A power distribution network dispatching method considering vehicle-to-grid interaction mode division

Abstract

The application relates to a power distribution network scheduling method considering a vehicle-to-grid interaction mode division, and the steps include: establishing a road network model, jointly corresponding road network nodes and power distribution network nodes, and constructing a road network-power distribution network coupling model; dividing EV user types and constructing corresponding travel characteristic models; based on the response modes of different EV users participating in vehicle-to-grid interaction under subjective will, establishing respective response models; based on the established models, considering distributed photovoltaic and energy storage in the power distribution network, taking the minimum network loss cost, EV scheduling cost and bus voltage deviation as the target, constructing a day-ahead-real-time two-stage power distribution network operation optimization scheduling problem considering the diversification of EV automobile scheduling modes, and solving the problem to obtain a real-time scheduling scheme. Compared with the prior art, the application mobilizes the EVs connected to the network to participate in the power distribution network scheduling, effectively adjusts the power flow distribution of the power distribution network, reduces the network loss caused by the power flow outflow due to the power supply of the main network, and further reduces the operation cost of the power distribution network.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power distribution network optimal scheduling, and particularly relates to a two-stage power distribution network optimal scheduling strategy considering different EV scheduling modes under the background of large-scale access of electric vehicles (EV) to urban power distribution network through vehicle-to-grid (V2G) technology. BACKGROUND

[0002] The construction of urban large-scale charging stations has increased the simultaneous access rate of EVs, and the power grid faces the risk of random load access. The risk brought by this uncertainty may be further amplified by the disordered charging behavior of EVs in large-scale charging stations, which poses a great threat to the reliability of the power system. Therefore, the orderly charging of EVs accessing the power grid has attracted much attention in recent years. Li Jinpeng, Feng Hua, Chen Xiaogang, etc. considered the multiple uncertain factors after EVs accessed the power distribution network, established a distribution robust optimization model to predict the temporal and spatial distribution of EV charging load, and intuitively managed the overall reliability of the system. Jia Shicheng, Liao Kai, Yang Jianwei, etc. considered the differences in charging load in the time and space dimensions, proposed a power distribution network regional division method based on spectral clustering, and effectively reduced the operation cost of the power distribution network. F. Chen, F. Li, R. Hong, M. Guo, Z. Dai and R. Mo designed a control strategy for the orderly charging of electric vehicles under the premise of considering the continuity of electric vehicle charging, improved the operation efficiency of the distribution network, and also reduced the electricity cost of electric vehicle users. The above-mentioned literatures only consider the uncertainty of EVs as loads during charging, and do not take into account the energy storage characteristics of EVs, and therefore cannot fully realize the potential support of EVs for the optimal scheduling of power distribution network.

[0003] The proposal of V2G technology makes electric vehicles can accept charging and discharging scheduling when accessing charging piles, and no longer only acts as a power load accessing the distribution network system, but also can inject the power stored in the battery into the power grid when necessary, thereby providing flexible power support Shang Y T, Yu, H, Niu S Y, et al. On the other hand, through active order control of EV charging and discharging, the system adequacy level can be improved Lü Si, Wei Zhinong, Sun Guoqiang, et al. In the current research, many scholars have carried out in-depth research and discussion on the application of V2G technology in the power system. Such as Wang Ying, He Jinghan, Xu Yin, etc. and Su Si, Wei Cunhao, Chen Qifang, etc. The dispatching of emergency power supply vehicles as the main power supply to the regional power grid, but the potential of a large number of small EVs in urban distribution networks as distributed energy storage resources has not been fully explored. Ge Xiaolin, Cao Shipeng, Fu Yang, etc. establish an EV scheduling model based on V2G with the minimum charging cost and network loss cost as the target, and prove the great potential of EVs as flexible resources in improving the flexibility and reliability of the power system. Jin Guobin, Li Shuang, Li Guoqing, etc. apply EV clusters to the emergency optimal dispatching of AC-DC hybrid distribution networks, and the experimental results show that the time and space energy regulation characteristics of EVs can realize emergency optimal dispatching of distribution network economy and reliability, but further discussion on the influence of user subjective will on EV participation in dispatching response is not carried out.

[0004] In summary, the current research on EVs participating in distribution network optimal dispatching through V2G charging piles under normal conditions still focuses on EVs as centralized loads participating in dispatching, or simply as distributed energy storage for charging and discharging scheduling of EV individuals. The influence of the subjective will of different types of EV users on the cooperation degree of EVs participating in dispatching is not classified. Under the influence of user subjective will, the change of the electrical characteristics of EVs when accessing the distribution network through V2G charging piles will change the influence of the distribution network dispatching plan. SUMMARY

[0005] The purpose of the present application is to overcome the defects of the prior art and provide a distribution network dispatching method considering the division of vehicle-to-grid interaction mode in a typical urban scenario.

