Power transmission and distribution network risk-driven collaborative planning method for elastic improvement under typhoon disasters

Abstract

The invention relates to a power transmission and distribution network risk-driven collaborative planning method for elastic improvement under typhoon disasters. The method comprises the following steps: acquiring basic data; based on the basic data, considering the economy of the power transmission and distribution network in a normal load increase scene and the loss load expectation and the loss load tail risk based on risk preference factors in a typhoon disaster scene, and respectively constructing a power transmission network elastic improvement planning model and a power distribution network elastic improvement planning model; and establishing a distributed solving framework based on an improved alternating direction multiplier method, and carrying out cooperative solving on the power transmission network elastic lifting planning model and the power distribution network elastic lifting planning model to obtain a power transmission and distribution network cooperative planning scheme. Compared with the prior art, the elastic planning resources in the power transmission link and the power distribution link of the power system are combined to carry out power transmission and distribution network collaborative planning, the system load loss under the disaster can be further reduced by utilizing the transmission and distribution mutual assistance capability under the typhoon disaster, the reliability is high, and the economical efficiency of the planning scheme is improved.

Description

Technical Field

[0001] The present invention relates to the field of power system planning, and in particular to a risk-driven collaborative planning method for transmission and distribution networks aimed at improving resilience under typhoon disasters. Background Art

[0002] The deterioration of the global ecological environment has led to frequent typhoon-related extreme weather disasters, which have a serious impact on the transmission and distribution links of power systems, thereby threatening the normal use of electricity by end users. Therefore, it is necessary to improve the planning capabilities of transmission and distribution system planning to prevent typhoon disasters. The occurrence of low-probability, high-impact typhoon disasters is a random event, and the impact of uncertain typhoon disasters must be reasonably estimated during planning to avoid large-scale load losses. In this context, it is necessary to conduct research on a coordinated planning method for transmission and distribution networks to improve resilience under typhoon disasters. This method can provide a reference for quantifying the risk of load loss under typhoon disasters, formulating coordinated planning schemes for transmission and distribution networks, and improving the economic efficiency of planning schemes while reducing load losses under typhoon disasters.

[0003] The prior art CN11787516B discloses a method for improving the source-grid-load collaborative resilience by considering the uncertainty of multiple faults, constructs a source-grid-load collaborative resilience model considering the uncertainty set of the probability distribution of multiple types of faults, adopts the primal-dual parallel decomposition algorithm to solve the source-grid-load collaborative resilience model, and obtains a source-grid-load collaborative planning strategy. This method characterizes the uncertainty of the probability distribution of three typical types of faults under typhoon disasters, namely the most serious, most likely, and chain failures, by taking into account the uncertainty set of component faults with the uncertainty of the probability distribution of multiple types of faults under typhoon disasters. By taking coordinated source-grid-load resilience improvement measures, it effectively reduces line investment and power outage losses in the most serious fault scenarios under typhoon disasters, thereby coordinating the economic and resilience improvement goals of the power system. However, the above method mainly considers the resilience improvement of the transmission network, and does not comprehensively consider the resilience planning resources in the transmission and distribution links. The planning reliability needs to be improved. Summary of the Invention

[0004] The purpose of the present invention is to overcome the defects of the above-mentioned existing technologies and provide a risk-driven collaborative planning method for transmission and distribution networks that improves resilience under typhoon disasters. It can coordinate the elastic resources in the transmission and distribution links to resist typhoon disasters, reduce the load loss of the transmission and distribution network under uncertain typhoon disasters from the perspective of risk measurement, and have high planning reliability.

[0005] The purpose of the present invention can be achieved by the following technical solutions:

[0006] A risk-driven collaborative planning method for transmission and distribution network resilience improvement under typhoon disasters includes the following steps:

[0007] Obtaining basic data, including transmission and distribution network load data, power generation capacity and grid system data, typhoon forecast data, planned operation unit costs, and risk preference factors;

[0008] Based on the basic data, a transmission network resilience improvement planning model and a distribution network resilience improvement planning model are constructed, respectively, taking into account the economic efficiency of the transmission and distribution network in the normal load growth scenario and the load loss expectation and load loss tail risk based on risk preference factors in the typhoon disaster scenario.

