Multi-type charging facility planning method and device based on user charging behavior decision

Abstract

The invention discloses a multi-type charging facility planning method and device based on user charging behavior decision, and the method comprises the steps: building an electric vehicle multi-type charging demand prediction model based on Monte Carlo stochastic simulation and origin-destination OD analysis; performing prediction processing through the electric vehicle multi-type charging demand prediction model to obtain spatial and temporal distribution of electric vehicle multi-type charging demands; constructing a multi-type charging facility collaborative optimization planning model according to the spatial-temporal distribution in combination with set constraints; based on second-order cone constraints, converting the multi-type charging facility collaborative optimization planning model into a mixed integer second-order cone planning model; a solver is set to solve the mixed integer second-order cone programming model, and the site selection position of a charging station, the configuration number of slow charging / fast charging / overcharging facilities and a power distribution scheme are obtained. According to the method, the difference of charging facilities in the station can be fully considered while the charging demand of the user is fully considered, and the optimal scheme is output.

Description

Technical Field

[0001] The present invention relates to the technical field of optimized configuration and control of electric vehicle charging facilities, and in particular to a method and device for planning multiple types of charging facilities based on user charging behavior decisions. Background Art

[0002] Electric vehicles (EVs), as clean energy transportation vehicles, are gradually replacing traditional fuel vehicles to reduce carbon emissions from the transportation system. In recent years, driven by breakthroughs in power battery technology and government incentives, the EV industry has entered a stage of large-scale development. By the end of 2024, my country's EV ownership had reached 22.09 million. With the widespread adoption of EVs, they have not only become a primary means of daily transportation for residents but have also been widely used in public services such as urban public transportation, taxi operations, and logistics.

[0003] With the rapid growth of EV users, charging infrastructure development faces critical challenges. Due to the heterogeneity of user travel purposes and behavior patterns, different groups have significantly different demands for charging facilities. Traditional single-type charging facilities are no longer able to meet the growing and diverse charging service needs of EV users. Against this backdrop, the construction of hybrid charging stations that integrate multiple types of charging facilities has become a new trend in cities. These stations integrate slow, fast, and ultra-fast charging facilities within a single station. This hybrid charging station architecture precisely matches the temporal and spatial heterogeneity of user needs, enabling flexible charging service provision and effectively improving the user experience. From an operational perspective, such facilities can both expand user coverage and achieve economies of scale, thus forming a key direction for the evolution of charging infrastructure. Therefore, optimizing the spatial layout of charging stations with multiple types of charging facilities and cost-effectively combining multiple types of facilities within stations to meet diverse user needs has dual value in improving the theoretical framework of urban charging network planning and guiding industry practice.

[0004] Currently, most approaches to electric vehicle charging facility planning focus solely on the overall charging station, failing to fully consider the diverse characteristics of charging facilities within the station. Consequently, the resulting solutions struggle to meet the growing and diverse charging service demands of EV users. Among the few studies that do consider the differences in charging facilities within a station, most assume that all EV users charge at their destination, using parking time after arriving at their destination. Consequently, charging demand is allocated across different types of facilities based on the user's parking time and the power of the charging facility. However, in reality, urban public charging stations primarily address the emergency charging needs of EVs while driving. This involves proactively traveling to a nearby charging station for immediate recharging when an EV's battery is nearing depletion, and then departing immediately upon completion.

[0005] Therefore, how to invent a collaborative planning method for multiple types of charging facilities, which fully considers the charging needs of users while fully considering the differences in charging facilities within the station, obtains a site selection plan for the charging station, and configures the types and quantities of multiple types of charging piles in the station, has become an urgent problem to be solved. Summary of the Invention

[0006] To this end, the present invention provides a multi-type charging facility planning method and apparatus based on user charging behavior decisions. By considering the differences in behavioral characteristics of different user types and simulating their travel and charging behaviors, this method predicts the charging needs of multiple types of EV users. Based on the prediction results, the method comprehensively considers factors such as investment cost, user convenience, and grid carrying capacity. With the goal of minimizing the annualized charging station construction cost, operation and maintenance cost, and user station search cost, and taking into account the power and cost differences of different types of charging facilities within the station and their mutual influence, a collaborative charging station planning model integrating multiple types of facilities is constructed. This model derives a site selection plan for the charging station, as well as the type and quantity of multiple types of charging piles to be deployed within the station.

[0007] To achieve the above objectives, the present invention provides the following technical solution: a multi-type charging facility planning method based on user charging behavior decisions, comprising:

[0008] Based on Monte Carlo random simulation and origin-destination OD analysis, a multi-type charging demand prediction model for electric vehicles is constructed; prediction processing is performed using the multi-type charging demand prediction model for electric vehicles to obtain the spatiotemporal distribution of multi-type charging demand for electric vehicles;

[0009] According to the spatiotemporal distribution of the multi-type charging demand of electric vehicles and the set constraints, a collaborative optimization planning model for multi-type charging facilities is constructed;

[0010] The distribution network safety constraints in the set constraints are converted into second-order cone constraints through the second-order cone relaxation strategy; based on the second-order cone constraints, the multi-type charging facility collaborative optimization planning model is converted into a mixed integer second-order cone programming model; the mixed integer second-order cone programming model is solved by setting a solver to obtain the site selection location of the charging station, the configuration number of slow charging / fast charging / supercharging facilities and the power allocation plan.

[0011] As a preferred solution for the multi-type charging facility planning method based on user charging behavior decisions, the multi-type charging demand prediction model for electric vehicles includes: a destination charging decision model and an emergency charging decision model;

[0012] In the destination charging decision model, the relationship between the user's charging time and expected stay time is expressed as:

[0013]

[0014] Where i is the user; T i S 、T i F 、T i U The time required for users to complete charging by selecting slow charging, fast charging, and super charging facilities in the station; T i stay is the estimated length of stay;

[0015] The expression of the emergency charging decision model is:

[0016]

[0017] Where, For charging costs; Waiting time cost; Represent the charging prices of fast charging and super charging respectively; c w,i is the unit time value of user i.

[0018] As a preferred solution for the multi-type charging facility planning method based on user charging behavior decisions, in the process of constructing the multi-type charging facility collaborative optimization planning model, the set constraints include: charging station quantity constraint, parking space quantity constraint, charging demand satisfaction constraint, charging demand logic constraint and distribution network security constraint;

[0019] The expression for the number constraint of charging stations is:

[0020]

[0021] Where, X j N is the 0-1 decision variable for charging station location selection; max and N min The upper and lower limits for the number of charging stations to be built;

[0022] The expression of the parking space quantity constraint is:

[0023]

[0024] Where, They represent the number of slow charging piles, fast charging piles, and super charging piles installed at the charging station at location j; L j is the maximum number of parking spaces that can be built at charging station j, subject to the regional land area limit;

[0025] The expression of the charging demand satisfying constraint is:

[0026]

[0027] Where Y ijis a 0-1 decision variable, which is 1 if the charging demand at point i is assigned to the charging station at point j, otherwise it is 0; are the number of EVs that require slow charging, fast charging, and super charging at node i in period t, respectively;

[0028] The expression of the charging demand logic constraint is:

[0029]

[0030] Where D ij is the total distance EV travels from point i to point j; D max Indicates the maximum distance for charging demand guidance;

[0031] The expression of the distribution network security constraint is:

[0032]

[0033] Where, Ω N ,Ω L is the set of nodes and branches of the distribution network; v(j) and u(j) are the sets of downstream and upstream nodes of node j; is the active load and reactive load of node j; is the EV load at node j; P ij , Q ij is the active and reactive power flowing from the upstream branch to point j; P jl , Q jl is the active and reactive power flowing from point j to the downstream branch; R ij 、X ij is the resistance and reactance of branch ij; U i is the voltage amplitude of node i; I ij is the current on branch ij; l is the upstream node of node j; U j is the voltage amplitude at node j.

