Post-disaster power grid emergency power supply recovery method and system considering electric vehicle energy space-time optimization

Abstract

The invention discloses a post-disaster power grid emergency power supply recovery method and system considering electric vehicle energy space-time optimization, and the method comprises the steps: building target optimization problems before and after a disaster according to a power supply recovery problem after an extreme scene; adopting a Big-M method and a second-order cone relaxation method to construct constraints of the optimization problem; a two-stage robust optimization model is adopted in pre-disaster pre-layout, a mixed integer second-order cone programming model is adopted in post-disaster recovery, the model is solved through a GUROBI solver, and an optimal planning scheme is obtained. The method has the advantages that collaborative scheduling of the electric vehicle and the energy storage system in post-disaster power grid recovery is optimized, the power grid recovery efficiency is improved, and the method has high practicability and application prospects.

Description

Technical Field

[0001] The present invention relates to the technical field of power system optimization and restoration, and in particular to a post-disaster power grid emergency power supply restoration method and system taking into account the spatiotemporal optimization of electric vehicle energy. Background Art

[0002] As the impact of global climate change continues to intensify, the frequency and intensity of natural disasters have also increased significantly, especially extreme weather and geological disasters such as typhoons, earthquakes, and floods. These disasters not only damage infrastructure, but also pose a serious threat to the stable operation of the power system. The power supply restoration of traditional power grids often relies on ground restoration work, but after extreme disasters, power grid facilities are severely damaged and the restoration time is long. In addition, traditional restoration methods are difficult to quickly and effectively mobilize available resources for emergency power supply. The restoration of power grids after disasters faces huge challenges, especially in remote areas and disaster areas with complex environments. The efficiency of restoring power supply needs to be improved urgently.

[0003] In recent years, with the rapid development of electric vehicles (EV) and mobile energy storage technology, the application of electric vehicles in post-disaster emergency power grid restoration has gradually attracted attention. Electric vehicles can not only be used as a means of load regulation, but also have the function of energy storage. They can release the energy in the battery through charging stations in a short time after the disaster and restore the power grid. Therefore, the reasonable dispatch of electric vehicles and mobile energy storage equipment, combined with other distributed power sources (such as photovoltaics, wind power, etc.), can effectively shorten the time for power grid recovery and improve the power supply capacity and resilience of the power grid.

[0004] However, most of the existing post-disaster grid restoration methods focus on traditional restoration strategies for power systems, and lack consideration of the integrated application of modern electric vehicles and mobile energy storage, especially the spatiotemporal distribution and scheduling optimization of these renewable resources and mobile energy storage in post-disaster recovery. Electric vehicle scheduling in existing technologies is mostly limited to ordinary charging optimization, and lacks spatiotemporal optimization scheduling of electric vehicle energy charging and discharging processes, which cannot fully tap its potential in post-disaster emergency power supply. In addition, there are usually many uncertainties in the resource allocation and scheduling problems in the pre-disaster power grid and post-disaster recovery process, such as the output error of photovoltaic power generation, the fluctuation of load demand, etc. The existing methods lack consideration of these uncertainties and it is difficult to provide efficient and robust power supply restoration solutions.

[0005] Therefore, a new post-disaster emergency power supply restoration method is urgently needed, which can quickly and intelligently dispatch resources such as electric vehicles and mobile energy storage after a disaster, comprehensively consider different factors (such as power load demand, distributed power output, traffic network conditions, etc.), perform spatiotemporal optimization scheduling, and solve uncertainty problems through robust optimization models to improve the speed and reliability of power grid recovery. Summary of the invention

[0006] In view of the defects of the prior art, the present invention provides a post-disaster power grid emergency power supply restoration method and system taking into account the spatiotemporal optimization of electric vehicle energy.

