A distributed recovery method for full-parallel hot start of a power transmission and distribution system

Abstract

The application discloses a kind of distributed recovery methods of full parallel heat start of power transmission and distribution system, the method comprises: the coordinated recovery problem model of power transmission and distribution system is built;Using ATC method, centralized load recovery problem is respectively decoupled into TSR, DSR subproblem;Introduce diagonal quadratic approximation parallel TSO+DSO recovery problem solution process;Combining heat start mode, the distributed load recovery problem of proposed TSO+DSO is accelerated to solve;TSO and DSO will optimization result as dispatching instruction, realize power transmission and distribution system collaborative recovery.The proposed TSO+DSO full parallel distributed recovery solving procedure of the application reduces the calculation time of each iteration.

Description

Technical Field

[0001] This invention relates to the field of load restoration in power transmission and distribution systems, and more particularly to a distributed restoration method for fully parallel hot start of power transmission and distribution systems. Background Technology

[0002] In recent years, with the frequent occurrence of extreme events, the resilience of power systems has received increasing attention. In modern large-capacity power systems, the coupling relationship between transmission system operators (TSOs) and distribution system operators (DSOs) during load processes closely considers their physical connections. Therefore, after a large-scale power outage, coordination between TSOs and DSOs is beneficial for utilizing the resources of the entire system, facilitating the service restoration process, and avoiding safety violations. Typically, transmission and distribution systems are physically coupled and controlled independently by the TSO and DSO, respectively. Under normal circumstances, the coordinated operation of TSOs and DSOs has been addressed in previous studies, but current research still faces the following challenges:

[0003] 1) In existing research on TSO+DSO collaborative operation, considering autonomy and privacy, the TSO+DSO collaborative operation mode is essentially a collaborative optimization problem involving multiple system operators. Therefore, centralized methods are not applicable. To address this, a decentralized scheme based on decomposition, where TSO and DSO operate separately, is introduced. However, previous decentralized recovery schemes are sequential solution processes. In each iteration, TSO(DSO) needs to be solved using updated shared boundary variables obtained from DSO(TSO). Therefore, the computation time is the sum of the solution time of TSO and the longest solution time of DSO. Furthermore, current decentralized recovery algorithms are all started based on given initial iteration values, i.e., cold start, resulting in poor convergence. These characteristics reduce computational efficiency.

[0004] 2) Existing coordinated service restoration of TSO+DSO relies on local generators to quickly and fully restore load, but the flexible regulation resources of the distribution system are not fully considered, limiting the power support capacity between TSO and DSO. Furthermore, considering the various flexible regulation measures in the distribution system for coordinated TSO+DSO restoration could potentially further improve the overall system service restoration performance, such as load restoration level / speed, and simultaneously alleviate voltage deviations; however, this aspect currently lacks in-depth research.

[0005] Overall, there is currently a lack of a distributed recovery method that takes into account the flexibility adjustment measures of the power distribution system and can coordinate the optimal operation of the power transmission and distribution system in the event of a power outage. Summary of the Invention

[0006] This invention provides a decentralized service restoration scheme (D-TDSR) for fully parallel hot-start of transmission and distribution systems. This method allows for the complete parallel implementation of TSO and DSO. The proposed D-TDSR is based on the decomposition of the transmission service restoration (TSR) and distribution service restoration (DSR) models, and an iterative program is designed using the analytic target cascading (ATC) method. Furthermore, a diagonal quadratic approximation is introduced to relax the Lagrange penalty function of ATC, allowing the TSR (DSR) to be solved together with the latest boundary variables obtained from the DSR (TSR). The proposed fully parallel distributed restoration solution program for TSO+DSO reduces the computation time for each iteration, as detailed below:

[0007] A distributed recovery method for fully parallel hot-start of a power transmission and distribution system, the method comprising:

[0008] A model of the coordinated recovery problem of the power transmission and distribution system is constructed; the centralized load recovery problem is decoupled into TSR and DSR subproblems using the ATC method.

