Charging station and power distribution network collaborative planning method, equipment and medium considering v2g energy storage and line margin

Abstract

The present application relates to a kind of charging station and power distribution network collaborative planning method, equipment and medium considering V2G energy storage and line margin, the method includes: based on electric vehicle load space-time distribution, with the minimum comprehensive cost of charging station and the maximum equivalent capacity of V2G energy storage as optimization goal, construct charging station multi-objective planning model, the site selection of charging station is determined capacity;Comprehensively consider the coupling relationship of traffic network and power distribution network and the line margin of power distribution network, with the minimum line margin and comprehensive cost as optimization goal, construct power distribution network multi-objective expansion planning model, carry out expansion planning to power distribution network;Charging station multi-objective planning model and power distribution network multi-objective expansion planning model are solved by using normalization method to constraint method, obtain the final charging station site selection and the result of determining capacity and power distribution network construction scheme.Compared with prior art, the present application improves the economy and flexibility of system operation, promotes new energy consumption, and can guarantee the safe, economic and stable operation of power distribution network.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power distribution network planning, in particular to a charging station and power distribution network collaborative planning method considering V2G energy storage and line margin, equipment and medium. BACKGROUND

[0002] The access of high penetration rate and high power electric vehicles (EV) charging load will lead to the growth of distribution network load, transformer overload and the increase of distribution network investment and operation cost, which will bring great impact on the accommodation capacity and safe operation of the distribution network.

[0003] Electric vehicles have energy storage characteristics and can be charged bidirectionally with the power grid, which is called V2G technology of electric vehicles. The core idea of V2G technology is to use the storage capacity of a large number of electric vehicles as a buffer for the power distribution network and renewable energy. By reasonably planning the location of charging stations, the adjustable potential of electric vehicle energy storage can be maximized to alleviate the problems of low efficiency of the power distribution network and fluctuation of renewable energy. In order to cope with the challenges brought by the rapidly growing charging demand of electric vehicles, the distribution system and charging stations should be planned and operated in a more coordinated manner. However, the existing planning methods usually aim to minimize the investment cost of the power distribution network, while ignoring the risk of deteriorating traffic conditions. Therefore, it is of great significance to collaboratively plan V2G charging stations and power distribution networks.

[0004] In order to meet the growing demand for electric vehicle charging, the siting and sizing problem of electric vehicle charging stations has become an important problem worthy of study. In recent years, there have been many studies on electric vehicle V2G technology, mainly from the perspective of the interaction between electric vehicle energy storage characteristics and the power grid, exploring how electric vehicle V2G technology can better perform peak shaving and valley filling and improve the stability of the power grid. However, in the study of charging station planning considering the accommodation capacity of electric vehicle charging load in the distribution network, the consideration of V2G energy storage characteristics is not perfect, and the V2G energy storage resources are not reasonably utilized to alleviate the fluctuation of the power distribution system operation and other problems. SUMMARY

[0005] The purpose of the present application is to overcome the defects of the prior art and provide a charging station and power distribution network collaborative planning method considering V2G energy storage and line margin, equipment and medium, which can improve the safety and stability of the power distribution network, reduce the investment cost of the power distribution network reconstruction and physical energy storage, and provide a reference for actual engineering. The purpose of the present application can be achieved by the following technical solutions:

[0006] According to a first aspect of the present application, a charging station and power distribution network collaborative planning method considering V2G energy storage and line margin is provided, which comprises:

[0007] Based on the temporal and spatial distribution of electric vehicle load, the comprehensive cost of charging stations C cs Minimum and V2G energy storage equivalent capacity E total The maximum is the optimization goal, a multi-objective planning model for charging stations is constructed to select the location and capacity of charging stations;

[0008] Taking into account the coupling relationship between the transportation network and the distribution network, as well as the line margin of the distribution network, and with minimizing the line margin and comprehensive cost as the optimization goal, a multi-objective expansion planning model for the distribution network is constructed to carry out expansion planning for the distribution network.

[0009] The normalized normal constraint method is used to collaboratively solve the multi-objective planning model of charging stations and the multi-objective expansion planning model of distribution networks to obtain the final charging station site selection and sizing results and distribution network construction plan.

[0010] Preferably, the multi-objective planning model of the charging station is mathematically expressed as:

[0011] minF CS =(C CS ,-E total )

[0012] C CS =C Cinv +C Cope

[0013]

[0014] Where: C Cope is the total annual operation and maintenance cost of the charging station; C Cinv is the total annual investment cost of the charging station; N CS The number of planned charging stations; is the number of charging piles in the i-th charging station; A is the investment cost of the charging station equipment; B is the unit price of the charging pile in the charging station; C is the construction cost of the charging station; r0 is the discount rate; T is the operating life of the charging pile;

[0015] Constraints include the number of charging piles and the service distance of charging stations.