[0007] In order to achieve the above invention object, the technical solution adopted by the present invention is as follows:

[0008] A post-disaster power grid emergency power supply restoration method considering the spatiotemporal optimization of electric vehicle energy includes the following steps:

[0009] a) In the pre-disaster stage, a target optimization problem based on the power supply restoration problem after extreme scenarios is established, where the pre-disaster objective function is to determine the pre-configured location and quantity of mobile energy storage, and consider the prediction error of photovoltaic unit output and the uncertainty of spatiotemporal variation of photovoltaic output;

[0010] b) In the post-disaster stage, the power supply restoration optimization problem is established. The post-disaster objective function is to minimize the load active power reduction and restore the power supply of the power grid by dynamically dispatching mobile energy storage, electric vehicles and other available resources to maximize the resilience of the distribution network;

[0011] c) Using the Big-M method and the second-order cone relaxation method, the constraints of the optimization problem are constructed to ensure the rationality of the configuration and scheduling of mobile energy storage, taking into account the constraints of the distribution network radiation topology, load reduction power, and distributed power output;

[0012] d) A two-stage robust optimization model is applied for pre-disaster layout, a mixed integer second-order cone programming model is used for post-disaster recovery, and the GUROBI optimization solver is used to obtain the optimal power restoration plan.

[0013] Furthermore, the pre-disaster objective function is:

[0014]

[0015] in, is a Boolean variable, indicating the connection status between the mobile energy storage and the node. is the load active power reduction of node i, C ME is the cost per unit of mobile energy storage.

[0016] Furthermore, the post-disaster objective function is:

[0017]

[0018] Among them, Ω T is the set of fault durations, The active power reduction of the load.

[0019] Furthermore, the photovoltaic output The change of is characterized by the following formula:

[0020]

[0021] in, To predict PV output, τ is the uncertainty.

[0022] Furthermore, the constraints include:

[0023] Node mobile energy storage configuration constraints limit the maximum number of mobile energy storage configured for each node;

[0024] Distribution network radial topology constraints ensure that the radial topology of the power grid meets the requirements of load recovery;

[0025] Load reduction power constraints ensure that the load reduction power meets the power requirements of each node during the grid restoration process;

[0026] Distributed power generation output constraints to ensure that the output of distributed power generation is within a reasonable range;

[0027] Distribution network operation constraints to ensure that the operation status of the distribution network meets the current, voltage and power balance requirements;

[0028] Voltage and current constraints ensure that the voltage and current status of the distribution network meet the stability and safety requirements of the power system during the post-disaster recovery process;

[0029] Spatiotemporal dynamic scheduling constraints ensure that electric vehicles are reasonably scheduled according to the actual conditions of the transportation network and power grid during the post-disaster recovery process, optimizing the spatiotemporal distribution of electric energy.

[0030] Furthermore, the node mobile energy storage configuration constraint formula is as follows:

[0031]

[0032] in, is the maximum amount of mobile energy storage, It is a Boolean variable, indicating whether mobile energy storage is configured at node i.

[0033] The distribution network radiation topology constraint formula is as follows:

[0034]

[0035] Among them, Ω n represents the branch set, N B is the number of nodes, α ij is the branch disconnection state, γ(i) represents the reverse neighbor set of node i, δ(i) represents the node set adjacent to node i, represents a special state associated with node i, Represents the special state flow of node i.

[0036] The load reduction power constraint formula is as follows:

[0037]

[0038] in, To reduce power for load, Cut power for maximum load.

[0039] Furthermore, the output constraint formula of distributed generation is as follows:

[0040]

[0041] in, Indicates whether the distributed power source is connected to the node, For the active power output of distributed power sources, Indicates whether a distributed generation source is connected to the node.

[0042] The distribution network operation constraint formula is as follows:

[0043]

[0044] Among them, P ij is the branch active power, I ji is the branch current, R ji The resistance of the branch.

[0045] The voltage and current constraints are as follows:

[0046]

[0047] Among them, Q ij is the branch reactive power, V i The voltage at node i, I ij is the branch current.

[0048] The second-order cone relaxation method is transformed into:

[0049]

[0050] The time-space dynamic scheduling constraint formula is as follows:

[0051]

[0052] Among them, Ω ME is a collection of mobile energy storage device ME nodes, is a Boolean variable for spatiotemporal dynamic scheduling, Indicates whether node i is connected to node j at time t0, Indicates whether node i is connected to node j at time t, and whether the mobile energy storage device on node i is connected to node j at this moment. Indicates whether node i is connected to node k at time t+Δt, represents the time interval between the energy storage devices of nodes i and j being switched to node k, Indicates the minimum time interval or waiting time required for scheduling equipment during the scheduling process. represents the charging power of node i at time t, represents the discharge power of node i at time t, Ω N Represents the set of all nodes that may participate in scheduling.

