CN107886186A - A kind of charging pile method to set up based on travelling data and Wei Nuotu zonings - Google Patents
A kind of charging pile method to set up based on travelling data and Wei Nuotu zonings Download PDFInfo
- Publication number
- CN107886186A CN107886186A CN201710957855.7A CN201710957855A CN107886186A CN 107886186 A CN107886186 A CN 107886186A CN 201710957855 A CN201710957855 A CN 201710957855A CN 107886186 A CN107886186 A CN 107886186A
- Authority
- CN
- China
- Prior art keywords
- charging
- area
- sub
- time period
- vehicle
- 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.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 45
- 238000013316 zoning Methods 0.000 title abstract 2
- 238000005457 optimization Methods 0.000 claims abstract description 12
- 239000002245 particle Substances 0.000 claims abstract description 11
- 238000010586 diagram Methods 0.000 claims description 25
- 238000004364 calculation method Methods 0.000 claims description 16
- 238000012423 maintenance Methods 0.000 claims description 5
- 239000004576 sand Substances 0.000 claims description 2
- 238000010276 construction Methods 0.000 abstract description 8
- 101100400378 Mus musculus Marveld2 gene Proteins 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000002860 competitive effect Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000002050 diffraction method Methods 0.000 description 1
- 230000009977 dual effect Effects 0.000 description 1
- 230000005611 electricity Effects 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 230000002068 genetic effect Effects 0.000 description 1
- UYXTWWCETRIEDR-UHFFFAOYSA-N glycerol tributanoate Natural products CCCC(=O)OCC(OC(=O)CCC)COC(=O)CCC UYXTWWCETRIEDR-UHFFFAOYSA-N 0.000 description 1
- 230000005484 gravity Effects 0.000 description 1
- 230000002401 inhibitory effect Effects 0.000 description 1
- 238000011835 investigation Methods 0.000 description 1
- 230000005658 nuclear physics Effects 0.000 description 1
- JLKIGFTWXXRPMT-UHFFFAOYSA-N sulphamethoxazole Chemical compound O1C(C)=CC(NS(=O)(=O)C=2C=CC(N)=CC=2)=N1 JLKIGFTWXXRPMT-UHFFFAOYSA-N 0.000 description 1
Classifications
-
- 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
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
- G06Q10/043—Optimisation of two dimensional placement, e.g. cutting of clothes or wood
Landscapes
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Engineering & Computer Science (AREA)
- Strategic Management (AREA)
- Economics (AREA)
- Marketing (AREA)
- Game Theory and Decision Science (AREA)
- Entrepreneurship & Innovation (AREA)
- Development Economics (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Tourism & Hospitality (AREA)
- Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Charge And Discharge Circuits For Batteries Or The Like (AREA)
Abstract
The present invention proposes a kind of charging pile method to set up based on travelling data and Wei Nuotu zonings, belongs to electric automobile field.This method first with dimension promise drawing method to need set charging pile region division subregion；Using travelling data, calculate the maximum charge per sub-regions and load, select maximum therein and its corresponding subregion；Value models are established to the subregion, using particle cluster algorithm to model solution, obtain the optimum results of charging station location that the subregion newly increases and charging pile quantity；Then the charging station newly increased is added in the map in the region, the optimization that all charging stations are re-started with sub-zone dividing and charging station calculates, until constraints exceeds the default upper limit, setting completed for charging pile in the region.The present invention is carried out the reasonable plant-site selection and constant volume of charging pile setting using vehicle operation data, has very strong of overall importance and accuracy to obtain charging electric vehicle efficiently and reduce electrically-charging equipment construction cost as target.
Description
Technical Field
The invention belongs to the field of electric automobiles, and particularly relates to a charging pile setting method based on driving data and a Voronoi diagram division area.
Background
Compared with the traditional vehicle, the electric vehicle has the advantages of no pollution, high energy utilization rate, simple structure, low noise and the like. Starting from dealing with energy structure adjustment, environmental protection and cultivation of future scientific and technological competitive advantages, the development trend of electric automobiles is inevitable. With the steady increase of the holding capacity of electric automobiles, the problems of inhibiting the development of the electric automobiles appear, wherein the charging piles are unreasonably distributed and have low utilization rate, so that the popularization and the popularization of the electric automobiles are seriously restricted.
To solve this problem, scholars at home and abroad have conducted preliminary studies on the optimized layout of the charging station. The existing electric vehicle charging pile setting is mainly divided into 2 aspects of urban area highway charging pile setting and intercity highway charging pile setting; the charging pile of the expressway is mainly limited by considering the driving range of the electric vehicle, and the charging pile of the urban highway is also required to consider the requirements (proper charging time, proper charging position and the like) of electric vehicle users besides the driving range. The common point of the two charging pile settings is that charging demands (such as charging load prediction based on user travel demands and charging demand prediction based on space-time distribution) are predicted by a certain method, so that the charging piles are planned and dimensioned.
The existing charging pile setting method comprises the following steps: selecting a certain area (city or town), acquiring the GDP growth rate and the electric automobile holding capacity of the area over the years, planning functional buildings (residential land, public facility land, industrial land and the like) in the area, dividing traffic districts according to different functions, predicting the electric automobile holding capacity and the charging pile demand through an elastic coefficient method, quantizing and calibrating the travel generation intensity, the travel attraction intensity and the traffic impedance of different traffic districts through a gravity model method, predicting the travel distribution capacity of the electric automobiles among the districts, and predicting the charging demand of each traffic district according to the peak hour flow ratio and the different charging tendency crowd occupation ratios. The method includes the steps that the inherent characteristics of short driving mileage and long charging time of the electric vehicle are considered, the charging behavior of the electric vehicle is assumed, and a charging station site selection model taking the minimum total time consumption of all users as an objective function is established by combining the limit conditions of the development scale of a charging facility. The traffic cell is considered as a centralized demand generation point by the model, and whether a charging station is arranged at each point or not is judged by utilizing a genetic algorithm.
