Urban power distribution network emergency response decision-making method based on multi-subject V2G response

Abstract

The invention relates to the field of power systems, and particularly discloses an urban power distribution network emergency response decision-making method based on multi-subject V2G response, and the method comprises the steps: constructing a multi-subject V2G response estimation model through NetLogo; the V2G response estimation model simulates behaviors of multiple subjects by integrating a vehicle following model, an improved discrete selection Logit model and a path planning model; the power distribution network operator transmits a V2G response to the personalized travel advisor, the personalized travel advisor collects data of the electric vehicle and sends a request to the electric vehicle, and the intention of an electric vehicle owner is judged through a vehicle following model and an improved discrete selection Logit model; an MILP in the Matlab is utilized to construct a recovery model; the method is used for repairing faults in extreme events. The method has the advantages that the recovery efficiency of the power grid after an extreme event is improved, the operation cost is reduced, and effective decision support is provided for a power distribution network operator in actual recovery planning.

Description

Technical Field

[0001] The present invention relates to the field of power systems, and in particular to an emergency response decision method for an urban distribution network based on multi-agent V2G response. Background Art

[0002] As global warming intensifies, urban areas are facing increasingly frequent extreme weather events, such as heavy rains, floods or hurricanes. Power shortages or blackouts caused by these extreme events pose a major threat to the lives of urban residents and economic activities. Urban power grids are particularly vulnerable to these disasters due to the density and interdependence of their infrastructure.

[0003] The resilience of urban distribution networks is directly related to the overall resilience of cities in dealing with extreme events. How to improve the disaster resistance of power grids and quickly restore power supply has become an urgent problem for the power industry and urban planners. In order to meet such challenges, it is urgent to use interdisciplinary technical means to prevent and mitigate the impact of extreme events on the power grid, including preventive decision analysis, disaster damage assessment, and restoration resource optimization. Theoretical research in recent years has emphasized the importance of improving the disaster resistance of distribution networks for power grid restoration. Researchers have explored the collaboration of multiple restoration methods that span multiple stages and spatiotemporal dimensions, mainly including topology control, power support, and load extraction, such as network reconstruction, emergency maintenance personnel dispatch, and mobile emergency resource dispatch.

[0004] The coupling between urban distribution networks and transportation networks is becoming increasingly close. Using transportation networks as carriers can effectively support power supply and fault repair in distribution networks. In addition, with the popularity of electric vehicles (EVs), their interaction with the power grid has brought new challenges and opportunities. EVs not only increase the load demand on the power grid, but also provide a new means of power support and recovery through V2G technology. V2G technology allows electric vehicles to feed surplus energy back to the power grid, providing auxiliary services to the power grid, such as demand-side response, load regulation, frequency regulation, and voltage regulation.

[0005] However, there is still limited research on how to effectively integrate V2G technology to enhance grid resilience under extreme events. Existing research often ignores the potential benefits of electric vehicle battery functions for V2G response. Therefore, exploring the synergy between V2G services and transportation networks and distribution networks, as well as how to effectively integrate the multi-agent characteristics and actions required for electric vehicle aggregation, has important research value and application prospects for achieving V2G response and improving grid resilience. Summary of the invention

[0006] The purpose of the present invention is to overcome the shortcomings of the prior art and provide an urban distribution network emergency response decision-making method based on multi-agent V2G response.

[0007] The purpose of the present invention is achieved through the following technical solutions: A method for making emergency response decisions for urban distribution networks based on multi-agent V2G responses, comprising:

[0008] Use NetLogo to build a multi-agent based V2G response estimation model;

[0009] The V2G response estimation model simulates the behavior of multiple agents by integrating a vehicle following model, an improved discrete choice Logit model and a path planning model, wherein the multiple agents include electric vehicles, charging stations, personalized travel advisors, charging station aggregators and distribution network operators;

[0010] The distribution network operator transmits the V2G response to the personalized travel advisor, which collects the data of the electric vehicle and sends a request to the electric vehicle. The vehicle following model and the improved discrete choice Logit model are used to determine the intention of the electric vehicle owner. The path planning model is used to provide the responding electric vehicle with a path planning destination of the charging station. The electric vehicle drives to the charging station according to the planned path and starts discharging.

[0011] A recovery model was constructed using MILP in Matlab; it is used to repair failures in extreme events.

[0012] Specifically, the specific steps of determining the intention of the electric vehicle owner are as follows:

[0013] Step S1: determine whether the electric vehicle responds; if it responds, proceed to step S2.