[0053] Furthermore, the charging and discharging capabilities of the electric vehicle and the state of charge of its battery are constrained as follows:

[0054]

[0055] in, represents the charging power of node i at time t, Indicates whether node i is in charging state at time t. If the charging state is enabled, the value is 1; otherwise, it is 0. represents the maximum charging power of node i, The discharge power of node i at time t, Boolean variable indicating whether node i is in the discharge state at time t. If the discharge state is enabled, the value is 1; otherwise, it is 0. The maximum discharge power of node i, Indicates whether node i is in charging state at time t. If the charging state is enabled, the value is 1; otherwise, it is 0. Indicates whether node i is in the discharge state at time t. If the discharge state is enabled, the value is 1; otherwise, it is 0.

[0056] The present invention also discloses a post-disaster power grid emergency power supply restoration system, which can be used to implement the above-mentioned post-disaster power grid emergency power supply restoration method, specifically including:

[0057] a) Pre-disaster target optimization model building module, which is used to establish a target optimization problem based on the power supply restoration problem after extreme scenarios in the pre-disaster stage, determine the pre-configured location and quantity of mobile energy storage, and consider the prediction error of photovoltaic unit output and the uncertainty of spatiotemporal variation of photovoltaic output;

[0058] b) Post-disaster recovery optimization model building module, which is used to establish the power supply recovery optimization problem in the post-disaster stage, with the goal of minimizing the load active power reduction and restoring the power supply of the power grid by dynamically dispatching mobile energy storage, electric vehicles and other resources to maximize the resilience of the distribution network;

[0059] c) Constraint construction module, which constructs the constraints of the optimization problem through the Big-M method and the second-order cone relaxation method to ensure the rationality of the mobile energy storage configuration and scheduling, taking into account the constraints of the distribution network radiation topology, load reduction power, and distributed power output;

[0060] d) Optimization solution module, which is used to perform pre-disaster layout by applying a two-stage robust optimization model, adopt a mixed integer second-order cone programming model for post-disaster recovery, and use the GUROBI optimization solver to solve the optimal power supply restoration plan;

[0061] e) Display module, which is used to display in real time the progress of grid restoration, the dispatch status of mobile energy storage and electric vehicles, the power restoration situation, and the parameters and decision results in the relevant optimization process to support the decision-making of dispatchers and the monitoring of system status.

[0062] The present invention also discloses a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned post-disaster power grid emergency power supply recovery method when executing the program.

[0063] The present invention also discloses a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the above-mentioned post-disaster power grid emergency power supply recovery method is implemented.

[0064] Compared with the prior art, the advantages of the present invention are:

[0065] The present invention can provide power support quickly after a disaster by fully dispatching electric vehicles and mobile energy storage equipment, combined with distributed energy systems (such as photovoltaics, wind power, etc.), thus shortening the time for power grid recovery. Compared with traditional recovery methods, the present invention can achieve faster power supply recovery and reduce the impact of post-disaster power outages on social life and economic activities.

[0066] The present invention adopts a time-space optimization scheduling strategy, comprehensively considering factors such as power demand, the charging and discharging capacity and timeliness of various energy storage resources (electric vehicles, battery energy storage, etc.), and the output changes of distributed power sources, to effectively allocate various types of power resources after the disaster. Through precise scheduling optimization, the efficiency of the use of existing power resources can be maximized to avoid waste or excessive use of resources.

[0067] The present invention takes into account various uncertainties that may arise in the process of post-disaster power grid restoration, such as uncertainty in the charging status of electric vehicles, fluctuations in photovoltaic and wind power generation caused by weather changes, etc., and processes them through a robust optimization model to ensure that the power grid restoration plan can remain efficient and reliable in a variety of emergency situations. This enhances the adaptability and resilience of the power grid in the face of extreme disasters.

[0068] The present invention can not only dispatch a single electric vehicle or energy storage device, but also conduct integrated dispatch of a large number of widely distributed electric vehicles to achieve the coordinated work of large-scale dynamic resources. This technology can significantly enhance the role of mobile energy storage devices such as electric vehicles in post-disaster power supply restoration, especially in disaster areas with limited transportation. The dispatch system can automatically optimize the power supply plan and minimize the risk of power shortage.

[0069] The present invention combines intelligent algorithms and dispatch models to automatically dispatch resources and optimize in real time after a disaster, avoiding delays and errors caused by human intervention. The dispatch process responds to grid status and resource changes in real time, ensuring that all available resources are optimally utilized during grid recovery.