However, these existing methods have problems in that data (such as GDP growth rate over the years, electric vehicle holding capacity over the years, functional building planning in the areas, etc. in the above methods) are obtained from investigation, belong to statistical data, and have obvious disadvantages in predicting charging demand through the statistical data, and the data amount is small; and due to the influence of human factors, the subjectivity of part of data is too strong, such as the intensity generated by traveling in a traffic cell, the travel attraction intensity and the traffic impedance, and an accurate value cannot be obtained through calibration, so that an accurate result cannot be obtained.
Voronoi diagram (Voronoi), also called thiessen polygon or Dirichlet diagram: the method comprises the steps of dividing a plane into N regions by N discrete data points which are distinguished on the plane, wherein each discrete data point corresponds to one region, and the distance from each point in the N regions to the discrete data point corresponding to the region is the nearest. The method has an important position in the geometric discipline of calculation, and has wide application in the fields of geography, meteorology, crystallography, aerospace, nuclear physics, robots and the like due to the characteristic that the distance from the region divided according to the point set to the point is nearest. If the obstacle points are concentrated, the method can be used for avoiding obstacles and finding the best path. At present, the voronoi diagram has not been applied to electric automobile and fills electric pile and set up the field yet.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides a charging pile setting method based on driving data and a Voronoi diagram division area. According to the method, a Veno diagram is used for dividing regions, vehicle driving data are analyzed, the charging facility has the dual attributes of urban traffic public service facilities and common power utilization facilities, the charging efficiency of the electric vehicle is obtained and the construction cost of the charging facility is reduced on the basis of meeting the charging requirement of the electric vehicle in the region, reasonable site selection and constant volume are carried out on a charging pile, and a foundation is laid for large-scale application of the electric vehicle.
The invention provides a charging pile setting method based on driving data and a Voronoi diagram divided region, which is characterized by comprising the following steps of:
1) dividing an area needing to be provided with a charging pile into sub-areas;
acquiring a map of an area where charging piles need to be set; marking all existing charging stations in the area on a map of the area and calculating the distance between every two charging stations; if the distances between all the charging stations are larger than a set distance threshold value L, setting each charging station in the area as a discrete data point on the map; if the distance between the two charging stations is smaller than or equal to the set distance threshold value L, combining the two charging stations on a map into a new charging station, taking the center point of the positions of the two charging stations as the position of the new charging station, and setting each charging station in the area as a discrete data point on the map; dividing the region into different sub-regions by a Voronoi diagram method by utilizing the discrete data points, wherein the number of the sub-regions is equal to that of the discrete data points;
2) calculating the maximum charging load of each sub-area by using the driving data, selecting the maximum value of the maximum charging load, and recording the sub-area corresponding to the maximum value as A; the method comprises the following specific steps:
2-1) dividing the whole day into 24 time periods by 1 hour, and making i represent a time period serial number, i is 1,2,3 … 24;
2-2) selecting any one subregion, and calculating the number of vehicles required for effective charging of the subregion;
the vehicle with the effective charging demand refers to an electric vehicle with an actual charging demand in a subregion in any time period, and comprises the following two parts:
the vehicle is effectively parked: the one-time parking time of the electric vehicle in any time period of the subregion is more than 30 minutes, and the remaining capacity percentage SOC is lower than a set remaining capacity percentage threshold value E;
passing through the vehicle: the difference between the electric automobile traffic flow and the effective parking quantity in any time period in the subarea;
the calculation expression of the number of vehicles required to be charged effectively for any of the sub-area time periods i is as follows:
N0i＝αNei+βNpi
in the formula, N0iThe number of vehicles required for effective charging in the time period i in the sub-area; n is a radical ofeiThe number of effective parking vehicles in the time period i in the sub-area is; n is a radical ofpiThe number of passing vehicles in the time period i in the subregion, α an effective parking weight coefficient, β a passing vehicle weight coefficient;
2-3) calculating the average charge demand of the vehicles in the subareas;
the average charging demand of the vehicle refers to the average charging demand of the vehicle per unit time of the effective charging demand in any time period in the subregion; selecting any time period according to the effective parking vehicles and the passing vehicles obtained by dividing in the step 2-1), respectively counting the SOC of each effective parking vehicle and the minimum SOC of each passing vehicle in the time period, and performing accumulated summation to be respectively recorded as sigma SOCeiSum sigma SOCpiIf the average power battery capacity of the vehicle is A, the sub-region time is determinedAverage charge demand Q for vehicles of section iiThe calculation expression of (a) is:
Qi＝[α·(Nei*100％-∑SOCei)+β·(Npi*100％-∑SOCpi)]·A/N0i
2-4) calculating the charging load of all time periods in a sub-region within a period of time to obtain the maximum charging load of the sub-region;
acquiring the quantity of charging piles of charging stations in each sub-area, wherein the calculation expression of the charging load in any time period of the sub-area is as follows:
Ci＝Qi·N0i/(Nc·q)
in the formula, CiCharging a load for a sub-area in a time period i; n is a radical ofcThe number of the charging piles in the subarea is determined; q is the average charging capacity of the charging piles in the subareas;
intercepting a period of time, calculating the charging load of all time periods in the sub-area within the intercepted period of time,
selecting a time period corresponding to the maximum value and recording the charging load of the sub-area in the time period as the maximum charging load C of the sub-areamax；
2-5) traversing all sub-regions obtained by dividing in the step 1), repeating the steps 2-2) to 2-4), and calculating to obtain C corresponding to each sub-regionmaxSelecting C thereinmaxThe subarea with the largest value is marked as a subarea A;
3) comparing the maximum charging load of the subarea corresponding to the A with a charging load threshold value [ C ] and judging that: if the maximum charging load of the subarea A is higher than the set charging load threshold, adding charging stations to the subarea A, and entering the step 4); otherwise, the setting of the charging pile in the area is finished;
4) establishing a value model for the sub-region A, solving the model by utilizing a particle swarm algorithm, and optimizing the newly added charging station positions and the number of charging piles of the sub-region; the method comprises the following specific steps:
4-1) determining an objective function of the model; the expression is as follows
G＝a·Dj+b·M
In the formula, DjThe sum of the minimum distances traveled by all vehicles in the subarea to the current charging station j of the subarea A is represented by M, and the target cost of setting a new charging station in the subarea A is represented by M; a is a distance optimization weight, and b is a cost optimization weight;
Djthe calculation expression of (a) is as follows:
in the formula, DjiRepresenting the distance between the ith vehicle and the j point of the charging station, wherein k is the total number of the vehicles;
the target cost M includes: charging station investment cost MsAnd the total charging pile investment cost MqAnd the operation and maintenance cost M of the charging stationomAnd the charging profit Mp(ii) a The expression is as follows:
M＝Min(Ms+Mq+Mom-Mp)
4-2) solving the model;
optimizing the positions of the newly added charging stations and the number of the charging piles in the sub-area A by using a particle swarm optimization and taking the number of the charging piles which are added cumulatively in the whole area, the number of the charging stations which are added cumulatively, the target cost M and the charging load threshold value [ C ] as constraint conditions and taking G as a target function; only one charging station is added in the sub-area A needing to be added with the charging station each time, and the result output by the particle swarm algorithm is as follows: adding a charging station in the subarea, wherein the position coordinates of the newly added charging station and the number of charging piles in the newly added charging station are obtained;
5) adding the newly added charging stations obtained in the step 4) into the map in the step 1), returning to the step 1), and performing sub-area division and optimal calculation on all the charging stations again until the number of the charging stations which are added in the area, the number of the charging piles which are added in the area or the target cost exceed the constraint condition, and finishing the setting of the charging piles in the area.
