CN114897302A - Method for charging and discharging electric automobile in space-time order under V2G mode - Google Patents

Method for charging and discharging electric automobile in space-time order under V2G mode Download PDF

Info

Publication number
CN114897302A
CN114897302A CN202210367205.8A CN202210367205A CN114897302A CN 114897302 A CN114897302 A CN 114897302A CN 202210367205 A CN202210367205 A CN 202210367205A CN 114897302 A CN114897302 A CN 114897302A
Authority
CN
China
Prior art keywords
electric automobile
charging
time
discharging
electric
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
CN202210367205.8A
Other languages
Chinese (zh)
Other versions
CN114897302B (en
Inventor
万佑红
曹宇航
黄文睿
王由鋆
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Nanjing University of Posts and Telecommunications
Original Assignee
Nanjing University of Posts and Telecommunications
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Nanjing University of Posts and Telecommunications filed Critical Nanjing University of Posts and Telecommunications
Priority to CN202210367205.8A priority Critical patent/CN114897302B/en
Priority claimed from CN202210367205.8A external-priority patent/CN114897302B/en
Publication of CN114897302A publication Critical patent/CN114897302A/en
Application granted granted Critical
Publication of CN114897302B publication Critical patent/CN114897302B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L55/00—Arrangements for supplying energy stored within a vehicle to a power network, i.e. vehicle-to-grid [V2G] arrangements
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L53/00—Methods of charging batteries, specially adapted for electric vehicles; Charging stations or on-board charging equipment therefor; Exchange of energy storage elements in electric vehicles
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L53/00—Methods of charging batteries, specially adapted for electric vehicles; Charging stations or on-board charging equipment therefor; Exchange of energy storage elements in electric vehicles
    • B60L53/60—Monitoring or controlling charging stations
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L53/00—Methods of charging batteries, specially adapted for electric vehicles; Charging stations or on-board charging equipment therefor; Exchange of energy storage elements in electric vehicles
    • B60L53/60—Monitoring or controlling charging stations
    • B60L53/63—Monitoring or controlling charging stations in response to network capacity
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L53/00—Methods of charging batteries, specially adapted for electric vehicles; Charging stations or on-board charging equipment therefor; Exchange of energy storage elements in electric vehicles
    • B60L53/60—Monitoring or controlling charging stations
    • B60L53/67—Controlling two or more charging stations
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0637—Strategic management or analysis, e.g. setting a goal or target of an organisation; Planning actions based on goals; Analysis or evaluation of effectiveness of goals
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/067—Enterprise or organisation modelling
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Energy or water supply
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/28—Arrangements for balancing of the load in a network by storage of energy
    • H02J3/32—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means
    • H02J3/322—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means the battery being on-board an electric or hybrid vehicle, e.g. vehicle to grid arrangements [V2G], power aggregation, use of the battery for network load balancing, coordinated or cooperative battery charging
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/10—Power transmission or distribution systems management focussing at grid-level, e.g. load flow analysis, node profile computation, meshed network optimisation, active network management or spinning reserve management
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S30/00—Systems supporting specific end-user applications in the sector of transportation
    • Y04S30/10—Systems supporting the interoperability of electric or hybrid vehicles
    • Y04S30/12—Remote or cooperative charging

Landscapes

  • Engineering & Computer Science (AREA)
  • Business, Economics & Management (AREA)
  • Human Resources & Organizations (AREA)
  • Power Engineering (AREA)
  • Strategic Management (AREA)
  • Economics (AREA)
  • Mechanical Engineering (AREA)
  • Transportation (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Theoretical Computer Science (AREA)
  • Educational Administration (AREA)
  • Marketing (AREA)
  • Tourism & Hospitality (AREA)
  • Physics & Mathematics (AREA)
  • General Business, Economics & Management (AREA)
  • General Physics & Mathematics (AREA)
  • Game Theory and Decision Science (AREA)
  • Quality & Reliability (AREA)
  • Development Economics (AREA)
  • Operations Research (AREA)
  • Health & Medical Sciences (AREA)
  • Public Health (AREA)
  • Water Supply & Treatment (AREA)
  • General Health & Medical Sciences (AREA)
  • Primary Health Care (AREA)
  • Charge And Discharge Circuits For Batteries Or The Like (AREA)

Abstract

The invention provides a method for space-time ordered charging and discharging of an electric automobile in a V2G mode, belonging to ordered charging and discharging of the electric automobile and comprising the following steps: step S1: based on the fact that an owner usually moves to and fro among different destinations in one day, a community theory is combined, a charging station area is divided into different areas, the different areas are used as a basic model of electric automobile driving in space, a daily trip chain of the electric automobile is constructed, and therefore an electric automobile ordered charging and discharging space model based on the trip chain is constructed; step S2: based on the selection of a trip chain, constructing an electric automobile ordered charging and discharging time model by adopting a corresponding user behavior characteristic rule; step S3: considering the interactivity and integrity between the electric automobile and the power distribution network, constructing a neighborhood world evolution model under the access of the electric automobile; step S4: and analyzing the influence of charging and discharging of the electric automobile connected to the power distribution network from two aspects of topological characteristics and electrical characteristics.

