Distribution network multi-scenario dispatching method, device, equipment and medium based on travel chain

Abstract

The present invention relates to the field of electric vehicle dispatching technology, and in particular to a multi-scenario dispatching method, device, equipment and medium for a distribution network based on a travel chain. The method includes: obtaining historical movement data of electric vehicles, calculating the state transfer matrix of electric vehicles through historical movement data, and establishing a travel chain behavior model of electric vehicles; performing sampling simulation based on the behavior model, and outputting the spatiotemporal state data of electric vehicles in each time period; receiving distribution network prediction data, and calculating the operating state of the distribution network; determining the constraints of distribution network operation, photovoltaic facilities and electric vehicle clusters, determining the dispatching scenario of electric vehicles and the objective function of optimized dispatching based on the operating state of the distribution network, and establishing an optimized dispatching model; solving the optimized dispatching model to obtain an optimized dispatching method. In the present invention, the interactive potential of electric vehicles is fully utilized, the pressure caused by the charging load of electric vehicles on the distribution network is alleviated, and the safe, stable and reliable operation of the distribution network is ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of electric vehicle scheduling, and in particular to a multi-scenario scheduling method, device, equipment and medium for a distribution network based on a travel chain. Background Art

[0002] In recent years, the number of electric vehicles in my country has continued to rise, and the access of large-scale electric vehicle clusters to the power grid has brought certain challenges to the stability and economy of power system operation. At the same time, relying on a large number of charging piles, electric vehicles, as flexible resources, have huge interactive potential in multiple scenarios such as supporting important loads and promoting the consumption of new energy. How to give full play to the adjustable capabilities of electric vehicles and efficiently dispatch electric vehicles in different scenarios is the current key issue.

[0003] The information disclosed in this background technology section is only intended to deepen the understanding of the overall background technology of the present invention, and should not be regarded as acknowledging or suggesting in any form that the information constitutes the prior art already known to those skilled in the art. Summary of the invention

[0004] The present invention provides a multi-scenario scheduling method, device, equipment and medium for a distribution network based on a travel chain, thereby effectively solving the problems in the background technology.

[0005] In order to achieve the above object, the technical solution adopted by the present invention is: a multi-scenario dispatching method for distribution network based on travel chain, comprising the following steps:

[0006] Acquire historical motion data of the electric vehicle, calculate the state transfer matrix of the electric vehicle through the historical motion data, and thus establish a travel chain behavior model of the electric vehicle;

[0007] Perform sampling simulation based on the electric vehicle travel chain behavior model to output the spatiotemporal state data of the electric vehicle in each time period;

[0008] Receive distribution network forecast data and calculate the operating status of the distribution network;

[0009] Determine the constraints of the distribution network operation and photovoltaic facilities through the operating status of the distribution network, determine the constraints of the electric vehicle cluster through the spatiotemporal state data of the electric vehicles in each time period, determine the scheduling scenario of the electric vehicles and the objective function of the optimized scheduling based on the operating status of the distribution network, and establish an optimized scheduling model;

[0010] The optimization scheduling model is solved to obtain an optimization scheduling method.

[0011] Furthermore, the calculation of the state transfer matrix of the electric vehicle includes:

[0012] Get the electric vehicle's affiliated motion data, with Δ t time interval, dividing a day into T In each time period, the movement status information, stay area information and charging status information of electric vehicle clusters of a set size in the area are counted;

[0013] The calculation formula for calculating the electric vehicle state transfer matrix is ​​as follows:

[0014] ;

[0015] ;

[0016] ;

[0017] In the formula, M t express t The transfer matrix at time, λ Si,Si Indicates that the electric vehicle remains in the stationary state S i The probability of a node, λ Si,SH Indicates that from staying in S i The node state enters the moving state S H The probability of; the elements in the other rows of the matrix have the same meaning except the last row. The elements in the last row represent the movement state S H The probability of going to each node is λ SH,S1 Indicates that the electric car in motion is heading to the node S 1 probability; N i,s,t The node in the current period S i The total number of vehicles staying at the node, N i,m,t is the number of movements in the next time period; N s,t for t The total number of vehicles in motion during the time period; N i,t for t Time period S i The number of vehicles whose node is the destination.

[0018] Furthermore, the spatiotemporal state data of the electric vehicle in each time period includes: motion state information, geographic location information and battery power information of the electric vehicle.

[0019] Furthermore, the sampling simulation is performed based on the electric vehicle travel chain behavior model to output the spatiotemporal state data of the electric vehicle in each time period, including:

[0020] Generate initial data of electric vehicles, including power information and location information;

[0021] Determine the current state of the electric vehicle;

[0022] If the electric vehicle is in a stationary state, judging whether the vehicle will move based on its stationary area information and the state transfer matrix;

[0023] If the electric vehicle is stationary and has no movement, the battery level will be used to determine whether it is low. If the battery level is low, charging will be performed. If the battery level is high, the battery level will be used to determine whether there is a willingness to charge based on the battery level information and the rechargeable status of the stationary point. If there is a willingness to charge, charging will be performed.

[0024] If the electric vehicle is moving or about to move, the destination is determined according to the state transfer matrix, the distance is determined by path planning according to the regional mileage information, and whether the power is sufficient to complete the journey is determined. If the power is sufficient, the corresponding movement time is calculated to complete the journey. Otherwise, the movement is completed after the nearest charging is selected, and the charging time and movement time are calculated;

[0025] When an electric vehicle is charged, the statistics are sampled for charging time, charging duration and charging power, and the real-time status is updated;

[0026] It is determined whether the result generated by the electric vehicle travel chain behavior model meets the set requirements. If so, the loop is terminated; if not, the initial data of the electric vehicle and subsequent steps are repeatedly generated.

[0027] Furthermore, the receiving of distribution network prediction data and calculating the operating status of the distribution network comprises the following steps:

[0028] Calculate photovoltaic output based on light intensity data:

[0029] ;

[0030] Where: R c It is the light intensity of a certain intensity, representing the dividing point between the nonlinear region and the linear region in the photovoltaic output curve, and the unit is kW / m 2 ; is the light intensity at the photovoltaic location. G r pv It is the rated light intensity, which is also the dividing point between the linear area and the constant area of ​​the curve, and the unit is kW / m 2 ; Pr pv is the rated output power of photovoltaic power generation;

[0031] Calculate the failure probability of the overhead lines of the distribution network:

[0032] ;

[0033] Where: represents the failure probability of the ith overhead line; and Respectively represent i The first overhead line j The wire and k The failure probability of each tower is affected by the load generated by external forces and its own strength; n 1,i and n 2,i Respectively represent i The number of conductors and towers on the overhead lines;

[0034] Calculate the failure probability of PV facilities based on wind speed data:

[0035] ;

[0036] Where: is the failure probability of photovoltaic power generation, is the failure probability of photovoltaic under normal conditions; θ pv is the angle between the photovoltaic panel and the wind direction; V is the wind speed at the current location, and They are the lower and upper wind speed limits for photovoltaic panel failure, respectively;

[0037] The Monte Carlo method is used to sample the fault states of photovoltaic, lines and towers in the distribution network. N possible initial fault moments; considering the fault conditions of photovoltaic power generation equipment and lines, calculate the distribution of information entropy of the distribution network system, select the typical scenario corresponding to the entropy value with the largest probability, and judge whether the operation state of the distribution network is normal or emergency. The calculation formula is:

[0038] ;

[0039] ;

[0040] In the formula, H ( X n ) indicates the n System information entropy of each scene;n L , n pv Respectively represent the number of lines and photovoltaics in the system; , Respectively represent i Line, k Photovoltaic t The probability of failure at a given moment; , are 0-1 variables, representing the n In this scenario i Line, k Taiwan Photovoltaic t The fault state at the moment, and when the value is 0, it means that no fault occurs at this moment, and when the value is 1, it means that this moment is the initial fault moment of this component; when T The moment a line fault occurs, an emergency is deemed to exist.