[0014] Step S2, the personalized travel consultant will sort the shortest paths from the starting point to the charging station in the path list according to the vertex algorithm;

[0015]

[0016] In the formula, is the shortest path, is the location of the electric vehicle, Step S3 is the location of the charging station. It is assumed that the initial SOC of the electric vehicle obeys a normal distribution with a mean σ and a standard deviation μ, as shown in the following formula:

[0017]

[0018] Where V i and V i cap are the initial battery capacity and total battery capacity of the electric vehicle, is the initial SOC of electric vehicle i, K pri and K com represents the collection of private and commercial electric vehicles;

[0019] Step S4, calculating speed;

[0020] Step S5: Calculate energy consumption:

[0021]

[0022]

[0023] In the formula, is the energy consumption of electric vehicles in driving state, γ C is the regression coefficient corresponding to the driving state, is the instantaneous speed at time t, is the longitude and latitude coordinates of electric vehicle i at time t; T is the total time of the response process, and Δt represents the time difference;

[0024] Step S6, calculate the final SOC. When the responding electric vehicle arrives at the charging station, the remaining SOC of the responding electric vehicle is the initial SOC minus the energy consumption of the journey from the starting point to the charging station. The specific formula is as follows:

[0025]

[0026] In the formula, In response to the remaining SOC of the electric vehicle, is the initial SOC of electric vehicle i, is energy consumption; step S7, calculating discharge;

[0027] Step S8, calculating the response duration;

[0028] Step S9: determining the intention of the electric car owner.

[0029] Specifically, in step S1, the remaining SOC of the responding electric vehicle is calculated to determine whether the electric vehicle has the V2G response capability, as shown in the following formula:

[0030]

[0031] In the formula, The qualified SOC level is higher than 60%. In response to the remaining SOC of the electric vehicle.

[0032] Specifically, in step 24, the vehicle following model is used to simulate the interaction of electric vehicles in real driving scenarios. The speed change of the electric vehicle is to maintain a safe distance with adjacent entities, including maintaining a safe distance with other electric vehicles and red lights. The electric vehicle senses the distance in front of it and other entities. Keep it at a safe distance d buffThe comparison is done to determine whether to accelerate, decelerate, or stop. The acceleration and deceleration are set to and Electric car speed Cannot exceed Specifically, in step S7, it is assumed that the discharge process is uniform, and the available power of the responsive electric vehicle should satisfy the following formula:

[0033]

[0034] In the formula, and are the minimum and maximum discharge power limits of electric vehicles, respectively. When an electric vehicle supplies power to the grid, if the SOC drops to the reserved value At 20% the battery will stop discharging.

[0035] Specifically, the calculation formula for the response duration in step S8 is as follows:

[0036]

[0037] in, is the response duration, η dis is the discharge efficiency, is the unit discharge power, and Indicates the V2G travel time and V2G response end time.

[0038] Specifically, in step S9, an improved discrete choice Logit model is used to capture the decision-making process of electric vehicle owners based on their personal attributes, and the V2G response willingness of electric vehicle owners is converted into the following expression:

[0039]

[0040] In the formula They represent the benefits, electricity costs, battery degradation costs and opportunity costs of EV i participating in V2G response respectively; π ch is the discharge and charge electricity price, η ch is the charging efficiency, τ represents the opportunity cost coefficient, is the economic benefit coefficient of the car owner, represents the daily travel power consumption of electric vehicle i. The decision-making process of electric vehicles is a rough estimate of benefits and costs, so the discharge power Replaced by the estimated dispatchable power Specifically, the recovery model aims to reduce the weighted load reduction and V2G compensation cost during the post-event recovery period, and the objective function is:

[0041]

[0042] In the formula, m f is the load weight, is the load reduction after power supply to node f is restored, C V2G,t compensation to aggregators for EV owners;

[0043] The constraints of the recovery model include:

[0044] Active and reactive power balance constraints for each node in the network

[0045]

[0046] p b,t and q b,t are the active and reactive node injection power of node b respectively, is the reactive power of the generator at node b, is the load reactive power, B is the set of all nodes, p fb,t and q fb,t For line l fb The active and reactive transmission power, r fb 、x fb represent impedance and reactance respectively, are the active powers of the generator, V2G and load at node b respectively;

[0047] Node and branch voltage amplitude and branch current limit constraints

[0048]