[0070] The optimized dispatching scheme of the present invention can effectively reduce the waste of resources in the process of post-disaster power grid restoration, avoid over-reliance on traditional power grid restoration methods, and reduce the cost required for repairing traditional power grid facilities. At the same time, the use of new resources such as electric vehicles and energy storage equipment reduces the demand for fuel and labor resources and reduces the overall cost of post-disaster recovery. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] Figure 1 It is a flow chart of a post-disaster power grid emergency power supply restoration method that takes into account the spatiotemporal optimization of electric vehicle energy according to an embodiment of the present invention.

[0072] Figure 2 It is a topological diagram of the distribution network structure according to an embodiment of the present invention.

[0073] Figure 3 It is a load recovery ratio diagram of the multi-energy coordinated power supply and the important load and the secondary important load in each time period in the city center according to the embodiment of the present invention. DETAILED DESCRIPTION

[0074] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below with reference to the accompanying drawings and examples.

[0075] The present invention provides a post-disaster power grid emergency power supply restoration method considering the spatiotemporal optimization of electric vehicle energy, comprising the following steps:

[0076] S1: Based on the power supply restoration problem after extreme scenarios, target optimization problems are established before and after the disaster.

[0077] Considering the uncertainty of photovoltaic unit output in the pre-disaster stage, the sum of mobile energy storage configuration cost and load reduction cost, that is, the minimum system risk cost, is taken as the goal. A two-stage robust optimization model is established to determine the pre-configured quantity and location of mobile energy storage. In the first stage, the connection status of mobile energy storage and the switch status of the line are regarded as decision variables. In the second stage, the minimum load power shortage is regarded as a decision variable. The objective function is as follows:

[0078]

[0079] The explanation of each variable in the formula is shown in Table 1:

[0080] Table 1 Explanation of variables

[0081]

[0082]

[0083] Among them, since the photovoltaic output will change over time, this paper uses the following formula to describe the prediction error:

[0084]

[0085] in, To predict PV output, τ is the uncertainty.

[0086] After the disaster, the distribution network loses the main power supply and several lines fail. The real-time traffic capacity of the transportation network during the disaster load recovery period is affected by the disaster. Assuming that the duration of the fault can be predicted by evaluating the severity of the disaster and the available repair resources, the goal is to minimize the reduction in power load during the fault and establish a multi-source coordinated post-disaster recovery optimization model; by dynamically dispatching mobile energy storage, electric vehicles and diesel generators, the resilience of the distribution network is maximized. The objective function is as follows:

[0087]

[0088] Where Ω T is the set of fault durations, The active power reduction of the load.

[0089] S2: Use the Big-M method and the second-order cone relaxation method to construct the constraints of the optimization problem;

[0090] (1) Node mobile energy storage configuration constraints

[0091]

[0092] in, is the maximum amount of mobile energy storage, It is a Boolean variable with a value of 0 or 1, thus limiting the mobile energy storage configuration of each node to a maximum of 1.

[0093] (2) Distribution network radiation topology constraints

[0094]

[0095] -M1α ij ≤F ij ≤M1α ij

[0096] Among them, Ω n is a set, representing a branch; N B is the number of nodes. The constraint adopts the Big-M method. The Big-M method is a technique for dealing with certain types of constraints in linear programming, especially when dealing with linear programming problems involving 0-1 integer variables. This method is particularly suitable for converting the dependencies between logical conditions and decision variables into linear constraints. The basic idea of ​​the Big-M method is to introduce a sufficiently large constant M to ensure that under certain conditions, the values ​​of certain variables are correctly restricted or forced.

[0097] (3) Load reduction power constraints

[0098]

[0099] (4) Distributed power generation output constraints

[0100]

[0101] in, Indicates whether the distributed power source is connected to the node, is the power factor of the distributed power supply.

[0102] (5) Distribution network operation constraints

[0103]

[0104] (6) For the nonlinear constraint between voltage, current and power

[0105]

[0106] It is transformed into the following second-order cone constraint by the second-order cone relaxation method:

[0107]

[0108] (7) Spatiotemporal dynamic scheduling constraints

[0109]

[0110]

[0111] (8) Electric vehicle charging and discharging capabilities and battery state of charge constraints

[0112]

[0113] S3: A two-stage robust optimization model is used for pre-disaster layout, and a mixed integer second-order cone programming model is used for post-disaster recovery. The GUROBI solver is used to solve the model to obtain the optimal planning scheme.

