load, which can be calculated using the following formula:

[0220] ,

[0221] in This represents the relative gas load level in scenario s.

[0222] Reference Figure 7 and Figure 8 In a preferred embodiment, the specific method of step 4 is as follows: the two-level planning model for the investment and construction of wind turbine generators and flexible soft switches consists of an upper-level problem and a set of lower-level problems. The upper-level problem aims to minimize the annual investment and operating costs. Based on the investment decisions of the upper-level problem, the lower-level problems reflect the operating performance of the integrated energy system under various typical scenarios.

[0223] Reference Figures 9-16 In a preferred embodiment, step 4 is implemented as follows:

[0224] Step 12: The upper-level problem is to plan the location and capacity of wind turbines and flexible switches, and minimize the annualized investment and operating costs of the system. The objective function is as follows:

[0225] ,

[0226] The annual investment costs of WT and SOP are defined as follows:

[0227] , ,

[0228] The annual electricity purchase cost is defined as follows:

[0229] ,

[0230] The annual natural gas purchase cost is defined as follows:

[0231] ,

[0232] The binary expansion of the capacity limits of the voltage source converter and WT in SOP is shown below:

[0233] ,

[0234] ,

[0235] ,

[0236] ;

[0237] Step 13: The lower-level problem utilizes a linear weighted sum method to minimize both network loss and voltage deviation. The objective function for the lower-level problem is shown in the following equation:

[0238] ,

[0239] The definitions of network loss, SOP loss, and voltage deviation are as follows:

[0240] ,

[0241] ,

[0242] ;

[0243] Step 14: SOP Operation Modeling The steady-state operation model of SOP running under PQ-VdcQ control mode is described as follows:

[0244] Power balance:

[0245] ,

[0246] VSC loss:

[0247] ,

[0248] SOP capacity limitations:

[0249] ,

[0250] VSC reactive power limit:

[0251] ;

[0252] Step 15: Active Distribution Network Operation Modeling: The operation constraints of the active distribution network are modeled using a second-order cone relaxation DistFlow model, as described below:

[0253] ,

[0254] ,

[0255] ,

[0256] ,

[0257] ,

[0258] ,

[0259] ,

[0260] ,

[0261] ,

[0262] ;

[0263] Step 16: Natural Gas Distribution Network Operation Modeling: The steady-state Weymouth natural gas network model is shown below:

[0264] ,

[0265] ,

[0266] ,

[0267] ,

[0268] .

[0269] Reference Figure 16 In a preferred embodiment, step 5 is specifically implemented by using the following approximation or convex relaxation techniques to convert the second-layer model into a mixed-integer second-order cone programming model.

[0270] Reference Figures 9-16 In a preferred embodiment, step 5 is implemented as follows:

[0271] Step 17: Bilinear terms in the hydrogen-natural gas mixing model: Discretize the bilinear product terms in the equation using the binary expansion method, and use the bilinear product terms as the basis for the discretization. For example, the volume fraction of hydrogen can be converted into the following form:

[0272] ,

[0273] ,

[0274] ,

[0275] ,

[0276] ,

[0277] in, It's the step length. It is the number of segments. For segmented indicator variables, As an auxiliary variable, M is a very large number;

[0278] Step 18: Hydrogen-to-Natural Gas Consumption Model: The 1 / HHVs term in the hydrogen-to-natural gas consumption model can be approximated by the McCormick envelope, as shown below:

[0279] ,

[0280] ,

[0281] ,

[0282] ,

[0283] ,

[0284] ,

[0285] in, The range is arrive ;

[0286] Step 19: The Weymouth steady-state natural gas flow can be relaxed using a second-order cone form, as shown in the following equation:

[0287] ,

[0288] To make the relaxation tighter, a penalty term is added to the objective function of the lower-level problem, as shown in the following equation:

[0289] ,

[0290] In this formula, This represents a smaller penalty coefficient.

