Gas-electric combined distribution network distribution robust extension planning method considering system toughness constraint

Abstract

The invention relates to the technical field of pneumatic-electric combined system planning and toughness improvement, in particular to a pneumatic-electric combined distribution network distribution robust extension planning method considering system toughness constraints. Firstly, a pneumoelectric joint distribution network expansion planning model with the sum of operation cost and planning cost as a target function is established; in the aspect of improving the toughness, the capacity of replacing fuel supply loads of the dual-fuel unit when natural gas is insufficient under disasters is considered, then modeling is carried out on investment and construction of the dual-fuel unit and a tie line, and constraints such as power distribution network reconstruction are considered on the operation level; secondly, considering the uncertainty of extreme disaster influence by using a KL divergence-based distribution robust optimization method, and establishing a two-stage distribution robust extension planning model considering the toughness constraint of the gas-electricity combined distribution network system under the extreme disaster; according to the method, the economical efficiency of the gas-electricity combined distribution network during normal operation can be improved, and meanwhile the toughness capacity during extreme disasters is enhanced.

Description

Technical Field

[0001] The present invention relates to the field of gas-electricity combined system planning and resilience improvement, and in particular to a method for planning robust expansion of a gas-electricity combined distribution network taking into account system resilience constraints. Background Art

[0002] Global climate change has become one of the most severe challenges facing the world. In order to meet this challenge, starting from the energy structure to carry out low-carbon optimization and gradually replace traditional fossil energy with clean renewable energy has become the only way to develop a low-carbon power system. At the same time, research shows that the integrated energy system is of great significance in reducing carbon emissions and improving energy utilization, making the power system more and more closely coupled with other energy systems such as natural gas and heat. Natural gas power generation, with its significant advantages such as low carbon, clean and high efficiency, also occupies an important position in the path to achieving the "dual carbon" goal. However, with the deepening of gas-electricity coupling, the risk of fault propagation between systems is also increasing. In a major power outage accident, an extreme cold wave caused insufficient supply of the natural gas system, resulting in large-scale shutdowns. Therefore, the reliability evaluation indicators of traditional power systems have limitations in dealing with similar disasters, and the concept of resilience has gradually attracted attention, which emphasizes the system's ability to recover quickly in the face of extreme events with low probability and high impact.

[0003] With the development of smart grids and the widespread application of distributed energy, methods for improving the resilience of power systems are becoming increasingly diverse. Current research mainly focuses on two categories: resilience improvement methods based on operation and resilience improvement methods based on planning. Among them, optimization methods at the operation level include network reconstruction, emergency resource scheduling, and island division, while the planning level involves line reinforcement, vegetation management, investment in backup generators and energy storage equipment, etc. However, existing pre-disaster planning studies often ignore the economic benefits of flexible resources during normal operation, resulting in high investment costs. By extending planning and incorporating the economic benefits of normal system operation into the objective function, a more balanced investment strategy can be achieved. In addition, distributed robust optimization, as an important method for measuring uncertainty, is gradually gaining wider application. Summary of the invention

[0004] The present invention provides a distributed robust expansion planning method for a gas-electricity combined distribution network that takes into account the system resilience constraints under extreme disasters in view of the existing technology. In the gas-electricity combined distribution network planning problem, the investment and construction of various equipment and the upgrade of lines are taken into account, and the role of dual-fuel units and interconnection lines in improving system resilience is considered. The distributed robust method based on Kullback-Leibler (KL) divergence is used to model the uncertainty of the impact of extreme disasters, and a two-stage distributed robust planning model is obtained. The technical solution is as follows:

[0005] The robust expansion planning method of gas-electricity combined distribution network considering system resilience constraints includes the following steps:

[0006] Step 1: Considering the construction of gas turbines, dual-fuel units, power-to-gas equipment, power storage equipment, gas storage equipment and interconnection lines, as well as the coordinated planning of power supply lines and natural gas pipeline upgrades, the goal is to minimize the sum of the operating cost and construction cost of the gas-electricity combined distribution network, and considering the safe operation constraints of the gas distribution network and the operation constraints of the coupling equipment, a gas-electricity combined distribution network expansion planning model is established;

[0007] Step 2: Based on the gas-electricity combined distribution network expansion planning model, the operation constraints of the gas-electricity combined distribution network under the influence of extreme disasters are modeled, and the fuel conversion effect of dual-fuel units when natural gas is insufficient under extreme disasters is considered, and the flexibility of distribution network reconstruction is increased after the construction of interconnection lines. The minimum load loss of the gas-electricity combined distribution network system during the period of extreme disasters is calculated, and then the resilience constraint is constructed by limiting the upper limit of the allowable load loss, and a robust expansion planning model of the gas-electricity combined distribution network that considers the resilience constraint and balances economy and resilience is obtained;

[0008] Step 3: Based on the robust expansion planning model of the gas-electricity combined distribution network that balances economy and resilience, taking into account the uncertainty of the probability of extreme disaster scenarios, the linearized KL divergence is used to describe the distance between the actual distribution of extreme disasters and the empirical distribution, and an extreme disaster uncertainty set is established;

[0009] Step 4: According to the extreme disaster uncertainty set, a two-stage distributed robust expansion planning model based on KL divergence is established; the basic scenario in the first stage is used to ensure the economy of system planning, and the resilience index under the extreme disaster scenario is used as a constraint in the second stage to obtain the optimal decision that meets the set resilience constraint;

[0010] Step 5: Use the column and constraint generation algorithm to decouple the two-stage robust expansion planning model into the main problem and sub-problems and solve them iteratively.

[0011] The invention has the following advantages and beneficial effects:

[0012] 1) The present invention takes into account that the introduction of dual-fuel units and interconnection lines significantly enhances the system's ability to respond to extreme disasters. The dual-fuel units reduce the system's dependence on natural gas supply by switching to fuel-fired power generation during faults, ensuring the continuity of power supply. The construction of interconnection lines provides greater flexibility for the system, so that when a disaster occurs, the distribution network can be reconstructed to form a gas-electricity island. The energy within the island can be optimally distributed through optimized scheduling, effectively ensuring the supply of critical electricity or natural gas loads, thereby further improving the overall resilience and reliability of the system.

[0013] 2) The present invention combines multiple flexible resources such as dual-fuel units, distribution network interconnection lines, distributed units, power-to-gas equipment and energy storage devices to achieve dual optimization under normal operation and extreme disaster conditions. Compared with the prior art, the present invention uses a distributed robust method to measure the uncertainty of extreme disaster scenarios. Compared with the robust optimization method, the conservatism is lower and the economy is higher. It can reduce the investment cost of redundant resources and achieve a trade-off between economy and resilience in the planning stage, which has high application value. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 This is a logical diagram of line connectivity when extreme disasters occur.

[0015] Figure 2 Detailed solution flow chart for the present invention.

[0016] Figure 3 This is a gas-electricity combined distribution network system adopted in an embodiment of the present invention.

[0017] Figure 4 This is the distribution network operation mode in Example 2.

[0018] Figure 5 This is the energy storage and P2G operation curve of Example 3.

[0019] Figure 6 Comparison of load loss penalties under the worst distribution of Examples 1-4.

[0020] Figure 7 The figure shows the comparison of the working status of the dual-fuel unit under the basic scenario and the extreme disaster scenario in the embodiment.

[0021] Figure 8 The figure is a comparison of P2G power consumption in the basic scenario and the extreme disaster scenario in the embodiment.