[0049] Where V b,t is the node voltage of node b at time t, V f,t is the voltage of node f at time t; E is the set of all fault lines; and are the two top beam values; I fb,t is the current of the fault line fb at time t;

[0050] Second-order cone constraints

[0051]

[0052] Among them, || ||2 represents the l2 norm,

[0053] Load shedding constraints

[0054]

[0055] In the formula, and are the active and passive load reductions, θ is the constant used for constraint;

[0056] V2G discharge power constraints:

[0057]

[0058] In the formula, is the V2G discharge power,

[0059] Radial Network Constraints

[0060]

[0061] In the formula, l fb The flow direction is determined by the binary variable and Decision, cannot be 1 at the same time, if The flow direction is positive; if The flow direction is negative. If z fb,t =0, then l fb When an error occurs, the reference positive direction of the tributary is represented by α and β. When α=1 and β=0, the flow direction is from node f to b.

[0062] Specifically, the method further includes constructing an emergency maintenance personnel scheduling model, and the constraint conditions for constructing the emergency maintenance personnel scheduling model are:

[0063] Maintenance space constraints:

[0064]

[0065]

[0066] In the formula, C ERC For the purpose of emergency repair, is a binary variable of the fault line, and the number of emergency maintenance personnel is N ERC , d c,u is the total distance of the repair path, S OD For each emergency repairman’s driving path, if the repair path of emergency repairman k is from fault line c to u, its value is 1 or 0, S F , S O , S D and S E is the set of fault lines, starting points, end points and emergency repair personnel, y c,k is a binary variable indicating whether the fault line has been repaired. If the fault line c has been repaired by the emergency repairman k, its value will be 1. For repair tasks;

[0067] Describe the driving and repair trajectories of emergency maintenance personnel:

[0068]

[0069] In the formula, and are the starting and ending points of the destination pair m, is the node number of the station in the road network. Emergency maintenance personnel k starts from the station and drives in the order of the fault line to complete the repair task and return to the starting point;

[0070] Restoration time constraints:

[0071]

[0072] Emergency maintenance personnel Arriving at fault line c at time, time Time after repair exist Arrives at the next fault line u at time, where h is a positive real number.

[0073] The present invention has the following advantages:

[0074] The present invention proposes a method for resilient recovery of urban distribution networks by comprehensively utilizing network reconstruction, emergency maintenance personnel dispatch model and V2G response. By accurately simulating and predicting V2G response through a multi-agent-based model and using mixed integer linear programming for coordinated optimization, this strategy not only improves the recovery efficiency of the power grid after extreme events, but also reduces operating costs, providing effective decision support for distribution network operators in actual recovery planning. BRIEF DESCRIPTION OF THE DRAWINGS

[0075] Figure 1 It is a schematic diagram of the recovery method flow of the present invention; DETAILED DESCRIPTION

[0076] 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 in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention, that is, the embodiments described are only part of the embodiments of the present invention, rather than all of the embodiments. The components of the embodiments of the present invention described and shown in the drawings herein can be arranged and designed in various different configurations.

[0077] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention claimed for protection, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making creative work are within the scope of protection of the present invention.

[0078] It should be noted that relational terms such as "first" and "second" are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "including a..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0079] The present invention is further described below in conjunction with the accompanying drawings, but the protection scope of the present invention is not limited to the following description. Figure 1 As shown, a method for making emergency response decisions for urban distribution networks based on multi-agent V2G responses includes:

[0080] Use NetLogo to build a multi-agent based V2G response estimation model;

[0081] The V2G response estimation model simulates the behavior of multiple agents by integrating a vehicle following model, an improved discrete choice Logit model and a path planning model. The multiple agents include electric vehicles, charging stations, personalized travel consultants, charging station aggregators and distribution network operators. Each agent acts as an independent unit with incomplete information and problem-solving capabilities. There is no global control mechanism in the entire framework, but the entire estimation process is completed through synchronous parallel calculations. The V2G response estimation model simulates the individual behavior and collective V2G response effect of electric vehicles at both micro and macro levels, so that the two levels can coexist at the same time. At the micro level, the behavior and decision of electric vehicles depend on their own attributes and intentions, such as technical parameters such as electric vehicle type, battery capacity, initial battery state of charge (SOC), speed, cruising range and duration, as well as the driver's intentions, such as travel purpose, battery degradation and income, etc. The parameters at the macro level come from the external environment, such as transportation network, weather, extreme disaster impact and mechanism, etc.