[0009] A distributed solution framework based on the improved alternating direction multiplier method is established to collaboratively solve the transmission network resilience improvement planning model and the distribution network resilience improvement planning model to obtain a transmission and distribution network collaborative planning scheme.

[0010] Furthermore, the typhoon prediction data includes predicted output data of wind farms and distributed wind power under extreme weather conditions, predicted typhoon paths, and predicted initial central pressure differences of typhoons.

[0011] Furthermore, the objective function of the transmission network resilience improvement planning model is expressed as:

[0012]

[0013] Where, and They represent the annual planned investment cost of the transmission network, the operating cost of the transmission network, and the tail risk value of the transmission network load loss; γ TNS is the transmission network risk preference factor; δ is the confidence level; is the normal load growth scenario set; is a set of typhoon disaster scenes; Δ du is the duration of the planning period; π s is the scene probability; C ls,TNS is the unit load shedding cost of the transmission network; The transmission network node loses load.

[0014] Furthermore, the objective function of the distribution network flexibility improvement planning model is expressed as:

[0015]

[0016] Where, and They represent the annual planned investment cost of the distribution network, the operating cost of the distribution network, and the tail risk value of the distribution network load loss; γ DNS is the risk preference factor of the distribution network; δ is the confidence level; is the normal load growth scenario set; is a set of typhoon disaster scenes; Δ du is the duration of the planning period; πs is the scene probability; C ls,DNS is the unit load loss cost of the distribution network; Shed load for distribution network nodes.

[0017] Furthermore, the collaborative solution specifically includes the following steps:

[0018] 1) obtaining the Lagrangian functions of the transmission network planning model and the distribution network planning model based on the principle of the alternating direction multiplier method, adding the Lagrangian functions to the objective functions of the transmission network planning model and the distribution network planning model respectively, and reconstructing the transmission network planning model and the distribution network planning model;

[0019] 2) Solving the reconstructed transmission network planning model and distribution network planning model to obtain the transmission and distribution network boundary power in the optimization results, and updating the transmission and distribution boundary power mean according to the transmission and distribution boundary power values;

[0020] 3) Update the Lagrange multipliers and penalty term parameters of the reconstructed transmission network planning model and distribution network planning model;

[0021] 4) Determine whether the residual power at the transmission and distribution network interface is less than the convergence threshold. If so, terminate the iteration and output the transmission and distribution network collaborative planning scheme. If not, return to step 2) and solve again based on the updated transmission and distribution interface power mean, Lagrange multiplier, and penalty term parameters.

[0022] Furthermore, the update formula of the transmission and distribution boundary power mean is:

[0023]

[0024] Where, is the average power at the transmission and distribution interface, The power of the transmission and distribution network boundary nodes is obtained by solving the reconstructed transmission network planning model and distribution network planning model respectively.

[0025] Furthermore, the update formula of the Lagrange multiplier and penalty term parameters is:

[0026]

[0027] Where, and are the Lagrange multipliers of the transmission network planning model and the distribution network planning model respectively; and are the penalty parameters of the transmission network planning model and the distribution network planning model respectively; The power of the transmission and distribution network boundary nodes obtained by solving the reconstructed transmission network planning model and distribution network planning model, is the average power at the transmission and distribution interface; A1 and A2 are the residual power at the transmission and distribution interface; K u , K d and ν are the update coefficients of the penalty term parameters; the superscripts (n) and (n+1) represent the nth and n+1th iterations, respectively.

[0028] Furthermore, the calculation formulas for the transmission and distribution boundary power residual values A1 and A2 are:

[0029]

[0030] Where, is the transmission and distribution interface power vector of scenario s at the nth iteration; and is the mean power vector of the transmission and distribution boundary of scenario s at the n+1th and nth iterations.

[0031] The present invention also provides a computer-readable storage medium, comprising one or more programs for execution by one or more processors of an electronic device, wherein the one or more programs include instructions for executing the risk-driven collaborative planning method for transmission and distribution networks for improving resilience under typhoon disasters as described above.

