Layout method of multi-cycle electric vehicle charging facility

Abstract

The invention discloses a multi-cycle electric vehicle charging facility layout method. The method comprises the following steps: acquiring road network data, electric vehicle inventory data and charging facility layout data of an area to be laid out in the initial year; constructing a basic road network based on the road network data and the charging facility layout data of the initial year, estimating the charging demand of the current year according to the electric vehicle ownership data of the current year, and distributing the charging demand to each node of the constructed basic road network to form a charging demand distribution map of the initial year; judging whether the current period is within the planned age limit or not; if yes, the charging demand of the current period is estimated based on the electric vehicle inventory data of the period, the charging demand of the current period is updated in the charging demand distribution map, and the charging demand distribution map of the current period is formed. According to the method, the charging setting position and the electric quantity configuration can be arranged based on the inventory and demand distribution condition of the urban electric vehicles in each period within the planned age limit, and the actual condition is better met.

Description

Technical Field

[0001] The invention relates to a layout method of multi-cycle electric vehicle charging facilities. Background Art

[0002] At present, electric vehicles are developing rapidly, the penetration rate of new energy vehicles is getting higher and higher, and the demand for charging facilities is also increasing. However, the number and layout of existing charging facilities are not matched with the development scale of electric vehicles, and charging difficulties have gradually become one of the problems hindering the further development of the industry. Therefore, the reasonable layout of charging facilities is of great significance to the sustainable development of the electric vehicle industry. How to reasonably plan the charging facilities of electric vehicles to meet the growing demand for electric vehicle charging has become an important issue that needs to be studied. At present, the common charging station planning is either based only on the fixed planning time and the number of electric vehicles in the initial year, without taking into account the changes in the annual growth of the number of electric vehicles; or when the charging demand cannot be met, only the construction of new charging stations is considered as the only solution, ignoring the possibility that charging stations will be expanded first for cost savings in actual engineering construction. Summary of the invention

[0003] The technical problem to be solved by the present invention is to overcome the defects of the prior art and provide a layout method for multi-cycle electric vehicle charging facilities, which can layout the location and power configuration of charging settings based on the number of urban electric vehicles and demand distribution in each cycle within the planning period, which is more in line with the actual situation and makes the construction of charging settings more economical.

[0004] In order to solve the above technical problems, the technical solution of the present invention is: a layout method of multi-cycle electric vehicle charging facilities, characterized in that the method also includes the following steps:

[0005] S01: Obtain the road network data, electric vehicle ownership data and charging facility layout data of the initial year of the area to be deployed;

[0006] S02: Construct a basic road network based on the road network data and charging facility layout data of the initial year, estimate the charging demand of the year according to the electric vehicle ownership data of the year, and distribute the charging demand to each node of the constructed basic road network to form a charging demand distribution map of the initial year;

[0007] S03: Determine whether the current cycle is within the planned period; if so, estimate the charging demand of the current cycle based on the electric vehicle ownership data of the cycle, and update the charging demand of the current cycle in the charging demand distribution map to form the charging demand distribution map of the current cycle;

[0008] S04: Solving the constructed charging facility layout optimization model based on the charging demand of the current cycle and the charging facility layout data of the current cycle to determine whether the existing charging facilities in the current cycle meet the charging demand. If so, entering the next cycle, i.e. returning to step S03; if not, solving how to expand the charging facilities in the current cycle to meet the charging demand on the road network through the charging facility layout optimization model;

[0009] S05: If the charging facilities in the current cycle meet the charging demand after the capacity expansion, the charging facilities expansion information of the current cycle is updated and the next cycle is entered, that is, the process returns to step S03; if the charging facilities in the current cycle cannot meet the charging demand after the capacity expansion, the charging facilities layout optimization model is used to solve how to build new charging facilities in the current cycle to meet the charging demand on the road network; wherein, the ordinary nodes without charging facilities are the backup nodes for the newly built charging facilities;

[0010] S06: Update the new charging facility information of the current cycle and enter the next cycle, that is, return to step S03;

[0011] S07: The cycle is executed until the end of the cycle, and the layout of charging facilities and investment costs of each cycle are obtained; wherein, the construction method of the charging facility layout optimization model is as follows:

[0012] The basic road network is constructed based on the road network data and the charging facility layout data. The charging demand is estimated according to the electric vehicle ownership data, and the charging demand is allocated to each node of the constructed basic road network to form a charging demand distribution map to describe the relationship between charging facilities and charging demand.

