Multi-type charging facility locating and sizing method and device based on super charging behavior

Abstract

The invention discloses a multi-type charging facility locating and sizing method and device based on a super charging behavior, and the method comprises the steps: constructing a resident trip chain model according to the trip time-space information of residents, and obtaining the trip behavior data of an electric vehicle; according to the obtained travel behavior data of the electric vehicle, constructing a multi-type charging demand prediction model, and obtaining space-time distribution of charging demands of the electric vehicle; according to a P-center theory, constructing a charging station space site selection model, and obtaining the optimal position distribution of the charging stations; and according to the space-time distribution of the charging demand of the electric vehicle and the optimal position distribution of the charging station, constructing a capacity configuration model of the multi-type charging piles in the charging station, carrying out optimal configuration on the multi-type charging piles in the charging station, and completing site selection and capacity setting of the multi-type charging facilities. Through scientific planning and optimal configuration, diversified charging requirements of slow charging, fast charging and ultra-fast charging of a user are met, the use experience of the electric vehicle is improved, mileage anxiety is reduced, and healthy development of the electric vehicle industry is promoted.

Description

Technical Field

[0001] The present invention relates to the technical field of optimal configuration and regulation of electric vehicle charging facilities, and in particular to a method and device for site selection and capacity determination of multiple types of charging facilities based on super charging behavior. Background Art

[0002] With the rapid development of the global economy, energy demand continues to rise, and resource and environmental issues are becoming increasingly prominent. Countries have strengthened carbon emission management and reduced fossil energy consumption to meet the challenges of climate change and environmental protection. The transportation industry is one of the main areas of fossil energy consumption, of which road transportation accounts for nearly 45% of global oil demand, especially the fuel demand of cars and trucks, which accounts for about 25% and 19% respectively. Therefore, promoting energy conservation and emission reduction in the automotive industry has become an urgent issue for all countries. In this context, electric vehicles (EVs), as a substitute for fuel vehicles, have gradually become a new direction for the development of the automotive industry with their advantages of low pollution and low carbon emissions. With the rapid popularization of electric vehicles, higher requirements are placed on the construction of charging facilities.

[0003] However, the current EV range and charging speed are still far behind those of traditional fuel vehicles, which leads to "range anxiety" among users during use, affecting the promotion and popularization of electric vehicles. In recent years, the construction of charging facility networks has been gradually improved, and the number and types of charging piles have continued to increase. However, the unreasonable distribution of charging facilities and the limitations of site and grid capacity are still the main problems facing the current construction of charging piles. In order to meet these challenges, the planning and layout of charging facilities and the reasonable configuration of multiple types of charging facilities are particularly important. In particular, the emergence of ultra-fast charging piles provides a new solution for improving charging speed and user experience, but also puts higher requirements on the grid carrying capacity and equipment safety. In current research, the research on load forecasting methods for the diversity of EV user charging needs is relatively limited, and generally fails to fully consider the mutual influence between the configuration schemes of EV multiple types of charging facilities and the demand types. Most of the existing research on the coordinated planning of multiple types of charging facilities independently considers slow charging, fast charging, and super fast charging facilities and their corresponding demand types, and the research on the mutual influence relationship between different facilities is still not in-depth enough.

[0004] Therefore, how to invent a method for site selection and capacity determination of multiple types of charging facilities has become an urgent problem that needs to be solved. Summary of the invention

[0005] To this end, the present invention provides a method and device for site selection and capacity determination of multiple types of charging facilities based on supercharging behavior. Through scientific planning and optimized configuration, it meets the user's diversified charging needs for slow charging, fast charging, and ultra-fast charging, improves the user experience of electric vehicles, reduces mileage anxiety, and promotes the healthy development of the electric vehicle industry.

[0006] In order to achieve the above-mentioned object, the present invention provides the following technical solution: a method for site selection and capacity determination of multiple types of charging facilities based on supercharging behavior, comprising:

[0007] According to the residents' travel time and space information, a resident travel chain model is constructed; through the resident travel chain model, parameter calculation and behavior analysis of electric vehicle travel behavior are performed to obtain electric vehicle travel behavior data;

[0008] Constructing a multi-type charging demand prediction model based on the obtained electric vehicle travel behavior data; performing Monte Carlo simulation through the multi-type charging demand prediction model to obtain the spatiotemporal distribution of electric vehicle charging demand;

[0009] According to the P-center theory, a charging station spatial location model is constructed; the charging station spatial location model is used to perform location processing to obtain the optimal location distribution of the charging station;

[0010] According to the spatiotemporal distribution of electric vehicle charging demand and the optimal location distribution of charging stations, a capacity configuration model for multiple types of charging piles in a charging station is constructed; through the capacity configuration model for multiple types of charging piles in a charging station, multiple types of charging piles inside the charging station are optimally configured to complete the site selection and capacity determination of multiple types of charging facilities.

[0011] As a preferred solution for the site selection and sizing method of multiple types of charging facilities based on super charging behavior, in the process of obtaining electric vehicle travel behavior data by performing parameter calculation and behavior analysis on electric vehicle travel behavior through the resident travel chain model, the electric vehicle travel behavior data includes: electric vehicle travel time information, electric vehicle travel location information, electric vehicle initial charge state, electric vehicle charging start charge state, electric vehicle charging pile power selection and electric vehicle charging time; the electric vehicle travel time information includes: the first travel time of the day, the return time and the length of stay; the electric vehicle travel location information includes: travel destination type, single driving distance and spatial transfer probability.

[0012] As a preferred solution for the method of site selection and capacity determination of multi-type charging facilities based on super charging behavior, in the process of performing Monte Carlo simulation through the multi-type charging demand prediction model to obtain the spatiotemporal distribution of electric vehicle charging demand, the steps of the Monte Carlo simulation are:

[0013] Simulate the travel process of electric vehicles, extract the geographic information of departure points and destinations, travel time, and stay time information, and obtain the temporal and spatial distribution of electric vehicle travel;

[0014] The charging process of electric vehicles is simulated, the initial state of charge is extracted, the state of charge during the trip is calculated, and the charging decision is determined.

[0015] As a preferred solution for the method of site selection and capacity determination of multiple types of charging facilities based on super charging behavior, the expression of the charging station spatial site selection model is:

[0016] minω

[0017]

[0018]

[0019]

[0020]

[0021]

[0022] Where ω is the maximum distance from all charging demand points to their respective charging stations; d ij is the distance between point i and point j; Y ij is a 0-1 decision variable. If the charging demand at point i is assigned to the charging station at point j, then Y ij =1, otherwise Y ij =0;X i is a 0-1 decision variable, X i =1 means to build a charging station at point i, X i =0 means no charging station is built at point i; I is the location set of charging demand points; N evcs The number of charging stations to be planned.

[0023] As a preferred solution for the method of site selection and capacity determination of multiple types of charging facilities based on supercharging behavior, the objective function of the capacity configuration model of multiple types of charging piles in the charging station is:

[0024] min Cost = C I +C R +C OM

[0025] Where minCost is the minimum total cost; C I is the annualized EV charging station investment and construction cost; C R is the annualized distribution network capacity expansion cost; C OM is the annual operation and maintenance cost of EV charging stations.