[0291] Reference Figures 9-16 In a preferred embodiment, step 6 specifically involves solving the bilevel programming problem using an algorithm based on reconstruction decomposition, wherein the compact form of the bilevel programming model is as follows:

[0292] ,

[0293] Where n represents the number of second-order cone constraints.

[0294] Reference Figures 10-16 In a preferred embodiment, the bi-level programming problem mentioned in step 6 can be decomposed into a main problem and two sub-problems, the main problem being as follows:

[0295] ,

[0296] In this formula, K represents the current iteration number of the main problem. These are the dual variables of the constraints during the k-th iteration. and These are the two dual variables of the i-th second-order cone constraint in the k-th iteration. The bilinear terms in the model can be handled using the big-M method.

[0297] The first subproblem is represented as:

[0298] ;

[0299] The second subproblem addresses the multiple solutions to the first subproblem, yielding a solution that optimizes the MP objective function. The second subproblem is expressed as:

[0300] ,

[0301] In the formula Let represent the optimal solution of SP2. Substitute the values ​​into MP to solve the problem, and iterate through MP and the two SPs until convergence.

[0302] Working principle: Step one involves inputting the network parameters of the integrated energy distribution network system to be planned (electricity and natural gas) and the parameters of various facilities to be planned. The integrated energy distribution network test system consists of an IEEE 33-node active distribution network system and a 10-node natural gas distribution network system, including 3 P2G facilities, 7 WT planning locations, and 5 SOP planning locations, such as... Figure 17 As shown, the technical specifications of the equipment to be planned are given, and other parameter settings in the planning model are as follows. Figure 18 As shown, Figure 19 Parameters for different gases are given, with the WPPL threshold set to 0.5 to achieve high wind power penetration. Renewable energy and load scenarios are constructed using historical datasets, and K-means clustering is performed. In step 2, the electricity load and wind load conform to the standardized distribution of the dataset used. It is assumed that the gas load follows the standardized curve of the Irish Energy Regulatory Commission dataset. The annual load distribution related to the peak load is as follows: Figure 3 As shown, Figure 3 The monthly and daily average wind power related to capacity are displayed, and 12 clustering scenarios are obtained using the K-means algorithm. Figure 20 The paper provides the power load level, gas load level, wind intensity coefficient, and related operating hours for 12 scenarios. Step 3 constructs a mixed hydrogen-natural gas operation model based on the network and facility parameters provided in Step 1 and the scenario clustering results obtained in Step 2. This model includes modeling the power-to-gas facility, mixed gas components, and natural gas consumption. Then, a two-layer planning model for wind turbine and soft-switching investment and construction planning is constructed. Using convex approximation and relaxation techniques, the nonlinear model in Step 4 is transformed into an easily solvable two-layer mixed integer second-order cone model. This model is then solved using an iterative algorithm based on reconstruction decomposition. Finally, the planning and operation results are output, thus achieving the goal of obtaining the optimal planning strategy through an iterative algorithm based on decomposition and reconstruction.