[0013] According to the charging demand distribution map, the goal is to minimize the consumption cost of the road sections passed by the road network to complete the charging demand in each cycle and the construction cost of the charging facility expansion or new charging facilities in all cycles, and to construct a charging facility layout optimization model with the constraints of meeting the charging demand of all nodes on the road network and the capacity limit of the charging facilities.

[0014] Furthermore, the charging facility layout optimization model is as follows:

[0015] Objective function:

[0016]

[0017] First constraint:

[0018] Second constraint:

[0019] Third constraint:

[0020] Fourth constraint:

[0021] Fifth constraint:

[0022]

[0023] Sixth constraint:

[0024] Seventh Constraint:

[0025] Eighth constraint:

[0026]

[0027] Among them, in formula (1), ω1 and ω2 are the weights of the objective function; represents the cost of passing through section ij in period t; is a 0-1 decision variable. When , it means that the road segment ij is passed within the cycle t and the charging demand on this path is served by the charging facility m, otherwise a t represents the cost of increasing the capacity of charging facilities per unit of electricity within period t, Δq m,t represents the additional power generated by the expansion of charging facility m within period t; b t represents the cost of building a new charging facility in period t, is a 0-1 decision variable. When , it means that the planner selects from the candidate node set N in period t t Choose to build a new charging facility at node i, otherwise The first term of formula (1) represents the total path cost of completing the charging demand on the road network in all cycles, and the second term represents the construction cost of expanding the charging facilities or building new charging facilities in the entire planning period;

[0028] Formula (2) constrains the charging demand on the road network to be met within each cycle t, where: is a 0-1 decision variable. When , it means that charging facility m serves the charging demand of node i in period t. otherwise

[0029] Formula (3) constrains the power consumption of each charging facility. Within a period t, each charging facility m serves the charging needs of several nodes, but cannot exceed its own power limit q m,t ;

[0030] Formula (4) indicates that each node demand can only be served by a charging facility at most once, which is a distribution constraint;

[0031] Formula (5) indicates that each node in the road network follows the flow balance constraint, where b = 0 when the node is a normal node; b = 1 when the node is a charging facility;

[0032] Formula (6) represents the number constraint of charging facilities for expansion, which is no greater than the number of charging facilities in the current cycle |M t |; is the decision variable for charging facility capacity expansion. , it means that the planner chooses to expand the capacity of charging facility m in period t, otherwise

[0033] Formula (7) is the power constraint of charging facility m after expansion, q m,t Represents the total power of charging facility m in cycle t. The expanded power of charging facility m is not greater than the power q of the charging facility in the previous cycle. m,t-1 ;

[0034] Formula (8) is the constraint on the number of new charging facilities, which is no greater than the number of candidate sites in the current cycle |N t |;

[0035] Formula (9) is a coupling constraint between two types of variables, indicating that the charging facility must visit the node before serving the node demand;

[0036] Formulas (10) to (13) are constraints on the value range of decision variables.

[0037] After adopting the above technical solution, the present invention has the following beneficial effects:

[0038] 1. The method of the present invention takes into account the layout of charging facilities in multiple periods. It arranges the location and power configuration of charging stations based on the growth of the number of electric vehicles in the city and the change of demand distribution in each period within the planning time sequence, which is more in line with the actual situation.