[0016] Preferably, the V2G energy storage equivalent capacity E total The acquisition process includes:

[0017] Define electric vehicles as energy storage participating in regulation in constant power mode, and the sustainable service time of a single electric vehicle is:

[0018]

[0019]

[0020] Where: T ch,n 、Tdis,n respectively, charging time and discharging time of single electric vehicle participating in energy storage; SOC n i is the state of charge of the nth electric vehicle at time t; SOC i ch,n is the expected charging capacity of the electric vehicle participating in energy storage; SOC dis,n is the energy ratio of the reserved trip; E bat,n is the available capacity of the battery of each electric vehicle; P ch,n , P dis,n and η ch,n , η dis,n are the charging and discharging power and efficiency of the nth electric vehicle, respectively;

[0021] According to the available service time of a single electric vehicle, the number of electric vehicles is accumulated to obtain the aggregated energy storage capacity as the equivalent capacity E total of V2G energy storage.

[0022] Preferably, the charging station service distance constraint is specifically:

[0023] The actual distance d1 between adjacent charging stations satisfies:

[0024]

[0025] In the formula: is the service distance of the charging station.

[0026] Preferably, the total annual operation and maintenance cost C Cope of the charging station is valued as a percentage of the initial investment cost, and the expression is:

[0027]

[0028] In the formula: η is the conversion factor.

[0029] Preferably, the multi-objective expansion planning model of the power distribution network has a mathematical expression as follows:

[0030] min F PD = (C PD , F t,σ )

[0031] C PD = C Pinv + C Pope

[0032] C Pinv = β L C L + β m C m ​​

[0033]

[0034]

[0035] In the formula: C PD is the comprehensive operation cost of the distribution network, F t,σ is the standard deviation of the line margin; C Pinv is the reconstruction investment cost of the distribution network; C Pope is the operation and maintenance cost of the distribution network; C L , C m are the total cost of newly built lines and the total cost of substation reconstruction, respectively, Ω L , Ω tc , Ω te are the sets of lines that can be newly built, the sets of substations that can be expanded, and the sets of substations that can be newly built, respectively; are the unit newly built cost of the distribution line ij, the expansion cost of the substation m, and the newly built cost of the substation m, respectively; l ij is the length of the distribution line ij; x ij , are binary decision variables, respectively representing whether the distribution line is newly built, whether the substation is expanded, and whether the substation is newly built; β L , β m are the capital recovery coefficients of the distribution line and the substation, respectively;

[0036] The standard deviation of the line margin F t,σ is calculated by the following expression:

[0037]

[0038] In the formula: F t,σ is the standard deviation of the overall network line margin at time t; K is the total of the lines built after planning; K ij is the set of lines built after planning; F ij,t and F i'j',t are the line margins after planning and before planning, respectively;

[0039] The constraint conditions include line transmission power constraints, system safety constraints, line transmission power constraints, substation capacity constraints, substation capacity constraints, and topology constraints.

[0040] Preferably, when the charging station multi-objective planning model and the distribution network multi-objective expansion planning model are collaboratively solved by using the normalized normal constraint method, the charging station multi-objective planning model and the distribution network multi-objective expansion planning model are rewritten into a single-layer multi-objective model in a compact form, and the expression is as follows:

[0041]

[0042] Where: f1, f2 are two objective functions, x C is decision variable, G C (·) and H C (·) are inequality constraints and equality constraints, respectively.

[0043] Preferably, the normalized normal constraint method is used to solve each single objective problem, which specifically includes:

[0044] 1) Single objective optimization of f1 is considered only, the constraints remain unchanged, and the minimum value of f1 is f1 min , and the corresponding variables in x C are substituted into f2 to obtain f2 , and the solution is obtained

[0045] 2) Single objective optimization of f2 is considered only, the constraints remain unchanged, and the minimum value of f2 is f2 , and the corresponding variables in x C are substituted into f1 to obtain f1 max , and the solution is obtained

[0046] 3) The two sets of solutions are taken as two extreme points of the Pareto front, and the solution space of the multi-objective model is normalized:

[0047]

[0048] 4) The vector of point A1 on the utopia line A pointing to point A2 is defined as , and the utopia line A is equally divided into (a+1) equidistant points A j , and the calculation expression is:

[0049]

[0050] Where: is the vector between each equidistant point; j takes values from 0 to a;

[0051] 5) The perpendicular line of the utopia line is drawn at the equidistant point , and the intersection of the perpendicular line and the Pareto front is B j , and B j is defined as and are the vectors from the origin to and , respectively.

[0052] Combined with the constraints of the multi-objective model, the constraint equation Single-objective optimization is performed with f2 as the target, and the following single-objective optimization problem is constructed:

[0053]

[0054] The split points are obtained The optimal solution B on the corresponding Pareto front j Where the solution space of the single-objective optimization problem is the vertical line The upper half of the divided region;

[0055] 6) Repeat step 5), and substitute each split point into the equation to obtain the corresponding solution in the Pareto front corresponding to each split point, and finally a uniform Pareto front can be obtained; finally, a suitable compromise solution is selected from the Pareto front through the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method.

[0056] According to a second aspect of the present application, an electronic device is provided, comprising a memory and a processor, the memory having a computer program stored thereon, and the processor implementing any of the methods when executing the program.