Description

Method for charging and discharging electric automobile in space-time order under V2G mode
Technical Field
The invention relates to a space-time ordered charging and discharging method for an electric automobile in a V2G mode, and belongs to ordered charging and discharging of the electric automobile.
Background
With the continuous deterioration of global environment and the increasing prominence of the problem of energy shortage, electric vehicles are widely used in recent years as a novel vehicle which uses clean energy, has zero emission and low noise. As the number of electric vehicles increases, their impact on the power system increases. After a large-scale electric automobile is connected to the grid, the charging load of the electric automobile is overlapped with the peak value of the original power grid load, so that the peak load of the power distribution network is increased, the load peak-valley difference is increased, and the safe operation of the power distribution network is influenced, and therefore the problem of orderly charging and discharging of the electric automobile becomes a hotspot of current research.
The existing research method for orderly charging and discharging electric automobiles has many defects: (1) mostly, the method is based on probability statistics, and cannot reflect the interaction between the electric automobile and the power distribution network; (2) most of research methods based on the time horizon are that the electric automobile is immediately and intensively charged after the valley period comes, so that the effect of transferring the charging load to the valley period is achieved, but a new load peak is formed at the beginning of the valley period, and the stability of the power distribution network is influenced; (3) the influence of operations such as charging or discharging of an electric automobile connected to a power distribution network on the topology level of the power distribution network is ignored, the power distribution network is dynamic and developed due to connection or disconnection, and the essence of network functions determined by the network topology is ignored, so that the conventional static network cannot be used for describing the evolution process.
In view of the above, it is necessary to provide a method for space-time ordered charging and discharging of an electric vehicle in a V2G mode to solve the above problems.
Disclosure of Invention
The invention aims to provide a method for charging and discharging an electric automobile in a V2G mode in a time-space ordered manner, which is used for controlling the electric automobile to be charged and discharged at a specific time, can avoid the problems of large-scale charging in peak time periods and insufficient electric energy utilization in valley time periods, realizes peak clipping and valley filling, stabilizes power flow of a power grid and improves electric energy quality.
The invention provides a space-time ordered charging and discharging method of an electric automobile in a V2G mode, which comprises the following steps:
step S1: based on the fact that an owner usually moves to and fro among different destinations in one day, a community theory is combined, a charging station area is divided into different areas, the different areas are used as a basic model of electric automobile driving in space, a daily trip chain of the electric automobile is constructed, and therefore an electric automobile ordered charging and discharging space model based on the trip chain is constructed;
step S2: based on the selection of a trip chain, constructing an electric automobile ordered charging and discharging time model by adopting a corresponding user behavior characteristic rule;
step S3: considering the interactivity and integrity between the electric automobile and the power distribution network, constructing a neighborhood world evolution model under the access of the electric automobile;
step S4: and analyzing the influence of charging and discharging of the electric automobile connected to the power distribution network from two aspects of topological characteristics and electrical characteristics.
As a further technical solution of the present invention,
in the aforementioned method for space-time ordered charging and discharging of the electric vehicle in the V2G mode, the step S1 specifically includes the following steps:
step S11: dividing the charging station area into different areas by combining modularity and community structure theory;
the calculation formula of the modularity Q is as follows:
Figure RE-GDA0003732712790000021
in the formula, s i-in ，s i-out Respectively representing the incoming strength and the outgoing strength of the charging station node i, i.e. the sum of the weights of the edges connected to the charging station node i(ii) a W represents the sum of the weights of all lines in the network; w is a ij Representing the weight of a connecting edge between a charging station node i and a charging station node j in the network; when the charging station node i and the charging station node j are in the same community, δ (i, j) is 1, otherwise δ (i, j) is 0;
step S12: constructing an electric vehicle trip chain based on the charging station areas divided by the communities;
the community structure is defined as: the community division is to divide nodes with similar electrical characteristics in a power network into corresponding areas, divide the whole charging station area into different areas, and decompose the charging and discharging behaviors of the electric vehicle in the whole power distribution network into the charging and discharging behaviors of the electric vehicle in each area.
In the aforementioned method for space-time ordered charging and discharging of the electric vehicle in the V2G mode, the step S2 specifically includes the following steps:
step S21: constructing user behavior characteristics based on a trip chain;
step S22: constructing different ordered charging models according to the situation that the electric automobile is in a non-residential area or a residential area;
step S23: and constructing different ordered discharge models according to the situation that the electric automobile is in a non-residential area or a residential area.
In the foregoing method for space-time ordered charging and discharging of the electric vehicle in the V2G mode, in step S22, the ordered charging model of the electric vehicle in the non-residential area is as follows:
Figure RE-GDA0003732712790000031
in the formula, SOC I,I+1 The electric quantity is consumed for the electric automobile from the area I to the area I + 1;
Figure RE-GDA0003732712790000032
the real-time electric quantity of the electric automobile arriving at the area I;
Figure RE-GDA0003732712790000033
the real-time electric quantity of the electric automobile leaving the area I; distance I,I+1 Is a regionDistance of domain I to region I + 1; w is the power consumption of the electric automobile per kilometer; s is the battery capacity; t is charge Charging the electric vehicle for a period of time; p is the charge and discharge power of the electric automobile; t is t charge Starting charging time for the electric automobile;
Figure RE-GDA0003732712790000034
Figure RE-GDA0003732712790000035
the time when the electric automobile leaves the area I;
Figure RE-GDA0003732712790000036
the time when the electric automobile arrives at the area I;
Figure RE-GDA0003732712790000037
the residence time of the electric automobile in the area I is shown; rand is [0, 1]]A random number within the interval;
in step S22, the ordered charging model of the electric vehicle in the residential area is:
Figure RE-GDA0003732712790000041
in the formula, SOC max The maximum capacity of the battery; SOC n arr The real-time electric quantity reaching the residential area; Δ t v ＝t v2 -t v1 ，t v1 Is the beginning time of the valley period, t v2 The end time of the valley period.
In the foregoing method for space-time ordered charging and discharging of the electric vehicle in the V2G mode, in step S23, the ordered discharging model of the electric vehicle in the non-residential area is as follows:
Figure RE-GDA0003732712790000042
in the formula, T discharge The discharge time of the electric automobile is set; SOC control A regulation threshold for electric vehicle discharge; t is t discharge The discharge starting time of the electric automobile;
in step S23, the ordered discharge model of the electric vehicle in the residential area is:
Figure RE-GDA0003732712790000043
in the formula, SOC min Is the minimum capacity of the battery; Δ t ═ t p2 -t n arr ，t p2 Is the peak period end time, t n arr The time of the electric automobile arriving at the residential area.
In the aforementioned method for space-time ordered charging and discharging of the electric vehicle in the V2G mode, the step S3 specifically includes the following steps:
step S31: simplified IEEE33 node distribution network system, the initial network comprising N 1 The direction of the line is the flow direction of the electric energy, and the weight of the line is the equivalent impedance value of the line;
step S32: an optimization factor is allocated to each charging station node i in the area according to the distance and the electric vehicle connection number of the charging stations
Figure RE-GDA0003732712790000044
Selecting the most suitable charging station for the electric vehicle; preference factor
Figure RE-GDA0003732712790000045
The calculation formula of (2) is as follows:
Figure RE-GDA0003732712790000051
in the formula, k i-in Is the degree of entry of node i, and is denoted by k i-in The edge takes the node i as an end point; k is a radical of i-out For the out degree of node i, it has k i-out The edge takes the node i as a starting point; n is a radical of 1 Indicating the number of charging stations; d ij The physical distance between the charging station node i and the newly added electric vehicle node j is obtained; p is more than or equal to 0, and q is less than or equal to 1; o is a neighborhood range including part of the charging station nodes；