[0303] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A planning method for an integrated energy distribution network system combining electricity and natural gas, characterized in that, The specific steps include the following: Step 1: Input the network parameters of the integrated electricity and natural gas energy distribution network system to be planned, as well as the parameters of various facilities to be planned; Step 2: Construct renewable energy and load scenarios using historical datasets, and perform clustering using the K-means algorithm; Step 3: Based on the network and facility parameters provided in Step 1 and the scenario clustering results obtained in Step 2, construct a hydrogen-to-natural gas operation model, including modeling the power-to-gas facility, the mixed gas composition, and natural gas consumption; Step 4: Construct a two-layer planning model for the investment and construction planning of wind turbine generators and soft switches; Step 5: Using convex approximation and relaxation techniques, the nonlinear model in Step 4 is transformed into an easily solvable two-layer mixed integer second-order cone model; Step 6: Solve using an iterative algorithm based on reconstruction decomposition; Step 7: Output the planning and execution results; The specific method of step 4 is as follows: The two-level programming model for the investment and construction of wind turbines and flexible soft switches consists of an upper-level problem and a set of lower-level problems; the specific implementation steps of step 4 are as follows: Step 12: The upper-level problem is to plan the location and capacity of wind turbines and flexible switches, and minimize the annualized investment and operating costs of the system. The objective function is as follows: ， in the formula This represents the total annual cost of the integrated energy system; This represents the annual investment cost for soft switching equipment. This represents the annual investment cost of wind turbines. This represents the total annual cost for the distribution network (ADN) to purchase electricity from the upstream power grid. This represents the total annual cost for the gas distribution network (GDN) to purchase natural gas from upstream suppliers. The annual investment costs of WT and SOP are defined as follows: ， ， in the formula The discount rate represents the opportunity cost and risk of capital; y represents the lifespan of the equipment. This indicates the annual investment cost of a wind turbine (WT). This represents the capacity of the wind turbine installed at node i; This indicates the annual investment cost of the voltage source converter (VSC). This indicates the apparent power capacity of the voltage prototype converter. The annual electricity purchase cost is defined as follows: ， in the formula This represents the number of annual operating hours for scenario s; Indicates electricity price; This represents the power purchased by the substation in scenario s. The annual natural gas purchase cost is defined as follows: ， in the formula Indicates the price of natural gas; This represents the volume of natural gas purchased from valve station m in scenario s. The binary expansion of the capacity limits of the voltage source converter and WT in SOP is shown below: ， ， ， ； in the formula Represents the 0-1 decision variables for SOP installation; Indicates the maximum capacity allowed to be installed under the SOP; This represents the 0-1 decision variables for WT installation; This indicates the maximum capacity that WT allows to be installed. This indicates the minimum capacity that VSC allows to be installed; Indicates the first The step size of VSC capacity in each SOP; The number of binary variables representing the VSC capacity; Indicates the first The k-th 0-1 decision variable in the binary expansion of the VSC capacity in each SOP; Indicates the minimum capacity that WT allows to be installed; This represents the step size of the i-th WT capacity; The number of binary variables representing the capacity of WT; This represents the k-th 0-1 decision variable in the binary expansion of the WT capacity at node i; This represents the set of all wind turbines; Step 13: The lower-level problem utilizes a linear weighted sum method to minimize both network loss and voltage deviation. The objective function for the lower-level problem is shown in the following equation: ， in the formula This represents the overall operational performance index of the lower-level problem; This represents the annual line loss of the distribution network; This indicates the annual operating loss of the SOP; This represents the integral of the annual voltage deviation; Indicates the multi-objective weighting coefficients. The definitions of network loss, SOP loss, and voltage deviation are as follows: ， ， ； in the formula This represents the number of annual operating hours for scenario s; This represents the square of the current flowing through line ij in scenario s; This represents the resistance value of line ij; This indicates that it belongs to SOP. The active power loss of the VSC located at node i in scenario s; Indicates connection to SOP The nodes are accumulated; This represents the actual voltage value of node i in scenario s; Indicates the reference voltage; This represents the sum of all power grid