[0026] The present invention also provides a device for selecting a location and determining the capacity of multiple types of charging facilities based on supercharging behavior, based on the above method for selecting a location and determining the capacity of multiple types of charging facilities based on supercharging behavior, including:

[0027] The electric vehicle travel behavior data acquisition module is used to construct a resident travel chain model based on the residents' travel time and space information; the electric vehicle travel behavior is parameterized and analyzed through the resident travel chain model to obtain the electric vehicle travel behavior data;

[0028] A module for acquiring the spatiotemporal distribution of electric vehicle charging demand, which is used to construct a multi-type charging demand prediction model based on the acquired electric vehicle travel behavior data; and to perform Monte Carlo simulation through the multi-type charging demand prediction model to acquire the spatiotemporal distribution of electric vehicle charging demand;

[0029] The module for acquiring the optimal location distribution of charging stations is used to construct a spatial location selection model of charging stations according to the P-center theory; and to perform location selection processing through the spatial location selection model of charging stations to obtain the optimal location distribution of charging stations;

[0030] The module for optimizing the configuration of multiple types of charging piles is used to construct a capacity configuration model for multiple types of charging piles in a charging station according to the spatiotemporal distribution of electric vehicle charging demand and the optimal location distribution of the charging station; the capacity configuration model for multiple types of charging piles in the charging station is used to optimize the configuration of multiple types of charging piles in the charging station, and the site selection and capacity determination of multiple types of charging facilities are completed.

[0031] As a preferred solution for the site selection and capacity determination device of multiple types of charging facilities based on super charging behavior, in the electric vehicle travel behavior data acquisition module, in the process of obtaining electric vehicle travel behavior data by performing parameter calculation and behavior analysis on electric vehicle travel behavior through the resident travel chain model, the electric vehicle travel behavior data includes: electric vehicle travel time information, electric vehicle travel location information, electric vehicle initial charge state, electric vehicle charging start charge state, electric vehicle charging pile power selection and electric vehicle charging time; the electric vehicle travel time information includes: the first travel time of the day, the return time and the length of stay; the electric vehicle travel location information includes: travel destination type, single driving distance and spatial transfer probability.

[0032] As a preferred solution for the multi-type charging facility site selection and capacity determination device based on super charging behavior, the spatiotemporal distribution acquisition module of the electric vehicle charging demand includes submodules:

[0033] The travel process simulation submodule is used to simulate the travel process of electric vehicles, extract the geographical information of departure points and destinations, and the travel time and stay time information to obtain the temporal and spatial distribution of the electric vehicle's travel;

[0034] The charging process simulation submodule is used to simulate the charging process of the electric vehicle, extract the initial state of charge, calculate the state of charge during the trip, and determine the charging decision.

[0035] As a preferred solution of the multi-type charging facility site selection and capacity determination device based on super charging behavior, in the charging station optimal location distribution acquisition module, the expression of the charging station space site selection model is:

[0036] minω

[0037]

[0038]

[0039]

[0040]

[0041]

[0042] Where ω is the maximum distance from all charging demand points to their respective charging stations; d ij is the distance between point i and point j; Y ij is a 0-1 decision variable. If the charging demand at point i is assigned to the charging station at point j, then Y ij =1, otherwise Y ij =0;X i is a 0-1 decision variable, X i =1 means to build a charging station at point i, X i =0 means no charging station is built at point i; I is the location set of charging demand points; N evcs The number of charging stations to be planned.

[0043] As a preferred solution of the multi-type charging facility site selection and capacity determination device based on super charging behavior, in the multi-type charging pile optimization configuration module, the objective function of the capacity configuration model of the multi-type charging piles in the charging station is:

[0044] min Cost = C I +C R +C OM

[0045] Where minCost is the minimum total cost; C I is the annualized EV charging station investment and construction cost; C R is the annualized distribution network capacity expansion cost; C OM is the annual operation and maintenance cost of EV charging stations.

[0046] The present invention has the following advantages: constructing a resident travel chain model according to the travel time and space information of residents; performing parameter calculation and behavior analysis on electric vehicle travel behavior through the resident travel chain model to obtain electric vehicle travel behavior data; constructing a multi-type charging demand prediction model according to the obtained electric vehicle travel behavior data; performing Monte Carlo simulation through the multi-type charging demand prediction model to obtain the spatiotemporal distribution of electric vehicle charging demand; constructing a charging station spatial site selection model according to the P-center theory; performing site selection processing through the charging station spatial site selection model to obtain the optimal location distribution of the charging station; constructing a capacity configuration model of multiple types of charging piles in the charging station according to the spatiotemporal distribution of electric vehicle charging demand and the optimal location distribution of the charging station; optimizing the configuration of multiple types of charging piles in the charging station through the capacity configuration model of multiple types of charging piles in the charging station, and completing the site selection and capacity determination of multiple types of charging facilities. The present invention first conducts an in-depth analysis of the travel characteristics of EV users on a typical day, obtains spatiotemporal information such as the time distribution of EV travel, the length of stay at each type of destination, and the spatial transfer probability matrix, and reasonably models the charging behavior and multi-type demand decision of EV users. Combining travel characteristics and charging behavior characteristics, the Monte Carlo simulation based on travel chain theory is used to obtain the spatiotemporal distribution of multiple types of charging demands. On this basis, the present invention builds a site selection model for EV charging stations and an optimal configuration model for multiple types of charging facilities. The site selection model aims to minimize the maximum distance from the charging demand point to the charging station, taking into account the convenience and fairness of user charging; the configuration model aims to minimize the operating cost and the expansion cost of the distribution network, and fully considers the mutual influence between different types of charging facilities and the constraints of the safe operation of the distribution network. Through scientific planning and optimal configuration, the present invention meets the user's diversified charging needs of slow charging, fast charging, and ultra-fast charging, improves the user experience of electric vehicles, reduces mileage anxiety, and promotes the healthy development of the electric vehicle industry. At the same time, this study provides a new planning idea for the widespread application of ultra-fast charging piles, so as to achieve a more friendly access to the power grid for charging facilities, reduce construction costs, and improve investment benefits, providing valuable reference for decision makers in related fields. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the implementation methods of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for the implementation methods or the description of the prior art. Obviously, the drawings in the following description are only exemplary, and for ordinary technicians in this field, other implementation drawings can be derived from the provided drawings without creative work.

[0048] The structures, proportions, sizes, etc. illustrated in this specification are only used to match the contents disclosed in the specification so as to facilitate understanding and reading by persons familiar with the technology. They are not used to limit the conditions under which the present invention can be implemented, and therefore have no substantial technical significance. Any structural modification, change in proportion or adjustment of size shall still fall within the scope of the technical contents disclosed in the present invention without affecting the effects and purposes that can be achieved by the present invention.