[0039] 2. The method of the present invention comprehensively considers the expansion and new construction of charging facilities to meet the growing charging demand on the road network as the number of electric vehicles continues to increase, and gives priority to the expansion method, which is more economical. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 A flow chart of a method for laying out multi-cycle electric vehicle charging facilities of the present invention;

[0041] Figure 2 A charging demand distribution diagram in the layout method of multi-cycle electric vehicle charging facilities of the present invention;

[0042] Figure 3 A charging demand distribution diagram of an initial road network in an embodiment of the present invention;

[0043] Figure 4 is a charging demand distribution diagram of the charging station expansion in an embodiment of the present invention (node ​​5 expansion);

[0044] Figure 5 It is a charging demand distribution diagram of a newly built charging station in an embodiment of the present invention (node ​​1 is newly built). DETAILED DESCRIPTION

[0045] In order to make the contents of the present invention more clearly understood, the present invention is further described in detail below based on specific embodiments in conjunction with the accompanying drawings.

[0046] like Figures 1 to 3 As shown, a layout method of multi-cycle electric vehicle charging facilities is characterized in that the method also includes the following steps:

[0047] S01: Obtain the road network data, electric vehicle ownership data and charging facility layout data of the initial year of the area to be deployed;

[0048] S02: Construct a basic road network based on the road network data and charging facility layout data of the initial year, estimate the charging demand of the year according to the electric vehicle ownership data of the year, and distribute the charging demand to each node of the constructed basic road network to form a charging demand distribution map of the initial year;

[0049] S03: Determine whether the current cycle is within the planned period; if so, estimate the charging demand of the current cycle based on the electric vehicle ownership data of the cycle, and update the charging demand of the current cycle in the charging demand distribution map to form the charging demand distribution map of the current cycle;

[0050] S04: Solving the constructed charging facility layout optimization model based on the charging demand of the current cycle and the charging facility layout data of the current cycle to determine whether the existing charging facilities in the current cycle meet the charging demand. If so, entering the next cycle, i.e. returning to step S03; if not, solving how to expand the charging facilities in the current cycle to meet the charging demand on the road network through the charging facility layout optimization model;

[0051] S05: If the charging facilities in the current cycle meet the charging demand after the capacity expansion, the charging facilities expansion information of the current cycle is updated and the next cycle is entered, that is, the process returns to step S03; if the charging facilities in the current cycle cannot meet the charging demand after the capacity expansion, the charging facilities layout optimization model is used to solve how to build new charging facilities in the current cycle to meet the charging demand on the road network; wherein, the ordinary nodes without charging facilities are the backup nodes for the newly built charging facilities;

[0052] S06: Update the new charging facility information of the current cycle and enter the next cycle, that is, return to step S03;

[0053] S07: The cycle is executed until the end of the cycle, and the layout of charging facilities and investment costs of each cycle are obtained; wherein, the construction method of the charging facility layout optimization model is as follows:

[0054] The basic road network is constructed based on the road network data and the charging facility layout data. The charging demand is estimated according to the electric vehicle ownership data, and the charging demand is allocated to each node of the constructed basic road network to form a charging demand distribution map to describe the relationship between charging facilities and charging demand.

[0055] According to the charging demand distribution map, the goal is to minimize the consumption cost of the road sections passed by the road network to complete the charging demand in each cycle and the construction cost of the charging facility expansion or new charging facilities in all cycles, and to construct a charging facility layout optimization model with the constraints of meeting the charging demand of all nodes on the road network and the capacity limit of the charging facilities.

[0056] In this embodiment, the construction of the charging demand distribution map can be specifically as follows: Figure 2 As shown; the data on the number of electric vehicles can be derived from government public data (such as statistical yearbooks), comprehensive transportation annual reports and other official documents, and the data on the number of electric vehicles in the corresponding region can be proportionally split by the population of the corresponding region, but is not limited to this. The charging demand can be estimated by multiplying the corresponding electric vehicle ownership data by a certain coefficient. Of course, it can also be estimated by other existing technologies, such as the calculation method of charging demand in a method for planning urban electric vehicle charging facilities that considers different types of charging needs disclosed in publication number CN111199080A.