[0041] Furthermore, the constraints of the distribution network operation, photovoltaic facilities and electric vehicle clusters include:

[0042] Output constraints of photovoltaic facilities:

[0043] ;

[0044] In the formula, , Respectively represent nodes j Photovoltaic t Grid-connected active power and reactive power in each time period; Representation Node j Photovoltaic t The upper limit of active power that can be connected to the grid during the time period; Representation Node j Photovoltaic t The upper limit of reactive power that can be connected to the grid during a period;

[0045] The power constraint that the bus node obtains from the upper power grid:

[0046] ;

[0047] In the formula, , Indicates busbar node j exist t Active power and reactive power obtained from the upper layer at all times; 、 Indicates busbar node j The upper limit of active power and reactive power that can be obtained from the upper layer;

[0048] Energy storage charging and discharging constraints:

[0049] ;

[0050] In the formula, It is the active power output by the energy storage at time t. When it is greater than 0, it indicates that the energy storage is discharging, and when it is less than 0, it indicates that the energy storage is charging. and are the charging and discharging power of the energy storage at node j at time t; Indicates the charging and discharging time of distributed energy storage; , are the maximum values ​​of energy storage charging power and discharging power at node j respectively; They are efficiency; is the energy state at time t; , are the minimum and maximum state of charge, respectively. is the rated capacity of energy storage at node j; and are the initial capacity and initial state of charge of energy storage respectively; , They are the charging and discharging states of the energy storage at time t. In a time period, the energy storage can only be in one of the states of charging and discharging, which are represented by 0 and 1;

[0051] Electric vehicle charging station charging and discharging constraints:

[0052] ;

[0053] ;

[0054] In the formula, , express t The period j The charging and discharging power of each charging station; , Respectively represent j Charging stations t The maximum value of charging and discharging power during a period; , The jth charging station is represented by t Moment and t +1 moment of power; Respectively represent nodes j EV charging stations t Active power output during the time period; , Indicates the maximum and minimum power allowed by the jth charging station; Δ t represents the unit step length; η chand η dch Respectively represent the charge and discharge efficiency; D n,t Used to indicate the grid-connected status of electric vehicles. D n,t =1 means grid connection, D n,t =0 means off-grid; , They represent the maximum value of charging and discharging power of a single electric vehicle respectively; S min , S max Indicates the maximum and minimum power allowed for a single electric vehicle; S n,arrive Indicates the amount of electricity when the electric vehicle is connected to the grid. S n,leave It indicates the power of the electric vehicle when it is off-grid, that is, the expected charging power; Indicates charging station j exist t The number of charging stations available during the time period;

[0055] Constraints to ensure the network operates in a radial manner:

[0056] ;

[0057] In the formula, z ij is a 0-1 variable indicating the line connection status. z ij =1, indicating that the line is operating normally; when z ij =0, indicating that the line is disconnected; β ij and β ji All are 0-1 variables, indicating nodes i and nodes j The parent-child relationship of j For Node i When the parent node of β ij =1 and β ji =0; otherwise, β ij =0 and β ji =1; if the node i and nodes j If they are not connected to each other, β ij = β ji =z ij =0; B represents the set of all nodes in the distribution network, B( i ) represents a node i The set of all nearby nodes, B / 1 represents all nodes except node 1;

[0058] Power flow constraints:

[0059] ;

[0060] ;

[0061] ;

[0062] Where: i , j , s Both represent nodes. δ ( j )and π ( j ) represent nodes respectively j The downstream node set and the upstream node set of ; P ij,t and Q ij,t Respectively indicate time periods t Internal Line ij Real-time active power and reactive power flowing upstream; r ij , x ij Line ij Resistance and reactance values; and They represent the upper grid injection t Active power and reactive power in the time period; Respectively represent nodes j EV charging stations t Active power output during the time period; and Indicates the node in normal state j Load in time period t Reactive power demand within and Respectively represent nodes j Load in time period t Active power and reactive power lost within the M 1 (1- z ij,t ) item, indicating that when the line ij When disconnected ij The voltage between them is an infinite value; where:V i,t and V j,t Respectively represent nodes i and nodes j exist t Voltage value of the time period; V 0 Indicates the rated voltage value; M 1 represents a very large constant; z ij,t Indicates line ij exist t The fault status of the time period, L represents the line set; For line ij The maximum transmission capacity.

[0063] Furthermore, the objective function of determining the dispatching scenario of electric vehicles and optimizing the dispatching based on the operating status of the distribution network includes:

[0064] When the scenario is an emergency scenario, the objective function is to minimize the weighted load loss under extreme conditions:

[0065] ;

[0066] In the formula, T last is the duration of the emergency scenario, N B is the total number of system nodes, ω j is the unit load loss cost,

[0067] The charging and discharging constraints of the electric vehicle charging station are changed to:

[0068] ;

[0069] ;

[0070] Receive distribution network forecast data, including distribution network load output data, light intensity data, wind intensity data, line and tower status data, and then calculate the operating status of the distribution network;

[0071] To guide EVs that stay at other nodes to the charging station node, the path planning model aims at the shortest running mileage and uses an integer programming model to represent the planning constraints:

[0072] ;

[0073] ;

[0074] Where:c mn is a Boolean variable. c mn =1 indicates that the EV has passed the path mn ; l mn Represents a connection node in the transportation network m and n The path distance between nodes m and nodes n If there is no directly connected road between them, l mn =∞; N trans Represents a set of transportation network nodes;

[0075] In normal scenarios, multi-objective optimization is performed with the goal of minimizing the peak-to-valley difference and total operating cost within the scheduling cycle. The calculation formula is as follows:

[0076] ;

[0077] In the formula, is the optimization target in normal scenarios. , are the weights corresponding to the peak-to-valley difference and the total operating cost target, f dpv and f cost They are peak-to-valley difference target and total operating cost target; The root node is The active power injected into the area at the time, The average active power injected into the region by the root node during the scheduling period; Time-of-use electricity price; and are the discharge electricity price and charging electricity price of electric vehicles respectively is the scheduling time interval, The scheduling cycle.

[0078] Furthermore, solving the optimization scheduling model includes:

[0079] An auxiliary variable and two inequality constraints are introduced to linearize the absolute value term of the peak-to-valley difference in the objective function, and the original model is converted into a MILP model for solution:

[0080] ;

[0081] In the formula, Auxiliary variables introduced for linearization.

[0082] The present invention also includes a distribution network multi-scenario dispatching device based on a travel chain, using the above method, including:

[0083] A behavior model building unit, used to obtain historical motion data of the electric vehicle, calculate the state transfer matrix of the electric vehicle through the historical motion data, and thus establish a travel chain behavior model of the electric vehicle;

[0084] A sampling simulation unit, used for performing sampling simulation based on the electric vehicle travel chain behavior model, and outputting the spatiotemporal state data of the electric vehicle in each time period;

[0085] An operation status calculation unit, used for receiving distribution network prediction data and calculating the operation status of the distribution network;

[0086] An optimization scheduling model building unit determines the constraints of the distribution network operation and photovoltaic facilities through the operating status of the distribution network, determines the constraints of the electric vehicle cluster through the spatiotemporal state data of the electric vehicles in each time period, determines the scheduling scenario of the electric vehicles and the objective function of the optimization scheduling based on the operating status of the distribution network, and establishes an optimization scheduling model;

[0087] A solving unit is used to solve the optimization scheduling model to obtain an optimization scheduling method.