[0032] The present invention also provides an electronic device comprising one or more processors, a memory and one or more programs stored in the memory, wherein the one or more programs include instructions for executing the risk-driven collaborative planning method for transmission and distribution networks for improving resilience under typhoon disasters as described above.

[0033] Compared with the prior art, the present invention has the following beneficial effects:

[0034] 1) The present invention combines the elastic planning resources in the transmission and distribution links of the power system to carry out coordinated planning of the transmission and distribution network, taking into account the potential of the transmission and distribution networks to comprehensively improve the system resilience under typhoon disasters. It can use the mutual assistance capabilities of transmission and distribution under typhoon disasters to further reduce system load losses under disasters and improve the economic efficiency of the planning scheme.

[0035] 2) The present invention comprehensively considers the flexible planning resources in the transmission and distribution links from the perspective of risk measurement. It can take typhoon disaster prevention into consideration in the planning while meeting the load growth demand under normal scenarios, and measure the tail risk of load loss in the transmission and distribution network under typhoon disasters, so that transmission and distribution network decision makers can formulate corresponding transmission and distribution network planning strategies according to different risk preferences. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 It is a flow chart of a specific implementation method of the present invention;

[0037] Figure 27 is a coordinate diagram of a 6-test node transmission network and a lower-level distribution network according to an embodiment of the present invention;

[0038] Figure 3 This is a structural diagram of the 18-test node distribution network 1 and 3 according to an embodiment of the present invention;

[0039] Figure 4 This is a structural diagram of an 18-test node distribution network 2 according to an embodiment of the present invention. DETAILED DESCRIPTION

[0040] The present invention is described in detail below with reference to the accompanying drawings and specific embodiments. This embodiment is implemented based on the technical solution of the present invention, and provides a detailed implementation method and specific operation process, but the protection scope of the present invention is not limited to the following embodiments.

[0041] This embodiment provides a risk-driven collaborative planning method for transmission and distribution network resilience improvement under typhoon disasters. First, based on the conditional value at risk theory, the tail risk of load reduction in the transmission and distribution network under typhoon disasters is considered, and a multi-scenario planning model for transmission and distribution system resilience improvement is established, namely, a transmission network resilience improvement planning model and a distribution network resilience improvement planning model. The planning decisions of the transmission network resilience improvement planning model include line expansion and reinforcement, and the planning decisions of the distribution network resilience improvement planning model include line reinforcement, mobile power configuration, and interconnector switch configuration. Then, the Lagrangian function of the objective function of the transmission and distribution system planning model is constructed respectively. The transmission and distribution system planning model is reconstructed based on the improved alternating direction multiplier method, and a solution framework is built to realize the distributed solution of the transmission system and distribution system planning models. Finally, according to the convergence result of the distributed algorithm, a risk-driven transmission and distribution system collaborative planning scheme is obtained. The above method can coordinate the elastic resources in the transmission and distribution links to resist typhoon disasters, reduce the load loss of the transmission and distribution network under uncertain typhoon disasters from the perspective of risk measurement, and provide a reference basis for formulating a transmission and distribution network collaborative planning scheme.

[0042] like Figure 1 As shown, the risk-driven collaborative planning method for transmission and distribution network resilience improvement under typhoon disasters provided in this embodiment specifically includes the following steps:

[0043] Step S1: Obtain basic data, which includes transmission and distribution network load data, power installed capacity and grid system data, typhoon forecast data, planned operation unit cost and risk preference factor. Among them, typhoon forecast data includes predicted output data of wind farms and distributed wind power under extreme weather conditions, predicted typhoon paths and predicted typhoon initial central pressure difference.