[0022] Fig. 9 The transmission power comparison of the tie line (18-33) in the basic scenario and the extreme scenario in the embodiment.

[0023] Figure 10(a) shows the reconstruction results (t=15) of gas-fired power combined island scenario 1 and the intra-day load loss situation under extreme disaster scenarios.

[0024] Figure 10(b) shows the reconstruction results (t=15) of gas-fired power combined island scenario 2 and the intra-day load loss situation under extreme disaster scenarios.

[0025] Figure 10(c) shows the reconstruction results (t=15) of gas-electricity combined island scenario 3 and the intra-day load loss situation under extreme disaster scenarios.

[0026] Fig.11 This is a sensitivity analysis of the system cost in the embodiment. DETAILED DESCRIPTION

[0027] The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments.

[0028] Step 1: Consider the coordinated planning of the construction and investment of gas turbines, dual-fuel units, power-to-gas equipment, power storage equipment, gas storage equipment and interconnection lines, power supply lines and natural gas pipeline upgrades, take the minimum sum of the operating cost and construction cost of the gas-electricity combined distribution network as the goal, consider the safe operation constraints of the gas distribution network, the distribution network and the operation constraints of the coupling equipment, and establish a gas-electricity combined distribution network expansion planning model.

[0029] Among them, the established gas-electricity joint distribution network expansion planning model is as follows:

[0030] The objective function is the construction cost of the gas-electricity combined distribution network C I and operating cost C o The sum is minimized as follows:

[0031] minOF=min(C I +C o ) (1)

[0032] The expansion plan targets investments in gas turbines, dual-fuel units, power-to-gas, replacement of distribution network lines and gas distribution network pipelines, interconnection lines, and power and gas storage equipment. Each facility has k alternative plans, with specific investment and construction costs C I The expression is as follows.

[0033]

[0034] Where: is the average annual unit price of investment in k-type distributed units; They represent the cost of investing in power-to-gas equipment, distribution network lines, gas distribution network pipelines, interconnecting lines, gas storage equipment and electricity storage equipment respectively; is a candidate set of distributed units, including gas turbines and dual-fuel units, L s A collection of alternative contact lines. It is a collection of corresponding power-to-gas equipment, distribution network lines, gas distribution network pipelines, gas storage equipment and power storage equipment; is the investment state variable of distributed generators. When it is 1, it means that investment has been made, and when it is 0, it means the original facilities of the system. The superscript k represents the model of the generator to be selected; They represent the construction status variables of power-to-gas equipment, distribution network lines, gas distribution network pipelines, interconnecting lines, gas storage equipment and electricity storage equipment respectively.

[0035]

[0036] Where: r is the typical day index, h is the hour index, Indicates the number of days included in a typical day r; Ω GU represents the set of gas generating units connected to the distribution network; L is the set of distribution network lines; Represents the unit fuel price when the dual-fuel unit g is running when the diesel generator or oil-fired generator is running; is the fuel quantity of dual-fuel unit g, P g,rh is the output of the g-th distributed unit in the corresponding dispatching period, is the energy cost coefficient, SU g,rh and SD g,rh The amount of fuel consumed for the startup and shutdown of distributed units; is the cost of a single operation of the section switch and the tie switch on line (i, j), γ ij,r is the number of switch operations on line (i, j) in a typical day r; is the wind turbine operation and maintenance cost coefficient, is the dispatch output of the wind turbine; The penalty price for unit wind curtailment, is the abandoned wind power; is the gas purchase price of node n in the gas distribution network, Purchase gas power for the system; is the electricity purchase price of the electricity purchasing node, Purchase active power for the system; is the unit power failure load penalty price of the load node, ΔP j,rh is the power loss; is the penalty price per unit gas loss load of gas distribution network node n, ΔG n,rh is the lost power.

[0037] The constraints include construction-related constraints, distribution network operation constraints, gas distribution network operation constraints, and coupling equipment operation constraints, as follows:

[0038] (1) Investment and construction constraints

[0039]

[0040] Where: are the construction state variables of the original distribution network lines and gas distribution network pipelines respectively; L is the set of distribution network lines, and PL is the set of gas distribution network pipelines.

[0041] (2) Distribution network operation constraints

[0042] 1) Node power balance constraints

[0043]

[0044] Where: S(j) is the set of devices connected to the distribution network node j, δ(j) is the set of receiving nodes corresponding to the distribution network node j; represents the active power released and stored by the storage device connected to the distribution network node j; P ij,rh is the active power flow of line (i, j); is the active load of distribution network node j, is the power consumption of the power-to-gas equipment q; It is the reactive power flowing into the power purchasing node of the distribution network. is the reactive power generated by the wind turbine. represents the reactive power released and stored by the storage device connected to the distribution network node j, Q ij,rh is the reactive power transmitted by line (i, j), Q g,rh is the reactive power generated by the distributed generator g, is the predicted reactive load of distribution network node j, φ j,rh is the proportional coefficient that needs to be satisfied for removing active and reactive power at the same distribution network node j.

[0045] 2) Line transmission power constraints and node voltage constraints

[0046]

[0047] Where: are the lower and upper limits of the voltage amplitude required for node j in the distribution network, respectively; is the maximum active power that the original line and the k-type line (i, j) can transmit; V j,rh is the voltage amplitude of the distribution network node j.

[0048] 3) Distribution network reconstruction constraints

[0049]

[0050] Where: a ij,rh is a binary variable, representing the actual connectivity status of line (i, j). When it is 1, it means it is connected, and when it is 0, it means the line is disconnected; ε ij,rh is a binary variable representing whether the switch of line (i, j) is closed at time h on typical day r. When it is 1, it means it is closed. The binary variable β ij Represents the virtual connection status of line (i, j), n s is the substation node, N is the set of distribution network nodes, represents the virtual traffic on line (i, j) sent to node ω, λ ij is a binary variable representing the directed connectivity status of link (i, j).

[0051] Among them, the line connectivity logic diagram when extreme disasters occur is as follows: Figure 1 shown.

[0052] 4) Distribution network flow constraints

[0053]

[0054]

[0055] Where: V j,rh is the voltage amplitude at node j, are the resistance and reactance values ​​of the original line / k-type line (i, j), V0 is the reference value of the voltage amplitude; M is a very large number. is the slack variable. ij,rh is a binary variable indicating whether the switch of line (i, j) r has a state change at time h on a typical day. When it is 1, it represents a switch action. ij,r is the number of switching operations of the switch on line (i, j) in typical day r.

[0056] 5) Wind turbine operation constraints

[0057]

[0058] Where: is the predicted output of the unit at time h on typical day r; θ j is the power factor angle of the wind turbine at node j.

[0059] 6) Distributed unit operation constraints

[0060]

[0061] Where: are the up and down climbing rates of the unit, su g and sd g are the fuel consumption of the unit during startup and shutdown, I g,rh is a binary variable representing the start / stop status of the generator at time h. When it is 1, it means it is on. is the original set of units in the system, They are respectively the upper and lower limits of active output of the original / k type units. The upper and lower limits of reactive power output are for the original / k type units.

[0062] 7) Grid transmission power limitation

[0063]

[0064] Where: are the maximum / minimum active and reactive transmission powers of node j, respectively.