[0082] The distribution network operator transmits the V2G response to the personalized travel advisor, which collects the data of the electric vehicle and sends a request to the electric vehicle. The vehicle following model and the improved discrete choice Logit model are used to determine the intention of the electric vehicle owner. The path planning model is used to provide the responding electric vehicle with a path planning destination of the charging station. The electric vehicle drives to the charging station according to the planned path and starts discharging.

[0083] A recovery model was constructed using MILP in Matlab; it is used to repair failures in extreme events.

[0084] Furthermore, the specific steps of determining the intention of the electric vehicle owner are as follows:

[0085] Step S1: Determine whether the electric vehicle responds; the battery capacity of the electric vehicle affects the driving distance and determines whether the electric vehicle has V2G response capability. The CS aggregator requires the remaining SOC of the responding electric vehicle. At least 60%, when the EV responds to a V2G request, it consumes a portion of the energy in the driving distance before reaching the nearest CS. If this energy consumption makes the qualified SOC level If the rate is higher than 60%, electric vehicles are eligible to respond, and the specific formula is as follows:

[0086]

[0087] In the formula, The qualified SOC level is higher than 60%. In response to the remaining SOC of the electric vehicle; if the response is received, the process proceeds to step S2;

[0088] Step S2: The personalized travel consultant will sort the shortest paths from the starting point to the charging station in the path list according to the vertex algorithm; the shortest path in the path list By electric car and target CS The position is calculated by Dijkstra algorithm:

[0089]

[0090] In the formula, is the shortest path, is the location of the electric vehicle, The location of the charging station; j∈SV2G Represents a collection of target cs;

[0091] Step S3, setting the initial SOC of the electric vehicle to obey the normal distribution with mean σ and standard deviation μ, as shown in the following formula:

[0092]

[0093]

[0094] Where V i and V i cap are the initial battery capacity and total battery capacity of the electric vehicle, is the initial SOC of electric vehicle i, Kpri and K com represents the collection of private and commercial electric vehicles;

[0095] Step S4: Calculate the speed, and simulate the interaction of electric vehicles in real driving scenarios through the vehicle following model. The speed change of the electric vehicle is to maintain a safe distance with adjacent entities, including other electric vehicles and red lights. The electric vehicle senses the distance in front of other entities. Keep it at a safe distance d buff The comparison is done to determine whether to accelerate, decelerate, or stop. The acceleration and deceleration are set to and Electric car speed Cannot exceed Step S5: Calculate energy consumption:

[0096]

[0097] In the formula, is the energy consumption of electric vehicles in driving state, γ C is the regression coefficient corresponding to the driving state, is the instantaneous speed at time t, is the longitude and latitude coordinates of electric vehicle i at time t, and T is the total time of the response process;

[0098] Step S6, calculate the final SOC. When the responding electric vehicle arrives at the charging station, the remaining SOC of the responding electric vehicle is the initial SOC minus the energy consumption of the journey from the starting point to the charging station. The specific formula is as follows:

[0099]

[0100] In the formula, In response to the remaining SOC of the electric vehicle, is the initial SOC of electric vehicle i, is energy consumption; step S7, calculate discharge; assuming that the discharge process is uniform, the available power of the responsive electric vehicle should satisfy the following formula:

[0101]

[0102] In the formula, and are the minimum and maximum discharge power limits of electric vehicles, respectively. When an electric vehicle supplies power to the grid, if the SOC drops to the reserved value When set to 20%, the battery will stop discharging;

[0103] Step S8, calculate the response duration; the calculation formula of the response duration is as follows:

[0104]

[0105] in, is the response duration, η dis is the discharge efficiency, is the unit discharge power, and Indicates the V2G travel time and V2G response end time;

[0106] Step S9, determine the intention of the electric vehicle owner; use the improved discrete choice Logit model to capture the decision-making process of the electric vehicle owner based on his or her personal attributes, and the electric vehicle owner's V2G response willingness is converted into the following expression:

[0107]

[0108] In the formula They represent the benefits, electricity costs, battery degradation costs and opportunity costs of EV i participating in V2G response respectively; π ch is the discharge and charge electricity price, η ch is the charging efficiency, τ represents the opportunity cost coefficient, is the economic benefit coefficient of the car owner, represents the daily travel power consumption of electric vehicle i, α degra The electric vehicle decision process is a rough estimate of the benefits and costs, so the discharge power Replaced by the estimated dispatchable power If EV owners are idle, they will weigh the economic benefits of participating in the V2G response to decide whether to respond.