Step S33: if the electric automobile continues to charge and discharge at the next moment, the electric automobile keeps being connected with the power distribution network, and the position of the electric automobile keeps unchanged; if the electric automobile finishes charging and discharging at the next moment, the connection with the power distribution network is disconnected;
step S34: and updating the network topology and the node information.
In the foregoing method for space-time ordered charging and discharging of an electric vehicle in the V2G mode, the neighborhood range O in step S23 is defined as:
Figure RE-GDA0003732712790000052
coordinate v of charging station node i i Is defined as:
v i ＝(x i ,y i ),i∈1,2,...,N 1 ；
coordinate v of electric automobile node j j Is defined as:
v j ＝(x j ,y j ),j∈1,2,...,N 2 。
in the aforementioned method for space-time ordered charging and discharging of the electric vehicle in the V2G mode, the step S4 specifically includes the following steps:
step S41: respectively obtaining the charging and discharging requirements of the electric automobile in different areas and different trip chains;
step S42: defining topological entropy, and describing the orderliness of the system from the point of node degree distribution; the topological entropy is:
Figure RE-GDA0003732712790000061
in the formula, P 1 (k) The number of electric vehicles connected for a charging station node belongs to (U) k ，U k+1 ]Probability of constant sequence interval;
step S43: defining a power flow entropy, and describing the orderliness of the system from the point of view of line load rate distribution; the power flow entropy is as follows:
Figure RE-GDA0003732712790000062
in the formula, P2(k) is the probability that the line load rate belongs to the (Uk, Uk +1] constant sequence interval when the electric vehicle is connected into the power distribution network for charging and discharging.
Step S44: the method was analyzed to be effective in improving the quality of electric power.
The method for charging and discharging the electric vehicle in the V2G mode in time-space order includes the step S42 constant sequence interval U 2 Comprises the following steps:
U 2 ＝[U 1 ，U 2 ，...，U n ]；
probability P 2 (k) Comprises the following steps:
Figure RE-GDA0003732712790000063
in the formula I k The line load rate of the electric automobile connected to the power distribution network during charging and discharging belongs to (U) k ，U k+1 ]The number of lines of (a);
load factor mu of line i i The following were used:
Figure RE-GDA0003732712790000064
in the formula, P i 0 For the actual power flow, P, during operation of the transmission line i i max The maximum active transmission capacity of the transmission line i.
Compared with the prior art, the invention adopting the technical scheme has the following technical effects: the invention realizes the ordered charging and discharging of the electric automobiles in time and space, can realize the targets of discharging in peak time period and charging in valley time period by selecting different trip chains or electric automobiles positioned in different areas, not only meets the power consumption requirements of users, balances the electric automobile access quantity of each charging station, improves the utilization rate of the charging stations, effectively inhibits the tide fluctuation of the power distribution network, reduces the adverse effects of node voltage reduction and network loss increase caused by the charging and discharging of the electric automobiles accessed to the power distribution network, and is beneficial to improving the safety and the stability of the power distribution network.
Drawings
FIG. 1 is a topology structure diagram of an IEEE33 node power distribution network system after community division;
FIG. 2 is a schematic diagram of a travel chain according to the present invention;
FIG. 3 is a flow chart of the time-space ordered charging and discharging method of the electric vehicle under the V2G mode according to the invention;
FIG. 4 is a diagram illustrating a distribution of charging and discharging demand times of electric vehicles in different areas according to the present invention;
FIG. 5 is a distribution diagram of the charging and discharging demand time of electric vehicles with different trip chains according to the present invention;
FIG. 6 is a graph of topological entropy as a function of time for different charge and discharge modes of the present invention;
FIG. 7 is a diagram of the entropy of the power flow with time under different charging and discharging modes according to the present invention;
FIG. 8 shows node voltage amplitudes in different charge and discharge modes according to the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in detail with reference to the accompanying drawings and specific embodiments.
The invention provides a method for space-time ordered charging and discharging of an electric automobile in a V2G mode, which mainly comprises the following steps:
step S1: based on the fact that an owner usually moves to and fro among different destinations in one day, a community theory is combined, a charging station area is divided into different areas, the different areas are used as a basic model of electric automobile driving in space, a daily trip chain of the electric automobile is constructed, and therefore an electric automobile ordered charging and discharging space model based on the trip chain is constructed;
step S2: based on the selection of a trip chain, constructing an electric automobile ordered charging and discharging time model by adopting a corresponding user behavior characteristic rule;
step S3: considering the interactivity and integrity between the electric automobile and the power distribution network, constructing a neighborhood world evolution model under the access of the electric automobile;
step S4: and analyzing the influence of charging and discharging of the electric automobile connected to the power distribution network from two aspects of topological characteristics and electrical characteristics.
The following will explain step S1-step S4 in detail:
in step S1, combine community structure theory, divide into residential area, workspace and commercial district with the charging station region, the charge-discharge action in three regions of overall analysis constructs the electric automobile trip chain, carries out orderly charge-discharge to electric automobile in the space, and concrete step includes:
step S11: dividing the charging station area into different areas by combining modularity and community structure theory;
step S111: and adopting a modularity index based on line equivalent impedance as a power distribution network community division basis. The modularity is an index for measuring the structural strength of the network community, and the numerical value is determined by the actual connection condition and the edge weight of the network. In the power network, the weight values of the lines among the nodes are represented by line equivalent impedance values, and the line equivalent impedance values can more effectively reflect the electrical connection among the nodes.
The calculation formula of the modularity Q is as follows:
Figure RE-GDA0003732712790000081
in the formula s i-in ，s i-out Respectively representing the incoming strength and the outgoing strength of the charging station node i, wherein the strength is the weight sum of edges connected with the charging station node i; w represents the sum of the weights of all lines in the network; w is a ij Representing the weight of a connecting edge between a charging station node i and a charging station node j in the network; when the charging station node i and the charging station node j are in the same community, δ (i, j) is 1, otherwise δ (i, j) is 0.
In step S112, the community division specifically includes:
step (1): initially, assuming that each charging station node is an independent community, the module value Q is 0,
the elements of the initial modularity increment matrix are calculated as follows:
Figure RE-GDA0003732712790000091
after the initial modularity increment matrix is obtained, the maximum heap H formed by the maximum elements of each row of the initial modularity increment matrix can be obtained;
step (2): selecting the largest Delta Q from the largest heap H ij Merging the corresponding communities i and j, and marking the merged communities with a label j; updating modularity increment matrix delta Q ij Maximum heap H and auxiliary vector s;
step (2.1): delta Q ij Updating: deleting the element in the ith row and the ith column, and updating the element in the jth row and the jth column to obtain:
Figure RE-GDA0003732712790000092
step (2.2): update of maximum heap H: updating the maximum element of the corresponding row and column in the maximum heap;
step (2.3): auxiliary vector s i Updating:
s’ j ＝s i +s j ，s’ i ＝0；
recording the combined module value Q ═ Q + delta Q ij ；
And (3) repeating the step (2) until the maximum elements in the modularity degree increment matrix change from positive values to negative values.
The modularity Q has only one maximum peak throughout the algorithm. When the largest elements in the modularity increment matrix are all smaller than zero, the Q value may only decrease all the time. Therefore, as long as the largest element in the modularity increment matrix changes from positive to negative, the merging can be stopped, and the result at this time is considered to be the optimal community structure of the network.
The topology structure diagram of the divided IEEE33 node power distribution network system is shown in figure 1.
Step S12: the method comprises the following steps of constructing an electric automobile trip chain based on a charging station area divided by communities:
a travel chain refers to a connection mode in which an individual completes one or more activities and different travel purposes are performed in a certain time sequence. The travel chain can well describe the activity rule of the electric automobile in space-time, and comprises a time characteristic quantity, a space characteristic quantity and a charge characteristic quantity, wherein the time characteristic quantity refers to the change rule of the electric automobile in travel in time, the space characteristic quantity refers to the transfer characteristic of the electric automobile in travel in space, and the charge state quantity refers to the change quantity of the charge state of the electric automobile in the travel process.
Fig. 2 is a schematic diagram of a spatiotemporal trip chain for completing a one-day trip. As can be seen from FIG. 2, the time characteristic includes the time t of the electric vehicle's starting point 0 start Time t of departure from zone I to zone I +1 I,I+1 Time t of arrival at region I +1 I+1 arr Dwell time t in region I +1 I+1 stay Time t of leaving region I +1 I+1 leave (ii) a The spatial feature quantity comprises an originating region I, and the distance from the region I to a region I +1 I,I+1 Charging and discharging position loc of electric vehicle j in region I j (ii) a The characteristic charge quantity includes the initial ground charge quantity SOC 0 start Real-time electric quantity of arrival at area I
Figure RE-GDA0003732712790000101
Real-time power leaving zone I
Figure RE-GDA0003732712790000102
Real-time electric quantity returned to residential area after finishing all strokes
Figure RE-GDA0003732712790000103
In order to analyze the travel rule of the electric automobile in one day, the method takes a residential area as the starting and ending point of the round trip of the electric automobile in one day, namely the electric automobile starts from the residential area and finally returns to the residential area, and the travel destinations are divided into three categories, namely the residential area H (HOME), the working area W (WORK) and the commercial area M (MARKET), according to the community division result.
In step S2, an electric vehicle trip chain is combined, and a corresponding user behavior characteristic rule is adopted to construct an electric vehicle ordered charging and discharging time model, which specifically includes:
step S21: constructing user behavior characteristics based on a trip chain: selecting a travel chain for the electric automobile, generating initial travel time and travel distance, calculating destination reaching time and electric quantity consumption, and judging charging, discharging or idling according to the real-time SOC value on the premise that the residual electric quantity meets the power consumption requirement of the residual travel.
Step 211: and obtaining different initial travel times according to different selected travel chains of the electric automobile.
When the electric automobile selects the H-W-H or H-W-M-H trip chain, the initial trip time approximately obeys the parameter N (mu) e1 ，σ e1 2 ) Normal distribution of (a):
Figure RE-GDA0003732712790000104
in the formula (I), the compound is shown in the specification,
Figure RE-GDA0003732712790000105
the initial travel time of the electric automobile with the first travel destination in the industrial area; mu.s e1 To initial travel time expectation, μ e1 ＝6.92；σ e1 To the initial travel time standard deviation, σ e1 ＝1.24。
When the electric automobile selects the H-M-H trip chain, the initial trip time approximately obeys the parameter N (mu) e2 ，σ e2 2 ) Normal distribution of (a):
Figure RE-GDA0003732712790000111
in the formula (I), the compound is shown in the specification,
Figure RE-GDA0003732712790000112
for the initial travel time of an electric vehicle whose first travel destination is the commercial district, 50% of users are subjected toInitial travel time expected value mu e2 Initial travel time standard deviation σ of 8.92 e2 Normal distribution of 3.24; 50% of users obey the initial travel time expectation value mu e2 16.47, initial travel time standard deviation σ e2 Normal distribution of 3.41.
Step 212: each section of travel mileage of the electric automobile with three travel chains approximately obeys parameters as
Figure RE-GDA0003732712790000113
Figure RE-GDA0003732712790000114
Normal distribution of (a):
Figure RE-GDA0003732712790000115
in the formula, L represents the driving mileage of the electric automobile; mu.s D Mu is the expected value of the mileage D ＝3.2；σ D Is the standard deviation of the mileage D ＝0.88。
Step 213: and different return times are obtained according to different trip chains selected by the electric automobile.
When the electric automobile selects the H-W-H trip chain, the return time approximately obeys the parameter N (mu) s ，σ s 2 ) Normal distribution of (a):
Figure RE-GDA0003732712790000116
in the formula, t end Representing an electric vehicle return time; mu.s s 17.47 denotes the expected value, σ s 1.8 represents the standard deviation.
When the electric automobile selects the H-M-H trip chain, the return time of the H-M-H
Figure RE-GDA0003732712790000117
Figure RE-GDA0003732712790000118
In the formula, t 0 start Is the departure time from a residential area; speed H-M-H Selecting the running speed of the electric automobile of the H-W-H trip chain;
Figure RE-GDA0003732712790000121
the residence time of the electric automobile in the commercial district M is represented; d H-M And D M-H The distance between the residential area H and the commercial area M is equal.
When the electric automobile selects the H-W-M-H trip chain, the return time of the H-W-M-H
Figure RE-GDA0003732712790000122
Figure RE-GDA0003732712790000123
In the formula, speed H-W-M-H Selecting the running speed of the electric automobile of the H-W-M-H trip chain;
Figure RE-GDA0003732712790000124
and
Figure RE-GDA0003732712790000125
the residence time of the electric automobile in the industrial area W and the commercial area M; d H-W 、D W-M And D M-H The distances from residential area H to industrial area W, industrial area W to business area M, and business area M to residential area H, respectively.
Step 214: and different stay times are obtained according to different travel chains selected by the electric automobile.
When the electric automobile selects H-W-M-H or H-M-H trip chain, the residence time in M is uniform distribution satisfying [1, 3] H.
When the electric automobile selects the H-W-H trip chain, the stay time at W
Figure RE-GDA0003732712790000126
Step 215: the SOC real-time size of the electric automobile is as follows:
Figure RE-GDA0003732712790000127
in the formula, SOC start Representing the real-time SOC before the start of the current section of the travel; w represents the power consumption of the electric automobile per kilometer; s represents the battery capacity.
Step S22: and constructing an ordered charging model of the electric automobile by combining real-time electric quantity according to the fact that the electric automobile is located in a non-residential area or a residential area.
Step 221: the electric vehicle drives away from the residential area.
When the electric automobile is in other areas I of non-residential areas, whether to charge or not depends on the real-time electric quantity of the electric automobile
Figure RE-GDA0003732712790000128
And the required power consumption SOC from the region I to the region I +1 I,I+1 If the real-time electric quantity can not meet the consumption requirement of the next section of travel, calculating the required electric quantity to obtain the charging time T charge . Charging time T of electric automobile charge Less than the residence time
Figure RE-GDA0003732712790000129
The charging starting time is any time t meeting the charging requirement in the stay time charge (ii) a On the contrary, the charging start time is the time of reaching the area I, and the specific expression is as follows:
Figure RE-GDA0003732712790000131
in the formula (I), the compound is shown in the specification,
Figure RE-GDA0003732712790000132
rand is [0, 1]]A random number within the interval.
Step S222: the electric vehicle returns to the residential area.
The electric vehicle responding to the peak-valley electricity price is charged or not after returning to the residential area according to the residual electricity quantity of the electric vehicle
Figure RE-GDA0003732712790000133
Time of return
Figure RE-GDA0003732712790000134
And a peak-to-valley electricity rate period. Charging time T of electric automobile charge When the duration of the valley period is less than the duration of the valley period, the charging starting time is any time t meeting the requirement of completing the charging in the valley period charge (ii) a Conversely, the charging start time is the valley period start time, and the specific expression is as follows:
Figure RE-GDA0003732712790000135
in the formula,. DELTA.t v Duration of valley price, Δ t v ＝t v2 -t v1 。
Step S23: and constructing an electric automobile ordered discharge model by combining real-time electric quantity according to the fact that the electric automobile is located in a non-residential area or a residential area.
Step S231: the electric vehicle drives away from the residential area.
When the electric automobile is in other areas of the non-residential area, whether to discharge or not depends on the real-time electric quantity of the electric automobile
Figure RE-GDA0003732712790000136
And the required power consumption SOC from the region I to the region I +1 I,I+1 If the real-time electric quantity can meet the consumption requirement of the next section of travel, a certain regulation and control threshold value SOC is arranged in the air control Then calculating the feedback electric quantity to obtain the discharge time length T discharge . Discharge time T of electric automobile discharge Less than the residence time