[0088] The present invention also includes a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the method described above is implemented.

[0089] The present invention also includes a storage medium on which a computer program is stored. When the computer program is executed by a processor, the method described above is implemented.

[0090] The beneficial effects of the present invention are as follows: the present invention calculates the state transfer matrix of electric vehicles through historical motion data, thereby establishing an electric vehicle travel chain behavior model, and based on the sampling simulation of the electric vehicle behavior model, outputs the spatiotemporal state data of the electric vehicle in each time period, determines the scheduling scenario of the electric vehicle and the optimization objective function based on the operating status of the distribution network, and solves the electric vehicle optimization scheduling model, which can optimize the scheduling of electric vehicle clusters in different scenarios, give full play to the interactive potential of electric vehicles, improve the load distribution of the system, alleviate the pressure of the electric vehicle charging load on the distribution network, and ensure the safe, stable and reliable operation of the distribution network. BRIEF DESCRIPTION OF THE DRAWINGS

[0091] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0092] Figure 1 is a flow chart of the method in Example 1;

[0093] Figure 2 It is a structural schematic diagram of the device in Example 1;

[0094] Figure 3 It is a flow chart of a multi-scenario dispatching method for a distribution network based on an electric vehicle travel chain in Example 2;

[0095] Figure 4 This is a sampling simulation flow chart based on the electric vehicle travel chain in Example 2;

[0096] Figure 5 This is a structural diagram of the power supply unit in Example 2;

[0097] Figure 6 This is a schematic diagram of the load situation before and after the electric vehicle dispatch in a normal scenario in Example 2;

[0098] Figure 7 is the active power output diagram of resources such as electric vehicles in an emergency scenario in Example 2;

[0099] Figure 8 A schematic diagram of the structure of a computer device. DETAILED DESCRIPTION

[0100] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0101] Embodiment 1:

[0102] like Figure 1 As shown: A multi-scenario dispatching method for distribution network based on travel chain includes the following steps:

[0103] Obtain the historical movement data of electric vehicles, calculate the state transfer matrix of electric vehicles through the historical movement data, and thus establish the travel chain behavior model of electric vehicles;

[0104] Based on the electric vehicle travel chain behavior model, sampling simulation is performed to output the spatiotemporal state data of electric vehicles in each time period;

[0105] Receive distribution network forecast data and calculate the operating status of the distribution network;

[0106] Determine the constraints of the distribution network operation and photovoltaic facilities through the operating status of the distribution network, determine the constraints of the electric vehicle cluster through the spatiotemporal state data of the electric vehicles in each time period, determine the scheduling scenario of the electric vehicles and the objective function of the optimized scheduling based on the operating status of the distribution network, and establish an optimized scheduling model;

[0107] The optimal scheduling model is solved to obtain the optimal scheduling method.

[0108] The state transfer matrix of electric vehicles is calculated through historical motion data, so as to establish an electric vehicle travel chain behavior model. Based on the sampling simulation of the electric vehicle behavior model, the spatiotemporal state data of the electric vehicle in each time period is output. The scheduling scenario of the electric vehicle and the optimization objective function are determined based on the operating status of the distribution network, and the electric vehicle optimization scheduling model is solved. It can optimize the scheduling of electric vehicle clusters in different scenarios, give full play to the interactive potential of electric vehicles, improve the load distribution of the system, alleviate the pressure of electric vehicle charging load on the distribution network, and ensure the safe, stable and reliable operation of the distribution network.

[0109] The travel chain describes the process of a user starting from a starting point based on the purpose of travel, passing through several destinations in a certain time sequence, and finally arriving at the end point to end the entire trip. It includes variables that are interconnected in time and space. The electric vehicle travel chain is described as: a time-space chain that links the driving and stopping of electric vehicles in the process of travel in the order of their occurrence.

[0110] The travel chain contains two types of variables to describe the running and parking status of electric vehicles at different times. Among them, the variables that describe the spatial transfer of electric vehicles are called spatial variables, including the driving destination and mileage; the variables that describe the changes in the driving and parking time of electric vehicles are called time variables, including the start time of driving, the end time of driving, the driving duration, and the parking duration.

[0111] In this embodiment, calculating the state transfer matrix of the electric vehicle includes:

[0112] Get the electric vehicle's affiliated motion data, with Δ t time interval, dividing a day into T In each time period, the movement status information, stay area information and charging status information of electric vehicle clusters of a set size in the area are counted;

[0113] The calculation formula for calculating the electric vehicle state transfer matrix is ​​as follows:

[0114] ;

[0115] ;

[0116] ;

[0117] In the formula, M t express t The transfer matrix at time, λ Si,Si Indicates that the electric vehicle remains in the stationary state S i The probability of a node, λ Si,SH Indicates that from staying in S i The node state enters the moving state S H The probability of; the elements in the other rows of the matrix have the same meaning except the last row. The elements in the last row represent the movement state S H The probability of going to each node is λ SH,S1 Indicates that the electric car in motion is heading to the node S 1 probability; N i,s,t The node in the current period S i The total number of vehicles staying at the node, N i,m,t is the number of movements in the next time period; N s,t for t The total number of vehicles in motion during the time period; N i,t for t Time period S i The number of vehicles whose node is the destination.

[0118] The spatiotemporal status data of electric vehicles in different time periods include: the movement status information, geographic location information and battery power information of the electric vehicle.

[0119] Among them, sampling simulation is carried out based on the electric vehicle travel chain behavior model, and the spatiotemporal state data of electric vehicles in each period are output, including:

[0120] Generate initial data of electric vehicles, including power information and location information;

[0121] Determine the current state of the electric vehicle;

[0122] If the electric vehicle is in a stationary state, determine whether the vehicle will move based on its stationary area information and the state transfer matrix;

[0123] If the electric vehicle is stationary and has no movement, the battery level will be used to determine whether it is low. If the battery level is low, charging will be performed. If the battery level is high, the battery level will be used to determine whether there is a willingness to charge based on the battery level information and the rechargeable status of the stationary point. If there is a willingness to charge, charging will be performed.

[0124] If the electric vehicle is moving or about to move, its destination is determined according to the state transfer matrix, and the distance is determined through path planning according to the regional mileage information. At the same time, it is determined whether the power is sufficient to complete the journey. If the power is sufficient, the corresponding movement time is calculated to complete the journey. Otherwise, the nearest charging is selected to complete the movement, and the charging time and movement time are calculated;

[0125] When an electric vehicle is charged, the statistics are sampled for charging time, charging duration and charging power, and the real-time status is updated;

[0126] Determine whether the result generated by the electric vehicle travel chain behavior model meets the set requirements. If so, end the loop; if not, repeat the generation of the electric vehicle's initial data and subsequent steps.

[0127] The specific constraints of electric vehicle clusters need to be determined based on the spatiotemporal state data of electric vehicles.

[0128] For example, the upper and lower limits of the charging and discharging capacity of the electric vehicle charging station in the constraint conditions are related to the number of electric vehicles connected to the charging station at this moment, which needs to be determined by counting the spatiotemporal state data of electric vehicles. In addition, the total power of the charging station is also related to the power of the electric vehicles connected and left at the moment, which needs to be determined by the spatiotemporal state data.

[0129] Alternatively, in emergency scenarios, path planning for electric vehicles requires spatiotemporal state data to determine the location of the EV on the transportation network at that moment.