[0034] As a preferred solution of the multi-type charging facility planning method based on user charging behavior decision-making, the objective function expression of the multi-type charging facility collaborative optimization planning model is:

[0035] min C=C I +C M +C EV

[0036] Where, C is the annual average comprehensive cost of charging facilities; C I is the annualized charging station construction cost; C M The annual operation and maintenance cost of the charging station; C EV The cost of searching for sites for users throughout the year.

[0037] As a preferred solution for the multi-type charging facility planning method based on user charging behavior decisions, in the process of solving the mixed integer second-order cone programming model through the setting solver, the Cplex solver is called in MATLAB through the Yalmip toolbox to solve the mixed integer second-order cone programming model.

[0038] The present invention also provides a multi-type charging facility planning device based on user charging behavior decision-making, based on the above multi-type charging facility planning method based on user charging behavior decision-making, including:

[0039] An electric vehicle multi-type charging demand prediction model construction module is used to construct an electric vehicle multi-type charging demand prediction model based on Monte Carlo random simulation and origin-destination OD analysis; the multi-type electric vehicle charging demand prediction model is used to perform prediction processing to obtain the spatiotemporal distribution of the multi-type electric vehicle charging demand;

[0040] A multi-type charging facility collaborative optimization planning model construction module is used to construct a multi-type charging facility collaborative optimization planning model based on the spatiotemporal distribution of the multi-type charging demand of the electric vehicles and the set constraints;

[0041] A module for solving the collaborative optimization planning model for multiple types of charging facilities is used to convert the distribution network safety constraints in the set constraints into second-order cone constraints through a second-order cone relaxation strategy; based on the second-order cone constraints, the collaborative optimization planning model for multiple types of charging facilities is converted into a mixed-integer second-order cone programming model; and the mixed-integer second-order cone programming model is solved by setting a solver to obtain the site selection location of the charging station, the number of slow-charging / fast-charging / supercharging facilities, and the power allocation plan.

[0042] As a preferred solution of the multi-type charging facility planning device based on user charging behavior decision-making, in the electric vehicle multi-type charging demand prediction model construction module, the electric vehicle multi-type charging demand prediction model includes: a destination charging decision model and an emergency charging decision model;

[0043] In the destination charging decision model, the relationship between the user's charging time and expected stay time is expressed as:

[0044]

[0045] Where i is the user; T i S 、T i F 、T i U The time required for users to complete charging by selecting slow charging, fast charging, and super charging facilities in the station; T i stayis the estimated length of stay;

[0046] The expression of the emergency charging decision model is:

[0047]

[0048] Where, For charging costs; Waiting time cost; Represent the charging prices of fast charging and super charging respectively; c w,i is the unit time value of user i.

[0049] As a preferred solution for the multi-type charging facility planning device based on user charging behavior decision-making, in the multi-type charging facility collaborative optimization planning model construction module, in the process of constructing the multi-type charging facility collaborative optimization planning model, the set constraints include: charging station quantity constraint, parking space quantity constraint, charging demand satisfaction constraint, charging demand logic constraint and distribution network safety constraint;

[0050] The expression for the number constraint of charging stations is:

[0051]

[0052] Where, X j N is the 0-1 decision variable for charging station location selection; max and N min The upper and lower limits for the number of charging stations to be built;

[0053] The expression of the parking space quantity constraint is:

[0054]

[0055] Where, They represent the number of slow charging piles, fast charging piles, and super charging piles installed at the charging station at location j; L j is the maximum number of parking spaces that can be built at charging station j, subject to the regional land area limit;

[0056] The expression of the charging demand satisfying constraint is:

[0057]

[0058] Where Y ij is a 0-1 decision variable, which is 1 if the charging demand at point i is assigned to the charging station at point j, otherwise it is 0; are the number of EVs that require slow charging, fast charging, and super charging at node i in period t, respectively;

[0059] The expression of the charging demand logic constraint is:

[0060]

[0061] Where D ij is the total distance EV travels from point i to point j; D max Indicates the maximum distance for charging demand guidance;

[0062] The expression of the distribution network security constraint is:

[0063]

[0064] Where, Ω N ,Ω L is the set of nodes and branches of the distribution network; v(j) and u(j) are the sets of downstream and upstream nodes of node j; is the active load and reactive load of node j; is the EV load at node j; P ij , Q ij is the active and reactive power flowing from the upstream branch to point j; P jl , Q jl is the active and reactive power flowing from point j to the downstream branch; R ij 、X ij is the resistance and reactance of branch ij; U i is the voltage amplitude of node i; I ij is the current on branch ij; l is the upstream node of node j; U j is the voltage amplitude at node j.

[0065] As a preferred solution of the multi-type charging facility planning device based on user charging behavior decision-making, in the multi-type charging facility collaborative optimization planning model construction module, the objective function expression of the multi-type charging facility collaborative optimization planning model is:

[0066] min C=C I +C M +C EV

[0067] Where, C is the annual average comprehensive cost of charging facilities; C I is the annualized charging station construction cost; C M The annual operation and maintenance cost of the charging station; C EV The cost of searching for sites for users throughout the year.

[0068] As a preferred solution for a multi-type charging facility planning device based on user charging behavior decisions, in the multi-type charging facility collaborative optimization planning model solving module, in the process of solving the mixed integer second-order cone programming model through the setting solver, the Cplex solver is called in MATLAB through the Yalmip toolbox to solve the mixed integer second-order cone programming model.

[0069] The present invention has the following advantages: the present invention constructs a multi-type charging demand prediction model for electric vehicles based on Monte Carlo random simulation and start-end point OD analysis; performs prediction processing through the multi-type charging demand prediction model for electric vehicles to obtain the spatiotemporal distribution of multi-type charging demands of electric vehicles; constructs a collaborative optimization planning model for multi-type charging facilities based on the spatiotemporal distribution of multi-type charging demands of electric vehicles and combined with set constraints; converts the distribution network safety constraints in the set constraints into second-order cone constraints through a second-order cone relaxation strategy; converts the collaborative optimization planning model for multi-type charging facilities into a mixed integer second-order cone programming model based on the second-order cone constraints; solves the mixed integer second-order cone programming model by setting a solver to obtain the site selection location of the charging station, the number of slow charging / fast charging / super charging facilities and the power allocation scheme. The present invention dynamically predicts the spatiotemporal distribution of various types of charging demands in a city by constructing a differentiated user behavior model (covering destination charging and emergency charging scenarios); on this basis, with the goal of minimizing the annualized construction cost, operation and maintenance cost, and user station search cost, a collaborative planning model for various types of charging facilities is established, comprehensively considering the power differences, cost differences, and distribution network carrying capacity constraints of charging facilities, and realizing the joint optimization of charging station site selection and facility configuration through a mixed integer second-order cone programming algorithm. This method solves the problems of neglecting user behavior heterogeneity and insufficient facility coordination in traditional planning, achieves a precise match between charging facilities and user needs, significantly improves facility utilization, reduces overall operating costs, and takes into account grid security constraints, providing an economical and efficient technical solution for urban charging network planning, with both social benefits and promotion value. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] To more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for the embodiments or the description of the prior art. Obviously, the drawings described below are merely exemplary, and those skilled in the art can, without inventive effort, derive other implementation drawings based on the provided drawings.

[0071] The structures, proportions, sizes, etc. illustrated in this specification are intended solely to complement the contents disclosed herein and to facilitate understanding and reading by persons skilled in the art. They are not intended to limit the conditions under which the present invention may be implemented and therefore have no substantive technical significance. Any structural modifications, changes in proportions, or adjustments in sizes, without affecting the efficacy and objectives of the present invention, shall remain within the scope of the technical contents disclosed herein.