[0006] The purpose of the present application can be achieved by the following technical solutions:

[0007] A distribution network dispatching method considering the division of vehicle-to-grid interaction mode, the method steps comprising:

[0008] Obtain the vehicle OD travel probability matrix, establish the road network model and jointly correspond the road network nodes and the distribution network nodes, and construct the road network-distribution network coupling model;

[0009] Dividing EV user types by travel mode, and constructing travel characteristic models corresponding to user types; wherein the EV user type division includes private car users, taxi users and bus users;

[0010] According to the division of EV user types, based on different response modes of different EV users participating in vehicle network interaction, response models are established; wherein the response mode includes: fully responding EV, partially responding EV and not participating in responding EV;

[0011] Based on the established model, considering distributed photovoltaic and energy storage in the distribution network, with the minimum network loss cost, EV scheduling cost and bus voltage deviation as the target, a two-stage optimal scheduling of distribution network day-ahead-real-time operation considering the diversification of EV car scheduling mode is constructed, and the real-time scheduling scheme is obtained by solving the distribution network day-ahead-real-time operation optimization scheduling problem.

[0012] As a preferred technical solution, the road network-distribution network coupling model is as follows:

[0013]

[0014] In the formula: P(R) is each node in the road network, n i represents the i-th road node, k is the number of road network nodes, E(R) is the road section, L road is the road length matrix, V road is the road resistance coefficient matrix, l ij is the length between node n i and node n j , v ij is the direct road resistance coefficient between node i and node j;

[0015] EV vehicles drive in the road network, and according to the given optimal route, they drive into the traffic node for charging and discharging action, and transmit the charging load information from the traffic node to the corresponding distribution network node;

[0016] Through the mapping function, the load information in the road network node is stored in the corresponding distribution network node load information matrix, realizing the information coupling of urban road network-distribution network.

[0017] As a preferred technical solution, the travel characteristics of private car users are as follows:

[0018] The probability density function expression of the first travel time of private car users on the same day is as follows:

[0019]

[0020] In the formula: T s1 is the first travel start time, α is the scale parameter, β is the first shape parameter, and μ is the second shape parameter;

[0021] The private vehicle stays in the residential area and the working area for a time obeying Gaussian distribution, and stays in the commercial area for a time obeying exponential distribution.

[0022] As a preferred technical solution, the taxi user trip characteristics are as follows:

[0023] The taxi user first trip time probability density function expression is as follows:

[0024]

[0025] In the formula: T s1 is the first trip start driving time, alpha is the scale parameter, beta is the first shape parameter, and mu is the second shape parameter;

[0026] When the taxi user vehicle battery SOC reaches the minimum charging threshold, the nearby charging station is parked and charged, and the parking time can be expressed as:

[0027]

[0028] In the formula: S car,n is the nth vehicle battery capacity, is the maximum capacity that the nth vehicle battery can accept, is the nth vehicle battery capacity when entering the network, P ch is the charging pile charging power per unit time.

[0029] As a preferred technical solution, the bus user considers the fixed charging load characteristics of the bus station during non-business hours.

[0030] As a preferred technical solution, the complete response EV charging and discharging model is as follows:

[0031]

[0032] In the formula: is the nth EV capacity at t period, and are the nth EV charging and discharging power at t period, and respectively represent the charging and discharging efficiency of the EV;

[0033] The vehicle scheduling cost is represented as:

[0034]

[0035] In the formula: N1 represents the total number of EVs that can be completely scheduled, is the t time point of the distribution network to EV electricity purchase price, The EV receives a full subsidy for each unit of time.

[0036] As a preferred technical solution, the partial response EV is a large-scale flexible load resource, and the charging and discharging model of the EV is:

[0037]

[0038] The charging constraint when accessing the charging pile is:

[0039]

[0040] Through the scheduling instruction, the partial charging load is transferred and distributed to other time periods within the parking time range under the condition that the total EV charging load in a scheduling period is unchanged;

[0041] The running period before transfer is The acceptable period after transfer is The EV scheduling cost as a flexible load is:

[0042]

[0043] In the formula, C tr is the scheduling cost of unit power transfer, is the nth EV load power transferred in the period.

[0044] As a preferred technical solution, the non-participating response EV is a rigid load and does not accept the regulation of the distribution network,

[0045] The charging model of the access charging station is the same as the flexible load charging model:

[0046]

[0047] The charging constraint is:

[0048]

[0049] As a preferred technical solution, the day-ahead stage of the two-stage optimal scheduling of the distribution network day-ahead-real-time operation takes the minimum operation cost of the distribution network and the minimum load fluctuation as the target to construct the distribution network day-ahead multi-objective function:

[0050] Minimize the total operation cost of the charging station and the urban distribution network:

[0051]

[0052] In the formula, is the total operation cost of the distribution network in the day-ahead stage; C buy is the purchase cost of the distribution network from the upper-level power grid, C PV is the abandoned light cost of the rooftop photovoltaic power generation, CESS C is the operation cost of the energy storage station, loss F is the network loss cost, EV C is the total cost of the EV scheduling;

[0053] The scheduling objects are the power purchased from the upper-level main grid, the output power of the rooftop photovoltaic, and the charging / discharging power of the energy storage station of the distribution network;

[0054]

[0055] F EV = F EV，1 + F EV,2

[0056] In the formula, τ is the scheduling period, Δt is the scheduling time interval, λ t is the real-time power purchase price; is the active power purchased from the upper-level grid at the t period; λ RL is the capacity price; is the capacity purchased from the upper-level grid at the t moment; c PV is the unit light rejection penalty; Ω PV is the set of nodes in which the photovoltaic is connected in the distribution network; is the maximum active power output by the photovoltaic at the i node at the t moment; is the actual power of the wind power and the photovoltaic connected to the grid at the i node at the t moment; c ESS is the charging / discharging cost coefficient of the energy storage; and are the charging / discharging power of the energy storage at the t period; J is the total number of nodes in the distribution network; r ij is the resistance of the branch ij; U i,t is the voltage of the i node at the t moment; P ij,t and Q ij,t are the active and reactive power flowing through the branch ij at the t moment; C e is the unit active network loss penalty;

[0057] The day load fluctuation is minimized by considering the basic load and the EV charging / discharging load in the network:

[0058]

[0059] In the formula: is the load mean square error in the day-ahead stage, indicating the load fluctuation of the distribution network; is the basic load at the t moment; are the charging and discharging power of the nth EV; P av is the daily average load.

[0060] As a preferred technical scheme, the real-time stage of the two-stage optimal scheduling of the power distribution network in the day-ahead-real-time operation is only used for scheduling optimization of the power distribution network in a period in which the EV response changes and a period after the period, so as to minimize the operation cost of the power distribution network and the load fluctuation, and the objective function is:

[0061]

[0062] In the formula, C is the total operation cost of the power distribution network in the real-time stage; C is the total operation cost of the power distribution network in the real-time stage; C is the total operation cost of the power distribution network in the real-time stage;

[0063] Compared with the prior art, the power distribution network scheduling strategy method considering the division of the vehicle-network interaction mode has the following beneficial effects:

[0064] The power distribution network scheduling strategy method considering the division of the vehicle-network interaction mode is proposed, the OD travel probability matrix is obtained through the analysis of the vehicle passing data of each node, the urban road network model is created, the road network nodes are jointly corresponding to the power distribution network nodes, and the urban road network-power distribution network coupling model is constructed. The vehicle data and travel data of the urban residents are analyzed, different EV user types are divided, the optimal fitting function is found out through various fitting method tests, and the Monte Carlo simulation is performed on the electricity consumption characteristics of different types of EV users. According to the division of different types of EV users, different response modes of different users participating in the vehicle-network interaction under different subjective wills are proposed, and respective response models are suggested, so that the participation of the EV vehicles in the power distribution network scheduling mode is more accurately divided. The EVs connected to the network are fully mobilized to participate in the power distribution network scheduling, the power flow distribution of the power distribution network is effectively adjusted, the network loss caused by the power flow sending due to the power supply of the main network is reduced, and then the operation cost of the power distribution network is reduced. BRIEF DESCRIPTION OF DRAWINGS

[0065] Figure 1 It is a schematic diagram of the road-network coupling relationship;

[0066] Figure 2 It is a user travel state transition probability column chart;

[0067] Figure 3 It is a vehicle scheduling mode schematic diagram;

[0068] Figure 4 It is a day-ahead-real-time scheduling solution process schematic diagram;

[0069] Figure 5 It is a road network 26 node-power distribution network 33 node coupling model schematic diagram;

[0070] Figure 6 It is a photovoltaic output curve diagram;

[0071] Figure 7 It is an EV output and load demand schematic diagram under the two-stage scheduling;

[0072] Figure 8 Node voltage comparison diagram;

[0073] Figure 9 Network loss comparison diagram for each period. DETAILED DESCRIPTION

[0074] The present application will be described in detail below in conjunction with the accompanying drawings and specific embodiments. The embodiments are implemented on the premise of the technical solution of the present application, and detailed implementation methods and specific operation processes are given, but the protection scope of the present application is not limited to the following embodiments.

[0075] Example 1

[0076] The present application proposes a power distribution network scheduling strategy method considering the division of vehicle-to-grid interaction mode in a typical urban scenario, which mainly includes the following contents:

[0077] S1, through the analysis of the vehicle passing data of each node, the OD travel probability matrix is obtained, the urban road network model is established, the road network nodes are jointly corresponding to the power distribution network nodes, and the urban road network-power distribution network coupling model is constructed.

[0078] S2, analyze the vehicle data and travel data of urban residents, divide different EV user types, find the optimal fitting function through various fitting method tests, and perform Monte Carlo simulation on the electricity consumption characteristics of different types of EV users.

[0079] S3, according to the division of different types of EV users, different response modes of different users participating in vehicle-to-grid interaction under different subjective will are proposed, and respective response models are suggested, so that the EV vehicles participating in the distribution network scheduling mode are more accurately divided.

[0080] S4, based on the above model, considering the distributed photovoltaic and energy storage in the distribution network, considering the economics of power distribution network operation, in order to enhance the reliability of power distribution network operation, taking the minimum network loss cost, EV scheduling cost and bus voltage deviation as the target, a day-ahead-real-time two-stage optimal scheduling considering the diversification of EV car scheduling mode in power distribution network operation is designed. Finally, the Cplex solver is used to solve the power distribution network optimization scheduling problem containing vehicle-to-grid interaction.

[0081] (1) Urban road network-power distribution network coupling model

[0082] As shown in Figure 2 , the road network is an important carrier reflecting the space-time characteristics of EVs, and is also the basis for characterizing the travel characteristics and charging load demand of EVs. The present application divides the urban area into three categories: residential area, working area and commercial area, and models them by graph theory method. The road network model is as follows:

[0083]

[0084] In the formula: N(R) represents each node in the road network, n i Let L represent the i-th road node, k be the number of road network nodes, E(R) be the road segment, and L be the number of nodes in the road network. road Let V be the road length matrix. road Let l be the road resistance coefficient matrix. ij For node n i With node n j Length between, v ij Let be the direct road resistance coefficient between node i and node j.

[0085] EV vehicles travel within the road network, entering traffic nodes according to a given optimal route to perform charging and discharging operations, and transmitting charging load information from the traffic node to the corresponding power distribution network node. For example... Figure 1 As shown. Information processing is performed on the distribution network side, through the mapping function f: Load information in road network nodes Store the load information matrix of the corresponding distribution network node. In order to achieve information coupling between urban road network and power distribution network.

[0086] (2) OD travel probability matrix

[0087] The concept of random walk is introduced, referring to the gradual, random movement of an object within a certain spatial range. That is, assuming an object is located at a node in a network, it moves to other nodes with a fixed probability. In a transportation network, the random movement of vehicles between road segments can be considered as random walk. Therefore, by analyzing urban road data published by the transportation department, the EV traffic volume on each road segment at different times can be obtained, and then the origin-destination (OD) probability corresponding to each traffic node can be obtained using TransCAD software. Figure 3 This is a bar chart illustrating the user travel state transition probability based on the travel data used in this invention. It represents the probability of an EV departing from each road network traffic node and traveling to one of the four city zones. Based on this, the probability of user destination selection can be analyzed. The OD travel probability matrix is ​​as follows:

[0088]

[0089] In the formula: a ij Indicates that the vehicle is represented by node n i Starting from node n, travel to the destination. j The probability of being the destination.

[0090] (3) EV travel characteristics analysis

[0091] The behavior habit factors of EV users are closely related to the charging load characteristics, and accurate analysis of the above influencing factors is the theoretical basis for accurately predicting the charging load. Based on the travel survey report, the travel data of urban residents users are selected, and the EV vehicles are divided into three types according to the travel mode: private cars, taxis and buses. Through data arrangement, the known form of travel data probability distribution statistics is obtained, and then the fitting analysis of the to-be-fitted travel variables is carried out to carry out probability modeling. The probability density function expression of the first travel time of the private car user on the same day is as follows:

[0092]

[0093] In the formula: T s1 is the first travel start time, alpha is the scale parameter, beta is the first shape parameter, and mu is the second shape parameter.

[0094] The private car stays in the residential area and the working area for a time obeying Gaussian distribution, as shown in formula (4), and stays in the commercial area for a time obeying exponential distribution, as shown in formula (5).

[0095]

[0096] The taxi user mainly runs to pick up passengers, so the first travel time probability on the same day is the same as that of the private car. At the same time, based on the characteristics of the taxi carrying passengers, the stopping time during the running process is ignored, when the battery SOC reaches the minimum charging threshold, the vehicle drives into the charging station for parking and charging until the battery SOC reaches the maximum value and drives away, so the stopping time can be expressed as:

[0097]

[0098] In the formula: S car,n is the battery capacity of the nth vehicle, is the maximum capacity that the battery of the nth vehicle can accept, is the battery capacity of the nth vehicle when it enters the network, P ch is the charging power per unit time of the charging pile.

[0099] The bus user is uniformly managed by its bus operating company, and is charged in the fixed company parking lot during the non-business time period, and its daily operation strictly follows the specified timetable and operation route, and the daily consumption of the vehicle is basically the same, so only the fixed and unchanged charging load characteristics of the bus station during the non-business time period are considered.

[0100] (4) V2G charging and discharging model

[0101] EV cluster connected with V2G charging pile in distribution network can be scheduled by the system, and the EVs can decide the charging and discharging state according to the scheduling instruction of the system. The V2G charging and discharging model is shown as follows:

[0102]

[0103] In the formula: is the capacity of the nth EV in the period t, and are the charging and discharging power of the nth EV in the period t, and respectively represent the charging and discharging efficiency of the EV.

[0104] The V2G charging and discharging process meets the constraint condition:

[0105]

[0106] In the formula: and are the upper and lower limits of the battery capacity of the EV, and are the upper and lower limits of the charging power of the nth EV, respectively.

[0107] The EV user receiving the charging scheduling will report the charging target power set by the scheduling center to the scheduling center to determine that the battery capacity of the vehicle meets:

[0108]

[0109] At the same time, the scheduling duration that the vehicle can accept after reaching the charging target can be obtained:

[0110]

[0111] The vehicle battery meets:

[0112]

[0113] In the formula: is the minimum capacity that the nth vehicle battery can accept.