[0130] Receiving distribution network forecast data and calculating the operating status of the distribution network includes the following steps:

[0131] Calculate photovoltaic output based on light intensity data:

[0132] ;

[0133] Where: R c It is the light intensity of a certain intensity, representing the dividing point between the nonlinear region and the linear region in the photovoltaic output curve, and the unit is kW / m 2 ; is the light intensity at the photovoltaic location, G r pvIt is the rated light intensity, which is also the dividing point between the linear area and the constant area of ​​the curve, and the unit is kW / m 2 ; P r pv is the rated output power of photovoltaic power generation;

[0134] Calculate the failure probability of the overhead lines of the distribution network:

[0135] ;

[0136] Where: represents the failure probability of the ith overhead line; and Respectively represent i The first overhead line j The wire and k The failure probability of each tower is affected by the load generated by external forces and its own strength; n 1,i and n 2,i Respectively represent i The number of conductors and towers on the overhead lines;

[0137] Calculate the failure probability of PV facilities based on wind speed data:

[0138] ;

[0139] Where: is the failure probability of photovoltaic power generation, is the failure probability of photovoltaic under normal conditions; θ pv is the angle between the photovoltaic panel and the wind direction; V is the wind speed at the current location, and They are the lower and upper wind speed limits for photovoltaic panel failure, respectively;

[0140] The Monte Carlo method is used to sample the fault states of photovoltaic, lines and towers in the distribution network. N possible initial fault moments; considering the fault conditions of photovoltaic power generation equipment and lines, calculate the distribution of information entropy of the distribution network system, select the typical scenario corresponding to the entropy value with the largest probability, and judge whether the operation state of the distribution network is normal or emergency. The calculation formula is:

[0141] ;

[0142] ;

[0143] In the formula,H ( X n ) indicates the n System information entropy of each scene; n L , n pv Respectively represent the number of lines and photovoltaics in the system; , Respectively represent i Line, k Photovoltaic t The probability of failure at a given moment; , are 0-1 variables, representing the n In this scenario i Line, k Taiwan Photovoltaic t The fault state at the moment, and when the value is 0, it means that no fault occurs at this moment, and when the value is 1, it means that this moment is the initial fault moment of this component; when T The moment a line fault occurs, an emergency is deemed to exist.

[0144] In this embodiment, the constraints of the distribution network operation, photovoltaic facilities and electric vehicle clusters include:

[0145] Output constraints of photovoltaic facilities:

[0146] ;

[0147] In the formula, , Respectively represent nodes j Photovoltaic t Grid-connected active power and reactive power in each time period; Representation Node j Photovoltaic t The upper limit of active power that can be connected to the grid during the time period; Representation Node j Photovoltaic t The upper limit of reactive power that can be connected to the grid during a period;

[0148] The power constraint that the bus node obtains from the upper power grid:

[0149] ;

[0150] In the formula, , Indicates busbar node j exist t Active power and reactive power obtained from the upper layer at all times; 、 Indicates busbar node j The upper limit of active power and reactive power that can be obtained from the upper layer;

[0151] Energy storage charging and discharging constraints:

[0152] ;

[0153] In the formula, It is the active power output by the energy storage at time t. When it is greater than 0, it indicates that the energy storage is discharging, and when it is less than 0, it indicates that the energy storage is charging. and are the charging and discharging power of the energy storage at node j at time t; Indicates the charging and discharging time of distributed energy storage; , are the maximum values ​​of energy storage charging power and discharging power at node j respectively; They are efficiency; is the energy state at time t; , are the minimum and maximum state of charge, respectively. is the rated capacity of energy storage at node j; and are the initial capacity and initial state of charge of energy storage respectively; , They are the charging and discharging states of the energy storage at time t. In a time period, the energy storage can only be in one of the states of charging and discharging, which are represented by 0 and 1;

[0154] Electric vehicle charging station charging and discharging constraints:

[0155] ;

[0156] ;

[0157] In the formula, , express t The period j The charging and discharging power of each charging station; , Respectively represent j Charging stations t The maximum value of charging and discharging power during a period; , The jth charging station is represented by t Moment and t +1 moment of power; Respectively represent nodes j EV charging stations t Active power output during the time period; , Indicates the maximum and minimum power allowed by the jth charging station; Δ t represents the unit step length; η ch and η dch Respectively represent the charge and discharge efficiency; D n,t Used to indicate the grid-connected status of electric vehicles. D n,t =1 means grid connection, D n,t =0 means off-grid; , They represent the maximum value of charging and discharging power of a single electric vehicle respectively; S min , S max Indicates the maximum and minimum power allowed for a single electric vehicle; S n,arrive Indicates the amount of electricity when the electric vehicle is connected to the grid. S n,leave It indicates the power of the electric vehicle when it is off-grid, that is, the expected charging power; Indicates charging station j exist t The number of charging stations available during the time period;

[0158] Constraints to ensure the network operates in a radial manner:

[0159] ;

[0160] In the formula, z ij is a 0-1 variable indicating the line connection status. z ij =1, indicating that the line is operating normally; when z ij =0, indicating that the line is disconnected; β ij and β ji All are 0-1 variables, indicating nodes i and nodes j The parent-child relationship of j For Node i When the parent node of β ij =1 and β ji =0; otherwise, β ij =0 and β ji =1; if the node i and nodesj If they are not connected to each other, β ij = β ji = z ij =0; B represents the set of all nodes in the distribution network, B( i ) represents a node i The set of all nearby nodes, B / 1 represents all nodes except node 1;

[0161] Power flow constraints:

[0162] ;

[0163] ;

[0164] ;

[0165] Where: i , j , s Both represent nodes. δ ( j )and π ( j ) represent nodes respectively j The downstream node set and the upstream node set of ; P ij,t and Q ij,t Respectively indicate time periods t Internal Line ij Real-time active power and reactive power flowing upstream; r ij , x ij Line ij Resistance and reactance values; and They represent the upper grid injection t Active power and reactive power in the time period; Respectively represent nodes j EV charging stations t Active power output during the time period; and Indicates the node in normal state j Load in time period t Reactive power demand within and Respectively represent nodes j Load in time period t Active power and reactive power lost within the M 1 (1-z ij,t ) item, indicating that when the line ij When disconnected ij The voltage between them is an infinite value; where: V i,t and V j,t Respectively represent nodes i and nodes j exist t Voltage value of the time period; V 0 Indicates the rated voltage value; M 1 represents a very large constant; z ij,t Indicates line ij exist t The fault status of the time period, L represents the line set; For line ij The maximum transmission capacity.

[0166] As a preferred embodiment of the above embodiment, the dispatching scenario of electric vehicles and the objective function of optimizing the dispatching are determined based on the operating status of the distribution network, including:

[0167] When the scenario is an emergency scenario, the objective function is to minimize the weighted load loss under extreme conditions:

[0168] ;

[0169] In the formula, T last is the duration of the emergency scenario, N B is the total number of system nodes, ω j is the unit load loss cost,

[0170] The charging and discharging constraints of the electric vehicle charging station are changed to:

[0171] ;

[0172] ;

[0173] Receive distribution network forecast data, including distribution network load output data, light intensity data, wind intensity data, line and tower status data, and then calculate the operating status of the distribution network;

[0174] To guide EVs that stay at other nodes to the charging station node, the path planning model aims at the shortest running mileage and uses an integer programming model to represent the planning constraints:

[0175] ;

[0176] ;

[0177] Where: c mn is a Boolean variable. c mn =1 indicates that the EV has passed the path mn ; l mn Represents a connection node in the transportation network m and n The path distance between nodes m and nodes n If there is no directly connected road between them, l mn =∞; N trans Represents a set of transportation network nodes;

[0178] In normal scenarios, multi-objective optimization is performed with the goal of minimizing the peak-to-valley difference and total operating cost within the scheduling cycle. The calculation formula is as follows:

[0179] ;

[0180] In the formula, is the optimization target in normal scenarios. , are the weights corresponding to the peak-to-valley difference and the total operating cost target, f dpv and f cost They are peak-to-valley difference target and total operating cost target; The root node is The active power injected into the area at the time, The average active power injected into the region by the root node during the scheduling period; Time-of-use electricity price; and are the discharge electricity price and charging electricity price of electric vehicles respectively is the scheduling time interval, The scheduling cycle.