nodes. Represents the set of all nodes. Step 14: SOP Operation Modeling The steady-state operation model of SOP running under PQ-VdcQ control mode is described as follows: Power balance: ， in the formula This indicates that it belongs to SOP. The active power injected into the grid by the VSC located at node i in scenario s; This indicates the active power consumed by the VSC during operation. VSC loss: ， in the formula SOP The power loss ratio factor of the VSC at node i; Represents the absolute value of the transmitted power. SOP capacity limitations: ， in the formula This represents the reactive power injected into the grid by the VSC in scenario s; Indicates the rated capacity of the VSC; Describes the 2-norm of a vector. VSC reactive power limit: ； in the formula Indicates the reactive power coefficient. Step 15: Active Distribution Network Operation Modeling: The operation constraints of the active distribution network are modeled using a second-order cone relaxation DistFlow model, described as follows: ， in the formula Represents a node In the scene The net injected active power, with positive values ​​indicating injection nodes and negative values ​​indicating outflow from nodes. Indicates from node Flow to all downstream nodes The sum of the active power of the branches, This indicates that there are nodes on all the nodes. Inflow node The active power of the branch, minus the active power loss of that branch. ,in branch road The active power at the head end, The square of the branch current, For branch resistance, Represents all nodes The set, ， in the formula Represents a node In the scene Net injected reactive power; Indicates from node The sum of reactive power flowing downstream; This represents the reactive power flowing in from upstream minus reactive power losses. ,in For branch reactor, ， in the formula Represents a node and nodes The square of the voltage amplitude; This represents the square of the branch impedance magnitude; Indicates the square of the branch current; Indicates a branch The active and reactive power at the head end; Represents the set of all branches. ， ， in the formula Represents a node The active power output of the wind turbine generator; Indicates if node If it is a substation node, add the active power injected by the substation; otherwise, add 0. This indicates that all SOPs are connected to the node. The sum of active power injected by the VSC on the device; Representing a scene The relative active load level; Represents a node The baseline active load; Represents a node The active power of the smaller electrolytic cell. ， formula This represents the reactive power injected into the substation; This represents the reactive power injected into the VSC at the SOP. This represents the baseline reactive load of the node. ， in the formula Represents the square of the current in the branch; This represents the square of the maximum allowable current in the branch. ， in the formula Represents a node The square of the voltage; This represents the lower and upper limits of the square of the voltage. ， in the formula Representing a scene The number of hours allowed to continue; Representing a scene The total active power output of all wind turbines; Representing a scene The sum of active power injected by all substations; This indicates the set wind power penetration threshold. ； in the formula Represents a node Wind turbine in the scene Those who have made meritorious contributions; Representing a scene The wind power intensity coefficient; Indicates the installed capacity of the node fan; Step 16: Natural Gas Distribution Network Operation Modeling: The steady-state Weymouth natural gas network model is shown below: ， in the formula This represents the node-gas valve station association matrix, used to allocate the injection volume of gas valve stations (GVS) to the corresponding nodes. The matrix elements represent the connection relationship between the nodes and GVS. Represents a node Total injection volume of hydrogen-rich compressed natural gas (HCNG) at the location; Represents a node In the scene The actual gas demand; Indicates from node Flow to all downstream nodes The sum of gas flow rates; Indicates from all upstream nodes Inflow node The sum of gas flow rates, , in the formula Indicates pipeline The Wiemos constant; Represents a node In the scene The absolute pressure of the gas; Represents a node In the scene The absolute pressure of the gas; Indicates pipeline In the scene Gas flow rate; This represents the collection of all gas pipelines. ， in the formula Represents a node In the scene The gas pressure; Represents a node Minimum allowable pressure; Represents a node Maximum allowable pressure; Represents the set of all gas nodes. ， in the formula Indicates pipeline In the scene Gas flow rate; Indicates the minimum allowable flow rate of the pipeline; Indicates the maximum allowable flow rate of the pipeline. , in the formula This indicates that the gas valve station (GVS) is at the node. The volumetric flow rate of natural gas injected at this point represents the amount of natural gas entering the gas distribution network from the upstream gas source; This indicates the minimum injection flow allowed by GVS; This indicates the maximum injection traffic allowed by GVS; This represents the set of all nodes that have gas valve stations installed.

