A method, system, device, medium, and product for optimizing power grid defense.

Abstract

This invention discloses a power grid defense optimization method, system, equipment, medium, and product, relating to the field of power grid monitoring technology. It acquires power grid defense-related data and disaster evolution cycles. Based on pre-acquired emergency timing characteristics, it constructs a corresponding spatiotemporal power supply map using defense-related data and disaster evolution cycles. Feasible power supply chains are screened from the spatiotemporal power supply map according to defense-related data and preset schedulability constraints to obtain time-feasible power supply chains. Corresponding resilience gap constraints are constructed using defense-related data and each time-feasible power supply chain. Based on schedulability constraints and resilience gap constraints, an active defense optimization model is optimized and solved to obtain the corresponding defense optimization scheme. This invention solves the technical problem that existing power grid defense optimization methods often use static fault scenarios for optimization, making it difficult to adapt to actual engineering needs under extreme disaster scenarios and reducing the disaster defense capability of the power grid.

Description

Technical Field

[0001] This invention relates to the field of power grid monitoring technology, and in particular to a power grid defense optimization method, system, device, medium, and product. Background Technology

[0002] Against the backdrop of global climate change and the frequent occurrence of extreme weather disasters, typhoons, rainstorms, and blizzards are causing increasingly severe damage to power systems, placing unprecedented pressure on power grid security and supply. The construction of new power systems is driving the power grid towards integrated transmission and distribution, and coordinated interaction between power generation, grid, load, and storage, placing higher demands on the grid's resilience in disaster response and rapid power restoration. Traditional power supply methods focused on reactive repair are no longer adequate for extreme disaster scenarios. Proactive defense, advance planning, and dynamic dispatch have become the core development directions for power grid disaster prevention and mitigation. Constructing optimized power grid defense methods that consider the spatiotemporal evolution of disasters, coordinated resource allocation, and precise load supply is a key technological support for ensuring the safe and stable operation of the power system.

[0003] Currently, most existing power grid defense optimization methods adopt static fault scenarios for optimization, without considering the spatiotemporal characteristics of disaster dynamic progression. They cannot accurately describe the temporal damage state of power grid components and the time continuity of power supply paths, making it difficult to adapt to the actual engineering needs under extreme disaster scenarios and reducing the disaster defense capability of the power grid. Summary of the Invention

[0004] This invention provides a power grid defense optimization method, system, device, medium, and product, which solves the technical problem that existing power grid defense optimization methods mostly adopt static fault scenarios for optimization, do not consider the spatiotemporal characteristics of the dynamic progression of disasters, cannot accurately describe the temporal damage state of power grid components and the time continuity of power supply paths, are difficult to adapt to the actual engineering needs under extreme disaster scenarios, and reduce the disaster defense capability of the power grid.

[0005] The first aspect of this invention provides a power grid defense optimization method, comprising:

[0006] Acquire defense-related data and disaster evolution cycles of the power grid, and construct a corresponding spatiotemporal power supply map based on the pre-acquired emergency timing characteristics using the defense-related data and the disaster evolution cycles;

[0007] Based on the defense-related data and preset schedulability constraints, feasible power supply chains are screened in the spatiotemporal power supply map to obtain the corresponding time-feasible power supply chains;

[0008] The defense-related data and each of the time-feasible power chains are used to construct the corresponding resilience gap constraints.

[0009] With the goal of minimizing resilience costs, defense constraints are set, and defense scheduling data is used as decision variables to construct an active defense optimization model;

[0010] The active defense optimization model is optimized and solved based on the schedulability constraint and the resilience gap constraint to obtain the corresponding defense optimization scheme.

[0011] Optionally, the defense-related data includes power grid topology and resource access information. The step of constructing a corresponding spatiotemporal power supply map based on preset emergency timing characteristics using the defense-related data and the disaster evolution cycle includes:

[0012] The disaster evolution cycle is divided into multiple time layers based on the preset emergency timing characteristics;

[0013] A corresponding spatiotemporal power supply map is constructed using each of the aforementioned time layers, the power grid topology, and the resource access information.

[0014] Optionally, the defense-related data further includes multiple key loads, movement trajectories, intensity grids, and duration data. The step of filtering feasible power supply chains in the spatiotemporal power supply map based on the defense-related data and preset schedulability constraints to obtain the corresponding time-feasible power supply chains includes:

[0015] The spatiotemporal power supply map is spatiotemporally overlaid using the movement trajectory, the intensity grid, and the duration data to obtain multiple disaster corridors;

[0016] The available timing status of each power grid element in the spatiotemporal power supply diagram is determined based on each of the disaster corridors.

[0017] Extract the candidate power supply chains corresponding to each of the key loads from the spatiotemporal power supply diagram;

[0018] When the timing availability status of the components corresponding to the candidate power supply chain is the component availability status, the candidate power supply chain is determined as the target power supply chain.

[0019] When the target power supply chain meets the preset schedulability constraints, the target power supply chain is determined as a time-feasible power supply chain.

[0020] By employing the aforementioned technical solution, and through the multi-dimensional spatiotemporal overlay of disaster movement trajectory, intensity grid, and duration data to generate dynamic disaster corridors from spatiotemporal power supply maps, the dynamic evolution patterns and differentiated damage ranges of disasters in the spatiotemporal dimension can be accurately depicted. This overcomes the limitations of traditional static disaster assessments, which cannot adapt to dynamic disaster attacks. Based on the disaster corridor, the temporal availability status of each power grid component is accurately determined, enabling time-by-time, refined, and dynamic assessment of power grid component failure due to disasters, ensuring that component status identification closely matches real disaster conditions. Furthermore, by verifying the availability and dispatchability constraints of each candidate power supply chain, corresponding time-feasible power supply chains are selected. Topologically invalid paths that have failed due to disasters are eliminated, and theoretical paths that meet topological connectivity requirements but cannot be actually dispatched are filtered out. This approach balances the physical operational safety of the power grid with the practical feasibility of dispatching, significantly improving the accuracy and reliability of effective power supply path selection.

[0021] Optionally, the step of determining the timing availability status of each power grid element in the spatiotemporal power supply diagram based on each of the disaster corridors includes:

[0022] Calculate the degree of disaster overlap between each power grid element and each disaster corridor in the spatiotemporal power supply diagram;

[0023] Based on the preset spatiotemporal exposure function, the spatiotemporal exposure index corresponding to each of the power grid components is determined according to the overlap of each disaster, the intensity of each disaster in the spatiotemporal power supply diagram, and the duration of each disaster.

[0024] When the spatiotemporal exposure index is less than a preset index threshold, the timing availability state of the component corresponding to the spatiotemporal exposure index is determined as the component availability state.

[0025] When the spatiotemporal exposure index is greater than or equal to the indication threshold, the timing availability status of the component corresponding to the spatiotemporal exposure index is determined to be the component unavailable status.

[0026] Optionally, the step of constructing corresponding resilience gap constraints using the defense-related data and each of the time-feasible power chains includes:

[0027] Based on a preset power supply function, the time-layer power supply power corresponding to each critical load in the defense-related data is determined according to each of the time-feasible power supply chains.