[0009] A parallel solution to the TSO+DSO recovery problem is introduced by introducing a diagonal quadratic approximation.

[0010] The proposed distributed load restoration problem of TSO+DSO is accelerated by combining the hot start mode; TSO and DSO use the optimization results as scheduling instructions to achieve coordinated restoration of the power transmission and distribution system.

[0011] The model for the coordinated recovery of the power transmission and distribution system is as follows:

[0012] The objective function is constructed based on maximizing load recovery and reducing voltage deviation; constraints are established including those for the transmission system, distribution system, and boundary variables.

[0013] Furthermore, the objective function is:

[0014]

[0015] In the formula, These represent the active and reactive power of the load at node i, respectively. These are binary variables representing whether load i in the power transmission system and power distribution system has recovered or not, respectively. and These represent the load priorities in the power transmission and distribution systems, respectively. This represents the voltage amplitude at node i in the distribution network.

[0016] The specific process for solving the TSO+DSO recovery problem by introducing diagonal quadratic approximation parallelization is as follows:

[0017] Parallel solution for diagonal quadratic approximation; a two-stage method is used to solve the D-TDSR recovery strategy, which improves the convergence of the distributed recovery algorithm while obtaining an approximate solution.

[0018] Furthermore, the proposed distributed load recovery problem of TSO+DSO, which combines a hot-start mode to accelerate the solution, specifically involves:

[0019] In the inner loop, the TSR and DSR subproblems are solved in parallel; the outer loop penalty parameters are updated based on the inner loop results.

[0020] The method also includes reconstructing the network and setting soft openings.

[0021] The beneficial effects of the technical solution provided by this invention are:

[0022] 1) This invention uses the hot start mode to run a fully parallel D-TDSR scheme. The proposed D-TDSR method can obtain good initial values ​​of the target and response variables of ATC from the previous recovery step, thereby starting the recovery process. Compared with the cold start mode (i.e., arbitrarily given corresponding initial values), this improvement greatly accelerates the convergence process of the distributed recovery algorithm.

[0023] 2) This invention utilizes various flexible adjustment resources to promote load recovery performance, coordinates the power output of local generators, the distribution network reconfiguration strategy based on tie switches (TS), and the power regulation capability of smart soft open points (SOPs). TSOs and DSOs issue power control commands, load control commands, and equipment control commands to generators, SOPs, load nodes, and tie lines in the power transmission and distribution system, controlling the operation of corresponding components to improve the mutual support capability of TSOs and DSOs, increase the load recovery speed, and alleviate voltage deviations; thus achieving coordinated recovery of the power transmission and distribution system. Attached Figure Description

[0024] Figure 1 A flowchart of a distributed recovery method for a fully parallel hot start of a power transmission and distribution system;

[0025] Figure 2 This is a schematic diagram of a power transmission and distribution system test case;

[0026] Figure 3 This is a schematic diagram showing the results of load recovery in the power transmission and distribution system. Detailed Implementation

[0027] To make the objectives, technical solutions, and advantages of the present invention clearer, the embodiments of the present invention will be described in further detail below.

[0028] Example 1

[0029] This invention proposes a fully parallel distributed load recovery (DSR) method using TSO+DSO. This method constructs a centralized service recovery problem model by coupling the TSR and DSR sub-problems; and decouples the proposed D-TDSR model into local recovery models of TSO and DSO during the iteration process using the objective cascade analysis method. Furthermore, by introducing a diagonal quadratic approximation into ATC, all local repair models are solved in a fully parallel manner, improving the computational efficiency of each iteration. Finally, a warm-start mode is adopted to accelerate the convergence speed of the proposed D-TDSR scheme. The method includes the following steps:

[0030] 101: Construct a model for the coordinated recovery problem of power transmission and distribution systems;

[0031] 102: The centralized load restoration problem is decoupled into TSR and DSR sub-problems using the ATC method;

[0032] 103: Introducing the parallelization of diagonal quadratic approximation to solve the TSO+DSO recovery problem;

[0033] 104: Accelerating the solution of the proposed distributed load recovery problem of TSO+DSO by combining the hot start mode;

[0034] 105: TSO and DSO use the optimization results as scheduling instructions to achieve coordinated recovery of the power transmission and distribution system.