[0072] Figure 1 This is a flow chart of the multi-type charging facility planning method based on user charging behavior decision-making provided in Example 1 of the present invention;

[0073] Figure 2 This is a schematic diagram of a multi-type charging demand prediction process in a multi-type charging facility planning method based on user charging behavior decision-making provided in Example 1 of the present invention;

[0074] Figure 3 This is a schematic diagram of a 25-node traffic network in a city in a possible embodiment provided in Example 1 of the present invention;

[0075] Figure 4 This is a schematic diagram of an IEEE 69-node distribution network in a possible embodiment provided in Example 1 of the present invention;

[0076] Figure 5 This is a schematic diagram of the distribution of EV charging demand in different areas throughout the day in a possible embodiment provided in Example 1 of the present invention; (a) is a residential area; (b) is an office area; (c) is a commercial area;

[0077] Figure 6 This is a schematic diagram of the distribution of EV charging demand in an urban road network in a possible embodiment provided in Example 1 of the present invention; wherein (a) is 13:00-14:00; (b) is 21:00-22:00;

[0078] Figure 7 This is a schematic diagram of the spatiotemporal distribution of the overall EV charging load in a possible embodiment provided in Example 1 of the present invention;

[0079] Figure 8 This is a schematic diagram of a planning scheme including multiple types of charging facilities in a possible embodiment provided in Example 1 of the present invention;

[0080] Figure 9 This is a schematic diagram of a planning scheme including only fast charging facilities in a possible embodiment provided in Example 1 of the present invention;

[0081] Figure 10 A schematic diagram of a charging station planning scheme evaluation radar in a possible embodiment provided in Example 1 of the present invention;

[0082] Figure 11 This is a schematic diagram of the architecture of a multi-type charging facility planning device based on user charging behavior decisions provided in Example 2 of the present invention. DETAILED DESCRIPTION

[0083] The following describes the implementation of the present invention using specific embodiments. Those skilled in the art will readily understand the other advantages and benefits of the present invention from the disclosure herein. Obviously, the embodiments described are only a portion of the present invention, not all of it. All other embodiments derived by persons of ordinary skill in the art based on the embodiments of the present invention without inventive effort are intended to fall within the scope of protection of the present invention.

[0084] Example 1

[0085] See also Figure 1 Embodiment 1 of the present invention provides a multi-type charging facility planning method based on user charging behavior decision-making, including the following steps:

[0086] S1. Based on Monte Carlo random simulation and origin-destination OD analysis, a multi-type charging demand forecasting model for electric vehicles is constructed; and prediction processing is performed using the multi-type charging demand forecasting model to obtain the spatiotemporal distribution of the multi-type charging demand for electric vehicles.

[0087] S2. Constructing a collaborative optimization planning model for multiple types of charging facilities based on the spatiotemporal distribution of the multiple types of charging demands of the electric vehicles and the set constraints;

[0088] S3. The distribution network safety constraints in the set constraints are converted into second-order cone constraints through the second-order cone relaxation strategy; based on the second-order cone constraints, the multi-type charging facility collaborative optimization planning model is converted into a mixed integer second-order cone programming model; the mixed integer second-order cone programming model is solved by setting a solver to obtain the site selection location of the charging station, the configuration number of slow charging / fast charging / super charging facilities and the power allocation plan.

[0089] In this embodiment, in step S1, a multi-type charging demand prediction model for electric vehicles is constructed based on Monte Carlo random simulation and origin-destination OD analysis; prediction processing is performed using the multi-type charging demand prediction model for electric vehicles to obtain the spatiotemporal distribution of the multi-type charging demand for electric vehicles;

[0090] Specifically, such as Figure 2 As shown, first analyze the types and behavioral characteristics of electric vehicles:

[0091] Based on the vehicle classification standards in the "China New Energy Vehicle Big Data Research Report," this paper divides EVs into the following three categories based on their application: private cars; commercial passenger vehicles, which include taxis, ride-hailing services, and shared rental vehicles. Public buses, which primarily recharge at dedicated stations rather than public ones, are not included in this study; and freight vehicles, which include logistics vehicles and heavy trucks. This classification system fully accounts for the differences in operating models and charging behavior across different vehicle types.

[0092] The starting time of EV travel is affected by the corresponding user's working hours and travel habits. This paper refers to the NCHRP365 definition of different types of EV travel time t s The statistical data of , the fitting probability density function is shown in formula (1):

[0093]

[0094] Where, α k 、μ k , σ k are the mixing ratio, mean and standard deviation of the kth Gaussian distribution respectively; K is the number of mixed Gaussian distributions; the above parameters are taken for the three types of electric vehicles respectively.

[0095] With advances in battery technology, EV range has significantly increased. According to a research report, the average monthly charging frequency for private cars in 2022 will be 6.5 times, meaning most owners will charge their vehicles every few days. Among commercial passenger vehicles, taxis, ride-hailing services, and shared rental vehicles average 30.1, 30.9, and 21.8 monthly charging times, respectively. Among freight vehicles, logistics vehicles and heavy trucks average 22.3 and 29.4 monthly charging times, respectively. Therefore, for frequently used commercial passenger vehicles and freight vehicles, daily charging is the primary method, with a few vehicles adopting multiple daily charging cycles.

[0096] Next, the road network structure and functional areas are analyzed and divided:

[0097] Based on graph theory, an urban road network can be abstracted as a set of nodes and edges, G(V,E), where V represents the node set, representing the intersections between road segments; and E represents the edge set, representing the entity set of the road segment. Based on the topological structure of the road network, an adjacency matrix is constructed to accurately represent the topological connectivity between nodes.

[0098] There is a significant coupling correlation between the division of urban functional areas and the behavior of EV users. Differences in destination attributes directly affect EV travel choices, which makes accurate urban functional space deconstruction a key prerequisite for charging demand prediction. In recent years, geographic information point of interest (POI) data has gradually been used in urban activity research due to its convenient acquisition and wide coverage. POI abstracts geographic entities related to people's daily lives as points, and each POI point contains information such as type, name, longitude and latitude. The present invention uses POI data to identify the functional attributes of road network nodes. For each road network node, all POI points within a 1km area around it are obtained through an open source geographic data platform, and they are divided into three categories: residential, office, and commercial, and the number of POI points of each type in the area is counted. The entity objects contained in the three functional types are as follows:

[0099] As shown in Table 1:

[0100] Function Type Entity Object Weight coefficient resident Residential areas, communities, villas 30 Office Office buildings, institutions, government agencies, schools 40 Business Restaurants, shopping malls, shopping centers, tourist attractions 50

[0101] Table 1 POI data classification

[0102] To account for the spatial influence of building footprints and morphological characteristics, a weight coefficient is introduced to calculate the weighted number and proportion of each type of POI within the area surrounding the node. The functional type with the largest proportion is selected as the type of the road network node, thereby achieving the functional division of the urban road network. Taking the commercial type as an example, the calculation process is as follows:

[0103]

[0104] Where, ω POI,B is the weighting coefficient of the commercial category; m POI,B The number of commercial POI points; are the weighted numbers of residential, office, and commercial POI points respectively; CR POI,B The percentage of commercial POI points.

[0105] In this embodiment, OD analysis is used to characterize EV travel characteristics. For each type of electric vehicle, historical data from the traffic management department is used to obtain its traffic flow information at each section of the road network. OD matrix inversion technology is then used to infer the OD matrix of each type of EV at different time periods, thereby constructing a travel probability matrix for EVs between road network nodes, as shown in Equation (4):

[0106]

[0107] Where n is the number of nodes in the road network; is the probability that an EV starting from node i chooses node j as its destination within the T period; its value range is [0,1] and satisfies

[0108] For each electric vehicle, after determining the destination, the Floyd algorithm is used to obtain the shortest path and mileage from the current location to the destination. At this time, the EV's driving speed v on the road segment (i, j) is ij (t) Available speed-flow model calculation:

[0109]

[0110] Where v0 is the zero flow velocity of EV; q ij (t) is the traffic flow of the road section (i, j) at time t; C ij is the maximum traffic capacity of road section (i, j); a, b, d are the adaptive coefficients under different road grades; β is the congestion correlation coefficient.

[0111] Assuming that the power consumption of EV increases linearly with the mileage during driving, the remaining power of EV battery at time t is Cap t The following formula can be used for calculation:

[0112] Cap t =Cap r -l t ΔCap(6)

[0113] Where, Cap r is the rated capacity of the EV battery; l t is the total distance traveled by the EV from departure to time t; ΔCap is the energy consumption per unit mileage of the EV.

[0114] In this embodiment, urban public charging stations mainly meet emergency charging needs. However, due to the lack of supporting charging facilities in some residential areas or workplaces, a small number of destination charging needs are also met in public charging stations. Therefore, the present invention considers the EV user's choice behavior among multiple types of charging facilities in the charging station under the above two scenarios. This is also the premise for scientifically configuring the number of different types of charging piles in the station. Assuming that private cars are all charged at the destination, while commercial passenger cars and freight vehicles are all charged in an emergency, and all EV users expect to fully charge their batteries, the required charging amount E of user i is i It can be calculated using formula (7):

[0115] E i =Cap r -Cap t (7)

[0116] Where, Cap t is the remaining charge of the EV battery at time t.

[0117] In this embodiment, the electric vehicle multi-type charging demand prediction model includes: a destination charging decision model and an emergency charging decision model;

[0118] Among them, the destination charging decision model:

[0119] When an EV user arrives at the destination, if the remaining power cannot meet the user's next travel needs, the user will choose to charge at the current destination. This process can be determined by formula (8).

[0120] Cap t -Δl·ΔCap<0.2Cap r (8)

[0121] Where Δl is the distance of the next trip; 0.2Cap r This is the lower limit of EV battery capacity. If the capacity is lower than this value, the battery life may be damaged due to excessive discharge.

[0122] The length of time EV users stay at a destination is correlated with the type of destination. According to the fitting results of parking time at different locations in the NHTS dataset, the parking time of EVs in office areas and commercial areas is It follows the generalized extreme value distribution as follows:

[0123]

[0124] Where μ W 、μ B is the location parameter; σ W , σ B is the scale parameter; γ W , γ B is the shape parameter.

[0125] Parking duration in residential areas It obeys the Weibull distribution, as shown below:

[0126]

[0127] Where λ H is the shape parameter; k H is the shape parameter.

[0128] For destination charging services, users generally expect to complete the charging process during their stay so that they can ensure that their EV has sufficient power while planning work or leisure activities. Based on this, users follow the following principles when choosing the type of charging facility: First, due to mileage anxiety, users tend to choose charging facilities with higher power to ensure that the battery can be fully charged as much as possible or at least reach a higher charging level during their stay; second, when multiple charging facilities can meet user needs, users will choose the charging facility with the lowest power to avoid additional space charges due to completing charging in advance, as well as battery loss and service fees caused by higher charging power. Therefore, the charging time T of user i is i and the expected stay time T i stay The following relationship is satisfied:

[0129]

[0130] Where i is the user; T i S 、T i F 、T i U The time required for users to complete charging by selecting slow charging, fast charging, and super charging facilities in the station; T i stay is the estimated length of stay;

[0131] From formula (14), we can see that when T i stay ∈[0,T i F ], user demand is super charging pile; when T i stay ∈[T i F ,T i S ], user demand is fast charging pile; T i stay ∈[T i S ,+∞], the user demand is slow charging pile. In engineering applications, the estimated completion time of the three charging methods can be presented to users through the charging application, so that users can make reasonable choices of charging facility types based on their personal travel arrangements.

[0132] Emergency charging decision model:

[0133] When the remaining power of an EV drops below the set threshold during driving, it will generate a charging demand at the current location and will need to go to the nearest charging station for temporary recharging. This process can be determined by formula (15):

[0134] Capt <SOC c Cap r (15)

[0135] Where, SOC c is the battery percentage threshold, and its value is [0.2,0.3].

[0136] In the emergency charging scenario, the charging decisions made by users have temporary characteristics. Such users are highly sensitive to charging time and cannot tolerate excessively long charging times. Based on this, the present invention assumes that such users will not consider slow charging options when choosing charging services. Compared with conventional charging modes, the electricity price of ultra-fast charging mode is higher, resulting in some users being willing to pay a premium for extremely short charging time, while other users are willing to sacrifice a certain amount of waiting time in exchange for lower costs. This shows that the selection behavior of different user groups shows significant heterogeneity. The present invention assumes that EV users only consider the trade-off between charging costs and waiting time when choosing charging services, and choose the method with the lowest overall cost between fast charging and ultra-fast charging. The specific decision model is shown in (16)-(18):

[0137]

[0138] Where, For charging costs; Waiting time cost; Represent the charging prices of fast charging and super charging respectively; c w,i is the unit time value of user i, calculated by the average income method, as shown in the following formula:

[0139] c w,i =INC i / T m (19)

[0140] Where, INC i is the average monthly income of user i, obtained by polynomial fitting of the statistical data of residents’ monthly income; T m The monthly working hours are 176 hours according to the standard working hours system.

[0141] In this embodiment, in step S2, a collaborative optimization planning model for multiple types of charging facilities is constructed based on the spatiotemporal distribution of the multiple types of charging demands of the electric vehicles and the set constraints;

[0142] Specifically, the collaborative optimization planning model of multi-type charging facilities aims to minimize the annual average comprehensive cost of charging facilities, and its objective function expression is:

[0143] min C=C I +C M +CEV (20)

[0144] Where, C is the annual average comprehensive cost of charging facilities; C I is the annualized charging station construction cost; C M The annual operation and maintenance cost of the charging station; C EV The cost of searching for sites for users throughout the year.

[0145] The annualized charging station construction cost refers to the average annual investment cost of building an electric vehicle charging station, as shown in formula (21):

[0146] C I =R d (C U +C L +C A )(twenty one)

[0147] Where C U The purchase cost of the charging pile; C L is the site cost; C A For supporting equipment and construction costs; R d is an auxiliary variable for annualization, calculated using formula (22):

[0148]

[0149] Where r represents the discount rate; y CF Indicates the service life of the charging facility.

[0150] The charging pile acquisition cost refers to the total cost of purchasing various types of charging piles. Given that charging piles of different power levels have different prices, this cost is closely related to the number of slow charging, fast charging, and super charging piles in the plan:

[0151]

[0152] Where, Respectively represent the purchase cost of a set of slow charging piles, fast charging piles, and super charging piles; They represent the number of slow charging piles, fast charging piles, and super charging piles installed at the charging station at location j.

[0153] Site cost is the land lease cost for building a charging station in the selected area, which is related to the location and size of the site where the charging station is to be built:

[0154]

[0155] Where, is the site price per unit area in area j; Φ is the ratio coefficient of the area occupied by roads, greening and other auxiliary facilities in the charging station to the parking space area; Nj represents the number of parking spaces at the charging station at location j, which is approximately equal to the number of charging piles and is calculated using formula (25); m is the area of a single parking space.

[0156] The supporting equipment and construction costs include two parts: the first is the expenditure on purchasing power equipment such as transformers and cables, which is positively correlated with the capacity of the charging station; the second is other auxiliary equipment and overall construction costs, which are the fixed costs of building the charging station; as shown in formula (26):

[0157]

[0158] Where c p is the unit capacity cost of power equipment; P S 、P F 、P U are the rated power of slow charging, fast charging and super charging facilities respectively; X j is the 0-1 decision variable for charging station location selection; c a is the fixed cost of a single charging station.

[0159] In this embodiment, the annual operation and maintenance cost of the charging station refers to the maintenance cost of the charging station during the annual operation cycle, as shown in formula (27):

[0160]

[0161] Where, They represent the annual operation and maintenance costs of a set of slow charging piles, fast charging piles, and super charging piles respectively.

[0162] In this embodiment, the user's annual station search cost refers to the total cost incurred by all EV users in driving to the charging station to receive charging services within a year. This indicator reflects the convenience of the charging station's site plan for users.

[0163]

[0164] Where Z is the total number of EVs that need to be charged; c k is the charging price of the facilities required by user k; Y ij D is a 0-1 decision variable, which is 1 if the charging demand at point i is assigned to the charging station at point j, otherwise it is 0; ij is the total distance the EV travels from point i to point j.

[0165] In this embodiment, the set constraints include: charging station quantity constraint, parking space quantity constraint, charging demand satisfaction constraint, charging demand logic constraint and distribution network security constraint;

[0166] The expression for the number constraint of charging stations is:

[0167]

[0168] Where, X j N is the 0-1 decision variable for charging station location selection; max and N min The upper and lower limits for the number of charging stations to be built;

[0169] The expression of the parking space quantity constraint is:

[0170]

[0171] Where, They represent the number of slow charging piles, fast charging piles, and super charging piles installed at the charging station at location j; L j is the maximum number of parking spaces that can be built at charging station j, subject to the regional land area limit;

[0172] The expression of the charging demand satisfying constraint is:

[0173]

[0174] Where Y ij is a 0-1 decision variable, which is 1 if the charging demand at point i is assigned to the charging station at point j, otherwise it is 0; are the number of EVs that require slow charging, fast charging, and super charging at node i in period t, respectively;

[0175] Among them, the meaning of formula (32) to formula (34) is that the charging facilities configured at each charging station must be able to meet the demand within the station at any time period. The demand of each charging station includes both the demand at the node where it is located and the demand of the nodes around the charging station that have not been selected for station construction. These demands will be directed to the adjacent charging stations. Since the charging station is equipped with various types of charging facilities, there is a transfer of charging demand from low power to high power between different types of facilities. When there is a shortage of slow charging piles, EVs with slow charging needs can be connected to idle fast charging or super charging piles for charging; similarly, when there is a shortage of fast charging piles, EVs with fast charging needs can be connected to idle super charging piles for charging.

[0176] The expression of the charging demand logic constraint is:

[0177]

[0178] Where D ij is the total distance EV travels from point i to point j; D max Indicates the maximum distance for charging demand guidance;

[0179] Formula (35) ensures that the charging needs of all EVs must be met at the charging station and cannot be directed to nodes without a station. Formula (36) requires that the demand of each EV can only be directed to one charging station to avoid overlapping service ranges of different charging stations. Formula (37) restricts the guidance to a certain spatial range.

[0180] In this embodiment, the Distflow equation is used to represent the power flow constraint of the distribution network; the expression of the distribution network security constraint is:

[0181]

[0182] Where, Ω N ,Ω L is the set of nodes and branches of the distribution network; v(j) and u(j) are the sets of downstream and upstream nodes of node j; is the active load and reactive load of node j; is the EV load at node j; P ij , Q ij is the active and reactive power flowing from the upstream branch to point j; P jl , Q jl is the active and reactive power flowing from point j to the downstream branch; R ij 、X ij is the resistance and reactance of branch ij; U i is the voltage amplitude of node i; I ij is the current on branch ij; l is the upstream node of node j; U j is the voltage amplitude at node j.

[0183] In this embodiment, to ensure safe operation, the distribution network should meet the voltage amplitude constraint and branch current constraint in any time period:

[0184]

[0185] Where U max and U min Respectively represent the upper and lower limits of the voltage deviation of the distribution network node; I ijmax Indicates the maximum allowable value of branch current.

[0186] In this embodiment, in step S3, the distribution network safety constraints in the set constraints are converted into second-order cone constraints through the second-order cone relaxation strategy; based on the second-order cone constraints, the multi-type charging facility collaborative optimization planning model is converted into a mixed integer second-order cone programming model; the mixed integer second-order cone programming model is solved by setting a solver to obtain the site selection location of the charging station, the configuration number of slow charging / fast charging / super charging facilities and the power allocation plan.

[0187] Specifically, a second-order cone relaxation method was used to transform Equations (38)-(42) into second-order cone constraints, and a mixed-integer second-order cone programming model was constructed. The mixed-integer second-order cone programming model was solved by calling the Cplex solver using the Yalmip toolbox in MATLAB. The location of the charging station, the number of slow-charging / fast-charging / supercharging facilities, and the power allocation plan were obtained.

[0188] In a possible embodiment, a specific test example is provided as follows:

[0189] Test case and parameter setting:

[0190] An actual area of a city in my country was selected as an example to verify the multi-type charging facility planning method proposed in this invention. Figure 3 As shown in Figure 1, the residential area contains {2, 3, 6, 12, 13, 15, 19, 20, 22} nodes, the work area contains {1, 5, 7, 10, 11, 16, 18, 23} nodes, and the commercial area contains {4, 8, 9, 14, 17, 21, 24, 25} nodes. In order to simplify the traffic simulation process, this embodiment assumes that the vehicle's initial position, destination, and charging station construction location are all located on the road network nodes. In addition, this embodiment uses the IEEE 69-node system as an example of the distribution network in this area, and its topology is as follows: Figure 4 shown.

[0191] Assume there are 2,000 electric vehicles in the area, with 40% private cars, 40% commercial passenger vehicles, and 20% freight vehicles. The battery state of charge for 60% of the private cars before travel is set to follow N(0.5, 0.12), while the remaining 40% of the private cars follow N(0.9, 0.12). Commercial passenger vehicles and freight vehicles follow N(0.9, 0.12). Parameters such as electric vehicle battery capacity, power consumption per mile, and travel time are shown in Table 2:

[0192]

[0193]

[0194] Table 2 Electric vehicle related parameters It is planned to build 6-10 charging stations in this area. The data of three types of charging facilities are shown in Table 3:

[0195] parameter Slow charging pile Fast charging pile Supercharging pile Rated power / kW 7 60 300 Charging price / (yuan / kWh) 1.4 1.4 2 Unit purchase cost / yuan 4000 35000 80000 Unit operation and maintenance cost / yuan 400 3500 8000 Service life / year 10 10 10

[0196] Table 3 Charging facility related parameters

[0197] The site prices for residential areas, office areas, and commercial areas are 0.2, 0.25, and 0.3 million yuan / m2 respectively. The remaining parameters are shown in Table 4:

[0198] parameter Numerical parameter Numerical parameter Numerical <![CDATA[v0]]> 48km / h <![CDATA[c a ]]> 100,000 yuan <![CDATA[μ W ]]> 438.445 a 1.726 <![CDATA[L j ]]> 60 <![CDATA[σ W ]]> 164.506 b 3.15 <![CDATA[D max ]]> 4km <![CDATA[γ W ]]> 0.234 d 3 <![CDATA[U max ]]> 1.05pu <![CDATA[μ B ]]> 68.52 r 0.03 <![CDATA[U min ]]> 0.95pu <![CDATA[σ B ]]> 41.761 Φ 0.4 <![CDATA[I ijmax ]]> 600A <![CDATA[γ B ]]> -0.657 m <![CDATA[12m 2 ]]> <![CDATA[λ H ]]> 195.787 / / <![CDATA[c p ]]> 0.25 yuan / VA <![CDATA[k H ]]> 1.153 / /

[0199] Table 4 Charging facility related parameters

[0200] Simulation results analysis:

[0201] 1. Multi-type charging demand forecast results

[0202] The distribution characteristics of multi-type charging demand for electric vehicles were analyzed using the prediction results of a multi-type charging demand forecasting model. Statistics on the charging behavior decisions of 2,000 EVs revealed that the demand for slow charging, fast charging, and supercharging was 254, 1,257, and 489, respectively. In destination charging scenarios, the user's dwell time directly influences their choice of charging method. When the dwell time is less than 0.5 hours, most users choose supercharging; when the dwell time exceeds 6.4 hours, users tend to prefer slow charging; and when the dwell time is between the two, fast charging becomes the primary choice. This result demonstrates that different charging methods can effectively meet the needs of users with different dwell times. In emergency charging scenarios, the driver's income has a significant impact on the choice of charging method. Drivers with a monthly income of more than 8,600 yuan are generally more willing to choose the more expensive supercharging service to save time and meet emergency travel needs. Conversely, when the monthly income is less than 8,600 yuan, lower charging costs become the primary consideration, leading more drivers to choose fast charging. This research result can provide a reference for the market pricing strategy of charging services, helping charging operators to better consider the needs of users at different income levels and thus develop more targeted service plans.

[0203] In order to analyze the impact of urban functional attributes on EV charging behavior, the time distribution curves of EV slow charging, fast charging and super charging demand in different regions were statistically analyzed, such as Figure 5 The analysis results show that the demand for slow charging in residential areas is significantly higher than in other areas, and exhibits a clear periodic peak-valley pattern. Specifically, slow charging demand reaches a low point around 8:00 a.m. and peaks in the early morning hours, which aligns with the behavior of most residents who work during the day and charge their vehicles at home after get off work.

[0204] Demand for fast charging and supercharging has increased significantly in office and commercial areas. Peak charging demand occurs primarily between 12:00 PM and 3:00 PM, and after 8:00 PM. This phenomenon is likely due to the tendency of commercial and freight truck drivers to charge their vehicles during lunch breaks and after nighttime operations. Furthermore, some office workers utilize slow charging facilities at their workplaces, leading to relatively high demand for slow charging in office areas during working hours.

[0205] Depend on Figure 5It can be seen that despite the high demand for supercharging, it completes the charging process in a very short time, so there is no significant cumulative effect in the time dimension. In contrast, although slow charging has the lowest demand, its longer charging time leads to a gradual accumulation of time. This research result reveals the advantage of high-power charging facilities in improving vehicle turnover efficiency, indicating that they can serve more users in the same amount of time.

[0206] Selecting two charging peak periods, 13:00-14:00 and 21:00-22:00, to conduct a statistical analysis on the distribution of EV charging demand in the urban road network, the results are as follows: Figure 6 shown. Figure 6 (a) reveals that during the period of 13:00-14:00, charging demand is more concentrated on sections 7-8, 9-17, and 22-23, mainly distributed in office and commercial areas. Figure 6 (b) shows that during the period of 21:00-22:00, there is significant charging demand near nodes 6, 15 in residential areas and 8, 14, and 25 in commercial areas, which indicates that the distribution of charging demand is closely related to the daily life patterns of residents.

[0207] The simulation results show the temporal and spatial distribution characteristics of the total EV charging load in the test area within one day. Figure 7 As shown in the figure, the charging load is primarily concentrated during the daytime hours of 9:00 AM to 4:00 PM and the evening hours of 7:00 PM to 11:00 PM. Specific nodes, such as nodes 7, 14, and 15, exhibit high levels of charging load. Among these nodes, node 7 reaches its peak at 12:30 PM, with a peak load of 1881 kW.

[0208] 2. Planning results of various types of charging facilities

[0209] Based on the EV charging demand forecast results, the optimal planning scheme for the region is solved by constructing a multi-type EV charging facility planning model, such as Figure 8 As shown in Figure 2, in terms of spatial distribution, the locations of charging stations are dispersed across the city, and generally coincide with areas where charging demand is concentrated.

[0210] In terms of charging facility types, there are significant differences in the number of three types of charging piles configured at different charging stations. Nodes 15 and 19 are located near residential areas, and users tend to charge slowly for a long time, so the proportion of slow charging piles is relatively high. In contrast, nodes 2, 10, and 16 are located near commercial or office areas, and users mostly stay for a short time. Their charging needs are mainly fast charging or ultra-fast charging, and their demand for slow charging is relatively low. Therefore, a higher proportion of high-power fast and ultra-fast charging facilities are built. In addition, Figure 8The number of fast-charging stations is high, which is consistent with the forecast that electric vehicles will have the highest demand for fast charging. The number of supercharging stations is relatively low, partly due to their high cost compared to slow and fast charging stations, and partly due to the extremely fast vehicle turnover of supercharging stations, so a small number of them can meet user needs.

[0211] 3. Comparative Analysis Results with Single Fast Charging Scheme

[0212] To further illustrate the advantages of multiple types of charging facilities, this example uses evaluation indicators to compare and analyze planning schemes that include multiple types of charging facilities with planning schemes that only include a single fast-charging facility. It also assumes that the length of time EV users spend at charging stations is not affected by the planning scheme.

[0213] The simulation results of the two planning schemes are shown in Table 5:

[0214]

[0215]

[0216] Table 5 Comparison of different planning schemes

[0217] The average annual comprehensive cost of the multi-type facility planning scheme was 3.8708 million yuan, including annualized charging station construction costs of 2.5774 million yuan, operation and maintenance costs of 957,000 yuan, and user station search costs of 336,400 yuan, respectively. The charging facility utilization rate was 47.38%, ensuring that all user needs were met. Compared to the scheme with only a single fast-charging facility, the coordinated planning of multi-type charging facilities reduced the planning cost by 5.24%. This result stems from the discrepancy between the matching between EV user needs and charging facilities—that is, whether user needs are matched with appropriate charging facilities. Equipping EVs that are originally suitable for slow charging with fast-charging facilities inevitably leads to unnecessary consumption of social resources and increases the economic cost of the planning scheme. Furthermore, these vehicles will remain in the charging facilities after charging is complete until they leave, reducing their utilization efficiency. Compared to slow and fast charging facilities, ultra-fast charging facilities have a higher utilization rate due to their rapid vehicle turnover. Therefore, the results show that the multi-type planning scheme improves charging facility utilization by 20.21% compared to the fast-charging-only scheme. On the other hand, if EVs that originally require ultra-fast charging only use fast charging facilities, they will not be able to fully charge within the charging station's dwell time, forcing them to leave without their needs being met. In the fast-charging-only solution, 17.46% of user needs are not met, which will affect the user's charging experience.

[0218] In order to make the comparison more intuitive, this embodiment uses a radar chart to present the evaluation results of the planning schemes. Figure 10As shown. Figure 10 It can be seen that the planning scheme containing multiple types of charging facilities is superior to the planning scheme containing only a single fast charging in terms of economy, charging facility utilization, user experience, etc.

[0219] In summary, the present invention has the following advantages: the present invention constructs a multi-type charging demand prediction model for electric vehicles based on Monte Carlo random simulation and start-end point OD analysis; predicts and processes the multi-type charging demand prediction model for electric vehicles to obtain the spatiotemporal distribution of the multi-type charging demand for electric vehicles; constructs a collaborative optimization planning model for multi-type charging facilities based on the spatiotemporal distribution of the multi-type charging demand for electric vehicles and the set constraints; converts the distribution network safety constraints in the set constraints into second-order cone constraints through a second-order cone relaxation strategy; converts the collaborative optimization planning model for multi-type charging facilities into a mixed integer second-order cone programming model based on the second-order cone constraints; solves the mixed integer second-order cone programming model by setting a solver to obtain the site selection location of the charging station, the number of slow charging / fast charging / super charging facilities, and the power allocation scheme. The present invention dynamically predicts the spatiotemporal distribution of various types of charging demands in a city by constructing a differentiated user behavior model (covering destination charging and emergency charging scenarios); on this basis, with the goal of minimizing the annualized construction cost, operation and maintenance cost, and user station search cost, a collaborative planning model for various types of charging facilities is established, comprehensively considering the power differences, cost differences, and distribution network carrying capacity constraints of charging facilities, and realizing the joint optimization of charging station site selection and facility configuration through a mixed integer second-order cone programming algorithm. This method solves the problems of neglecting user behavior heterogeneity and insufficient facility coordination in traditional planning, achieves a precise match between charging facilities and user needs, significantly improves facility utilization, reduces overall operating costs, and takes into account grid security constraints, providing an economical and efficient technical solution for urban charging network planning, with both social benefits and promotion value.

[0220] It should be noted that the method of the embodiments of the present disclosure can be performed by a single device, such as a computer or server. The method of the embodiments of the present disclosure can also be applied in a distributed scenario, where multiple devices cooperate to perform the method. In such a distributed scenario, one of the multiple devices may only perform one or more steps of the method of the embodiments of the present disclosure, and the multiple devices will interact with each other to complete the method.

[0221] It should be noted that the above description is limited to some embodiments of the present disclosure. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in an order different from that described in the above embodiments and still achieve the desired results. Furthermore, the processes depicted in the accompanying drawings do not necessarily require the specific order or sequential order shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0222] Example 2

[0223] See also Figure 11 Embodiment 2 of the present invention further provides a multi-type charging facility planning device based on user charging behavior decision-making, including:

[0224] The electric vehicle multi-type charging demand prediction model construction module 001 is used to construct a multi-type charging demand prediction model for electric vehicles based on Monte Carlo random simulation and origin-destination OD analysis; the multi-type charging demand prediction model for electric vehicles is used to perform prediction processing to obtain the spatiotemporal distribution of the multi-type charging demand of electric vehicles;

[0225] A multi-type charging facility collaborative optimization planning model construction module 002 is used to construct a multi-type charging facility collaborative optimization planning model based on the spatiotemporal distribution of the multi-type charging demands of the electric vehicles and in combination with set constraints;

[0226] The multi-type charging facility collaborative optimization planning model solving module 003 is used to convert the distribution network safety constraints in the set constraints into second-order cone constraints through the second-order cone relaxation strategy; based on the second-order cone constraints, the multi-type charging facility collaborative optimization planning model is converted into a mixed integer second-order cone programming model; and the mixed integer second-order cone programming model is solved by setting a solver to obtain the site selection location of the charging station, the configuration number of slow charging / fast charging / super charging facilities and the power allocation plan.

[0227] In this embodiment, in the electric vehicle multi-type charging demand prediction model construction module 001, the electric vehicle multi-type charging demand prediction model includes: a destination charging decision model and an emergency charging decision model;

[0228] In the destination charging decision model, the relationship between the user's charging time and expected stay time is expressed as:

[0229]

[0230] Where i is the user; T i S 、T i F 、T iU The time required for users to complete charging by selecting slow charging, fast charging, and super charging facilities in the station; T i stay is the estimated length of stay;

[0231] The expression of the emergency charging decision model is:

[0232]

[0233] Where, For charging costs; Waiting time cost; Represent the charging prices of fast charging and super charging respectively; c w,i is the unit time value of user i.

[0234] In this embodiment, in the multi-type charging facility collaborative optimization planning model construction module 002, in the process of constructing the multi-type charging facility collaborative optimization planning model, the set constraints include: charging station quantity constraint, parking space quantity constraint, charging demand satisfaction constraint, charging demand logic constraint and distribution network safety constraint;

[0235] The expression for the number constraint of charging stations is:

[0236]

[0237] Where, X j N is the 0-1 decision variable for charging station location selection; max and N min The upper and lower limits for the number of charging stations to be built;

[0238] The expression of the parking space quantity constraint is:

[0239]

[0240] Where, They represent the number of slow charging piles, fast charging piles, and super charging piles installed at the charging station at location j; L j is the maximum number of parking spaces that can be built at charging station j, subject to the regional land area limit;

[0241] The expression of the charging demand satisfying constraint is:

[0242]

[0243] Where Y ij is a 0-1 decision variable, which is 1 if the charging demand at point i is assigned to the charging station at point j, otherwise it is 0; are the number of EVs that require slow charging, fast charging, and super charging at node i in period t, respectively;

[0244] The expression of the charging demand logic constraint is:

[0245]

[0246] Where D ij is the total distance EV travels from point i to point j; D max Indicates the maximum distance for charging demand guidance;

[0247] The expression of the distribution network security constraint is:

[0248]

[0249] Where, Ω N ,Ω L is the set of nodes and branches of the distribution network; v(j) and u(j) are the sets of downstream and upstream nodes of node j; P ij , Q ij is the active and reactive power flowing from the upstream branch to point j; P jl , Q jl is the active and reactive power flowing from point j to the downstream branch; is the EV load at node j; P ij , Q ij are the active power and reactive power flowing through branch ij respectively; R ij 、X ij is the resistance and reactance of branch ij; U i is the voltage amplitude of node i; I ij is the current on branch ij; l is the upstream node of node j; U j is the voltage amplitude at node j.

[0250] In this embodiment, in the multi-type charging facility collaborative optimization planning model construction module 002, the objective function expression of the multi-type charging facility collaborative optimization planning model is:

[0251] min C=C I +C M +C EV

[0252] Where, C is the annual average comprehensive cost of charging facilities; C I is the annualized charging station construction cost; C M The annual operation and maintenance cost of the charging station; C EV The cost of searching for sites for users throughout the year.

[0253] In this embodiment, in the multi-type charging facility collaborative optimization planning model solving module 003, in the process of solving the mixed integer second-order cone programming model by the setting solver, the Cplex solver is called in MATLAB through the Yalmip toolbox to solve the mixed integer second-order cone programming model.

[0254] It should be noted that the information interaction, execution process, etc. between the modules of the above-mentioned system are based on the same concept as the method embodiment in Example 1 of the present application, and the technical effects they bring are the same as those of the method embodiment of the present application. For specific contents, please refer to the description in the method embodiment shown above in the present application, and no further details will be given here.

[0255] Example 3

[0256] Embodiment 3 of the present invention provides a non-transitory computer-readable storage medium, in which a program code for a multi-type charging facility planning method based on user charging behavior decisions is stored. The program code includes instructions for executing the multi-type charging facility planning method based on user charging behavior decisions of embodiment 1 or any possible implementation thereof.

[0257] Computer-readable storage media can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that includes one or more available media. The available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media (e.g., solid-state drives (SSDs)).

[0258] Example 4

[0259] Embodiment 4 of the present invention provides an electronic device, including: a memory and a processor;

[0260] The processor and the memory communicate with each other via a bus; the memory stores program instructions that can be executed by the processor, and the processor calls the program instructions to execute the multi-type charging facility planning method based on user charging behavior decisions of Example 1 or any possible implementation thereof.

[0261] Specifically, the processor can be implemented by hardware or by software. When implemented by hardware, the processor can be a logic circuit, an integrated circuit, etc.; when implemented by software, the processor can be a general-purpose processor, which is implemented by reading software code stored in a memory. The memory can be integrated into the processor or located outside the processor and exist independently.

[0262] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present invention is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable systems. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from a website, computer, server or data center to another website, computer, server or data center via a wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode.

[0263] Obviously, those skilled in the art will appreciate that the various modules or steps of the present invention described above can be implemented using a general-purpose computing system. They can be centralized on a single computing system or distributed across a network of multiple computing systems. Alternatively, they can be implemented using program code executable by a computing system, and thus, they can be stored in a storage system and executed by the computing system. In some cases, the steps shown or described herein can be performed in a different order than that shown, or they can be fabricated into separate integrated circuit modules, or multiple modules or steps can be fabricated into a single integrated circuit module. Thus, the present invention is not limited to any particular combination of hardware and software.

[0264] Although the present invention has been described in detail above using general descriptions and specific embodiments, it will be apparent to those skilled in the art that modifications and improvements may be made thereto. Therefore, such modifications and improvements, without departing from the spirit of the present invention, are intended to be within the scope of protection claimed herein.

Claims

1. A multi-type charging facility planning method based on user charging behavior decision-making, characterized by: include: Based on Monte Carlo random simulation and origin-destination OD analysis, a multi-type charging demand prediction model for electric vehicles is constructed; prediction processing is performed using the multi-type charging demand prediction model for electric vehicles to obtain the spatiotemporal distribution of multi-type charging demand for electric vehicles; According to the spatiotemporal distribution of the multi-type charging demand of electric vehicles and the set constraints, a collaborative optimization planning model for multi-type charging facilities is constructed; The distribution network safety constraints in the set constraints are converted into second-order cone constraints through the second-order cone relaxation strategy; based on the second-order cone constraints, the multi-type charging facility collaborative optimization planning model is converted into a mixed integer second-order cone programming model; the mixed integer second-order cone programming model is solved by setting a solver to obtain the site selection location of the charging station, the configuration number of slow charging / fast charging / supercharging facilities and the power allocation plan.