[0044] Step S2: Considering the load growth demand under normal scenarios and the elasticity improvement demand under typhoon scenarios, based on the conditional value at risk theory, with the transmission network investment cost, operating cost under normal scenarios, load loss expectation under typhoon scenarios and load loss tail risk as the objective function, and with the transmission network investment constraint, operating constraint under normal / typhoon scenarios and CVaR constraint under typhoon scenarios as the constraint conditions, a transmission network elasticity improvement planning model is established; at the same time, with the distribution network investment cost, operating cost under normal scenarios, load loss expectation under typhoon scenarios and load loss tail risk as the objective function, and with the distribution network investment constraint, operating constraint under normal / typhoon scenarios and CVaR constraint under typhoon scenarios as the constraint conditions, a distribution network elasticity improvement planning model is established.

[0045] In this embodiment, the transmission network resilience improvement planning model is based on the basic data of the distribution network in step S1, taking into account the economic goals of the transmission network in the normal load growth scenario and the load loss expectation and load loss tail risk goals based on the risk preference factor in the typhoon disaster scenario, and using the transmission network investment constraints, transmission network operation constraints and conditional risk value constraints as constraints to construct a flexible transmission network planning model. The specific planning model is shown below.

[0046] Objective function:

[0047]

[0048]

[0049] Where: and They represent the annual planned investment cost of the transmission network, the operating cost of the transmission network, and the tail risk value of the transmission network load loss; γ TNS is the transmission network risk preference factor; δ is the confidence level; is the normal load growth scenario set; is a set of typhoon disaster scenes; Δ du The duration of the planning period; is the initial planned investment cost of the transmission network; IR is the annual interest rate; Y is the planning period; and are the unit reinforcement cost and unit investment cost of the transmission line respectively; and are 0-1 variables representing the reinforcement and construction status of transmission lines, where 1 indicates reinforcement / construction and 0 indicates no reinforcement / construction; π s is the scenario probability; C ls,TNS and C wc,TNS They are the unit startup cost, unit operation cost, unit load shedding cost and unit wind curtailment cost of conventional units in the transmission network; SU g,sA 0-1 variable representing the startup status of a conventional unit, 1 indicates startup, and 0 indicates shutdown; Output for conventional units; The transmission network node loses load; The wind power abandoned by the transmission grid; Γ TNS , Γ TNS* ,Ω TNS ,Ξ TNS and Θ TNS They are the existing transmission line set, the line set to be built set, the conventional unit set, the node set and the wind farm set.

[0050] Constraints:

[0051] 1. Transmission network investment constraints

[0052]

[0053] Where: H,TNS Budget for transmission line reinforcement.

[0054] 2. Transmission grid operation constraints in normal / typhoon scenarios

[0055] (1) Node power balance constraint

[0056]

[0057] Where: f l,s 、 and is the wind farm output, line flow, node load and transmission and distribution boundary power of the transmission network; s is the load growth factor; TD It is the set of transmission and distribution boundary nodes.

[0058] (2) DC power flow constraints on existing lines

[0059]

[0060] Where: B is a 0-1 variable representing the fault status of the transmission line, 0 represents fault and 1 represents no fault; l is the transmission line susceptance; θ se(l),s and θ re(l),s is the node voltage phase angle; M is a large positive number.

[0061] (3) Existing line capacity constraints

[0062]

[0063] Where: F l maxThe transmission limit of the transmission line.

[0064] (4) DC power flow constraints on the lines to be built

[0065]

[0066] (5) Capacity constraints of lines to be built

[0067]

[0068] (6) Output constraints of conventional units

[0069]

[0070] Where: and These are the upper and lower limits of conventional unit output.

[0071] (7) Wind farm output constraints

[0072]

[0073] Where: Predict output coefficient for wind farms; is the wind farm installed capacity growth factor; Forecast output for wind farms.

[0074] (8) Load shedding constraints

[0075]

[0076] 3. CVaR Constraints in Typhoon Scenarios

[0077]

[0078] Where: is the load loss of the transmission network under typhoon scenario s; VaR TNS To quantify the load loss risk of the transmission network under typhoon scenarios; is an auxiliary variable.

[0079] In this embodiment, the distribution network resilience improvement planning model is based on the distribution network basic data of step S1, taking into account the economic goals of the distribution network in the normal load growth scenario and the load loss expectation and load loss tail risk goals based on the risk preference factor in the typhoon disaster scenario, and constructing a flexible distribution network planning model with distribution network investment constraints, distribution network operation constraints and conditional risk value constraints as constraints. The specific model is shown below.