[0035] Specifically, the power transmission and distribution system coordinated restoration problem model in step 101 is as follows:

[0036] The objective function is constructed based on maximizing load recovery and minimizing voltage deviation.

[0037] Establish constraints, including: power transmission system constraints (power flow constraints and safety constraints), power distribution system constraints (power flow constraints, SOP operation constraints, radial topology constraints, and safety operation constraints), and boundary variable constraints.

[0038] Specifically, in step 102, the distributed algorithm ATC is used to decouple the centralized load recovery problem into TSR and DSR recovery sub-problems, as follows:

[0039] Decompose the shared boundary variables to satisfy consistency constraints;

[0040] It is decomposed into one TSR subproblem and multiple DSR subproblems.

[0041] Specifically, step 103 introduces the diagonal quadratic approximation (DQA) method to parallelize the TSO+DSO recovery problem.

[0042] Parallel solutions for diagonal quadratic approximation;

[0043] A two-stage method is adopted to solve the recovery strategy of D-TDSR, which improves the convergence of the distributed recovery algorithm and obtains an approximate solution.

[0044] Specifically, step 104, which combines a hot-start mode to accelerate the solution of the proposed TSO+DSO distributed load recovery problem, involves the following:

[0045] In the inner loop, the TSR subproblem and the DSR subproblem are solved in parallel.

[0046] After the inner loop converges, the outer loop penalty parameters are updated based on the inner loop results. The solution process stops once the recovery step is complete.

[0047] In step 105, the TSO and DSO use the optimization results as scheduling instructions to achieve coordinated restoration of the power transmission and distribution system.

[0048] The optimization results of generator output, SOP output, load status, and tie switch status are collected and used as dispatch instructions.

[0049] By controlling the output of generators and SOPs, as well as the switching status of load nodes and tie lines through dispatch commands, the coordinated restoration of the power transmission and distribution system can be achieved.

[0050] In summary, the embodiments of the present invention promote the coordinated operation of TSO and DSO through steps 101-105, making full use of the flexible adjustment resources in the power distribution system, such as network reconfiguration and soft switching, thereby improving load recovery efficiency, reducing voltage deviation, and meeting the industrial needs in practical applications.

[0051] Example 2

[0052] The scheme in Example 1 will be further described below with specific calculation formulas and examples:

[0053] 201: Constructing a model for the coordinated recovery of power transmission and distribution systems;

[0054] 1. Objective function:

[0055] The embodiments of the present invention construct an optimization model with the goal of maximizing the load recovery level and reducing voltage deviation, as shown in Equation (1).

[0056]

[0057] In the formula, These represent the active and reactive power of the load at node i, respectively. These are binary variables representing whether load i in the power transmission system and power distribution system has recovered or not, respectively. and These represent the load priorities in the power transmission and distribution systems, respectively. This represents the voltage amplitude at node i in the distribution network.

[0058] 2. Power transmission system constraints:

[0059]

[0060]

[0061]

[0062]

[0063]

[0064]

[0065]

[0066]

[0067]

[0068]

[0069]

[0070]

[0071]

[0072]