[0049] Figure 1 A schematic diagram of a flow chart of a method for selecting locations and determining capacities of multiple types of charging facilities based on supercharging behavior provided in Example 1 of the present invention;

[0050] Figure 2 A schematic diagram of a multi-type charging demand prediction process in a multi-type charging facility site selection and capacity determination method based on super charging behavior provided in Example 1 of the present invention;

[0051] Figure 3 A schematic diagram of the traffic-distribution network coupling in a selected area in a possible embodiment provided in Embodiment 1 of the present invention;

[0052] Figure 4 A schematic diagram of the spatiotemporal distribution of EV slow charging demand in a possible embodiment provided in Embodiment 1 of the present invention;

[0053] Figure 5 A schematic diagram of the spatiotemporal distribution of EV fast charging demand in a possible embodiment provided in Embodiment 1 of the present invention;

[0054] Figure 6 A schematic diagram of the spatiotemporal distribution of EV superfast charging demand in a possible embodiment provided in Embodiment 1 of the present invention;

[0055] Figure 7 This is a schematic diagram of a charging station site selection result in a possible embodiment provided in Embodiment 1 of the present invention;

[0056] Figure 8 A schematic diagram of a configuration scheme of EV multi-type charging piles in a possible embodiment provided in Embodiment 1 of the present invention;

[0057] Fig. 9 A schematic diagram of a configuration scheme of EV multi-type charging piles under scenario 2 in a possible embodiment provided in embodiment 1 of the present invention;

[0058] Fig.10 A schematic diagram of an EV charging facility configuration scheme under scenario three in a possible embodiment provided in embodiment 1 of the present invention;

[0059] Fig.11This is a schematic diagram of the architecture of a device for selecting and determining the location and capacity of multiple types of charging facilities based on supercharging behavior provided in Example 2 of the present invention. DETAILED DESCRIPTION

[0060] The following is a description of the implementation of the present invention by specific embodiments. People familiar with the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0061] Example 1

[0062] See also Figure 1 Embodiment 1 of the present invention provides a method for selecting a location and determining a capacity of multiple types of charging facilities based on supercharging behavior, comprising the following steps:

[0063] S1. Construct a resident travel chain model based on the residents' travel time and space information; perform parameter calculation and behavior analysis on electric vehicle travel behavior through the resident travel chain model to obtain electric vehicle travel behavior data;

[0064] S2. Construct a multi-type charging demand prediction model based on the obtained electric vehicle travel behavior data; perform Monte Carlo simulation through the multi-type charging demand prediction model to obtain the spatiotemporal distribution of electric vehicle charging demand;

[0065] S3. Construct a charging station spatial location model based on the P-center theory; perform location processing using the charging station spatial location model to obtain the optimal location distribution of the charging station;

[0066] S4. Construct a capacity configuration model for multiple types of charging piles in a charging station based on the spatiotemporal distribution of electric vehicle charging demand and the optimal location distribution of charging stations. Optimize the configuration of multiple types of charging piles in the charging station through the capacity configuration model for multiple types of charging piles in the charging station to complete the site selection and capacity determination of multiple types of charging facilities.

[0067] In this embodiment, in step S1, a resident travel chain model is constructed according to the travel time and space information of residents; the electric vehicle travel behavior is parameterized and analyzed through the resident travel chain model to obtain the electric vehicle travel behavior data;

[0068] The electric vehicle travel behavior data includes: electric vehicle travel time information, electric vehicle travel location information, electric vehicle initial charge state, electric vehicle charging start charge state, electric vehicle charging pile power selection and electric vehicle charging time; the electric vehicle travel time information includes: the first travel time of the day, the return time and the length of stay; the electric vehicle travel location information includes: travel destination type, single driving distance and spatial transfer probability.

[0069] Specifically, analyzing the temporal and spatial variation probability travel patterns of electric vehicle users is the basic work for studying the charging demand of electric vehicles. The travel chain model can be used to reflect the dynamic characteristics of travel patterns, which includes time chains and space chains. Similarly, its characteristic variables can also be divided into two categories. The temporal and spatial characteristic variables of the user's travel process are analyzed and modeled below.

[0070] 1. Electric vehicle travel time information includes the first trip time, return time and stay duration of the day;

[0071] First departure time and return time of the day:

[0072] The probability distribution curves of initial travel time and return time of private electric vehicles on a typical weekday provided by the National Cooperative Highway Research Program (NCHRP187) are used as reference. The fitted probability density functions are shown in equations (1) and (2). The initial travel time and return time of private cars are generated by sampling using the Monte Carlo method.

[0073]

[0074] In the formula, t s represents the initial travel time; λ1, λ2, λ3 are weighting coefficients; α1, α2, α3 represent the mean parameters of the three normal distributions; β1, β2, β3 represent the standard deviation parameters.

[0075]

[0076] In the formula, t b Indicates the return time; μ b represents the mean; σ b Represents standard deviation.

[0077] Duration of stay:

[0078] Excluding traffic congestion and short stops for non-main travel purposes, EVs can only be charged after arriving at the travel destination. The length of the stop affects the length of its charging time, and the length of the EV's stop is closely related to the type of stop location. Therefore, depending on the travel destination, the length of time users stay at the destination is also different. Since the length of stay in residential areas, commercial areas, and office areas is quite different, three different probability distribution functions are used to represent the length of time EVs stay in three different areas.

[0079] The probability density of the length of stay in the office area and the probability density of the length of stay in the commercial area both obey the generalized extreme value distribution. The probability density function of the length of stay in the office area is shown in equations (3) and (4), and the probability density function of the length of stay in the commercial area is shown in equations (5) and (6).

[0080]

[0081] In the formula, is the length of stay of EVs at residential type destinations; is the length of stay of EVs at office area type destinations; is the length of time that EVs stay at commercial district type destinations; μ p2 , μ p3 is the location parameter; σ p2 , σ p3 is the scale parameter; γ p2 , γ p3 is the shape parameter.

[0082] The probability density of the length of stay in a residential area can be expressed by Weibull distribution, as shown in formula (7).

[0083]

[0084] In the formula, λ p1 is the scale parameter; k p1 is the shape parameter.

[0085] Second, the electric vehicle travel location information includes: travel destination type, single travel distance and spatial transfer probability;

[0086] Travel destination type:

[0087] According to the different needs of social production and residents' lives, the urban structure can be divided into many areas with different travel characteristics, such as industrial areas, residential areas, commercial areas, administrative areas, cultural areas, etc. In EV load forecasting, the division of urban functional areas is the basis for analyzing the travel behavior of EV users, and it is also important geographical information for analyzing the charging behavior of users. Considering that the planning area assumed by the present invention does not cover industrial areas and tourist areas, in order to simplify the problem, the present invention divides the travel destinations into three categories, namely residential areas, office areas and commercial areas.

[0088] Single trip distance:

[0089] Considering that the grid-shaped road network in urban areas is relatively common, the Euclidean distance between the starting point and the destination selected in the trip chain analysis is used to approximate the single driving distance, which has a certain degree of universality. As shown in formula (8):

[0090]

[0091] Where, d i represents the single driving distance of the ith driving; x i+1 ,y i+1 represents the coordinates of the destination of the i-th trip; x i ,y i Represents the coordinates of the starting point of the i-th trip.

[0092] Spatial transition probability:

[0093] The travel chain is a special Markov chain. The travel destination of an EV at a certain moment can be approximately considered to be only related to the destination reached in the last trip, which conforms to the no aftereffect of the Markov random process. According to the Markov chain theory, its state transition probability is shown in formula (9):

[0094] P(A i →A j )=P(A j |A i )=p ij (9)

[0095] In the formula, A i is the current state; A j The state of the next moment.

[0096] Consider each driving destination as a state. According to Markov theory, for the i-th trip, the EV is at the current location A. i Transfer to destination A j The probability can be written in matrix form, as shown in formula (10):

[0097]

[0098] Among them, p ij Satisfy the conditions shown in formula (11):

[0099]

[0100] The three types of places that the present invention focuses on, namely residential areas, office areas and commercial areas, are defined as A1, A2 and A3 respectively. The destinations are classified and counted, and the transfer probability of an EV from one type of destination to another type of destination is shown in formula (12).