[0028] The time-layer power supply requirement corresponding to each of the key loads is processed with the corresponding time-layer power supply to obtain multiple first differences;

[0029] When the first difference is greater than or equal to the preset capacity gap benchmark value, the first difference is determined as the corresponding capacity gap variable.

[0030] When the first difference is less than the capacity gap reference value, the capacity gap reference value is determined as the corresponding capacity gap variable;

[0031] The corresponding resilience gap constraints are constructed using each of the aforementioned capacity gap variables and each of the aforementioned time-feasible power chains.

[0032] By adopting the above technical solution, the power supply of critical loads at each time level is accurately calculated based on the preset power supply function and feasible power supply chains at each time level. This enables a quantitative representation of the actual power supply capacity of the power grid under dynamic disaster conditions. Compared with the traditional fixed power calculation method, it has stronger time-series adaptability. By processing the difference between the power supply demand of critical loads at each time level and the actual power supply at each time level, the first difference can be obtained by accurately capturing the power supply and demand deviation of loads at each time period. At the same time, the capacity gap variable is assigned differently based on the relationship between the first difference and the capacity gap benchmark value. This can accurately identify the power supply gap conditions and avoid invalid calculations and model disturbances caused by small power deviations, ensuring that the capacity gap quantification results are accurate and stable. Finally, the quantified capacity gap variable and the effective feasible power supply chains at each time level are combined to construct a resilience gap constraint, which can specifically limit the range of power supply capacity loss of critical loads under extreme disasters, accurately regulate the bottom line of power grid supply, and make up for the shortcomings of traditional power grid resilience assessment, which cannot quantify the power supply gap in time series and whose constraint conditions do not fit the actual operating conditions well.

[0033] Optionally, the step of optimizing the active defense optimization model based on the schedulability constraint and the resilience gap constraint to obtain the corresponding defense optimization scheme includes:

[0034] By combining the schedulability constraint, the resilience gap constraint, and the active defense optimization model, the corresponding target defense optimization model is obtained.

[0035] The target defense optimization model is iteratively solved using a decomposition algorithm to obtain the corresponding defense optimization scheme.

[0036] A second aspect of the present invention provides a power grid defense optimization system, comprising:

[0037] The data acquisition module is used to acquire defense-related data and disaster evolution cycles of the power grid. Based on the pre-acquired emergency timing characteristics, the module constructs a corresponding spatiotemporal power supply map using the defense-related data and the disaster evolution cycle.

[0038] The filtering module is used to filter feasible power supply chains in the spatiotemporal power supply map based on the defense-related data and preset schedulability constraints, so as to obtain the corresponding time-feasible power supply chains.

[0039] The first construction module is used to construct corresponding resilience gap constraints using the defense-related data and each of the time-feasible power chains;

[0040] The second building module is used to set defense constraints with the goal of minimizing resilience cost and to build an active defense optimization model with defense scheduling data as the decision variable.

[0041] The optimization module is used to optimize and solve the active defense optimization model based on the schedulability constraint and the resilience gap constraint, so as to obtain the corresponding defense optimization scheme.

[0042] A third aspect of the present invention provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor performs the steps of the power grid defense optimization method described above.

[0043] The fourth aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed, implements the power grid defense optimization method as described above.

[0044] The fifth aspect of the present invention provides a computer program product comprising a computer program stored on a non-transitory computer-readable storage medium, the computer program comprising program instructions, wherein when the program instructions are executed by a computer, the computer performs the power grid defense optimization method as described above.

[0045] As can be seen from the above technical solutions, the present invention has the following advantages:

[0046] This invention acquires defense-related data and disaster evolution cycles of the power grid. Based on pre-acquired emergency timing characteristics, it constructs a corresponding spatiotemporal power supply map using the defense-related data and disaster evolution cycles. Feasible power supply chains are screened based on the defense-related data and preset schedulability constraints to obtain corresponding time-feasible power supply chains. Resilience gap constraints are then constructed using the defense-related data and each time-feasible power supply chain. With minimizing resilience cost as the optimization objective, defense constraints are set, and defense scheduling data is used as decision variables to construct an active defense optimization model. The active defense optimization model is then optimized and solved based on schedulability constraints and resilience gap constraints to obtain the corresponding defense optimization scheme. This overcomes the technical problem that existing power grid defense optimization methods often use static fault scenarios for optimization, making it difficult to adapt to actual engineering needs under extreme disaster scenarios and reducing the disaster defense capability of the power grid. Compared with traditional power grid defense optimization methods, this invention constructs a spatiotemporal power supply map based on emergency timing characteristics, power grid defense-related data, and disaster evolution cycles. This map can fully reproduce the dynamic changes in power grid topology and resource status throughout the entire disaster process, achieving spatiotemporal integration of power grid and disaster information. Feasible power supply chains are then screened based on dispatchability constraints, eliminating paths that are inoperable due to disasters, ensuring that the selected time-feasible power supply chains are ready for on-site execution. Resilience gap constraints are then established based on these time-feasible power supply chains, accurately quantifying the degree of power supply loss for critical loads and defining operational boundaries from a power supply security perspective. An active defense optimization model is constructed with the goal of minimizing resilience costs, and multiple constraints are applied for solution. This model can balance emergency resource input, equipment operating costs, and power outage losses while considering equipment operating rules, timing requirements, and power supply resilience, resulting in a feasible defense optimization solution that effectively improves the power grid's proactive resilience and supply guarantee level under extreme disasters. Attached Figure Description

[0047] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0048] Figure 1 This is a flowchart illustrating the steps of a power grid defense optimization method provided in Embodiment 1 of the present invention.

[0049] Figure 2 This is a flowchart illustrating the steps of a power grid defense optimization method provided in Embodiment 2 of the present invention.

[0050] Figure 3 This is a schematic diagram of the spatiotemporal overlay of disaster corridors and power grid topology provided in Embodiment 2 of the present invention;

[0051] Figure 4 The convergence curve of the decomposition solution of the target active defense optimization model provided in Embodiment 2 of the present invention is shown.

[0052] Figure 5 This is a comparison chart of critical load resilience gaps throughout the entire disaster process provided in Embodiment 2 of the present invention;

[0053] Figure 6 This is a schematic diagram of the pre-disaster setup and in-disaster support status of energy storage within the power supply unit provided in Embodiment 2 of the present invention;

[0054] Figure 7 This is a structural block diagram of a power grid defense optimization system provided in Embodiment 3 of the present invention;

[0055] Figure 8 This is a structural block diagram of an electronic device provided in Embodiment 4 of the present invention. Detailed Implementation

[0056] This invention provides a power grid defense optimization method, system, device, medium, and product to address the technical problem that existing power grid defense optimization methods often use static fault scenarios for optimization, fail to consider the spatiotemporal characteristics of dynamic disaster progression, cannot accurately describe the temporal damage state of power grid components and the time continuity of power supply paths, are difficult to adapt to the actual engineering needs under extreme disaster scenarios, and reduce the disaster defense capability of the power grid.

[0057] 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.

[0058] Please see Figure 1 , Figure 1 The flowchart illustrates the steps of a power grid defense optimization method provided in Embodiment 1 of the present invention.