[0080] Objective function:

[0081]

[0082] Where: and They represent the annual planned investment cost of the distribution network, the operating cost of the distribution network, and the tail risk value of the distribution network load loss; γ DNS is the risk preference factor of the distribution network; Initial planning of investment costs for distribution networks; and They are the unit reinforcement cost of distribution lines, the unit investment cost of mobile power supplies, and the unit investment cost of tie switches; and are 0-1 variables representing the reinforcement status of distribution lines, the construction status of mobile power supplies, and the construction status of tie switches, respectively, where 1 indicates reinforcement / construction and 0 indicates no reinforcement / construction; C op,DNS 、C om,DNS 、C ls,DNS and C wc,DNS They are the unit power purchase cost of the upper power grid of the distribution network, the unit operating cost of conventional distributed power sources, the unit operating cost of mobile power sources, the unit load loss cost and the unit wind curtailment cost; and are the upper grid transmission power, distributed power output, mobile power output, node load shedding and wind power abandonment respectively; Γ DNS ,Ω DNS+ ,Ξ DNS and Ξ DT They are respectively a collection of distribution network lines, mobile power sources to be built, nodes and upper-level power grid handover points.

[0083] Constraints:

[0084] 1. Distribution network investment constraints

[0085]

[0086] Where: H,DNS Budget for distribution line reinforcement.

[0087] 2. Distribution network operation constraints in normal / typhoon scenarios

[0088] (1) Distribution network Distflow constraints

[0089]

[0090]

[0091] Where: p ij,s ,q ij,s is the branch active and reactive power flow; L ij,s is the square of the branch current amplitude; R ij and Xij are the resistance and reactance of the branch respectively; They are respectively the active output of conventional distributed power sources, distributed wind power active output and mobile power active output; They are respectively the active output of conventional distributed power sources, the active output of distributed wind power and the reactive output of mobile power sources; and It is the active and reactive power at the junction of the distribution network and the upper power grid; and is the active and reactive loads of the distribution network nodes; Load shedding for distribution network nodes; U i,s 、U j,s is the square of the node voltage amplitude; φ o is the power factor at the transmission and distribution junction.

[0092] (2) Current and voltage amplitude constraints

[0093]

[0094] Where: and is the upper and lower limits of the branch current amplitude; V i max and V i min are the upper and lower limits of the node voltage amplitude; α ij,s A 0-1 variable representing the branch connectivity status, 1 indicates the branch is closed, and 0 indicates the branch is disconnected.

[0095] (3) Line active and reactive power constraints

[0096]

[0097] (4) Output constraints of conventional distributed power generation

[0098]

[0099] Where: P i CDmax 、P i CDmin The upper and lower limits of the active power output of conventional distributed power sources; These are the upper and lower limits of reactive power output of conventional distributed power sources.

[0100] (5) Distributed wind power output constraints

[0101]

[0102] Where: c WD,DNS Predict output coefficient for distributed wind power; is the growth coefficient of distributed wind power installed capacity; P i WDmax Forecast output of distributed wind power. i WD is the power factor of the node where the distributed wind power is located.

[0103] (6) Mobile power output constraints

[0104]

[0105] Where: is a 0-1 state variable representing whether the mobile power supply is connected to the distribution network node, 1 means connected to the node, and 0 means not connected; and The upper and lower limits of the active output of the mobile power supply; and It is the upper and lower limits of the reactive power output of the mobile power supply.

[0106] (7) Load shedding constraints

[0107]

[0108] (8) Radial Constraints

[0109]

[0110] Where: card(·) represents the number of elements in the set; β ji,s is a 0-1 state variable representing whether node i is the parent node of node j, 1 means yes and 0 means no.

[0111] (9) Line status constraints

[0112]

[0113] Where: is a 0-1 state variable representing whether the branch is faulty, 0 indicates faulty and 1 indicates no fault; τ ij It is a 0-1 state variable representing whether the branch is a tie line, 1 means yes, 0 means no.