[0057] According to a third aspect of the present application, a computer-readable storage medium is provided, having a computer program stored thereon, and the program being executed by a processor to implement any of the methods.

[0058] Compared with the prior art, the present application has the following beneficial effects:

[0059] The present application comprehensively considers the V2G characteristics of electric vehicles and the line margin to establish a charging station planning model and a distribution network expansion planning model. In the planning of the charging station, the minimum annual comprehensive cost of the charging station and the maximum V2G energy storage capacity are taken as the optimization objectives, and in the expansion planning of the distribution network, the minimum annual comprehensive cost of the distribution network and the minimum line margin are taken as the optimization objectives. The two models are related to each other through a traffic-distribution network coupling model, and both the double-layer models are multi-objective optimization problems. The multi-objective optimization problems of the charging station and the distribution network are respectively processed by using a normalization method and a constraint method to realize collaborative solution. Simulation results show that as the charging load connected to the distribution network continuously increases, the distribution network needs to expand the capacity to adapt to the growing electric vehicle load, and at the same time, the increase of the charging load penetration rate can provide V2G energy storage capacity for the distribution network, thereby reducing the investment cost of the physical energy storage, improving the economy and flexibility of system operation, promoting new energy consumption, and ensuring the safe, economic and stable operation of the distribution network. BRIEF DESCRIPTION OF DRAWINGS

[0060] Figure 1 The overall framework for collaborative planning is shown in Figure 1;

[0061] Figure 2 The 29-node road network topology is shown in Figure 2;

[0062] Figure 3 Fig. 54 is a topology diagram of a 54-node distribution network;

[0063] Figure 4 Fig. 54 is a topology diagram of a 54-node distribution network;

[0064] Figure 5 Fig. 54 is a topology diagram of a 54-node distribution network;

[0065] Figure 6 Fig. 54 is a topology diagram of a 54-node distribution network;

[0066] Figure 7 Fig. 54 is a topology diagram of a 54-node distribution network. DETAILED DESCRIPTION

[0067] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative labor should fall within the protection scope of the present application.

[0068] Embodiment 1

[0069] As shown in the embodiment, the embodiment provides a charging station and distribution network collaborative planning method considering V2G energy storage and line margin, the method comprising: Figure 1 1) Based on the time and space distribution of electric vehicle load, taking the minimum comprehensive cost C cs of the charging station and the maximum equivalent capacity E total of the V2G energy storage as the optimization objective, a charging station multi-objective planning model is constructed to select the site and capacity of the charging station;

[0070] The charging station multi-objective planning model has a mathematical expression as follows:

[0071] min F CS = (C CS , -E total )

[0072] C CS = C Cinv + C Cope

[0073]

[0074]

[0075] In the formula, C Cope is the total annual operation cost of the charging station; C Cinv is the total annual investment cost of the charging station; and N CS is the number of the planned charging stations. ​is the number of charging piles in the ith charging station; A is the investment cost of charging station equipment; B is the unit price of charging piles in the charging station; C is the construction cost of the charging station; r0is the discount rate; T is the service life of the charging pile;

[0076] The constraint conditions include a charging pile quantity constraint and a charging station service distance constraint; the charging station service distance constraint is specifically that the actual distance d1between adjacent charging stations satisfies:

[0077]

[0078] In the formula: is the charging station service distance.

[0079] wherein, the V2G energy storage equivalent capacity E total The obtaining process includes:

[0080] If the electric vehicle is defined as participating in regulation in a constant power mode, the time that a single electric vehicle can sustain service is:

[0081]

[0082]

[0083] In the formula: T ch,n , T dis,n are the charging time and discharging time of a single electric vehicle participating in energy storage; SOC n (t i ) is the battery state of charge of the nth vehicle at time t i ; SOC ch,n is the expected charging capacity of the electric vehicle participating in energy storage; SOC dis,n is the energy ratio reserved for travel; E bat,n is the available capacity of the battery of each electric vehicle; P ch,n , P dis,n and η ch,n , η dis,n are the charging and discharging power and charging and discharging efficiency of the nth electric vehicle;

[0084] According to the available service time of a single electric vehicle, the aggregated energy storage capacity obtained by accumulating the number of electric vehicles is taken as the V2G energy storage equivalent capacity E total . The V2G energy storage equivalent capacity E total , the obtaining process includes:

[0085] If the electric vehicle is defined as participating in regulation in a constant power mode, the time that a single electric vehicle can sustain service is:

[0086]

[0087]

[0088] In the formula: T ch,n , T dis,n respectively are the charging time and discharging time of a single electric vehicle participating in energy storage; SOC n (t i ) is the state of charge of the nth vehicle at time t i ; SOC ch,n is the expected charging capacity of the electric vehicle participating in energy storage; SOC dis,n is the energy ratio reserved for travel; E bat,n is the available capacity of the battery of each electric vehicle; P ch,n , P dis,n and η ch,n , η dis,n respectively are the charging and discharging power and charging and discharging efficiency of the nth electric vehicle;

[0089] According to the available service time of a single electric vehicle, the number of electric vehicles is accumulated to obtain the aggregated energy storage capacity as the equivalent capacity E total of V2G energy storage.