[0065] 8) Operation constraints of power storage equipment

[0066]

[0067] Where: is the maximum / minimum charging and discharging active power of the k-type energy storage device; is the maximum / minimum charging and discharging reactive power of the k-type energy storage device; is the minimum and maximum capacity of the k-type power storage device; is the capacity of the storage at time h, The efficiency of discharging and charging energy storage devices.

[0068] (3) Gas distribution network operation constraints:

[0069] 1) Node natural gas balance constraints

[0070]

[0071] Where: S(n) is the set of devices connected to node n of the gas distribution network, δ(n) is the set of receiving nodes corresponding to node n, is the gas discharge and storage power of the gas storage device connected to the node n of the gas distribution network, f mn,rh is the flow of the natural gas pipeline (m,n), is the gas production power of the power-to-gas device q; is the predicted gas load of node n, G g,rh The gas power consumption of a gas turbine or a dual-fuel unit that uses natural gas to generate electricity.

[0072] 2) Gas distribution network pipeline flow constraints

[0073]

[0074] Where: π m,rh and π n,rh are the air pressures at nodes m and n at this time, is the characteristic coefficient of the original / k type pipeline (m,n).

[0075] 3) Pipeline transmission air flow limitation and node air pressure constraint

[0076]

[0077]

[0078] Where: are the lower and upper limits of the gas pressure allowed at node n in the gas distribution network, respectively; They are the maximum gas flow rates allowed to be transmitted by the original and k-type natural gas pipelines (m, n) respectively.

[0079] 4) Gas purchase power and gas loss load constraints

[0080]

[0081] Where: are the minimum and maximum gas purchasing powers allowed for node n.

[0082] 5) Gas storage equipment constraints

[0083]

[0084] Where: is the maximum / minimum gas storage and release power of the K-type gas storage device; is the minimum and maximum capacity of the K-type gas storage equipment; is the capacity of the gas storage equipment in the h period, The deflation and storage efficiency of gas storage equipment.

[0085] (4) Coupling equipment operation constraints

[0086] 1) Gas turbine operation constraints

[0087] The ramping constraints, start-stop fuel constraints, and active and reactive output constraints of the gas turbine are shown in the unit operation constraints of equations (27)-(30). The gas consumption of the gas turbine is calculated as follows:

[0088]

[0089] Where: G g,rh is the gas consumption power of the gas turbine g, is the gas consumption coefficient of the gas turbine; HHV is the high calorific value of natural gas, which is 1.026MBtu / kcf.

[0090] 2) Dual-fuel unit operation constraints

[0091] The ramping constraint and start-stop fuel constraint of the dual-fuel unit are the same as those of equations (27) and (28). During operation, the dual-fuel unit can only choose one of diesel or natural gas for power generation. This state constraint is shown in equation (41). The active output constraint is shown in equations (42) and (43). The reactive output constraint is similar to the active output constraint. The gas consumption constraint of the dual-fuel unit is similar to that of the gas turbine, i.e., (46); there is an upper limit on the number of times the unit can change the fuel type in a day. This constraint is described by equation (44).

[0092]

[0093]

[0094] Where: Ω DF represents the set of dual-fuel units, It is a binary variable, and when it is 1, it represents gas / oil power generation of dual-fuel unit; Represents the corresponding power of the dual-fuel unit using two fuels; is a binary variable. When it is 1, it means that the unit g switches from fuel oil to gas power generation during the h period. Similarly, It means the unit is switched from gas to oil power generation; The maximum number of fuel changes allowed for unit g in one day.

[0095] 3) Power-to-gas equipment operation constraints

[0096]

[0097] Where: φ is the energy conversion coefficient, which is 3.4MBtu / MW. is the efficiency of the qth P2G device; are the minimum and maximum gas production power of the qth P2G device, A collection of existing devices.

[0098] 4) Other constraints

[0099] In order to avoid meaningless energy closed loops, the power-to-gas device and the gas turbine or gas-fired dual-fuel power generation unit connected to the same node cannot be turned on at the same time, as shown below.

[0100]

[0101] Except for the Weymouth equation, all the constraints in the planning model established by the present invention are linear constraints. The first-order Taylor series expansion method is now used to linearize the Weymouth equation:

[0102]

[0103]

[0104] Where: The b-th pressure breakpoint of for The b-th pressure breakpoint of are the coefficients in the first-order Taylor series process.

[0105] Step 2: Based on the planning model obtained in step 1, the operating constraints of the gas-electricity combined distribution network under the influence of extreme disasters are modeled, and the fuel conversion of dual-fuel units when natural gas is insufficient under extreme disasters is taken into account. The flexibility of the distribution network reconstruction is increased after the construction of the interconnection line. The minimum load loss of the gas-electricity combined distribution network system during the period of extreme disasters is calculated, and then the resilience constraint is constructed by limiting the upper limit of the allowable load loss. The robust expansion planning method of the gas-electricity combined distribution network that considers the resilience constraint and balances economy and resilience is obtained.

[0106] The planning model based on step 1 considers the resilience constraint and balances the economic and resilience of the gas-electricity combined distribution network. The specific expansion planning model includes:

[0107] Step 2.1: Consider the constraints of the gas-electricity distribution network after extreme disasters;

[0108] The connectivity of distribution network lines is affected by extreme disastersζ ij,rh 、Line switch closed state ε ij,rh and the construction status of the contact line The specific logical relationship determined is as follows Figure 1 Its mathematical expression is shown in the following formula.

[0109]

[0110] Where: ij,rh is a binary variable representing whether the line (i, j) is affected by extreme disasters. When it is 1, it means it is affected. The above formula is nonlinear, and an auxiliary binary variable is introduced. The linearization is performed as follows:

[0111]

[0112] The connection status of the pipeline (m,n) of the gas distribution network a mn,rh Only affected by extreme disasters, that is, a mn,rh =1-ζ mn,rh , the gas distribution network flow constraint under the influence of extreme disasters is obtained as follows:

[0113]

[0114] Step 2.2: Construct resilience constraints for the gas-electricity combined distribution network.

[0115] The power loss load and gas loss load penalties are selected as the quantification of the resilience of the gas-electricity combined distribution network, and R is set 0represents the upper limit of the penalty cost of load loss during the fault period, and the resilience constraint is constructed as shown in formula (63). By setting the resilience constraint, the gas-electricity joint system needs to plan additional flexibility resources to meet this resilience constraint, thus achieving a trade-off between the economic efficiency of expansion planning and the resilience of the gas-electricity joint distribution network system.

[0116]

[0117] Where: R 0 is the toughness index set, H F Represents the time period during which the system has not yet resumed normal operation after being hit by a disaster.

[0118] Step 3: Based on the robust expansion planning model of the gas-electricity combined distribution network that balances economy and resilience obtained in step 2, taking into account the uncertainty of the probability of extreme disaster scenarios, the linearized KL divergence is used to describe the distance between the actual distribution of extreme disasters and the empirical distribution, and an extreme disaster uncertainty set is established.

[0119] The details are as follows:

[0120] Step 3.1: Generate N extreme disaster scenarios randomly through Monte Carlo simulation method, and reduce the scenarios through simultaneous back-substitution method to obtain S representative disaster scenarios and their corresponding empirical distributions.

[0121]

[0122] Step 3.2: Characterize the empirical distribution using the linearized KL divergence and the actual distribution The distance is expressed as And set the threshold d that the distance should not exceed KL , and finally we get the uncertainty set U that describes the probability of extreme disaster scenarios.