2. The planning method for an integrated electricity and natural gas energy distribution network system according to claim 1, characterized in that, The network and facility parameters in step 1 are: power distribution network parameters, gas distribution network parameters, electricity-to-gas facility parameters, SOP parameters, and wind turbine parameters.

3. The planning method for an integrated electricity and natural gas energy distribution network system according to claim 1, characterized in that, The specific implementation method of step 3 is as follows: Step 8: Construct a mixed hydrogen natural gas operation model: It consists of four parts: mixed hydrogen natural gas source, mixed hydrogen natural gas mixing, mixed hydrogen natural gas distribution, and mixed hydrogen natural gas consumption. The mixed hydrogen natural gas source includes the upstream natural gas supply, as well as hydrogen and methane produced by the power-to-gas facility, which are mixed with natural gas to form mixed hydrogen natural gas, and then transported to users through the natural gas distribution network. Step 9: Model the power-to-gas conversion facility: The power-to-gas conversion facility includes an electrolyzer and a methanation reactor, wherein the operating constraints of the electrolyzer are shown in the following formula: ， ， in the formula Indicates the use of electricity to gas conversion facilities In scenario s, the volumetric flow rate of hydrogen generated by the electrolyzer through water electrolysis; Indicates electricity-to-gas conversion facility The efficiency of hydrogen production in the electrolyzer; This represents the active power consumed by the electrolyzer at grid node j and scenario s. This indicates the maximum active power that the electrolytic cell is allowed to consume; This represents a set constraint that stores all data related to the electricity-to-gas conversion facility. The numbers of the connected power grid nodes; s is the index of the electricity-to-gas conversion facility, indicating which electricity-to-gas conversion facility it is; s is the index of the operating scenario, indicating which operating scenario it is. Indicates the maximum allowable power consumption of the electrolytic cell. The output of the methanation reactor is shown in the following formula: ， in the formula Indicates the use of electricity to gas conversion facilities Under scenario s, the volumetric flow rate of methane output from the methanation reactor; Indicates the use of electricity to gas conversion facilities Conversion efficiency of the methanation reactor; Indicates the use of electricity to gas conversion facilities The volumetric flow rate of hydrogen entering the methanation reactor under scenario s; This indicates the higher heating value of methane. The gas volume in the electro-gas conversion facility satisfies the formula shown below: ， in the formula Indicates the use of electricity to gas conversion facilities The volume of hydrogen directly injected into the natural gas network under scenario s; Step 10: Modeling the hydrogen-natural gas mixture: Under the same pressure, according to Dalton's law, the volume of the hydrogen-natural gas mixture is equal to the sum of the volumes of each component, as shown in the following formula: ， in the formula This represents the total volumetric flow rate of the hydrogen-mixed natural gas formed after mixing, under scenario s. This represents the volume of natural gas purchased by gas valve station m from upstream in scenario s; This indicates the sum of the corresponding quantities for each electricity-to-gas conversion facility; This means calculating and summing the results for each node belonging to the natural gas valve station set. The volume fraction of hydrogen is expressed as follows: ， in the formula Indicates in the scene Below is the volume percentage of hydrogen in the hydrogen-mixed natural gas. The volume fraction of methane is expressed as follows: ， in the formula Indicates in the scene Below is the volume percentage of artificially injected methane in the hydrogen-mixed natural gas. The volume fraction of all gaseous components must satisfy the constraint condition shown in the following equation: ， in the formula It is a gas type index, representing the different components in a gas mixture; Indicates the volume fraction of each component. The high calorific value (HHV) of hydrogen-mixed natural gas is estimated using the following formula: ， in the formula This indicates the higher calorific value of the hydrogen-mixed natural gas in scenario s; Represents pure component gas High calorific value, The Warber index (WI) is shown in the following formula: ， in the formula This represents the Wobbe index of hydrogen-mixed natural gas in scenario s. In the formula The specific gravity of the hydrogen-mixed natural gas is expressed by the following formula: ， in the formula Represents pure component gas relative density, Through simulation, and using the 12T standard of my country's GB / T13611-2018, the feasible operating region of the mixed hydrogen and natural gas is approximated by a quadrilateral region with respect to the feasible volume fraction. Step 11: Modeling the consumption of mixed hydrogen and natural gas: The gas load at each node is modeled as a linear combination of volumetric load and energy load, calculated using the following formula: ， In the formula This represents the actual volumetric flow rate of mixed hydrogen natural gas consumed by the user at gas network node m in scenario s. This indicates the proportion of energy-type loads in the total load within the gas network; This represents the relative gas load level in scenario s; This represents the baseline demand for volumetric loads at node m. This represents the baseline energy demand of the energy-type load at node m of the gas network under scenario s.

4. The planning method for an integrated energy distribution network system of electricity and natural gas according to claim 1, characterized in that, The specific method of step 5 is as follows: the second-layer model is converted into a mixed-integer second-order cone programming model by using the following approximation or convex relaxation techniques.