[0114] (5) Fully responsive EV

[0115] In the EV scheduling time, if the user chooses to fully cooperate with the scheduling center control, the EV can be discharged to the grid through the V2G device, that is, the vehicle can be fully idle in the parking time to fully respond to the scheduling instruction, mainly represented by private cars and buses.

[0116] In the family travel data statistics, private car travel is generally based on work and home, and most of them are divided into daytime work and evening home, and the vehicle is idle for a long time in a certain place. The bus only needs to ensure that there is enough driving power when it is put into use the next day, and it also exists for a long time after being parked in the company charging station. Therefore, it can also participate in complete scheduling.

[0117] In the case of accepting complete scheduling, EV can access the power grid node as a general energy storage to accept unified deployment from the scheduling center. Its charge-discharge model is consistent with the V2G charge-discharge model (7). By taking advantage of the vehicle energy storage characteristics, not only can the load peak-valley difference be smoothed out to maintain power balance, but also the urban rooftop photovoltaic power consumption capacity can be improved. As a general energy storage, the vehicle scheduling cost can be represented as:

[0118]

[0119] In the formula: N1 represents the total number of EVs that can accept complete scheduling, is the power purchase price of the power grid to EV at time t, is the subsidy per unit time that EV accepts complete scheduling.

[0120] (6) Partially responsive EV

[0121] There are some EV users who do not want to participate in the operation of the V2G mode, but can accept orderly charging. This part of EV can be used as a large-scale flexible load resource, that is, on the premise of meeting the user's travel demand, the vehicle's battery power reaches the target power when it leaves the pile. As a flexible load, the EV charge-discharge model is:

[0122]

[0123] When this part of EV load interacts with the power grid, its power demand can be flexible and variable within the parking period. EV can determine the charging time according to the system's scheduling instructions. The charging constraint of EV as a flexible load when accessing the charging pile is:

[0124]

[0125] Through the scheduling instruction, the total EV charging load in a scheduling period can be transferred and distributed to other time periods within the parking time range. The running period before transfer is The acceptable period after transfer is The scheduling cost of EV as a flexible load is

[0126]

[0127] In the formula: C trThe scheduling cost for unit power transfer, The nth EV load power for transfer within a time period.

[0128] (7) Not participating in response EV

[0129] There are some EV users who do not participate in grid scheduling during charging, mainly taxi users who use vehicle driving as an operating means. This object only needs charging as the main demand and does not perform discharging operation, so its charging model at the charging station is consistent with the flexible load charging model (13). This type of EV load can be regarded as a rigid load, that is, in general cases, the power demand must be guaranteed and the load cannot accept power grid regulation.

[0130] Since the actual demand of this type of user is to minimize the vehicle parking time and to put the vehicle into operation as soon as possible, only the fast charging model is selected, and the charging constraint is:

[0131]

[0132] (8) Day-ahead-real-time scheduling strategy

[0133] 1) Day-ahead stage

[0134] It is assumed that the travel plan of each EV is unchanged in the day-ahead stage, the response mode is unchanged, and the minimum power grid operation cost and the minimum load fluctuation are taken as the target to construct the multi-objective function of the power grid in the day-ahead stage.

[0135] The minimum power grid operation cost target function is shown in equation (17), which is to minimize the total operation cost of the charging station and the urban power grid, including the purchase cost from the upper grid, the rooftop photovoltaic power generation cost, the charging and discharging cost of the energy storage station, the network loss cost of the power grid, and the EV scheduling cost.

[0136]

[0137] In the formula, is the total operation cost of the power grid in the day-ahead stage; C buy is the purchase cost of the power grid from the upper grid, C PV is the abandoned light cost of the rooftop photovoltaic power generation, C ESS is the operation cost of the energy storage station, C loss is the network loss cost, F EV is the total EV scheduling cost.

[0138] At the same time, the scheduling objects are the power purchase power of the power grid from the upper main grid, the output power of the rooftop photovoltaic power, and the charging and discharging power of the energy storage station.

[0139]

[0140] FEV = F EV，1 + F EV,2 (22)

[0141] where τ is the dispatch period, Δt is the dispatch time interval, λ t is the real-time electricity purchase price; is the active power purchased by the distribution network from the upper-level network at time t, λ RL is the capacity price; is the capacity purchased by the distribution network from the upper-level network at time t, c PV is the unit light curtailment penalty; Ω PV is the set of nodes in the distribution network where photovoltaic is connected; is the maximum active power generated by the photovoltaic at node i at time t; is the actual power generated by the wind power and photovoltaic at node i at time t, c ESS is the energy storage charging and discharging cost coefficient; and are the charging and discharging power of the energy storage at time t, respectively; J is the total number of nodes in the distribution network; r ij is the resistance of branch ij; U i,t is the voltage at node i at time t; P ij,t and Q ij,t are the active and reactive power flowing through branch ij at time t; C e is the unit active network loss penalty.

[0142] In the present application, the load of the distribution network mainly considers the basic load and the EV charging and discharging load in the network, and the minimum daily load fluctuation objective function is:

[0143]

[0144] wherein: is the load mean square error in the day-ahead stage, indicating the load fluctuation of the distribution network; P av is the daily average load; is the basic load at time t; are the charging power and discharging power of the nth EV, respectively.