[0123]

[0124] Where: s is the probability of scene s in the actual distribution. is the auxiliary variable in the piecewise linearization, are the left and right endpoints of the interval during the piecewise linearization process, are the left and right endpoints of each interval, b s,n and α s,n is an auxiliary variable.

[0125] Step 4: Based on steps 2 and 3, a two-stage distributed robust expansion planning model based on KL divergence is established. The first stage basic scenario mainly ensures the economy of system planning, and the second stage takes the resilience index under extreme disaster scenarios as a constraint to obtain the optimal decision that meets the set resilience constraints.

[0126] Formula (71) represents the objective function and constraints of the basic scenario in the first stage with economy as the goal. The second stage ensures the resilience of system operation under the worst scenario. In formula (72), W(x) represents the constraints such as the objectives and resilience indicators for the worst scenario of extreme disasters in the second stage.

[0127]

[0128] Where: x represents the investment and construction decision variable, v represents the binary variable other than the investment and construction decision, and y represents the continuous variable; c a 、c b 、c d , b, d, e, h, A, B, C, D, E, F, G, H and I are the corresponding coefficient vectors and matrices in the abstract objective function and constraints. s is the index of the extreme disaster scenario, S is the number of scenarios, ρ s is the probability of scene s, U = {ρ s |Kρ s ≤l} represents the fuzzy set of extreme disaster scenarios; z s Represents the amount of power and gas loss in the system in scenario s; represents the unit price of load loss penalty; R is the maximum allowable load loss index set.

[0129] Step 5: Use the column and constraint generation algorithm to decouple the two-stage distributed robust programming model obtained in step 4 into the main problem and sub-problems and solve them iteratively. Use the IEEE 33-node distribution network system and the improved Belgian 20-node gas distribution network coupled system for example analysis to verify the ability of the invented model to improve the economy of the normal operation of the gas-electricity combined distribution network and enhance its resilience during extreme disasters.

[0130] The column-and-constraint generation (CCG) algorithm is used to iteratively solve the two-stage model. The master-problem (MP), i.e., the first-stage planning model, is the known probability distribution of extreme disaster scenarios, and the expansion decision that minimizes the sum of investment and operation costs is solved. The matrix form of the master problem is expressed as follows, where u is the iteration index, N is I Iteration count value.

[0131]

[0132]

[0133] The sub-problem (SP) is the second-stage model that obtains the worst probability distribution of the response to the extreme disaster scenario distribution based on the expansion decision obtained from the main problem. And verify whether this worst-case scenario meets the resilience index requirements of the gas-electricity combined distribution network in the form of a max-min model.

[0134]

[0135] The upper-level variables of the subproblem model do not affect the feasible domain of the lower level, and thus can be decoupled into two independent solution models, where the lower-level subproblem (lower-level SP, LSP) can be expressed as (71), that is, s minimization loss load problems that can be solved in parallel. After solving the lower-level subproblem, we get Pass it to the upper-level subproblem (upper-level SP, USP), that is, formula (72), and get the corresponding main problem decision x * The worst probability distribution

[0136]

[0137] The specific solution process of the model is as follows Figure 2 , the specific steps are as follows:

[0138] 1) Set the iteration count N I =0 and toughness index R.

[0139] 2) Solve the main problem (73)-(75) and obtain the planning decision and pass it to the subproblem.

[0140] 3) Solve the problem of The LSP (77) at the time of S is used to obtain the minimum load loss of the system under S scenarios. Will Pass it to USP(78) to solve the worst probability distribution

[0141] 4) Verify whether the solution satisfies the toughness constraint, i.e., formula (79). If the toughness index is satisfied, then output is the optimal planning result, and the solution is completed. If it is not satisfied, update the probability distribution in the main problem to And set N I =N I +1, go back to step 2 and continue to alternately solve the main problem and sub-problems.

[0142]

[0143] The model proposed in this paper couples the standard IEEE 33-node distribution network system with the improved Belgian 20-node gas distribution network. Figure 3 As shown. There is a diesel generator set in the system, represented by G1; two distributed wind turbines, represented by WT; the coupling equipment includes gas turbines and power-to-gas equipment, represented by GT and P2G respectively. In order to verify the impact of different planning schemes on the normal operation and resilience of the system, five schemes are set to calculate the optimal investment and construction strategies for comparative analysis. The specific settings are shown in Table 1.

[0144] The cost and planning results of the five schemes are shown in Table 2 and Table 3. In Table 3, the superscript represents the equipment model selected for investment and construction. When the value is 0, it represents the original equipment. E and P represent the pipelines of the feeder and gas distribution network respectively. The subscript represents the specific upgraded line or pipeline, for example represents the upgrade of the distribution network line (1,2) to model 2; G, D and A represent gas turbines, dual-fuel units and power-to-gas equipment respectively. Due to the characteristics of the coupled equipment, the subscripts represent the distribution network nodes and gas distribution network nodes to which the equipment is connected, for example represents the selection of a model 1 gas turbine that connects the distribution network node 3 and the gas distribution network node 9; B and T represent power storage and gas storage equipment, with the subscripts corresponding to the power / gas distribution network nodes; S represents the tie line.

[0145] Table 1 Specific settings for schemes 1-5

[0146] plan Lines and pipes Gas turbines and dual fuel units Power-to-gas Electricity and gas storage Uncertainty Contact Line 1 √ × × × × × 2 √ √ × × × × 3 √ √ √ √ × × 4 √ √ √ √ √ × 5 √ √ √ √ √ √

[0147] Table 2 Cost of Schemes 1-5

[0148] plan <![CDATA[Investment and construction cost (10 6 yuan)]]> <![CDATA[Operating cost (10 6 Yuan)]]> <![CDATA[Total cost (10 6 yuan)]]> 1 0.760 19.175 19.935 2 0.810 18.029 18.839 3 1.015 12.009 13.024 4 1.310 11.975 13.285 5 1.251 11.959 13.210

[0149] Table 3 Planning results of Schemes 1-5

[0150]

[0151]

[0152] In Option 1, only line and pipeline upgrades are allowed. In the planning results, four distribution network lines and two gas distribution network pipelines are upgraded, and the lines connecting substations or natural gas suppliers are upgraded to models that allow for larger capacity to meet load growth. However, after the upgrade, the feeder The power transmitted is still close to its maximum power limit. If the load increases further, the capacity of these two feeders will need to be expanded.

[0153] Scheme 2 adds the site selection and type planning of distributed units on the basis of Scheme 1. As shown in Table 3, Scheme 2 adds gas turbines to Scheme 1. The number of distribution network line upgrades is reduced to 0, while the number of gas distribution network pipeline upgrades is increased from 2 to 4. As shown in Table 2, the operating cost is reduced by 1.146×10 6 Yuan, the investment and construction cost increased by 0.05×10 6 Compared with Scheme 1, the total cost is reduced by 1.096×10 6 The output and purchased power curve of the distributed units in the gas-electricity combined distribution network on a typical day in Scheme 2 is as follows Figure 4 As shown, the net load is the total power load minus the wind power dispatch output and power-to-gas consumption. Figure 4 It was observed that the construction of distributed gas turbines reduced the system's purchased power and alleviated the system's original feeder The pressure of transmitting power to downstream nodes does not require the feeder to be upgraded to meet the load demand. In Scheme 2, the gas-electricity joint distribution network did not choose to invest in dual-fuel units because the alternative dual-fuel units consumed more and were less economical than gas turbines. On the other hand, since the distributed units invested and built are all gas turbines, the pressure on the gas distribution network to transmit natural gas has increased, which has led to an increase in the decision to upgrade the gas distribution network pipeline compared to Scheme 1. Distributed units can reduce the power purchased by the distribution network, postpone the upgrade of the lines, and improve economic efficiency, but on the other hand, because they rely on the supply of fuel, they increase the purchase and transmission pressure of the gas distribution network.