[0114] 3. CVaR Constraints in Typhoon Scenarios

[0115]

[0116] Where: is the load loss of the distribution network under typhoon scenario s; VaR DNS To quantify the risk of load loss in the distribution network under typhoon scenarios; is an auxiliary variable.

[0117] Step S3: Establish a distributed solution framework based on the improved alternating direction multiplier method, collaboratively solve the transmission network resilience improvement planning model and the distribution network resilience improvement planning model, and obtain a transmission and distribution network collaborative planning solution.

[0118] In this embodiment, collaborative solution specifically includes the following steps:

[0119] S301. Based on the principle of the alternating direction multiplier method, the Lagrangian functions of the transmission network planning model and the distribution network planning model are obtained, the Lagrangian functions are added to the objective functions of the transmission network planning model and the distribution network planning model respectively, and the transmission network planning model and the distribution network planning model are reconstructed, as shown below.

[0120] Lagrangian function of the transmission network planning model:

[0121]

[0122] Lagrangian function of the distribution network planning model:

[0123]

[0124] Where: L TNS and are the Lagrangian functions of the transmission network and distribution network planning models respectively; and are the Lagrange multipliers of the transmission and distribution network planning models, respectively; and are the penalty parameters of the transmission network and distribution network planning models respectively; is the average power at the transmission and distribution interface obtained by optimization in the two models, which is calculated by the following formula:

[0125]

[0126] The Lagrangian function is added to the objective functions of the two planning models respectively, and the models are expressed in matrix form, as shown below.

[0127] Reconstructed transmission network planning model:

[0128]

[0129] Where: C is the unit investment cost vector of the transmission network; x TNS is a 0-1 variable representing the investment status in transmission network planning, 1 indicates investment and 0 indicates no investment; H is the unit operating cost vector of the transmission network planning model; y s are the transmission network operation state variables; A, F, c, D, and e are the constant coefficient vectors corresponding to the constraints in the transmission network planning model.

[0130] Reconstructed distribution network planning model:

[0131]

[0132] Where: Q is the unit investment cost vector of the distribution network; is a 0-1 variable representing the investment status in distribution network planning, 1 means investment and 0 means no investment; G is the unit operating cost vector of the distribution network planning model; z s,m are the distribution network operation state variables; B, L, h, J, and v are the constant coefficient vectors corresponding to the constraints in the distribution network planning model.

[0133] S302, initialize Lagrange multiplier and and penalty parameters and Set the convergence thresholds ε1 and ε2 of the algorithm, and set n=1.

[0134] S303, let n = n + 1, solve the reconstructed transmission network planning model and distribution network planning model, and obtain the transmission and distribution network boundary power in the optimization results respectively. and The average power of the transmission and distribution interface is calculated based on the two transmission and distribution interface power values. to update.

[0135] S304, updating the Lagrange multipliers and penalty term parameters of the reconstructed transmission network planning model and distribution network planning model. The update formula is as follows:

[0136]

[0137] Where: and are the Lagrange multipliers of the transmission network planning model and the distribution network planning model respectively; and are the penalty parameters of the transmission network planning model and the distribution network planning model respectively; The power of the transmission and distribution network boundary nodes obtained by solving the reconstructed transmission network planning model and distribution network planning model, is the average power at the transmission and distribution interface; A1 and A2 are the residual power at the transmission and distribution interface; K u , K d and ν are the update coefficients of the penalty term parameters.

[0138] The calculation formulas for the residual power values A1 and A2 at the transmission and distribution interface are:

[0139]

[0140] Where: is the transmission and distribution interface power vector of scenario s at the nth iteration; and is the mean power vector of the transmission and distribution boundary of scenario s at the n+1th and nth iterations.

[0141] S305. Determine whether the residual power at the transmission and distribution network boundary is less than the convergence threshold, that is, satisfying A1≤ε1, A2≤ε2. If so, terminate the iteration and output the transmission and distribution network collaborative planning scheme. If not, return to step S303 and re-solve based on the updated transmission and distribution boundary power mean, Lagrange multiplier and penalty term parameters, and continue iteration.

[0142] This embodiment uses an improved Garver-6 node transmission network system combined with three distribution network systems for testing. The total load of the transmission network is 570MW. Conventional units are located at nodes 3 and 6, with installed capacities of 145MW and 345MW respectively. A wind farm is located at node 2, with an installed capacity of 210MW. The specific coordinate diagram is as follows: Figure 2 As shown. For distribution network 2, conventional distributed power sources are located at nodes 3 and 14, with a capacity of 8MW each, and distributed wind power is located at nodes 7 and 16, with a capacity of 5MW each. For distribution networks 1 and 3, conventional distributed power sources are located at nodes 11 and 14, with a capacity of 8MW each, and distributed wind power is located at nodes 5 and 13, with a capacity of 5MW each. The capacity of the selected mobile power sources in the three distribution networks is 5MW. The network structures of distribution networks 1, 3, and 2 are shown as follows: Figure 3 and Figure 4 This embodiment was simulated on the MATLAB platform and solved using the GUROBI toolbox. Table 1 shows a comparison of the transmission and distribution coordinated planning scheme and the separate planning schemes for the transmission and distribution networks. Table 2 also shows a comparison of the transmission and distribution coordinated planning results and the separate planning results for the transmission and distribution networks. This verifies that the proposed invention can reduce load losses in the transmission and distribution network during typhoons while improving the economic efficiency of the planning scheme. Table 3 shows the transmission network planning results under different transmission network risk preference factors, demonstrating that planning decision makers can develop corresponding planning strategies based on their risk preferences for typhoon disasters.

[0143] Table 1 Comparison between the transmission and distribution coordinated planning scheme and the transmission network and distribution network separate planning schemes

[0144]

[0145] Table 2 Comparison of transmission and distribution coordinated planning results and transmission and distribution network separate planning results

[0146]

[0147] Table 3 Transmission network planning results under different transmission network risk preference factors

[0148]

[0149] Figure 1 Shown is a specific implementation flow chart of a distributed collaborative planning method for improving the resilience of the transmission and distribution network taking into account the risk of load loss under typhoon disasters, which has reflected the essential characteristics and effectiveness of the present invention. It can be modified equivalently according to actual usage needs under the guidance of the present invention, and all of them are within the scope of protection of this solution.

[0150] If the above method is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, and other media that can store program code.

[0151] The above describes in detail the preferred embodiments of the present invention. It should be understood that those skilled in the art can make numerous modifications and variations based on the concepts of the present invention without inventive effort. Therefore, any technical solutions that can be derived by those skilled in the art through logical analysis, reasoning, or limited experimentation based on the concepts of the present invention and the prior art should be within the scope of protection defined by the claims.

Claims

1. A risk-driven collaborative planning method for transmission and distribution network resilience improvement under typhoon disasters, characterized by: The following steps are involved: Obtaining basic data, including transmission and distribution network load data, power generation capacity and grid system data, typhoon forecast data, planned operation unit costs, and risk preference factors; Based on the basic data, a transmission network resilience improvement planning model and a distribution network resilience improvement planning model are constructed, respectively, taking into account the economic efficiency of the transmission and distribution network in the normal load growth scenario and the load loss expectation and load loss tail risk based on risk preference factors in the typhoon disaster scenario. A distributed solution framework based on the improved alternating direction multiplier method is established to collaboratively solve the transmission network resilience improvement planning model and the distribution network resilience improvement planning model to obtain a transmission and distribution network collaborative planning scheme.

2. The risk-driven collaborative planning method for transmission and distribution network to improve resilience under typhoon disasters according to claim 1 is characterized in that: The typhoon prediction data includes the predicted output data of wind farms and distributed wind power under extreme weather conditions, the predicted typhoon path, and the predicted initial central pressure difference of the typhoon.

