CN109934414B - Incremental distribution network planning method with electric vehicle charging station - Google Patents
Incremental distribution network planning method with electric vehicle charging station Download PDFInfo
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Abstract
The invention relates to a planning method for an incremental power distribution network with an electric vehicle charging station. And starting from the charging convenience degree of the user, the weighting of the P-median and the maximum coverage model is established to address the charging station, so that the installation position of the charging station is determined. On the basis of obtaining the installation position of the charging station, a model with the minimum annual comprehensive cost is established to plan the capacity of the charging station, the position capacity of the DG and the line selection. The invention comprehensively considers the unified planning of the electric vehicle charging station, the DG and the net rack, and addresses the charging station from the viewpoint of charging convenience of users, so that the position of the charging station can be more reasonable.
Description
Technical Field
The invention belongs to the technical field of power distribution network planning, and particularly relates to an incremental power distribution network planning method with electric vehicle charging stations.
Background
The traditional incremental distribution network investment business reform is continuously trying to perfect, and the incremental distribution network investment pre-planning is particularly important. The distributed power generation technology and the electric vehicle technology are used as important power assistance for energy conservation and emission reduction, and are two important directions for future intelligent development, the traditional planning is usually carried out firstly, then the DG planning is carried out, so that the planning result cannot achieve the optimal effect of global planning, and the planning is rarely carried out together with the electric vehicle charging station, but the electric vehicle charging station, the DG and the grid are closely connected, unified planning is carried out, the three can reach the optimal state, the problem that the planning is not proper due to separate planning is avoided, secondary planning is carried out, manpower and material resources are wasted, for the site selection of the electric vehicle charging station, the economic performance is mostly adopted, the characteristics of a public service area of the charging station are considered, the site selection is carried out on the charging station from the perspective of charging convenience of a user, and the position of the charging station can be more reasonable.
Disclosure of Invention
The invention aims to provide an incremental power distribution network planning method including an electric vehicle charging station, which comprehensively considers the planning of the electric vehicle charging station, a DG and a network frame, establishes a user charging convenience model from the perspective of a user, selects a site for the charging station, and establishes an incremental power distribution network planning model with the minimum total cost to perform site selection and volume fixing on the charging station and the DG and construct the network frame.
In order to realize the purpose, the technical scheme of the invention is as follows: an incremental distribution network planning method comprising an electric vehicle charging station comprises the following steps:
step S1: extracting system information, traffic road information, conventional load prediction information, electric vehicle load prediction information and line parameter information;
step S2: starting from the user charging convenience perspective, establishing a P-median and the weighting of a maximum coverage model as a user convenience model;
and step S3: solving the user convenience model to obtain the installation position of the electric vehicle charging station;
and step S4: on the basis of obtaining the installation position of the electric vehicle charging station, establishing an incremental power distribution network planning model with the minimum annual comprehensive cost;
step S5: and solving the incremental power distribution network planning model by using an immune algorithm to obtain the installation capacity of the charging station, the installation position capacity of the distributed power supply and the line site selection.
In an embodiment of the present invention, a specific implementation manner of step S2 is:
step S21: the weighting of the P-median model and the maximum coverage model is used as a convenience model of the user, and the mathematical model is as follows:
in the formula: omega is a compromise weight value and takes the value of [0, 1')]The decimal fraction of the inner; n is the number of nodes; p i The charging demand of the electric vehicle as the i node; l is ij Is the shortest distance between nodes ij; m ij Providing charging service for the i node for a charging station installed at the j point; p is q For the charging demand of the electric automobile of the q node, if the charging demand of the q node can be met, N q Is 1, otherwise is 0;
z is an index for evaluating the site selection performance and represents the convenience of a user; the front half part is a P-median model which represents the total weight distance of the users, namely the sum of the distances from all the electric vehicle users to the nearest charging station; the second half is a maximum coverage model, which represents that the charging requirement of the user is met to the maximum extent under the condition of the limitation of the number and the capacity of the charging stations;
step S22: the following constraints are placed on the user convenience model for charging station site selection:
(1) Electric vehicle charging limitation
In the formula: j denotes a set of all charging stations; the formula shows that only one charging station provides charging service for the i-node electric vehicle;
(2) Number of charging stations limitation
In the formula: x is the number of j Installing a charging station at the point j; p is the number of installed charging stations; this constraint limits the number of charging stations;
(3) Charging station capacity limitation
In the formula: b (J) represents that the charging station at the J point provides charging service for the q node; c j A configured capacity for j-node charging stations; this formula limits the charging capacity of the charging station;
(4) Charging distance limitation
In the formula: d is the farthest distance that the charging station can cover, and is a fixed value.