[0073] in, These represent the active power and reactive power output by generator i, respectively. A binary variable representing whether the load at node i in the transmission network has been restored; These represent the active power and reactive power of node i, respectively. These represent the active and reactive power transmitted from the transmission network to the distribution network, respectively. These represent the active and reactive power flows of branch ij, respectively. The reactive power component of branch ij is related to voltage; Ω TB For the collection of power transmission network branches; and The target voltage amplitudes for nodes i and j; g ij +jb ij Let θ be the admittance of branch ij in the power transmission system; i ,θ j The phase angles at nodes i and j are respectively; cos ij cos(θ) i -θ j Approximate value of ). δ represents the voltage magnitude at node i; i Δθ represents the change in voltage magnitude at node i. max The maximum allowable phase angle difference between the two nodes; h is the number of hyperplanes in the cosine function approximation; d is d = 2Δθ max / (h+1) Hyperplane spacing; s ij a is the maximum apparent power capacity of branch ij; c ,b c ,c c These represent the approximation coefficients within the polygon; These represent the generator's minimum, maximum, and initial active power outputs, respectively; r i Let be the ramp slope of generator i; τ be the recovery step time interval; This represents the minimum reactive power output of the generator. Let be the power factor of generator i; This refers to the maximum active power allowed to be transmitted from the transmission network to the distribution network. These are the minimum and maximum allowable values ​​for the voltage. This indicates that in recovery step t k A binary variable representing whether the load i in the power transmission system has recovered.

[0074] 3. Power distribution system constraints:

[0075] (1) Current constraint:

[0076]

[0077]

[0078]

[0079]

[0080]

[0081]

[0082] in, These represent the active and reactive power flows of branch ij, respectively. These represent the active and reactive power injected into node j, respectively. A binary variable representing whether the load at node j in the distribution network has been restored; These represent the active power and reactive power of node j, respectively. These represent the active and reactive power flows of branch lines j and k, respectively; Ω DB For distribution network branch collection; These represent the active power and reactive power output by generator j, respectively. These represent the active power and reactive power output from the J-port of the intelligent soft switch, respectively. These represent the active and reactive power received by the distribution network from the transmission network, respectively. Represent the voltage magnitudes at nodes i and j, respectively; r ij ,x ij Represent the resistance and reactance of branch ij, respectively; V0 is the reference voltage amplitude; M is a relatively large positive number; α ij For binary variables: 1 when branch ij is off, and 0 when branch ij is on.

[0083] (2) SOP operational constraints:

[0084]

[0085]

[0086] in, These represent the active power and reactive power output from the i-port of the intelligent soft switch, respectively. These represent the minimum and maximum reactive power output of the intelligent soft switch, respectively. For intelligent soft switching capacity; a c ,b c ,c c These represent the approximation coefficients within the polygon.

[0087] (3) Radial topological constraints

[0088] α ij =β ij +β ji , ij∈Ω DB (25)

[0089]

[0090]

[0091] αij∈{0 , 1},ij∈Ω DB (28)

[0092] 0≤βij≤1, 0≤βji≤1, ij∈Ω DB (29)

[0093] Where, α ij For binary variables: 1 when branch ij is off, 0 when branch ij is on; β ij This is a binary variable; if node j is the parent node of node i, it is 1; otherwise, it is 0. ji This is a binary variable; if node i is the parent node of node j, it is 1; otherwise, it is 0. DB For the collection of distribution network branches; Ω DR Ω is the set of root nodes. DL This is the set of load nodes.

[0094] (4) Safety constraints

[0095]

[0096]

[0097]

[0098]

[0099]

[0100]

[0101]

[0102] in, These represent the generator's minimum, maximum, and initial active power outputs, respectively; r i Let be the ramp slope of generator i; τ be the recovery step time interval; These represent the active power and reactive power output by generator i, respectively. This represents the minimum reactive power output of the generator. α is the power factor of generator i; ij For binary variables: 1 when branch ij is off, 0 when branch ij is on; These represent the active and reactive power transmitted by branch ij, respectively. These are the minimum and maximum active power values ​​that branch ij is allowed to transmit, respectively. These are the minimum and maximum reactive power values ​​that branch ij is allowed to transmit, respectively. Let be the voltage amplitude at node i; These are the minimum and maximum allowable node voltage amplitudes, respectively; To receive active power from the transmission network for the distribution network; This represents the maximum active power received by the distribution network from the transmission network. This indicates that in recovery step t k A binary variable indicating whether the load i in the power distribution system has recovered.