[0090] The total annual operation and maintenance cost C Cope of the charging station is valued according to the percentage of the initial investment cost, and the expression is:

[0091]

[0092] In the formula: η is the conversion factor.

[0093] 2) Considering the coupling relationship between the transportation network and the distribution network and the line margin of the distribution network, a multi-objective expansion planning model of the distribution network is constructed to minimize the line margin and the comprehensive cost as the optimization objective, and the distribution network is planned for expansion;

[0094] The multi-objective expansion planning model of the distribution network has the mathematical expression:

[0095] min F PD =(C PD ,F t,σ )

[0096] C PD =C Pinv +C Pope

[0097] C Pinv =β L C L +β m C m

[0098]

[0099]

[0100] In the formula: C PD F is the comprehensive operation cost of the distribution network; F t,σ is the standard deviation of the line margin; C Pinv is the reconstruction investment cost of the distribution network; C Pope is the operation and maintenance cost of the distribution network; C L , C m are the total cost of newly built lines and the total cost of substation reconstruction, respectively; Ω L , Ω tc , Ω te are the sets of lines that can be newly built, substation that can be expanded, and substation that can be newly built, respectively; are the unit cost of newly built lines, the expansion cost of substation, and the newly built cost of substation, respectively; l ij is the length of the distribution line ij; x ij , are binary decision variables, representing whether the distribution line is newly built, whether the substation is expanded, and whether the substation is newly built, respectively; β L , β m are the capital recovery coefficients of the distribution lines and the substations, respectively;

[0101] The standard deviation of the line margin is calculated as follows:

[0102]

[0103] In the formula: F t,σ is the standard deviation of the overall network line margin at time t; K is the total of the lines built after planning; K ij is the set of lines built after planning; F ij,t and F i'j',t are the line margins after planning and before planning, respectively;

[0104] The constraint conditions include line transmission power constraints, system safety constraints, line transmission power constraints, substation capacity constraints, substation capacity constraints, and topology constraints.

[0105] 3) The multi-objective planning model of the charging station and the multi-objective expansion planning model of the distribution network are solved collaboratively by using the normalized normal constraint method, to obtain the final results of the charging station site selection and capacity determination and the construction scheme of the distribution network.

[0106] The electronic device of the present application includes a central processing unit (CPU) that can perform various appropriate actions and processes in accordance with computer program instructions stored in a read only memory (ROM) or computer program instructions loaded from a storage unit into a random access memory (RAM). Various programs and data required for device operation can also be stored in the RAM. The CPU, ROM, and RAM are connected to each other by a bus. An input / output (I / O) interface is also connected to the bus.

[0107] A plurality of components in the device are connected to the I / O interface, including: an input unit such as a keyboard, mouse, etc.; an output unit such as various types of displays, speakers, etc.; a storage unit such as a magnetic disk, optical disk, etc.; and a communication unit such as a network card, modem, wireless communication transceiver, etc. The communication unit allows the device to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0108] The processing unit performs the various methods and processes described above. For example, in some embodiments, the methods can be implemented as a computer software program tangibly embodied in a machine readable medium, such as the storage unit. In some embodiments, part or all of the computer program can be loaded and / or installed on the device via the ROM and / or the communication unit. When the computer program is loaded into the RAM and executed by the CPU, one or more steps of the methods described above can be performed. Alternatively, in other embodiments, the CPU can be configured to perform the methods by any other appropriate means, such as by means of firmware.

[0109] The functionality described above in this document can be performed, at least in part, by one or more hardware logic components. For example, and without limitation, example types of hardware logic components that can be used include Field-programmable Gate Arrays (FPGAs), Application-specific Integrated Circuits (ASICs), Application-specific Standard Products (ASSPs), System-on-a-chip systems (SOCs), Complex Programmable Logic Devices (CPLDs), etc.

[0110] Program code to implement methods of the present application can be written in any combination of one or more programming languages. The program code can be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the program code, when executed by the processor or controller, causes the machine to perform the functions / acts specified in the flow diagrams and / or block diagrams. The program code can execute entirely on a machine, partly on a machine, as a stand-alone software package, partly on a machine and partly on a remote machine or entirely on a remote machine or server.

[0111] In the context of the present invention, machine-readable medium can be a tangible medium that can contain or store a program for use with an instruction execution system, device or equipment or used in combination with an instruction execution system, device or equipment. Machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. Machine-readable medium can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. More specific examples of machine-readable storage media can include electrical connections based on one or more lines, portable computer disks, hard disks, random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memory), optical fibers, portable compact disk read-only memories (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0112] Example 2

[0113] This embodiment provides a method for collaborative planning of charging stations and distribution networks taking into account V2G energy storage and line margins. The method includes:

[0114] (1) Establish a V2G charging station planning model and a dynamic traffic network model. Combined with the user balance flow distribution principle, the driving path of electric vehicles is accurately simulated to obtain the spatiotemporal distribution of electric vehicle loads. Based on the charging load prediction results of electric vehicles, the location and capacity of charging stations are determined with the goal of maximizing the V2G energy storage capacity and minimizing the overall cost.