Figure RE-GDA0003732712790000137
At the beginning ofThe discharge time is any time t for meeting the discharge requirement in the residence time discharge (ii) a On the contrary, the discharge starting time is the time of reaching the region I, and the specific expression is as follows:
Figure RE-GDA0003732712790000141
step S232: the electric vehicle returns to the residential area.
The electric vehicle which returns in response to the peak time period of the peak-valley electricity price depends on the residual electricity quantity of the electric vehicle whether to discharge or not after the electric vehicle returns to the residential area
Figure RE-GDA0003732712790000142
And return time
Figure RE-GDA0003732712790000143
And a peak-to-valley electricity rate period.
Figure RE-GDA0003732712790000144
In order to obtain as much income as possible, the owner of the electric vehicle has a discharge time period T charge Less than Δ t, discharge start time t discharge The whole discharging process can be ensured to be within the peak electrovalence interval; conversely, the discharge start time is the return time
Figure RE-GDA0003732712790000145
The discharge-to-valley period comes, and the specific expression is as follows:
Figure RE-GDA0003732712790000146
in the formula (I), the compound is shown in the specification,
Figure RE-GDA0003732712790000147
in step S3, considering the interactivity and integrity between the electric vehicle and the power distribution network, a neighborhood world evolution model under the access of the electric vehicle is constructed, and the specific steps include:
step S31: simplified IEEE33 node distribution network system, the initial network comprising N 1 Each node and m edges, wherein the direction of the line is the flow direction of the electric energy, and the weight of the line is the equivalent impedance value of the line;
step S32: an optimization factor phi is allocated to each charging station node i in the area according to the distance and the number of electric vehicle connections of the charging station i Selecting the most suitable charging station for the electric vehicle:
Figure RE-GDA0003732712790000148
in the formula, k i-in Is the degree of entry of node i, and is denoted by k i-in The edge takes the node i as an end point; k is a radical of i-out For the out degree of node i, it has k i-out The edge takes the node i as a starting point; n is a radical of 1 Indicating the number of charging stations; d ij The physical distance between the charging station node i and the newly added electric vehicle node j is obtained; p is more than or equal to 0, and q is less than or equal to 1; and O is a neighborhood range and comprises part of the charging station nodes. If the electric automobile is charged, a charging station is selected for the electric automobile within the neighborhood by the given probability p, the newly added electric automobile is connected with the node with smaller emergence degree in the range of the charging station by the probability q, and is connected with the node with closer distance in the range of the charging station by the probability (1-q); selecting a charging station for the electric vehicle outside the neighborhood by the probability (1-p), connecting the charging station with a node with smaller outing degree in the charging station range by the probability q, and connecting the charging station with a node which is closer to the charging station range by the probability (1-q); the same applies to discharging.
The probability that the newly joined electric vehicle node is connected to the specific charging station node i is as follows:
Figure RE-GDA0003732712790000151
the neighborhood world O is defined as:
Figure RE-GDA0003732712790000152
the coordinates of the charging station node i are defined as v i ＝(x i ，y i ) The coordinate of the node j of the electric automobile is defined as v j ＝(x j ，y j ) Physical distance d between charging station node i and electric vehicle node j ij Is defined as:
Figure RE-GDA0003732712790000153
step S33: if the electric automobile continues to charge and discharge at the next moment, the electric automobile keeps being connected with the power distribution network, and the position of the electric automobile keeps unchanged; if the electric automobile finishes charging and discharging at the next moment, the connection with the power distribution network is disconnected;
step S34: and updating the network topology and the node information.
As shown in fig. 3, a flow chart of a space-time ordered charging and discharging method for an electric vehicle in a V2G mode is shown, and the specific flow is as follows:
(1) firstly, before the electric automobile j starts a travel of one day, knowing each travel destination in one day to obtain a corresponding travel chain type;
(2) starting a journey, generating travel time and travel distance, calculating time and electric quantity consumption of arriving at a destination I, and updating in real time
Figure RE-GDA0003732712790000161
Generating a dwell time;
(3) according to the current
Figure RE-GDA0003732712790000162
And the lower section stroke electric quantity consumption SOC I,I+1 Is simultaneously greater than the minimum value SOC of the battery capacity min And regulating threshold SOC control In time, feedback of electrical energy to the distribution network may be considered. Calculating the dischargeable time of the electric automobile, obtaining the discharge starting time, generating a neighborhood world, and distributing an optimal factor phi to charging station nodes inside and outside the region i Selecting proper charging station to discharge, and updating in real time after discharge is finished
Figure RE-GDA0003732712790000163
The topology and node information of the power distribution network prepares for starting the next section of the journey; if the difference between the two is smaller than the SOC control No charge and discharge behaviors are carried out, and the device is idled until the next stroke begins;
(4) if it is present
Figure RE-GDA0003732712790000164
The difference value between the numerical value and the lower stroke electric quantity consumption is less than the minimum value SOC of the battery capacity min And then the electric automobile needs to be charged at the moment so as to ensure that the electric quantity of the lower section of the travel is sufficient. Calculating the charging time of the electric automobile, obtaining the charging starting time, generating a neighborhood world, and distributing an optimal factor phi for charging station nodes inside and outside the region i Selecting proper charging station to charge, and updating in real time after charging is finished
Figure RE-GDA0003732712790000165
The topology and node information of the power distribution network are prepared to start the next section of travel;
(5) judging whether the lower section of stroke is the last section of stroke, if not, returning to the step (2) for execution; if yes, returning to the residential area;
(6) if the electric automobile returns to the residential area in the peak time period, the electric automobile considers the discharge, if the electric automobile returns to the residential area in the peak time period
Figure RE-GDA0003732712790000166
The electric automobile selects to discharge, and the optimal factor phi of charging station nodes inside and outside the area is calculated i Selecting proper charging station to discharge, and discharging to the valley period to come or real-time SOC (state of charge) ═ SOC min (ii) a If it is
Figure RE-GDA0003732712790000167
After the valley period comes, selecting a proper time point for charging;
(7) if the electric automobile returns to the residential area in the valley period, the optimal factor phi of the charging station nodes inside and outside the area is directly calculated i Selecting proper charging station to charge, and finishing chargingThen, the SOC is updated 0 start Network topology and node information;
(8) when j is<N 2 If so, j is j +1, and the step (2) is returned to be executed; when j is more than or equal to N 2 Calculating the charge and discharge load in one day;
(9) and finishing the execution.
In step S4, the influence of charging and discharging the electric vehicle into the distribution network is analyzed from both the topological characteristic and the electrical characteristic, and the specific steps include:
step S41: the charging and discharging requirements of the electric vehicle in different areas and different trip chains are obtained through monte carlo simulation, as shown in fig. 4 and 5.
As shown in FIG. 4, the starting power SOC of the electric vehicle for one day 0 start The power utilization requirements of most users can be met, so that the charging requirements of the users are concentrated after 23:00, namely the electricity price valley period. During peak electricity price, on the premise that an owner meets the next section of electricity demand, the owner actively selects to feed back electric energy to the power distribution network for gaining income, most vehicles returning to a residential area discharge in a 17:00-21:00 time period, most owners with destinations of an industrial area and a commercial area select 12:00-16:00 time period and 17:00-22:00 time period to discharge, power supply pressure of the power distribution network in the peak time period is effectively relieved, charging is uniformly arranged after the valley time period comes, and the effects of peak clipping and valley filling are achieved.