[0101] 3. Analysis of charging behavior of electric vehicle users includes initial state of charge and state of charge at the beginning of charging;

[0102] Initial state of charge:

[0103] Considering the influence of the configuration of slow charging piles in residential areas, the SOC state of EV when leaving home is represented by the SOC state distribution probability density function, which satisfies the normal distribution N(μ s ,σ s ), as shown in formula (13):

[0104]

[0105] Where, s is the SOC state at departure; μ s represents the mean value of the probability density function of SOC state distribution; σ s Represents the standard deviation of the SOC state distribution probability density function.

[0106] Charging start state of charge:

[0107] Since most EV users have range anxiety and too low SOC will have an adverse effect on the vehicle battery life, users generally do not wait until the EV is completely exhausted before charging. When the power is below a certain threshold, users will choose to charge. In addition, due to the influence of range anxiety, users tend to maintain a high level of SOC when traveling in order to maximize the travel mileage. Based on this, each charging behavior of the user will require a higher SOC level as much as possible before stopping charging.

[0108] Under the premise of charging at the destination, EV users will have the following two types of charging behaviors:

[0109] One situation is: since the current SOC of the EV is directly related to whether the next trip can be completed, when the EV user arrives at destination i, if the remaining power cannot support the user's next trip, the user will choose to charge at the current destination. Formula (14) is used to measure whether the remaining power meets the power required for the next trip and judge the necessity of charging. If it meets the requirement, charging is performed at the destination, otherwise charging is not necessary.

[0110] E i,end -k per,km d i+1 >0.2E total (14)

[0111] In the formula, E i,end is the remaining power after reaching the destination for the i-th trip; k per,km Indicates power consumption per kilometer; d i+1Indicates the distance of the next trip; 0.2E total Indicates the lower limit of EV battery power. If the value is lower than this, the battery may be damaged. total is the EV battery capacity.

[0112] Another situation is: regardless of whether the remaining power can meet the power required for the next trip, charging starts as long as the EV battery power is lower than a certain value. The charging threshold is related to the user's "mileage anxiety" psychological factor, and the charging condition can be expressed by formula (15).

[0113] E i,end <S c *E total (15)

[0114] In the formula, S c Indicates the charging threshold SOC level that the user psychologically expects.

[0115] 4. Analysis of various types of charging behaviors of electric vehicle users.

[0116] First, due to the influence of "mileage anxiety", users tend to keep the battery SOC at a high level as much as possible. Therefore, in each charging, users will try to charge the battery as much as possible; secondly, since the charging cost of high-power charging piles will be higher, especially the high service fee of supercharging piles, users will choose low-power piles for economic considerations; in addition, since charging parking spaces generally do not allow EVs to stay for a long time after charging, unnecessary fast charging may cause users to spend extra energy to return to the charging station to move the car. Therefore, in the face of EV charging piles with multiple powers, users will rationally choose charging piles that can match them according to their expected stay time and the SOC state of the EV. Under the three power level charging pile conditions of the present invention, the principles for selecting charging piles are as follows: first, select charging facilities with sufficient power to ensure that the battery can be basically filled or charged as much as possible within the expected parking time of the owner; second, on the premise of ensuring that it can be charged to a higher SOC, select charging facilities with the minimum power to reduce costs.

[0117] Under the above principles, the charging pile selection model can be expressed as formula (16):

[0118]

[0119] Where P EVn Indicates the charging power selected by the user; P F , P FCF , P UCF Indicates the rated charging power that slow charging piles, fast charging piles and super charging piles can provide; Indicates the user's expected stay time, i=1, 2, 3 respectively indicate that the user stays in the residential area, office area, and commercial area; Etotal is the EV battery capacity; SOC n Indicates EV n state of charge.

[0120] 5. Analysis of charging time.

[0121] If the user selects charging facilities with different powers, the actual charging time required will also be different, which is related to the current power and the selected charging pile power. The calculation is shown in formula (17):

[0122]

[0123] In the formula, t charge Indicates the user's charging time.

[0124] In this embodiment, in step S2, a multi-type charging demand prediction model is constructed based on the obtained electric vehicle travel behavior data; a Monte Carlo simulation is performed through the multi-type charging demand prediction model to obtain the spatiotemporal distribution of electric vehicle charging demand;

[0125] The steps of the Monte Carlo simulation are:

[0126] S21, simulating the travel process of the electric vehicle, extracting the geographical information of the departure point and destination, the travel time, and the stay time information, and obtaining the temporal and spatial distribution of the travel of the electric vehicle;

[0127] S22, simulating the charging process of the electric vehicle, extracting the initial state of charge, calculating the state of charge during the trip, and determining the charging decision.

[0128] Specifically, Figure 2 As shown in the figure, assuming that the total number of EVs is N and the EV number is set to n, the specific steps of the Monte Carlo simulation based on the trip chain theory are as follows:

[0129] T1. Input the plane coordinate data of each type of area;

[0130] T2, input EV travel time information, EV travel space information, and probability density functions of EV charging behavior characteristics;

[0131] T3, initialize EV number n=1;

[0132] T4, randomly select the starting point and initial charge state of vehicle n;

[0133] T5, generate the first trip time and return time of the EV, and set the number of trips i = 1;

[0134] T6. Randomly generate the destination of the i-th trip of vehicle n according to the EV position transfer probability matrix and calculate the travel distance;

[0135] T7. Randomly generate the residence time of vehicle n at different types of destinations according to formulas (3)-(7), and sum up all the residence times and travel times to obtain the total travel time;

[0136] T8. Judge whether it is time to return according to the total travel time and the return time. If it is time to return, end this travel chain, simulate the next vehicle, and go to step T11; if it is not time to return, go to step T9;

[0137] T9. Update the current SOC state of the EV;

[0138] T10. Judge whether the charging threshold is reached. If the charging threshold is reached, charge, record the time and location information of this charging demand, judge the charging method of the user, calculate the charging duration of the EV, let i = i + 1, and then go to step T6; if the charging threshold is not reached, let i = i + 1, and directly jump to step T6 to simulate the next section of the journey;

[0139] T11. Judge whether the number of simulated EVs reaches the upper limit. If it reaches the upper limit, end the entire simulation process; if n < N, let n = n + 1, return to step T4 to simulate the journey of the next EV until all the charging demand simulations of the EVs are completed.

[0140] In this embodiment, in step S3, according to the P - center theory, a spatial location model of the charging station is constructed; location processing is performed through the spatial location model of the charging station to obtain the optimal location distribution of the charging stations;

[0141] Specifically, in the location selection, only starting from the convenience of users, on the premise of a given number of charging stations to be planned, it is required that the maximum distance from the charging station to the demand point is the smallest, which can be described as the "P - center problem".

[0142] The maximum value of the distances from all charging demand points to their affiliated charging stations is calculated as shown in formula (18):

[0143]

[0144] In the formula, ω is the maximum value of the distances from all charging demand points to their affiliated charging stations; d ij is the distance between point i and point j; Y ij is a 0 - 1 decision variable. If the charging demand at point i is assigned to charging station j, then Y ij = 1, otherwise Y ij = 0; I is the set of locations of charging demand points.

[0145] The spatial location model of the charging station is as follows:

[0146] minω(19)

[0147]

[0148]

[0149] Where, X i is a 0-1 decision variable, X i =1 means to build a charging station at point i, X i =0 means no charging station is built at point i; N evcs The number of charging stations to be planned.

[0150] Formula (20) ensures that the total number of sites is N evcs ; Formula (21) ensures that the facility point assigned to the demand point is the proposed site; Formula (22) ensures that only one site is assigned to a demand point; Formula (23) represents X i , Y ij All are 0-1 variables.