[0103] 4. Boundary variable constraints:

[0104]

[0105] in, These represent the active and reactive power and voltage amplitude transmitted from the transmission network to the distribution network, respectively. These represent the active and reactive power and voltage amplitude received by the distribution network from the transmission network, respectively.

[0106] In summary, the above equations (1)-(37) constitute the optimized model for distributed coordinated recovery of power transmission and distribution systems established in the embodiments of the present invention.

[0107] 202: The distributed algorithm ATC is used to decouple the centralized load restoration problem into transmission network restoration and distribution network restoration sub-problems.

[0108] 1. Centralized load restoration model

[0109]

[0110] Where f represents the objective function of centralized load restoration; g and h represent the inequality and equality constraints of centralized load restoration, respectively; x T and x D These are local variables of the power transmission and distribution systems; b represents shared boundary variables, including the objective variable. and response variables The consistency constraint is satisfied. To decompose the centralized load recovery problem, the objective and response variables are subjected to Lagrange relaxation, as shown below:

[0111]

[0112] Here, λ is the vector of Lagrange multipliers; w is the vector of penalty weights. Both are parameters of the penalty function. "o" represents the Hadamard product. Adding this penalty function to the objective function of the transmission and distribution network subproblem enables the decomposition of the centralized recovery problem, as shown below.

[0113] 2. Subproblem of power transmission network restoration

[0114] Based on the ATC method, the power transmission system recovery subproblem is shown in equation (40).

[0115]

[0116] Among them, g T and h T These are the inequalities and equality constraints of the power transmission network recovery subproblem; N represents the number of distribution systems it is connected to.

[0117] 3. Distribution network restoration sub-problem:

[0118] Similarly, the distribution network recovery subproblem can be represented by equation (41).

[0119]

[0120] Among them, g D,n and h D,n These are the inequalities and equality constraints of the nth distribution network recovery subproblem.

[0121] 203: Parallelizing the solution process of the TSO+DSO recovery problem by introducing a diagonal quadratic approximation method;

[0122] In each iteration, TSR(DSR) uses the response variable r obtained from DSR(TSR) in the current iteration. k (target variable t) k Solving this problem using traditional methods is inefficient. To address this issue, the DQA method is used to separate the quadratic terms in the penalty function.

[0123]

[0124] Among them, t k-1 and r k-1 Let TSR and DSR be the target and response variables obtained in the previous iteration k-1, respectively, and be constants in the current iteration. Therefore, the quadratic term can be approximated as:

[0125]

[0126] Where C is a constant. It can be seen that the quadratic term in equation (43) is transformed into a separable term, which can be solved using a fully parallel process. Now, using the ATC of DQA relaxation treatment, the objective functions of the TSR and DSR subproblems are redefined as:

[0127]

[0128]

[0129] At this point, the penalty term of the TSR (DSR) subproblem depends on the objective variable t (response r), while using the response r obtained from the DSR (TSR) subproblem in the previous iteration k-1. k-1 (target t) k-1Therefore, equations (44) and (45) allow for parallel solutions to the TSR and DSR subproblems.

[0130] 204: Iterative solution process.

[0131] After the above processing, the original centralized recovery problem is transformed into a distributed recovery problem. This invention proposes a three-layer nested iterative process consisting of an inner loop, an outer loop, and a multi-step recovery iteration layer to solve the distributed recovery model, as follows: Figure 1 As shown, in the inner loop, the power transmission and distribution system recovery subproblem is solved in parallel; after the inner loop converges, the outer loop updates the penalty coefficient based on the inner loop result; convergence is achieved until the maximum number of iterations is met.