[0181] Solve the optimization scheduling model, including:

[0182] An auxiliary variable and two inequality constraints are introduced to linearize the absolute value term of the peak-to-valley difference in the objective function, and the original model is converted into a MILP model for solution:

[0183] ;

[0184] In the formula, Auxiliary variables introduced for linearization.

[0185] like Figure 2 As shown, this embodiment also includes a distribution network multi-scenario scheduling device based on a travel chain, using the above method, including:

[0186] A behavior model building unit is used to obtain the historical motion data of the electric vehicle, calculate the state transfer matrix of the electric vehicle through the historical motion data, and thus establish a travel chain behavior model of the electric vehicle;

[0187] A sampling simulation unit is used to perform sampling simulation based on the behavior model, and output the spatiotemporal state data of the electric vehicle in each time period;

[0188] An operation status calculation unit, used for receiving distribution network prediction data and calculating the operation status of the distribution network;

[0189] An optimization scheduling model building unit determines the constraints of the distribution network operation and photovoltaic facilities through the operating status of the distribution network, determines the constraints of the electric vehicle cluster through the spatiotemporal state data of the electric vehicles in each time period, determines the scheduling scenario of the electric vehicles and the objective function of the optimization scheduling based on the operating status of the distribution network, and establishes an optimization scheduling model;

[0190] The solving unit is used to solve the optimization scheduling model to obtain the optimization scheduling method.

[0191] Embodiment 2:

[0192] like Figure 3 As shown, a multi-scenario dispatching method for a distribution network based on an electric vehicle travel chain comprises the following steps:

[0193] Step S1: Collect historical movement data of electric vehicles, use the historical data to calculate the electric vehicle state transfer matrix, and then establish an electric vehicle travel chain behavior model;

[0194] Step S2: performing sampling simulation based on the electric vehicle behavior model, and outputting the spatiotemporal state data of the electric vehicle in each time period, wherein the spatiotemporal state data includes the motion state information, geographic location information, and battery power information of the electric vehicle;

[0195] Step S3: receiving distribution network prediction data, including distribution network load output data, light intensity data, wind intensity data, line and tower status data, and then calculating the operating status of the distribution network;

[0196] Step S4: determining the constraints of the distribution network operation, photovoltaic facilities and electric vehicle clusters;

[0197] Step S5: Determine the dispatching scenario of the electric vehicle and the optimization objective function based on the operating status of the distribution network, wherein different scenario modes include normal scenarios and emergency scenarios, and solve the electric vehicle optimization dispatching model.

[0198] In the above steps, step S1 specifically includes the following steps:

[0199] S11: Collect historical movement data of electric vehicles, using Δ t time interval, dividing a day into T In each time period, the movement status information, stay area information (including movement, stay) and charging status information of electric vehicle clusters of a certain size in the area are counted;

[0200] S12: Using the historical movement data of the electric vehicle, the calculation formula for calculating the state transfer of the electric vehicle is as follows:

[0201] ;

[0202] ;

[0203] ;

[0204] in, M t express t The transfer matrix at time, λ Si,Si Indicates that the electric vehicle remains in the stationary state S i The probability of a node, λ Si,SH Indicates that from staying in S i The node state enters the moving state S H The probability of. The elements in the other rows of the matrix have the same meaning except the last row. The elements in the last row represent the movement state. S H The probability of going to each node is λ SH,S1 Indicates that the electric car in motion is heading to the node S 1 probability. N i,s,t The node in the current period S i The total number of vehicles staying at the node, N i,m,t is the number of movements in the next time period; N s,t for t The total number of vehicles in motion during the time period; Ni,t for t Time period S i The number of vehicles whose node is the destination.

[0205] like Figure 4 As shown, step S2 specifically includes the following steps:

[0206] S21: Generate initial data of the electric vehicle, including power information and location information.

[0207] S22: Determine the current state of the electric vehicle, and determine whether the vehicle is in a stationary state. If it is not in a stationary state, enter the next period, otherwise go to step S23;

[0208] S23: determining whether the electric vehicle is in motion;

[0209] For electric vehicles in a stationary state, according to their stationary area information, the state transfer matrix M t Determine whether the vehicle will move, if so, go to step S24, otherwise go to step S25;

[0210] Step S24: If the electric vehicle moves, determine M t Determine the destination, determine the route through route planning based on the regional mileage information, and determine whether the battery is sufficient to complete the journey. If the battery is sufficient, calculate the corresponding movement time and complete the journey. Otherwise, choose the nearest location to charge and then complete the movement, and calculate the charging time and movement time;

[0211] Step S25: If the electric vehicle is in a stationary state and has no movement, determine whether the electric vehicle is low in power according to the power level of the electric vehicle. Assuming that the low-power EV will definitely participate in charging, the high-power EV will determine whether it is willing to charge according to the power information and the rechargeable status of the stationary point. If it is willing to charge, go to step S26, otherwise go to step S27;

[0212] Step S26: If the electric vehicle is involved in charging, the charging time, charging duration and other information are sampled according to the statistical data, and the real-time status is updated;

[0213] Step S27: Determine whether the result generated by the model meets the set requirements. If so, end the loop; otherwise, return to step S22.

[0214] Step S3: receiving distribution network prediction data, including distribution network load output data, light intensity data, wind intensity data, line and tower status data, and then calculating the operating status of the distribution network;

[0215] In the above steps, step S3 specifically includes the following steps:

[0216] S31: Calculate photovoltaic output based on light intensity data:

[0217] ;

[0218] R c It is the light intensity of a certain intensity, representing the dividing point between the nonlinear region and the linear region in the photovoltaic output curve, and the unit is kW / m 2 ; is the light intensity at the photovoltaic location, G r pv It is the rated light intensity, which is also the dividing point between the linear area and the constant area of ​​the curve, and the unit is kW / m 2 ; P r pv is the rated output power of photovoltaic power generation.

[0219] S32: Calculating the failure probability of the overhead line of the distribution network;

[0220]

[0221] in: represents the failure probability of the ith overhead line; and represent the first j The wire and k The failure probability of a tower is affected by the load generated by external forces such as wind and its own strength. n 1,i and n 2,i They represent the number of conductors and towers on the i-th overhead line respectively.

[0222] S33: Calculate the failure probability of photovoltaic facilities based on wind speed data:

[0223]

[0224] in: is the failure probability of photovoltaic power generation, is the failure probability of photovoltaic under normal conditions; θ pv is the angle between the photovoltaic panel and the wind direction; V is the wind speed at the current location, and They are the lower and upper wind speed limits for photovoltaic panel failure.