2. The multi-type charging facility planning method based on user charging behavior decision-making according to claim 1 is characterized in that: The electric vehicle multi-type charging demand prediction model includes: a destination charging decision model and an emergency charging decision model; In the destination charging decision model, the relationship between the user's charging time and expected stay time is expressed as: Where i is the user; T i S 、T i F 、T i U The time required for users to complete charging by selecting slow charging, fast charging, and super charging facilities in the station; T i stay is the estimated length of stay; The expression of the emergency charging decision model is: Where, For charging costs; Waiting time cost; Represent the charging prices of fast charging and super charging respectively; c w,i is the unit time value of user i.

3. The multi-type charging facility planning method based on user charging behavior decision-making according to claim 2 is characterized in that: In the process of constructing the collaborative optimization planning model for multiple types of charging facilities, the set constraints include: a constraint on the number of charging stations, a constraint on the number of parking spaces, a constraint on the satisfaction of charging demand, a constraint on the logic of charging demand, and a constraint on the safety of the distribution network; The expression for the number constraint of charging stations is: Where, X j N is the 0-1 decision variable for charging station location selection; max and N min The upper and lower limits for the number of charging stations to be built; The expression of the parking space quantity constraint is: Where, They represent the number of slow charging piles, fast charging piles, and super charging piles installed at the charging station at location j; L j is the maximum number of parking spaces that can be built at charging station j, subject to the regional land area limit; The expression of the charging demand satisfying constraint is: Where Y ij is a 0-1 decision variable, which is 1 if the charging demand at point i is assigned to the charging station at point j, otherwise it is 0; are the number of EVs that require slow charging, fast charging, and super charging at node i in period t, respectively; The expression of the charging demand logic constraint is: Where D ij is the total distance EV travels from point i to point j; D max Indicates the maximum distance for charging demand guidance; The expression of the distribution network security constraint is: Where, Ω N ,Ω L is the set of nodes and branches of the distribution network; v(j) and u(j) are the sets of downstream and upstream nodes of node j; is the active load and reactive load of node j; is the EV load at node j; P ij , Q ij is the active and reactive power flowing from the upstream branch to point j; P jl , Q jl is the active and reactive power flowing from point j to the downstream branch; R ij 、X ij is the resistance and reactance of branch ij; U i is the voltage amplitude of node i; I ij is the current on branch ij; l is the upstream node of node j; U j is the voltage amplitude at node j.

4. The multi-type charging facility planning method based on user charging behavior decision-making according to claim 3 is characterized in that: The objective function expression of the multi-type charging facility collaborative optimization planning model is: my C=C I +C M +C EV Where, C is the annual average comprehensive cost of charging facilities; C I is the annualized charging station construction cost; C M The annual operation and maintenance cost of the charging station; C EV The cost of searching for sites for users throughout the year.

5. The multi-type charging facility planning method based on user charging behavior decision-making according to claim 4 is characterized in that: In the process of solving the mixed integer second-order cone programming model by using the setting solver, the Cplex solver is called in MATLAB through the Yalmip toolbox to solve the mixed integer second-order cone programming model.

6. A multi-type charging facility planning device based on user charging behavior decision-making, adopting the multi-type charging facility planning method based on user charging behavior decision-making according to any one of claims 1 to 5, characterized in that: include: An electric vehicle multi-type charging demand prediction model construction module is used to construct an electric vehicle multi-type charging demand prediction model based on Monte Carlo random simulation and origin-destination OD analysis; the multi-type electric vehicle charging demand prediction model is used to perform prediction processing to obtain the spatiotemporal distribution of the multi-type electric vehicle charging demand; A multi-type charging facility collaborative optimization planning model construction module is used to construct a multi-type charging facility collaborative optimization planning model based on the spatiotemporal distribution of the multi-type charging demand of the electric vehicles and the set constraints; A module for solving the collaborative optimization planning model for multiple types of charging facilities is used to convert the distribution network safety constraints in the set constraints into second-order cone constraints through a second-order cone relaxation strategy; based on the second-order cone constraints, the collaborative optimization planning model for multiple types of charging facilities is converted into a mixed-integer second-order cone programming model; and the mixed-integer second-order cone programming model is solved by setting a solver to obtain the site selection location of the charging station, the number of slow-charging / fast-charging / supercharging facilities, and the power allocation plan.

7. The multi-type charging facility planning device based on user charging behavior decision-making according to claim 6 is characterized in that: In the electric vehicle multi-type charging demand prediction model construction module, the electric vehicle multi-type charging demand prediction model includes: a destination charging decision model and an emergency charging decision model; In the destination charging decision model, the relationship between the user's charging time and expected stay time is expressed as: Where i is the user; T i S 、T i F 、T i U The time required for users to complete charging by selecting slow charging, fast charging, and super charging facilities in the station; T i stay is the estimated length of stay; The expression of the emergency charging decision model is: Where, For charging costs; Waiting time cost; Represent the charging prices of fast charging and super charging respectively; c w,i is the unit time value of user i.

8. The multi-type charging facility planning device based on user charging behavior decision-making according to claim 7 is characterized in that: In the multi-type charging facility collaborative optimization planning model construction module, in the process of constructing the multi-type charging facility collaborative optimization planning model, the set constraints include: charging station quantity constraint, parking space quantity constraint, charging demand satisfaction constraint, charging demand logic constraint and distribution network safety constraint; The expression for the number constraint of charging stations is: Where, X j N is the 0-1 decision variable for charging station location selection; max and N min The upper and lower limits for the number of charging stations to be built; The expression of the parking space quantity constraint is: Where, They represent the number of slow charging piles, fast charging piles, and super charging piles installed at the charging station at location j; L j is the maximum number of parking spaces that can be built at charging station j, subject to the regional land area limit; The expression of the charging demand satisfying constraint is: Where Y ij is a 0-1 decision variable, which is 1 if the charging demand at point i is assigned to the charging station at point j, otherwise it is 0; are the number of EVs that require slow charging, fast charging, and super charging at node i in period t, respectively; The expression of the charging demand logic constraint is: Where D ij is the total distance EV travels from point i to point j; D max Indicates the maximum distance for charging demand guidance; The expression of the distribution network security constraint is: Where, Ω N ,Ω L is the set of nodes and branches of the distribution network; v(j) and u(j) are the sets of downstream and upstream nodes of node j; is the active load and reactive load of node j; is the EV load at node j; P ij , Q ij is the active and reactive power flowing from the upstream branch to point j; P jl , Q jl is the active and reactive power flowing from point j to the downstream branch; R ij 、X ij is the resistance and reactance of branch ij; U i is the voltage amplitude of node i; I ij is the current on branch ij; l is the upstream node of node j; U j is the voltage amplitude at node j.

9. The multi-type charging facility planning device based on user charging behavior decision-making according to claim 8, characterized in that: In the multi-type charging facility collaborative optimization planning model construction module, the objective function expression of the multi-type charging facility collaborative optimization planning model is: my C=C I +C M +C EV Where, C is the annual average comprehensive cost of charging facilities; C I is the annualized charging station construction cost; C M The annual operation and maintenance cost of the charging station; C EV The cost of searching for sites for users throughout the year.

10. The multi-type charging facility planning device based on user charging behavior decision-making according to claim 9, characterized in that: In the multi-type charging facility collaborative optimization planning model solving module, in the process of solving the mixed integer second-order cone programming model through the setting solver, the Cplex solver is called in MATLAB through the Yalmip toolbox to solve the mixed integer second-order cone programming model.

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