[0151] In this embodiment, in step S4, a capacity configuration model of multiple types of charging piles in a charging station is constructed based on the spatiotemporal distribution of electric vehicle charging demand and the optimal location distribution of the charging station; the multiple types of charging piles in the charging station are optimally configured through the capacity configuration model of the multiple types of charging piles in the charging station, and the site selection and capacity determination of multiple types of charging facilities are completed.

[0152] Specifically, based on the above site selection results, the optimization configuration of multiple types of charging facilities such as slow charging, fast charging, and super charging is carried out. The goal of optimizing the configuration of charging piles is mainly to reduce the total cost of operators and power grids, while ensuring the safe and stable operation of the distribution network and bringing users a good charging experience. To simplify the calculation, the present invention makes the following assumptions: First, charging piles and EVs can only correspond one to one, and there is no situation where one pile can be charged multiple times; second, when an EV has a charging demand, it completes the charging at the nearest charging station.

[0153] Objective function for optimizing configuration of multiple types of charging piles:

[0154] From the perspective of operators, the optimal configuration of multiple types of charging piles at each charging station is carried out with the annual operator construction and operation cost and the minimum distribution network capacity expansion cost borne by the operator as the objective function. The total cost includes the annual EV charging station investment and construction cost, the annual EV charging station operation and maintenance cost, and the annual configuration of distributed power sources to expand the distribution network capacity. The objective function of the capacity configuration model of multiple types of charging piles in the charging station is shown in formula (24):

[0155] min Cost = C I +C R +C OM (twenty four)

[0156] Where minCost is the minimum total cost; CI is the annualized EV charging station investment and construction cost; C R is the annualized distribution network capacity expansion cost; C OM is the annual operation and maintenance cost of EV charging stations.

[0157] Among them, the annualized EV charging station investment and construction cost is:

[0158] Assuming that the investment cost is mainly the cost of purchasing charging piles, the investment cost calculation for three types of charging piles with different power levels, slow charging, fast charging and super fast charging, is shown in formula (25):

[0159]

[0160] Where N bus Indicates the total number of nodes; They represent the cost of investing in a set of slow charging piles, fast charging piles, and super charging piles respectively; Respectively represent the number of slow charging piles, fast charging piles, and super charging piles installed at node i; R d It is used to The auxiliary variable of annualization is calculated as shown in formula (26):

[0161]

[0162] Where d is the discount rate; y is CF Indicates the economic life of the charging pile.

[0163] Annualized distribution network capacity expansion cost:

[0164]

[0165] In the formula, c g P represents the cost of configuring distributed power generation per unit capacity; i bcm It indicates the maximum capacity of distributed generation added to compensate for the power shortage caused by EVs in the distribution network.

[0166] Annual EV charging station operation and maintenance costs:

[0167]

[0168] In the formula, They represent the annual operation and maintenance costs of slow charging piles, fast charging piles, and super charging piles respectively.

[0169] In this embodiment, the optimal configuration constraint of charging facilities is also included:

[0170] 1. Distribution network flow constraints:

[0171] The radial distribution network Distflow power flow equation is used for power flow calculation.

[0172]

[0173]

[0174] In the formula, u(j) represents the set of downstream nodes of node j; v(j) represents the set of upstream nodes of node j; at a certain time section, P ij , Q ij They represent the active power and reactive power flowing through branch ij respectively; U i is the voltage amplitude at node i; represents the active load and reactive load of node j; is the EV load at node j, is the distributed generation compensation power of node j; X ij is the reactance of branch ij.

[0175] 2. Voltage amplitude constraint:

[0176]

[0177] Where U min and U max These are the upper and lower limits of allowable voltage fluctuations.

[0178] 3. Branch current constraints:

[0179]

[0180] In the formula, I ijmax I is the maximum current allowed to pass through branch ij; ij is the current passing through branch ij.

[0181] 4. Restrictions on the number of charging piles in a charging station:

[0182] Considering that high-power charging piles can be used as a supplement when low-power charging piles are insufficient, the following charging pile quantity constraint can be adopted to allow low-power EV loads to be transferred to charging piles with a higher level of charging power.

[0183]

[0184] In the formula, They respectively represent the number of EVs that travel from point i to the charging facility at point j in a certain period of time.

[0185] 5. Quantification of load of electric vehicle charging stations:

[0186] According to the aforementioned charging pile selection principle, when the user arrives at the charging station, on the one hand, he should choose a charging pile with sufficient power to fully charge the car, and on the other hand, he should choose a charging pile with the smallest charging power among the charging piles that meet the first requirement. Therefore, for the three types of charging piles and three types of charging requirements proposed in the present invention, there are four possible demand-facility correspondence scenarios.

[0187] Scenario 1: There are sufficient three types of charging facilities;

[0188]

[0189] At this time, various demands are borne by the charging piles with corresponding power levels, and the load of the EV charging station at node j can be calculated using formula (39).

[0190] Scenario 2: Insufficient slow charging piles;

[0191]

[0192] At this time, part of the slow charging demand is transferred to the fast charging pile, and the EV charging station load calculation at node j is shown in formula (41).

[0193] Scenario 3: Insufficient fast charging piles;

[0194]

[0195] At this time, part of the fast charging demand is transferred to the ultra-fast charging pile, and the EV charging station load calculation at node j is shown in formula (43).

[0196] Scenario 4: Both fast charging and slow charging piles are insufficient;

[0197]

[0198] At this time, part of the fast charging and slow charging demands are transferred to the supercharging pile, and the EV charging station load calculation at node j is shown in formula (45).

[0199] 6. Distributed power generation power limit:

[0200]

[0201] In the formula, represents the distributed generation compensation power of node j at a certain moment; Indicates the maximum power that a distributed power source can provide.

[0202] In a possible embodiment, taking the geographic-distribution network coupling system in a certain region of my country as an example, a specific example of site selection and capacity determination is provided as follows:

[0203] In order to verify the multi-type charging facility site selection and sizing method based on super charging behavior proposed in the present invention, a 31-node distribution network-transportation network coupling area is used as an example for simulation. The horizontal direction is 6.8km and the vertical direction is 3.1km. There are 31 small blocks in the area, including 12 residential areas, 12 commercial areas and 7 office areas, of which areas 1, 4, 8, 9, 10, 11, 12, 13, 14, 19, 20, and 22 are residential areas; areas 2, 3, 7, 15, 16, 17, 18, 21, 24, 25, 29, and 30 are commercial areas; areas 5, 6, 23, 26, 27, 28, and 31 are office areas. Assume that the transportation network is coupled with a 31-node 10kV voltage level radial distribution network, and the coupling relationship is as follows: Figure 3 To simplify the calculation, it is assumed that the distribution network nodes and EV load aggregation points are the geometric center points of the corresponding blocks.

[0204] 1. The specific parameters of the case are as follows:

[0205] 1) Taking 2025 as the planning year, the number of EVs in the region is 10,000, and the selected EV battery capacity is assumed to be 70kWh, the power consumption per 100 kilometers is 15kWh / km, and the average driving speed is 40km / h.

[0206] 2) The discount rate used in the annualized calculation is 0.03.

[0207] 3) The parameters of the three types of charging piles selected and their investment, construction, operation and maintenance costs are shown in Table 1.

[0208] Slow charging station Fast charging station Ultra-fast charging station Rated charging power 7kW 30kW 250kW Unit investment and construction cost 400$ / set 3250$ / set 20000$ / set Unit operation and maintenance cost 40$ / year 325$ / year 2000$ / year Economic life 10 years 10 years 10 years

[0209] Table 1 Parameters of electric vehicle charging piles

[0210] 4) The unit capacity increase cost of configuring distributed power sources is set at 106.5$ / kVA.