The invention has the characteristics and beneficial effects that:
according to the charging pile setting method based on the driving data and the division of the area by the Voronoi diagram, the total number of the charging piles, the charging load threshold value, the per-hour income of each unit charging pile, the costs of the charging piles and the charging stations and the weight of the cost and the distance can be preset according to the requirements of users. According to the method, areas are divided by using the acquired map information and the charging pile charging station position information through a Veno diagram method, vehicle longitude and latitude information, parking event information and charging event information are extracted from vehicle driving data, charging requirements in each area are calculated, a comprehensive value function of a certain charging pile setting scheme is used according to parameters and position information set by a user, and an optimal solution is selected through a particle swarm algorithm. The method utilizes the vehicle driving data to reasonably select the site and fix the volume of the charging pile, and has strong global property and accuracy. On the basis of meeting the charging requirements of electric vehicles in the region, the charging device aims at obtaining high charging efficiency of the electric vehicles and reducing the construction cost of charging facilities, reasonably selects and fixes the volume of the charging pile, and lays a foundation for large-scale application of the electric vehicles.
Compared with the traditional method, the method uses more accurate data sources, uses a more reasonable region division method, has high reliability of the quantized value in the middle process, can carry out reasonable planning on the position capacity of the newly-added charging station in the region (the charging demands obtained by data of different time phases represent the reasonable position and capacity of the charging station at that time), and has real-time performance, high accuracy and high effectiveness.
Drawings
FIG. 1 is a block diagram of the overall flow of the method of the present invention.
Fig. 2 is a schematic diagram of an optimization result of a charging pile according to an embodiment of the present invention.
Detailed Description
The invention provides a charging pile setting method based on driving data and a Voronoi diagram division area, which is further described in detail below with reference to the accompanying drawings and specific embodiments.
The invention provides a charging pile setting method based on driving data and a Voronoi diagram divided region, the overall flow is shown in figure 1, and the method comprises the following steps:
1) dividing an area needing to be provided with a charging pile into sub-areas;
obtaining a map of a charging pile area to be set (the area can be a city or a village and town, and is selected according to the requirements of a planner; marking all existing charging stations in the area on a map of the area and calculating the distance between every two charging stations; if the distances between all the charging stations are larger than a set distance threshold value L, setting each charging station in the area as a discrete data point on the map; if the distance between two charging stations is smaller than or equal to the set distance threshold value L, combining the two charging stations on the map into a new charging station, taking the center point of the positions of the two charging stations as the position of the new charging station, and setting each charging station in the area as a discrete data point on the map (the distance threshold value can be set by a user according to needs, and is preset to 200m in the embodiment); dividing the region into different sub-regions by a Voronoi method by using discrete data points, wherein the number of the sub-regions is equal to that of the discrete data points;
the key to building a voronoi diagram is to reasonably concatenate the discrete data points into a triangulation network, i.e., to build a Delaunay triangulation network. The Delaunay triangulation algorithm is adopted for constructing the Delaunay triangulation network, and the algorithm mainly comprises the steps of generating the dual-element Delaunay triangulation network when the Veno diagram is generated, finding out the circle center of a circumscribed circle of each triangle of the triangulation network, and finally connecting the circle centers of the circumscribed circles of adjacent triangles to form the polygonal network taking the vertex of each triangle as a generating element. The specific steps for establishing the voronoi diagram are as follows:
1-1) setting discrete data points (in the present invention, each existing charging station location is a discrete data point, it should be specifically noted that, when the distance between two charging station locations is less than a set distance threshold, the two charging stations are regarded as a charging station, the location of the charging station is taken as the center point of the two charging station locations, and the distance threshold can be set by a user according to needs, which is preset to 200m in this embodiment), and constructing a Delaunay triangulation network. The discrete data points and each formed triangle are numbered to form a triangle linked list, and the triangle linked list is used for recording three discrete data points forming each triangle.
1-2) calculating the center of a circumscribed circle of each triangle and recording;
1-3) randomly selecting a triangle to be marked as a current triangle pTri, traversing a triangle linked list, and searching adjacent triangles which are shared with three edges of the current triangle pTri and respectively marking as a TriA, a TriB and a TriC; and judging that:
if the co-edge adjacent triangle exists, connecting the outer center of the searched co-edge adjacent triangle with the outer center of the pTri, and storing the formed line segment into the Voronoi edge chain table; if a certain edge of the current triangle does not have an adjacent triangle with a common edge, the perpendicular bisector ray of the edge is solved and stored in the Voronoi edge linked list.
1-4) traversing each triangle, finding all the Voronoi edges and storing the Voronoi edges into a Voronoi edge linked list; and drawing a voronoi diagram (the voronoi diagram obtained in the embodiment, namely the voronoi diagram of the area needing to be provided with the charging pile) by using all voronoi edges in the voronoi edge linked list.