[0225] S34: Monte Carlo method is used to sample the fault status of photovoltaic, lines and towers in the distribution network, and N possible initial fault moments are extracted respectively. Considering the fault conditions of photovoltaic power generation equipment and lines, the distribution of information entropy of the distribution network system is calculated, and the typical scenario corresponding to the entropy value with the highest probability of occurrence is selected to determine whether the operation status of the distribution network is normal or emergency. The calculation formula is:

[0226] ;

[0227] ;

[0228] in, H(X n ) Indicates n System information entropy of each scene; n L , n pv Indicates the number of lines and photovoltaics in the system; , Respectively represent i Line, k The failure probability of a photovoltaic unit at time t; , are 0-1 variables, representing the n In this scenario i Line, k Taiwan Photovoltaic t The fault state at the moment, and when the value is 0, it means that no fault occurs at this moment, and when the value is 1, it means that this moment is the initial fault moment of this component. T The moment a line fault occurs, an emergency is deemed to exist.

[0229] In the above steps, step S4 specifically includes the following steps:

[0230] S41: Describe the output of the photovoltaic facility:

[0231] ;

[0232] in, , Respectively represent nodes j Photovoltaic t Grid-connected active power and reactive power in each time period; represents the upper limit of the grid-connected active power of the PV on node j in period t; represents the upper limit of the reactive power that can be connected to the grid of the PV on node j in period t;

[0233] S42: Describe the power constraints that the bus node obtains from the upper power grid:

[0234] ;

[0235] in, , Indicates busbar node j The active power and reactive power obtained from the upper layer at time t; 、 Indicates busbar node j The upper limit of active power and reactive power that can be obtained from the upper layer;

[0236] S43: Describe the charging and discharging constraints of energy storage:

[0237] ;

[0238] in, It is the active power output by the energy storage at time t. When it is greater than 0, it indicates that the energy storage is discharging, and when it is less than 0, it indicates that the energy storage is charging. and are the charging and discharging power of the energy storage at node j at time t; Indicates the charging and discharging time of distributed energy storage; , are the maximum values ​​of energy storage charging power and discharging power at node j respectively; They are efficiency; is the energy state at time t; , are the minimum and maximum state of charge, respectively. is the rated capacity of energy storage at node j; and are the initial capacity and initial state of charge of energy storage respectively; , They are the charging and discharging states of the energy storage at time t. In a time period, the energy storage can only be in one of the states of charging and discharging, which are represented by 0 and 1.

[0239] S44: Based on the spatiotemporal information of the electric vehicle cluster obtained by sampling, the charging and discharging constraints of the electric vehicle charging station are described:

[0240]

[0241]

[0242] in, , express t The period j The charging and discharging power of each charging station; , Respectively represent j Charging stations t The maximum value of charging and discharging power during a period; , The jth charging station is represented by t Moment and t +1 moment of power; Respectively represent nodes j EV charging stations t Active power output during the time period; , Indicates the maximum and minimum power allowed by the jth charging station; Δ t represents the unit step length; η ch and η dch Respectively represent the charge and discharge efficiency; D n,t Used to indicate the grid-connected status of electric vehicles. D n,t =1 means grid connection, D n,t =0 means off-grid; , They represent the maximum value of charging and discharging power of a single electric vehicle respectively; S min , S max Indicates the maximum and minimum power allowed for a single electric vehicle; S n,arrive Indicates the amount of electricity when the electric vehicle is connected to the grid. S n,leave It indicates the power of the electric vehicle when it is off-grid, that is, the expected charging power; Indicates charging station j exist t The number of charging stations available during the time period.

[0243] S45: Based on graph theory, we build constraints to ensure that the network runs in a radial manner:

[0244] ;

[0245] in, z ij is a 0-1 variable indicating the line connection status. z ij =1, indicating that the line is operating normally; when z ij =0, indicating that the line is disconnected. β ij and βji All are 0-1 variables, indicating nodes i and nodes j The parent-child relationship of j For Node i When the parent node of β ij =1 and β ji =0; otherwise, β ij =0 and β ji =1; if the node i and nodes j If they are not connected to each other, β ij = β ji = z ij = 0. B represents the set of all nodes in the distribution network, B( i ) represents a node i The set of all nearby nodes, B / 1 represents all nodes except node 1.

[0246] S46: The linear power flow model of the distribution network obtained after approximate processing is used to process the power flow constraints:

[0247] ;

[0248] ;

[0249] ;

[0250] in: i , j , s Both represent nodes. δ ( j )and π ( j ) represent nodes respectively j The downstream node set and the upstream node set; P ij,t and Q ij,t Respectively indicate time periods t Internal Line ij Real-time active power and reactive power flowing upstream; r ij , x ij Line ij Resistance and reactance values; and They represent the upper grid injection tActive power and reactive power in the time period; Respectively represent nodes j EV charging stations t Active power output during the time period; and Indicates the node in normal state j Load in time period t Reactive power demand within and Respectively represent nodes j Load in time period t The active power and reactive power lost in the M 1 (1- z ij,t ) item, indicating that when the line ij When disconnected ij The voltage between them is an infinite value. Among them: V i,t and V j,t Respectively represent nodes i and nodes j exist t Voltage value of the time period; V 0 Indicates the rated voltage value; M 1 Represents a very large constant. z ij,t Indicates line ij exist t The fault status of the time period, L represents the line set. For line ij The maximum transmission capacity.

[0251] In the above steps, step S5 specifically includes the following steps:

[0252] S51: According to the scenario determined in step S3, the scenario includes an emergency scenario and a normal scenario, and the corresponding objective function of electric vehicle scheduling is determined.

[0253] S52: When the scenario is an emergency scenario, the objective function is to minimize the weighted load loss under extreme conditions. The calculation formula is as follows:

[0254] ;

[0255] in, T last is the duration of the emergency scenario, N B is the total number of system nodes, ω jis the unit load loss cost, and the unit loss cost of important loads is higher.

[0256] In an emergency scenario, the electric vehicle cluster will stop charging. Each charging station will urgently requisition the EVs staying at the charging station node for distribution network load recovery. Therefore, the charging and discharging constraints of the electric vehicle charging station represented by equations (12) and (13) are changed to:

[0257] ;

[0258] ;

[0259] To guide EVs that stay at other nodes to the charging station node, the path planning model aims at the shortest running mileage and uses an integer programming model to represent the planning constraints:

[0260] ;

[0261] ;

[0262] in: c mn is a Boolean variable. c mn =1 indicates that the EV has passed the path mn . l mn Represents a connection node in the transportation network m and n The path distance between nodes m and nodes n If there is no directly connected road between them, l mn =∞. N trans Represents a collection of transportation network nodes.

[0263] S53: In normal scenarios, multi-objective optimization is performed with the goal of minimizing the peak-to-valley difference and total operating cost within the scheduling cycle. The calculation formula is as follows:

[0264] ;

[0265] in, is the optimization target in normal scenarios. , are the weights corresponding to the peak-to-valley difference and the total operating cost target, f dpv and f cost They are peak-to-valley difference target and total operating cost target; is the active power injected into the area by the root node at time, The average active power injected into the region by the root node during the scheduling period; Time-of-use electricity price; and are the discharge electricity price and charging electricity price of electric vehicles respectively is the scheduling time interval, The scheduling cycle.

[0266] By introducing an auxiliary variable and two inequality constraints, the absolute value term of the peak-to-valley difference in the objective function is linearized, and the original model is converted into a MILP model for solution:

[0267] ;

[0268] in, Auxiliary variables introduced for linearization.