In an embodiment of the present invention, a specific implementation manner of step S4 is:
step S41: an incremental distribution network planning model is established at the minimum annual comprehensive cost, wherein the annual cost of a charging station, the annual cost of a distributed power supply, the line expansion cost, the electricity purchasing cost and the government subsidy are fully considered, and the mathematical model is as follows:
minC＝C line +C DG +C evcs +C buy -C en
in the formula: c line Annual line costs; c DG Annual cost for distributed power, C evcs Annual charge for charging, C buy For electricity purchase and C en Subsidizing the government;
(1) Annual cost of the line
In the formula: m is the number of branches; c. C line Investment cost per unit length of line; l i Is the m-th branchRoad investment costs; r is the discount rate, and is taken as 0.1; n is the economic service life of the line, 30a is taken as an overhead line, and 40a is taken as a cable line; alpha is whether a line is erected or not, and takes a value of [0,1 ]](ii) a Lambda is the electricity price; p is lossi The loss number of the mth branch is; tau is the number of annual maximum load loss hours;
(2) Annual cost of distributed power supply
In the formula: n is the number of nodes; p is i Is the capacity of the DG installed at the inode; c. C 1 Is the investment cost per unit capacity of DG; c. C 2 The operation and maintenance cost is DG unit generated energy; t is max The annual maximum number of electricity generation hours for DG; delta is whether DG is installed or not, and takes a value of [0,1 ]]；
(3) Annual cost of electric vehicle charging station
In the formula: c fixi Fixed investment cost for installing charging stations for the i-node; c. C vari In order to convert to one year, the average variable investment corresponding to the unit capacity of the charging station of the ith node is calculated, and the unit is ten thousand yuan/MW; c. C operi In order to convert to one year, the average operation cost of the i-th node charging station in unit capacity is ten thousand yuan/MW; p evcsi Charging station capacity for an ith node;
(4) Cost of electricity purchase
In the formula: lambda is the electricity price; p loadi Is the load of the ith node;
(5) Government patch
In the formula: gamma is the subsidy cost of the unit DG generated energy;
step S42: the constraint conditions of the incremental distribution network planning model containing the electric vehicle charging station are as follows:
1) System power balance constraints
2) Node voltage constraint
3) DG capacity constraint
4) DG permeability constraint
5) Charging station capacity constraints
6) The line radiates connectivity constraints.
Compared with the prior art, the invention has the following beneficial effects: the invention comprehensively considers the electric vehicle charging station, the DG and the net rack, considers the convenience of charging for users from the perspective of users, and better conforms to the characteristics of the installation place of the charging station than from the perspective of economy, thereby providing a certain suggestion for planners and improving the convenience and social benefits of charging for users.
Drawings
Fig. 1 is a node diagram of a calculation example 13.
Fig. 2 is a diagram of the model solution results.
Detailed Description
The technical scheme of the invention is specifically explained below with reference to the accompanying drawings.
The invention provides a planning method for an incremental distribution network with electric vehicle charging stations, which comprises the following steps:
step S1: extracting system information, traffic road information, conventional load prediction information, electric vehicle load prediction information and line parameter information;
step S2: from the perspective of charging convenience of a user, establishing a P-median and a weighting of a maximum coverage model as a user convenience model;
and step S3: solving the user convenience model so as to obtain the installation position of the electric vehicle charging station;
and step S4: on the basis of obtaining the installation position of the electric vehicle charging station, establishing an incremental power distribution network planning model with the minimum annual comprehensive cost;
step S5: and solving the incremental distribution network planning model by using an immune algorithm to obtain the installation capacity of the charging station, the installation position capacity of the distributed power supply and the line site selection.
The following are specific implementation examples of the present invention.