2) Calculating the maximum charging load of each sub-area by using the driving data, selecting the maximum value of the maximum charging load, and recording the sub-area corresponding to the maximum value as A;
the charging demand is the basis for the planning of electric vehicle charging facilities. The charging demand is calculated mainly based on the number of vehicles with effective charging demand, the average charging demand of the vehicles, and the sub-area charging load.
In the invention, gps sensing equipment and driving data acquisition equipment need to be installed on a vehicle, and a certain T-box equipment in the market is used for acquiring driving data, and the method comprises the following steps: the parking position (latitude and longitude coordinates), parking time data (time range), charging position (latitude and longitude coordinates), charging time data (time range) data, vehicle SOC (percentage of remaining charge) of the vehicle, from which the charging demand and charging load of the vehicle are calculated. The method comprises the following specific steps:
2-1) dividing the whole day into 24 time periods by 1 hour, and making i represent a time period serial number, i is 1,2,3 … 24; the time period may be 00: 00-01: 00, 01: 00-02: 00 … and so on, i equals 1, then the time period represented is 00: 00-01: 00;
2-2) selecting any one subregion, and calculating the number of vehicles required for effective charging of the subregion;
the vehicle with the effective charging demand refers to an electric vehicle with an actual charging demand in a subregion in any time period, and comprises the following two parts:
the vehicle is effectively parked: and recording the electric automobile with one-time parking time length of more than 30 minutes and SOC (percentage of remaining charge) lower than a set threshold value E (80% in the embodiment) of percentage of remaining charge in any time period of the sub-area as an effective parking vehicle in the time period. The SOC electric quantity percentage threshold value E can be adjusted according to the SOC statistical result of the existing vehicle starting to charge.
Passing through the vehicle: the difference between the electric automobile traffic flow and the effective parking quantity in any time period in the subarea.
The charging possibility of the effective parking vehicle and the passing vehicle in the subarea is obviously different, the charging possibility of the effective parking vehicle is far greater than that of the passing vehicle, and therefore the effective parking vehicle is given higher weight. The calculation expression of the number of vehicles required for effective charging of the sub-area time period i is as follows:
N0i＝αNei+βNpi
in the formula, N0iThe number of vehicles required for effective charging in the time period i in the sub-area; n is a radical ofeiThe number of effective parking vehicles in the time period i in the sub-area is; n is a radical ofpiThe number of passing vehicles in the time period i in the sub-area is set; (N)eiAnd NpiAll values of (A) are obtained through driving data)
α is an effective parking weight coefficient (value range: 0-1, 0.5 in this embodiment), β is a passing vehicle weight coefficient (value range: 0-0.5, 0.15 in this embodiment);
2-3) calculating the average charge demand of the vehicles in the subareas;
the average charge demand of the vehicle refers to the average charge demand per unit time of the vehicle for which the effective charge demand is in any time period in the sub-area. Selecting any time period according to the effective parking vehicles and the passing vehicles obtained by dividing in the step 2-1), respectively counting the SOC of each effective parking vehicle and the SOC of each passing vehicle in the time period, and performing accumulated summation to be respectively recorded as sigma SOCeiSum sigma SOCpiIf the average power battery capacity of the vehicle is A, the average charging demand Q of the vehicle in the time period i in the sub-areaiThe calculation expression of (a) is:
Qi＝[α·(Nei*100％-∑SOCei)+β·(Npi*100％-∑SOCpi)]·A/N0i
2-4) calculating the charging load of all time periods in a sub-region within a period of time to obtain the maximum charging load of the sub-region;
the sub-area charging load is defined as the ratio of the average charging demand of the vehicles in any time period in the sub-area to the charging capacity of the charging piles in the sub-area.
Acquiring the quantity of charging piles of charging stations in each sub-area, wherein the calculation expression of the charging load in any time period of the sub-area is as follows:
Ci＝Qi·N0i/(Nc·q)
in the formula, CiCharging the sub-region in time period i with a load, NcThe number of the charging piles in the subarea is determined; q is the average charging capacity of the charging piles in the subareas (the average value of the charging capacities of all the charging piles in the subarea charging stations is obtained, and the unit is kw);
intercepting a period of time (such as one month), calculating the charging loads of all time periods in the sub-area within the intercepted period of time, selecting the time period corresponding to the maximum value, and recording the sub-area charging load of the time period as the maximum charging load C of the sub-areamax。
2-5) traversing all sub-regions obtained by dividing in the step 1), repeating the steps 2-2) to 2-4), and calculating to obtain C corresponding to each sub-regionmaxSelecting C thereinmaxThe subarea with the largest value is marked as a subarea A;
3) comparing the maximum charging load of the sub-area corresponding to the A with a preset charging load threshold value [ C ] (the value range is generally 0-2, and is set as 1.2 in the invention) and judging: if the maximum charging load of the subarea A is higher than the set charging load threshold, adding charging stations for the subarea A, and entering the step 4), calculating positions where the subareas are possibly provided with the charging stations, and optimizing the positions of the charging stations newly added for the subareas and the number of charging piles; otherwise, the charging station is not needed to be additionally arranged in the whole area, and the arrangement of the charging piles in the area is finished.