As can be seen from fig. 5, the charging demands under three kinds of travel chains are mainly concentrated in the time interval of 01:00-07:00, after the vehicle owner finishes the destination included in the travel chain, the vehicle returns to the residential area and is not immediately charged, after the valley time period comes, a proper time point is selected to be charged to full charge after the valley time period comes according to the situation of the remaining electric quantity of the vehicle owner, the advantages of low electricity price and low electricity consumption of residents in the valley time period are fully utilized, both the user and the distribution network are benefited, but the immediate charging just before the valley time period is avoided, otherwise, a new load peak is generated at the beginning of the valley time period; the discharge demand of the electric automobile is concentrated in the 11:00-22:00 time period, so that the electricity utilization pressure of residential areas, industrial areas and commercial areas can be effectively relieved, and the load peak value is reduced.
Step S42: and (3) defining topological entropy, and describing the ordering of the system from the point of view of node degree distribution.
The complex network theory considers that: the node degree can represent the position of the node in the network, and the larger the node degree is, the more critical the node is in the network. The network topology entropy can quantitatively describe the degree distribution of the nodes, and the topology entropy is defined as:
Figure RE-GDA0003732712790000171
in the formula, P 1 (k) The number of electric vehicles connected for a charging station node belongs to (U) k ，U k+1 ]Probability of constant sequence interval.
Constant sequence interval U 1 Comprises the following steps:
U 1 ＝[U 1 ，U 2 ，...，U m ]；
probability P 1 (k) Comprises the following steps:
Figure RE-GDA0003732712790000181
in the formula, n k The number of electric vehicles which represent the connection of the charging station nodes belongs to (U) k ，U k+1 ]The number of charging station nodes of (1).
When the degrees of all the charging station nodes are in the same range, the topological entropy is 0, and the network electric vehicle nodes are distributed most orderly. When all the charging station node degrees are in different intervals, the topological entropy reaches the maximum value. Topology entropy describes the ordering of a network from the point of view of its topological properties. The smaller the topological entropy, the higher the ordering of the system.
As shown in fig. 6, the simulation shows a graph of the change of the topological entropy with time in three charge and discharge modes, and the topological entropy describes the orderliness of the system from the point of view of node degree distribution. As can be seen from fig. 6, in the disordered charge-discharge mode, only the distance between the electric vehicles and the charging stations is considered, the number of electric vehicles connected to the charging stations is not considered, and the electric vehicles are randomly connected to the distribution network for charge and discharge only according to the own requirements, so that the number distribution difference of the connection of the charging stations in each area is large, and adverse effects are caused on the stable and safe operation of the distribution network. Simulation in the ordered charging mode shows that the overall topological entropy of the network is improved. Under the method, the topological entropy of the network is reduced to 0.40 from 1.19 in the disordered charging and discharging mode and 0.84 in the ordered charging mode, the overall reduction amplitude reaches 66.4% and 51.8%, the charging station is more balanced in use, and the safe operation of the power distribution network is facilitated.
Step S43: and (3) defining the power flow entropy, and describing the ordering of the system from the point of view of line load rate distribution.
The power distribution network system flow is the power distribution condition of each line in the power distribution network. Through load flow calculation, the load distribution of each line can be known, and the running state of the system can be observed. In the text, a Newton-Raphson method is adopted to calculate the power flow distribution situation when the electric automobile is connected to the power distribution network, and the power flow entropy is defined as follows:
Figure RE-GDA0003732712790000191
in the formula, P 2 (k) The line load rate belongs to when the electric automobile is connected into a power distribution network for charging and discharging (U) k ，U k+1 ]Probability of constant sequence interval.
Constant sequence interval U 2 Comprises the following steps:
U 2 ＝[U 1 ，U 2 ，...，U n ]；
probability P 2 (k) Comprises the following steps:
Figure RE-GDA0003732712790000192
in the formula I k The line load rate of the electric automobile connected to the power distribution network during charging and discharging belongs to (U) k ，U k+1 ]The number of lines of (2).
Load factor mu of line i i Comprises the following steps:
μ i ＝|P i 0 /P i max | i＝1,2,...,l；
in the formula, P i 0 For the actual power flow, P, during operation of the transmission line i i max The maximum active transmission capacity of the transmission line i.
When the load rates of all lines are in the same load rate range, the power flow entropy is 0, and the network power flow distribution is in the most ordered state. When the load rates of all lines are in different load rate intervals, the power flow entropy reaches the maximum value at the moment. The power flow entropy describes the ordering of the network from the point of view of the electrical characteristics of the network. The smaller the power flow entropy, the higher the ordering of the system.
As shown in fig. 7, the simulation shows the variation curve of the power flow entropy with time under three charge and discharge modes, and the power flow entropy describes the orderliness of the system from the perspective of power flow distribution. As can be seen from fig. 7, under the guidance of ordered charging and discharging of the method, the electric vehicle feeds back electric energy to the power distribution network at peak time intervals, and acquires electric energy from the power distribution network at valley time intervals, so that the distribution of the power flow entropy of the power distribution network is relatively balanced, the fluctuation of the power flow entropy is relatively small, and the ordered degree of the network is greatly improved; in the disordered charge-discharge mode, the power flow entropy fluctuation caused by random charge-discharge of the electric automobile is large, which shows that the disordered charge-discharge of the electric automobile causes the power flow distribution of the whole power distribution network to be very disordered; the distribution of the power flow in the ordered charging mode is slightly improved, but still needs to be improved.
Step S44: the effectiveness of the method in improving the quality of electrical energy was analyzed.
As shown in fig. 8, the simulation shows the change of the node voltage amplitude with time in three charge and discharge modes. As can be seen from fig. 8(a), in the disordered charge-discharge mode, the owner randomly accesses the power distribution network for charge-discharge in 24 time periods, without considering the safety and reliability of the operation of the power distribution network, so that most electric vehicles are charged in a peak time period, the voltage amplitude of 16:00-24:00 time periods is small, especially the node 18, the node voltage reaches the minimum value of 0.915 at 21:00 time, excessive electric vehicle charge loads overlap with the basic load peak time period, the voltage amplitude is greatly reduced, and the electric energy quality is greatly influenced; when the electric automobile adopts the ordered charging mode as shown in fig. 8(b), compared with the voltage amplitude of the disordered charging and discharging mode, the voltage amplitude of the basic load in the peak period is improved, the node voltage of the node 18 at the 21:00 moment is raised back to 0.928, and the falling period of the voltage amplitude is gradually transferred to the electricity utilization valley period; in a V2G mode, in fig. 8(c), a vehicle owner who goes to a working area, a business area or returns to a residential area after finishing a day trip mostly selects to feed redundant electric energy back to a power distribution network in the area on the premise of meeting the self electric quantity requirement or finishing all the trips today, the node voltage of the node 18 at the 21:00 moment rises back to 1.02, the time period of voltage amplitude falling is completely transferred to the electricity consumption valley period, after the electric vehicle is controlled to be charged and discharged at a specific time through a reasonable regulation and control means, the electric energy quality of the power distribution network is greatly improved, and the power distribution network improves the acceptance capacity of the electric vehicle.
In summary, the invention provides a method for time-space ordered charging and discharging of an electric vehicle in a V2G mode from a community level in combination with a user trip chain, which not only can meet the power consumption demand of a user, but also can effectively reduce adverse effects of node voltage reduction caused by charging and discharging of the electric vehicle during access, reduce network loss, balance power flow of a power distribution network, realize peak clipping and valley filling, and is beneficial to improving the safety and reliability of the power distribution network.
The above description is only an embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can understand that the modifications or substitutions within the technical scope of the present invention are included in the scope of the present invention, and therefore, the scope of the present invention should be subject to the protection scope of the claims.