[0154] Compared with Plan 2, Plan 3 adds coordinated planning of power-to-gas, power storage and gas storage equipment. As shown in Tables 2 and 3, at this time, one power-to-gas, power storage and gas storage equipment is selected, and the decision to upgrade the pipeline is reduced from 4 in Plan 2 to 3: the original pipeline of the system No longer need to upgrade, choose to upgrade to Gas distribution network pipeline The model selected for upgrading is the smaller capacity model 1. Table 2 shows that the investment and construction cost of Scheme 3 is the largest among Schemes 1-3, while the operating cost is reduced by 6.02×10 compared with Scheme 2. 6 The total cost was reduced by 31.95%, a significant decrease, making it the most economical of the top three planning schemes without considering extreme disasters, verifying the economic superiority of coordinated planning of lines and pipelines, distributed units, power-to-gas and energy storage.

[0155] The reduction in operating costs is mainly due to the power-to-gas and power storage equipment The construction of the gas-electricity combined distribution network realizes the absorption of excess wind energy in the distribution network, reducing the penalty cost of wind abandonment to 0. In Scheme 2, the gas-electricity combined distribution network system does not have energy storage equipment and does not allow reverse flow. The wind abandonment power is shown in Figure 5 The power-to-gas conversion project of Option 3 is also proposed. The power consumption of the power-to-gas conversion and the daily state of charge (SOC) curve of the power and gas storage are shown in Figure 2. It can be seen that the power consumption of the power-to-gas conversion and the wind abandonment of node 17 in scheme 2 are the same in each scheduling period, that is, the power-to-gas conversion only converts excess wind energy. This is because the power-to-gas conversion efficiency is low, only 60%-70%, so the operation mode of consuming power to generate gas is not economical. On the other hand, it can be seen from Figure 5 See power storage equipment The storage period is 1:00-6:00. Part of the stored electricity is excess wind power, and the other part is low-priced electricity purchased from the substation. Although there are losses in the storage and release process of power storage equipment, its efficiency is much greater than the conversion efficiency of power-to-gas. The operation mode of storing low-priced electricity and releasing it during the peak load period is more economical. Figure 5 The SOC curve of the China Gas Storage follows a similar pattern, storing gas during periods of low gas prices and releasing it when gas prices peak. Energy storage equipment improves the economy of the system by shaving peaks and filling valleys. At the same time, energy storage equipment also has more advantages in absorbing new energy than electricity-to-gas.

[0156] Schemes 1-3 do not consider the requirements for system resilience when extreme disasters occur. Schemes 4 and 5 consider the resilience index under extreme disasters. In order to observe the additional investment in resilience resources, the resilience index R needs to be less than the expected load loss penalty corresponding to the decision of Scheme 3. The resilience index R is selected as 2.5×10 6 The maximum distance d allowed between the empirical distribution and the actual distribution KL Set to 0.05, the distribution network allows reverse flow after a fault, while the gas distribution network does not. The Monte Carlo method is used to generate 5000 extreme disaster scenarios, and three representative extreme disaster scenarios are obtained through synchronous back-substitution elimination. The damage status and initial probability distribution See Table 4.

[0157] Table 4 Introduction to extreme disaster scenarios of the present invention

[0158]

[0159] As can be seen from Table 2, Scheme 4 has an increase in total cost compared to the calculation results of the deterministic model (i.e., Scheme 3). This part of the total cost increase is the result of the combined effect of the increased investment and construction costs and the reduced operating costs. Comparing the planning decisions of Scheme 4 with those of Scheme 3, it is observed that the gas turbine at the distribution network node 30 Dual fuel unit Replacement, also invested in the construction of dual-fuel units The power-to-gas type at the distribution network node 17 is changed to the more powerful type 1. The investment and construction decisions of Schemes 1-3 correspond to the worst distribution results obtained by Scheme 4, and the expected load loss of Schemes 1-4 is calculated and compared. Figure 6 The labels in the figure are the minimum load loss penalties corresponding to the scenarios. After considering the system resilience index under extreme disasters, the total cost of Scheme 4 increased by about 2% compared with Scheme 3, while in the worst distribution scenario, the load loss penalty of Scheme 4 was 27.08% lower than that of Scheme 3. The increase in system cost of Scheme 4 comes from the additional investment in flexible resources such as distributed units, which has led to a significant reduction in load loss in extreme disaster scenarios. Figure 6 It can be seen that the expected maximum load loss penalty corresponding to the construction decisions of Schemes 1-3 is greater than the resilience index R, while Scheme 4, which adds the resilience index as a constraint to the planning model, ensures that the resilience of the distribution network system in this scenario meets the standard, proving the correctness of the model of the present invention.

[0160] The working modes of the two dual-fuel units in Scheme 4 under the basic scenario and three extreme disaster scenarios are shown in Figure 7 The figure shows the dual-fuel unit built at node 30. There has been a shift from gas-fired power generation in basic scenarios to only using oil-fired power generation in extreme disaster scenarios. The operating characteristics of dual-fuel units that can generate electricity using oil in the event of a disaster make them more reliable than gas turbines that rely on the transportation capacity of the gas distribution network. After an extreme disaster occurs, dual-fuel units can support important power loads.

[0161] Power-to-gas The output curves for basic scenarios and extreme disaster scenarios are as follows: Figure 8 As shown. It can be seen that the power consumption of power-to-gas is also much greater than that of only converting wind in the basic scenario. In the example setting of the present invention, since the gas distribution network has fewer means to restore the load and limits the flow direction when it fails, its load loss penalty coefficient is higher than that of the distribution network. At the same time, since the load node 16 of the gas distribution network has the largest load and the highest penalty coefficient, when this node loses connection with the gas supplier, the power-to-gas It can support nodes 15 and 16 and ensure the supply of critical gas loads.

[0162] Option 5 allows investment in tie line S after construction of tie line 18-33 Compared with the construction results of Scheme 4, the location of the gas storage equipment has changed, and the dual-fuel unit Replace the gas turbine selected in Option 3 with a better economy Unit. Gas turbine In scenarios 1-3, the unit remains shut down after the failure due to lack of gas source. This decision is different from the dual-fuel unit selected in scenario 4. The increased load loss is compensated by the flexibility brought by the interconnection line combined with the distribution network reconstruction. The power transmitted by interconnection line 18-33 in the basic scenario and the three extreme disaster scenarios in Scheme 5 is as follows: Fig. 9 In the basic scenario and scenario 1 with less disaster impact, the contact line S 18-33 The upper switch is disconnected and the transmission power is 0; in extreme disaster scenarios 2 and 3, in order to ensure the supply of key load node 33 after the disaster, the switch on the tie line is closed to achieve power transmission. 18-33 The power supply mode for the important load node 33 is added, thus achieving the same resilience index requirements as Scheme 4. The calculated cost results can be seen from Table 2. Considering the construction of the interconnection line, due to the gas turbine For dual fuel units The replacement of the interconnection line reduced both the construction cost and the operating cost, reducing the total cost by 0.565%. This result proves the economic and flexibility of the combined consideration of the construction of the interconnection line and the network reconstruction.

[0163] The reconstruction and load loss results of the three fault scenarios are Figure 10(a)-Figure 10(c) As shown in the figure, ES and GS represent the electric storage device and gas storage device respectively. After the fault starts, the gas turbine The unit remains shut down in each scenario and is omitted in the figure; the dual-fuel unit in the three scenarios after the failure occurs Only fuel oil is used for power generation, so the connection line between the dual-fuel unit and the gas distribution network is ignored. In scenario 1, there are fewer faults. The distribution network and the gas distribution network are both connected to the main grid. At this time, the distribution network nodes 12-18 rely on the fuel oil power generation of the dual-fuel unit to maintain the supply to the key load nodes 13 and 15. In scenario 2, the fault situation is more serious. The tie line S 18-33 The closure of the power grid and the existence of coupling equipment form a gas-electricity island 1. In the gas-electricity island 1, the limited electric power or natural gas can be optimally allocated to minimize the load loss penalty cost within the island. It can also be seen from Figure 10(b) that the supply of key loads of the distribution network is achieved as much as possible. The situation in scenario 3 is similar to scenario 2. As shown in Figure 10(c), one power island and two gas-electricity islands are formed. The load in power island 1 is supported by diesel generators. There is a coupling device gas turbine in gas-electricity island 1. The supply of power load in the isolated island is guaranteed; the closed tie line S 18-33The key load node 33 was connected and the power supply to this node load was restored. The planning of the construction of the interconnection line and the formation of the gas-electricity joint island increased the flexibility of the system, which is more conducive to ensuring the continuous power supply to the key electric load and gas load, and improving the resilience of the gas-electricity joint distribution network.

[0164] In the model of the present invention, the requirements for the resilience index R are set by the system planning staff according to the needs, and its specific value determines the size of the resilience resources invested in the system. When constructing the fuzzy set U of disaster uncertainty, the parameter d that limits the maximum distance between the empirical distribution and the actual distribution is KL The value of also plays a decisive role in the worst distribution selected by the distribution robustness model. Based on Scheme 5, the toughness index R and the maximum distance d allowed between the empirical distribution and the actual distribution are changed. KL And record the results to get the trend of total system cost and parameter changes such as Fig.11 As shown in Figure 2, it can be seen that the system cost decreases with the decrease of the resilience index R and the maximum distance d allowed by the empirical distribution and the actual distribution. KL The decrease in the resilience index R means that the upper limit of the minimum load loss allowed during a gas-electricity distribution network disaster is reduced. At this time, the investment in flexible resources such as distributed units should be increased, thereby increasing the total cost. On the other hand, d KL The increase of means that the allowable distance between the worst distribution and the initial distribution increases, which increases the probability corresponding to the worst failure scenario, thereby increasing the difficulty of satisfying the resilience constraint. Therefore, such a change trend appears in the sensitivity analysis.

[0165] In combination with the trend of multi-energy coupling in recent years and the resilience requirements brought about by the increasing frequency of extreme natural disasters, this paper proposes a robust expansion planning model for gas-electricity combined distribution networks that takes into account the resilience constraints of the system under extreme disasters. The planning model is obtained by jointly considering a variety of flexible resources that can improve the economy during normal operation and the resilience of the system during failures. The following conclusions are obtained through example analysis:

[0166] 1) The economic superiority of coordinated planning of lines and pipelines, distributed units, power-to-gas and energy storage and the role of absorbing new energy have been verified. When the two-stage distributed robust method is used to consider the uncertainty of faults, the total cost increases compared with the deterministic model. Under the constraints of the resilience index of the gas-electricity joint distribution network disaster scenario, more flexible resources need to be invested to support important loads.

[0167] 2) Due to its ability to switch fuels in the event of extreme disasters, dual-fuel units can use fuel oil to generate electricity during fault periods, decoupling the gas-electricity distribution network and reducing the distribution network's dependence on the gas distribution network under extreme disasters. Power-to-gas equipment can also effectively guarantee the supply of critical natural gas loads in extreme disaster scenarios.

[0168] 3) Comparative analysis has verified that the construction of interconnection lines can improve system flexibility. The formation of a gas-electricity joint island through the reconstruction of the distribution network can help to optimally distribute energy within the island and ensure the supply of critical electricity or natural gas loads.

Claims

1. A robust expansion planning method for a gas-electricity combined distribution network taking into account system resilience constraints, characterized in that: The following steps are involved: Step 1: Considering the construction of gas turbines, dual-fuel units, power-to-gas equipment, power storage equipment, gas storage equipment and interconnection lines, as well as the coordinated planning of power supply lines and natural gas pipeline upgrades, the goal is to minimize the sum of the operating cost and construction cost of the gas-electricity combined distribution network, and considering the safe operation constraints of the gas distribution network and the operation constraints of the coupling equipment, a gas-electricity combined distribution network expansion planning model is established; Step 2: Based on the gas-electricity combined distribution network expansion planning model, the operation constraints of the gas-electricity combined distribution network under the influence of extreme disasters are modeled, and the fuel conversion effect of dual-fuel units when natural gas is insufficient under extreme disasters is considered, and the flexibility of distribution network reconstruction is increased after the construction of interconnection lines. The minimum load loss of the gas-electricity combined distribution network system during the period of extreme disasters is calculated, and then the resilience constraint is constructed by limiting the upper limit of the allowable load loss, and a robust expansion planning model of the gas-electricity combined distribution network that considers the resilience constraint and balances economy and resilience is obtained; Step 3: Based on the robust expansion planning model of the gas-electricity combined distribution network that balances economy and resilience, taking into account the uncertainty of the probability of extreme disaster scenarios, the linearized KL divergence is used to describe the distance between the actual distribution of extreme disasters and the empirical distribution, and an extreme disaster uncertainty set is established; Step 4: According to the extreme disaster uncertainty set, a two-stage distributed robust expansion planning model based on KL divergence is established; the basic scenario in the first stage is used to ensure the economy of system planning, and the resilience index under the extreme disaster scenario is used as a constraint in the second stage to obtain the optimal decision that meets the set resilience constraint; Step 5: Use the column and constraint generation algorithm to decouple the two-stage robust expansion planning model into the main problem and sub-problems and solve them iteratively.

2. The method for planning robust expansion of gas-electricity combined distribution network considering system resilience constraints according to claim 1, characterized in that: The objective function of the gas-electricity combined distribution network expansion planning model established in step 1 is the gas-electricity combined distribution network construction cost C I and operating cost C o The sum OF is minimized as follows: minOF=min(C I +C o ) (1) Construction cost C I The expression is as follows: Where: is the average annual unit price of investment in k-type distributed units; c inv,tl , and They represent the cost of investing in power-to-gas equipment, distribution network lines, gas distribution network pipelines, interconnecting lines, gas storage equipment and electricity storage equipment respectively; is a candidate set of distributed units, including gas turbines and dual-fuel units, L s A collection of alternative contact lines. and It is a collection of corresponding power-to-gas equipment, distribution network lines, gas distribution network pipelines, gas storage equipment and power storage equipment; is the investment state variable of the distributed generator. When it is 1, it means that the investment has been made, and when it is 0, it means the original facilities of the system. The superscript k represents the model of the generator to be selected. Similarly, and They represent the investment and construction state variables of k-type power-to-gas equipment, distribution network lines, gas distribution network pipelines, tie lines, gas storage equipment, and electricity storage equipment respectively; Operating cost o The expression is as follows: Where: r is the typical day index, h is the hour index, Indicates the number of days included in a typical day r; Ω GU represents the set of gas generating units connected to the distribution network; L is the set of distribution network lines; Represents the unit fuel price when the dual-fuel unit g is running when the diesel generator or oil-fired generator is running; is the fuel quantity of the dual-fuel unit g, P g,rh is the output of the g-th distributed unit in the corresponding dispatching period, is the energy cost coefficient, SU g,rh and SD g,rh The amount of fuel consumed for the startup and shutdown of distributed units; is the cost of a single operation of the section switch and the tie switch on line (i, j), γ ij,r is the number of switch operations on line (i, j) in a typical day r; is the wind turbine operation and maintenance cost coefficient, is the dispatch output of the wind turbine; The penalty price for unit wind curtailment, is the abandoned wind power; is the gas purchase price of node n in the gas distribution network, Purchase gas power for the system; is the electricity purchase price of the electricity purchasing node, Purchase active power for the system; is the unit power failure load penalty price of the load node, ΔP j,rh is the power loss; is the penalty price per unit gas loss load of gas distribution network node n, ΔG n,rh is the lost power.

3. The method for planning robust expansion of gas-electricity combined distribution network considering system resilience constraints according to claim 2, characterized in that: The constraints of the gas-electricity combined distribution network expansion planning model established in step 1 include investment and construction constraints, distribution network operation constraints, gas distribution network operation constraints and coupling equipment operation constraints, which are as follows: (1) Investment and construction constraints Where: and are the original distribution network lines and gas distribution network pipeline investment and construction state variables respectively; PL is the set of gas distribution network pipelines; (2) Distribution network operation constraints 1) Node power balance constraints Where: S(j) is the set of devices connected to the distribution network node j, δ(j) is the set of receiving nodes corresponding to the distribution network node j; and represents the active power released and stored by the storage device connected to the distribution network node j; P ij,rh is the active power flow of line (i, j); is the active load of distribution network node j, is the power consumption of the power-to-gas equipment q; It is the reactive power flowing into the power purchasing node of the distribution network. is the reactive power generated by the wind turbine. and represents the reactive power released and stored by the storage device connected to the distribution network node j, Q ij,rh is the reactive power transmitted by line (i, j), Q g,rh is the reactive power generated by the distributed generator g, is the predicted reactive load of distribution network node j, φ j,rh is the proportional coefficient that needs to be satisfied for removing active and reactive power at the same distribution network node j; 2) Line transmission power constraints and node voltage constraints Where: and are the lower and upper limits of the voltage amplitude required for node j in the distribution network, respectively; and is the maximum active power that the original line and the k-type line (i, j) can transmit; V j,rh is the voltage amplitude of the distribution network node j; 3) Distribution network reconstruction constraints Where: a ij,rh It is a binary variable, representing the actual connectivity status of line (i, j). When it is 1, it means it is connected, and when it is 0, it means the line is disconnected. ε ij,rh is a binary variable representing whether the switch of line (i, j) is closed. When it is 1, it means the switch is closed, and when it is 0, it means the switch is open; Binary variable β ij Represents the virtual connection status of line (i, j), n s is the substation node, N is the set of distribution network nodes, represents the virtual traffic on line (i, j) sent to node ω, λ ij is a binary variable representing the directed connectivity status of line (i, j); 4) Distribution network flow constraints Where: V i,rh is the voltage amplitude at node i, and is the resistance value of the original line and the k-type line (i, j); and is the reactance value of the original line and the k-type line (i, j); V0 is the reference value of the voltage amplitude; M is a very large number; is the slack variable; σ ij,rh is a binary variable indicating whether the switch state of line (i, j) changes at time h on typical day r. When it is 1, it represents a switch action. is the maximum number of switching actions in one day; γ ij,r is the number of switching operations of the switch on line (i, j) in typical day r; 5) Wind turbine operation constraints Where: is the predicted output of the unit at time h on typical day r; θ j is the power factor angle of the wind turbine at node j; 6) Distributed unit operation constraints Where: and are the unit's ramp-up rate and ramp-down rate, su g and sd g I is the amount of fuel consumed by the unit when it is started and shut down. g,rh is a binary variable representing the start / stop status of the generator at time h. When it is 1, it means it is on. is the original set of units in the system, and They are respectively the upper limits of active output of the original units and the K-type units; and They are the lower limits of active output of the original units and the K-type units respectively; and The upper limits of reactive power output are for the original units and the K-type units respectively; and The lower limits of reactive power output are for the original units and the K-type units respectively; 7) Grid transmission power limitation Where: and are the maximum and minimum active transmission power of node j, respectively; and are the maximum and minimum reactive transmission powers of node j, respectively; 8) Operation constraints of power storage equipment Where: and are the maximum and minimum charging active powers of the k-type energy storage device respectively; and are the maximum and minimum discharge active powers of the k-type energy storage device respectively; and are the maximum and minimum charging reactive powers of the k-type energy storage device respectively; and are the maximum and minimum discharge reactive powers of the k-type energy storage device respectively; and is the minimum and maximum capacity of the k-type power storage device; is the capacity of the storage at time h, and the efficiency of discharging and charging the energy storage device; (3) Gas distribution network operation constraints: 1) Node natural gas balance constraints Where: S(n) is the set of devices connected to the gas distribution network node n, δ(n) is the set of receiving nodes corresponding to the gas distribution network node n, and is the gas discharge and storage power of the gas storage device connected to the gas distribution network node n, f mn,rh is the flow of the natural gas pipeline (m,n), is the gas production power of the power-to-gas device q; is the predicted gas load of node n in the gas distribution network, G g,rh Gas power consumption for gas turbines or dual-fuel units using natural gas for power generation; 2) Gas distribution network pipeline flow constraints Where: π m,rh and π n,rh are the air pressures at nodes m and n at this time, and is the characteristic coefficient of the original natural gas pipeline and the k-type natural gas pipeline (m, n); 3) Pipeline transmission air flow limitation and node air pressure constraint Where: and are the lower and upper limits of the gas pressure allowed at node n in the gas distribution network, respectively; and are the maximum gas flow rates allowed to be transmitted by the original natural gas pipeline and the k-type natural gas pipeline (m, n) respectively; 4) Gas purchase power and gas loss load constraints Where: and is the minimum and maximum gas purchase power allowed by the gas distribution network node n; ΔG n,rh is the gas loss power; 5) Gas storage equipment constraints Where: and are the maximum and minimum gas storage powers of type k gas storage equipment respectively; and are the maximum and minimum deflation powers of the k-type gas storage equipment, respectively; and is the minimum and maximum capacity of the K-type gas storage equipment; is the capacity of the gas storage equipment in the h period, and The deflation and storage efficiency of gas storage equipment; (4) Coupling equipment operation constraints 1) Gas turbine operation constraints The ramping constraints, start-stop fuel constraints, and active and reactive output constraints of the gas turbine are shown in the unit operation constraints of equations (27)-(30), that is, equations (27)-(30). The gas consumption of the gas turbine is calculated as follows: Where: G g,rh is the gas consumption power of the gas turbine g, is the gas consumption coefficient of the gas turbine; HHV is the high calorific value of natural gas; 2) Dual-fuel unit operation constraints The ramping constraint and start-stop fuel constraint of the dual-fuel unit are the same as those of equations (27) and (28). During operation, the dual-fuel unit can only choose one of diesel or natural gas for power generation. This state constraint is shown in equation (41). The active output constraint is shown in equations (42) and (43). The gas consumption constraint of the dual-fuel unit is shown in equation (46). There is an upper limit on the number of times the unit can change the fuel type in a day. This constraint is described by equation (44). Where: Ω DF represents the set of dual-fuel units, It is a binary variable, and when it is 1, it represents gas power generation of dual-fuel unit; It is a binary variable, and when it is 1, it represents fuel-fired power generation of a dual-fuel unit; and Represents the corresponding power of the dual-fuel unit using two fuels; is a binary variable. When it is 1, it means that the unit g switches from fuel oil to gas power generation during the h period. It represents that unit g switches from gas to oil power generation during period h; is the maximum number of fuel changes allowed for unit g in one day; 3) Power-to-gas equipment operation constraints Where: φ is the energy conversion coefficient, is the efficiency of the qth P2G device; and are the minimum and maximum gas production power of the qth P2G device, A collection of existing equipment; and are the minimum and maximum gas production power of type k P2G device q respectively; 4) Other constraints Restrictions: The power-to-gas device and the gas turbine or gas-fired dual-fuel unit connected to the same node cannot be started at the same time, as shown below: The first-order Taylor series expansion method is used to linearize the Weymouth equation: Where: for The b-th pressure breakpoint of for The b-th pressure breakpoint of and are the coefficients in the first-order Taylor series process.

