A power network topology optimization method and device

Abstract

The present application relates to the technical field of power supply network evaluation and optimization, and particularly provides a power supply network topology optimization method and device, comprising: solving a first optimization model constructed in advance to obtain optimal connection conditions between nodes in a power supply network topology; adjusting the connection conditions between the nodes in the power supply network topology by using the optimal connection conditions between the nodes; solving a second optimization model constructed in advance to obtain optimal power flow parameters of the power supply network topology; and adjusting the power flow parameters of the power supply network topology by using the optimal power flow parameters; the technical scheme provided by the present application solves a double-layer optimization model by using an intelligent optimization algorithm, and then optimizes and adjusts the power supply network topology, so that the reliability of the power supply network topology can be improved by more than 30%.

Description

Technical Field

[0001] This invention relates to the field of power supply network assessment and optimization technology, specifically to a method and apparatus for optimizing power supply network topology. Background Technology

[0002] Aerospace autonomous power supply platforms are primarily powered by a combination of solar cells and energy storage batteries. With the development of solar cell technology, lighter and more efficient solar thin films are being widely used in off-grid platforms such as solar-powered drones, satellites, and space stations, providing long-term power supply capabilities. Due to high launch costs and long operational cycles, these platforms have high requirements for power supply duration and reliability.

[0003] Node failures can originate from human interference, attacks, and the inherent failure probability of the equipment itself. Furthermore, consecutive node failures can lead to the failure of a wider range of nodes, and the failure of critical nodes can have a significant impact on the system. Improving node reliability relies on breakthroughs in specialized technologies. Given the significant challenges in enhancing node reliability, topology optimization is crucial for improving the reliability of power supply networks. Therefore, there is an urgent need for an optimization method that can improve the reliability of power supply network topology. Summary of the Invention

[0004] To overcome the above-mentioned shortcomings, this invention proposes a method and apparatus for optimizing the topology of a power supply network.

[0005] Firstly, a method for optimizing the topology of a power supply network is provided, the method comprising:

[0006] Solve the pre-constructed first optimization model to obtain the optimal connectivity between nodes in the power supply network topology;

[0007] The optimal connectivity between nodes is used to adjust the connectivity between nodes in the power supply network topology.

[0008] Solve the pre-built second optimization model to obtain the optimal power flow parameters of the power supply network topology;

[0009] The power flow parameters of the power supply network topology are adjusted using the optimal power flow parameters.

[0010] The pre-constructed first optimization model includes an objective function and its corresponding constraints aimed at minimizing the minimum load shedding amount of the power supply network topology under the NK cascading failure scenario. The pre-constructed second optimization model includes an objective function and its corresponding constraints aimed at minimizing the load shedding amount of the power supply network topology. N is the total number of nodes in the power supply network topology, and K is the number of faulty nodes.

[0011] Preferably, the objective function, which aims to minimize the minimum load shedding in the power supply network topology under the NK cascading failure scenario, is calculated as follows:

[0012]

[0013] In the above formula, M represents the total number of NK cascading failure scenarios, m represents the total number of NK cascading failure scenarios after scenario reduction, and ω j Let j be the weight of node j. Let be the minimum load shedding amount at node j in the i-th NK cascading failure scenario.

[0014] Furthermore, the mathematical model for the constraints of the objective function aimed at minimizing the minimum load shedding amount in the NK cascading failure scenario of the power supply network topology is as follows:

[0015]

[0016] In the above formula, a xj Let a be the connectivity coefficient of the alternative branches between node x and node j in the power supply network topology. When the alternative branches between node x and node j are connected, a xj =1, when the alternative branches between node x and node j are not connected, a xj =0, N1 is the minimum number of branches for normal network operation, and N2 is the total number of alternative lines in the network.

[0017] Furthermore, the power flow parameters include at least one of the following: the line capacity of the lines between each node, and the phase angle of each node.

[0018] Furthermore, the objective function, which aims to minimize the load shedding of the power supply network topology, is calculated as follows:

[0019]

[0020] In the above formula, SL j Let be the load shedding amount for node j.