[0115] (2) Establish a distribution network expansion planning model, comprehensively consider the coupling relationship between the transportation network and the distribution network and the distribution network line margin, and carry out expansion planning for the distribution system with the goal of minimizing the line margin and comprehensive cost.

[0116] (3) The normalized normal constraint method is used to deal with the multi-objective problems of charging stations and distribution networks respectively, and the corresponding solutions in the Pareto front of each split point can be obtained. Finally, a uniform Pareto front can be obtained. Finally, the appropriate compromise solution is selected from the Pareto front by the approximate ideal solution ranking method.

[0117] Next, the method of this embodiment is further explained from several aspects such as model establishment, design principle, design method, and effectiveness verification.

[0118] 1. Multi-objective planning model for charging stations

[0119] The planning and construction of charging stations is a multi-objective optimization problem. It is necessary to consider not only their operating costs and benefits, but also the maximization of their V2G energy storage capacity. The comprehensive cost C of the charging station is the most important factor. cs Minimum, and V2G energy storage equivalent capacity E total The maximum is the optimization target, and the mathematical expression of the model is:

[0120] minF CS = (C CS -E total )

[0121] C CS = C Cinv +C Cope

[0122] In the formula: C Cinv is the total annual investment cost of the charging station, C Cope is the total annual operation and maintenance cost of the charging station.

[0123]

[0124] In the formula: N CSs is the number of planned charging stations; N CS is the number of planned charging stations; N chi is the number of charging piles in the i-th charging station; A is the investment cost of the construction area, cable, transformer and other equipment of the charging station; B is the unit price of the charging piles in the charging station; C is the construction cost of the charging station; r0 is the discount rate; T is the operating life of the charging pile.

[0125] The operation and maintenance cost of the charging station includes aging cost, maintenance material cost and labor cost, etc., which is valued according to the percentage of the initial investment cost, and the calculation expression is:

[0126]

[0127] In the formula: η is the conversion factor.

[0128] The equivalent energy storage capacity of V2G that can be provided by electric vehicles is closely related to the number of electric vehicles, the state of charge (SOC) of electric vehicles, the power of electric vehicles, the energy consumption per 100 kilometers, and the travel habits of vehicle owners.

[0129] First, the available service time of the energy storage capacity is modeled. It is assumed that the constant power mode is adopted in the process of electric vehicles participating in regulation as energy storage, and the service time of a single electric vehicle is:

[0130]

[0131]

[0132] In the formula: T ch,n , T dis,n are the charging time and discharging time of a single electric vehicle participating in energy storage; SOC n (t i ) is the state of charge of the electric vehicle at time ti State of Charge of the nth vehicle at time t; SOC ch,n Desired State of Charge of the participating EVs; SOC dis,n Energy ratio for the reserved trip; E bat,n Available capacity of the battery of the nth EV; P ch,n , P dis,n , η ch,n , η dis,n are the charging and discharging power and efficiency of the nth EV, respectively.

[0133] Based on the available service time of the EVs and the cumulative number of vehicles, the aggregated energy storage capacity can be obtained. The present application aggregates the V2G energy storage of large-scale EVs as a whole and calculates the total energy storage capacity. Only one centralized charging and discharging variable is needed in the optimization calculation, including the charging and discharging power of the EVs and the available energy storage capacity. The specific aggregation process is as follows:

[0134] Total power of EV access:

[0135] P ch,total,t =∑λP ch,t

[0136] P dis,total,t =∑λP dis,t

[0137] P car,t =P dis,total,t +P ch,total,t

[0138] In the formula, P ch,total,t P dis,total,t are the total charging and discharging power of the EVs at time t; λ is the charging and discharging state of the vehicle.

[0139] The equivalent total energy storage capacity of the EVs is calculated based on the energy storage capacity of a single EV. The total energy storage capacity available in a certain time period can be obtained by accumulating the energy storage capacity of a single EV in the time period. The calculation formula is:

[0140]

[0141] In the formula, E total,ch(T) , E total,dis(T) are the total available energy storage capacity in the time period T; t1 and t2 are the start time and end time of the time period T; N is the number of EVs participating in the dispatch.

[0142] In addition, the charging speed of the EV should not exceed the limit of its maximum charging power, and the discharging speed should not exceed the rated power of the EV. The expression is:

[0143]

[0144] P n,ch,t,max , P n,dis,t,max respectively represent the maximum charging power and the rated discharging power of the nth electric vehicle at time t.

[0145] E total = E total,ch,gy (T) + E total,dis,gy (T)

[0146] E total,ch,gy (T) and E total,dis,gy (T) represent the total energy storage capacity of the electric vehicle charging and discharging.

[0147] Constraints:

[0148] 1) Charging pile number constraint:

[0149] N CS.min ≤ N CS ≤ N CS.max

[0150] N CS.min , N CS.max are the minimum and maximum installation numbers of charging piles respectively.