The invention plans an electric vehicle charging station, a distributed power supply and a line, and provides an incremental power distribution network planning method considering the electric vehicle charging station, which comprises the following steps:
step S1: extracting system information; the method comprises the steps of extracting traffic road information, conventional load prediction information, electric vehicle load prediction information and line parameter information, expanding 4 nodes into 13 nodes (shown in figure 1), selecting 4 nodes, 5 nodes, 6 nodes, 9 nodes, 10 nodes and 11 nodes as charging station alternative nodes, limiting the maximum installation capacity to 1MW, limiting the installation number to 3 nodes, and setting the farthest distance for charging the electric vehicles to be 5km. Considering that the charging station can consume the DG resources on site, the charging station node or the middle and rear section positions of the nearby nodes and lines are used as DG installation alternative nodes, the maximum installation capacity is limited to 0.6MW, the unit investment cost is 0.8 ten thousand yuan/kVA, the operation and maintenance cost is 0.2 yuan/kWh, the discount rate of investment DG is 0.1, the investment recovery year is 20 years, the permeability is 30%, the power factor is 0.9, and the maximum electricity generation utilization hours in DG year is 1600 hours. The discount rate of the line is 0.1, the planning year is 25 years, the maximum annual load loss hour is 1600 hours, the unit electricity price is 0.5 yuan/kWh, and the government subsidy is 0.2 yuan/kWh.
TABLE 1 line parameters
Table 1 shows the line parameters.
Step S2: from the perspective of charging convenience of a user, establishing a P-median and a weighting of a maximum coverage model as a user convenience model;
and step S3: solving the shortest distance between the nodes through a Floyd algorithm, and solving a user convenience model so as to obtain the installation position of the electric vehicle charging station;
and step S4: on the basis of obtaining the installation position of the electric vehicle charging station, establishing an incremental power distribution network planning model with the minimum annual comprehensive cost;
step S5: and solving the model by using an immune algorithm to obtain the installation capacity of the charging station, the installation position capacity of the distributed power supply and the address of the line. The planning result is as follows: the electric vehicle charging stations are built at nodes 10 and 11, the capacities are 501.77kW, 998.23kW and 298.7kW respectively, the DGs are built at nodes 7, 9, 10, 12 and 13, and the capacities are 423.93kW, 592.89kW, 553.79kW, 456.26kW and 398.62kW respectively.
TABLE 2 predicted values of load and electric vehicle
FIG. 2 is a diagram of the model solution result established by the invention, and Table 2 shows the predicted load value and the predicted electric vehicle value.
The above are preferred embodiments of the present invention, and all changes made according to the technical scheme of the present invention that produce functional effects do not exceed the scope of the technical scheme of the present invention belong to the protection scope of the present invention.
Claims (1)
1. An incremental distribution network planning method comprising an electric vehicle charging station is characterized by comprising the following steps:
step S1: extracting system information, traffic road information, conventional load prediction information, electric vehicle load prediction information and line parameter information;
step S2: from the perspective of charging convenience of a user, establishing a P-median and a weighting of a maximum coverage model as a user convenience model;
and step S3: solving the user convenience model to obtain the installation position of the electric vehicle charging station;
and step S4: on the basis of obtaining the installation position of the electric vehicle charging station, establishing an incremental power distribution network planning model with the minimum annual comprehensive cost;
step S5: solving the incremental distribution network planning model by using an immune algorithm to obtain the installation capacity of a charging station, the installation position capacity of a distributed power supply and the line address;
the specific implementation manner of the step S2 is as follows:
step S21: the weighting of the P-median model and the maximum coverage model is used as a convenience model of a user, and the mathematical model is as follows:
in the formula: omega is a compromise weight value and takes the value of [0, 1')]The decimal fraction of the inner; n is the number of nodes; p i The charging demand of the electric vehicle as the i node; l is ij Is the shortest distance between the nodes i and j; m ij Charging service provided for the node i for a charging station installed at the node j; p is q For the charging demand of the electric automobile of the q node, if the charging demand of the q node can be met, N q Is 1, otherwise is 0;