[0269] In the embodiment of the present invention, an actual power supply unit in Jiangsu is used to simulate the distribution network structure. Figure 5 The access locations of the three photovoltaic facilities are nodes 8, 12 and 18, and the rated power is 1MW; the access locations of the two energy storages are 22 and 23, with a rated capacity of 1WMh and a rated power of 500kW; the access locations of the four EV charging stations are nodes 9, 15, 21 and 25; the simulation results of the embodiment of the present invention are explained:

[0270] Depend on Figure 6 It can be seen that, in normal scenarios, after using the method provided by the present invention to dispatch power in the distribution network, the peak load of the system is effectively reduced by controlling the charging and discharging of the electric vehicle cluster, from 11.72MW to 7.69MW, which improves the load distribution of the system and alleviates the pressure of the electric vehicle charging load on the distribution network. Figure 7 It can be seen that in emergency scenarios, the discharge power of energy storage gradually decreases due to its own capacity limitation, and the photovoltaic output is affected by the light intensity and its own fault state. Using the method provided by the present invention to control the discharge of electric vehicles can provide more abundant electricity for the distribution network in emergency scenarios. Therefore, the multi-scenario scheduling method for distribution networks based on electric vehicle travel chains proposed in the present invention can promote the safe and stable operation of distribution networks and has practical application value.

[0271] See also Figure 8 A computer device 400 provided in an embodiment of the present application includes: a processor 410 and a memory 420, wherein the memory 420 stores a computer program executable by the processor 410, and when the computer program is executed by the processor 410, the above method is executed.

[0272] The embodiment of the present application further provides a storage medium 430 on which a computer program is stored. When the computer program is run by the processor 410, the above method is executed.

[0273] Among them, the storage medium 430 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable red-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, disk or optical disk.

[0274] In the description of the present invention, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. "Multiple" means two or more, unless otherwise clearly and specifically defined.

[0275] In the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", "connected", "fixed" and the like should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, it can be the internal connection of two elements or the interaction relationship between two elements. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0276] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, unless they are contradictory.

[0277] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code that includes one or more executable instructions for implementing the steps of a specific logical function or process, and the scope of the preferred embodiments of the present invention includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present invention belong.

[0278] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples (non-exhaustive list) of computer-readable media include the following: an electrical connection with one or more wires (electronic device), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or processing in other suitable ways if necessary, and then stored in a computer memory.

[0279] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0280] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0281] The storage medium mentioned above may be a read-only memory, a magnetic disk or an optical disk, etc. Although the embodiments of the present invention have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limiting the present invention. A person of ordinary skill in the art may change, modify, replace and modify the above embodiments within the scope of the present invention.

Claims

1. A multi-scenario dispatching method for distribution network based on travel chain, It is characterized in that The steps include: Acquire historical motion data of the electric vehicle, calculate the state transfer matrix of the electric vehicle through the historical motion data, and thus establish a travel chain behavior model of the electric vehicle; Perform sampling simulation based on the electric vehicle travel chain behavior model to output the spatiotemporal state data of the electric vehicle in each time period; Receive distribution network forecast data and calculate the operating status of the distribution network; Determine the constraints of the distribution network operation and photovoltaic facilities through the operating status of the distribution network, determine the constraints of the electric vehicle cluster through the spatiotemporal state data of the electric vehicles in each time period, determine the scheduling scenario of the electric vehicles and the objective function of the optimized scheduling based on the operating status of the distribution network, and establish an optimized scheduling model; Solving the optimization scheduling model to obtain an optimization scheduling method; The constraints on the operation of distribution networks, PV facilities and electric vehicle clusters include: Output constraints of photovoltaic facilities: In the formula, They represent the grid-connected active power and reactive power of the PV on node j in period t respectively; represents the upper limit of the active power that can be connected to the grid at the PV plant on node j during period t; represents the upper limit of the reactive power that can be connected to the grid of the PV on node j in period t; The power constraint that the bus node obtains from the upper power grid: In the formula, represents the active power and reactive power obtained by bus node j from the upper layer at time t; Indicates the upper limit of active power and reactive power that bus node j can obtain from the upper layer; Energy storage charging and discharging constraints: In the formula, is the active power output by the energy storage at time t. When it is greater than 0, it indicates that the energy storage is discharging, and when it is less than 0, it indicates that the energy storage is charging; and are the charging and discharging power of the energy storage at node j at time t; Δt represents the charging and discharging time of the distributed energy storage; are the maximum values ​​of energy storage charging power and discharging power at node j respectively; They are efficiency; is the energy state at time t; S SOC，min , S SOC，max are the minimum and maximum state of charge, respectively. is the rated capacity of energy storage at node j; and are the initial capacity and initial state of charge of energy storage respectively; They are the charging and discharging states of the energy storage at time t. In a time period, the energy storage can only be in one of the states of charging and discharging, which are represented by 0 and 1; Electric vehicle charging station charging and discharging constraints: In the formula, represents the charging and discharging power of the jth charging station during period t; They represent the maximum value of charging and discharging power of the jth charging station in time period t; S j，t , S j，t+1 The jth charging station represents the power at time t and time t+1 respectively; They represent the active power output of the EV charging station at node j during period t; S j，t，min , S j，t，max represents the maximum and minimum power allowed by the jth charging station; Δt represents the unit step length; η ch and η dch Respectively represent the charge and discharge efficiency; D n,t Used to indicate the grid-connected state of electric vehicles. n,t =1 means grid connection, D n,t =0 means off-grid; They represent the maximum value of the charging and discharging power of a single electric vehicle; S min , S max Indicates the maximum and minimum power allowed for a single electric vehicle; S n,arrive Indicates the amount of electricity when the electric vehicle is connected to the grid, S n,leave It indicates the power of the electric vehicle when it is off-grid, that is, the expected charging power; represents the number of charging piles available at charging station j during time period t; Constraints to ensure the network operates in a radial manner: In the formula, z ij is a 0-1 variable indicating the line connection status. ij =1, it indicates that the line is operating normally; when z ij =0, indicating that the line is disconnected; β ij and β ji are all 0-1 variables, indicating the parent-child relationship between node i and node j. When node j is the parent node of node i, β ij =1 and β ji =0; otherwise, β ij = 0 and β ji =1; if nodes i and j are not connected to each other, then β ij =β ji =z ij =0; B represents the set of all nodes in the distribution network, B(i) represents the set of all nodes near node i, and B / 1 represents all nodes except node 1; Power flow constraints: Where: i, j, s all represent nodes, δ(j) and π(j) represent the downstream node set and upstream node set of node j respectively; P ij,t and Q ij,t They represent the real-time active power and reactive power flowing through line ij in time period t respectively; r ij 、x ij are the resistance and reactance of line ij respectively; and They represent the active power and reactive power injected by the upper power grid in period t respectively; They represent the active power output by the EV charging station at node j during period t; and represents the reactive power demand of the load at node j in time period t under normal conditions; and Respectively represent the active power and reactive power lost by the load at node j in time period t; where M 1 (1-z ij,t ) term, indicating that when line ij is disconnected, the voltage between ij is an infinite value; where: V i,t and V j,t Respectively represent the voltage values ​​of node i and node j in time period t; V 0 Indicates the rated voltage value; M 1 represents a very large constant; z ij,t represents the fault status of line ij in period t, and L represents the line set; is the maximum transmission capacity of line ij; The objective function of determining the dispatching scenario of electric vehicles and optimizing the dispatching based on the operating status of the distribution network includes: When the scenario is an emergency scenario, the objective function is to minimize the weighted load loss under extreme conditions: Where, T last is the duration of the emergency scenario, N B is the total number of system nodes, ω j is the unit load loss cost, The charging and discharging constraints of the electric vehicle charging station are changed to: Receive distribution network forecast data, including distribution network load output data, light intensity data, wind intensity data, line and tower status data, and then calculate the operating status of the distribution network; To guide EVs that stay at other nodes to the charging station node, the path planning model aims at the shortest running mileage and uses an integer programming model to represent the planning constraints: Where: c mn is a Boolean variable. mn =1 indicates that the EV passes through path mn; l mn Represents the path distance between nodes m and n in the traffic network. If there is no direct road between nodes m and n, then l is taken. mn =∞; N trans Represents a set of transportation network nodes; In normal scenarios, multi-objective optimization is performed with the goal of minimizing the peak-to-valley difference and total operating cost within the scheduling cycle. The calculation formula is as follows: In the formula, F norm is the optimization target in normal scenarios, ω dpv ,ω cost is the weight corresponding to the peak-to-valley difference and the total operating cost target, f dpv and f cost They are peak-to-valley difference target and total operating cost target; is the active power of the root node injected into the area at time t, P 1 gain，av The average active power injected into the region by the root node during the scheduling period; Time-of-use electricity price; and are the discharge electricity price and charging electricity price of electric vehicles respectively, Δt is the scheduling time interval, and T is the scheduling period.