[0114] According to the above-mentioned post-disaster power grid emergency power supply restoration considering the spatiotemporal optimization of electric vehicle energy, the advantages and effects of the present invention have been verified by calculating the IEEE33-node distribution network topology in the city center.

[0115] like Figure 2 As shown in the figure, the system contains 33 nodes, connected to 5 diesel generators, 5 photovoltaic units, and 3 electric vehicle charging piles. The primary and secondary loads in the topology are regarded as important loads, and the tertiary loads are regarded as secondary loads. The degree of traffic congestion in different regions is different. In order to simplify the model, the traffic coupling coefficients in different regions are regarded as the same.

[0116] Since the distribution network reconstruction is taken into account, the topology of the distribution network may change continuously, and the transportation network cannot change with the topology of the power grid. Therefore, it is assumed that the road network and the distribution network have the same topology, and the transportation distance between adjacent electrical nodes is 2 km.

[0117] The remaining simulation parameters are shown in Table 2 and Table 3:

[0118] Table 2 Power supply parameters

[0119]

[0120] Table 3 Other parameters

[0121]

[0122] Set the PV output uncertainty to 0.35 and the parameter to 4.

[0123] Since the initial scene is randomly generated and uncertainty is taken into account during the simulation, the simulation results are different each time, so representative results are selected for analysis. The conclusions are shown in the table:

[0124] Table 4 Pre-layout cost in each scenario

[0125]

[0126] According to the results in the table above, it can be clearly seen that compared with considering the pre-configuration of mobile energy storage, the cost of pre-layout of the city center topology increased by 56.925% when the pre-configuration of mobile energy storage was not considered. It can be seen that pre-configuration of mobile energy storage before a disaster can effectively reduce operating costs.

[0127] Since the scenarios in the post-disaster stage are randomly generated, in order to simplify the simulation, fixed mobile energy storage pre-configuration nodes are required. Before the disaster, mobile energy storage is pre-configured to nodes 12 and 24, dividing the distribution network into three islands.

[0128] Assuming that the disaster occurs at 4:00, causing failures in branches 1, 3, 7, 28, and 35, and that the repairs are expected to be completed by 15:00, the multi-energy coordinated power supply and the load recovery ratio diagrams of important loads and secondary important loads in the city center at different time periods are obtained ( Figure 3 ).

[0129] It can be seen that during the disaster recovery period, the load recovery ratio of important load nodes was above 95%, and the load recovery ratio of secondary important load nodes was also around 90%, with the maximum power supply load being 1930.49kW.

[0130] In another embodiment of the present invention, a post-disaster power grid emergency power supply restoration system is provided. The system can be used to implement the above-mentioned post-disaster power grid emergency power supply restoration method, specifically including:

[0131] a) Pre-disaster target optimization model building module, which is used to establish a target optimization problem based on the power supply restoration problem after extreme scenarios in the pre-disaster stage, determine the pre-configured location and quantity of mobile energy storage, and consider the prediction error of photovoltaic unit output and the uncertainty of spatiotemporal variation of photovoltaic output;

[0132] b) Post-disaster recovery optimization model building module, which is used to establish the power supply recovery optimization problem in the post-disaster stage, with the goal of minimizing the load active power reduction and restoring the power supply of the power grid by dynamically dispatching mobile energy storage, electric vehicles and other resources to maximize the resilience of the distribution network;

[0133] c) Constraint construction module, which constructs the constraints of the optimization problem through the Big-M method and the second-order cone relaxation method to ensure the rationality of the mobile energy storage configuration and scheduling, taking into account the constraints of the distribution network radiation topology, load reduction power, and distributed power output;

[0134] d) Optimization solution module, which is used to perform pre-disaster layout by applying a two-stage robust optimization model, adopt a mixed integer second-order cone programming model for post-disaster recovery, and use the GUROBI optimization solver to solve the optimal power supply restoration plan;

[0135] e) Display module, which is used to display in real time the progress of grid restoration, the dispatch status of mobile energy storage and electric vehicles, the power restoration situation, and the parameters and decision results in the relevant optimization process to support the decision-making of dispatchers and the monitoring of system status.