[0211] 5) The lower and upper limits of the allowable range of node voltage fluctuation in the system are set to 0.95 pu and 1.05 pu respectively, and the maximum allowable value of each branch current of the 10 kV distribution system is 600 A.

[0212] 6) Some travel time and space characteristic parameters are shown in Table 2.

[0213] parameter Value parameter Value <![CDATA[λ1]]> 0.018 <![CDATA[α3]]> 12.750 <![CDATA[α1]]> 7.483 <![CDATA[β3]]> 8.069 <![CDATA[β1]]> 0.307 <![CDATA[μ b ]]> 17.6 <![CDATA[λ2]]> 0.112 <![CDATA[σ b ]]> 3.4 <![CDATA[α2]]> 7.280 <![CDATA[μ s ]]> 0.95 <![CDATA[β2]]> 1.572 <![CDATA[σ s ]]> 0.34 <![CDATA[λ3]]> 0.017 <![CDATA[S c ]]> 30％ <![CDATA[μ p2 ]]> 438.45 <![CDATA[μ p3 ]]> 68.52 <![CDATA[σ p2 ]]> 164.51 <![CDATA[σ p3 ]]> 41.76 <![CDATA[γ p2 ]]> 0.23 <![CDATA[γ p3 ]]> 0.66 <![CDATA[λ p1 ]]> 195.787 <![CDATA[k p1 ]]> 1.153

[0214] Table 2 Parameters related to EV users’ daily travel characteristics

[0215] 2. Prediction results of multiple types of charging loads:

[0216] Through the time and location information of EV travel, a travel chain simulation model is built, and the EV user behavior characteristics such as charging starting SOC preference, travel starting SOC, charging decision behavior, etc. are considered. A Monte Carlo simulation based on travel chain theory is carried out to obtain the spatiotemporal distribution of EV charging demand points, providing a data basis for the optimal configuration of EV charging facilities.

[0217] 1) Slow charging demand distribution:

[0218] The EV slow charging load distribution obtained by simulation is as follows: Figure 4 As shown. The present invention divides a day into 96 moments, with each 15 minutes as a time section. From a time point of view, the slow charging demand of EV users is mostly distributed between 7:00 in the morning and 3:00 in the afternoon. This is because on weekdays, private car users are mostly in the workplace at this time, and the stay time in the workplace is generally longer, and the expected charging time is relatively abundant, so it is generally chosen to slow charge near the work destination to save charging costs and maintain battery life. After 7:00 p.m., the slow charging demand of public charging stations has dropped significantly. This is because most residents go home from work after 7:00 p.m., and some EVs use private household piles for slow charging, and the public slow demand is less. From the location point of view, areas 5, 6, 23, 26, 27, 28, and 31 with higher slow charging demand are all office areas. This is because the slow charging load in residential areas at night is mostly met by private household piles, which is not within the scope of consideration of the public charging station of the present invention, while the EV stays for a shorter time in commercial areas, generating more fast charging demand and less slow charging demand. As mentioned above, private car users on weekdays are mostly active in the workplace, which creates a demand for slow charging in the workplace.

[0219] 2) Fast charging demand distribution:

[0220] The EV fast charging load distribution obtained by simulation is as follows: Figure 5 As shown in the figure, from the location point of view, areas 2, 3, 7, 15, 16, 17, 18, 21, 24, 25, 29, and 30 with higher demand for fast charging are commercial areas. This is because users' activities in commercial areas generally do not last too long, usually no more than three hours. They hope to charge their EVs while shopping, which will generate more demand for fast charging.

[0221] From a time perspective, EV users' demand for fast charging starts to increase around 7:00 a.m. and peaks around 10:00 a.m. In addition, there is also a high demand for fast charging around 3:00 p.m. Since users usually go to commercial areas for shopping in the morning and return at noon, or at noon and return at night, their demand for fast charging is generated after arriving at the commercial area, that is, after 7:00 a.m. and around 3:00 p.m.

[0222] 3) Supercharging demand distribution:

[0223] The distribution of ultra-fast charging demand obtained by simulation is as follows: Figure 6 As shown in the figure, it can be seen that the demand for EV super-fast charging is relatively scattered and the demand is generally small. This is because in the destination charging mode, EV owners generally keep the EV state of charge at a high level, and there is not much demand for emergency super-fast charging. Supercharging demand will only occur when the SOC level is very low and the expected stay time is very short, and the probability of its occurrence is low.

[0224] 3. Site selection and capacity determination results of various types of charging facilities:

[0225] The site selection results are as follows Figure 7 As shown in the figure, charging demand point nodes 1 to 31 are taken as candidate sites. Based on the principle of minimizing the distance for users to find the station, nodes 3, 6, 8, 12, 14, 24, 27, and 29 are finally selected as the locations for charging station construction.

[0226] The present invention analyzes the optimal configuration results of charging facilities in three scenarios, and the difference between the various scenarios is mainly the different optional ranges of charging facility types.

[0227] Scenario 1: All three types of charging facilities are configured, and the optimized configuration results are as follows: Figure 8 As shown, the average total annual cost of building charging facilities is US$117,000, of which the cost of grid expansion is US$25,000.

[0228] Under the condition of joint configuration of slow charging piles, fast charging piles, and ultra-fast charging piles, the number of configurations of each node varies greatly. Since there are a large number of office workplaces near nodes 6, 24, and 27, users prefer long-term slow charging, so the configuration of slow charging piles accounts for a high proportion. In contrast, nodes 8 and 12 are close to commercial areas, and users mostly stay for a short time. Most of the power replenishment needs are fast charging or ultra-fast charging, and the demand for slow charging is not high, so the proportion of fast charging facilities built is relatively high.

[0229] Scenario 2: Only slow charging piles and ultra-fast charging piles are configured. The optimized configuration results are as follows: Fig. 9 As shown, the average total annual cost of charging facility construction is US$371,000, of which the cost of grid expansion is US$108,000.

[0230] Under the condition that only slow charging piles and ultra-fast charging piles are configured, all fast charging and ultra-fast charging needs are met by ultra-fast charging piles because there is no allocation function of fast charging piles. Compared with scenario 1, the number of ultra-fast charging piles configured at each node has increased significantly. Due to the relatively expensive construction and operation costs of ultra-fast charging piles, as well as their large power fluctuations, the annual total cost of the planning scheme and the cost of expanding the distribution network have increased significantly.

[0231] Scenario 3: Only fast charging piles and ultra-fast charging piles are configured. The optimized configuration results are as follows: Fig.10As shown, the average total annual cost of charging facility construction is US$233,000, of which the expansion cost is US$42,000.

[0232] Under the condition of only configuring fast charging piles and ultra-fast charging piles, the slow charging demand is borne by the fast charging piles, which basically does not affect the planning of ultra-fast charging piles, but the total construction of fast charging piles increases significantly, and the annual total cost is also higher than that of scenario 1. However, its expansion cost does not change much compared with the layout of three types of charging piles, because the power of a single fast charging pile is not very high, and the maximum power impact on the distribution network is not high, so the expansion cost is slightly higher than that of scenario 1.