[0132] The specific process is as follows:

[0133] 1) Given the initial values ​​of the inner and outer loop indices, i.e., k o =0, k I =0; given the initial value λ of the penalty function coefficient vector. n =0 and the initial value w of the penalty function weight vector n =0.5; The initial values ​​of the target variable and the response variable are obtained by the warm start method, that is, in each recovery step, the initial values ​​of the target variable and the response variable are obtained from the optimization solution of the previous recovery step;

[0134] 2) Let k I =k I +1, solve the power transmission and distribution network recovery subproblem in parallel to obtain new target variable and response variable values;

[0135] 3) Determine if the inner loop meets the convergence condition, i.e., the maximum difference between the target (and response) values ​​in two consecutive iterations is less than an acceptable threshold. If convergence is achieved, proceed to step 4); otherwise, return to step 2). They are respectively the kth I and k I The value of the target variable in the inner loop -1st time. They are respectively the kth I and k I The response variable value in the inner loop of iteration -1, where ε1 is the convergence threshold;

[0136] 4) Determine if the outer loop meets the convergence criterion. and If convergence occurs, proceed to step 5; otherwise, let k... o =k o +1,k I =0, and update the penalty function coefficient vector and penalty function weight vector using formulas (46) and (47), and then return to step 2);

[0137] in, They are respectively the kth I and k I The sum of the objective functions of all power transmission and distribution network restoration subproblems in the inner loop of -1, where ε2 and ε3 are the convergence thresholds.

[0138]

[0139]

[0140] in, They are respectively the kth o and k o +1 times the penalty function weight vector in the outer loop, They are respectively the kth o and k o The penalty function coefficient vector in the outer loop +1, where ρ is a constant.

[0141] 5) Determine whether the given maximum recovery step has been reached, i.e. If the condition is met, stop the calculation and output the final recovery result; otherwise, let t k =t k +1, Go to step 1) and begin the next iteration. Where t... k This represents the current iteration step. The maximum number of allowed iterations, This is the optimal solution for the current iteration step of the generator. r is the initial value of the generator output. i τ is the ramp slope of generator i; τ is the recovery step time interval.

[0142] 205: Restore operation control.

[0143] Step 205 includes:

[0144] 1. Obtain scheduling instructions

[0145] Based on the above optimization results, TSO and DSO collect the output values ​​of generators in the transmission system and distributed power sources in the distribution system as power control commands; collect the optimization results of binary state variables of whether each load node in the transmission system and distribution system recovers as load control commands; and collect the power output of SOP and the state variables of tie switches as equipment control commands.

[0146] 2. Operation Control

[0147] TSO and DSO issue power control commands, load control commands, and equipment control commands to generators, SOPs, load nodes, and tie lines in the power transmission and distribution system to control the operation of corresponding components and achieve coordinated recovery of the power transmission and distribution system.

[0148] In summary, the embodiments of the present invention achieve coordinated operation of TSO and DSO through the above steps 201-205, making full use of the flexible adjustment resources in the power distribution system to improve load recovery efficiency, reduce voltage deviation, and meet various needs in practical applications.

[0149] Example 3

[0150] The feasibility of the schemes in Example 1 and Example 2 is verified below with specific examples, as detailed in the following description:

[0151] like Figure 2 As shown, this example system consists of one IEEE 30 bus transmission system and two IEEE 33 bus distribution systems. The result of the power transmission and distribution network being restored in 10 steps is as follows. Figure 3 As shown, it can be seen that as the number of iterations increases, the output power of the generator gradually increases, which leads to an increase in the amount of load recovery, and finally all loads are recovered, verifying the feasibility of the method proposed in the embodiment of the present invention.

[0152] The following three scenarios are set up for comparative analysis:

[0153] Scenario 1: The distribution network does not contain tie switches or smart soft switches;

[0154] Scenario 2: The distribution network contains tie switches and undergoes network reconfiguration;

[0155] Scenario 3: The distribution network contains tie switches and smart soft switches, which operate in coordination.

[0156] Table 1 Voltage distribution of distribution network under different scenarios

[0157]

[0158]

[0159] The optimization results for the three scenarios are shown in Table 1. It can be observed that the power transmission and distribution network, under different load restoration priorities, i.e. or Scenario 3 has the greatest system flexibility, and therefore, its nodes have the smallest voltage deviation. This verifies that the method proposed in this embodiment can effectively reduce the voltage deviation of the power distribution network while achieving load restoration.