[0136] In another embodiment of the present invention, a terminal device is provided, which includes a processor and a memory, wherein the memory is used to store a computer program, the computer program includes program instructions, and the processor is used to execute the program instructions stored in the computer storage medium. The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, which is suitable for implementing one or more instructions, and is specifically suitable for loading and executing one or more instructions to implement the corresponding method flow or corresponding function; the processor described in the embodiment of the present invention can be used for the operation of the post-disaster power grid emergency power supply recovery method, including the following steps:

[0137] In another embodiment of the present invention, a storage medium is provided, specifically a computer-readable storage medium (Memory), which is a memory device in a terminal device for storing programs and data. It is understandable that the computer-readable storage medium here can include both the built-in storage medium in the terminal device and the extended storage medium supported by the terminal device. The computer-readable storage medium provides a storage space, which stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by the processor are also stored in the storage space, and these instructions can be one or more computer programs (including program codes). It should be noted that the computer-readable storage medium here can be a high-speed RAM memory, or a non-volatile memory (non-volatile memory), such as at least one disk storage.

[0138] One or more instructions stored in a computer-readable storage medium can be loaded and executed by a processor to implement the corresponding steps of the post-disaster power grid emergency power supply restoration method in the above-mentioned embodiment; one or more instructions in the computer-readable storage medium are loaded and executed by the processor.

[0139] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented 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.

[0140] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0141] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0142] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0143] Those skilled in the art will appreciate that the embodiments described herein are intended to help readers understand the implementation methods of the present invention, and should be understood that the protection scope of the present invention is not limited to such special statements and embodiments. Those skilled in the art can make various other specific variations and combinations that do not deviate from the essence of the present invention based on the technical revelations disclosed by the present invention, and these variations and combinations are still within the protection scope of the present invention.

Claims

1. A post-disaster power grid emergency power supply restoration method considering the spatiotemporal optimization of electric vehicle energy, characterized in that: The following steps are involved: a) In the pre-disaster stage, a target optimization problem based on the power supply restoration problem after extreme scenarios is established, where the pre-disaster objective function is to determine the pre-configured location and quantity of mobile energy storage, and consider the prediction error of photovoltaic unit output and the uncertainty of spatiotemporal variation of photovoltaic output; b) In the post-disaster stage, the power supply restoration optimization problem is established. The post-disaster objective function is to minimize the load active power reduction and restore the power supply of the power grid by dynamically dispatching mobile energy storage, electric vehicles and other available resources to maximize the resilience of the distribution network; c) Using the Big-M method and the second-order cone relaxation method, the constraints of the optimization problem are constructed to ensure the rationality of the configuration and scheduling of mobile energy storage, taking into account the constraints of the distribution network radiation topology, load reduction power, and distributed power output; d) A two-stage robust optimization model is applied for pre-disaster layout, a mixed integer second-order cone programming model is used for post-disaster recovery, and the GUROBI optimization solver is used to obtain the optimal power restoration plan.

2. The post-disaster power grid emergency power supply restoration method according to claim 1 is characterized by: The pre-disaster objective function is: in, is a Boolean variable, indicating the connection status between the mobile energy storage and the node. is the load active power reduction of node i, C ME is the cost per unit of mobile energy storage.

3. The post-disaster power grid emergency power supply restoration method according to claim 2 is characterized in that: The post-disaster objective function is: Among them, Ω T is the set of fault durations, The active power reduction of the load.

4. The post-disaster power grid emergency power supply restoration method according to claim 1 is characterized in that: The photovoltaic output The change of is characterized by the following formula: in, To predict the PV output, τ is the uncertainty.

5. The post-disaster power grid emergency power supply restoration method according to claim 1 is characterized in that: The constraints include: Node mobile energy storage configuration constraints limit the maximum number of mobile energy storage configured for each node; Distribution network radial topology constraints ensure that the radial topology of the power grid meets the requirements of load recovery; Load reduction power constraints ensure that the load reduction power meets the power requirements of each node during the grid restoration process; Distributed power generation output constraints to ensure that the output of distributed power generation is within a reasonable range; Distribution network operation constraints to ensure that the operation status of the distribution network meets the current, voltage and power balance requirements; Voltage and current constraints ensure that the voltage and current status of the distribution network meet the stability and safety requirements of the power system during the post-disaster recovery process; Spatiotemporal dynamic scheduling constraints ensure that electric vehicles are reasonably scheduled according to the actual conditions of the transportation network and power grid during the post-disaster recovery process, optimizing the spatiotemporal distribution of electric energy.