[0021] Furthermore, the mathematical model for the constraints corresponding to the objective function that aims to minimize the load shedding of the power supply network topology is as follows:

[0022] a xj P xj x xj =θ x -θ j

[0023] P PV,x +P Es,x +P in,x -P out,x -PLe,x =SL_pInS x -SL_nInS x

[0024]

[0025] 0≤θ x ≤2π

[0026]

[0027] In the above formula, P xj Let x be the line capacity of the connection between node x and node j. xj Let θ be the admittance between node x and node j. x Let θ be the phase angle of node x. j Let P be the phase angle of node j. PV,x Let P be the photovoltaic power generation at node x. Es,x P outputs power to the energy storage of node x. in,x Let P be the inflow power at node x. out,x Let P be the outflow power of node x. Le,x SL_pInS represents the operating power of node x. x For the positive relaxation of the power balance at node x, SL_nInS x For the negative relaxation of the power balance at node x, This represents the maximum line capacity of the line between node x and node j. Let x be the maximum photovoltaic power generation at node x.

[0028] Secondly, a power supply network topology optimization device is provided, the power supply network topology optimization device comprising:

[0029] The first acquisition module is used to solve the pre-built first optimization model to obtain the optimal connectivity between nodes in the power supply network topology.

[0030] The first adjustment module is used to adjust the connectivity between nodes in the power supply network topology by utilizing the optimal connectivity between the nodes.

[0031] The second acquisition module is used to solve the pre-built second optimization model to obtain the optimal power flow parameters of the power supply network topology.

[0032] The second adjustment module is used to adjust the power flow parameters of the power supply network topology using the optimal power flow parameters.

[0033] The pre-constructed first optimization model includes an objective function and its corresponding constraints aimed at minimizing the minimum load shedding amount of the power supply network topology under the NK cascading failure scenario. The pre-constructed second optimization model includes an objective function and its corresponding constraints aimed at minimizing the load shedding amount of the power supply network topology. N is the total number of nodes in the power supply network topology, and K is the number of faulty nodes.

[0034] Thirdly, a storage medium is provided, the storage medium including a stored program, wherein, when the program is executed, the device where the storage medium is located is controlled to execute the power supply network topology optimization method.

[0035] Fourthly, a processor is provided for running a program, wherein the program executes the power supply network topology optimization method during runtime.

[0036] The above-described technical solutions of the present invention have at least one or more of the following beneficial effects:

[0037] This invention provides a method and apparatus for optimizing the topology of a power supply network, comprising: solving a pre-constructed first optimization model to obtain the optimal connectivity between nodes in the power supply network topology; adjusting the connectivity between nodes in the power supply network topology using the optimal connectivity; solving a pre-constructed second optimization model to obtain the optimal power flow parameters of the power supply network topology; and adjusting the power flow parameters of the power supply network topology using the optimal power flow parameters. The pre-constructed first optimization model includes an objective function and its corresponding constraints aimed at minimizing the minimum load shedding amount of the power supply network topology under an NK cascading failure scenario. The pre-constructed second optimization model includes an objective function and its corresponding constraints aimed at minimizing the load shedding amount of the power supply network topology, where N is the total number of nodes in the power supply network topology. The technical solution provided by this invention solves the two-layer optimization model using an intelligent optimization algorithm, thereby optimizing and adjusting the power supply network topology, which can improve the reliability of the power supply network topology by more than 30%. Attached Figure Description

[0038] Figure 1 This is a schematic diagram of the main steps of the power supply network topology optimization method according to an embodiment of the present invention;

[0039] Figure 2 This is a schematic diagram of the power supply network topology according to an embodiment of the present invention;

[0040] Figure 3 This is a schematic diagram of the power supply network topology under the N-2 cascading failure scenario according to an embodiment of the present invention;

[0041] Figure 4 This is a main structural block diagram of the power supply network topology optimization device according to an embodiment of the present invention. Detailed Implementation

[0042] The specific embodiments of the present invention will be further described in detail below with reference to the accompanying drawings.

[0043] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0044] See appendix Figure 1 , Figure 1 This is a schematic flowchart illustrating the main steps of a power supply network topology optimization method according to an embodiment of the present invention. Figure 1 As shown, the power supply network topology optimization method in this embodiment of the invention mainly includes the following steps:

[0045] Step S101: Solve the pre-built first optimization model to obtain the optimal connectivity between nodes in the power supply network topology;

[0046] Step S102: Adjust the connectivity between nodes in the power supply network topology using the optimal connectivity between the nodes;

[0047] Step S103: Solve the pre-built second optimization model to obtain the optimal power flow parameters of the power supply network topology;

[0048] Step S104: Adjust the power flow parameters of the power supply network topology using the optimal power flow parameters;

[0049] The pre-constructed first optimization model includes an objective function and its corresponding constraints aimed at minimizing the minimum load shedding amount of the power supply network topology under the NK cascading failure scenario. The pre-constructed second optimization model includes an objective function and its corresponding constraints aimed at minimizing the load shedding amount of the power supply network topology. N is the total number of nodes in the power supply network topology, and K is the number of faulty nodes.