4) Establishing a value model for the sub-region A, solving the model by utilizing a particle swarm algorithm, and optimizing the newly added charging station positions and the number of charging piles of the sub-region; the method comprises the following specific steps:
4-1) determining an objective function of the model; the expression is as follows
G＝a·Dj+b·M
The objective function is a cost function and comprises a distance part and a cost part; in the formula, DjThe sum of the minimum distances traveled by all vehicles in the subarea to the current charging station j of the subarea A is represented by M, and the target cost of setting a new charging station in the subarea A is represented by M; a is a distance optimization weight (the value range is 0-1, in this embodiment, 0.7), and b is a cost optimization weight (the value range is 0-1, in this embodiment, 0.3);
Djthe calculation expression of (a) is as follows:
in the formula, DjiRepresenting the distance between the ith vehicle and the j point of the charging station, wherein k is the total number of the vehicles;
the cost of engineering needs to be considered in the planning of the charging facility, and the target cost needs to be considered while the convenience of use of the user is met. Target cost M charging station investment cost M is mainly considereds(including land use costs and construction costs), total charging pile investment costs MqAnd the operation and maintenance cost M of the charging stationomAnd the charging profit Mp(deducting the cost of electricity purchase). The expression is as follows:
M＝Min(Ms+Mq+Mom-Mp)
wherein,
charging station construction unit cost
Charging pile number Mq (number of charging piles) and unit construction cost
The unit charging pile unit time operation maintenance cost is the time
Number of charging piles per unit of profit per unit of time
4-2) solving the model;
and optimizing the positions (longitude and latitude coordinates) of the newly-added charging stations in the sub-area A and the number of the charging piles by using a particle swarm optimization algorithm and taking the number of the charging piles which are added cumulatively in the whole area, the number of the charging stations which are added cumulatively, the target cost M and the charging load threshold value [ C ] as constraint conditions (the constraint conditions can be set by a planner according to the self conditions), and taking G as an objective function to obtain the optimal charging station position which is added in the sub-area A. In the invention, it is assumed that only one charging station is added in the sub-area a where the charging stations need to be added each time, and the result output by the particle swarm algorithm is as follows: and adding a charging station in the subarea, wherein the position coordinates of the newly added charging station and the number of the charging piles in the newly added charging station are obtained.
5) Adding the newly added charging stations obtained in the step 4) into the map in the step 1), returning to the step 1), and performing sub-area division and optimal calculation on all the charging stations again until the number of the charging stations which are added in the area, the number of the charging piles which are added in the area or the target cost exceed the constraint condition, and finishing the setting of the charging piles in the area.
Fig. 2 is a schematic diagram of an optimization result of a charging pile in an embodiment of the present invention, where the area shown in fig. 2 is an area in Shanghai city, and in this embodiment, a charging pile planning scheme is obtained according to a certain user requirement (that is, parameter settings include a total number of charging piles, a total number of charging stations, a charging load threshold, a charging station construction unit cost, a charging pile construction unit cost, a unit charging pile unit time operation and maintenance cost, a unit charging pile unit time profit, a distance, and a cost weight). The circles in the figure represent the parking events, the darker the color represents the more parking events, the bold straight line in the figure is a region division graph drawn by using the method of the voronoi diagram, and the two plus signs in the process represent the optimal arrangement scheme calculated by using the method of the invention to add the charging stations at the two positions.
Claims (1)
1. A charging pile setting method based on driving data and a Voronoi diagram divided area is characterized by comprising the following steps:
1) dividing an area needing to be provided with a charging pile into sub-areas;
acquiring a map of an area where charging piles need to be set; marking all existing charging stations in the area on a map of the area and calculating the distance between every two charging stations; if the distances between all the charging stations are larger than a set distance threshold value L, setting each charging station in the area as a discrete data point on the map; if the distance between the two charging stations is smaller than or equal to the set distance threshold value L, combining the two charging stations on a map into a new charging station, taking the center point of the positions of the two charging stations as the position of the new charging station, and setting each charging station in the area as a discrete data point on the map; dividing the region into different sub-regions by a Voronoi diagram method by utilizing the discrete data points, wherein the number of the sub-regions is equal to that of the discrete data points;
2) calculating the maximum charging load of each sub-area by using the driving data, selecting the maximum value of the maximum charging load, and recording the sub-area corresponding to the maximum value as A; the method comprises the following specific steps:
2-1) dividing the whole day into 24 time periods by 1 hour, and making i represent a time period serial number, i is 1,2,3 … 24;
2-2) selecting any one subregion, and calculating the number of vehicles required for effective charging of the subregion;
the vehicle with the effective charging demand refers to an electric vehicle with an actual charging demand in a subregion in any time period, and comprises the following two parts:
the vehicle is effectively parked: the one-time parking time of the electric vehicle in any time period of the subregion is more than 30 minutes, and the remaining capacity percentage SOC is lower than a set remaining capacity percentage threshold value E;
passing through the vehicle: the difference between the electric automobile traffic flow and the effective parking quantity in any time period in the subarea;
the calculation expression of the number of vehicles required to be charged effectively for any of the sub-area time periods i is as follows:
N0i＝αNei+βNpi
in the formula, N0iThe number of vehicles required for effective charging in the time period i in the sub-area; n is a radical ofeiThe number of effective parking vehicles in the time period i in the sub-area is; n is a radical ofpiThe number of passing vehicles in the time period i in the subregion, α an effective parking weight coefficient, β a passing vehicle weight coefficient;
2-3) calculating the average charge demand of the vehicles in the subareas;
the average charge demand of the vehicle is the time when the vehicle unit is effectively charged in any time period in the subareaA mean charge demand; selecting any time period according to the effective parking vehicles and the passing vehicles obtained by dividing in the step 2-1), respectively counting the SOC of each effective parking vehicle and the minimum SOC of each passing vehicle in the time period, and performing accumulated summation to be respectively recorded as sigma SOCeiSum sigma SOCpiIf the average power battery capacity of the vehicle is A, the average charging demand Q of the vehicle in the sub-region time period iiThe calculation expression of (a) is:
Qi＝[α·(Nei*100％-∑SOCei)+β·(Npi*100％-∑SOCpi)]·A/N0i
2-4) calculating the charging load of all time periods in a sub-region within a period of time to obtain the maximum charging load of the sub-region; acquiring the quantity of charging piles of charging stations in each sub-area, wherein the calculation expression of the charging load in any time period of the sub-area is as follows:
Ci＝Qi·N0i/(Nc·q)
in the formula, CiCharging a load for a sub-area in a time period i; n is a radical ofcThe number of the charging piles in the subarea is determined; q is the average charging capacity of the charging piles in the subareas;
intercepting a period of time, calculating the charging load of all time periods in the sub-area within the intercepted period of time,
selecting a time period corresponding to the maximum value and recording the charging load of the sub-area in the time period as the maximum charging load C of the sub-areamax；
2-5) traversing all sub-regions obtained by dividing in the step 1), repeating the steps 2-2) to 2-4), and calculating to obtain C corresponding to each sub-regionmaxSelecting C thereinmaxThe subarea with the largest value is marked as a subarea A;
3) comparing the maximum charging load of the subarea corresponding to the A with a charging load threshold value [ C ] and judging that: if the maximum charging load of the subarea A is higher than the set charging load threshold, adding charging stations to the subarea A, and entering the step 4); otherwise, the setting of the charging pile in the area is finished;
4) establishing a value model for the sub-region A, solving the model by utilizing a particle swarm algorithm, and optimizing the newly added charging station positions and the number of charging piles of the sub-region; the method comprises the following specific steps:
4-1) determining an objective function of the model; the expression is as follows
G＝a·Dj+b·M
In the formula, DjThe sum of the minimum distances traveled by all vehicles in the subarea to the current charging station j of the subarea A is represented by M, and the target cost of setting a new charging station in the subarea A is represented by M; a is a distance optimization weight, and b is a cost optimization weight;
Djthe calculation expression of (a) is as follows:
in the formula, DjiRepresenting the distance between the ith vehicle and the j point of the charging station, wherein k is the total number of the vehicles;
the target cost M includes: charging station investment cost MsAnd the total charging pile investment cost MqAnd the operation and maintenance cost M of the charging stationomAnd the charging profit Mp(ii) a The expression is as follows:
M＝Min(Ms+Mq+Mom-Mp)
4-2) solving the model;
optimizing the positions of the newly added charging stations and the number of the charging piles in the sub-area A by using a particle swarm optimization and taking the number of the charging piles which are added cumulatively in the whole area, the number of the charging stations which are added cumulatively, the target cost M and the charging load threshold value [ C ] as constraint conditions and taking G as a target function; only one charging station is added in the sub-area A needing to be added with the charging station each time, and the result output by the particle swarm algorithm is as follows: adding a charging station in the subarea, wherein the position coordinates of the newly added charging station and the number of charging piles in the newly added charging station are obtained;
5) adding the newly added charging stations obtained in the step 4) into the map in the step 1), returning to the step 1), and performing sub-area division and optimal calculation on all the charging stations again until the number of the charging stations which are added in the area, the number of the charging piles which are added in the area or the target cost exceed the constraint condition, and finishing the setting of the charging piles in the area.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710957855.7A CN107886186A (en) | 2017-10-16 | 2017-10-16 | A kind of charging pile method to set up based on travelling data and Wei Nuotu zonings |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710957855.7A CN107886186A (en) | 2017-10-16 | 2017-10-16 | A kind of charging pile method to set up based on travelling data and Wei Nuotu zonings |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN107886186A true CN107886186A (en) | 2018-04-06 |
Family
ID=61781443
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201710957855.7A Pending CN107886186A (en) | 2017-10-16 | 2017-10-16 | A kind of charging pile method to set up based on travelling data and Wei Nuotu zonings |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN107886186A (en) |
Cited By (27)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108595868A (en) * | 2018-05-03 | 2018-09-28 | 新奥（中国）燃气投资有限公司 | A kind of information of building a station of gas station determines method and device |
| CN108932561A (en) * | 2018-06-22 | 2018-12-04 | 华北电力大学 | A kind of electric car charge path selection method considering non-linear charge function |
| CN109004677A (en) * | 2018-08-30 | 2018-12-14 | 南通大学 | A kind of charging pile quantity configuration method based on photovoltaic power generation Yu electronic vehicle flowrate |
| CN109145989A (en) * | 2018-08-22 | 2019-01-04 | 深圳市东部公共交通有限公司 | Bus station distribution method, device and terminal |
| CN109409582A (en) * | 2018-09-30 | 2019-03-01 | 贵州大学 | A kind of charging pile method for optimizing position |
| CN109636067A (en) * | 2019-01-09 | 2019-04-16 | 浙江工业大学 | A kind of electric car charging schedule optimization method based on particle swarm algorithm |