3. The risk-driven collaborative planning method for transmission and distribution network to improve resilience under typhoon disasters according to claim 1 is characterized in that: The objective function of the transmission network resilience improvement planning model is expressed as: Where, and They represent the annual planned investment cost of the transmission network, the operating cost of the transmission network, and the tail risk value of the transmission network load loss; γ TNS is the transmission network risk preference factor; δ is the confidence level; is the normal load growth scenario set; is a set of typhoon disaster scenes; Δ du is the duration of the planning period; π s is the scene probability; C ls,TNS is the unit load shedding cost of the transmission network; The transmission network node loses load.

4. The risk-driven collaborative planning method for transmission and distribution network to improve resilience under typhoon disasters according to claim 1 is characterized in that: The objective function of the distribution network flexibility improvement planning model is expressed as: Where, and They represent the annual planned investment cost of the distribution network, the operating cost of the distribution network, and the tail risk value of the distribution network load loss; γ DNS is the risk preference factor of the distribution network; δ is the confidence level; is the normal load growth scenario set; is a set of typhoon disaster scenes; Δ du is the duration of the planning period; π s is the scene probability; C ls,DNS is the unit load loss cost of the distribution network; Shed load for distribution network nodes.

5. The risk-driven collaborative planning method for transmission and distribution network to improve resilience under typhoon disasters according to claim 1 is characterized in that: The collaborative solution specifically includes the following steps: 1) obtaining the Lagrangian functions of the transmission network planning model and the distribution network planning model based on the principle of the alternating direction multiplier method, adding the Lagrangian functions to the objective functions of the transmission network planning model and the distribution network planning model respectively, and reconstructing the transmission network planning model and the distribution network planning model; 2) Solving the reconstructed transmission network planning model and distribution network planning model to obtain the transmission and distribution network boundary power in the optimization results, and updating the transmission and distribution boundary power mean according to the transmission and distribution boundary power values; 3) Update the Lagrange multipliers and penalty term parameters of the reconstructed transmission network planning model and distribution network planning model; 4) Determine whether the residual power at the transmission and distribution network interface is less than the convergence threshold. If so, terminate the iteration and output the transmission and distribution network collaborative planning scheme. If not, return to step 2) and solve again based on the updated transmission and distribution interface power mean, Lagrange multiplier, and penalty term parameters.

6. The risk-driven collaborative planning method for transmission and distribution network to improve resilience under typhoon disasters according to claim 5 is characterized in that: The updating formula of the mean power at the transmission and distribution interface is: Where, is the average power at the transmission and distribution interface, The power of the transmission and distribution network boundary nodes is obtained by solving the reconstructed transmission network planning model and distribution network planning model respectively.

7. The risk-driven collaborative planning method for transmission and distribution network to improve resilience under typhoon disasters according to claim 5 is characterized in that: The update formula of the Lagrange multiplier and penalty term parameters is: Where, and are the Lagrange multipliers of the transmission network planning model and the distribution network planning model respectively; and are the penalty parameters of the transmission network planning model and the distribution network planning model respectively; The power of the transmission and distribution network boundary nodes obtained by solving the reconstructed transmission network planning model and distribution network planning model, is the average power at the transmission and distribution interface; A1 and A2 are the residual power at the transmission and distribution interface; K u , K d and ν are the update coefficients of the penalty term parameters; the superscripts (n) and (n+1) represent the nth and n+1th iterations, respectively.

8. The risk-driven collaborative planning method for transmission and distribution network to enhance resilience under typhoon disasters according to claim 7 is characterized in that: The calculation formulas for the transmission and distribution boundary power residual values A1 and A2 are: Where, is the transmission and distribution interface power vector of scenario s at the nth iteration; and is the mean power vector of the transmission and distribution boundary of scenario s at the n+1th and nth iterations.

9. A computer-readable storage medium, characterized in that It includes one or more programs for execution by one or more processors of an electronic device, and the one or more programs include instructions for executing a risk-driven collaborative planning method for transmission and distribution networks for improving resilience under typhoon disasters as described in any one of claims 1-8.

10. An electronic device, characterized in that: It includes one or more processors, a memory and one or more programs stored in the memory, and the one or more programs include instructions for executing the risk-driven collaborative planning method for transmission and distribution network for improving resilience under typhoon disasters as described in any one of claims 1-8.

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