2. According to claim 1, the multi-scenario dispatching method for distribution network based on travel chain, It is characterized in that The calculating of the state transfer matrix of the electric vehicle comprises: Obtain the movement data of electric vehicles, divide a day into T time periods at a time interval of Δt, and count the movement status information, stay area information, and charging status information of electric vehicle clusters of a set size in the area; The calculation formula for calculating the electric vehicle state transfer matrix is ​​as follows: Where M t represents the transfer matrix at time t, λ Si,Si Indicates that the electric vehicle remains in the state of S i The probability of a node, λ Si,SH Indicates that from staying at S i The node state enters the motion state S H The probability of the other rows in the matrix, except for the last row, have the same meaning. The elements in the last row represent the probability of the motion state S H The probability of going to each node, where λ SH,S1 Indicates that the electric car in motion is heading to node S 1 The probability of i,s,t is the node S in the current period i The total number of vehicles staying at the node, N i,m,t is the number of movements in the next time period; N s,t N is the total number of vehicles in motion during period t; i,t S is the time period t i The number of vehicles whose node is the destination.

3. According to claim 1, the multi-scenario dispatching method for distribution network based on travel chain, It is characterized in that The spatiotemporal state data of the electric vehicle in each time period includes: motion state information, geographic location information and battery power information of the electric vehicle.

4. According to claim 3, the multi-scenario dispatching method for distribution network based on travel chain, It is characterized in that The sampling simulation is performed based on the electric vehicle travel chain behavior model to output the spatiotemporal state data of the electric vehicle in each time period, including: Generate initial data of electric vehicles, including power information and location information; Determine the current state of the electric vehicle; If the electric vehicle is in a stationary state, judging whether the vehicle will move based on its stationary area information and the state transfer matrix; If the electric vehicle is stationary and has no movement, the battery level will be used to determine whether it is low. If the battery level is low, charging will be performed. If the battery level is high, the battery level will be used to determine whether there is a willingness to charge based on the battery level information and the rechargeable status of the stationary point. If there is a willingness to charge, charging will be performed. If the electric vehicle is moving or about to move, the destination is determined according to the state transfer matrix, the distance is determined by path planning according to the regional mileage information, and whether the power is sufficient to complete the journey is determined. If the power is sufficient, the corresponding movement time is calculated to complete the journey. Otherwise, the movement is completed after the nearest charging is selected, and the charging time and movement time are calculated; When an electric vehicle is charged, the statistics are sampled for charging time, charging duration and charging power, and the real-time status is updated; It is determined whether the result generated by the electric vehicle travel chain behavior model meets the set requirements. If so, the loop is terminated; if not, the initial data of the electric vehicle and subsequent steps are repeatedly generated.

5. According to claim 1, the multi-scenario dispatching method for distribution network based on travel chain, It is characterized in that The receiving of distribution network prediction data and calculating the operating status of the distribution network comprises the following steps: Calculate photovoltaic output based on light intensity data: Where: R c It is the light intensity of a certain intensity, representing the dividing point between the nonlinear region and the linear region in the photovoltaic output curve, and the unit is kW / m 2 ; G pv is the light intensity at the photovoltaic position; It is the rated light intensity, which is also the dividing point between the linear area and the constant area of ​​the curve, and the unit is kW / m 2 ; is the rated output power of photovoltaic power generation; Calculate the failure probability of the overhead lines of the distribution network: Where: represents the failure probability of the ith overhead line; and They represent the failure probability of the jth conductor and the kth tower on the i-th overhead line, respectively. The failure probability is affected by the load generated by the external force and its own strength; n 1,i and n 2,i Respectively represent the number of conductors and towers on the i-th overhead line; Calculate the failure probability of PV facilities based on wind speed data: Where: is the failure probability of photovoltaic power generation, is the failure probability of photovoltaic under normal conditions; θ pv is the angle between the photovoltaic panel and the wind direction; V is the wind speed at the current location, and They are the lower and upper wind speed limits for photovoltaic panel failure, respectively; The Monte Carlo method is used to sample the fault states of photovoltaics, lines and towers in the distribution network, and N possible initial fault moments are extracted respectively; considering the fault conditions of photovoltaic power generation equipment and lines, the distribution of information entropy of the distribution network system is calculated, and the typical scenario corresponding to the entropy value with the largest probability of occurrence is selected to determine whether the operation state of the distribution network is normal or emergency. The calculation formula is: In the formula, H(X n ) represents the system information entropy of the nth scene; n L 、n pv Respectively represent the number of lines and photovoltaics in the system; They represent the failure probability of the i-th line and the k-th photovoltaic unit at time t respectively; They are all 0-1 variables, representing the fault status of the i-th line and the k-th photovoltaic unit at time t in the n-th scenario. When the value is 0, it means that no fault occurs at that moment, and when the value is 1, it means that the moment is the initial fault moment of this component. When there is a line fault within T, it is considered that an emergency state exists.

6. The multi-scenario dispatching method for distribution network based on travel chain according to claim 1, It is characterized in that The solving of the optimization scheduling model includes: An auxiliary variable and two inequality constraints are introduced to linearize the absolute value term of the peak-to-valley difference in the objective function, and the original model is converted into a MILP model for solution: In the formula, Auxiliary variables introduced for linearization.

7. A multi-scenario dispatching device for distribution network based on travel chain, It is characterized in that Use of the method according to any one of claims 1 to 6, comprising: A behavior model building unit, used to obtain historical motion data of the electric vehicle, calculate the state transfer matrix of the electric vehicle through the historical motion data, and thus establish a travel chain behavior model of the electric vehicle; A sampling simulation unit, used for performing sampling simulation based on the electric vehicle travel chain behavior model, and outputting the spatiotemporal state data of the electric vehicle in each time period; An operation status calculation unit, used for receiving distribution network prediction data and calculating the operation status of the distribution network; An optimization scheduling model building unit determines the constraints of the distribution network operation and photovoltaic facilities through the operating status of the distribution network, determines the constraints of the electric vehicle cluster through the spatiotemporal state data of the electric vehicles in each time period, determines the scheduling scenario of the electric vehicles and the objective function of the optimization scheduling based on the operating status of the distribution network, and establishes an optimization scheduling model; A solving unit is used to solve the optimization scheduling model to obtain an optimization scheduling method.

8. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, It is characterized in that When the processor executes the computer program, the method according to any one of claims 1 to 6 is implemented.

9. A storage medium having a computer program stored thereon, It is characterized in that When the computer program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

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