A joint optimization method and system for charging and swapping stations based on rural distribution network

Abstract

A joint optimization method for charging and swapping stations based on rural distribution networks, the method comprising: constructing a joint optimization configuration and operation model for charging and swapping stations, performing simulation calculations based on the constructed model, and optimizing the site selection and sizing schemes and joint operation schemes of charging stations and swapping stations; in the method, the constructed model aims to maximize the comprehensive benefits of charging and swapping services, optimizes the site selection and sizing, access methods and joint operation schemes of electric vehicle charging stations and swapping stations in rural areas, and fully considers the charging and swapping needs of external electric vehicles and local electric vehicles during the optimization, as well as the supporting capabilities of photovoltaic and distributed pumped-storage power stations in rural areas for charging loads, so that the final optimization result is more reasonable.

Description

Technical Field

[0001] The present invention belongs to the technical field of access and operation of charging and swapping facilities in rural distribution networks, and specifically relates to a joint optimization method and system for charging and swapping stations based on rural distribution networks. Background Art

[0002] At present, various types of charging and swapping facilities have been widely covered in major cities, but their popularity in rural areas still needs to be improved. There are two main types of electric vehicle recharge needs in rural areas. One is the recharge demand for various electric vehicles owned by local residents, and the other is the recharge demand for foreign electric vehicles going to rural areas for sightseeing or passing through. The former is evenly and widely distributed in rural areas, while the latter is mainly concentrated in important scenic spots and along main roads such as corridors. Therefore, optimizing the type, site selection and capacity of charging and swapping facilities is crucial to meeting the recharge needs of electric vehicles in rural areas.

[0003] In addition, due to the large power supply radius of the rural distribution network, the capacity of the distribution transformer and distribution line is not as good as that of the urban distribution network, so its carrying capacity for electric vehicle charging load is limited. Large-scale access to charging load will have a huge impact on the rural distribution network trend, which may lead to low voltage problems at the distribution network nodes, especially at the end of the line. Therefore, it is particularly important for the rural distribution network to optimize the access location and method of electric vehicle charging load. Summary of the invention

[0004] The purpose of the present invention is to address the above-mentioned problems existing in the prior art and to provide a joint optimization method and system for charging and swapping stations based on rural distribution networks, which maximizes the comprehensive benefits of charging and swapping services while taking into account the energy replenishment needs of rural electric vehicles and the safety of rural distribution networks.

[0005] To achieve the above objectives, the technical solution of the present invention is as follows:

[0006] In a first aspect, the present invention provides a joint optimization method for charging and swapping stations based on a rural distribution network, the joint optimization method comprising:

[0007] S1. Construct a joint optimization configuration and operation model for charging and swapping stations. The objective function of this model is to maximize the comprehensive benefits of charging and swapping services while ensuring the energy demand of rural electric vehicles and the safety of rural distribution networks.

[0008] S2. Perform simulation calculations based on the joint optimization configuration and operation model of charging and swapping stations to optimize the site selection, capacity determination, access method and joint operation plan of charging stations and swapping stations.

[0009] The objective function of the combined optimization configuration and operation model of the charging and swapping stations is:

[0010] max(IC +I S -C E )dC P [(1+α) γ -1] / αγ 2 ;

[0011]

[0012]

[0013]

[0014]

[0015] In the above formula, I C Daily charging service income; S Daily battery replacement service income; C E is the daily electricity purchase cost; C P is the annualized construction cost; d is the number of typical days in a year; α is the discount rate; γ is the investment period; γ EP is the average proportion of electric vehicles in traffic flow; γV is the flow capture rate of electric vehicles by the charging and swapping station; is the traffic flow of local vehicles using path k for the origin-destination pair od in time period t; is the traffic flow of out-of-town vehicles taking route k for the origin-destination pair od in time period t; is the correlation matrix between paths and roads; is the correlation matrix between roads and traffic nodes; are binary planning decision variables for charging stations and battery swap stations respectively; E v Energy requirements for a single charge of an electric vehicle; IN C The unit price of charging service; IN S The single price for battery replacement service; To balance the active power output of the node to the entire distribution network in time period t, e o It is a balance node; is the basic active load connected to the node e of the distribution network at time period t; is the real-time electricity price in period t; is the number of charging piles in the charging station at node j; is the number of battery swapping facilities in the battery swapping station at node j; is the number of battery charging facilities at node j; PR C , PR S , PR B These are the unit prices of electric vehicle charging facilities, battery replacement facilities, and battery charging facilities respectively.

[0016] The constraints of the joint optimization configuration and operation model of the charging and swapping stations include the planning and operation constraints of charging stations and swapping stations, the charging and swapping demand radius constraints, distributed pumped storage power station constraints, transportation network constraints, and distribution network constraints; wherein, the planning and operation constraints of the charging and swapping stations include the upper limit constraint on the number of batteries in the charging and swapping stations, the battery flow constraint of electric vehicles, the location constraint of the charging and swapping facilities, and the charging and swapping service capacity constraint; the charging and swapping demand radius constraint includes the charging and swapping demand radius constraint of local electric vehicles and the proportion constraint of charging and swapping demand generated in electric vehicles; the distributed The distributed pumped storage power station constraints include the capacity boundary constraints of the upper reservoir and the lower reservoir of the distributed pumped storage power station, the capacity state constraints of the upper reservoir and the lower reservoir of the distributed pumped storage power station, the constraints on the change of water volume per unit time of the distributed pumped storage power station, and the power constraints of the distributed pumped storage power station; the traffic network constraints include the road traffic flow composition constraints, the road traffic flow capacity constraints, and the total traffic demand constraints; the distribution network constraints include the active and reactive power balance constraints of the balancing nodes, the power balance constraints of the distribution network lines, the power constraints of the electric vehicle charging stations, and the voltage constraints of the distribution network nodes;

[0017] The upper limit of the number of batteries in the charging station and the battery swap station is:

[0018]

[0019]

[0020] In the above formula, FB t，j is the number of batteries that have completed charging at the battery swap station at node j in time period t; FB M The upper limit of the number of batteries that can be charged in the battery swap station; J S is the set of nodes with battery swap stations; EB t，j is the number of batteries to be charged at the charging station at node j in time period t; EBM is the upper limit of the number of batteries to be charged in the charging station; J C It is a collection of nodes with charging stations built;

[0021] The electric vehicle battery flow constraint is:

[0022]

[0023]

[0024] In the above formula, FB t-1，j EB is the number of batteries that have completed charging at the battery swap station at node j in time period t-1; t-1，j is the number of batteries to be charged at the charging station at node j in time period t-1; BS t，jis the number of batteries to be charged due to battery replacement at the battery replacement station at node j in time period t; t，j，m is the number of fully charged batteries transported from the charging station at node m to the battery swap station at node j during period t; BC t，j is the number of batteries that have completed charging at the charging station at node j during period t; t，j，n is the number of batteries to be charged transported from the charging station at node j to the battery swap station at node n during time period t;

[0025] The charging and swapping facilities are configured with the following location constraints:

[0026]

[0027]

[0028]

[0029] In the above formula, δ M is the large M constant;

[0030] The charging and swapping service capacity constraints are:

[0031]

[0032]

[0033]

[0034] In the above formula, T C 、T S 、T B They are the time consumed for a single electric vehicle charging, the time consumed for electric vehicle battery replacement, and the time consumed for battery charging; T U is the unit time;

[0035] The charging and swapping demand radius constraint of the local electric vehicle is:

[0036]

[0037]

[0038] In the above formula, D i，j is the distance between node i and the charging station or swap station located at node j; DM j，j is the distance between node i and node j; is a binary auxiliary variable; LS is the charging demand radius;

[0039] The proportion constraint of charging and swapping demand generated in the electric vehicle is:

[0040]

[0041] In the above formula, FS is the minimum value that meets the charging demand of out-of-town electric vehicles;

[0042] The capacity boundary constraints of the upper reservoir and the lower reservoir of the distributed pumped storage power station are:

[0043]

[0044]

[0045] In the above formula, are the water volumes of the upper and lower reservoirs of the distributed pumped storage power station in time period t; VU m 、VU M are the lower and upper limits of water volume in the upper reservoir of the distributed pumped storage power station; VD m 、VD M They are the lower and upper limits of water volume in the lower reservoir of the distributed pumped storage power station;

[0046] The capacity state constraints of the upper reservoir and the lower reservoir of the distributed pumped storage power station are:

[0047]

[0048]

[0049] In the above formula, are the water volumes of the upper and lower reservoirs of the distributed pumped storage power station in time period t-1; tn is the last time period in a typical day;

[0050] The constraint on the change of water volume per unit time in the distributed pumped storage power station is:

[0051]

[0052] In the above formula, ΔV t is the water volume change of the distributed pumped storage power station in time period t; ΔV M The upper limit of water volume change per unit time in a distributed pumped storage power station;

[0053] The power constraint of the distributed pumped storage power station is:

[0054]

[0055] In the above formula, is the output and input power of the distributed pumped storage power station in time period t; ρ is the density of water; g is the acceleration of gravity; h is the height difference between the upper and lower reservoirs of the distributed pumped storage power station;

[0056] The road traffic flow composition constraint is:

[0057]

[0058] In the above formula, is the traffic flow on road l in time period t;

[0059] The road traffic flow capacity constraint is:

[0060]

[0061] In the above formula, FC l is the upper limit of traffic flow on road l;

[0062] The total traffic demand constraint is:

[0063]

[0064]

[0065] In the above formula, LD t，od ND t，od is the traffic demand of local vehicles and out-of-town vehicles for the starting and ending points od in time period t;

[0066] The active and reactive power balance constraints of the balancing node are:

[0067]

[0068]

[0069] In the above formula, The reactive power output by the balancing node to the entire distribution network in time period t; are respectively the active and reactive power on the outgoing line of the balancing node at time period t; d o Outgoing line for balancing node;

[0070] The power balance constraint of the distribution network line is:

[0071]

[0072]

[0073]

[0074] In the above formula, are the active and reactive power on line w in time period t, respectively, and line d refers to all lines connected to the distribution network node e; is the active power output by the distribution network to the electric vehicle charging station connected to node e during time period t; is the photovoltaic output connected to node e by the distribution network during time period t; are the basic active and reactive loads connected to the node e of the distribution network at time period t; LC d is the capacity of the distribution network line d;

[0075] The power constraint of the electric vehicle charging station is:

[0076]

[0077] In the above formula, E B The energy requirements needed to charge the battery once; is the correlation matrix between transportation nodes and distribution network nodes;

[0078] The distribution network node voltage constraint is:

[0079]

[0080]

[0081]

[0082] In the above formula, are the resistance and reactance of the distribution network line d respectively; ΔU t，d is the voltage drop on the distribution network line d during time period t; U t，a , U t，b are the bus voltages of nodes a and b in the distribution network in time period t, respectively. Nodes a and b are the two endpoints of the distribution network line d; U N is the rated voltage of the distribution network bus; U m , U M The upper and lower limits of the distribution network bus voltage; is the reactive power of the distribution network node e in time period t; U t，e is the bus voltage of the node e in the distribution network during time period t.

[0083] In a second aspect, the present invention provides a joint optimization system for charging and swapping stations based on a rural distribution network, the joint optimization system comprising a model building module and a simulation calculation module;

[0084] The model building module is used to build a joint optimization configuration and operation model of charging and swapping stations. The objective function of the model is to maximize the comprehensive benefits of charging and swapping services under the premise of ensuring the energy replenishment demand of rural electric vehicles and the safety of rural distribution networks;

[0085] The simulation calculation module is used to perform simulation calculations based on the joint optimization configuration and operation model of the charging and swapping stations, and optimize the site selection, capacity determination, access method and joint operation plan of the charging stations and swapping stations.

[0086] The objective function of the combined optimization configuration and operation model of the charging and swapping stations is:

[0087] max(I C +I S -C E )dC P [(1+α) γ -1] / αγ 2 ;

[0088]

[0089]

[0090]

[0091]

[0092] In the above formula, I C Daily charging service income; S Daily battery replacement service income; C E is the daily electricity purchase cost; C P is the annualized construction cost; d is the number of typical days in a year; α is the discount rate; γ is the investment period; γ EP is the average proportion of electric vehicles in traffic flow; γ V The flow capture rate of electric vehicles at charging and swapping stations; is the traffic flow of local vehicles using path k for the origin-destination pair od in time period t; is the traffic flow of out-of-town vehicles taking route k for the origin-destination pair od in time period t; is the correlation matrix between paths and roads; is the correlation matrix between roads and traffic nodes; are binary planning decision variables for charging stations and battery swap stations respectively; E v Energy requirements for a single charge of an electric vehicle; IN C The unit price of charging service; IN S The single price for battery replacement service; To balance the active power output of the node to the entire distribution network in time period t, e o It is a balance node; is the basic active load connected to the node e of the distribution network at time period t; is the real-time electricity price in period t; is the number of charging piles in the charging station at node j; is the number of battery swapping facilities in the battery swapping station at node j; is the number of battery charging facilities at node j; PRC , PR S , PR B These are the unit prices of electric vehicle charging facilities, battery replacement facilities, and battery charging facilities respectively.

[0093] The constraints of the joint optimization configuration and operation model of the charging and swapping stations include the planning and operation constraints of charging stations and swapping stations, the charging and swapping demand radius constraints, distributed pumped storage power station constraints, transportation network constraints, and distribution network constraints; wherein, the planning and operation constraints of the charging and swapping stations include the upper limit constraint on the number of batteries in the charging and swapping stations, the battery flow constraint of electric vehicles, the location constraint of the charging and swapping facilities, and the charging and swapping service capacity constraint; the charging and swapping demand radius constraint includes the charging and swapping demand radius constraint of local electric vehicles and the proportion constraint of charging and swapping demand generated in electric vehicles; the distributed The distributed pumped-storage power station constraints include the capacity boundary constraints of the upper reservoir and the lower reservoir of the distributed pumped-storage power station, the capacity state constraints of the upper reservoir and the lower reservoir of the distributed pumped-storage power station, the constraints on the change of water volume per unit time of the distributed pumped-storage power station, and the power constraints of the distributed pumped-storage power station; the traffic network constraints include the road traffic flow composition constraints, the road traffic flow capacity constraints, and the total traffic demand constraints; the distribution network constraints include the active and reactive power balance constraints of the balancing nodes, the power balance constraints of the distribution network lines, the power constraints of the electric vehicle charging stations, and the voltage constraints of the distribution network nodes.

[0094] The upper limit of the number of batteries in the charging station and the battery swap station is:

[0095]

[0096]

[0097] In the above formula, FB t，j is the number of batteries that have completed charging at the battery swap station at node j in time period t; FB M The upper limit of the number of batteries that can be charged in the battery swap station; J S is the set of nodes with battery swap stations; EB t，j is the number of batteries to be charged at the charging station at node j in time period t; EB M The upper limit of the number of batteries to be charged in the charging station; J C It is a collection of nodes with charging stations built;

[0098] The electric vehicle battery flow constraint is:

[0099]

[0100]

[0101] In the above formula, FB t-1，jEB is the number of batteries that have completed charging at the battery swap station at node j in time period t-1; t-1，j is the number of batteries to be charged at the charging station at node j in time period t-1; BS t，j is the number of batteries to be charged due to battery replacement at the battery replacement station at node j in time period t; t，j，m is the number of fully charged batteries transported from the charging station at node m to the battery swap station at node j during period t; BC t，j is the number of batteries that have completed charging at the charging station at node j during period t; t，j，n is the number of batteries to be charged transported from the charging station at node j to the battery swap station at node n during time period t;

[0102] The charging and swapping facilities are configured with the following location constraints:

[0103]

[0104]

[0105]

[0106] In the above formula, δ M is the large M constant;

[0107] The charging and swapping service capacity constraints are:

[0108]

[0109]

[0110]

[0111] In the above formula, T C 、T S 、T B They are the time consumed for a single electric vehicle charging, the time consumed for electric vehicle battery replacement, and the time consumed for battery charging; T U is the unit time;

[0112] The charging and swapping demand radius constraint of the local electric vehicle is:

[0113]

[0114]

[0115]

[0116] In the above formula, D i，j is the distance between node i and the charging station or swap station located at node j; DM i，j is the distance between node i and node j; is a binary auxiliary variable; LS is the charging demand radius;

[0117] The proportion constraint of charging and swapping demand generated in the electric vehicle is:

[0118]

[0119] In the above formula, FS is the minimum value that meets the charging demand of out-of-town electric vehicles;

[0120] The capacity boundary constraints of the upper reservoir and the lower reservoir of the distributed pumped storage power station are:

[0121]

[0122]

[0123] In the above formula, are the water volumes of the upper and lower reservoirs of the distributed pumped storage power station in time period t; VU m 、VU M are the lower and upper limits of water volume in the upper reservoir of the distributed pumped storage power station; VD m 、VD M They are the lower and upper limits of water volume in the lower reservoir of the distributed pumped storage power station;

[0124] The capacity state constraints of the upper reservoir and the lower reservoir of the distributed pumped storage power station are:

[0125]

[0126]

[0127] In the above formula, are the water volumes of the upper and lower reservoirs of the distributed pumped storage power station in time period t-1; tn is the last time period in a typical day;

[0128] The constraint on the change of water volume per unit time in the distributed pumped storage power station is:

[0129]

[0130] In the above formula, ΔV t is the water volume change of the distributed pumped storage power station in time period t; ΔV M The upper limit of water volume change per unit time in a distributed pumped storage power station;

[0131] The power constraint of the distributed pumped storage power station is:

[0132]

[0133] In the above formula, is the output and input power of the distributed pumped storage power station in time period t; ρ is the density of water; g is the acceleration of gravity; h is the height difference between the upper and lower reservoirs of the distributed pumped storage power station;

[0134] The road traffic flow composition constraint is:

[0135]

[0136] In the above formula, is the traffic flow on road l in time period t;

[0137] The road traffic flow capacity constraint is:

[0138]

[0139] In the above formula, FC l is the upper limit of traffic flow on road l;

[0140] The total traffic demand constraint is:

[0141]

[0142]

[0143] In the above formula, LD t，od ND t，od is the traffic demand of local vehicles and out-of-town vehicles for the starting and ending points od in time period t;

[0144] The active and reactive power balance constraints of the balancing node are:

[0145]

[0146]

[0147] In the above formula, The reactive power output by the balancing node to the entire distribution network in time period t; are respectively the active and reactive power on the outgoing line of the balancing node at time period t; d o Outgoing line for balancing node;

[0148] The power balance constraint of the distribution network line is:

[0149]

[0150]

[0151]

[0152] In the above formula, are the active and reactive power on line w in time period t, respectively, and line d refers to all lines connected to the distribution network node e; is the active power output by the distribution network to the electric vehicle charging station connected to node e during time period t; is the photovoltaic output connected to node e by the distribution network during time period t; are the basic active and reactive loads connected to the node e of the distribution network at time period t; LC d is the capacity of the distribution network line d;

[0153] The power constraint of the electric vehicle charging station is:

[0154]

[0155] In the above formula, E B The energy requirements needed to charge the battery once; is the correlation matrix between transportation nodes and distribution network nodes;

[0156] The distribution network node voltage constraint is:

[0157]

[0158]

[0159]

[0160] In the above formula, are the resistance and reactance of the distribution network line d respectively; ΔU t，d is the voltage drop on the distribution network line d during time period t; U t，a , U t，b are the bus voltages of nodes a and b in the distribution network in time period t, respectively. Nodes a and b are the two endpoints of the distribution network line d; U N is the rated voltage of the distribution network bus; U m , U M The upper and lower limits of the distribution network bus voltage; is the reactive power of the distribution network node e in time period t; U t，e is the bus voltage of the node e in the distribution network during time period t.

[0161] In a third aspect, the present invention provides a joint optimization device for charging and swapping stations based on a rural distribution network, the joint optimization device comprising a memory and a processor;

[0162] The memory is used to store computer program code and transmit the computer program code to the processor;

[0163] The processor is used to execute the aforementioned method according to the instructions in the computer program code.

[0164] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program implements the aforementioned method when executed by a processor.

[0165] Compared with the prior art, the present invention has the following beneficial effects:

[0166] The present invention provides a joint optimization method for charging and swapping stations based on rural distribution networks, constructs a joint optimization configuration and operation model for charging and swapping stations, performs simulation calculations based on the constructed model, and optimizes the site selection and capacity determination scheme and joint operation scheme of charging stations and swapping stations; the model constructed by this design aims to maximize the comprehensive benefits of charging and swapping services, optimizes the site selection and capacity determination, access mode and joint operation scheme of electric vehicle charging stations and swapping stations in rural areas, and fully considers the charging and swapping needs of external electric vehicles and local electric vehicles during optimization, as well as the supporting capacity of photovoltaic and distributed pumped storage power stations in rural areas for charging loads, so that the final optimization result is more reasonable. Therefore, the present invention can maximize the comprehensive benefits of charging and swapping services under the premise of ensuring the energy replenishment needs of rural electric vehicles and the safety of rural distribution networks. BRIEF DESCRIPTION OF THE DRAWINGS

[0167] Figure 1 The figure is a flow chart of the joint optimization method of the present invention.

[0168] Figure 2 This is a structural diagram of the rural distribution network-transportation system that the present invention is directed to.

[0169] Figure 3 This is a schematic diagram of the joint service mode of the charging station and the battery swap station described in the present invention.

[0170] Figure 4 This is the topological structure diagram of the 18-node distribution network-22-node transportation network in Example 1.

[0171] Figure 5 This is the time-of-use electricity price used in Example 1.

[0172] Figure 6 This is the photovoltaic output characteristic curve used in Example 1.

[0173] Figure 7 The planned locations of the charging and swapping stations obtained by Scheme 1 in Example 1.

[0174] Figure 8 It is a structural schematic diagram of the joint optimization system described in the present invention.

[0175] Fig. 9 It is a schematic diagram of the structure of the joint optimization device described in the present invention. DETAILED DESCRIPTION

[0176] The present invention is further described in detail below in conjunction with specific implementations and drawings.

[0177] The structure of the rural distribution network-transportation system faced by the present invention is as follows Figure 2 As shown, it includes rural distribution network, transportation network, electric vehicle, charging station, and battery swap station; in terms of energy replenishment system, the charging station is connected to the head end of the rural distribution network, and the battery swap station is configured near the end of the line, and the battery replaced by the battery swap station is sent to the charging station for charging, so as to achieve the purpose of transferring load and reducing the low voltage risk of the distribution network; in terms of energy replenishment demand, two types of charging demand, external electric vehicles and local electric vehicles, are mainly considered, among which the charging demand of external electric vehicles is concentrated near national roads and provincial roads, while the charging demand of local electric vehicles is concentrated near township roads and village roads; in terms of distributed power supply, the distributed power supply connected to the rural distribution network is mainly photovoltaic and distributed pumped storage power station, both of which can provide certain power support for the charging load of electric vehicles connected to the rural distribution network. In order to reduce the operating pressure of the distribution network, distributed pumped storage power station, photovoltaic, and electric vehicle charging station all adopt local reactive power compensation. The present invention finally optimizes the site selection, capacity setting, access mode and joint operation of the charging and swapping station, so as to maximize the comprehensive benefits of charging and battery swapping services under the premise of ensuring the energy replenishment demand of rural electric vehicles and the safety of rural distribution network.

[0178] Embodiment 1:

[0179] See also Figure 1 , a joint optimization method of charging and swapping stations based on rural distribution network is specifically carried out in the following steps:

[0180] S1. Construct a joint optimization configuration and operation model for charging and swapping stations. The objective function of the model is to maximize the comprehensive benefits of charging and swapping services under the premise of ensuring the energy replenishment demand of rural electric vehicles and the safety of rural distribution networks. The constraints include the planning and operation constraints of charging stations and swapping stations, charging and swapping demand radius constraints, distributed pumped storage power station constraints, transportation network constraints, and distribution network constraints. Among them, the objective function of the joint optimization configuration and operation model for charging and swapping stations is:

[0181] max(I C +I S -C E )dC P [(1+α) γ -1] / αγ 2 ;

[0182]

[0183]

[0184]

[0185]

[0186] In the above formula, I C Daily charging service income; S Daily battery replacement service income; C E is the daily electricity purchase cost; C P is the annual construction cost; d is the number of typical days in a year; α is the discount rate; γ is the investment period; γEP is the average proportion of electric vehicles in traffic flow; γ V The flow capture rate of electric vehicles at charging and swapping stations; is the traffic flow of local vehicles using path k for the origin-destination pair od in time period t; is the traffic flow of out-of-town vehicles taking route k for the origin-destination pair od in time period t; is the correlation matrix between paths and roads. When path k contains road l on the contrary is the correlation matrix between roads and traffic nodes. When road l is connected to node j, on the contrary are binary planning decision variables for charging stations and battery swap stations, respectively. The value is 1 when a charging station / battery swap station is built at node j, otherwise it is 0; E v Energy requirements for a single charge of an electric vehicle; IN C The unit price of charging service; IN S The single price for battery replacement service; To balance the active power output of the node to the entire distribution network in time period t, e o It is a balance node; is the basic active load connected to the node e of the distribution network at time period t; is the real-time electricity price in period t; is the number of charging piles in the charging station at node j; is the number of battery swapping facilities in the battery swapping station at node j; is the number of battery charging facilities at node j; PR C , PR S , PR B They are the unit prices of electric vehicle charging facilities, battery replacement facilities, and battery charging facilities;

[0187] This embodiment mainly considers the following Figure 3The joint service mode of the charging station and the battery swap station shown is specifically that the battery swap station will transport the batteries to be charged due to the battery swap service to the charging station for charging, and the charging station will transport the charged batteries to the battery swap station. At the same time, electric vehicles can also go directly to the charging station for charging. In order to ensure the utilization efficiency of the battery, the battery swap station does not store the batteries to be charged, and the generated batteries to be charged are directly transported to the charging station. Similarly, the charging station does not store the batteries that have been charged, and the batteries that have been charged are directly transported to the battery swap station. Based on this, the planning and operation constraints of the charging station and the battery swap station include the upper limit constraint on the number of batteries in the charging station and the battery swap station, the battery flow constraint of the electric vehicle, the configuration location constraint of the charging and battery swapping facilities, and the charging and battery swapping service capacity constraint. The upper limit constraint on the number of batteries in the charging station and the battery swap station is:

[0188]

[0189]

[0190] In the above formula, FB t，j is the number of batteries that have completed charging at the battery swap station at node j in time period t; FBM is the upper limit of the number of batteries that have completed charging at the battery swap station; J S is the set of nodes with battery swap stations; EB t，j is the number of batteries to be charged at the charging station at node j in time period t; EB M The upper limit of the number of batteries to be charged in the charging station; J C It is a collection of nodes with charging stations built;

[0191] The electric vehicle battery flow constraint is:

[0192]

[0193]

[0194] In the above formula, FB t-1，j EB is the number of batteries that have completed charging at the battery swap station at node j in time period t-1; t-1，j is the number of batteries to be charged at the charging station at node j in time period t-1; BS t，j is the number of batteries to be charged due to battery replacement at the battery replacement station at node j in time period t; t，j，m is the number of fully charged batteries transported from the charging station at node m to the battery swap station at node j during period t; BC t，j is the number of batteries that have completed charging at the charging station at node j during period t; t，j，n is the number of batteries to be charged transported from the charging station at node j to the battery swap station at node n during time period t;

[0195] The charging and swapping facilities are configured with the following location constraints:

[0196]

[0197]

[0198]

[0199] In the above formula, δ M is the large M constant;

[0200] The charging and swapping service capacity constraints are:

[0201]

[0202]

[0203]

[0204] In the above formula, T C 、T S 、T B They are the time consumed for a single electric vehicle charging, the time consumed for electric vehicle battery replacement, and the time consumed for battery charging; T U is the unit time;

[0205] In the rural scenario considered in this embodiment, the charging and swapping demands of local electric vehicles are widely distributed, while the traffic capacity of the rural transportation network is relatively weak compared to that of the city. This places certain requirements on the service coverage capability of the charging and swapping facilities, so as to meet the charging and swapping demand radius constraints of electric vehicle users. The demand radius refers to the maximum distance from an electric vehicle at any location in the rural transportation network to the charging and swapping facilities. For non-local electric vehicles, their charging behavior mainly occurs along national and provincial roads, so it is necessary to ensure that the charging and swapping facilities in these areas can cover the charging and swapping demands generated by the traffic flow in these areas. Based on this, the charging and swapping demand radius constraints include the charging and swapping demand radius constraints of local electric vehicles and the proportion constraints of the charging and swapping demands generated in electric vehicles. The charging and swapping demand radius constraints of local electric vehicles are:

[0206]

[0207]

[0208]

[0209] In the above formula, D i，j is the distance between node i and the charging station or swap station located at node j; DM i，j is the distance between node i and node j; is a binary auxiliary variable, which takes the value of 1 if node j is within the charging demand radius of node i, otherwise it takes the value of 0; LS is the charging demand radius;

[0210] The proportion constraint of charging and swapping demand generated in the electric vehicle is:

[0211]

[0212] In the above formula, FS is the minimum value that meets the charging demand of out-of-town electric vehicles;

[0213] The distributed pumped-storage power station considered in this embodiment is connected at the balance node of the system and adopts a reversible unit, that is, the unit operates as a motor when pumping water and operates as a generator when releasing water; based on this, the distributed pumped-storage power station constraints include the capacity boundary constraints of the upper reservoir and the lower reservoir of the distributed pumped-storage power station, the capacity state constraints of the upper reservoir and the lower reservoir of the distributed pumped-storage power station, the constraints on the change of water volume per unit time of the distributed pumped-storage power station, and the power constraints of the distributed pumped-storage power station; the capacity boundary constraints of the upper reservoir and the lower reservoir of the distributed pumped-storage power station are:

[0214]

[0215]

[0216] In the above formula, are the water volumes of the upper and lower reservoirs of the distributed pumped storage power station in time period t; VU m 、VU M are the lower and upper limits of water volume in the upper reservoir of the distributed pumped storage power station; VD m 、VD M They are the lower and upper limits of water volume in the lower reservoir of the distributed pumped storage power station;

[0217] The capacity state constraints of the upper reservoir and the lower reservoir of the distributed pumped storage power station are:

[0218]

[0219]

[0220] In the above formula, are the water volumes of the upper and lower reservoirs of the distributed pumped storage power station in time period t-1; tn is the last time period in a typical day;

[0221] The constraint on the change of water volume per unit time in the distributed pumped storage power station is:

[0222]

[0223] In the above formula, ΔV t is the water volume change of the distributed pumped storage power station in time period t, when ΔV t >0, release water, ΔV t Pumping water when <0; ΔV M The upper limit of water volume change per unit time in a distributed pumped storage power station;

[0224] The power constraint of the distributed pumped storage power station is:

[0225]

[0226] In the above formula, is the output and input power of the distributed pumped storage power station in time period t; ρ is the density of water; g is the acceleration of gravity; h is the height difference between the upper and lower reservoirs of the distributed pumped storage power station;

[0227] In the rural power distribution-transportation system considered in this embodiment, there are two types of traffic demands: local electric vehicles and non-local electric vehicles. The former are widely distributed in the entire rural transportation network, while the latter are mainly distributed on national and provincial roads. Based on this, the traffic network constraints include road traffic flow composition constraints, road traffic flow capacity constraints, and total traffic demand constraints. The road traffic flow composition constraints are:

[0228]

[0229] In the above formula, is the traffic flow on road l in time period t;

[0230] The road traffic flow capacity constraint is:

[0231]

[0232] In the above formula, FC l is the upper limit of traffic flow on road l;

[0233] The total traffic demand constraint is:

[0234]

[0235]

[0236] In the above formula, LD t，od ND t，od is the traffic demand of local vehicles and out-of-town vehicles for the starting and ending points od in time period t;

[0237] The factors affecting the rural distribution network in this embodiment are the basic load of the distribution network, electric vehicle charging stations, distributed pumped storage power stations and photovoltaics; based on this, the distribution network constraints include active and reactive power balance constraints of balancing nodes, power balance constraints of distribution network lines, power constraints of electric vehicle charging stations, and voltage constraints of distribution network nodes; the active and reactive power balance constraints of the balancing nodes are:

[0238]

[0239]

[0240] In the above formula, The reactive power output by the balancing node to the entire distribution network in time period t; are respectively the active and reactive power on the outgoing line of the balancing node at time period t; d o Outgoing line for balancing node;

[0241] The power balance constraint of the distribution network line is:

[0242]

[0243]

[0244]

[0245] In the above formula, are the active and reactive power on line w in time period t, respectively, and line d refers to all lines connected to the distribution network node e; is the active power output by the distribution network to the electric vehicle charging station connected to node r during time period t; is the photovoltaic output connected to node e by the distribution network during time period t; are the basic active and reactive loads connected to the node e of the distribution network at time period t; LC d is the capacity of the distribution network line d;

[0246] The power constraint of the electric vehicle charging station is:

[0247]

[0248] In the above formula, E B The energy requirements needed to charge the battery once; is the correlation matrix between traffic nodes and distribution network nodes. When traffic node j is coupled with distribution network node e, on the contrary

[0249] The distribution network node voltage constraint is:

[0250]

[0251]

[0252]

[0253] In the above formula, are the resistance and reactance of the distribution network line d respectively; ΔU t，d is the voltage drop on the distribution network line d during time period t; U t，a , U t，b are the bus voltages of nodes a and b in the distribution network in time period t, respectively. Nodes a and b are the two endpoints of the distribution network line d; U N is the rated voltage of the distribution network bus; U m , U M The upper and lower limits of the distribution network bus voltage; is the reactive power of the distribution network node e in time period t; U t，e is the bus voltage of the distribution network node e in time period t;

[0254] S2. The problem of solving the joint optimization configuration and operation model of the charging and swapping stations obtained in step S1 corresponds to a mixed integer linear programming problem. The Matlab / Cplex solver can be used for simulation calculation to optimize the site selection, capacity determination, access method and joint operation plan of the charging stations and swapping stations.

[0255] Performance Verification:

[0256] In order to verify the effectiveness of the joint optimization of charging and swapping stations based on rural distribution network proposed in the present invention, the method of the present invention (referred to as Scheme 1) and the independent configuration and operation optimization method of charging and swapping stations (referred to as Scheme 2, in which the interaction between charging stations and swapping stations is not considered, and each swapping station is responsible for its own battery charging) are applied to the 18-node distribution network-22-node transportation network and simulation calculations are carried out. The topological structure of the 18-node distribution network-22-node transportation network is shown in the figure. Figure 4 As shown in the figure, the time-of-use electricity price and photovoltaic output characteristic curve used are as follows: Figure 5 and Figure 6 As shown;

[0257] Parameter setting: Based on the above 18-node distribution network-22-node transportation network, this example considers 3 charging stations and 3 battery swap stations; the unit configuration costs of electric vehicle charging piles, battery charging facilities, and electric vehicle battery swap facilities are 10,000 yuan, 5,000 yuan, and 80,000 yuan respectively; the unit time is 1 hour; the investment period is 10 years; the discount rate is 0.05; the typical number of days in a year is 365 days; the rated voltage of the distribution network is 10 kV; the upper and lower limits of the distribution network bus voltage are 10.5 kV and 9.5 kV respectively; the average charging and battery swapping of each electric vehicle is 2. The energy demand is 40 kWh; the rated power of electric vehicle charging piles and battery charging facilities is 80 kW and 40 kW respectively; the unit price of charging service and battery replacement service is 90 yuan / time and 200 yuan / time respectively; the time required for single electric vehicle charging, electric vehicle battery replacement and battery charging is 0.5 hour, 0.2 hour and 1 hour respectively; the average proportion of electric vehicles in traffic flow is 40%; the proportion of charging and battery replacement demand generated by electric vehicles is 3%; the charging demand radius is 4 kilometers; the lower and upper limits of the water volume of the upper reservoir of the distributed pumped storage power station are 3×10 4 and 1.5×10 5 cubic meters; the upper limit of water volume change per unit time in distributed pumped storage power stations is 2×10 4 cubic meters; the height difference between the upper and lower reservoirs of the distributed pumped storage power station is 50 meters; the density of water is 1×10 3 Kilograms / cubic meter.

[0258] Finally, the planned locations of each charging and swapping station in Scheme 1 are as follows: Figure 7 The configuration scheme of the charging and swapping station is shown in Table 1. The economic indicators of Scheme 1 and Scheme 2 are calculated, and the calculation results are shown in Table 2:

[0259] Table 1 Planning location and configuration of each charging and swapping station

[0260]

[0261] Table 2 Various economic indicators

[0262]

[0263] As shown in Table 2, when comparing Scheme 1 and Scheme 2, the sum of the annual charging service and battery replacement service revenues when Scheme 1 is adopted is higher than that of Scheme 2, which is increased by 3.68%; in terms of annual electricity purchase cost and annual construction cost, Scheme 1 is reduced by 17.52% and 7.94% respectively compared with Scheme 2; from the perspective of annual system comprehensive revenue, Scheme 1 is increased by 46.35% compared with Scheme 2. The above results show that the joint optimization method proposed in the present invention achieves the effect of maximizing the comprehensive revenue of charging and battery replacement services under the premise of ensuring the energy replenishment demand of rural electric vehicles and the safety of rural distribution networks.

[0264] Embodiment 2:

[0265] See also Figure 8 A charging and swapping station joint optimization system based on a rural distribution network includes a model building module and a simulation calculation module; the model building module is used to construct the following charging and swapping station joint optimization configuration and operation model:

[0266] The objective function is:

[0267] max(I C +I S -C E )dC P [(1+α) γ -1] / αγ 2 ;

[0268]

[0269]

[0270]

[0271]

[0272] In the above formula, I C Daily charging service income; S Daily battery replacement service income; C E is the daily electricity purchase cost; C P is the annualized construction cost; d is the number of typical days in a year; α is the discount rate; γ is the investment period; γ EP is the average proportion of electric vehicles in traffic flow; γ V The flow capture rate of electric vehicles at charging and swapping stations; is the traffic flow of local vehicles using path k for the origin-destination pair od in time period t; is the traffic flow of out-of-town vehicles taking route k for the origin-destination pair od in time period t; is the correlation matrix between paths and roads. When path k contains road l on the contrary is the correlation matrix between roads and traffic nodes. When road l is connected to node j, on the contrary are binary planning decision variables for charging stations and battery swap stations, respectively. The value is 1 when a charging station / battery swap station is built at node j, otherwise it is 0; E v Energy requirements for a single charge of an electric vehicle; IN CThe unit price of charging service; IN S The single price for battery replacement service; To balance the active power output of the node to the entire distribution network in time period t, e o It is a balance node; is the basic active load connected to the node e of the distribution network at time period t; is the real-time electricity price in period t; is the number of charging piles in the charging station at node j; is the number of battery swapping facilities in the battery swapping station at node j; is the number of battery charging facilities at node j; PR C , PR S , PR B They are the unit prices of electric vehicle charging facilities, battery replacement facilities, and battery charging facilities;

[0273] The constraints include the planning and operation constraints of charging stations and battery swap stations, the charging and swapping demand radius constraints, the distributed pumped-storage power station constraints, the transportation network constraints, and the distribution network constraints; wherein, the planning and operation constraints of the charging stations and battery swap stations include the upper limit of the number of batteries in the charging stations and battery swap stations, the battery flow constraints of electric vehicles, the location constraints of the charging and swapping facilities, and the charging and swapping service capacity constraints; the charging and swapping demand radius constraints include the charging and swapping demand radius constraints of local electric vehicles and the proportion of charging and swapping demand generated in electric vehicles; the distributed pumped-storage power station constraints include the distributed pumped-storage The capacity boundary constraints of the upper reservoir and the lower reservoir of the energy power station, the capacity state constraints of the upper reservoir and the lower reservoir of the distributed pumped storage power station, the constraints on the change of water volume per unit time of the distributed pumped storage power station, and the power constraints of the distributed pumped storage power station; the traffic network constraints include road traffic flow composition constraints, road traffic flow capacity constraints, and total traffic demand constraints; the distribution network constraints include active and reactive power balance constraints of balancing nodes, power balance constraints of distribution network lines, power constraints of electric vehicle charging stations, and voltage constraints of distribution network nodes; the specific expressions of the above constraints are the same as those in Example 1;

[0274] The simulation calculation module is used to perform simulation calculations based on the joint optimization configuration and operation model of the charging and swapping stations, and optimize the site selection, capacity determination, access method and joint operation plan of the charging stations and swapping stations.

[0275] Embodiment 3:

[0276] See also Fig. 9, a charging and swapping station joint optimization device based on a rural distribution network, comprising a memory and a processor; the memory is used to store computer program code and transmit the computer program code to the processor; the processor is used to execute the method described in Example 1 according to the instructions in the computer program code.

[0277] A computer-readable storage medium having a computer program stored thereon, wherein the computer program implements the method described in Example 1 when executed by a processor.

Claims

1. A joint optimization method for charging and swapping stations based on rural distribution networks, characterized in that: The joint optimization method comprises: S1. Construct a joint optimization configuration and operation model for charging and swapping stations. The objective function of this model is to maximize the comprehensive benefits of charging and swapping services while ensuring the energy demand of rural electric vehicles and the safety of rural distribution networks. S2. Perform simulation calculations based on the joint optimization configuration and operation model of charging and swapping stations to optimize the site selection, capacity determination, access mode and joint operation plan of charging stations and swapping stations; The objective function of the joint optimization configuration and operation model of the charging and swapping station is: max(I C +I S -C E )dC P [(1+a) γ -1] / ag 2 ; In the above formula, I C Daily charging service income; S Daily battery replacement service income; C E is the daily electricity purchase cost; C P is the annualized construction cost; d is the number of typical days in a year; α is the discount rate; γ is the investment period; γ EP is the average proportion of electric vehicles in traffic flow; γ V The flow capture rate of electric vehicles at charging and swapping stations; is the traffic flow of local vehicles using path k for the origin-destination pair od in time period t; is the traffic flow of out-of-town vehicles taking route k for the origin-destination pair od in time period t; is the correlation matrix between paths and roads; are binary planning decision variables for charging stations and battery swap stations respectively; E v Energy requirements for a single charge of an electric vehicle; IN C The unit price of charging service; IN S The single price for battery replacement service; To balance the active power output of the node to the entire distribution network in time period t, e o It is a balance node; is the basic active load connected to the node e of the distribution network at time period t; is the real-time electricity price in period t; is the number of charging piles in the charging station at node j; is the number of battery swapping facilities in the battery swapping station at node j; is the number of battery charging facilities at node j; PR C , PR S , PR B They are the unit prices of electric vehicle charging facilities, battery replacement facilities, and battery charging facilities; The constraints of the joint optimization configuration and operation model of the charging and swapping stations include the planning and operation constraints of charging stations and swapping stations, the charging and swapping demand radius constraints, distributed pumped storage power station constraints, transportation network constraints, and distribution network constraints; wherein, the planning and operation constraints of the charging and swapping stations include the upper limit constraint on the number of batteries in the charging and swapping stations, the battery flow constraint of electric vehicles, the location constraint of the charging and swapping facilities, and the charging and swapping service capacity constraint; the charging and swapping demand radius constraint includes the charging and swapping demand radius constraint of local electric vehicles and the proportion constraint of charging and swapping demand generated in electric vehicles; the distributed The distributed pumped storage power station constraints include the capacity boundary constraints of the upper reservoir and the lower reservoir of the distributed pumped storage power station, the capacity state constraints of the upper reservoir and the lower reservoir of the distributed pumped storage power station, the constraints on the change of water volume per unit time of the distributed pumped storage power station, and the power constraints of the distributed pumped storage power station; the traffic network constraints include the road traffic flow composition constraints, the road traffic flow capacity constraints, and the total traffic demand constraints; the distribution network constraints include the active and reactive power balance constraints of the balancing nodes, the power balance constraints of the distribution network lines, the power constraints of the electric vehicle charging stations, and the voltage constraints of the distribution network nodes; The upper limit of the number of batteries in the charging station and the battery swap station is: In the above formula, FB t，j is the number of batteries that have completed charging at the battery swap station at node j in time period t; FB M The upper limit of the number of batteries that can be charged in the battery swap station; J S is the set of nodes with battery swap stations; EB t，j is the number of batteries to be charged at the charging station at node j in time period t; EB M The upper limit of the number of batteries to be charged in the charging station; J C It is a collection of nodes with charging stations built; The electric vehicle battery flow constraint is: In the above formula, FB t-1，j EB is the number of batteries that have completed charging at the battery swap station at node j in time period t-1; t-1，j is the number of batteries to be charged at the charging station at node j in time period t-1; BS t，j is the number of batteries to be charged due to battery replacement at the battery replacement station at node j in time period t; t，j，m is the number of fully charged batteries transported from the charging station at node m to the battery swap station at node j during time period t; BC t，j is the number of batteries that have completed charging at the charging station at node j during period t; t，j，n is the number of batteries to be charged transported from the charging station at node j to the battery swap station at node n during time period t; The charging and swapping facilities are configured with the following location constraints: In the above formula, δ M is the large M constant; The charging and swapping service capacity constraints are: In the above formula, T C , T S , T B They are the time consumed for a single electric vehicle charging, the time consumed for electric vehicle battery replacement, and the time consumed for battery charging; T U is the unit time; The charging and swapping demand radius constraint of the local electric vehicle is: In the above formula, D i，j is the distance between node i and the charging station or swap station located at node j; DM i，j is the distance between node i and node j; is a binary auxiliary variable; LS is the charging demand radius; The proportion constraint of charging and swapping demand generated in the electric vehicle is: In the above formula, FS is the minimum value that meets the charging demand of out-of-town electric vehicles; The capacity boundary constraints of the upper reservoir and the lower reservoir of the distributed pumped storage power station are: In the above formula, are the water volumes of the upper and lower reservoirs of the distributed pumped storage power station in time period t; VU m 、VU M are the lower and upper limits of water volume in the upper reservoir of the distributed pumped storage power station; VD m 、VD M They are the lower and upper limits of water volume in the lower reservoir of the distributed pumped storage power station; The capacity state constraints of the upper reservoir and the lower reservoir of the distributed pumped storage power station are: In the above formula, are the water volumes of the upper and lower reservoirs of the distributed pumped storage power station in time period t-1; tn is the last time period in a typical day; The constraint on the change of water volume per unit time in the distributed pumped storage power station is: In the above formula, ΔV t is the water volume change of the distributed pumped storage power station in time period t; ΔV M The upper limit of water volume change per unit time in a distributed pumped storage power station; The power constraint of the distributed pumped storage power station is: In the above formula, is the output and input power of the distributed pumped storage power station in time period t; ρ is the density of water; g is the acceleration of gravity; h is the height difference between the upper and lower reservoirs of the distributed pumped storage power station; The road traffic flow composition constraint is: In the above formula, is the traffic flow on road l in time period t; The road traffic flow capacity constraint is: In the above formula, FC l is the upper limit of traffic flow on road l; The total traffic demand constraint is: In the above formula, LD t，od ND t，od are the traffic demands of local vehicles and out-of-town vehicles for od at the origin and destination points in time period t respectively; The active and reactive power balance constraints of the balancing node are: In the above formula, The reactive power output by the balancing node to the entire distribution network in time period t; are respectively the active and reactive power on the outgoing line of the balancing node at time period t; d o Outgoing line for balancing node; The power balance constraint of the distribution network line is: In the above formula, are the active and reactive power on line w in time period t, respectively, and line d refers to all lines connected to the distribution network node e; is the active power output by the distribution network to the electric vehicle charging station connected to node e during time period t; is the photovoltaic output connected to node e by the distribution network during time period t; are the basic active and reactive loads connected to the node e of the distribution network at time period t; LC d is the capacity of the distribution network line d; The power constraint of the electric vehicle charging station is: In the above formula, E B The energy requirements needed to charge the battery once; is the correlation matrix between transportation nodes and distribution network nodes; The distribution network node voltage constraint is: In the above formula, are the resistance and reactance of the distribution network line d respectively; ΔU t，d is the voltage drop on the distribution network line d during time period t; U t，a , U t，b are the bus voltages of nodes a and b in the distribution network in time period t, respectively. Nodes a and b are the two endpoints of the distribution network line d; U N is the rated voltage of the distribution network bus; U m , U M U is the upper and lower limits of the distribution network bus voltage; t，e is the bus voltage of the node e in the distribution network during time period t.