| CN109840640A (en) * | 2019-03-06 | 2019-06-04 | 清华大学 | A kind of site selecting method and system of electric automobile charging pile |
| CN109872070A (en) * | 2019-02-22 | 2019-06-11 | 中南大学 | Based on the static charging pile dispositions method divided |
| CN109872010A (en) * | 2019-03-15 | 2019-06-11 | 深圳供电局有限公司 | Task allocation method for inspection robot of transformer substation |
| CN110353639A (en) * | 2019-07-16 | 2019-10-22 | 脑玺(上海)智能科技有限公司 | A kind of blood supply area quantitative approach and system based on blood vessel enhancing radiography |
| CN109246602B (en) * | 2018-09-14 | 2020-06-23 | 杭州电子科技大学温州研究院有限公司 | Charging base station deployment method of wireless chargeable sensor network |
| CN111695942A (en) * | 2020-06-17 | 2020-09-22 | 云南省设计院集团有限公司 | Electric vehicle charging station site selection method based on time reliability |
| JP2020154586A (en) * | 2019-03-19 | 2020-09-24 | 本田技研工業株式会社 | Determination device, determination method, and program |
| CN111966768A (en) * | 2020-06-29 | 2020-11-20 | 北京百度网讯科技有限公司 | Address description method and device, electronic equipment and storage medium |
| CN112200471A (en) * | 2020-10-15 | 2021-01-08 | 国网北京市电力公司 | Charging pile quantity configuration method and device and charging pile distribution system |
| CN112379607A (en) * | 2021-01-18 | 2021-02-19 | 中联重科股份有限公司 | Simulation operation method and device, and quantity planning method, device and system |
| CN112446609A (en) * | 2020-11-25 | 2021-03-05 | 北京理工新源信息科技有限公司 | Charging station building method and system based on big data |
| CN112487587A (en) * | 2020-11-06 | 2021-03-12 | 国网浙江省电力有限公司衢州供电公司 | Point-line-plane comprehensive layout-based public charging facility site selection method |
| CN112966864A (en) * | 2021-03-02 | 2021-06-15 | 悉地（苏州）勘察设计顾问有限公司 | Urban public transport charging station layout planning method and device and readable storage medium |
| CN113077113A (en) * | 2021-05-10 | 2021-07-06 | 成都特来电新能源有限公司 | Intelligent planning and design method for charging infrastructure |
| CN113222325A (en) * | 2021-03-23 | 2021-08-06 | 南斗六星系统集成有限公司 | Big data-based new energy vehicle charging station site selection planning method and system |
| CN113362460A (en) * | 2021-04-28 | 2021-09-07 | 北京理工大学 | Global new energy automobile charging map construction and recommendation method |
| CN113673883A (en) * | 2021-08-24 | 2021-11-19 | 广东电网有限责任公司 | Charging pile site selection method based on visual power grid map |
| CN114565148A (en) * | 2022-02-23 | 2022-05-31 | 广州市城市规划勘测设计研究院 | Charging demand prediction method and device |
| CN117077918A (en) * | 2023-07-04 | 2023-11-17 | 南京工业职业技术大学 | Energy saving method and energy saving system based on electric power big data |
| CN117077985A (en) * | 2023-10-16 | 2023-11-17 | 浙江优能电力设计有限公司 | Electric vehicle charging pile planning method and system according to charging requirements |
| CN117895662A (en) * | 2024-03-14 | 2024-04-16 | 江苏智融能源科技有限公司 | Equipment demand monitoring system and method based on edge calculation |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102880921A (en) * | 2012-10-16 | 2013-01-16 | 山东电力集团公司电力科学研究院 | Method for optimizing site selection of electric vehicle charging stations |
| CN106529797A (en) * | 2016-10-28 | 2017-03-22 | 四川大学 | Electric automobile charging network planning method |
-
2017
- 2017-10-16 CN CN201710957855.7A patent/CN107886186A/en active Pending
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102880921A (en) * | 2012-10-16 | 2013-01-16 | 山东电力集团公司电力科学研究院 | Method for optimizing site selection of electric vehicle charging stations |
| CN106529797A (en) * | 2016-10-28 | 2017-03-22 | 四川大学 | Electric automobile charging network planning method |
Non-Patent Citations (2)
| Title |
|---|
| 李慧: "基于加权Voronoi图的变电站优化规划", 《中国优秀硕士学位论文全文数据库》 * |
| 肖波: "电动汽车充电站规划研究", 《中国优秀硕士学位论文全文数据库》 * |
Cited By (45)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108595868A (en) * | 2018-05-03 | 2018-09-28 | 新奥（中国）燃气投资有限公司 | A kind of information of building a station of gas station determines method and device |
| CN108932561A (en) * | 2018-06-22 | 2018-12-04 | 华北电力大学 | A kind of electric car charge path selection method considering non-linear charge function |
| CN108932561B (en) * | 2018-06-22 | 2021-07-30 | 华北电力大学 | Electric vehicle charging path selection method considering nonlinear charging function |
| CN109145989A (en) * | 2018-08-22 | 2019-01-04 | 深圳市东部公共交通有限公司 | Bus station distribution method, device and terminal |
| CN109145989B (en) * | 2018-08-22 | 2021-07-06 | 深圳市东部公共交通有限公司 | Bus stop layout method and device and computer terminal |
| CN109004677A (en) * | 2018-08-30 | 2018-12-14 | 南通大学 | A kind of charging pile quantity configuration method based on photovoltaic power generation Yu electronic vehicle flowrate |
| CN109004677B (en) * | 2018-08-30 | 2021-11-16 | 南通大学 | Charging pile quantity configuration method based on photovoltaic power generation and electric vehicle flow |
| CN109246602B (en) * | 2018-09-14 | 2020-06-23 | 杭州电子科技大学温州研究院有限公司 | Charging base station deployment method of wireless chargeable sensor network |
| CN109409582B (en) * | 2018-09-30 | 2021-05-25 | 贵州大学 | Charging pile position optimization method |
| CN109409582A (en) * | 2018-09-30 | 2019-03-01 | 贵州大学 | A kind of charging pile method for optimizing position |
| CN109636067B (en) * | 2019-01-09 | 2021-10-15 | 浙江工业大学 | Electric automobile charging scheduling optimization method based on particle swarm algorithm |
| CN109636067A (en) * | 2019-01-09 | 2019-04-16 | 浙江工业大学 | A kind of electric car charging schedule optimization method based on particle swarm algorithm |
| CN109872070A (en) * | 2019-02-22 | 2019-06-11 | 中南大学 | Based on the static charging pile dispositions method divided |
| CN109840640A (en) * | 2019-03-06 | 2019-06-04 | 清华大学 | A kind of site selecting method and system of electric automobile charging pile |
| CN109872010A (en) * | 2019-03-15 | 2019-06-11 | 深圳供电局有限公司 | Task allocation method for inspection robot of transformer substation |
| CN109872010B (en) * | 2019-03-15 | 2021-05-25 | 深圳供电局有限公司 | Task allocation method for inspection robot of transformer substation |
| JP2020154586A (en) * | 2019-03-19 | 2020-09-24 | 本田技研工業株式会社 | Determination device, determination method, and program |
| JP7273570B2 (en) | 2019-03-19 | 2023-05-15 | 本田技研工業株式会社 | Determination device, determination method and program |
| CN110353639A (en) * | 2019-07-16 | 2019-10-22 | 脑玺(上海)智能科技有限公司 | A kind of blood supply area quantitative approach and system based on blood vessel enhancing radiography |
| CN110353639B (en) * | 2019-07-16 | 2022-04-01 | 脑玺(上海)智能科技有限公司 | Blood supply area quantification method and system based on blood vessel enhanced radiography |
| CN111695942A (en) * | 2020-06-17 | 2020-09-22 | 云南省设计院集团有限公司 | Electric vehicle charging station site selection method based on time reliability |