[0050] In this embodiment, the objective function, which aims to minimize the minimum load shedding in the power supply network topology under the NK cascading failure scenario, is calculated as follows:

[0051]

[0052] In the above formula, M represents the total number of NK cascading failure scenarios, m represents the total number of NK cascading failure scenarios after scenario reduction, indicating that there are m possible failure combinations when two random nodes fail, ω j Let j be the weight of node j. Let be the minimum load shedding amount at node j in the i-th NK cascading failure scenario.

[0053] In one implementation, the mathematical model for the constraints of the objective function aimed at minimizing the minimum load shedding amount in the NK cascading failure scenario of the power supply network topology is as follows:

[0054]

[0055] In the above formula, a xj Let a be the connectivity coefficient of the alternative branches between node x and node j in the power supply network topology. When the alternative branches between node x and node j are connected, a xj =1, when the alternative branches between node x and node j are not connected, a xj =0, N1 is the minimum number of branches for normal network operation, and N2 is the total number of alternative lines in the network.

[0056] In this embodiment, the power flow parameters include at least one of the following: the line capacity of the lines between each node, and the phase angle of each node.

[0057] In one implementation, the objective function, which aims to minimize the load shedding of the power supply network topology, is calculated as follows:

[0058]

[0059] In the above formula, SL j Let be the load shedding amount for node j.

[0060] In one embodiment, the mathematical model for the constraints corresponding to the objective function that aims to minimize the load shedding of the power supply network topology is as follows:

[0061] a xj P xj x xj =θ x -θ j

[0062] P PV,x +P Es,x +P in,x -P out,x -P Le,x =SL_pInS x -SL_nInS x

[0063]

[0064] 0≤θ x ≤2π

[0065]

[0066] In the above formula, P xj Let x be the line capacity of the connection between node x and node j. xj Let θ be the admittance between node x and node j. x Let θ be the phase angle of node x. j Let P be the phase angle of node j. PV,x Let P be the photovoltaic power generation at node x. Es,x P outputs power to the energy storage of node x. in,x Let P be the inflow power at node x. out,x Let P be the outflow power of node x. Le,x SL_pInS represents the operating power of node x. x For the positive relaxation of the power balance at node x, SL_nInS x For the negative relaxation of the power balance at node x, This represents the maximum line capacity of the line between node x and node j. Let x be the maximum photovoltaic power generation at node x.

[0067] Based on the above solution, the present invention provides an optimal implementation scheme, specifically including:

[0068] Step 1: Collect distributed power supply network topology information, including network node information, network line information, and various basic parameters of the network such as line capacity and node rated power. Establish a mathematical model of the distributed power supply network topology, such as... Figure 2 ;

[0069] Step 2: Establish the first optimization model and calculate the minimum shear load of the current topology using the Yalmip linear solver;

[0070] Step 3: Establish and solve the second optimization model;

[0071] Step 4: Optimize network reliability using greedy and genetic algorithms;

[0072] Furthermore, the solution steps in step 2 are as follows:

[0073] Step 2.1: Obtain adjacency graph, network structure, network nodes, and network topology information;

[0074] Step 2.2: Set the optimization objective: minimum load shedding; set the decision variables: positive load shedding, negative load shedding, actual node power generation, phase angle variable, and power flow variable;

[0075] Step 2.3: Use the Yalmip tool to linearly solve for the minimum load shedding power flow allocation scheme;

[0076] Furthermore, the algorithm for establishing and solving the second optimization model in step 3 is as follows:

[0077] Step 3.1: Obtain current network information and topology;

[0078] Step 3.2: Define network failures. For N-1 failures, use sampling or traversal of the topology under all single component / line failures in the network; for N-2 failures, use sampling or traversal of the topology under two component / line failures in the network, such as... Figure 3 .

[0079] Step 3.3: Calculate the minimum load shedding amount and the average load shedding amount based on the topology under various fault conditions.

[0080] Furthermore, the method for optimizing network reliability using greedy algorithms and genetic algorithms in step 4 is as follows:

[0081] In a network, a fully connected graph represents the most reliable topology. However, due to limitations such as cost and space, a network can only contain a portion of the connections found in a fully connected graph. To solve for the optimal topology under optimal power flow configuration, a two-layer optimization model is established, with the following steps:

[0082] Step 4.1: Set optimization goal: Minimize average load shedding under network NK failure.