2. A joint optimization system for charging and swapping stations based on rural distribution networks, characterized in that: The joint optimization system includes a model building module and a simulation calculation module; The model building module is used to build a joint optimization configuration and operation model of charging and swapping stations. The objective function of the model is to maximize the comprehensive benefits of charging and swapping services under the premise of ensuring the energy replenishment demand of rural electric vehicles and the safety of rural distribution networks; The simulation calculation module is used to perform simulation calculations based on the joint optimization configuration and operation model of the charging and swapping stations, and optimize the site selection, capacity determination, access mode and joint operation plan of the charging stations and swapping stations; The objective function of the joint optimization configuration and operation model of the charging and swapping station is: max(I C +I S -C E )dC P [(1+a) γ -1] / ag 2 ; In the above formula, I C Daily charging service income; S Daily battery replacement service income; C E is the daily electricity purchase cost; C P is the annualized construction cost; d is the number of typical days in a year; α is the discount rate; γ is the investment period; γ EP is the average proportion of electric vehicles in traffic flow; γ V The flow capture rate of electric vehicles at charging and swapping stations; is the traffic flow of local vehicles using path k for the origin-destination pair od in time period t; is the traffic flow of out-of-town vehicles taking route k for the origin-destination pair od in time period t; is the correlation matrix between paths and roads; are binary planning decision variables for charging stations and battery swap stations respectively; E v Energy requirements for a single charge of an electric vehicle; IN C The unit price of charging service; IN S The single price for battery replacement service; To balance the active power output of the node to the entire distribution network in time period t, e o It is a balance node; is the basic active load connected to the node e of the distribution network at time period t; is the real-time electricity price in period t; is the number of charging piles in the charging station at node j; is the number of battery swapping facilities in the battery swapping station at node j; is the number of battery charging facilities at node j; PR C , PR S , PR B They are the unit prices of electric vehicle charging facilities, battery replacement facilities, and battery charging facilities; The constraints of the joint optimization configuration and operation model of the charging and swapping stations include the planning and operation constraints of charging stations and swapping stations, the charging and swapping demand radius constraints, distributed pumped storage power station constraints, transportation network constraints, and distribution network constraints; wherein, the planning and operation constraints of the charging and swapping stations include the upper limit constraint on the number of batteries in the charging and swapping stations, the battery flow constraint of electric vehicles, the location constraint of the charging and swapping facilities, and the charging and swapping service capacity constraint; the charging and swapping demand radius constraint includes the charging and swapping demand radius constraint of local electric vehicles and the proportion constraint of charging and swapping demand generated in electric vehicles; the distributed The distributed pumped storage power station constraints include the capacity boundary constraints of the upper reservoir and the lower reservoir of the distributed pumped storage power station, the capacity state constraints of the upper reservoir and the lower reservoir of the distributed pumped storage power station, the constraints on the change of water volume per unit time of the distributed pumped storage power station, and the power constraints of the distributed pumped storage power station; the traffic network constraints include the road traffic flow composition constraints, the road traffic flow capacity constraints, and the total traffic demand constraints; the distribution network constraints include the active and reactive power balance constraints of the balancing nodes, the power balance constraints of the distribution network lines, the power constraints of the electric vehicle charging stations, and the voltage constraints of the distribution network nodes; The upper limit of the number of batteries in the charging station and the battery swap station is: In the above formula, FB t，j is the number of batteries that have completed charging at the battery swap station at node j in time period t; FB M The upper limit of the number of batteries that can be charged in the battery swap station; J S is the set of nodes with battery swap stations; EB t，j is the number of batteries to be charged at the charging station at node j in time period t; EB M The upper limit of the number of batteries to be charged in the charging station; J C It is a collection of nodes with charging stations built; The electric vehicle battery flow constraint is: In the above formula, FB t-1，j EB is the number of batteries that have completed charging at the battery swap station at node j in time period t-1; t-1，j is the number of batteries to be charged at the charging station at node j in time period t-1; BS t，j is the number of batteries to be charged due to battery replacement at the battery replacement station at node j in time period t; t，j，m is the number of fully charged batteries transported from the charging station at node m to the battery swap station at node j during time period t; BC t，j is the number of batteries that have completed charging at the charging station at node j during period t; t，j，n is the number of batteries to be charged transported from the charging station at node j to the battery swap station at node n during time period t; The charging and swapping facilities are configured with the following location constraints: In the above formula, δ M is the large M constant; The charging and swapping service capacity constraints are: In the above formula, T C , T S , T B They are the time consumed for a single electric vehicle charging, the time consumed for electric vehicle battery replacement, and the time consumed for battery charging; T U is the unit time; The charging and swapping demand radius constraint of the local electric vehicle is: In the above formula, D i，j is the distance between node i and the charging station or swap station located at node j; DM i，j is the distance between node i and node j; is a binary auxiliary variable; LS is the charging demand radius; The proportion constraint of charging and swapping demand generated in the electric vehicle is: In the above formula, FS is the minimum value that meets the charging demand of out-of-town electric vehicles; The capacity boundary constraints of the upper reservoir and the lower reservoir of the distributed pumped storage power station are: In the above formula, are the water volumes of the upper and lower reservoirs of the distributed pumped storage power station in time period t; VU m , VU M are the lower and upper limits of water volume in the upper reservoir of the distributed pumped storage power station; VD m 、VD M They are the lower and upper limits of water volume in the lower reservoir of the distributed pumped storage power station; The capacity state constraints of the upper reservoir and the lower reservoir of the distributed pumped storage power station are: In the above formula, are the water volumes of the upper and lower reservoirs of the distributed pumped storage power station in time period t-1; tn is the last time period in a typical day; The constraint on the change of water volume per unit time in the distributed pumped storage power station is: In the above formula, ΔV t is the water volume change of the distributed pumped storage power station in time period t; ΔV M The upper limit of water volume change per unit time in a distributed pumped storage power station; The power constraint of the distributed pumped storage power station is: In the above formula, is the output and input power of the distributed pumped storage power station in time period t; ρ is the density of water; g is the acceleration of gravity; h is the height difference between the upper and lower reservoirs of the distributed pumped storage power station; The road traffic flow composition constraint is: In the above formula, is the traffic flow on road l in time period t; The road traffic flow capacity constraint is: In the above formula, FC l is the upper limit of traffic flow on road l; The total traffic demand constraint is: In the above formula, LD t，od ND t，od are the traffic demands of local vehicles and out-of-town vehicles for od at the origin and destination points in time period t respectively; The active and reactive power balance constraints of the balancing node are: In the above formula, The reactive power output by the balancing node to the entire distribution network in time period t; are respectively the active and reactive power on the outgoing line of the balancing node at time period t; d o Outgoing line for balancing node; The power balance constraint of the distribution network line is: In the above formula, are the active and reactive power on line w in time period t, respectively, and line d refers to all lines connected to the distribution network node e; is the active power output by the distribution network to the electric vehicle charging station connected to node e during time period t; is the photovoltaic output connected to node e by the distribution network during time period t; are the basic active and reactive loads connected to the node e of the distribution network at time period t; LC d is the capacity of the distribution network line d; The power constraint of the electric vehicle charging station is: In the above formula, E B The energy requirements needed to charge the battery once; is the correlation matrix between transportation nodes and distribution network nodes; The distribution network node voltage constraint is: In the above formula, are the resistance and reactance of the distribution network line d respectively; ΔU t，d is the voltage drop on the distribution network line d during time period t; U t，a , U t，b are the bus voltages of nodes a and b in the distribution network in time period t, respectively. Nodes a and b are the two endpoints of the distribution network line d; U N is the rated voltage of the distribution network bus; U m , U M U is the upper and lower limits of the distribution network bus voltage; t，e is the bus voltage of the node e in the distribution network during time period t.

3. A charging and swapping station joint optimization device based on rural distribution network, characterized by: The joint optimization device includes a memory and a processor; The memory is used to store computer program code and transmit the computer program code to the processor; The processor is configured to execute the method according to claim 1 according to instructions in the computer program code.

4. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method according to claim 1 is implemented.

Images