4. The method for robust expansion planning of gas-electricity combined distribution network considering system resilience constraints according to claim 3 is characterized in that: In step 2, a robust expansion planning model for the gas-electricity combined distribution network is established that considers resilience constraints and balances economy and resilience. The specific steps are as follows: Step 2.1: Consider the constraints of the gas-electricity distribution network after extreme disasters; The connectivity status of the distribution network lines is affected by extreme disasters. ij,rh 、Line switch closed state ε ij,rh and the construction status of the contact line The specific logical relationship is as follows: Where: ij,rh is a binary variable representing whether line (i, j) is affected by extreme disasters. When it is 1, it means it is affected; The above formula is nonlinear, so we introduce auxiliary binary variables and The linearization is performed as follows: The connection status of the pipeline (m,n) of the gas distribution network a mn,rh Pipeline variables ζ are only affected by extreme disasters mn,rh decision, i.e. a mn,rh =1-ζ mn,rh , the gas distribution network flow constraint under the influence of extreme disasters is obtained as follows: Where: is the slack variable; Step 2.2: Construct resilience constraints for gas-electricity distribution networks The power loss load and gas loss load penalties are selected as the quantification of the resilience of the gas-electricity combined distribution network, and R is set 0 The upper limit of the penalty cost of load loss during the fault period is expressed as the resilience indicator. The resilience constraint is constructed as shown in formula (63). Additional flexibility resources are planned to meet the resilience constraint, achieving a trade-off between the economic efficiency of the expansion plan and the resilience of the gas-electricity combined distribution network system: Where: H F Represents the time period during which the system has not yet resumed normal operation after being hit by a disaster.

5. The method for robust expansion planning of gas-electricity combined distribution network considering system resilience constraints according to claim 4 is characterized in that: In step 3, the linear KL divergence is used to measure the distance between the actual distribution of extreme disaster scenarios and the initial distribution, and an extreme disaster uncertainty set is established. The specific steps are as follows: Step 3.1: Generate N extreme disaster scenarios randomly through Monte Carlo simulation method, and reduce the scenarios through simultaneous back-substitution method to obtain S representative disaster scenarios and their corresponding empirical distributions. Where: is the probability of scenario s occurring in the empirical distribution; Step 3.2: Characterize the empirical distribution using the linearized KL divergence and the actual distribution The distance is expressed as And set the threshold d that the distance should not exceed KL , and finally we get the uncertainty set U describing the probability of extreme disaster scenarios; Where: s is the probability of scene s in the actual distribution; Δρ s is the length of each interval in the piecewise linearization process; θ s is the auxiliary variable in the piecewise linearization, and θ s =ρ s ln(ρ s ), and are the left and right endpoints of the interval during the piecewise linearization process, and are the left and right endpoints of each interval, b s,n and α s,n is an auxiliary variable, N pl is the number of segments in the piecewise linearization.

6. The method for robust expansion planning of gas-electricity combined distribution network considering system resilience constraints according to claim 5 is characterized in that: In step 4, a two-stage distributed robust programming model is established. The specific model is as follows: Formula (71) represents the objective function and constraints of the basic scenario in the first stage with economy as the goal, and the second stage ensures the resilience of system operation under the worst scenario. In formula (72), W(x) represents the objective and resilience index constraints for the worst scenario of extreme disasters in the second stage; Where: x represents the investment and construction decision variable, v represents the binary variable other than the investment and construction decision, and y represents the continuous variable; c a 、c b 、c d , b, d, e, h, l, A, B, C, D, E, F, G, H, I and K are the corresponding coefficient vectors and matrices in the abstract objective function and constraints; s is the index of the extreme disaster scenario, S is the number of scenarios, ρ s is the probability of scene s, U = {ρ s |Kρ s ≤l} represents the uncertainty set describing the probability of extreme disaster scenarios; z s Represents the amount of power and gas loss of the system in scenario s; v s Represents the binary variables of the system in scenario s except for the investment and construction decision, y s Represents the remaining continuous variables of the system in scenario s; represents the unit price of load loss penalty; R is the maximum allowable load loss index set.

7. The method for robust expansion planning of gas-electricity combined distribution network considering system resilience constraints according to claim 6 is characterized in that: The decoupling and iterative solution of the two-stage model in step 5 are specifically as follows: The two-stage model is iteratively solved using the column and constraint generation algorithm. The main problem, i.e., the first-stage planning model, is to solve the expansion decision that minimizes the sum of the investment cost and the operating cost based on the known probability distribution of extreme disaster scenarios. The matrix form of the main problem is expressed as follows, where u is the iteration number index, N is I Count the number of iterations: The sub-problem is that the second-stage planning model obtains the worst probability distribution of the response to the extreme disaster scenario distribution based on the expansion decision obtained from the main problem. And verify whether this worst scenario meets the resilience index requirements of the gas-electricity combined distribution network in the form of a max-min model: The sub-problem model is decoupled into two independent solution models, and the lower sub-problem is expressed as Equation (77), which is s parallel solutions to minimize the load loss problem. The power loss and gas loss obtained after solving the lower sub-problem are Pass it to the upper sub-problem, that is, formula (78), and get the corresponding main problem decision x * The worst probability distribution The specific steps of iterative solution of the model are as follows: 1) Set the iteration count N I =0 and toughness index R; 2) Solve the main problem through equations (73)-(75) and obtain the planning decision and pass it to the subproblem; 3) Solve the problem by using equation (77) in the planning decision The lower-level sub-problem when the system is under S scenarios is obtained. Will Pass it to the lower sub-problem, that is, equation (78), and solve N I The worst probability distribution after iterations 4) Verify whether the solution satisfies the toughness constraint of the following formula: If the toughness index is met, then output is the optimal planning result, and the solution is completed; if it is not satisfied, update the probability distribution in the main problem to N I The worst probability distribution after iterations And set N I =N I +1, go back to step 2) and continue to alternately solve the main problem and sub-problems.

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