[0233] In summary, the present invention constructs a resident travel chain model based on the travel time and space information of residents; performs parameter calculation and behavior analysis on electric vehicle travel behavior through the resident travel chain model to obtain electric vehicle travel behavior data; constructs a multi-type charging demand prediction model based on the obtained electric vehicle travel behavior data; performs Monte Carlo simulation through the multi-type charging demand prediction model to obtain the spatiotemporal distribution of electric vehicle charging demand; constructs a charging station spatial location model based on the P-center theory; performs location processing through the charging station spatial location model to obtain the optimal location distribution of the charging station; constructs a capacity configuration model of multiple types of charging piles in the charging station based on the spatiotemporal distribution of electric vehicle charging demand and the optimal location distribution of the charging station; optimizes the configuration of multiple types of charging piles in the charging station through the capacity configuration model of multiple types of charging piles in the charging station, and completes the location and capacity determination of multiple types of charging facilities. The present invention first conducts an in-depth analysis of the travel characteristics of EV users on a typical day, obtains spatiotemporal information such as the time distribution of EV travel, the length of stay at each type of destination, and the spatial transfer probability matrix, and reasonably models the charging behavior and multi-type demand decision of EV users. Combining travel characteristics and charging behavior characteristics, the Monte Carlo simulation based on travel chain theory is used to obtain the spatiotemporal distribution of multiple types of charging demands. On this basis, the present invention builds a site selection model for EV charging stations and an optimal configuration model for multiple types of charging facilities. The site selection model aims to minimize the maximum distance from the charging demand point to the charging station, taking into account the convenience and fairness of user charging; the configuration model aims to minimize the operating cost and the expansion cost of the distribution network, and fully considers the mutual influence between different types of charging facilities and the constraints of the safe operation of the distribution network. Through scientific planning and optimal configuration, the present invention meets the user's diversified charging needs of slow charging, fast charging, and ultra-fast charging, improves the user experience of electric vehicles, reduces mileage anxiety, and promotes the healthy development of the electric vehicle industry. At the same time, this study provides a new planning idea for the widespread application of ultra-fast charging piles, so as to achieve a more friendly access to the power grid for charging facilities, reduce construction costs, and improve investment benefits, providing valuable reference for decision makers in related fields.

[0234] It should be noted that the method of the embodiment of the present disclosure can be performed by a single device, such as a computer or a server. The method of the present embodiment can also be applied in a distributed scenario and completed by multiple devices cooperating with each other. In the case of such a distributed scenario, one of the multiple devices can only perform one or more steps in the method of the embodiment of the present disclosure, and the multiple devices will interact with each other to complete the described method.

[0235] It should be noted that the above describes some embodiments of the present disclosure. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the above embodiments and still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0236] Example 2

[0237] See also Fig.11 Embodiment 2 of the present invention also provides a device for selecting a location and determining a capacity of multiple types of charging facilities based on super charging behavior, including:

[0238] The electric vehicle travel behavior data acquisition module 001 is used to construct a resident travel chain model based on the residents' travel time and space information; perform parameter calculation and behavior analysis on the electric vehicle travel behavior through the resident travel chain model to obtain the electric vehicle travel behavior data;

[0239] The module 002 for acquiring the spatiotemporal distribution of the electric vehicle charging demand is used to construct a multi-type charging demand prediction model based on the acquired electric vehicle travel behavior data; and to perform Monte Carlo simulation through the multi-type charging demand prediction model to acquire the spatiotemporal distribution of the electric vehicle charging demand;

[0240] The module 003 for acquiring the optimal location distribution of charging stations is used to construct a spatial location selection model of charging stations according to the P-center theory; and to perform location selection processing through the spatial location selection model of charging stations to obtain the optimal location distribution of charging stations;

[0241] The module 004 for optimizing the configuration of multiple types of charging piles is used to construct a capacity configuration model of multiple types of charging piles in a charging station according to the spatiotemporal distribution of electric vehicle charging demand and the optimal location distribution of the charging station; the capacity configuration model of multiple types of charging piles in the charging station is used to optimize the configuration of multiple types of charging piles in the charging station, and the site selection and capacity determination of multiple types of charging facilities are completed.

[0242] In this embodiment, in the electric vehicle travel behavior data acquisition module 001, in the process of obtaining electric vehicle travel behavior data by performing parameter calculation and behavior analysis on the electric vehicle travel behavior through the resident travel chain model, the electric vehicle travel behavior data includes: electric vehicle travel time information, electric vehicle travel location information, electric vehicle initial charge state, electric vehicle charging start charge state, electric vehicle charging pile power selection and electric vehicle charging time; the electric vehicle travel time information includes: the first travel time and return time of the day and the length of stay; the electric vehicle travel location information includes: travel destination type, single driving distance and spatial transfer probability.

[0243] In this embodiment, the module 002 for acquiring the spatiotemporal distribution of the electric vehicle charging demand includes submodules:

[0244] The travel process simulation submodule 021 is used to simulate the travel process of the electric vehicle, extract the geographical information of the departure point and destination, the travel time and the stay time information, and obtain the temporal and spatial distribution of the travel of the electric vehicle;

[0245] The charging process simulation submodule 022 is used to simulate the charging process of the electric vehicle, extract the initial state of charge, calculate the state of charge during the trip, and determine the charging decision.

[0246] In this embodiment, in the optimal location distribution acquisition module 003 of the charging station, the expression of the charging station spatial location model is:

[0247] minω

[0248]

[0249]

[0250]

[0251]

[0252]

[0253] Where ω is the maximum distance from all charging demand points to their respective charging stations; d ij is the distance between point i and point j; Y ij is a 0-1 decision variable. If the charging demand at point i is assigned to the charging station at point j, then Y ij =1, otherwise Y ij =0;X i is a 0-1 decision variable, X i =1 means to build a charging station at point i, X i=0 means no charging station is built at point i; I is the location set of charging demand points; N evcs The number of charging stations to be planned.

[0254] In this embodiment, in the multi-type charging pile optimization configuration module 004, the objective function of the capacity configuration model of the multi-type charging piles in the charging station is:

[0255] min Cost = C I +C R +C OM

[0256] Where minCost is the minimum total cost; C I is the annualized EV charging station investment and construction cost; C R is the annualized distribution network capacity expansion cost; C OM is the annual operation and maintenance cost of EV charging stations.

[0257] It should be noted that the information interaction, execution process and other contents 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 will not be repeated here.

[0258] Example 3

[0259] Embodiment 3 of the present invention provides a non-transitory computer-readable storage medium, in which a program code for a method for site selection and capacity determination of multiple types of charging facilities based on supercharging behavior is stored, and the program code includes instructions for executing embodiment 1 or any possible implementation thereof.

[0260] The computer-readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server or a data center that includes one or more available media. The available medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid state drive (SSD)).

[0261] Example 4

[0262] Embodiment 4 of the present invention provides an electronic device, including: a memory and a processor;

[0263] 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 method for site selection and sizing of multiple types of charging facilities based on supercharging behavior in Example 1 or any possible implementation thereof.

[0264] 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 implemented by reading software codes stored in a memory. The memory can be integrated in the processor or can be located outside the processor and exist independently.

[0265] 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 a computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions can be transmitted from a website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website site, computer, server or data center.

[0266] Obviously, those skilled in the art should understand that the above modules or steps of the present invention can be implemented by a general computing system, they can be concentrated on a single computing system, or distributed on a network composed of multiple computing systems, and optionally, they can be implemented by a program code executable by a computing system, so that they can be stored in a storage system and executed by the computing system, and in some cases, the steps shown or described can be executed in a different order than here, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. Thus, the present invention is not limited to any specific combination of hardware and software.