Claims (10)

1. A method for orderly charging and discharging an electric automobile in a V2G mode in a space-time mode is characterized by comprising the following steps:
step S1: based on the fact that an owner usually moves to and fro among different destinations in one day, a community theory is combined, a charging station area is divided into different areas, the different areas are used as a basic model of electric automobile driving in space, a daily trip chain of the electric automobile is constructed, and therefore an electric automobile ordered charging and discharging space model based on the trip chain is constructed;
step S2: based on the selection of a trip chain, constructing an electric automobile ordered charging and discharging time model by adopting a corresponding user behavior characteristic rule;
step S3: considering the interactivity and integrity between the electric automobile and the power distribution network, constructing a neighborhood world evolution model under the access of the electric automobile;
step S4: and analyzing the influence of charging and discharging of the electric automobile connected to the power distribution network from two aspects of topological characteristics and electrical characteristics.
2. The method for space-time ordered charging and discharging of the electric automobile in the V2G mode according to claim 1, wherein: step S1 specifically includes the following steps:
step S11: dividing the charging station area into different areas by combining modularity and community structure theory;
the calculation formula of the modularity Q is as follows:
Figure FDA0003586365650000011
in the formula, s i-in ，s i-out Respectively representing the incoming strength and the outgoing strength of the charging station node i, wherein the strength is the weight sum of edges connected with the charging station node i; w represents the sum of the weights of all lines in the network; w is a ij Representing the weight of a connecting edge between a charging station node i and a charging station node j in the network; when the charging station node i and the charging station node j are in the same community, δ (i, j) is 1, otherwise δ (i, j) is 0;
step S12: constructing an electric vehicle trip chain based on the charging station areas divided by the communities;
the community structure is defined as follows: the community division is to divide nodes with similar electrical characteristics in a power network into corresponding areas, divide the whole charging station area into different areas, and decompose the charging and discharging behaviors of the electric vehicle in the whole power distribution network into the charging and discharging behaviors of the electric vehicle in each area.
3. The method for spatiotemporal ordered charging and discharging of the electric automobile in the V2G mode according to claim 1, wherein: the step S2 specifically includes the following steps:
step S21: constructing user behavior characteristics based on a trip chain;
step S22: constructing different ordered charging models according to the situation that the electric automobile is in a non-residential area or a residential area;
step S23: and constructing different ordered discharge models according to the situation that the electric automobile is in a non-residential area or a residential area.
4. The method for space-time ordered charging and discharging of the electric automobile in the V2G mode according to claim 3, wherein: the ordered charging model of the electric vehicle in the non-residential area in step S22 is:
Figure FDA0003586365650000021
in the formula, SOC I,I+1 The electric quantity is consumed for the electric automobile from the area I to the area I + 1;
Figure FDA0003586365650000022
the real-time electric quantity of the electric automobile arriving at the area I is obtained;
Figure FDA0003586365650000023
the real-time electric quantity of the electric automobile leaving the area I; distance I,I+1 The distance from the region I to the region I + 1; w is the power consumption of the electric automobile per kilometer; s is the battery capacity; t is charge Charging the electric vehicle for a period of time; p is the charge and discharge power of the electric automobile; t is t charge Starting charging time for the electric automobile;
Figure FDA0003586365650000024
Figure FDA0003586365650000025
the time when the electric automobile leaves the area I;
Figure FDA0003586365650000026
the time when the electric automobile arrives at the area I;
Figure FDA0003586365650000027
the residence time of the electric automobile in the area I is calculated; rand is [0, 1]]A random number within the interval;
the ordered charging model of the electric vehicle in the residential area in step S22 is:
Figure FDA0003586365650000028
in the formula, SOC max The maximum capacity of the battery; SOC n arr The real-time electric quantity reaching the residential area; Δ t v ＝t v2 -t v1 ，t v1 Is the beginning time of the valley period, t v2 The end time of the valley period.
5. The method for space-time ordered charging and discharging of the electric automobile in the V2G mode according to claim 4, wherein: the ordered discharge model of the electric vehicle in the non-residential area in step S23 is:
Figure FDA0003586365650000031
in the formula, T discharge The discharge time of the electric automobile is prolonged; SOC control A regulation threshold for electric vehicle discharge; t is t discharge The discharge starting time of the electric automobile;
the ordered discharge model of the electric vehicle in the residential area in step S23 is:
Figure FDA0003586365650000032
in the formula, SOC min Is the minimum capacity of the battery; Δ t ═ t p2 -t n arr ，t p2 Is the peak period end time, t n arr The time of the electric automobile arriving at the residential area.
6. The method for spatiotemporal ordered charging and discharging of the electric automobile in the V2G mode according to claim 1, wherein: the step S3 specifically includes the following steps:
step S31: simplified IEEE33 node distribution network system, the initial network comprising N 1 The direction of the line is the flow direction of the electric energy, and the weight of the line is the equivalent impedance value of the line;
step S32: an optimization factor is allocated to each charging station node i in the area in combination with the distance and the number of electric vehicle connections of the charging station
Figure FDA0003586365650000033
Selecting the most suitable charging station for the electric vehicle; the preferred factor
Figure FDA0003586365650000034
The calculation formula of (2) is as follows:
Figure FDA0003586365650000035
in the formula, k i-in Is the degree of entry of node i, and is denoted by k i-in The edge takes the node i as an end point; k is a radical of formula i-out For the out degree of node i, it has k i-out The edge takes the node i as a starting point; n is a radical of 1 Indicating the number of charging stations; d ij The physical distance between the charging station node i and the newly added electric vehicle node j is obtained; p is more than or equal to 0, and q is less than or equal to 1; o is a neighborhood range and comprises part of charging station nodes;
step S33: if the electric automobile continues to charge and discharge at the next moment, the electric automobile keeps being connected with the power distribution network, and the position of the electric automobile keeps unchanged; if the electric automobile finishes charging and discharging at the next moment, the connection with the power distribution network is disconnected;
step S34: and updating the network topology and the node information.
7. The method for space-time ordered charging and discharging of the electric automobile in the V2G mode according to claim 6, wherein: the neighborhood range O described in step S23 is defined as:
Figure FDA0003586365650000041
coordinate v of charging station node i i Is defined as:
v i ＝(x i ,y i ),i∈1,2,...,N 1 ；
coordinate v of electric automobile node j j Is defined as:
v j ＝(x j ,y j ),j∈1,2,...,N 2 。
8. the method for space-time ordered charging and discharging of the electric automobile in the V2G mode according to claim 1, wherein: step S4 specifically includes the following steps:
step S41: respectively obtaining the charging and discharging requirements of the electric automobile in different areas and different trip chains;
step S42: defining topological entropy, and describing the orderliness of the system from the point of node degree distribution; the topological entropy is:
Figure FDA0003586365650000042
in the formula, P 1 (k) The number of electric vehicles connected for a charging station node belongs to (U) k ，U k+1 ]Probability of constant sequence interval;
step S43: defining a power flow entropy, and describing the orderliness of the system from the point of view of line load rate distribution; the power flow entropy is as follows:
Figure FDA0003586365650000043
in the formula, P2(k) is the probability that the line load rate belongs to the (Uk, Uk +1] constant sequence interval when the electric vehicle is connected into the power distribution network for charging and discharging.
Step S44: the method was analyzed to be effective in improving the quality of electric power.
9. The method for space-time ordered charging and discharging of the electric automobile in the V2G mode according to claim 8, wherein: constant sequence interval U described in step S42 1 Comprises the following steps:
U 1 ＝[U 1 ，U 2 ，...，U m ]；
the probability P1(k) is:
Figure FDA0003586365650000051
in the formula, nk represents the number of the charging station nodes, to which the number of electric vehicles connected to the charging station node belongs to (Uk, Uk + 1).
10. The method for space-time ordered charging and discharging of the electric automobile in the V2G mode according to claim 8, wherein: constant sequence interval U described in step S42 2 Comprises the following steps:
U 2 ＝[U 1 ，U 2 ，...，U n ]；
probability P 2 (k) Comprises the following steps:
Figure FDA0003586365650000052
in the formula I k The line load rate of the electric automobile connected to the power distribution network during charging and discharging belongs to (U) k ，U k+1 ]The number of lines of (a);
load factor mu of line i i The following were used:
μ i ＝|P i 0 /P i max |i＝1,2,...,l；
in the formula, P i 0 For the actual power flow, P, during operation of the transmission line i i max The maximum active transmission capacity of the transmission line i.
CN202210367205.8A 2022-04-08 Method for time-space ordered charge and discharge of electric automobile in V2G mode Active CN114897302B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202210367205.8A CN114897302B (en) 2022-04-08 Method for time-space ordered charge and discharge of electric automobile in V2G mode