[0109] Simulation analysis at the macro level

[0110] Personalized travel advisor broadcasts V2G response signals to eligible electric vehicles, and the actual number of electric vehicles responding The number of electric vehicles that can be accommodated by the charging station CS is limited by the number of charging piles with V2G function. On the other hand, in the initial development stage of V2G response, the main participants are private electric vehicles, which account for the vast majority of the entire electric vehicle market. In addition, the daily online interaction of private electric vehicles with the power grid accounts for more than 80% of their operating time, making them more effective in receiving V2G response signals. Commercial electric vehicles also have great potential as response resources;

[0111] Step 1: Build a transportation network. The TN in the NetLogo platform is described as a directed graph consisting of nodes and edges representing geographic locations, major roads, and intersections, which is composed of patches. As the patches on the planned route change, electric vehicles travel from a location patch to a charging station (CS) patch within the TN, which may cause traffic congestion.

[0112] Step 2: Set the V2G response capability. When the responding electric vehicle i arrives at the charging station CSj, it provides dispatchable power to the charging station CSj. The dispatchable total power of electric vehicles is aggregated in CS j at time t and sent to the urban distribution network (DN). The CS aggregator pays compensation C to the electric vehicle owner. V2G,t , including unit power and single response cost. The specific formula is as follows:

[0113]

[0114] Among them, π V2G is the compensation price per unit of electricity, π rep Compensation for single response.

[0115] Step 3: Set V2G prices. In emergencies, the electricity market mechanism fails. If the pricing mechanism follows the normal market, it is difficult for V2G to respond to prices and attract the support of electric vehicles. Further, the distribution network operator adopts a recovery method in sequence, which includes network reconstruction, emergency maintenance personnel dispatching and V2G technology. Executing network reconstruction and emergency maintenance personnel dispatching can provide the best topology and fault line repair plan. Multi-agent modeling of V2G response estimates the potential power supply of electric vehicles. The repair aims to minimize the weighted load reduction and V2G compensation costs during the post-event recovery period. The recovery model aims to reduce the weighted load reduction and V2G compensation costs during the post-event recovery period. The objective function is:

[0116]

[0117] In the formula, m f is the load weight, is the load reduction after power supply to node f is restored, C V2G,t compensation to aggregators for EV owners;

[0118] The constraints of the recovery model include:

[0119] Active and reactive power balance constraints for each node in the network

[0120]

[0121] p b,t and q b,tare the active and reactive node injection power of node b respectively, is the reactive power of the generator at node b, is the load reactive power, B is the set of all nodes, p fb,t and q fb,t For line l fb The active and reactive transmission power, r fb 、x fb represent impedance and reactance respectively, are the active powers of the generator, V2G and load at node b respectively; node and branch voltage amplitude and branch current limit constraints

[0122]

[0123] Where V b,t is the node voltage of node b at time t, V f,t is the voltage of node f at time t; E is the set of all fault lines; and are the two top beam values; I fb,t is the current of the fault line fb at time t;

[0124] Second-order cone constraints

[0125]

[0126] Among them, || ||2 represents the l2 norm,

[0127] Load shedding constraints

[0128]

[0129]

[0130] In the formula, and are the active and passive load reductions, θ is the constant used for constraint;

[0131] V2G discharge power constraints:

[0132]

[0133] In the formula, is the V2G discharge power, S V2G is the discharge power constraint;

[0134] Radial Network Constraints

[0135]

[0136] In the formula, l fb The flow direction is determined by the binary variable and Decision, cannot be 1 at the same time, if The flow direction is positive; if The flow direction is negative. If z fb,t =0, then l fb The reference positive direction of the tributary is represented by α and β. When α = 1 and β = 0, the flow direction is from node f to b. slack is the z constraint.

[0137] Furthermore, the method further includes constructing an emergency maintenance personnel scheduling model, wherein the constraint conditions for constructing the emergency maintenance personnel scheduling model are:

[0138] Maintenance space constraints:

[0139]

[0140]

[0141] In the formula, C ERC For the purpose of emergency repair, is a binary variable of the fault line, and the number of emergency maintenance personnel is N ERC , d c,u is the total distance of the repair path, S OD For each emergency repairman’s driving path, if the repair path of emergency repairman k is from fault line c to u, its value is 1 or 0, S F , S O , S D and S E is the set of fault lines, starting points, end points and emergency repair personnel, y c,k is a binary variable indicating whether the fault line has been repaired. If the fault line c has been repaired by the emergency repairman k, its value will be 1. For repair tasks;

[0142] Describe the driving and repair trajectories of emergency maintenance personnel:

[0143]

[0144] In the formula, and are the starting and ending points of the destination pair m, is the node number of the station in the road network. Emergency maintenance personnel k starts from the station and drives in the order of the fault line to complete the repair task and return to the starting point;

[0145] Restoration time constraints:

[0146]

[0147] Emergency maintenance personnel Arriving at fault line c at time, time Time after repair exist Arrives at the next fault line u at time, where h is a very large positive real number.

[0148] The above is only a preferred embodiment of the present invention and does not limit the present invention in any form. Any technician familiar with the art can make many possible changes and modifications to the technical solution of the present invention by using the above-mentioned technical content without departing from the scope of the technical solution of the present invention, or modify it into an equivalent embodiment of equivalent changes. Therefore, any changes, modifications, equivalent changes and modifications made to the above embodiments based on the technology of the present invention without departing from the content of the technical solution of the present invention belong to the protection scope of the present technical solution.

Claims

1. A method for making emergency response decisions for urban distribution networks based on multi-agent V2G responses, characterized in that: include: Use NetLogo to build a multi-agent based V2G response estimation model; The V2G response estimation model simulates the behavior of multiple agents by integrating a vehicle following model, an improved discrete choice Logit model and a path planning model, wherein the multiple agents include electric vehicles, charging stations, personalized travel advisors, charging station aggregators and distribution network operators; The distribution network operator transmits the V2G response to the personalized travel advisor, which collects the data of the electric vehicle and sends a request to the electric vehicle. The vehicle following model and the improved discrete choice Logit model are used to determine the intention of the electric vehicle owner. The path planning model is used to provide the responding electric vehicle with a path planning destination of the charging station. The electric vehicle drives to the charging station according to the planned path and starts discharging. A recovery model was constructed using MILP in Matlab; it is used to repair failures in extreme events.

2. According to claim 1, a method for making decisions on emergency response of urban distribution network based on multi-agent V2G response, characterized in that: The specific steps of judging the intention of the electric vehicle owner are as follows: Step S1: determine whether the electric vehicle responds; if it responds, proceed to step S2. Step S2, the personalized travel consultant will sort the shortest paths from the starting point to the charging station in the path list according to the vertex algorithm; In the formula, is the shortest path, is the location of the electric vehicle, The location of the charging station; Step S3, setting the initial SOC of the electric vehicle to obey the normal distribution with mean σ and standard deviation μ, as shown in the following formula: Where V i and V i cap are the initial battery capacity and total battery capacity of the electric vehicle, is the initial SOC of electric vehicle i, K pri and K com represents the collection of private and commercial electric vehicles; Step S4, calculating speed; Step S5: Calculate energy consumption: In the formula, is the energy consumption of electric vehicles in driving state, γ C is the regression coefficient corresponding to the driving state, is the instantaneous speed at time t, is the longitude and latitude coordinates of electric vehicle i at time t, and T is the total time of the response process; Step S6, calculate the final SOC. When the responding electric vehicle arrives at the charging station, the remaining SOC of the responding electric vehicle is the initial SOC minus the energy consumption of the journey from the starting point to the charging station. The specific formula is as follows: In the formula, In response to the remaining SOC of the electric vehicle, is the initial SOC of electric vehicle i, For energy consumption; Step S7, calculating discharge; Step S8, calculating the response duration; Step S9: determining the intention of the electric car owner.

3. The urban distribution network emergency response decision-making method based on multi-agent V2G response according to claim 2 is characterized by: In step S1, the remaining SOC of the responding electric vehicle is calculated to determine whether the electric vehicle has the V2G response capability, as shown in the following formula: In the formula, The qualified SOC level is higher than 60%. In response to the remaining SOC of the electric vehicle.