[0151] 2) Charging station service distance constraint:

[0152] In order to avoid the waste of resources caused by too dense charging station site selection, the actual distance d1 between adjacent charging stations should satisfy:

[0153]

[0154] d1 is the charging station service distance.

[0155] 2. Multi-objective expansion planning model of distribution network

[0156] The objective of distribution network expansion planning is to minimize the sum of transformation investment cost C Pinv and operation and maintenance cost C Pope C PD , and due to the access of charging stations, the line channel margin needs to be considered, and the minimum standard deviation F t,σ of line margin is the optimization objective, so the distribution network expansion planning model is still a multi-objective optimization problem, as shown in the following formula:

[0157] min F PD = (C PD , F t,σ )

[0158] CPD = C Pinv + C Pope

[0159] Distribution network renovation investment cost:

[0160] C Pinv = β L C L + β m C m

[0161]

[0162]

[0163] wherein: C PD is the comprehensive operation cost of the distribution network, F t,σ is the standard deviation of line margin; C Pinv is the distribution network renovation investment cost; C Pope is the distribution network operation and maintenance cost; C L , C m are the total cost of newly built lines and the total cost of substation renovation, respectively, Ω L , Ω tc , Ω te are the sets of lines that can be newly built, substation that can be expanded, and substation that can be newly built, respectively; are the unit cost of newly built lines and the cost of expansion and new construction of substations, respectively; l ij is the length of the distribution line ij; x ij , are binary decision variables, representing whether the distribution line is newly built, the substation is expanded or newly built; β L , β m are the capital recovery coefficients of the distribution lines and substations.

[0164] Standard deviation of line margin:

[0165]

[0166] wherein: K is the total of lines built after planning; K ij is the set of lines built after planning; F t,σ is the standard deviation of the overall network margin at time t, and the smaller the value, the more stable the overall network, and vice versa, which will cause the harm of congestion; F ij,t and F i'j',t are the line margins after planning and before planning, respectively.

[0167] Constraint conditions:

[0168] 1) Line transmission power constraint:

[0169]

[0170] Where: P ij,max , Q ij,max They are the upper limits of active and reactive power transmitted by the line respectively.

[0171] 2) System security constraints:

[0172] To maintain the safe operation of the power distribution system, the node voltage and the current flowing through the line must be controlled within a safe range. The mathematical model is:

[0173]

[0174] Where: U i,max and U i,min are the upper and lower limits of the voltage at node i; U i,t is the voltage of node i at time t.

[0175] 3) Line transmission power constraints:

[0176]

[0177]

[0178]

[0179] Where: are the active power and reactive power transmitted by line ij respectively; is the apparent power capacity of line ij; g ij 、b ij are the conductance and susceptance of line ij respectively; U i 、U j are the voltage amplitudes at nodes i and j respectively; is the phase angle difference between the two ends of line ij; LD is the distribution line set.

[0180] 4) Line-end voltage constraints:

[0181]

[0182]

[0183] Where: are the impedances of the fixed line c and the newly built line d respectively; are the current amplitudes passing through fixed and newly built lines; (S Lf ) row.c 、(S Lc ) row.d are the columns of lines c and d in the node-branch matrix S of the network composed of fixed and newly built lines respectively; U sis the node voltage column vector; Lf and Lc are fixed line set and newly-built line set respectively.

[0184] 5) Substation capacity constraint:

[0185]

[0186]

[0187] wherein, and are active power and reactive power output of substation m respectively; and are apparent power capacity of expandable and newly-built substation respectively.

[0188] 6) Topology constraint:

[0189] When planning the power distribution network frame, the radial topology structure should be met, and the total number of branches is the difference between the total number of nodes and the number of root nodes, as shown in the following formula:

[0190]

[0191] wherein: D l represents the total line set of the power distribution network, x ij is the open state of the line, 0 represents open, 1 represents closed, n s represents the number of root nodes in the system.

[0192] Figure 2 is a 29-node road network topology graph, Figure 3 is a 54-node power distribution network topology graph.

[0193] 3, Normalized normal constraint method

[0194] The joint planning model of the charging station and the power distribution network proposed in the application is a multi-objective optimization problem in the upper and lower layers, since the objective functions of the double layers are the same, the normalized normal constraint method NNC is used to process the multi-objective problem, in order to facilitate discussion, the joint planning model is written into the following compact form of single-layer multi-objective model:

[0195]

[0196] wherein: f1 and f2 are two objective functions respectively, x C is a decision variable, G C (·) and H C (·) are inequality constraint conditions and equality constraint conditions respectively.

[0197] The NNC method is a relatively effective solution method for solving multi-objective optimization problems. Its core idea is to transform the multi-objective optimization problem into multiple single-objective optimization problems by adding new constraints. Based on the solution of each single-objective problem, a series of points on the Pareto front are obtained. For the above formula, if Figure 5 As shown, the specific steps of NNC method are as follows:

[0198] 1) Figure 4 Given the Pareto frontier, Utopia line and target space of the dual-objective optimization problem, we first consider the single-objective optimization of f1, and keep the constraints unchanged, and get f1 min The minimum value of is, and the optimized x C Substituting the corresponding variables into f2 yields Get the solution

[0199] 2) As above, only the single objective optimization of f2 is considered, and the constraints remain unchanged. The minimum value of f2 is At the same time, the optimized x C Substitute the corresponding variables into f1 to get f1 max , and get the solution

[0200] 3) The two sets of solutions obtained in the above steps are the two extreme points of the Pareto front, also known as anchor points. In order to avoid the different physical meanings and orders of magnitude of the two objective functions, the solution space of the multi-objective model needs to be normalized, such as Figure 4 shown.

[0201]

[0202] In the figure, f1 and f2 are the horizontal and vertical coordinates of the figure respectively. After normalization, the anchor points become A1(0,1) and A2(1,0) respectively, and the line connecting the anchor points is the Utopia line A.

[0203] 4) Define the vector from point A1 on Utopia line A to point A2 as At the same time, the Utopia line A is divided into a equal parts, resulting in (a+1) equally spaced division points Aj, which are calculated as follows.

[0204]

[0205] 5) At the split point Draw a vertical line to the Utopia line The intersection of this vertical line and the Pareto front is B j . B j definition and The origin points and Combined with the constraints of the multi-objective model, add the constraint formula Taking f2 as the main objective for single-objective optimization, the following single-objective optimization problem is constructed, and the split point can be obtained The optimal solution B on the corresponding Pareto front j The solution space of the single-objective optimization problem is the vertical line The upper half of the divided area.

[0206]

[0207] 6) Repeat the above steps to make each split point Substituting the solutions into the Pareto front sequentially, we can obtain the corresponding solution for each split point, and ultimately obtain a uniform Pareto front. Finally, we use the method of sorting the approximate ideal solution to select a suitable compromise solution from the Pareto front, and then conduct subsequent analysis and research.

[0208] Figure 6 Optimizing the Pareto frontier for charging stations, Figure 7 Optimizing the Pareto front for distribution networks.

[0209] The other configurations of this embodiment are the same as those of embodiment 1.

[0210] 4. Case analysis

[0211] To verify the economic and effectiveness of the charging station distribution network collaborative planning model proposed in this paper, the following three schemes are set up for comparative analysis:

[0212] Option 1: Considering the V2G energy storage characteristics and ignoring the line margin, the charging station distribution network is coordinated and planned.

[0213] Option 2: Coordinated planning of charging station distribution networks without considering V2G energy storage characteristics and taking line margin into account.

[0214] Option 3: Coordinated planning of charging station distribution networks that considers both V2G energy storage characteristics and line margins.

[0215] In summary, the application points out a charging station and power distribution network collaborative planning method considering V2G energy storage and line margin, comprehensively considers electric vehicle V2G characteristics and line margin to establish a charging station multi-objective planning model and a power distribution network multi-objective expansion planning model, takes the minimum charging station annual comprehensive cost and the maximum V2G energy storage capacity as optimization objectives in the planning of the charging station, takes the minimum power distribution network annual comprehensive cost and the minimum line margin standard deviation as optimization objectives in the expansion planning of the power distribution network, the two models are related to each other through a traffic-power distribution network coupling model, both the double-layer model are multi-objective optimization problems, and the multi-objective optimization problems of the charging station and the power distribution network are respectively processed by using a normalization method and a constraint method. Simulation results show that, as the charging load connected to the power distribution network continuously increases, the power distribution network needs to expand capacity to adapt to the continuously growing electric vehicle load, meanwhile, the increase of the charging load penetration rate can provide V2G energy storage capacity for the power distribution network, thereby reducing the investment cost of the physical energy storage, improving the economy and flexibility of system operation, promoting new energy consumption, and guaranteeing the safe, economical and stable operation of the power distribution network.

[0216] The above merely describes specific implementation manners of the present application, but the protection scope of the present application is not limited thereto, any skilled person in the art can easily think of various equivalent modifications or replacements within the technical range disclosed by the present application, and these modifications or replacements shall be encompassed within the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

Claims

1. A method for collaborative planning of charging stations and distribution networks taking into account V2G energy storage and line margin, characterized in that: The method includes: Based on the temporal and spatial distribution of electric vehicle load and the comprehensive cost of charging stations Minimum and V2G energy storage equivalent capacity The maximum is the optimization goal, a multi-objective planning model for charging stations is constructed to select the location and capacity of charging stations; Taking minimizing line margin and comprehensive cost of distribution network as optimization objectives, a multi-objective expansion planning model of distribution network is constructed to carry out expansion planning of distribution network. The normalized normal constraint method is used to collaboratively solve the multi-objective planning model of charging stations and the multi-objective expansion planning model of distribution networks to obtain the final charging station site selection and sizing results and distribution network construction plan.

2. A charging station and distribution network collaborative planning method taking into account V2G energy storage and line margin according to claim 1, characterized in that: The mathematical expression of the multi-objective planning model of the charging station is: , , , Where: is the total annual operation and maintenance cost of the charging station; is the total annual investment cost of the charging station; The number of planned charging stations; For the i The number of charging piles in each charging station; Investment costs for charging station equipment; The unit price of the charging pile in the charging station; Cost of building charging stations; is the discount rate; The operating life of the charging pile; Constraints include the number of charging piles and the service distance of charging stations.