5. The planning method for an integrated electricity and natural gas energy distribution network system according to claim 4, characterized in that, The specific implementation steps of step 5 are as follows: Step 17: Bilinear terms in the hydrogen-natural gas mixing model: Discretize the bilinear product terms in the equation using the binary expansion method, and use the bilinear product terms as the basis for the discretization. For example, the volume fraction of hydrogen can be converted into the following form: ， ， ， ， ， in, It's the step length. It is the number of segments. It is the total volumetric flow rate of the HCNG in the scenario. For segmented indicator variables, As an auxiliary variable, M is a very large number; Step 18: Hydrogen-to-Natural Gas Consumption Model: The 1 / HHVs term in the hydrogen-to-natural gas consumption model is approximated by the McCormick envelope, as shown below: ， ， ， ， ， ， in, This represents an auxiliary continuous variable introduced in the scenario, defined as the reciprocal of the high calorific value of HCNG; Represents a node The actual gas demand in the scenario; This indicates the proportion of energy-intensive loads to the total load; Represents a node The benchmark volumetric gas load; Represents a node In the scenario's energy-type gas load demand, the above formula... The range is arrive ; Step 19: The Weymouth steady-state natural gas flow can be relaxed using a second-order cone form, as shown in the following equation: ， in the formula Representing a scene Gas flow rate in the lower pipeline; This represents the Weymouth constant of the pipe; Representing a scene Next node The absolute pressure of the gas; Representing a scene Next node The absolute pressure of the gas; This represents the collection of all gas pipelines; To make the relaxation tighter, a penalty term is added to the objective function of the lower-level problem, as shown in the following equation: ， In this formula, This represents a smaller penalty coefficient. This represents the modified objective function value for the lower-level problem; This represents the objective function value of the original lower-level problem.

6. The planning method for an integrated energy distribution network system of electricity and natural gas according to claim 1, characterized in that, The specific method for step 6 is as follows: An algorithm based on reconstruction decomposition is used to solve the bilevel programming problem, wherein the compact form of the bilevel programming model is as follows: ， in This represents the 0-1 decision variable vector of the higher-level planning. Represents the continuously running variables of the lower-level program. This indicates the corresponding objective function in the upper layer. The cost coefficient vector, This indicates the corresponding objective function in the upper layer. The performance coefficient vector, Represents variables for upper-level investment decisions The set of all feasible region constraints that it must satisfy. This indicates an optimization problem for the entire lower-level operation. This represents the auxiliary 0-1 variable vector for lower-level planning. The coefficient matrix representing the linear constraints. The vector representing the constant terms of the linear constraints. This represents the coefficient matrix of the i-th second-order cone constraint. Let represent the coefficient vector of the linear part in the i-th second-order cone constraint, and n represent the number of second-order cone constraints.

7. The planning method for an integrated electricity and natural gas energy distribution network system according to claim 6, characterized in that, The bi-level programming problem mentioned in step 6 can be decomposed into a main problem and two sub-problems. The main problem is shown in the following equation: ， In this formula, K represents the current iteration number of the main problem. These are the dual variables of the constraints during the k-th iteration. This represents a fixed value obtained from solving subproblem 2. and These are the two dual variables of the i-th second-order cone constraint in the k-th iteration. The bilinear terms in the model are handled using the big-M method. The first subproblem is represented as: ； In the above formula This represents the optimal objective function value for the first subproblem (SP1). Indicates the corresponding objective function in the lower level The performance coefficient vector, The vector representing the constant terms of the linear constraints. This represents the optimal investment decision solution obtained in the k-th iteration of the main problem. The second subproblem addresses the multiple solutions to the first subproblem, yielding a solution that improves the MP objective function. The second subproblem is expressed as: ， In the formula The tuple represents the optimal objective function value of the second subproblem (SP2). Let represent the optimal solution of SP2. Substitute the values ​​into MP to solve the problem, and iterate through MP and the two SPs until convergence.

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