[0267] Although the present invention has been described in detail above by general description and specific embodiments, it is obvious to those skilled in the art that some modifications or improvements can be made to the present invention. Therefore, these modifications or improvements made without departing from the spirit of the present invention all belong to the scope of protection claimed by the present invention.

Claims

1. A method for site selection and capacity determination of multiple types of charging facilities based on super charging behavior, characterized in that: include: According to the residents' travel time and space information, a resident travel chain model is constructed; through the resident travel chain model, parameter calculation and behavior analysis of electric vehicle travel behavior are performed to obtain electric vehicle travel behavior data; Constructing a multi-type charging demand prediction model based on the obtained electric vehicle travel behavior data; performing Monte Carlo simulation through the multi-type charging demand prediction model to obtain the spatiotemporal distribution of electric vehicle charging demand; According to the P-center theory, a charging station spatial location model is constructed; the charging station spatial location model is used to perform location processing to obtain the optimal location distribution of the charging station; According to the spatiotemporal distribution of electric vehicle charging demand and the optimal location distribution of charging stations, a capacity configuration model for multiple types of charging piles in a charging station is constructed; through the capacity configuration model for multiple types of charging piles in a charging station, multiple types of charging piles inside the charging station are optimally configured to complete the site selection and capacity determination of multiple types of charging facilities.

2. The method for site selection and capacity determination of multiple types of charging facilities based on supercharging behavior according to claim 1 is characterized in that: In the process of performing parameter calculation and behavior analysis on electric vehicle travel behavior through the resident travel chain model to obtain electric vehicle travel behavior data, the electric vehicle travel behavior data includes: electric vehicle travel time information, electric vehicle travel location information, electric vehicle initial charge state, electric vehicle charging start charge state, electric vehicle charging pile power selection and electric vehicle charging time; the electric vehicle travel time information includes: the first travel time of the day, the return time and the length of stay; the electric vehicle travel location information includes: travel destination type, single driving distance and spatial transfer probability.

3. The method for selecting the location and determining the capacity of multiple types of charging facilities based on supercharging behavior according to claim 2 is characterized in that: In the process of performing Monte Carlo simulation through the multi-type charging demand prediction model to obtain the spatiotemporal distribution of electric vehicle charging demand, the steps of the Monte Carlo simulation are: Simulate the travel process of electric vehicles, extract the geographic information of departure points and destinations, travel time, and stay time information, and obtain the temporal and spatial distribution of electric vehicle travel; The charging process of electric vehicles is simulated, the initial state of charge is extracted, the state of charge during the trip is calculated, and the charging decision is determined.

4. The method for selecting the location and determining the capacity of multiple types of charging facilities based on supercharging behavior according to claim 3 is characterized in that: The expression of the charging station spatial location model is: Where ω is the maximum distance from all charging demand points to their respective charging stations; d ij is the distance between point i and point j; Y ij is a 0-1 decision variable. If the charging demand at point i is assigned to the charging station at point j, then Y ij =1, otherwise Y ij =0;X i is a 0-1 decision variable, X i =1 means to build a charging station at point i, X i =0 means no charging station is built at point i; I is the location set of charging demand points; N evcs The number of charging stations to be planned.

5. The method for site selection and capacity determination of multiple types of charging facilities based on supercharging behavior according to claim 4 is characterized in that: The objective function of the capacity configuration model of multiple types of charging piles in the charging station is: min Cost=C I +C R +C OM In the formula, min Cost is the minimum total cost; C I is the annualized EV charging station investment and construction cost; C R is the annualized distribution network capacity expansion cost; C OM is the annual operation and maintenance cost of EV charging stations.

6. A device for selecting and determining the location and capacity of multiple types of charging facilities based on supercharging behavior, adopting a method for selecting and determining the location and capacity of multiple types of charging facilities based on supercharging behavior according to any one of claims 1 to 5, characterized in that: include: The electric vehicle travel behavior data acquisition module is used to construct a resident travel chain model based on the residents' travel time and space information; the electric vehicle travel behavior is parameterized and analyzed through the resident travel chain model to obtain the electric vehicle travel behavior data; A module for acquiring the spatiotemporal distribution of electric vehicle charging demand, which is used to construct a multi-type charging demand prediction model based on the acquired electric vehicle travel behavior data; and to perform Monte Carlo simulation through the multi-type charging demand prediction model to acquire the spatiotemporal distribution of electric vehicle charging demand; The module for acquiring the optimal location distribution of charging stations is used to construct a spatial location selection model of charging stations according to the P-center theory; and to perform location selection processing through the spatial location selection model of charging stations to obtain the optimal location distribution of charging stations; The module for optimizing the configuration of multiple types of charging piles is used to construct a capacity configuration model for multiple types of charging piles in a charging station according to the spatiotemporal distribution of electric vehicle charging demand and the optimal location distribution of the charging station; the capacity configuration model for multiple types of charging piles in the charging station is used to optimize the configuration of multiple types of charging piles in the charging station, and the site selection and capacity determination of multiple types of charging facilities are completed.

7. The device for selecting a location and determining the capacity of multiple types of charging facilities based on supercharging behavior according to claim 6, characterized in that: In the electric vehicle travel behavior data acquisition module, in the process of obtaining electric vehicle travel behavior data by performing parameter calculation and behavior analysis on the electric vehicle travel behavior through the resident travel chain model, the electric vehicle travel behavior data includes: electric vehicle travel time information, electric vehicle travel location information, electric vehicle initial charge state, electric vehicle charging start charge state, electric vehicle charging pile power selection and electric vehicle charging time; the electric vehicle travel time information includes: the first travel time of the day, the return time and the length of stay; the electric vehicle travel location information includes: travel destination type, single driving distance and spatial transfer probability.

8. The device for selecting a location and determining the capacity of multiple types of charging facilities based on supercharging behavior according to claim 7, characterized in that: The module for acquiring the spatiotemporal distribution of the electric vehicle charging demand includes submodules: The travel process simulation submodule is used to simulate the travel process of electric vehicles, extract the geographical information of departure points and destinations, and the travel time and stay time information to obtain the temporal and spatial distribution of the electric vehicle's travel; The charging process simulation submodule is used to simulate the charging process of the electric vehicle, extract the initial state of charge, calculate the state of charge during the trip, and determine the charging decision.

9. The device for selecting locations and determining the capacity of multiple types of charging facilities based on supercharging behavior according to claim 8, characterized in that: In the optimal location distribution acquisition module of the charging station, the expression of the charging station spatial location model is: Where ω is the maximum distance from all charging demand points to their respective charging stations; d ij is the distance between point i and point j; Y ij is a 0-1 decision variable. If the charging demand at point i is assigned to the charging station at point j, then Y ij =1, otherwise Y ij =0;X i is a 0-1 decision variable, X i =1 means to build a charging station at point i, X i =0 means no charging station is built at point i; I is the location set of charging demand points; N evcs The number of charging stations to be planned.

10. The device for selecting the location and determining the capacity of multiple types of charging facilities based on super charging behavior according to claim 9, characterized in that: In the module for optimizing the configuration of multiple types of charging piles, the objective function of the capacity configuration model of multiple types of charging piles in the charging station is: min Cost=C I +C R +C OM In the formula, min Cost is the minimum total cost; C I is the annualized EV charging station investment and construction cost; C R is the annualized distribution network capacity expansion cost; C OM is the annual operation and maintenance cost of EV charging stations.

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