3. The method for collaborative planning of charging stations and distribution networks taking into account V2G energy storage and line margin according to claim 2, characterized in that: The V2G energy storage equivalent capacity The acquisition process includes: Define electric vehicles as energy storage participating in regulation in constant power mode, and the sustainable service time of a single electric vehicle is: , , Where: 、 They are respectively the charging time and discharging time of a single electric vehicle participating in energy storage; for The battery state of charge of the nth car at the moment; The expected charging capacity of electric vehicles participating in energy storage; Energy ratio for reserved travel; The available battery capacity for each electric vehicle; 、 and 、 are the charging and discharging power and charging and discharging efficiency of the nth electric vehicle respectively; According to the available service time of a single electric vehicle, the aggregated energy storage capacity is obtained by accumulating the number of electric vehicles, which is used as the V2G energy storage equivalent capacity. .

4. The method for collaborative planning of charging stations and distribution networks taking into account V2G energy storage and line margin according to claim 2, characterized in that: The charging station service distance constraint is specifically: Actual distance between adjacent charging stations satisfy: , Where: The service distance of the charging station.

5. The method for collaborative planning of charging stations and distribution networks taking into account V2G energy storage and line margin according to claim 2, characterized in that: The total annual operation and maintenance cost of the charging station The value is determined as a percentage of the initial investment cost, and the expression is: , Where: is the conversion factor.

6. The method for collaborative planning of charging stations and distribution networks taking into account V2G energy storage and line margin according to claim 1, characterized in that: The mathematical expression of the multi-objective expansion planning model of the distribution network is: , , , , , Where: is the comprehensive cost of the distribution network, is the line margin standard deviation; Investment costs for distribution network transformation; The operation and maintenance costs of the distribution network; 、 are the total cost of new line construction and total cost of substation reconstruction, 、 、 They are newly-built lines, newly-expandable substations, and newly-built substation collections; 、 、 Distribution lines Unit new construction cost, substation The expansion cost and the substation New construction costs; For distribution lines length; 、 、 are binary decision variables, representing whether a new distribution line is built, whether the substation is expanded, and whether a new substation is built; 、 are the capital recovery factors for distribution lines and substations, respectively; Line margin standard deviation The calculation expression is: , Where: for The standard deviation of the overall grid line margin at any moment; It is the total number of lines built after planning; It is the collection of lines built after planning; and are the line margins before and after planning, respectively; The constraints include line transmission power constraints, system security constraints, line transmission power constraints, substation capacity constraints, substation capacity constraints and topology constraints.

7. The method for collaborative planning of charging stations and distribution networks taking into account V2G energy storage and line margin according to claim 1, characterized in that: When the normalized normal constraint method is used to collaboratively solve the charging station multi-objective planning model and the distribution network multi-objective extended planning model, the charging station multi-objective planning model and the distribution network multi-objective extended planning model are rewritten into a compact single-layer multi-objective model, which is expressed as follows: , Where: 、 There are two objective functions, is the decision variable, and are inequality constraints and equality constraints, respectively.

8. The method for collaborative planning of charging stations and distribution networks taking into account V2G energy storage and line margin according to claim 7, characterized in that: The normalized normal constraint method is used to solve each single-objective problem, including: 1) Consider only The single objective optimization, the constraints remain unchanged, and we get The minimum value of , and the optimized Substitute the corresponding variables into Get , and get the solution ; 2) Consider only The single objective optimization, the constraints remain unchanged, and we get The minimum value of , and the optimized Substitute the corresponding variables into get , and get the solution ; 3) The two sets of solutions are regarded as the two extreme points of the Pareto frontier, and the solution space of the multi-objective model is normalized: , 4) Define the vector from point A1 on Utopia line A to point A2 as , and at the same time, the Utopia line A is divided into a equal parts, resulting in (a+1) equally spaced division points Aj. The calculation expression is: , Where: is the vector between each equally divided point; The value ranges from 0 to a; 5) At the split point Draw a vertical line to the Utopia line , vertical line The intersection with the Pareto front is , definition and The origin points and vector of Combine the constraints of the multi-objective model and add the constraint formula ,by For the single-objective optimization of the target, construct the following single-objective optimization problem: , Find the split point The optimal solution on the corresponding Pareto front , where the solution space of the single-objective optimization problem is the vertical line the upper half of the divided area; 6) Repeat step 5) for each segmentation point Substitute the solutions in turn to obtain the corresponding solutions in the Pareto front corresponding to each split point, and finally a uniform Pareto front can be obtained; finally, a suitable compromise solution is selected from the Pareto front by the approximate ideal solution sorting method.

9. An electronic device comprising a memory and a processor, wherein a computer program is stored in the memory, wherein: When the processor executes the program, the method according to any one of claims 1 to 8 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method according to any one of claims 1 to 8 is implemented.

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