| CN111966768B (en) * | 2020-06-29 | 2023-11-17 | 北京百度网讯科技有限公司 | Address description method, address description device, electronic equipment and storage medium |
| CN111966768A (en) * | 2020-06-29 | 2020-11-20 | 北京百度网讯科技有限公司 | Address description method and device, electronic equipment and storage medium |
| CN112200471A (en) * | 2020-10-15 | 2021-01-08 | 国网北京市电力公司 | Charging pile quantity configuration method and device and charging pile distribution system |
| CN112200471B (en) * | 2020-10-15 | 2023-11-21 | 国网北京市电力公司 | Charging pile quantity configuration method and device and charging pile distribution system |
| CN112487587A (en) * | 2020-11-06 | 2021-03-12 | 国网浙江省电力有限公司衢州供电公司 | Point-line-plane comprehensive layout-based public charging facility site selection method |
| CN112446609A (en) * | 2020-11-25 | 2021-03-05 | 北京理工新源信息科技有限公司 | Charging station building method and system based on big data |
| CN112446609B (en) * | 2020-11-25 | 2024-04-26 | 北京理工新源信息科技有限公司 | Big data-based charging station building method and system |
| CN112379607A (en) * | 2021-01-18 | 2021-02-19 | 中联重科股份有限公司 | Simulation operation method and device, and quantity planning method, device and system |
| CN112379607B (en) * | 2021-01-18 | 2021-04-13 | 中联重科股份有限公司 | Simulation operation method and device, and quantity planning method, device and system |
| CN112966864A (en) * | 2021-03-02 | 2021-06-15 | 悉地（苏州）勘察设计顾问有限公司 | Urban public transport charging station layout planning method and device and readable storage medium |
| CN113222325A (en) * | 2021-03-23 | 2021-08-06 | 南斗六星系统集成有限公司 | Big data-based new energy vehicle charging station site selection planning method and system |
| CN113362460A (en) * | 2021-04-28 | 2021-09-07 | 北京理工大学 | Global new energy automobile charging map construction and recommendation method |
| CN113362460B (en) * | 2021-04-28 | 2022-08-30 | 北京理工大学 | Construction and recommendation method for global new energy automobile charging map |
| CN113077113B (en) * | 2021-05-10 | 2023-12-12 | 成都特来电新能源有限公司 | Intelligent planning and designing method for charging infrastructure |
| CN113077113A (en) * | 2021-05-10 | 2021-07-06 | 成都特来电新能源有限公司 | Intelligent planning and design method for charging infrastructure |
| CN113673883A (en) * | 2021-08-24 | 2021-11-19 | 广东电网有限责任公司 | Charging pile site selection method based on visual power grid map |
| CN114565148B (en) * | 2022-02-23 | 2022-11-15 | 广州市城市规划勘测设计研究院 | Charging demand prediction method and device |
| CN114565148A (en) * | 2022-02-23 | 2022-05-31 | 广州市城市规划勘测设计研究院 | Charging demand prediction method and device |
| CN117077918A (en) * | 2023-07-04 | 2023-11-17 | 南京工业职业技术大学 | Energy saving method and energy saving system based on electric power big data |
| CN117077918B (en) * | 2023-07-04 | 2024-04-02 | 南京工业职业技术大学 | Energy saving method and energy saving system based on electric power big data |
| CN117077985A (en) * | 2023-10-16 | 2023-11-17 | 浙江优能电力设计有限公司 | Electric vehicle charging pile planning method and system according to charging requirements |
| CN117077985B (en) * | 2023-10-16 | 2024-04-09 | 浙江优能电力设计有限公司 | Electric vehicle charging pile planning method and system according to charging requirements |
| CN117895662A (en) * | 2024-03-14 | 2024-04-16 | 江苏智融能源科技有限公司 | Equipment demand monitoring system and method based on edge calculation |
| CN117895662B (en) * | 2024-03-14 | 2024-06-11 | 江苏智融能源科技有限公司 | Equipment demand monitoring system and method based on edge calculation |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107886186A (en) | A kind of charging pile method to set up based on travelling data and Wei Nuotu zonings | |
| CN106951978B (en) | Urban concentrated charging station planning method based on improved K-means algorithm | |
| Mozaffari et al. | Joint expansion planning studies of EV parking lots placement and distribution network | |
| Liu et al. | Optimal planning of charging station for electric vehicle based on particle swarm optimization | |
| CN110968837B (en) | Method for locating and sizing electric vehicle charging station | |
| CN105303882A (en) | Parking management system and method based on space-time dimension dynamic parking charging strategy | |
| Lin et al. | Optimal planning of intra-city public charging stations | |
| CN109583706A (en) | The polynary optimization evaluation method and system of distribution system receiving electric car ability | |
| Gong et al. | Load modeling method for EV charging stations based on trip chain | |
| Aljohani et al. | Dynamic real-time pricing structure for electric vehicle charging considering stochastic microgrids energy management system | |
| CN108805360A (en) | It is a kind of that the transportation cost that urban biomass carrier is transported to biomass power plant is determined into method | |
| Yin et al. | Planning of electric vehicle charging station based on real time traffic flow | |
| CN115907227B (en) | Double-layer collaborative optimization method for expressway fixed and mobile charging facilities | |
| CN111861145B (en) | Service area electric vehicle charging station configuration method considering highway network | |
| CN118071109B (en) | Electric vehicle charging station site selection and volume determination method, system, equipment and medium | |
| CN114418300A (en) | Multi-type electric vehicle charging facility planning method based on urban function partition and resident trip big data | |
| CN116362523B (en) | Coordinated optimization method for site selection and operation strategy of power exchange station considering temperature adaptability | |
| Sun et al. | Location and Capacity Determination Method of Electric Vehicle Charging Station Based on Simulated Annealing Immune Particle Swarm Optimization | |
| CN109146135B (en) | Optimization method for sharing electric bicycle station pile equipment | |
| Xiaodi et al. | The method of charging piles planning in parking lot | |
| CN114169774A (en) | Mountain city charging station planning system, method and storage medium | |
| Li et al. | Spatial-Temporal Distribution Forecasting of Electric Vehicle Charging and Discharging Loads Based on Urban V2G Application | |
| Zhang et al. | Optimal allocation of changing station for electric vehicle based on queuing theory | |
| Ferreira et al. | Simulation of electrical distributed energy resources for electrical vehicles charging process strategy | |
| Li et al. | Achieving accurate and balanced regional electric vehicle charging load forecasting with a dynamic road network: a case study of Lanzhou City |
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 | ||
| WD01 | Invention patent application deemed withdrawn after publication | ||
| WD01 | Invention patent application deemed withdrawn after publication |
Application publication date: 20180406 |