6. The post-disaster power grid emergency power supply restoration method according to claim 5 is characterized by: The node mobile energy storage configuration constraint formula is as follows: in, is the maximum amount of mobile energy storage, is a Boolean variable, indicating whether mobile energy storage is configured at node i; The distribution network radiation topology constraint formula is as follows: Among them, Ω n represents the branch set, N B is the number of nodes, α ij is the branch disconnection state, γ(i) represents the reverse neighbor set of node i, δ(i) represents the node set adjacent to node i, represents a special state associated with node i, represents the special state flow of node i; The load reduction power constraint formula is as follows: in, To reduce power for load, Cut power for maximum load.

7. The post-disaster power grid emergency power supply restoration method according to claim 6 is characterized by: The output constraint formula of distributed generation is as follows: in, Indicates whether the distributed power source is connected to the node, For the active power output of distributed power sources, Indicates whether a distributed generation source is connected to the node; The distribution network operation constraint formula is as follows: Among them, P ij is the branch active power, I ji is the branch current, R ji The resistance of the branch; The voltage and current constraints are as follows: Among them, Q ij is the branch reactive power, V i The voltage at node i, I ij is the current of the branch; The second-order cone relaxation method is transformed into: The time-space dynamic scheduling constraint formula is as follows: Among them, Ω ME is a collection of mobile energy storage device ME nodes, is a Boolean variable for spatiotemporal dynamic scheduling, Indicates whether node i is connected to node j at time t0, Indicates whether node i is connected to node j at time t, and whether the mobile energy storage device on node i is connected to node j at this moment. Indicates whether node i is connected to node k at time t+Δt, represents the time interval between the energy storage devices of nodes i and j being switched to node k, Indicates the minimum time interval or waiting time required for scheduling equipment during the scheduling process. represents the charging power of node i at time t, represents the discharge power of node i at time t, Ω N Represents the set of all nodes that may participate in scheduling.

8. The post-disaster power grid emergency power supply restoration method according to claim 1 is characterized by: The charging and discharging capabilities of the electric vehicle and the state of charge of its battery are constrained as follows: in, represents the charging power of node i at time t, Indicates whether node i is in charging state at time t; if the charging state is enabled, the value is 1; otherwise, it is 0. represents the maximum charging power of node i, The discharge power of node i at time t, Boolean variable indicating whether node i is in the discharge state at time t; if the discharge state is enabled, the value is 1; otherwise, it is 0. The maximum discharge power of node i, Indicates whether node i is in charging state at time t; if the charging state is enabled, the value is 1; otherwise, it is 0. Indicates whether node i is in the discharge state at time t; if the discharge state is enabled, the value is 1; otherwise, it is 0.

9. A post-disaster power grid emergency power supply restoration system, characterized by: The system can be used to implement the post-disaster power grid emergency power supply restoration method described in any one of claims 1 to 8, specifically including: a) Pre-disaster target optimization model building module, which is used to establish a target optimization problem based on the power supply restoration problem after extreme scenarios in the pre-disaster stage, determine the pre-configured location and quantity of mobile energy storage, and consider the prediction error of photovoltaic unit output and the uncertainty of spatiotemporal variation of photovoltaic output; b) Post-disaster recovery optimization model building module, which is used to establish the power supply recovery optimization problem in the post-disaster stage, with the goal of minimizing the load active power reduction and restoring the power supply of the power grid by dynamically dispatching mobile energy storage, electric vehicles and other resources to maximize the resilience of the distribution network; c) Constraint construction module, which constructs the constraints of the optimization problem through the Big-M method and the second-order cone relaxation method to ensure the rationality of the mobile energy storage configuration and scheduling, taking into account the constraints of the distribution network radiation topology, load reduction power, and distributed power output; d) Optimization solution module, which is used to perform pre-disaster layout by applying a two-stage robust optimization model, adopt a mixed integer second-order cone programming model for post-disaster recovery, and use the GUROBI optimization solver to solve the optimal power supply restoration plan; e) Display module, which is used to display in real time the progress of grid restoration, the dispatch status of mobile energy storage and electric vehicles, the power restoration situation, and the parameters and decision results in the relevant optimization process to support the decision-making of dispatchers and the monitoring of system status.

10. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and when the program is executed by a processor, the post-disaster power grid emergency power supply restoration method described in one of claims 1 to 8 is implemented.

Images