[0145] 2) Real-time stage

[0146] In the real-time stage, only the time period when the EV response changes and the subsequent time period are optimized for the distribution network scheduling, and the scheduling objective is consistent with the day-ahead, and the objective function is:

[0147]

[0148] wherein: is the total operation cost of the distribution network in the real-time stage; is the load mean square error in the real-time stage.

[0149] 3) solving process

[0150] The above scheduling model solving process is shown in Figure 4

[0151] First, import the normal load data, road network information, EV characteristic parameters, and photovoltaic data.

[0152] In the day-ahead stage:

[0153] For the nth EV trip data generation; calculate the trip power consumption, and judge whether the EV is connected to the charging station;

[0154] If the EV is not connected to the charging pile, return to the n+1 EV trip power consumption calculation; if the nth EV is connected to the charging pile, generate its parking time, and judge whether it participates in the scheduling;

[0155] If the EV participates in the scheduling; if the EV does not participate in the scheduling, generate the charging load;

[0156] Record the scheduling period, and loop the above steps until all EVs are traversed;

[0157] Optimize the day-ahead stage scheduling plan using Cplex solver.

[0158] In the real-time stage:

[0159] In the current period, judge whether the response of each EV changes;

[0160] If the EV response changes, update the response mode of the corresponding EV, get its node number in the distribution network and determine the charging and discharging plan;

[0161] After completing the response situation traversal update of all EVs, optimize the day-ahead stage scheduling plan, and enter the next time;

[0162] When the day-ahead stage scheduling plan optimization of the current period is completed, output the real-time stage scheduling plan.

[0163] (9) constraint conditions

[0164] 1) power flow constraint

[0165] The DistFlow model is used to calculate the distribution network power flow to obtain the power flow distribution, and the specific calculation model is as follows:

[0166]

[0167] In the formula: z:j→z represents the set of end nodes z with node j as the means; is the active and reactive power output of the load and distributed power at node j at time t; I​ij,t is the current of branch ij at time t; r ij is the resistance of branch ij; x ij is the reactance of branch ij; U j,t is the voltage of node j at time t.

[0168] The calculation constraints are:

[0169]

[0170] In the formula: U1 is the equilibrium node voltage; U ref is the reference value of U1; I ij,max represents the maximum current value allowed on branch ij.

[0171] In the DistFlow power flow equation, there is an equation constraint of quadratic nonlinearity, which will present non-convex characteristics to the optimization problem. Therefore, the second-order cone relaxation method is used to convert equation (29) into standard second-order cone constraint (30) for convexity.

[0172]

[0173] 2) Tie-line transmission constraints

[0174]

[0175] In the formula: P g,min , P g,max are the upper and lower limits of the active power transmitted by the upper-level power grid to the distribution network; Q g,min , Q g,max are the upper and lower limits of the reactive power transmitted by the upper-level power grid to the distribution network.

[0176] 3) Energy storage system operation constraints

[0177]

[0178] In the formula: is the energy storage capacity at time t; and represent the charging and discharging efficiency of the energy storage; E ESS,min and E ESS,max are the maximum and minimum capacities of the energy storage; and are the initial and final energy of the energy storage; and are the charging and discharging power of the energy storage at time t; and are the maximum charging and discharging power of the energy storage.

[0179] 4) Power balance constraints

[0180]

[0181] Embodiment 2

[0182] This embodiment provides one of the implementation examples of the power distribution network scheduling strategy method considering the vehicle network interaction mode division as described above, taking 26 road network nodes and 45 road data of a certain city area as the road network model, combining with the IEEE 33-node power distribution network system, a road network coupling model is established, as shown in Figure 6 The typical daily load curve of the city area is taken as an example, and the peak, valley and flat three-period electricity price data are adopted, and the detailed parameters are shown in Table 1. The rated voltage of the power distribution network is 12.66 kV, the rated capacity is 10 MW, the upper and lower limits of the voltage are 0.95 and 1.05 (unit value), and the maximum current that the branch can withstand is 500 A. The photovoltaic is connected to the power distribution network through nodes 8, 14, 17, 20, 23 and 26, and the rated power of the switching point connected to the photovoltaic is 0.2 MW. The power curve of the photovoltaic power generation device connected to different nodes is the same, and the time sequence output diagram is shown in Figure 7 The unit network loss cost of the power distribution network is 1.0 yuan / (kW·h). The capacity price is 0.0114 yuan / (kW·h), and the energy storage station is connected from nodes 6 and 12, and the parameters are shown in Table 2.

[0183] Table 1 Time-of-use electricity price

[0184]

[0185] Table 2 Energy storage parameters

[0186]

[0187] 1000 EVs in the area are taken as an example for research, and the EV parameters are shown in Table 3. Considering the EV load transfer problem, the initial power of the EV is set to be normally distributed with an expectation of 0.8 and a variance of 1. The day-ahead scheduling period is set to be 24 hours, with a time interval of 1 hour, 24 time periods are divided, the real-time scheduling time interval is 15 minutes, 96 time periods are divided, and the EV accepts the charging and discharging scheduling period is 15 minutes.