[0083] Step 4.2: Set decision variables: the on / off status of alternative lines in the network, and the distribution of power sources in the network.

[0084] Step 4.3: Set constraints: maximum number of connected branches in the network, maximum number of distributed power sources in the network.

[0085] Step 4.4: Optimize the solution.

[0086] The greedy algorithm employs a bottom-up heuristic strategy of adding connections. Specifically, in an empty graph with known nodes, connections are added one by one, ensuring that each added connection leads to the optimal topology. Once a certain number of connections is reached, more connections are added. When this limit is exceeded, the number of connections is gradually reduced back to the predetermined number. In each step, the added connection is required to achieve the optimal topology. The reverse reduction after exceeding the predetermined number of connections aims to avoid getting trapped in local optima.

[0087] The specific optimization using genetic algorithms is as follows: First, based on the decision-maker's risk preference, the optimization objective can be chosen as either minimum load shedding or average load shedding. When the decision-maker is risk-averse, minimum load shedding is chosen as the optimization objective; when the decision-maker is conservative, average load shedding is chosen. The decision variable is the network topology. Constraints include: power balance constraints, power flow constraints, phase angle constraints, line capacity constraints, operating power constraints, and electrical parameter value range constraints.

[0088] Based on the same inventive concept, this invention provides a power supply network topology optimization device, such as... Figure 4 As shown, the power supply network topology optimization device includes:

[0089] The first acquisition module is used to solve the pre-built first optimization model to obtain the optimal connectivity between nodes in the power supply network topology.

[0090] The first adjustment module is used to adjust the connectivity between nodes in the power supply network topology by utilizing the optimal connectivity between the nodes.

[0091] The second acquisition module is used to solve the pre-built second optimization model to obtain the optimal power flow parameters of the power supply network topology.

[0092] The second adjustment module is used to adjust the power flow parameters of the power supply network topology using the optimal power flow parameters.

[0093] The pre-constructed first optimization model includes an objective function and its corresponding constraints aimed at minimizing the minimum load shedding amount of the power supply network topology under the NK cascading failure scenario. The pre-constructed second optimization model includes an objective function and its corresponding constraints aimed at minimizing the load shedding amount of the power supply network topology. N is the total number of nodes in the power supply network topology, and K is the number of faulty nodes.

[0094] Preferably, the objective function, which aims to minimize the minimum load shedding in the power supply network topology under the NK cascading failure scenario, is calculated as follows:

[0095]

[0096] In the above formula, M represents the total number of NK cascading failure scenarios, m represents the total number of NK cascading failure scenarios after scenario reduction, and ω j Let j be the weight of node j. Let be the minimum load shedding amount at node j in the i-th NK cascading failure scenario.

[0097] Furthermore, the mathematical model for the constraints of the objective function aimed at minimizing the minimum load shedding amount in the NK cascading failure scenario of the power supply network topology is as follows:

[0098]

[0099] In the above formula, a xj Let a be the connectivity coefficient of the alternative branches between node x and node j in the power supply network topology. When the alternative branches between node x and node j are connected, a xj =1, when the alternative branches between node x and node j are not connected, a xj =0, N1 is the minimum number of branches for normal network operation, and N2 is the total number of alternative lines in the network.

[0100] Furthermore, the power flow parameters include at least one of the following: the line capacity of the lines between each node, and the phase angle of each node.

[0101] Furthermore, the objective function, which aims to minimize the load shedding of the power supply network topology, is calculated as follows:

[0102]

[0103] In the above formula, SL j Let be the load shedding amount for node j.

[0104] Furthermore, the mathematical model for the constraints corresponding to the objective function that aims to minimize the load shedding of the power supply network topology is as follows:

[0105] a xj P xj x xj =θ x -θ j

[0106] P PV,x +P Es,x +P in,x -P out,x -P Le,x =SL_pInS x -SL_nInS x

[0107]

[0108] 0≤θ x ≤2π

[0109]

[0110] In the above formula, P xj Let x be the line capacity of the connection between node x and node j.xj Let θ be the admittance between node x and node j. x Let θ be the phase angle of node x. j Let P be the phase angle of node j. PV,x Let P be the photovoltaic power generation at node x. Es,x P outputs power to the energy storage of node x. in,x Let P be the inflow power at node x. out,x Let P be the outflow power of node x. Le,x SL_pInS represents the operating power of node x. x For the positive relaxation of the power balance at node x, SL_nInS x For the negative relaxation of the power balance at node x, This represents the maximum line capacity of the line between node x and node j. Let x be the maximum photovoltaic power generation at node x.

[0111] Furthermore, the present invention provides a storage medium comprising a stored program, wherein, when the program is executed, it controls the device where the storage medium is located to execute the power supply network topology optimization method.

[0112] Furthermore, the present invention provides a processor for running a program, wherein the program executes the power supply network topology optimization method during runtime.

[0113] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0114] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0115] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0116] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0117] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for optimizing the topology of a power supply network, characterized in that, The method includes: Solve the pre-constructed first optimization model to obtain the optimal connectivity between nodes in the power supply network topology; The optimal connectivity between nodes is used to adjust the connectivity between nodes in the power supply network topology. Solve the pre-built second optimization model to obtain the optimal power flow parameters of the power supply network topology; The power flow parameters of the power supply network topology are adjusted using the optimal power flow parameters. The pre-constructed first optimization model includes an objective function and its corresponding constraints aimed at minimizing the minimum load shedding amount of the power supply network topology under the NK cascading failure scenario. The pre-constructed second optimization model includes an objective function and its corresponding constraints aimed at minimizing the load shedding amount of the power supply network topology. N is the total number of nodes in the power supply network topology, and K is the number of faulty nodes. The objective function, which aims to minimize the minimum load shedding in the power supply network topology under the NK cascading failure scenario, is calculated as follows: In the above formula, M represents the total number of NK cascading failure scenarios, and m represents the total number of NK cascading failure scenarios after scenario reduction. Let j be the weight of node j. This represents the minimum load shedding amount at node j in the i-th NK cascading failure scenario; The mathematical model for the constraints of the objective function that aims to minimize the minimum load shedding in the power supply network topology under the NK cascading failure scenario is as follows: In the above formula, Let be the connectivity coefficient of the alternative branches between node x and node j in the power supply network topology. This coefficient represents the connectivity coefficient when the alternative branches between node x and node j are connected. When the alternative branches between node x and node j are not connected, , This represents the minimum number of branches required for the network to function properly. This represents the total number of alternative routes in the network. The objective function, which aims to minimize the load shedding of the power supply network topology, is calculated as follows: In the above formula, Let J be the load shedding amount at node j; The mathematical model for the constraints of the objective function that aims to minimize the load shedding of the power supply network topology is as follows: In the above formula, Let be the line capacity of the connection between node x and node j. Let x be the admittance between node x and node j. Let x be the phase angle of node x. Let J be the phase angle of node j. Let x be the photovoltaic power generation capacity of node x. To provide power for the energy storage of node x. Let be the inflow power at node x. Let be the outflow power of node x. The operating power of node x. For the positive relaxation of the power balance at node x, For the negative relaxation of the power balance at node x, This represents the maximum line capacity of the line between node x and node j. Let x be the maximum photovoltaic power generation at node x.

2. The method as described in claim 1, characterized in that, The power flow parameters include at least one of the following: the line capacity of the lines between nodes, and the phase angle of each node.

3. An apparatus based on the power supply network topology optimization method according to any one of claims 1-2, characterized in that, The device includes: The first acquisition module is used to solve the pre-built first optimization model to obtain the optimal connectivity between nodes in the power supply network topology. The first adjustment module is used to adjust the connectivity between nodes in the power supply network topology by utilizing the optimal connectivity between the nodes. The second acquisition module is used to solve the pre-built second optimization model to obtain the optimal power flow parameters of the power supply network topology. The second adjustment module is used to adjust the power flow parameters of the power supply network topology using the optimal power flow parameters. The pre-constructed first optimization model includes an objective function and its corresponding constraints aimed at minimizing the minimum load shedding amount of the power supply network topology under the NK cascading failure scenario. The pre-constructed second optimization model includes an objective function and its corresponding constraints aimed at minimizing the load shedding amount of the power supply network topology. N is the total number of nodes in the power supply network topology, and K is the number of faulty nodes.

4. A storage medium, characterized in that, The storage medium includes a stored program, wherein, when the program is executed, it controls the device containing the storage medium to perform the method described in any one of claims 1 to 2.

5. A processor, characterized in that, The processor is used to run a program, wherein the program executes the method according to any one of claims 1 to 2 when it runs.

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