[0160] For a computational efficiency example of the warm-start mode, please see the description below:

[0161] The following two scenarios are set up for comparative analysis:

[0162] Scenario 1: Cold start: In each recovery step, the initial values ​​of the target and response variables are set to zero;

[0163] Scenario 2: Warm start: In each recovery step, the initial values ​​of the target and response variables are obtained from the previous step.

[0164] Table 2 Total number of iterations with different launch modes

[0165]

[0166] Table 2 shows a comparison of the two scenarios. The cold-start mode based on ATC converges after 267 iterations, while the warm-start mode requires only 159 iterations. Compared with the cold-start scheme, the warm-start mode improves the convergence speed of the ATC scheme by 57.8%. Furthermore, the total number of iterations for D-TDSR based on ATC and diagonal quadratic approximation relaxation ATC is comparable in the warm-start mode, but DATC, due to its fully parallel solution method, has a shorter solution time and higher efficiency. These results demonstrate that the feasibility and effectiveness of the fully parallel warm-start recovery method proposed in this embodiment of the invention are guaranteed.

[0167] This invention proposes a distributed recovery method that coordinates TSO and DSO. After a large-scale power outage, all resources of the entire system are utilized to improve load recovery performance. Numerical results show that the participation of intelligent soft switches in the DSO in system recovery can improve the load recovery level / speed while reducing voltage deviation. The recovery scheme proposed in this invention is based on a fully parallel mode of diagonal quadratic approximation relaxation ATC, reducing the computation time of each iteration. Furthermore, a hot-start mode is proposed, allowing the initial values ​​of the target and response variables to be obtained from the previous step. Compared with the cold-start method, the application of hot-start significantly accelerates the convergence speed of D-TDSR.

[0168] Unless otherwise specified, the model numbers of the various devices in this embodiment of the invention are not limited, and any device that can perform the above functions is acceptable.

[0169] Those skilled in the art will understand that the accompanying drawings are merely schematic diagrams of a preferred embodiment, and the sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0170] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A distributed recovery method for full parallel hot start of a power transmission and distribution system, characterized by, The method includes: A model of the coordinated recovery problem of the power transmission and distribution system is constructed; the centralized load recovery problem is decoupled into TSR and DSR subproblems using the ATC method. A parallel solution to the TSO+DSO recovery problem is introduced by introducing a diagonal quadratic approximation. The proposed distributed load restoration problem of TSO+DSO is accelerated by combining a hot start mode; TSO and DSO use the optimization results as scheduling instructions to achieve coordinated restoration of the power transmission and distribution system; The model for the coordinated recovery problem of the power transmission and distribution system is as follows: The objective function is constructed based on maximizing load recovery and minimizing voltage deviation; constraints are established including: transmission system constraints, distribution system constraints, and boundary variable constraints. The objective function is: ； where, PQ, i represent the active and reactive power of the load at node i, respectively; , B, i and B, i represent the binary variables for the restoration of the load i in the transmission and distribution systems, respectively; and P, i and P, i represent the priority of the load in the transmission and distribution systems, respectively; V, i represents the voltage magnitude at node i in the distribution network. The specific process for solving the TSO+DSO recovery problem by introducing diagonal quadratic approximation parallelization is as follows: Parallel solutions for diagonal quadratic approximation; a two-stage method is used to solve the D-TDSR recovery strategy, which improves the convergence of the distributed recovery algorithm while obtaining an approximate solution; the specific steps of combining the hot-start mode to accelerate the solution of the proposed TSO+DSO distributed load recovery problem are as follows: In the inner loop, the TSR and DSR subproblems are solved in parallel; the outer loop penalty parameters are updated based on the inner loop results.

2. The method of claim 1, wherein the method is a full parallel hot start distributed recovery method for a power transmission and distribution system. The method also includes network reconstruction and soft-open point configuration.

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