[0057] Specifically, the charging facility layout optimization model is as follows:

[0058] Objective function:

[0059]

[0060] First constraint:

[0061] Second constraint:

[0062] Third constraint:

[0063] Fourth constraint:

[0064] Fifth constraint:

[0065]

[0066] Sixth constraint:

[0067] Seventh Constraint:

[0068] Eighth constraint:

[0069]

[0070] Among them, in formula (1), ω1 and ω2 are the weights of the objective function; represents the cost of passing through section ij in period t; is a 0-1 decision variable. When , it means that the road segment ij is passed within the cycle t and the charging demand on this path is served by the charging facility m, otherwise a t represents the cost of increasing the capacity of charging facilities per unit of electricity within period t, Δq m,t represents the additional power generated by the expansion of charging facility m within period t; b t represents the cost of building a new charging facility in period t, is a 0-1 decision variable. When , it means that the planner selects from the candidate node set N in period t t Choose to build a new charging facility at node i, otherwise The first term of formula (1) represents the total path cost of completing the charging demand on the road network in all cycles, and the second term represents the construction cost of expanding the charging facilities or building new charging facilities in the entire planning period;

[0071] Formula (2) constrains the charging demand on the road network to be met within each cycle t, where: is a 0-1 decision variable. When , it means that charging facility m serves the charging demand of node i in period t. otherwise

[0072] Formula (3) constrains the power consumption of each charging facility. Within a period t, each charging facility m serves the charging needs of several nodes, but cannot exceed its own power limit q m,t ;

[0073] Formula (4) indicates that each node demand can only be served by a charging facility at most once, which is a distribution constraint;

[0074] Formula (5) indicates that each node in the road network follows the flow balance constraint, where b = 0 when the node is a normal node; b = 1 when the node is a charging facility;

[0075] Formula (6) represents the number constraint of charging facilities for expansion, which is no greater than the number of charging facilities in the current cycle |M t |; is the decision variable for charging facility capacity expansion. , it means that the planner chooses to expand the capacity of charging facility m in period t, otherwise

[0076] Formula (7) is the power constraint of charging facility m after expansion, q m,t Represents the total power of charging facility m in cycle t. The expanded power of charging facility m is not greater than the power q of the charging facility in the previous cycle. m,t-1 ;

[0077] Formula (8) is the constraint on the number of new charging facilities, which is no greater than the number of candidate sites in the current cycle |N t |;

[0078] Formula (9) is a coupling constraint between two types of variables, indicating that the charging facility must visit the node before serving the node demand;

[0079] Formulas (10) to (13) are constraints on the value range of decision variables.

[0080] The collections, indexes, parameters, and variables used in the charging facility layout optimization model are shown in the following table:

[0081]

[0082]

[0083] If there is Figure 3 The road network shown (including 6 nodes, 2 of which are charging stations, 4 are ordinary nodes, and also candidate nodes for new charging stations; 10 one-way sections). The initial charging capacity of the charging facility p 5 =p 6 =5, the charging demand at the common node is d1=d2=d3=d4=1, and the cost c consumed by passing the road section 15 =c 35 =c 26 =c 46 =1, c 13 =c 34 =c 42 =c 21 =2, c 14 =c 23 =3, the charging capacity of the charging facility expansion Δq 5 =Δq 6 =(0,10).

[0084] If the total cost is the lowest after node 5 is expanded, the charging demand distribution diagram after node 5 is expanded is as follows: Figure 4 As shown; if the solution requires node 1 to build new charging facilities, then the charging demand distribution diagram of node 1 after construction is as follows Figure 5 shown.

[0085] The specific embodiments described above further illustrate the technical problems, technical solutions and beneficial effects solved by the present invention. It should be understood that the above are only specific embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A layout method for multi-cycle electric vehicle charging facilities, characterized in that: The method also includes the following steps: S01: Obtain the road network data, electric vehicle ownership data and charging facility layout data of the initial year of the area to be deployed; S02: construct a basic road network based on the road network data and charging facility layout data of the initial year, estimate the charging demand of the year according to the electric vehicle ownership data of the year, and distribute the charging demand to each node of the constructed basic road network to form a charging demand distribution map of the initial year; S03: Determine whether the current cycle is within the planned period; if so, estimate the charging demand of the current cycle based on the electric vehicle ownership data of the cycle, and update the charging demand of the current cycle in the charging demand distribution map to form the charging demand distribution map of the current cycle; S04: Solving the constructed charging facility layout optimization model based on the charging demand of the current cycle and the charging facility layout data of the current cycle to determine whether the existing charging facilities in the current cycle meet the charging demand. If so, entering the next cycle, i.e. returning to step S03; if not, solving how to expand the charging facilities in the current cycle to meet the charging demand on the road network through the charging facility layout optimization model; S05: If the charging facilities in the current cycle meet the charging demand after the capacity expansion, the charging facilities expansion information of the current cycle is updated and the next cycle is entered, that is, the process returns to step S03; if the charging facilities in the current cycle cannot meet the charging demand after the capacity expansion, the charging facilities layout optimization model is used to solve how to build new charging facilities in the current cycle to meet the charging demand on the road network; wherein, the ordinary nodes without charging facilities are the backup nodes for the newly built charging facilities; S06: Update the new charging facility information of the current cycle and enter the next cycle, that is, return to step S03; S07: The cycle is executed until the end of the cycle, and the layout of charging facilities and investment costs of each cycle are obtained; wherein, the construction method of the charging facility layout optimization model is as follows: The basic road network is constructed based on the road network data and the charging facility layout data. The charging demand is estimated according to the electric vehicle ownership data, and the charging demand is allocated to each node of the constructed basic road network to form a charging demand distribution map to describe the relationship between charging facilities and charging demand. According to the charging demand distribution map, the goal is to minimize the consumption cost of the road sections passed by the road network to complete the charging demand in each cycle and the construction cost of the charging facility expansion or new charging facilities in all cycles, and to construct a charging facility layout optimization model with the constraints of meeting the charging demand of all nodes on the road network and the capacity limit of the charging facilities.

2. The layout method according to claim 1, characterized in that: The charging facility layout optimization model is as follows: Objective function: First constraint: Second constraint: Third constraint: Fourth constraint: Fifth constraint: Sixth constraint: Seventh Constraint: Eighth constraint: Among them, in formula (1), ω1 and ω2 are the weights of the objective function; represents the cost of passing through section ij in period t; is a 0-1 decision variable. When , it means that the road segment ij is passed within the cycle t and the charging demand on this path is served by the charging facility m, otherwise a t represents the cost of increasing the capacity of charging facilities per unit of electricity within period t, Δq m,t represents the additional power generated by the expansion of charging facility m within period t; b t represents the cost of building a new charging facility in period t, is a 0-1 decision variable. When , it means that the planner selects from the candidate node set N in period t t Choose to build a new charging facility at node i, otherwise The first term of formula (1) represents the total path cost of completing the charging demand on the road network in all cycles, and the second term represents the construction cost of expanding the charging facilities or building new charging facilities in the entire planning period; Formula (2) constrains the charging demand on the road network to be met within each cycle t, where: is a 0-1 decision variable. When , it means that charging facility m serves the charging demand of node i in period t. otherwise Formula (3) constrains the power consumption of each charging facility. Within a period t, each charging facility m serves the charging needs of several nodes, but cannot exceed its own power limit q m,t ; Formula (4) indicates that each node demand can only be served by a charging facility at most once, which is a distribution constraint; Formula (5) indicates that each node in the road network follows the flow balance constraint, where b = 0 when the node is a normal node; b = 1 when the node is a charging facility; Formula (6) represents the number constraint of charging facilities for expansion, which is no greater than the number of charging facilities in the current cycle |M t |; is the decision variable for charging facility capacity expansion. , it means that the planner chooses to expand the capacity of charging facility m in period t, otherwise Formula (7) is the power constraint of charging facility m after expansion, q m,t Represents the total power of charging facility m in cycle t. The expanded power of charging facility m is not greater than the power q of the charging facility in the previous cycle. m,t-1 ; Formula (8) is the constraint on the number of new charging facilities, which is no greater than the number of candidate sites in the current cycle |N t |; Formula (9) is a coupling constraint between two types of variables, indicating that the charging facility must visit the node before serving the node demand; Formulas (10) to (13) are constraints on the value range of decision variables.

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