4. The urban distribution network emergency response decision-making method based on multi-agent V2G response according to claim 2 is characterized by: In step 24, the vehicle following model is used to simulate the interaction of electric vehicles in real driving scenarios. The speed of the electric vehicle is changed to maintain a safe distance from adjacent entities, including other electric vehicles and red lights. The electric vehicle senses the distance to other entities in front. Keep it at a safe distance d buff The comparison is done to determine whether to accelerate, decelerate, or stop. The acceleration and deceleration are set to and Electric car speed Cannot exceed 5. The urban distribution network emergency response decision-making method based on multi-agent V2G response according to claim 2 is characterized by: In step S7, it is assumed that the discharge process is uniform, and the available power of the responsive electric vehicle should satisfy the following formula: In the formula, and are the minimum and maximum discharge power limits of electric vehicles, respectively. When an electric vehicle supplies power to the grid, if the SOC drops to the reserved value At 20% the battery will stop discharging.

6. The urban distribution network emergency response decision-making method based on multi-agent V2G response according to claim 5 is characterized by: The calculation formula of the response duration in step S8 is as follows: in, is the response duration, η dis is the discharge efficiency, is the unit discharge power, and Indicates the V2G travel time and V2G response end time.

7. The urban distribution network emergency response decision-making method based on multi-agent V2G response according to claim 6 is characterized by: In step S9, an improved discrete choice Logit model is used to capture the decision-making process of electric vehicle owners based on their personal attributes, and the V2G response willingness of electric vehicle owners is converted into the following expression: In the formula They represent the benefits, electricity costs, battery degradation costs and opportunity costs of EV i participating in V2G response respectively; π ch is the discharge and charge electricity price, η ch is the charging efficiency, τ represents the opportunity cost coefficient, is the economic benefit coefficient of the car owner, represents the daily travel power consumption of electric vehicle i, α degra The electric vehicle decision process is a rough estimate of the benefits and costs, so the discharge power Replaced by the estimated dispatchable power 8. The urban distribution network emergency response decision-making method based on multi-agent V2G response according to claim 1 is characterized by: The recovery model aims to reduce the weighted load reduction and V2G compensation costs during the post-event recovery period, and the objective function is: In the formula, m f is the load weight, is the load reduction after power supply is restored to node f, C V2G,t compensation to aggregators for EV owners; The constraints of the recovery model include: Active and reactive power balance constraints for each node in the network p b,t and q b,t are the active and reactive node injection power of node b respectively, is the reactive power of the generator at node b, is the load reactive power, B is the set of all nodes, p fb,t and q fb,t For line l fb The active and reactive transmission power, r fb 、x fb represent impedance and reactance respectively, are the active powers of the generator, V2G and load at node b respectively; Node and branch voltage amplitude and branch current limit constraints Where V b,t is the node voltage of node b at time t, V f,t is the voltage of node f at time t; E is the set of all fault lines; and are the two top beam values; I fb,t is the current of the fault line fb at time t; Second-order cone constraints Among them, || ||2 represents the l2 norm, Load shedding constraints In the formula, and are the active and passive load reductions, θ is the constant used for constraint; V2G discharge power constraints: In the formula, is the V2G discharge power; Radial Network Constraints In the formula, l fb The flow direction is determined by the binary variable and Decision, cannot be 1 at the same time, if The flow direction is positive; if The flow direction is negative. If z fb,t =0, then l fb The reference positive direction of the tributary is represented by α and β. When α = 1 and β = 0, the flow direction is from node f to b. slack is the z constraint.

9. The urban distribution network emergency response decision-making method based on multi-agent V2G response according to claim 6 is characterized by: The method further includes constructing an emergency maintenance personnel scheduling model, wherein the constraint conditions for constructing the emergency maintenance personnel scheduling model are: Maintenance space constraints: In the formula, C ERC For the purpose of emergency repair, is a binary variable of the fault line, and the number of emergency maintenance personnel is N ERC , d c,u is the total distance of the repair path, S OD For each emergency repairman’s driving path, if the repair path of emergency repairman k is from fault line c to u, its value is 1 or 0, S F , S O , S D and S E is the set of fault lines, starting points, end points and emergency repair personnel, y c,k is a binary variable indicating whether the fault line has been repaired. If the fault line c has been repaired by the emergency repairman k, its value will be 1. For repair tasks; Describe the driving and repair trajectories of emergency maintenance personnel: In the formula, and are the starting and ending points of the destination pair m, is the node number of the station in the road network. Emergency maintenance personnel k starts from the station and drives in the order of the fault line to complete the repair task and return to the starting point; Restoration time constraints: Emergency maintenance personnel Arriving at fault line c at time, time Time after repair exist Arrives at the next fault line u at time, where h is a positive real number.

Images