[0059] This invention provides a power grid defense optimization method, comprising:

[0060] Step 101: Obtain defense-related data and disaster evolution cycle of the power grid. Based on the pre-obtained emergency timing characteristics, construct the corresponding spatiotemporal power supply map using defense-related data and disaster evolution cycle.

[0061] Defense-related data refers to the comprehensive basic information required to support power grid disaster prevention, including power grid topology (i.e., the physical topology of the transmission and distribution network), resource access information, multiple critical loads, controllable switch status, parameters of distributed power sources and energy storage devices, location of mobile emergency power sources and repair resources, supply requirements of critical loads, movement trajectory of target meteorological disasters, intensity grid and duration data.

[0062] The disaster evolution cycle refers to the complete time interval from early warning, approach, invasion to dissipation of meteorological disasters. It is the full time scale for power grids to carry out pre-disaster prevention, in-disaster dispatch and post-disaster recovery.

[0063] Emergency timing characteristics refer to the time response patterns of power grid emergency resources and control actions, encompassing core features that determine the timing of defense actions, such as the arrival time of the disaster, the duration of switching operations, the duration of emergency resource dispatch, and the duration of equipment power supply.

[0064] Spatiotemporal power supply diagram refers to a transmission and distribution integrated network model based on graph theory, which integrates multi-time-layer power grid topology, source-load-storage resource status, cross-layer state inheritance and moving resource paths, and can intuitively reflect the spatiotemporal changes in power grid power supply capacity under the dynamic impact of disasters.

[0065] In this embodiment of the invention, the disaster evolution cycle is divided according to preset emergency timing characteristics to obtain multiple discrete time layers. A corresponding spatiotemporal power supply map is constructed using each time layer, power grid topology, and resource access information.

[0066] Step 102: Based on defense-related data and preset schedulability constraints, screen the spatiotemporal power supply map for feasible power supply chains to obtain the corresponding time-feasible power supply chains.

[0067] Dispatchability constraints refer to the set of time and operating conditions that ensure the power supply chain can be actually executed, including switching action time limits, mobile emergency power dispatch time limits, energy storage charge status, feeder capacity limits, and power grid safety operation constraints.

[0068] A time-feasible power supply chain refers to an effective power supply path that can stably supply power and meet supply guarantee requirements throughout the continuous time frame of the entire disaster process by verifying both dispatchability and time-series availability.

[0069] In this embodiment of the invention, the movement trajectory, intensity grid, and duration data of a disaster are spatiotemporally overlaid on a spatiotemporal power supply map to generate a disaster corridor that dynamically changes over time. Based on each disaster corridor, the timing availability status of each power grid element in the spatiotemporal power supply map is determined. All candidate power supply chains for critical loads are extracted from the spatiotemporal power supply map. Each element in the candidate power supply chain is verified one by one based on its timing availability status and schedulability constraints. Only candidate power supply chains that can stably supply power within a continuous time layer and meet the timing requirements for critical load supply are retained as target power supply chains.

[0070] It should be noted that the expression for the schedulability constraint is as follows:

[0071] ;

[0072] ;

[0073] ;

[0074] ;

[0075] ;

[0076] If a power supply chain forms an island, then the candidate power supply chains satisfy the power balance within the island in the schedulability constraint: ;

[0077] in, This represents the available timing state of components in the p-th power supply chain during time period t in scenario ω. Let ω represent the availability status of component e in time period t (a 0-1 variable), where 1 indicates that the component is intact and available, and 0 indicates that it is faulty / affected and unavailable. This is the index of the lines / devices (edges) contained in the power supply chain p. Here, q is the power supply chain index, and q is the critical load index. Let ω represent the availability status of node i in time period t (a 0-1 variable), where 1 indicates that the node can be connected / operating normally, and 0 indicates that the node is faulty / unavailable due to a disaster. This is the index of the nodes (such as buses and power nodes) contained in the power supply chain p. The time required for switching actions / transitions of the power supply chain p (such as the switching operation time for switching operations in islanding). The allowable outage time for the critical load q, In scenario ω, the accessibility status (0-1 variable) of power source u at node i during time period t is given. 1 indicates that the power source has been dispatched and is available for operation, while 0 indicates that it is not available or cannot be accessed. u is the index of the power source (such as distributed power source, energy storage, or mobile emergency power source) corresponding to power supply chain p. Let ω represent the active power delivered by power supply chain p to load q during time period t. Let p be the maximum power that the power supply chain can transmit in time period t under scenario ω. Let ω be the output power of power supply u in time period t. Let ω represent the active power demand of load l in time period t. Let l be the network loss within island / power chain p in time period t under scenario ω, where t is the time layer index and l is the load index. This is the set of all loads within the island / power chain p. It is the collection of all power sources (distributed power sources, energy storage, mobile power sources, etc.) within the island / power chain p.

[0078] Step 103: Construct corresponding resilience gap constraints using defense-related data and feasible power chains at various times.

[0079] In this embodiment of the invention, based on a preset power supply function, the time-layer power supply corresponding to each critical load in the defense-related data is determined according to each time-feasible power supply chain. The time-layer power supply requirement corresponding to each critical load is compared with the corresponding time-layer power supply to obtain multiple first differences. When the first difference is greater than or equal to a preset capacity gap benchmark value, the first difference is determined as the corresponding capacity gap variable. When the first difference is less than the capacity gap benchmark value, the capacity gap benchmark value is determined as the corresponding capacity gap variable. Corresponding resilience gap constraints are constructed using each capacity gap variable and each time-feasible power supply chain.

[0080] It should be noted that the toughness notch constraint is specifically as follows:

[0081] ;

[0082] ;

[0083] ;

[0084] ;

[0085] ;

[0086] in, Let ω be the capacity gap variable of load q within time period t. The target power supply demand of load q in time period t, Under scenario ω, the actual power supplied by load q through each feasible power supply chain within time period t. For scenario ω, the cumulative energy deficit of load q over the entire disaster cycle. It is the sum of the capacity gap variables for each time period multiplied by the time step. For scenario ω, the power supply redundancy gap of load q within time period t. The target number of redundant power supply chains for load q. Let t be the set of all time-feasible power supply chains that can supply power to load q, where t is the time period index. It is the set of all time steps within the disaster impact period.

[0087] Step 104: With the goal of minimizing resilience cost, set defense constraints and use defense scheduling data as decision variables to construct an active defense optimization model.

[0088] Defense scheduling data refers to the set of decision variables in the active defense model, including decision variables for defense actions (i.e., whether to execute a certain defense action, such as switching operation, emergency power supply access, line reinforcement), the overall availability status of the unit (i.e., whether a certain power supply unit is available as a whole in a certain scenario and a certain period of time), the activation status of the unit supplying power to the load (i.e., whether a certain power supply unit supplies power to a certain load in a certain scenario and a certain period of time), and the active power delivered by the unit to the load.

[0089] Defense constraints refer to the set of constraints that ensure the safe operation of the power grid throughout the entire disaster cycle, the supply of critical loads, and the feasibility of the power supply chain sequence. These constraints include the operational constraints of resilient power supply units, resource budget constraints, and action conflict constraints.

[0090] In this embodiment of the invention, with the minimum resilience cost as the optimization objective, defense constraints are set, and defense scheduling data is used as the decision variable to construct an active defense optimization model. The defense constraints include elastic power supply unit operation constraints, resource budget constraints, and action conflict constraints.

[0091] It should be noted that the active defense optimization model is as follows:

[0092] ;

[0093] ;

[0094] ;

[0095] ;

[0096] ;

[0097] ;

[0098] ;

[0099] ;

[0100] in, The cost of a single execution of defensive action a, For defensive actions The decision variables (0-1 variables, where 1 indicates that the action is performed and 0 indicates that the action is not performed). The probability of scenario ω occurring (scenario weights used for random / robust optimization). The outage loss weight of critical load q / unit outage loss cost. This represents the resilience gap for the critical load q under scenario ω. This is the total budget limit for defensive actions. For defensive actions Decision variables, As the first index for defensive actions, As the second index for defensive actions, It is a set of mutually exclusive defense action pairs (such as two different power supply schemes for the same node, or two operations that cannot be executed simultaneously). This represents the enabled state (0-1 variable, 1 indicates enabled) of unit m supplying power to load q within time period t under scenario ω. Let m be the overall available state of unit m within time period t under scenario ω (0-1 variables, where 1 indicates that the unit is available). Under scenario ω, the active power delivered by unit m to load q within time period t. Under scenario ω, the maximum power that unit m can transmit within time period t is determined by both power supply capacity and line capacity. Under scenario ω, the upper limit of the total available energy of unit m (such as the total capacity of energy storage batteries or the total power of mobile power banks). Let ω represent the available status of node / line k in unit m within time period t under scenario ω (0-1 variable, 1 indicates available). This refers to the node or line index in cell m. Let m be the set of nodes contained in cell m. Let m be the set of lines contained in unit m. A collection of defensive actions (such as switch operation, emergency power connection, and line reinforcement). A collection of disaster / failure scenarios. A set of critical load nodes. This is the set of loads powered by unit m, where m is the index of the power supply chain / island / emergency resource unit.

[0101] Step 105: Based on the schedulability constraint and resilience gap constraint, optimize the active defense optimization model to obtain the corresponding defense optimization scheme.

[0102] The active defense optimization scheme refers to the optimal set of defense scheduling instructions obtained from the model solution, which includes directly executable operation instructions such as switching operation sequence, emergency power supply access location, power supply chain switching strategy, and energy storage charging and discharging plan.

[0103] In this embodiment of the invention, schedulability constraints and resilience gap constraints are uniformly incorporated into the constraint set of the active defense optimization model. The active defense optimization model is solved using the branch and bound method or a commercial solver to obtain an active defense optimization scheme that includes switching operation sequence, emergency power supply access location, power supply chain switching strategy, and energy storage charging and discharging plan.

[0104] In this embodiment of the invention, by acquiring power grid defense-related data and disaster evolution cycles, and based on pre-acquired emergency timing characteristics, a corresponding spatiotemporal power supply map is constructed using the defense-related data and disaster evolution cycles. Feasible power supply chains are screened based on the defense-related data and preset schedulability constraints to obtain corresponding time-feasible power supply chains. Corresponding resilience gap constraints are constructed using the defense-related data and each time-feasible power supply chain. With minimizing resilience cost as the optimization objective, defense constraints are set, and defense scheduling data is used as decision variables to construct an active defense optimization model. The active defense optimization model is then optimized and solved based on schedulability constraints and resilience gap constraints to obtain the corresponding defense optimization scheme. This overcomes the technical problem that existing power grid defense optimization methods often use static fault scenarios for optimization, making it difficult to adapt to actual engineering needs under extreme disaster scenarios and reducing the disaster defense capability of the power grid. Compared with traditional power grid defense optimization methods, this invention constructs a spatiotemporal power supply map based on emergency timing characteristics, power grid defense-related data, and disaster evolution cycles. This map can fully reproduce the dynamic changes in power grid topology and resource status throughout the entire disaster process, achieving spatiotemporal integration of power grid and disaster information. Feasible power supply chains are then screened based on dispatchability constraints, eliminating paths that are inoperable due to disasters, ensuring that the selected time-feasible power supply chains are ready for on-site execution. Resilience gap constraints are then established based on these time-feasible power supply chains, accurately quantifying the degree of power supply loss for critical loads and defining operational boundaries from a power supply security perspective. An active defense optimization model is constructed with the goal of minimizing resilience costs, and multiple constraints are applied for solution. This model can balance emergency resource input, equipment operating costs, and power outage losses while considering equipment operating rules, timing requirements, and power supply resilience, resulting in a feasible defense optimization solution that effectively improves the power grid's proactive resilience and supply guarantee level under extreme disasters.

[0105] Please see Figure 2 , Figure 2 The flowchart illustrates the steps of a power grid defense optimization method provided in Embodiment 2 of the present invention.

[0106] This invention provides a power grid defense optimization method, comprising:

[0107] Step 201: Obtain defense-related data and disaster evolution cycle of the power grid. Based on the pre-acquired emergency timing characteristics, construct the corresponding spatiotemporal power supply map using defense-related data and disaster evolution cycle.

[0108] Furthermore, the defense-related data includes power grid topology and resource access information. Step 201 includes the following sub-steps:

[0109] S11. Divide the disaster evolution cycle according to the preset emergency timing characteristics to obtain multiple time layers.

[0110] The time layer refers to the equal or non-equal time units obtained by discretizing the disaster cycle according to the emergency timing characteristics. It is used to accurately describe the temporal changes of the power grid status and the impact of disasters.

[0111] In this embodiment of the invention, the disaster evolution cycle is discretized according to a unified time scale based on the disaster arrival time, switch operation duration, emergency resource scheduling duration, and equipment power supply duration, resulting in multiple time layers.

[0112] S12. Construct the corresponding spatiotemporal power supply diagram using information from each time layer, power grid topology, and resource access.

[0113] Power grid topology refers to the physical connection relationships and structural layout of components such as substations, lines, switches, and loads in a power grid, including transmission networks and distribution networks.

[0114] Resource access information refers to the access nodes, capacity ranges, operational constraints, and dispatchable status information of devices such as distributed power sources, energy storage, mobile emergency power sources, and controllable switches.

[0115] In this embodiment of the invention, the physical structure of the power transmission and distribution network in each time layer is uniformly mapped as nodes and edges in a graph structure. The access location and operating status of resources such as distributed power sources, energy storage, mobile emergency power sources, critical loads and controllable switches are synchronously embedded in the corresponding nodes and edges. At the same time, the inheritance relationship of the power grid component status and the transfer path of mobile resources are established between different time layers, forming a spatiotemporal power supply graph that can fully reflect the dynamic changes of the power grid topology, resource status and power supply capacity throughout the entire disaster process.

[0116] Step 202: Based on defense-related data and preset schedulability constraints, screen the spatiotemporal power supply map for feasible power supply chains to obtain the corresponding time-feasible power supply chains.

[0117] Furthermore, the defense-related data also includes multiple key loads, movement trajectories, intensity grids, and duration data. Step 202 includes the following sub-steps:

[0118] S21. Using movement trajectory, intensity grid and duration data, spatiotemporal power supply maps are spatiotemporally overlaid to obtain multiple disaster corridors.

[0119] The movement trajectory refers to the spatial path of extreme weather disasters over time, used to determine the dynamic location of the disaster's impact.

[0120] An intensity grid refers to the spatial distribution data of a disaster-affected area divided into grid cells, with each grid cell representing a corresponding disaster intensity value.

[0121] Duration data refers to the duration of the impact of a disaster in different regions and time periods, and is used to determine the cumulative time that a component is affected by the disaster.

[0122] Disaster corridors refer to the range of disaster impacts that are formed by the superposition of time and space and change dynamically over time, directly corresponding to the disaster-affected areas of power grid components.

[0123] In the embodiments of the present invention, see Figure 3 As shown, the movement trajectory, intensity grid, and duration data are synchronously mapped onto the spatiotemporal power supply map. Spatial location matching and time series alignment are performed with the line corridors, site locations, load nodes, and emergency resource access points in the spatiotemporal power supply map. By overlaying and calculating each time period, a disaster impact area that dynamically changes over time is formed, resulting in multiple disaster corridors.

[0124] S22. Determine the available status of the timing sequence of each power grid element in the spatiotemporal power supply diagram based on each disaster corridor.

[0125] Furthermore, S22 includes the following sub-steps:

[0126] S221. Calculate the degree of disaster overlap between each power grid element and each disaster corridor in the spatiotemporal power supply diagram.

[0127] Power grid components refer to the basic units that make up a power grid, including nodes, transmission lines, distribution feeders, switches, power sources, loads, energy storage, and emergency resource access points.

[0128] Disaster overlap is an indicator used to quantify the degree of overlap between the spatial location of power grid components and the coverage area of ​​disaster corridors. It has a value between 0 and 1, with a higher value indicating a higher overlap ratio.

[0129] In this embodiment of the invention, based on the spatial range of the disaster corridor corresponding to each time layer, it is determined whether the spatial location of the power grid node, line, switch and emergency access point and other components falls within the coverage area of ​​the current disaster corridor. The overlap ratio between the components and the disaster corridor is quantified by spatial geometric matching to obtain the disaster overlap degree of each power grid component in the corresponding time layer.

[0130] S222. Based on the preset spatiotemporal exposure function, determine the spatiotemporal exposure index corresponding to each power grid element according to the overlap of each disaster, the intensity of each disaster in the spatiotemporal power supply diagram, and the duration of each disaster.

[0131] Disaster intensity refers to the destructive power of a disaster in terms of time and spatial location, derived from meteorological intensity raster data.

[0132] Disaster duration refers to the cumulative duration during which a single power grid component is affected by a disaster within the current time frame.

[0133] The spatiotemporal exposure index is a quantitative indicator of the disaster risk of components obtained by comprehensively considering spatial coverage, disaster intensity, and duration of impact. The higher the value, the more severe the disaster.

[0134] In this embodiment of the invention, the overlap of each disaster, the intensity of each disaster in the spatiotemporal power supply diagram, and the duration of each disaster are input into a preset spatiotemporal exposure function to obtain a spatiotemporal exposure index that can accurately characterize the degree of disaster risk of each component.

[0135] It should be noted that the spatiotemporal exposure function is specifically as follows:

[0136] ;

[0137] in, Let ω be the spatiotemporal exposure index of the k-th power grid element within time period t. The first exposure weight coefficient, This is the second exposure weighting coefficient. The third exposure weighting coefficient, Let ω represent the disaster overlap of the k-th power grid element within time period t. Let ω represent the disaster intensity at the location of the k-th power grid component within time period t. Let k be the disaster tolerance threshold of the power grid element. Let ω represent the duration of the disaster for the k-th power grid element within time period t under scenario ω.

[0138] S223. When the spatiotemporal exposure index is less than the preset index threshold, the timing availability state of the component corresponding to the spatiotemporal exposure index is determined as the component availability state.

[0139] The index threshold refers to a critical value set in advance based on the disaster resistance capability, equipment type and operating standards of power grid components, which serves as a unified criterion for judging whether a component has failed due to a disaster.

[0140] In this embodiment of the invention, when the spatiotemporal exposure index is less than a preset index threshold, it is determined that the component has not been severely affected by the disaster in the current time layer and has normal working conditions, and the component's time-series availability state corresponding to the spatiotemporal exposure index is determined as the component's availability state.

[0141] S224. When the spatiotemporal exposure index is greater than or equal to the indication threshold, the timing availability status of the component corresponding to the spatiotemporal exposure index is determined as the component unavailable status.

[0142] In this embodiment of the invention, when the spatiotemporal exposure index is greater than or equal to the indication threshold, it is determined that the element has exceeded its own disaster resistance limit under the current disaster intensity, coverage and duration of action, and cannot guarantee safe and reliable operation. The element's temporal availability state corresponding to the spatiotemporal exposure index is determined as the element's unavailable state.

[0143] S23. Extract the candidate power supply chains corresponding to each critical load from the spatiotemporal power supply diagram.

[0144] Candidate power supply chains refer to all theoretically connected power supply paths from power nodes to critical load nodes, including main power supply paths, transfer power supply paths, distributed power supply paths, and emergency power supply paths.

[0145] In this embodiment of the invention, taking each critical load node as the power supply endpoint, within the spatiotemporal power supply diagram in which time layer division and component availability status marking have been completed, the power supply nodes are traced upstream along the power grid topology and power supply path direction. All theoretical power supply paths originating from various power sources such as main grid power, distributed power, energy storage devices, and mobile emergency power, and connected to critical loads through components such as feeders, switches, and tie branches, are extracted to obtain multiple candidate power supply chains.

[0146] S24. When the timing availability of the components corresponding to the candidate power supply chain is the same as that of the components, the candidate power supply chain is determined as the target power supply chain.

[0147] The target power supply chain refers to a valid power supply chain that has passed the full chain verification of component availability and has the basic topology connectivity conditions.

[0148] In this embodiment of the invention, when the timing availability state of the components corresponding to the candidate power supply chain is the component availability state, it is determined that the candidate power supply chain meets the continuous connectivity condition, and the candidate power supply chain is determined as the target power supply chain.

[0149] S25. When the target power supply chain meets the preset schedulability constraints, the target power supply chain is determined as a time-feasible power supply chain.

[0150] A time-feasible power supply chain refers to a power supply link that has passed the time availability check of all components and has the basic topology connectivity conditions.

[0151] In this embodiment of the invention, it is determined whether each target power supply chain meets a preset schedulability constraint. When a target power supply chain meets the schedulability constraint, the target power supply chain is determined as a time-feasible power supply chain.

[0152] It is worth mentioning that by integrating disaster movement trajectory, intensity grid, and duration data to perform multi-dimensional spatiotemporal overlay of the spatiotemporal power supply map to generate a dynamic disaster corridor, the dynamic evolution pattern and differentiated damage range of the disaster in the spatiotemporal dimension can be accurately depicted. This overcomes the limitations of traditional static disaster assessment, which cannot adapt to the dynamic impact of disasters. Based on the disaster corridor, the temporal availability status of each power grid component is accurately determined, enabling time-by-time, refined dynamic judgment of the failure status of power grid components due to disasters, ensuring that component status identification closely matches the actual disaster conditions. Furthermore, by verifying the availability and dispatchability constraints of each candidate power supply chain, the corresponding time-feasible power supply chains are selected. This eliminates topologically invalid paths that have failed due to disasters and filters out theoretical paths that meet topological connectivity requirements but cannot be actually dispatched. This approach balances the physical operational safety of the power grid with the practical feasibility of dispatching, significantly improving the accuracy and reliability of effective power supply path selection.

[0153] Step 203: Construct corresponding resilience gap constraints using defense-related data and feasible power chains at various times.

[0154] Furthermore, step 203 includes the following sub-steps:

[0155] S31. Based on the preset power supply function, determine the time layer power supply corresponding to each critical load in the defense-related data according to each feasible power supply chain at each time.

[0156] Critical loads refer to important load nodes that have the highest priority for power supply and require continuous power supply.

[0157] Time-layer power supply refers to the total power supply actually obtained by a critical load within a single time layer through all feasible time-layer power supply chains.

[0158] In this embodiment of the invention, the power supplied to the critical load by each feasible power supply chain at each time is input into a preset power supply function to obtain power supply at multiple time layers.

[0159] It should be noted that the power supply function is as follows:

[0160] ;

[0161] in, For critical load In the scene Time layer Time layer power supply, Time-feasible power supply chain In the time layer To critical load The power provided For critical load In the scene Time layer Time-feasible power supply chain set, For power supply chain indexing.

[0162] S32. The time-layer power supply requirements corresponding to each critical load are processed with the corresponding time-layer power supply to obtain multiple first differences.

[0163] Time-layer power supply requirements refer to the minimum power supply standards that critical loads must meet within a single time layer.

[0164] In this embodiment of the invention, the difference between the time-layer power supply requirement and the corresponding time-layer power supply for each critical load is calculated to obtain multiple first differences.

[0165] S33. When the first difference is greater than or equal to the preset capacity gap benchmark value, the first difference is determined as the corresponding capacity gap variable.

[0166] The capacity gap benchmark value refers to a pre-set judgment threshold value, which serves as a standard for distinguishing whether an effective power supply capacity gap has been formed. It is usually set to 0.

[0167] In this embodiment of the invention, when the first difference is greater than or equal to 0, the first difference is determined as the corresponding capacity gap variable.

[0168] S34. When the first difference is less than the capacity gap reference value, the capacity gap reference value is determined as the corresponding capacity gap variable.

[0169] In this embodiment of the invention, when the first difference is less than 0, the capacity gap variable is determined to be 0.

[0170] S35. Construct corresponding resilience gap constraints using each capacity gap variable and each time-feasible power supply chain.

[0171] In this embodiment of the invention, based on the capacity gap variables of each critical load at different time levels, the operating status, power transmission upper limit and timing operation rules of each time-feasible power supply chain are linked to construct corresponding resilience gap constraints.

[0172] It is worth mentioning that, based on the preset power supply function and feasible power supply chains at each time, the power supply capacity of critical loads at each time level is accurately calculated, realizing a quantitative representation of the actual power supply capacity of the power grid under dynamic disaster conditions. Compared with the traditional fixed power calculation method, it has stronger time-series adaptability. By processing the difference between the power supply demand of critical loads at each time level and the actual power supply at each time level, the first difference can be obtained by accurately capturing the power supply and demand deviation of loads at each time period. At the same time, the capacity gap variable is assigned differently according to the relationship between the first difference and the capacity gap benchmark value. This can accurately identify the power supply shortage conditions and avoid invalid calculations and model disturbances caused by small power deviations, ensuring that the capacity gap quantification results are accurate and stable. Finally, the quantified capacity gap variable and the effective feasible power supply chains at each time level are combined to construct a resilience gap constraint, which can specifically limit the range of power supply capacity loss of critical loads under extreme disasters, accurately regulate the bottom line of power grid supply, and make up for the shortcomings of traditional power grid resilience assessment, which cannot quantify the power supply gap in time series and whose constraint conditions do not fit the actual operating conditions well.

[0173] Step 204: With the goal of minimizing resilience cost, set defense constraints and use defense scheduling data as decision variables to construct an active defense optimization model.

[0174] In this embodiment of the invention, with the goal of minimizing resilience cost, operational constraints, resource budget constraints, and action conflict constraints are set for the flexible power supply unit, and active defense optimization model is constructed using defense scheduling data as decision variables.

[0175] It should be noted that the specific operational constraints of the flexible power supply unit are as follows:

[0176] ;

[0177] ;

[0178] ;

[0179] ;

[0180] ;

[0181] The specific resource budget constraints are as follows:

[0182] ;

[0183] The specific action conflict constraints are as follows:

[0184] ;

[0185] Step 205: Combine the schedulability constraint, resilience gap constraint, and active defense optimization model to obtain the corresponding target defense optimization model.

[0186] In this embodiment of the invention, the schedulability constraint and the resilience gap constraint are embedded into the active defense optimization model to obtain the corresponding target defense optimization model.

[0187] Step 206: Use a decomposition algorithm to iteratively solve the target defense optimization model to obtain the corresponding defense optimization scheme.

[0188] Decomposition algorithms refer to numerical algorithms that break down large-scale complex optimization models into multiple smaller, easier-to-solve sub-models for step-by-step computation, thereby reducing the difficulty of solving the problem and improving computational efficiency. Examples include the alternating direction multiplier method and the generalized Benders decomposition (GBD) algorithm.

[0189] In this embodiment of the invention, a decomposition algorithm is used to break down the complex target defense optimization model into multiple sub-problems and solve them sequentially. Defense scheduling data is continuously updated, various constraints are verified, and the objective function is converged according to the set iteration rules. The calculation is terminated when the iteration result meets the convergence accuracy requirements, and the optimal decision variable values ​​are output to obtain a defense optimization scheme that includes power supply chain switching, switching operation, and emergency resource allocation.

[0190] It is worth mentioning that the effectiveness of the power grid defense optimization method proposed in this application was verified using an improved IEEE 33-node integrated transmission and distribution system as the implementation object. The system includes 33 nodes and 32 default closed feeder branches, as well as 5 normally open tie switch branches. Three distributed power sources (located at nodes 12, 22, and 33 respectively), two fixed energy storage systems (located at nodes 18 and 25), and a reserved access point for mobile emergency power sources is configured at node 8. Nodes 15 and 24 are designated as primary critical loads with the highest supply protection weight. The disaster evolution cycle is set at 12 hours. Considering the switching action delay (e.g., 15 minutes) and mobile resource scheduling time, the entire defense and recovery process is divided into... Each discrete time layer, The target defense optimization model is constructed using steps 201-205. The generalized Benders decomposition (GBD) algorithm is used to iteratively solve the model to obtain the corresponding defense optimization scheme.

[0191] See Figure 4 As shown, the convergence process using the generalized Benders decomposition algorithm is observed. With increasing iterations, the lower bound representing the relaxation cost of the main problem and the upper bound representing the actual operational cost of the subproblems rapidly converge, reaching convergence in the 5th iteration. This demonstrates that the solution mechanism of this invention significantly reduces the computational complexity of large-scale spatiotemporal network models and enables rapid generation of pre-disaster defense strategies.

[0192] See Figure 5 As shown, during typhoon disasters ( to The capacity gap of the primary critical load (node ​​15) is compared between the method described in this application and the case without defensive measures. Under the traditional model without defensive measures, the load during the high-intensity disaster period ( to Facing the highest The large-scale power outage gap was addressed by the use of the resilient power supply unit in this application, which effectively eliminated the resilience gap and ensured the continuity of power supply under extreme disasters.

[0193] See Figure 6 As shown, this illustrates the operational status of the energy storage system (node ​​18) within the resilient power supply unit throughout the entire defense process. This includes the early warning phase before the disaster arrives (…). The system proactively decides to perform pre-charging of the energy storage, raising its state of charge (SOC) to 0.95; in the event of a power line breakage leading to islanded operation during a disaster ( to The rapid discharge of the energy storage system provides supporting power, perfectly demonstrating the foresight and effectiveness of the cross-time-layer joint scheduling proposed in this application.

[0194] In this embodiment of the invention, by acquiring power grid defense-related data and disaster evolution cycles, and based on pre-acquired emergency timing characteristics, a corresponding spatiotemporal power supply map is constructed using the defense-related data and disaster evolution cycles. Feasible power supply chains are screened based on the defense-related data and preset schedulability constraints to obtain corresponding time-feasible power supply chains. Corresponding resilience gap constraints are constructed using the defense-related data and each time-feasible power supply chain. With minimizing resilience cost as the optimization objective, defense constraints are set, and defense scheduling data is used as decision variables to construct an active defense optimization model. The active defense optimization model is then optimized and solved based on schedulability constraints and resilience gap constraints to obtain the corresponding defense optimization scheme. This overcomes the technical problem that existing power grid defense optimization methods often use static fault scenarios for optimization, making it difficult to adapt to actual engineering needs under extreme disaster scenarios and reducing the disaster defense capability of the power grid. Compared with traditional power grid defense optimization methods, this invention constructs a spatiotemporal power supply map based on emergency timing characteristics, power grid defense-related data, and disaster evolution cycles. This map can fully reproduce the dynamic changes in power grid topology and resource status throughout the entire disaster process, achieving spatiotemporal integration of power grid and disaster information. Feasible power supply chains are then screened based on dispatchability constraints, eliminating paths that are inoperable due to disasters, ensuring that the selected time-feasible power supply chains are ready for on-site execution. Resilience gap constraints are then established based on these time-feasible power supply chains, accurately quantifying the degree of power supply loss for critical loads and defining operational boundaries from a power supply security perspective. An active defense optimization model is constructed with the goal of minimizing resilience costs, and multiple constraints are applied for solution. This model can balance emergency resource input, equipment operating costs, and power outage losses while considering equipment operating rules, timing requirements, and power supply resilience, resulting in a feasible defense optimization solution that effectively improves the power grid's proactive resilience and supply guarantee level under extreme disasters.

[0195] Please see Figure 7 , Figure 7 This is a structural block diagram of a power grid defense optimization system provided in Embodiment 3 of the present invention.

[0196] This invention provides a power grid defense optimization system, comprising:

[0197] The data acquisition module 301 is used to acquire defense-related data and disaster evolution cycles of the power grid. Based on the pre-acquired emergency timing characteristics, it constructs a corresponding spatiotemporal power supply map using defense-related data and disaster evolution cycles.

[0198] The filtering module 302 is used to filter feasible power supply chains in the spatiotemporal power supply map based on defense-related data and preset schedulability constraints, and obtain the corresponding time-feasible power supply chains.

[0199] The first construction module 303 is used to construct the corresponding resilience gap constraints using defense-related data and feasible power chains at various times;

[0200] The second construction module 304 is used to set defense constraints with the goal of minimizing resilience cost and to construct an active defense optimization model with defense scheduling data as the decision variable.

[0201] The optimization module 305 is used to optimize the active defense optimization model based on schedulability constraints and resilience gap constraints, and obtain the corresponding defense optimization scheme.

[0202] Furthermore, the defense-related data includes power grid topology and resource access information. The acquisition module 301 includes:

[0203] The sub-module is used to divide the disaster evolution cycle according to the preset emergency timing characteristics, resulting in multiple time layers;

[0204] The first construction submodule is used to construct the corresponding spatiotemporal power supply map using information from various time layers, power grid topology, and resource access.

[0205] Furthermore, the defense-related data also includes multiple key loads, movement trajectories, intensity grids, and duration data. The filtering module 302 includes:

[0206] The spatiotemporal overlay submodule is used to overlay spatiotemporal power supply maps using movement trajectory, intensity grid, and duration data to obtain multiple disaster corridors.

[0207] The evaluation submodule is used to determine the timing availability status of each power grid element in the spatiotemporal power supply diagram based on each disaster corridor.

[0208] The filtering submodule is used to extract candidate power supply chains corresponding to each key load from the spatiotemporal power supply map;

[0209] When the timing availability status of all components corresponding to the candidate power supply chain is available, the candidate power supply chain is determined as the target power supply chain.

[0210] When the target power supply chain meets the preset schedulability constraints, the target power supply chain is determined as a time-feasible power supply chain.

[0211] Furthermore, the evaluation submodule includes:

[0212] The first analysis unit is used to calculate the degree of disaster overlap between each power grid element and each disaster corridor in the spatiotemporal power supply diagram.

[0213] The second analysis unit is used to determine the spatiotemporal exposure index of each power grid element based on the preset spatiotemporal exposure function, according to the overlap of each disaster, the intensity of each disaster in the spatiotemporal power supply diagram, and the duration of each disaster.

[0214] The third analysis unit is used to determine the component timing availability status corresponding to the spatiotemporal exposure index as the component availability status when the spatiotemporal exposure index is less than the preset index threshold.

[0215] When the spatiotemporal exposure index is greater than or equal to the indication threshold, the timing availability status of the component corresponding to the spatiotemporal exposure index is determined as the component unavailable status.

[0216] Furthermore, the first building module 303 includes:

[0217] The first analysis submodule is used to determine the time-layer power supply corresponding to each key load in the defense-related data based on the preset power supply function and the feasible power supply chain at each time.

[0218] The time-layer power supply requirements corresponding to each critical load are compared with the corresponding time-layer power supply to obtain multiple first differences.

[0219] When the first difference is greater than or equal to the preset capacity gap benchmark value, the first difference is determined as the corresponding capacity gap variable;

[0220] When the first difference is less than the capacity gap benchmark value, the capacity gap benchmark value is determined as the corresponding capacity gap variable;

[0221] The second analysis submodule is used to construct the corresponding resilience gap constraints using each capacity gap variable and each time-feasible power supply chain.

[0222] Furthermore, module 305 is optimized, including:

[0223] The simultaneous submodule is used to combine the schedulability constraint, resilience gap constraint and active defense optimization model to obtain the corresponding target defense optimization model.

[0224] The optimization submodule is used to iteratively solve the target defense optimization model using a decomposition algorithm to obtain the corresponding defense optimization scheme.

[0225] Please see Figure 8 , Figure 8 This is a structural block diagram of an electronic device provided in Embodiment 4 of the present invention.

[0226] An electronic device according to an embodiment of the present invention includes: a memory 401 and a processor 402. The memory 401 stores a computer program. When the computer program is executed by the processor 402, the processor 402 executes the power grid defense optimization method as described in any of the above embodiments.

[0227] Memory 401 may be an electronic memory such as flash memory, EEPROM (Electrically Erasable Programmable Read-Only Memory), EPROM, hard disk, or ROM. Memory 401 has storage space 403 for program code 413 for performing any of the method steps described above. For example, storage space 403 for program code may include individual program codes 413 for implementing the various steps in the methods described above. This program code may be read from or written to one or more computer program products. These computer program products include program code carriers such as hard disks, CDs, memory cards, or floppy disks. The program code may be compressed, for example, in a suitable form. When run by a computing processing device, this code causes the computing processing device to perform the various steps in the methods described above. This program code may be read from or written to one or more computer program products. These computer program products include program code carriers such as hard disks, CDs, memory cards, or floppy disks. The program code may be compressed, for example, in a suitable form. When this code is run by a computing processing device, it causes the device to perform the various steps in the power grid defense optimization method described above.

[0228] Embodiment 5 of the present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the power grid defense optimization method as described in any of the above embodiments.

[0229] Embodiment 6 of the present invention also provides a computer program product, which includes a computer program stored on a non-transitory computer-readable storage medium. The computer program includes program instructions, wherein when the program instructions are executed by a computer, the computer performs the power grid defense optimization method as described in any of the above embodiments.

[0230] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0231] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces, or indirect coupling or communication connection between apparatuses or units, and may be electrical, mechanical, or other forms.

[0232] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0233] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0234] If the integrated unit is implemented as 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, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0235] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A power grid defense optimization method, characterized in that, include: Acquire defense-related data and disaster evolution cycles of the power grid, and construct a corresponding spatiotemporal power supply map based on the pre-acquired emergency timing characteristics using the defense-related data and the disaster evolution cycles; Based on the defense-related data and preset schedulability constraints, feasible power supply chains are screened in the spatiotemporal power supply map to obtain the corresponding time-feasible power supply chains; The defense-related data and each of the time-feasible power chains are used to construct the corresponding resilience gap constraints. With the goal of minimizing resilience costs, defense constraints are set, and defense scheduling data is used as decision variables to construct an active defense optimization model; Based on the schedulability constraint and the resilience gap constraint, the active defense optimization model is optimized and solved to obtain the corresponding defense optimization scheme; The defense-related data also includes multiple key loads, movement trajectories, intensity grids, and duration data. The step of filtering feasible power supply chains in the spatiotemporal power supply map based on the defense-related data and preset schedulability constraints to obtain the corresponding time-feasible power supply chains includes: The spatiotemporal power supply map is spatiotemporally overlaid using the movement trajectory, the intensity grid, and the duration data to obtain multiple disaster corridors; The available timing status of each power grid element in the spatiotemporal power supply diagram is determined based on each of the disaster corridors. Extract the candidate power supply chains corresponding to each of the key loads from the spatiotemporal power supply diagram; When the timing availability status of the components corresponding to the candidate power supply chain is the component availability status, the candidate power supply chain is determined as the target power supply chain. When the target power supply chain meets the preset schedulability constraints, the target power supply chain is determined as a time-feasible power supply chain.

2. The power grid defense optimization method according to claim 1, characterized in that, The defense-related data includes power grid topology and resource access information. The step of constructing a corresponding spatiotemporal power supply map based on pre-acquired emergency timing characteristics using the defense-related data and the disaster evolution cycle includes: The disaster evolution cycle is divided into multiple time layers based on the pre-acquired emergency timing characteristics; A corresponding spatiotemporal power supply map is constructed using each of the aforementioned time layers, the power grid topology, and the resource access information.

3. The power grid defense optimization method according to claim 1, characterized in that, The step of determining the timing availability status of each power grid element in the spatiotemporal power supply diagram based on each of the disaster corridors includes: Calculate the degree of disaster overlap between each power grid element and each disaster corridor in the spatiotemporal power supply diagram; Based on the preset spatiotemporal exposure function, the spatiotemporal exposure index corresponding to each of the power grid components is determined according to the overlap of each disaster, the intensity of each disaster in the spatiotemporal power supply diagram, and the duration of each disaster. When the spatiotemporal exposure index is less than a preset index threshold, the timing availability state of the component corresponding to the spatiotemporal exposure index is determined as the component availability state. When the spatiotemporal exposure index is greater than or equal to the index threshold, the timing availability state of the component corresponding to the spatiotemporal exposure index is determined to be the component unavailable state.

4. The power grid defense optimization method according to claim 1, characterized in that, The step of constructing corresponding resilience gap constraints using the defense-related data and each of the time-feasible power chains includes: Based on a preset power supply function, the time-layer power supply power corresponding to each critical load in the defense-related data is determined according to each of the time-feasible power supply chains. The time-layer power supply requirement corresponding to each of the key loads is processed with the corresponding time-layer power supply to obtain multiple first differences; When the first difference is greater than or equal to the preset capacity gap benchmark value, the first difference is determined as the corresponding capacity gap variable. When the first difference is less than the capacity gap reference value, the capacity gap reference value is determined as the corresponding capacity gap variable; The corresponding resilience gap constraints are constructed using each of the aforementioned capacity gap variables and each of the aforementioned time-feasible power chains.

5. The power grid defense optimization method according to claim 1, characterized in that, The step of optimizing the active defense optimization model based on the schedulability constraint and the resilience gap constraint to obtain the corresponding defense optimization scheme includes: By combining the schedulability constraint, the resilience gap constraint, and the active defense optimization model, the corresponding target defense optimization model is obtained. The target defense optimization model is iteratively solved using a decomposition algorithm to obtain the corresponding defense optimization scheme.

6. A power grid defense optimization system, used to implement the power grid defense optimization method according to any one of claims 1-5, characterized in that, include: The data acquisition module is used to acquire defense-related data and disaster evolution cycles of the power grid. Based on the pre-acquired emergency timing characteristics, the module constructs a corresponding spatiotemporal power supply map using the defense-related data and the disaster evolution cycle. The filtering module is used to filter feasible power supply chains in the spatiotemporal power supply map based on the defense-related data and preset schedulability constraints, so as to obtain the corresponding time-feasible power supply chains. The first construction module is used to construct corresponding resilience gap constraints using the defense-related data and each of the time-feasible power chains; The second building module is used to set defense constraints with the goal of minimizing resilience cost and to build an active defense optimization model with defense scheduling data as the decision variable. The optimization module is used to optimize and solve the active defense optimization model based on the schedulability constraint and the resilience gap constraint, so as to obtain the corresponding defense optimization scheme.

7. An electronic device, characterized in that, It includes a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor causes the processor to perform the steps of the power grid defense optimization method as described in any one of claims 1-5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed, it implements the power grid defense optimization method as described in any one of claims 1-5.

9. A computer program product, characterized in that, The computer program product includes a computer program stored on a non-transitory computer-readable storage medium, the computer program including program instructions, wherein when the program instructions are executed by a computer, the computer performs the power grid defense optimization method as described in any one of claims 1-5.

Images