z is an index for evaluating the site selection performance and represents the convenience of a user; the front half part is a P-median model which represents the total weight distance of the users, namely the sum of the distances from all the electric vehicle users to the nearest charging station; the second half is a maximum coverage model, which represents that the charging requirement of the user is met to the maximum extent under the condition of the limitation of the number and the capacity of the charging stations;
step S22: the following constraints are provided for the user convenience model for charging station site selection:
(1) Electric vehicle charging limitation
In the formula: j denotes a set of all charging stations; the formula shows that only one charging station provides charging service for the i-node electric vehicle;
(2) Charging station quantity limitation
In the formula: x is the number of j Installing a charging station at the point j; p is the number of the charging stations; this constraint limits the number of charging stations;
(3) Charging station capacity limitation
In the formula: b (j) represents the charging service provided by the charging station of the j node for the q node; c j A configured capacity for j-node charging stations; this formula limits the charging capacity of the charging station;
(4) Charging distance limitation
In the formula: d is the farthest distance covered by the charging station and is a fixed value;
the specific implementation manner of the step S4 is as follows:
step S41: an incremental distribution network planning model is established at the minimum annual comprehensive cost, wherein the annual charge station expense, the annual charge cost of a distributed power supply, the line expansion expense, the electricity purchasing cost and the government subsidy are fully considered, and the mathematical model is as follows:
minC＝C line +C DG +C evcs +C buy -C en
in the formula: c line Annual line costs; c DG Annual cost for distributed power supply, C evcs Annual charge for charging, C buy For purchase of electricity and C en Subsidizing the government;
(1) Annual cost of the line
In the formula: m is the number of branches; c. C line Investment cost per unit length of line; l i Investment cost for the mth branch; r is the discount rate, and is taken as 0.1; a is the economic service life of the line; alpha is whether a line is erected or not, and takes a value of [0,1 ]](ii) a Lambda is the electricity price; p lossi The loss number of the mth branch is; tau is the annual maximum load loss hours;
(2) Annual cost of distributed power supply
In the formula: n is the number of nodes; p i Is the capacity of the i-node installed DG; c. C 1 Is the investment cost per unit capacity of DG; c. C 2 The operation and maintenance cost is DG unit generated energy; t is max The number of annual maximum generation hours for DG; delta is whether DG is installed or not, and takes a value of [0,1 ]]；
(3) Annual cost of electric vehicle charging station
In the formula: c fixi Fixed investment cost for installing charging stations for the i-node; c. C vari In order to convert to one year, the average variable investment corresponding to the unit capacity of the charging station of the ith node; c. C operi In order to convert to one year, the average operation cost of the unit capacity of the charging station of the ith node; p is evcsi Charging station capacity for an ith node;
(4) Cost of electricity purchase
In the formula: lambda is the electricity price; p is loadi Is the load of the ith node;
(5) Government patch
In the formula: gamma is the subsidy cost of the unit DG generated energy;
step S42: the constraint conditions of the incremental distribution network planning model containing the electric vehicle charging station are as follows:
1) System power balance constraints
2) Node voltage constraint
3) DG capacity constraint
4) DG permeability constraint
5) Charging station capacity constraints
6) The line radiates connectivity constraints.
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Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2017023852A1 (en) * | 2015-07-31 | 2017-02-09 | Exelon Generation Company, Llc | Methods and systems for determining economic viability of a microgrid |
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Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2017023852A1 (en) * | 2015-07-31 | 2017-02-09 | Exelon Generation Company, Llc | Methods and systems for determining economic viability of a microgrid |
| CN108154272A (en) * | 2017-12-29 | 2018-06-12 | 上海电力学院 | A kind of increment distribution network planning method for adapting to increment distribution network service and decontroling |
Non-Patent Citations (5)
| Title |
|---|
| Incremental Distribution Network Planning by Considering Electric Vehicle Charging Stations and Distributed Generators;Pengjia Shi et al.;《2019 IEEE 3rd Conference on Energy Internet and Energy》;20200409;全文 * |
| 含分布式电源及电动汽车充电站的配电网多目标规划研究;刘柏良 等;《电网技术》;20150228;第39卷(第2期);全文 * |
| 多源微网典型应用的优化设计;刘柏良;《中国博士学位论文全文数据库 工程科技II辑》;20160815;正文第1-50、81-99页 * |
| 考虑多方需求的电动汽车充电站规划研究;罗文等;《电力科学与工程》;20180628(第06期);全文 * |
| 考虑电动汽车充电站选址定容的配电网规划;陈婷 等;《电力系统及其自动化学报》;20130630;第25卷(第3期);全文 * |
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