[0188] Table 3 EV parameters

[0189]

[0190] Figure 7 The power and load demand transformation of the EVs in the day-ahead and real-time two-stage of the proposed method is shown in the figure. The daily load mean square error in the real-time scenario is reduced by 1579.7 compared with the day-ahead scenario, and the charging and discharging cost is reduced by 10084.67 yuan. This shows that through the two-stage optimization scheduling, the EVs participate in the power system scheduling more fully, which is more conducive to improving the stability and safety of the power system and reducing the operation cost of the power distribution network.

[0191] To further verify the adjustment ability of the proposed two-stage optimization scheduling strategy for the node voltage and network energy transmission loss of the urban distribution network, a comparison method is adopted to compare the node voltage and network loss before and after the proposed strategy. The node voltage comparison result is shown in FIG. 3. Figure 8 As can be seen from the figure, before the proposed optimization strategy is adopted, the node minimum voltage value of the distribution network drops to 0.9, and there is obvious under-limit, which has a great impact on the stable operation of the distribution network; after the proposed strategy is adopted, the node minimum voltage of the distribution network only drops to 0.95, the node voltage variation is within the safe range, and the node voltage deviation is improved. The network loss comparison result is shown in FIG. 4. Figure 9 As can be seen from the figure, by the proposed method, the EVs connected to the network are mobilized to participate in the distribution network scheduling, the distribution network power flow distribution is effectively adjusted, the network loss caused by the power flow outflow due to the main network power supply is reduced, and the operation cost of the distribution network is further reduced.

[0192] The above describes the preferred embodiments of the present application in detail. It should be understood that those skilled in the art can make many modifications and changes without creative labor based on the concept of the present application. Therefore, any technical solution obtained by logical analysis, reasoning or limited experiment based on the existing technology according to the concept of the present application shall be within the protection scope defined by the claims.

Claims

1.A power distribution network dispatching method considering vehicle-to-grid interaction mode division, characterized in that, The method steps include: Obtain the vehicle OD trip probability matrix, establish the road network model, and jointly correspond the road network nodes and the distribution network nodes to construct a road network-distribution network coupling model; Divide the EV user types by travel mode, and construct the travel characteristic model of the corresponding user types; wherein the EV user type division includes private car users, taxi users, and bus users; According to the division of the EV user types, based on different response modes of different EV users participating in the vehicle network interaction to different degrees, establish respective response models; wherein the response modes include fully responsive EVs, partially responsive EVs, and non-participating responsive EVs; the charging and discharging model of the fully responsive EVs is as follows: In the formula: is t the time interval between the two time periods; t1 and t2 are respectively the start time and the end time of the time period; V1 and V2 are respectively the capacity of the EV at the start time and the end time of the time period; P1 and P2 are respectively the charging and discharging power of the EV at the start time and the end time of the time period; and η1 and η2 are respectively the charging and discharging efficiency of the EV at the start time and the end time of the time period. n t n is the time interval between the two time periods; t1 and t2 are respectively the start time and the end time of the time period; V1 and V2 are respectively the capacity of the EV at the start​​​​​​​ The vehicle scheduling cost is represented as: In the formula: represents the total number of EVs that can accept full scheduling, is t the EV electricity purchase price at the moment of network distribution, is t the discharging power of the EV at the moment, is the duration of the vehicle after reaching the charging target that can accept scheduling, is the subsidy per unit time that the EV accepts full scheduling; Based on the established model, considering the distributed photovoltaic and energy storage in the distribution network, the minimum network loss cost, EV scheduling cost, and bus voltage deviation are taken as the target to construct a two-stage optimal scheduling of the distribution network day-ahead-real-time operation considering the diversification of the EV car scheduling mode, and a real-time scheduling scheme is obtained by solving the distribution network day-ahead-real-time operation optimization scheduling problem. 2.The power distribution network dispatching method considering the vehicle-to-grid interaction mode division of claim 1, wherein, The road network model in the road network-distribution network coupling model is as follows: In the formula: N ( R ) represents each node in the road network. Indicates the first i Road nodes, k The number of road network nodes. E ( R ) refers to road sections. This is a road length matrix. This is the road resistance coefficient matrix. For nodes With nodes Length between For nodes i With nodes j Direct path resistance coefficient; The EV vehicle drives in the road network, drives into the traffic node according to the given optimal route, and performs charging and discharging actions, and transmits the charging load information from the traffic node to the corresponding distribution network node; Through a mapping function, the load information in the road network node is stored in the corresponding distribution network node load information matrix to realize the information coupling of the urban road network-distribution network. 3.The power distribution network dispatching method considering the vehicle-to-grid interaction mode division of claim 1, wherein, The travel characteristics of the private car users are as follows: The first travel time probability density function expression of the private car users on the same day is as follows: In the formula: is the start time of the first trip, α is a scale parameter, β is a first shape parameter, μ is a second shape parameter; The private car stays in the residential area and the working area for a time following a Gaussian distribution, and stays in the commercial area for a time following an exponential distribution. 4.The power distribution network dispatching method considering the vehicle-to-grid interaction mode division of claim 1, wherein, The travel characteristics of the taxi users are as follows: The first travel time probability density function expression of the taxi users on the same day is as follows: In the formula: is the start time of the first trip, α is a scale parameter, β is a first shape parameter, μ is a second shape parameter; When the battery SOC of the taxi user vehicle reaches the minimum charging threshold, the taxi user drives into the nearby charging station for parking and charging, and the parking time can be expressed as: In the formula: is the maximum capacity of the battery of the nth vehicle, n is the maximum capacity of the battery of the nth vehicle, is the battery capacity of the nth vehicle when it enters the network, n is the battery capacity of the nth vehicle when it enters the network, is the charging efficiency of the EV, is the charging power per unit time of the charging pile. 5.The power distribution network dispatching method considering the vehicle-to-grid interaction mode division of claim 1, wherein, The bus user considers the fixed and unchanging charging load characteristics of the bus station during non-business hours. 6.The power distribution network dispatching method considering the vehicle-to-grid interaction mode division of claim 1, wherein, The partially responsive EVs are large-scale flexible load resources, and the charging and discharging model of the EVs is as follows: The charging constraint when accessing the charging pile is as follows: In the formula: For the first n Battery capacity of each vehicle when it is registered with the network; For the first n The target charging level set for each vehicle; Charging efficiency for EVs; , The first n The upper and lower limits of charging power for EVs; , The first n The registration and deregistration times of EVs; Under the condition that the total EV charging load amount in a scheduling period is unchanged through the scheduling instruction, part of the charging load is transferred and distributed to other time periods within the parking time range; The pre-transition runtime period is [ , ], the post-transition acceptable period is [ , ], and the EV scheduling cost of the flexible load is: In the formulae: is the scheduling cost per unit power transfer, is the first power transfer of the period, n is the load power of the i-th EV. 7.The power distribution network dispatching method considering the vehicle-to-grid interaction mode division of claim 1, wherein, The non-participating responsive EVs are rigid loads that do not accept the regulation of the distribution network, and the charging model of the charging station is the same as that of the flexible load charging model: The charging constraint is as follows: In the formula: is the battery capacity of the nth vehicle when it enters the network, n is the maximum capacity of the battery of the nth vehicle, is the maximum capacity of the battery of the nth vehicle, n is the upper limit of the charging power of the nth EV, is the upper limit of the charging power of the nth EV, n is the entry time of the nth vehicle into the network; is the entry time of the nth vehicle into the network; n is the entry time of the nth vehicle into the network; is the entry time of the nth vehicle into the network; 8.The power distribution network dispatching method considering the vehicle-to-grid interaction mode division of claim 1, wherein, The day-ahead stage of the distribution network day-ahead-real-time operation two-stage optimization scheduling takes the minimum distribution network operation cost and the minimum load fluctuation as the target to construct a distribution network day-ahead multi-objective function: Minimize the total cost of the charging station and the urban distribution network operation: In the formula, is the total operation cost of the distribution network in the day-ahead stage; is the purchase cost of the distribution network from the upper-level power grid, is the abandoned light cost of the roof photovoltaic power generation, is the operation cost of the energy storage power station, is the network loss cost, is the total EV scheduling cost; The scheduling object is the distribution network purchasing power from the superior main network, the rooftop photovoltaic output power, and the charging and discharging power of the energy storage power station; In the formula: τ is the scheduling period; is the scheduling time interval; is the real-time electricity purchase price; is t is the active power purchased by the distribution network from the upper-level power grid at the time period; is the capacity price; is t is the capacity purchased by the distribution network from the upper-level power grid at the time; is the unit light curtailment penalty; is the set of nodes in the distribution network where photovoltaic is connected; is t is the node i is the maximum active power generated by photovoltaic at the time period; is t is the node i is the actual power of wind power and photovoltaic connected to the power grid at the time; is the energy storage charging and discharging cost coefficient; and are the charging and discharging power of the energy storage at the time period, respectively; J is the total number of distribution network nodes; is the branch ij resistance; is t is the voltage of the node i at the time; and are the active and reactive power flowing through the branch ij at the time; is the unit active network loss penalty; is the vehicle scheduling cost when EV is fully responsive as a generalized energy storage; is the scheduling cost of the partially responsive EV vehicle as a flexible load; Considering the basic load and the EV charging and discharging load in the network, the daily load fluctuation is minimized: In the formula: is the load mean square error of the day-ahead stage, indicating the load fluctuation of the power distribution network; is the load mean square error of the day-ahead stage, indicating the load fluctuation of the power distribution network; t is the load at the moment; , are the charging power and discharging power of the i-th EV, respectively; n are the charging power and discharging power of the i-th EV, respectively; is the average load of the day. 9.The power distribution network dispatching method considering the vehicle-to-grid interaction mode division of claim 8, wherein, The real-time stage of the power distribution network day-ahead-real-time operation two-stage optimization scheduling only optimizes the power distribution network scheduling for the period in which the EV response condition changes and the period thereafter, with the lowest power distribution network operation cost and the minimum load fluctuation as the target, and the objective function is: In the formula: is the total cost of real-time phase operation of the distribution network; is the load mean square error of the real-time phase.

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