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202210367205.8A CN114897302B (en) 2022-04-08 Method for time-space ordered charge and discharge of electric automobile in V2G mode

Publications (2)

Publication Number Publication Date
CN114897302A true CN114897302A (en) 2022-08-12
CN114897302B CN114897302B (en) 2024-07-26

Family

ID=

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108510128A (en) * 2018-04-11 2018-09-07 华南理工大学广州学院 A kind of region electric vehicle charging load spatial and temporal distributions prediction technique
CN110728396A (en) * 2019-09-25 2020-01-24 国网山西省电力公司经济技术研究院 Electric vehicle charging load comprehensive modeling method considering space-time distribution
WO2021098352A1 (en) * 2019-11-22 2021-05-27 国网福建省电力有限公司 Active power distribution network planning model establishment method taking into consideration site selection and capacity determination of electric vehicle charging stations
WO2021143075A1 (en) * 2020-01-17 2021-07-22 南京东博智慧能源研究院有限公司 Demand response method taking space-time distribution of electric vehicle charging loads into consideration

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108510128A (en) * 2018-04-11 2018-09-07 华南理工大学广州学院 A kind of region electric vehicle charging load spatial and temporal distributions prediction technique
CN110728396A (en) * 2019-09-25 2020-01-24 国网山西省电力公司经济技术研究院 Electric vehicle charging load comprehensive modeling method considering space-time distribution
WO2021098352A1 (en) * 2019-11-22 2021-05-27 国网福建省电力有限公司 Active power distribution network planning model establishment method taking into consideration site selection and capacity determination of electric vehicle charging stations
WO2021143075A1 (en) * 2020-01-17 2021-07-22 南京东博智慧能源研究院有限公司 Demand response method taking space-time distribution of electric vehicle charging loads into consideration

Similar Documents

Publication Publication Date Title
Moghaddam et al. Smart charging strategy for electric vehicle charging stations
Wang et al. Two-stage optimal scheduling strategy for large-scale electric vehicles
CN108596667B (en) Electric automobile real-time charging electricity price calculation method based on Internet of vehicles
CN109948954B (en) Power distribution network bidirectional blocking scheduling method for distributed resources of power system
Wang et al. Optimal coordination of vehicle-to-grid batteries and renewable generators in a distribution system
CN111310966A (en) Micro-grid site selection and optimal configuration method containing electric vehicle charging station
Guo et al. Robust energy management for industrial microgrid considering charging and discharging pressure of electric vehicles
CN112381398B (en) Electric vehicle charging station site selection method and system considering limited trip of user
CN111798121B (en) Distributed collaborative optimization method for energy management scheduling of electric automobile
CN109167347B (en) Cloud-adaptive-particle-swarm-based multi-target electric vehicle charge-discharge optimization scheduling method
CN112217195B (en) Cloud energy storage charging and discharging strategy forming method based on GRU multi-step prediction technology
CN116001624A (en) Ordered charging method for one-pile multi-connected electric automobile based on deep reinforcement learning
CN111619391B (en) Electric vehicle ordered charging and discharging method based on cooperative game and dynamic time-of-use electricity price
Yang et al. An optimal battery allocation model for battery swapping station of electric vehicles
CN110276517A (en) A kind of electric automobile charging station site selecting method based on MOPSO algorithm
CN116632896B (en) Electric vehicle charging and discharging collaborative scheduling method and system of multi-light-storage charging station
CN115983598A (en) Micro-grid privacy protection and energy scheduling method based on distributed deep reinforcement learning
Zheng et al. Particle swarm optimisation for scheduling electric vehicles with microgrids
Taghizadeh et al. Deep reinforcement learning-aided bidding strategies for transactive energy market
Nethravathi et al. A novel residential energy management system based on sequential whale optimization algorithm and fuzzy logic
Lin et al. Aggregator pricing and electric vehicles charging strategy based on a two-layer deep learning model
Afshar et al. A distributed ev charging framework considering aggregators collaboration
Ri et al. eIFVT: exploiting an integrated FAHP-VWA-TOPSIS in whole-process of on-demand charging scheduling for WRSNs
CN114897302A (en) Method for charging and discharging electric automobile in space-time order under V2G mode
CN114897302B (en) Method for time-space ordered charge and discharge of electric automobile in V2G mode

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant