Charging facility scheduling method and device and electronic equipment

Abstract

The invention discloses a charging facility scheduling method and device and electronic equipment. The method comprises the following steps: receiving a charging facility scheduling request corresponding to a target area; in response to the charging facility scheduling request, determining a plurality of target passing places in the target area; determining electric vehicle parameters and charging facility parameters respectively corresponding to the plurality of target passing places; according to the corresponding charging facility parameters, the corresponding charging demand parameters and the corresponding number of passing vehicles, determining power utilization parameters corresponding to the multiple target passing places; determining a target scheduling amount of the corresponding to-be-scheduled facility according to the power utilization parameters corresponding to the plurality of target passing places; and performing scheduling according to the target scheduling amount of the to-be-scheduled facilities respectively corresponding to the plurality of target passing places. According to the invention, the technical problem that the scheduling cost of the electric vehicle charging facility is high when the electric vehicle charging demand is increased sharply in a short period in the prior art is solved.

Description

Technical Field

[0001] The present invention relates to the field of data processing, and in particular, to a charging facility scheduling method, apparatus, and electronic device. Background Art

[0002] In related technologies, using fixed charging piles to charge electric vehicles can meet the conventional charging needs to a certain extent. However, in situations such as peak travel periods when the charging demand for electric vehicles surges, it is difficult to quickly adjust the layout of fixed charging piles to cope with such short-term demand changes. As a result, when facing the surging charging demand for electric vehicles in the short term, there is a technical problem of high scheduling cost for electric vehicle charging facilities.

[0003] In view of the above problems, no effective solution has been proposed yet. Summary of the Invention

[0004] Embodiments of the present invention provide a charging facility scheduling method, apparatus, and electronic device to at least solve the technical problem of high scheduling cost for electric vehicle charging facilities in related technologies when facing the surging charging demand for electric vehicles in the short term.

[0005] According to an aspect of an embodiment of the present invention, a charging facility scheduling method is provided, including: receiving a charging facility scheduling request corresponding to a target area; in response to the charging facility scheduling request, determining a plurality of target passing places in the target area; determining electric vehicle parameters and charging facility parameters respectively corresponding to the plurality of target passing places, where the corresponding electric vehicle parameters are the charging demand parameters of electric vehicles passing through the corresponding target passing places within a predetermined time period and the corresponding number of passing vehicles; determining power consumption parameters respectively corresponding to the plurality of target passing places according to the charging facility parameters respectively corresponding to the plurality of target passing places, and the charging demand parameters and the corresponding number of passing vehicles of electric vehicles at the corresponding target passing places; determining the target scheduling amount of the facilities to be scheduled respectively corresponding to the plurality of target passing places according to the power consumption parameters respectively corresponding to the plurality of target passing places; and scheduling the facilities to be scheduled according to the target scheduling amount of the facilities to be scheduled respectively corresponding to the plurality of target passing places.

[0006] Optionally, determining the plurality of target passing places in the target area includes: determining a plurality of predetermined starting places in the target area, and predetermined destinations respectively corresponding to the plurality of predetermined starting places; determining a plurality of target travel routes according to the plurality of predetermined starting places and the predetermined destinations respectively corresponding to the plurality of predetermined starting places, where the plurality of target travel routes are in one-to-one correspondence with the plurality of predetermined starting places; and determining the plurality of target passing places in the target area according to the plurality of target travel routes.

[0007] Optionally, determining the target scheduling amount of the to-be-scheduled facilities corresponding to the multiple target passing locations respectively according to the electricity consumption parameters corresponding to the multiple target passing locations respectively includes: determining the facility type parameters corresponding to the multiple target passing locations according to the multiple target passing locations, where the facility type parameters are parameters representing the types of the to-be-scheduled facilities; determining the selection preference parameters corresponding to the multiple target passing locations respectively according to the electric vehicle parameters corresponding to the multiple target passing locations and the facility type parameters, where the selection preference parameters are parameters representing the preference degrees of the electric vehicles corresponding to the multiple target passing locations for the to-be-scheduled facilities corresponding to the facility type parameters; determining the charging power indexes of the pre-determined type scheduling facilities corresponding to the multiple target passing locations respectively according to the facility type parameters corresponding to the multiple target passing locations; determining the target scheduling amount of the to-be-scheduled facilities corresponding to the multiple target passing locations respectively according to the charging power indexes of the pre-determined type scheduling facilities corresponding to the multiple target passing locations respectively, the electricity consumption parameters corresponding to the multiple target passing locations respectively and the selection preference parameters.

[0008] Optionally, determining the selection preference parameters corresponding to the multiple target passing locations respectively according to the electric vehicle parameters corresponding to the multiple target passing locations and the facility type parameters includes: determining the number of charging vehicles corresponding to the pre-determined type scheduling facilities corresponding to the multiple target passing locations respectively according to the number of passing vehicles corresponding to the multiple target passing locations and the facility type parameters; determining the waiting time parameters corresponding to the pre-determined type scheduling facilities corresponding to the multiple target passing locations respectively; determining the sub-preference parameters of the pre-determined type scheduling facilities corresponding to the multiple target passing locations respectively according to the number of charging vehicles and the waiting time parameters of the pre-determined type scheduling facilities corresponding to the multiple target passing locations respectively; determining the selection preference parameters corresponding to the multiple target passing locations respectively according to the sub-preference parameters of the pre-determined type scheduling facilities corresponding to the multiple target passing locations respectively.

[0009] Optionally, determining the sub-preference parameters of the pre-determined type scheduling facilities corresponding to the multiple target passing locations respectively according to the number of charging vehicles and the waiting time parameters of the pre-determined type scheduling facilities corresponding to the multiple target passing locations respectively includes: determining the charging time parameters of the pre-determined type scheduling facilities corresponding to the multiple target passing locations respectively according to the number of charging vehicles and the charging power indexes of the pre-determined type scheduling facilities corresponding to the multiple target passing locations respectively; determining the charging price parameters of the pre-determined type scheduling facilities corresponding to the multiple target passing locations respectively; determining the sub-preference parameters of the pre-determined type scheduling facilities corresponding to the multiple target passing locations respectively according to the charging time parameters, the waiting time parameters and the charging price parameters of the pre-determined type scheduling facilities corresponding to the multiple target passing locations respectively.

[0010] Optionally, determining the target scheduling amounts of the facilities to be scheduled corresponding to the multiple target passing locations respectively according to the electricity consumption parameters corresponding to the multiple target passing locations respectively includes: determining the scheduling cost parameters corresponding to the multiple target passing locations according to the electricity consumption parameters corresponding to the multiple target passing locations respectively; determining the facility type parameters of the facilities to be scheduled corresponding to the multiple target passing locations according to the multiple target passing locations; determining the charging income parameters corresponding to the multiple target passing locations respectively according to the facility type parameters of the facilities to be scheduled corresponding to the multiple target passing locations; and determining the target scheduling amounts of the facilities to be scheduled corresponding to the multiple target passing locations respectively according to the scheduling cost parameters and the charging income parameters corresponding to the multiple target passing locations.

[0011] Optionally, determining the electricity consumption parameters corresponding to the multiple target passing locations respectively according to the charging facility parameters corresponding to the multiple target passing locations respectively, the charging demand parameters of the electric vehicles at the corresponding target passing locations, and the corresponding number of passing vehicles includes: determining the demand correction parameters corresponding to the multiple target passing locations respectively according to the charging facility parameters corresponding to the multiple target passing locations respectively; determining the corrected number of vehicles corresponding to the multiple target passing locations respectively according to the demand correction parameters and the number of passing vehicles corresponding to the multiple target passing locations respectively; and determining the electricity consumption parameters corresponding to the multiple target passing locations respectively according to the charging demand parameters and the corrected number of vehicles corresponding to the multiple target passing locations.

[0012] According to one aspect of the embodiments of the present invention, there is provided a charging facility scheduling device, including: a receiving module, configured to receive a charging facility scheduling request corresponding to a target area; a response module, configured to determine multiple target passing locations within the target area in response to the charging facility scheduling request; a first determination module, configured to determine the electric vehicle parameters and the charging facility parameters corresponding to the multiple target passing locations respectively, where the corresponding electric vehicle parameters are the parameters of the electric vehicles passing through the corresponding target passing locations within a predetermined time period, including the corresponding charging demand parameters and the corresponding number of passing vehicles; a second determination module, configured to determine the electricity consumption parameters corresponding to the multiple target passing locations respectively according to the charging facility parameters corresponding to the multiple target passing locations respectively, the charging demand parameters of the electric vehicles at the corresponding target passing locations, and the corresponding number of passing vehicles; a third determination module, configured to determine the target scheduling amounts of the facilities to be scheduled corresponding to the multiple target passing locations respectively according to the electricity consumption parameters corresponding to the multiple target passing locations respectively; and a fourth determination module, configured to schedule the facilities to be scheduled according to the target scheduling amounts of the facilities to be scheduled corresponding to the multiple target passing locations respectively.

[0013] According to one aspect of an embodiment of the present invention, an electronic device is provided, including: a processor; a memory for storing executable instructions of the processor; wherein, the processor is configured to execute the instructions to implement the charging facility scheduling method described in any one of the above.

[0014] According to one aspect of an embodiment of the present invention, a computer-readable storage medium is provided. When the instructions in the computer-readable storage medium are executed by a processor of an electronic device, the electronic device can execute the charging facility scheduling method described in any one of the above.

[0015] In an embodiment of the present invention, a charging facility scheduling request corresponding to a target area is received; in response to the charging facility scheduling request, a plurality of target passing places in the target area are determined; electric vehicle parameters and charging facility parameters respectively corresponding to the plurality of target passing places are determined, wherein the corresponding electric vehicle parameters are the charging demand parameters of the electric vehicles passing through the corresponding target passing places within a predetermined time period and the corresponding number of passing vehicles; according to the charging facility parameters respectively corresponding to the plurality of target passing places, as well as the charging demand parameters and the corresponding number of passing vehicles of the electric vehicles at the corresponding target passing places, the power consumption parameters respectively corresponding to the plurality of target passing places are determined; according to the power consumption parameters respectively corresponding to the plurality of target passing places, the target scheduling amounts of the facilities to be scheduled respectively corresponding to the plurality of target passing places are determined; according to the target scheduling amounts of the facilities to be scheduled respectively corresponding to the plurality of target passing places, the facilities to be scheduled are scheduled. By determining a plurality of target passing places in the target area according to the corresponding charging facility scheduling request, it helps to subsequently analyze the charging demands respectively corresponding to the plurality of target passing places. By determining the electric vehicle parameters and charging facility parameters respectively corresponding to the plurality of target passing places, it helps to subsequently accurately determine the specific power to be provided respectively corresponding to the plurality of target passing places. By accurately quantifying the power to be provided at each target passing place, that is, the power consumption parameter, it helps to subsequently accurately adjust the charging facilities and avoid unnecessary scheduling of charging facilities. By combining the power consumption parameters respectively corresponding to the plurality of target passing places, the target scheduling amounts of the facilities to be scheduled respectively corresponding to the plurality of target passing places are determined, thereby ensuring that the charging facilities can be efficiently and accurately scheduled to the corresponding target passing places during the peak charging demand period (such as the peak travel period). By scheduling the facilities to be scheduled according to the target scheduling amounts of the facilities to be scheduled respectively corresponding to the plurality of target passing places, it effectively avoids the mismatch between the power supply and demand caused by the over-concentration or dispersion of charging facilities, thereby avoiding unnecessary scheduling of charging facilities, effectively reducing the scheduling cost during the charging facility scheduling process, and further solving the technical problem in the related art that when facing the rapidly increasing electric vehicle charging demand in a short period, the scheduling cost of electric vehicle charging facilities is high. Description of the Drawings

[0016] The accompanying drawings described herein are used to provide a further understanding of the present invention and form a part of this application. The illustrative embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation to the present invention. In the drawings:

[0017] Figure 1 is a flowchart of the charging facility scheduling method according to an embodiment of the present invention;

[0018] Figure 2 is a framework flowchart of the charging facility scheduling method in an alternative embodiment of the present invention;

[0019] Figure 3 is a detailed flowchart of the charging facility scheduling method in an alternative embodiment of the present invention;

[0020] Figure 4 is a flowchart of solving using the simulated annealing algorithm in an alternative embodiment of the present invention;

[0021] Figure 5 is a structural block diagram of the charging facility scheduling device according to an embodiment of the present invention. Detailed implementation manners

[0022] In order to enable those skilled in the art to better understand the solution of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without making creative efforts shall fall within the protection scope of the present invention.

[0023] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned accompanying drawings are used to distinguish similar objects and do not necessarily need to be used to describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products, or devices.

[0024] First, some nouns or terms that appear in the process of describing the embodiments of the present application are applicable to the following explanations:

[0025] Simulated Annealing Algorithm: The simulated annealing algorithm is a heuristic global optimization method. By simulating the process of a substance cooling in a thermodynamic system, it gradually adjusts the state of the solution and finally reaches a state close to the global optimum. The characteristic of the simulated annealing algorithm is that it can effectively jump out of the local optimum and search for a better solution, especially suitable for optimization problems with complex constraint conditions.

[0026] Metropolis Process: In the simulated annealing algorithm, the Metropolis process is used to decide whether to accept a newly generated solution, even if this solution is not as good as the previous one in the current state. Its core idea is to introduce randomness in the search process, allowing the algorithm to jump out of the local optimum with a certain probability, thereby increasing the chance of finding the global optimum solution. This mechanism ensures the flexibility and global exploration ability of the algorithm when optimizing complex problems.

[0027] Mixed Multinomial Logit Model: The mixed multinomial logit model is an extension of the multinomial logit model. When dealing with classification decision problems, the latter assumes that the preferences of all choosers are homogeneous, that is, the choice behaviors of all choosers can be described by the same parameters. However, in reality, the preferences of choosers often exhibit heterogeneity, that is, different choosers may have different preferences or decision criteria. The mixed multinomial logit model captures this heterogeneity by introducing random parameters, allowing the parameters to vary randomly among individuals.

[0028] Embodiment 1

[0029] According to an embodiment of the present invention, an embodiment of a charging facility scheduling method is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order than here.

[0030] Figure 1 is a flowchart of the charging facility scheduling method according to an embodiment of the present invention, as Figure 1 shown, the method includes the following steps:

[0031] S102, receive a charging facility scheduling request corresponding to the target area.

[0032] In step S102 provided in this application, a charging facility scheduling request corresponding to the target area is received.

[0033] Among them, the target area is involved, and this target area is a pre-determined area that requires optimization and adjustment of charging facilities.

[0034] Among them, a charging facility scheduling request is involved. The charging facility scheduling request is initiated for a target area and is used to schedule charging facilities (such as increasing or decreasing the corresponding number of charging facilities, changing the type of charging facilities) to meet the charging needs of the target area.

[0035] By receiving the charging facility scheduling request initiated for the target area, it provides an analysis basis for subsequent steps, which helps to conduct targeted analysis on the charging facilities in the target area subsequently.

[0036] S104: In response to the charging facility scheduling request, determine multiple target passing locations within the target area.

[0037] In step S104 provided in this application, the charging facility scheduling request is responded to, and multiple target passing locations within the target area are determined.

[0038] Among them, multiple target passing locations are involved. The multiple target passing locations are locations that electric vehicles in the target area need to pass through. The multiple target passing locations can be service areas, rest stops, or charging stations in the target area. The multiple target passing locations are usually on the travel routes of electric vehicles and are locations where electric vehicles may charge.

[0039] In this step, the charging facility scheduling request is responded to, and according to the corresponding charging facility scheduling request, multiple target passing locations within the target area are determined, which helps to subsequently conduct targeted analysis on the charging needs corresponding to each of the multiple target passing locations, so as to ensure that the charging facilities are accurately scheduled to the target passing locations where electric vehicles are most likely to need charging, and reduce unnecessary scheduling of charging facilities.

[0040] S106: Determine the electric vehicle parameters and charging facility parameters corresponding to the multiple target passing locations respectively. Among them, the corresponding electric vehicle parameters are the charging demand parameters and the corresponding number of passing vehicles of the electric vehicles passing through the corresponding target passing locations within a predetermined time period.

[0041] In step S106 provided in this application, the electric vehicle parameters and charging facility parameters corresponding to the multiple target passing locations respectively are determined.

[0042] Among them, electric vehicle parameters are involved. The electric vehicle parameters are parameters used to represent the electric vehicle-related information corresponding to the multiple target passing locations respectively, including the charging demand parameters of the electric vehicles corresponding to the multiple target passing locations respectively, and the number of passing vehicles.

[0043] Among them, charging facility parameters are involved. The charging facility parameters are the parameters corresponding to multiple target passing locations and are the parameters of the existing charging facilities. The charging facility parameters reflect the operating characteristics or performance of the charging facilities included in the corresponding target passing locations, such as charging power, charging efficiency, charging facility type, facility quantity, etc.

[0044] Among them, charging demand parameters are involved. The charging demand parameters are the parameters used to represent the charging demand quantity of the corresponding electric vehicles in the corresponding target passing locations.

[0045] Among them, the number of passing vehicles is involved. The number of passing vehicles is used to represent the number of electric vehicles passing through in the corresponding target passing locations within a predetermined time period. The predetermined time period is a time period less than the predetermined time length threshold to address the technical problem of high charging facility scheduling costs caused by the short-term surge in electric vehicle charging demand.

[0046] Determine the electric vehicle parameters and charging facility parameters corresponding to multiple target passing locations respectively. Among them, the corresponding electric vehicle parameters are the charging demand parameters and the corresponding number of passing vehicles of the electric vehicles passing through the corresponding target passing locations within a predetermined time period.

[0047] By determining the electric vehicle parameters and charging facility parameters corresponding to multiple target passing locations respectively, it helps to subsequently accurately determine the specific electricity quantity that needs to be provided corresponding to multiple target passing locations, thereby effectively avoiding the problem of charging supply-demand mismatch, and thus helps to accurately and quickly adjust the charging facilities for different target passing locations subsequently, avoiding unnecessary charging facility scheduling, and further helping to reduce the charging facility scheduling costs.

[0048] S108. Determine the power consumption parameters corresponding to multiple target passing locations respectively according to the charging facility parameters corresponding to multiple target passing locations, as well as the charging demand parameters of the electric vehicles in the corresponding target passing locations and the corresponding number of passing vehicles.

[0049] In step S108 provided in this application, the power consumption parameters corresponding to multiple target passing locations are determined.

[0050] Among them, power consumption parameters are involved. The power consumption parameters are the parameters used to represent the electricity quantity that needs to be provided for the corresponding target passing locations.

[0051] By quantifying the power to be provided at each target route location, i.e., the power consumption parameter, based on the charging facility parameters corresponding to multiple target route locations, the charging demand parameters of electric vehicles at the corresponding target route locations, and the number of passing vehicles, it is possible to accurately adjust the charging facilities subsequently, avoid unnecessary dispatching of charging facilities, and thus contribute to the accurate and efficient dispatching of charging facilities, reducing the dispatching cost.

[0052] S110. Determine the target dispatching amounts of the facilities to be dispatched corresponding to multiple target route locations based on the power consumption parameters corresponding to the multiple target route locations.

[0053] In step S110 provided in this application, the target dispatching amounts of the facilities to be dispatched corresponding to multiple target route locations are determined.

[0054] Among them, the facilities to be dispatched are involved. The facilities to be dispatched are the charging facilities that need to be dispatched determined according to the power consumption parameters corresponding to multiple target route locations. For example, for some target route locations, if the existing charging facilities can meet the corresponding power consumption parameters and there are multiple idle charging facilities, these idle charging facilities can be shut down or dispatched to the target route locations with insufficient supply, and these charging facilities can be regarded as the facilities to be dispatched. For some target route locations, if the existing charging facilities cannot meet the corresponding power consumption parameters and new charging facilities need to be dispatched additionally to supplement the power supply, these new charging facilities can be regarded as the facilities to be dispatched.

[0055] Among them, the target dispatching amount is involved. The target dispatching amount is the number of charging devices that need to be dispatched determined based on the power consumption parameters representing the corresponding target route locations.

[0056] By combining the power consumption parameters corresponding to multiple target route locations, the target dispatching amounts of the facilities to be dispatched corresponding to multiple target route locations are determined, ensuring that the charging facilities can be efficiently and accurately dispatched to the corresponding target route locations during the subsequent peak charging demand periods (such as peak travel periods), thereby reducing unnecessary dispatching of charging facilities and contributing to reducing the dispatching cost of charging facilities.

[0057] S112. Dispatch the facilities to be dispatched based on the target dispatching amounts of the facilities to be dispatched corresponding to multiple target route locations.

[0058] In step S112 provided in this application, the facilities to be dispatched are dispatched based on the target dispatching amounts of the facilities to be dispatched corresponding to multiple target route locations.

[0059] By scheduling the facilities to be scheduled according to the target scheduling amounts of the facilities to be scheduled corresponding to multiple target passing locations respectively, it effectively avoids the mismatch between the power supply and demand caused by the over-concentration or dispersion of charging facilities, thus avoiding unnecessary scheduling of charging facilities and effectively reducing the scheduling cost during the scheduling process of charging facilities.

[0060] Through the above steps S102 - S112, receive a charging facility scheduling request corresponding to the target area; in response to the charging facility scheduling request, determine multiple target passing locations within the target area; determine the electric vehicle parameters and charging facility parameters corresponding to the multiple target passing locations respectively, where the corresponding electric vehicle parameters are the charging demand parameters and the corresponding number of passing vehicles of the electric vehicles passing through the corresponding target passing locations within a predetermined time period; based on the charging facility parameters corresponding to the multiple target passing locations respectively, as well as the charging demand parameters and the corresponding number of passing vehicles of the electric vehicles at the corresponding target passing locations, determine the power consumption parameters corresponding to the multiple target passing locations respectively; based on the power consumption parameters corresponding to the multiple target passing locations respectively, determine the target scheduling amounts of the facilities to be scheduled corresponding to the multiple target passing locations respectively; schedule the facilities to be scheduled according to the target scheduling amounts of the facilities to be scheduled corresponding to the multiple target passing locations respectively. By determining multiple target passing locations within the target area according to the corresponding charging facility scheduling request, it helps to conduct targeted analysis of the charging demands corresponding to the multiple target passing locations subsequently. By determining the electric vehicle parameters and charging facility parameters corresponding to the multiple target passing locations respectively, it helps to accurately determine the specific power to be provided corresponding to the multiple target passing locations subsequently. By accurately quantifying the power to be provided for each target passing location, that is, the power consumption parameter, it thus helps to accurately adjust the charging facilities subsequently and avoid unnecessary scheduling of charging facilities. By combining the power consumption parameters corresponding to the multiple target passing locations respectively to determine the target scheduling amounts of the facilities to be scheduled corresponding to the multiple target passing locations respectively, it ensures that the charging facilities can be efficiently and accurately scheduled to the corresponding target passing locations during the peak charging demand period (such as the peak travel period) subsequently. By scheduling the facilities to be scheduled according to the target scheduling amounts of the facilities to be scheduled corresponding to the multiple target passing locations respectively, it effectively avoids the mismatch between the power supply and demand caused by the over-concentration or dispersion of charging facilities, thus avoiding unnecessary scheduling of charging facilities and effectively reducing the scheduling cost during the scheduling process of charging facilities, and further solves the technical problem in the related art that there is a high scheduling cost for electric vehicle charging facilities when facing the rapidly increasing electric vehicle charging demand in a short period.

[0061] As an alternative embodiment, determining multiple target waypoints within a target area includes: determining multiple predetermined starting points within the target area, and corresponding predetermined destinations respectively corresponding to the multiple predetermined starting points; determining multiple target travel routes based on the multiple predetermined starting points and the corresponding predetermined destinations respectively corresponding to the multiple predetermined starting points, where the multiple target travel routes correspond one-to-one with the multiple predetermined starting points; and determining multiple target waypoints within the target area based on the multiple target travel routes.

[0062] In this embodiment, multiple target waypoints within the target area are determined.

[0063] Among them, a predetermined starting point is involved, which is the location where the electric vehicle departs within the target area. For example, if the electric vehicle departs from City A and heads to City B, then the corresponding predetermined starting point is City A.

[0064] Among them, a predetermined destination is involved, which is the destination that the electric vehicle heads to within the target area. For example, if the electric vehicle departs from City A and heads to City B, then the corresponding predetermined destination is City B.

[0065] Among them, a target travel route is involved, which is the optimal route determined based on multiple predetermined starting points and the corresponding predetermined destinations respectively corresponding to the multiple predetermined starting points for the electric vehicle to reach the corresponding predetermined destination from the corresponding predetermined starting point. This optimal route can be the route with the shortest time, the lowest travel cost, or the shortest distance, etc.

[0066] In the steps involved in this embodiment, first, multiple predetermined starting points within the target area and the corresponding predetermined destinations respectively corresponding to the multiple predetermined starting points are determined. Then, based on the multiple predetermined starting points and the corresponding predetermined destinations respectively corresponding to the multiple predetermined starting points, multiple target travel routes are determined, where the multiple target travel routes correspond one-to-one with the multiple predetermined starting points. Finally, based on the multiple target travel routes, multiple target waypoints within the target area are determined.

[0067] By determining multiple target travel routes based on multiple predetermined starting points and the corresponding predetermined destinations respectively corresponding to the multiple predetermined starting points, the prediction of the charging demand distribution of electric vehicles can be more accurately realized, so that it can be predicted which waypoints will have a sharp increase in charging demand. Thus, by determining multiple target waypoints within the target area based on the multiple target travel routes, the uncertainty in the facility scheduling process can be reduced, the pertinence and efficiency of the charging facility scheduling can be improved, the unnecessary scheduling costs can be reduced, and at the same time, the user charging experience can be enhanced.

[0068] As an alternative embodiment, determining the target scheduling amounts of the facilities to be scheduled corresponding to multiple target passing locations respectively, includes: determining the facility type parameters corresponding to the multiple target passing locations according to the multiple target passing locations, where the facility type parameter is a parameter indicating the type of the facility to be scheduled; determining the selection preference parameters corresponding to the multiple target passing locations respectively according to the electric vehicle parameters corresponding to the multiple target passing locations and the facility type parameters, where the selection preference parameter is a parameter indicating the preference degree of the electric vehicles corresponding to the multiple target passing locations for the facilities to be scheduled corresponding to the facility type parameters; determining the charging power indices of the pre-determined type of scheduling facilities corresponding to the multiple target passing locations respectively according to the facility type parameters corresponding to the multiple target passing locations; determining the target scheduling amounts of the facilities to be scheduled corresponding to the multiple target passing locations respectively according to the charging power indices of the pre-determined type of scheduling facilities corresponding to the multiple target passing locations respectively, the electricity consumption parameters corresponding to the multiple target passing locations and the selection preference parameters.

[0069] In this embodiment, the target scheduling amounts of the facilities to be scheduled corresponding to the multiple target passing locations respectively are determined.

[0070] Among them, the facility type parameter is involved, and the facility type parameter is a parameter used to represent the type of charging facilities. For example, the facility types corresponding to the facility type parameter may include: modular mobile charging facilities (BLMC), energy storage mobile charging facilities (BIMC) or other types of fixed charging facilities.

[0071] Among them, the selection preference parameter is involved, and the selection preference parameter is a parameter used to quantify the overall preference degree of the users of the corresponding electric vehicles for different types of charging facilities at different target passing locations. The selection preference parameter reflects the tendency of the users of the corresponding electric vehicles to select different types of charging facilities at the corresponding target passing locations.

[0072] Among them, the charging power index is involved, and the charging power index is an index used to represent the amount of electricity that the charging facilities of the corresponding facility type can provide. The charging power index can be that of the charging facilities.

[0073] In the steps involved in this embodiment, first, according to multiple target passing-through locations, facility type parameters corresponding to the multiple target passing-through locations are determined. Then, according to the electric vehicle parameters corresponding to the multiple target passing-through locations and the facility type parameters, selection preference parameters corresponding to the multiple target passing-through locations are determined respectively. Next, according to the facility type parameters corresponding to the multiple target passing-through locations, charging power indices of the scheduled facilities of the predetermined type corresponding to the multiple target passing-through locations are determined respectively. Finally, according to the charging power indices of the scheduled facilities of the predetermined type corresponding to the multiple target passing-through locations respectively, the power consumption parameters and the selection preference parameters corresponding to the multiple target passing-through locations respectively, the target scheduling amounts of the facilities to be scheduled corresponding to the multiple target passing-through locations are determined respectively.

[0074] By determining the facility type parameters corresponding to the multiple target passing-through locations, the charging demand characteristics of different target passing-through locations in the target area can be identified, which helps to determine which types of charging facilities are more suitable to be arranged at the corresponding target passing-through locations, and avoids the increase in scheduling costs caused by inappropriate facility types. By determining the selection preference parameters corresponding to the multiple target passing-through locations respectively, the preference degrees of users for different types of charging facilities at different target passing-through locations can be quantified. By adjusting the types and quantities of charging facilities in different target passing-through locations to match the user preferences, unnecessary scheduling of charging facilities can be avoided, which helps to further reduce the scheduling costs. Considering comprehensively the power consumption demands of the passing-through locations, user preferences and facility charging capabilities, it ensures that while meeting the charging demands, unnecessary scheduling operations are reduced, thus reducing the scheduling costs of charging facilities.

[0075] As an alternative embodiment, determining the selection preference parameters corresponding to the multiple target passing-through locations respectively according to the electric vehicle parameters corresponding to the multiple target passing-through locations and the facility type parameters includes: determining the number of charging vehicles corresponding to the scheduled facilities of the predetermined type corresponding to the multiple target passing-through locations respectively according to the number of passing-through vehicles corresponding to the multiple target passing-through locations and the facility type parameters; determining the waiting time parameters corresponding to the scheduled facilities of the predetermined type corresponding to the multiple target passing-through locations respectively; determining the sub-preference parameters of the scheduled facilities of the predetermined type corresponding to the multiple target passing-through locations respectively according to the number of charging vehicles and the waiting time parameters of the scheduled facilities of the predetermined type corresponding to the multiple target passing-through locations respectively; and determining the selection preference parameters corresponding to the multiple target passing-through locations respectively according to the sub-preference parameters of the scheduled facilities of the predetermined type corresponding to the multiple target passing-through locations respectively.

[0076] In this embodiment, the selection preference parameters corresponding to the multiple target passing-through locations are determined.

[0077] Among them, the number of charging vehicles is involved, and the number of charging vehicles represents the number of electric vehicles that need to be charged at the scheduled facilities of the predetermined type corresponding to the corresponding target passing-through location.

[0078] Among them, a waiting time parameter is involved, which is used to reflect the time that an electric vehicle needs to wait when using a specific type of charging facility (such as an energy storage mobile charging facility) at a corresponding target passing location.

[0079] Among them, a sub-preference parameter is involved, which is a parameter used to quantify the preference degree of the user of the corresponding electric vehicle for different types of charging facilities. This selection preference parameter reflects the tendency of the user of the corresponding electric vehicle when choosing a charging facility, and this tendency is generally affected by factors such as the charging speed, waiting time, charging cost, and availability of the charging facility.

[0080] In the steps involved in this embodiment, first, according to the number of passing vehicles corresponding to multiple target passing locations and the facility type parameter, the number of charging vehicles corresponding to the predetermined type of scheduling facility respectively corresponding to multiple target passing locations is determined. Then, the waiting time parameter corresponding to the predetermined type of scheduling facility respectively corresponding to multiple target passing locations is determined. Next, according to the number of charging vehicles and the waiting time parameter of the predetermined type of scheduling facility respectively corresponding to multiple target passing locations, the sub-preference parameter of the predetermined type of scheduling facility respectively corresponding to multiple target passing locations is determined. Finally, according to the sub-preference parameter of the predetermined type of scheduling facility respectively corresponding to multiple target passing locations, the selection preference parameter respectively corresponding to multiple target passing locations is determined.

[0081] By comprehensively considering the number of charging vehicles and the corresponding waiting time parameter of the predetermined type of scheduling facility respectively corresponding to multiple target passing locations, the charging preferences of users can be accurately analyzed. By determining the sub-preference parameter of the predetermined type of scheduling facility respectively corresponding to multiple target passing locations, it helps to accurately predict the possibility of the user's choice of different types of charging facilities in the corresponding target passing location, and further helps to ensure that the facility type scheduled to the target passing location matches the user's preference, reducing the scheduling of charging facilities of unnecessary facility types, thereby further reducing the scheduling cost and improving the utilization rate of charging facilities.

[0082] As an alternative embodiment, based on the charging vehicle quantity and waiting time parameters corresponding to multiple target passing-through locations for a predetermined type of scheduling facility, determine the sub-preference parameters for the predetermined type of scheduling facility corresponding to multiple target passing-through locations respectively, including: determining the charging time parameters for the predetermined type of scheduling facility corresponding to multiple target passing-through locations respectively according to the charging vehicle quantity and charging power index of the predetermined type of scheduling facility corresponding to multiple target passing-through locations respectively; determining the charging price parameters for the predetermined type of scheduling facility corresponding to multiple target passing-through locations respectively; and determining the sub-preference parameters for the predetermined type of scheduling facility corresponding to multiple target passing-through locations respectively according to the charging time parameters, waiting time parameters and charging price parameters of the predetermined type of scheduling facility corresponding to multiple target passing-through locations respectively.

[0083] In this embodiment, the sub-preference parameters for the predetermined type of scheduling facility corresponding to multiple target passing-through locations respectively are determined.

[0084] Among them, the charging time parameter is involved. The charging time parameter is the charging time required when charging using a predetermined type of scheduling facility (such as a modular or energy storage mobile charging facility) at a specific target passing-through location.

[0085] Among them, the charging price parameter is involved. The charging price parameter is a parameter set to reflect the charging price when using a predetermined type of scheduling facility at a specific target passing-through location, and may include electricity price, service fee, charging duration, etc.

[0086] In the steps involved in this embodiment, first, determine the charging time parameters for the predetermined type of scheduling facility corresponding to multiple target passing-through locations respectively according to the charging vehicle quantity and charging power index of the predetermined type of scheduling facility corresponding to multiple target passing-through locations respectively. Then, determine the charging price parameters for the predetermined type of scheduling facility corresponding to multiple target passing-through locations respectively. Finally, determine the sub-preference parameters for the predetermined type of scheduling facility corresponding to multiple target passing-through locations respectively according to the charging time parameters, waiting time parameters and charging price parameters of the predetermined type of scheduling facility corresponding to multiple target passing-through locations respectively.

[0087] By determining the charging time parameters and charging price parameters for the predetermined type of scheduling facility corresponding to multiple target passing-through locations respectively, the costs that electric vehicle users need to bear when selecting the equipment that actually needs to be charged can be quantified, which can help accurately predict the possibility of users' selection of different types of charging facilities.

[0088] As an alternative embodiment, determining the target scheduling amounts of the facilities to be scheduled corresponding to multiple target passing locations respectively, based on the electricity consumption parameters corresponding to the multiple target passing locations respectively, includes: determining the scheduling cost parameters corresponding to the multiple target passing locations based on the electricity consumption parameters corresponding to the multiple target passing locations respectively; determining the facility type parameters of the facilities to be scheduled corresponding to the multiple target passing locations based on the multiple target passing locations; determining the charging income parameters corresponding to the multiple target passing locations respectively based on the facility type parameters of the facilities to be scheduled corresponding to the multiple target passing locations; and determining the target scheduling amounts of the facilities to be scheduled corresponding to the multiple target passing locations respectively based on the scheduling cost parameters and the charging income parameters corresponding to the multiple target passing locations.

[0089] In this embodiment, the target scheduling amounts corresponding to the facilities to be scheduled are determined.

[0090] Among them, the scheduling cost parameters are involved. The scheduling cost parameters are the parameters used to represent the costs generated when scheduling the charging facilities to specific target passing locations. The scheduling cost parameters may include transportation costs, facility operation costs, personnel costs, energy costs, etc.

[0091] Among them, the charging income parameters are involved. The charging income parameters are the parameters used to reflect the income that can be obtained by the charging facilities corresponding to the target passing locations.

[0092] In the steps involved in this embodiment, first, the scheduling cost parameters corresponding to the multiple target passing locations are determined according to the electricity consumption parameters corresponding to the multiple target passing locations respectively. Next, the facility type parameters of the facilities to be scheduled corresponding to the multiple target passing locations are determined according to the multiple target passing locations. Then, the charging income parameters corresponding to the multiple target passing locations respectively are determined according to the facility type parameters of the facilities to be scheduled corresponding to the multiple target passing locations. Finally, the target scheduling amounts of the facilities to be scheduled corresponding to the multiple target passing locations are determined according to the scheduling cost parameters and the charging income parameters corresponding to the multiple target passing locations.

[0093] By determining the scheduling cost parameters corresponding to the multiple target passing locations and the corresponding charging income parameters, it provides data support for the economic analysis of the charging facility scheduling, thus ensuring that the scheduling of the charging facilities is economically feasible, and further helping to control the scheduling costs while meeting the charging demands to the greatest extent and avoiding resource waste.

[0094] As an alternative embodiment, according to the charging facility parameters corresponding to multiple target passing locations respectively, as well as the charging demand parameters of the electric vehicles at the corresponding target passing locations and the corresponding number of passing vehicles, the power consumption parameters corresponding to multiple target passing locations are determined, including: determining the demand correction parameters corresponding to multiple target passing locations respectively according to the charging facility parameters corresponding to multiple target passing locations respectively; determining the corrected vehicle numbers corresponding to multiple target passing locations respectively according to the demand correction parameters corresponding to multiple target passing locations respectively and the number of passing vehicles; and determining the power consumption parameters corresponding to multiple target passing locations respectively according to the charging demand parameters corresponding to multiple target passing locations respectively and the corrected vehicle numbers.

[0095] In this embodiment, the power consumption parameters corresponding to multiple target passing locations are determined.

[0096] Among them, the demand correction parameter is involved. The demand correction parameter is a parameter used to adjust the charging demand. The demand correction parameter can adjust the actual required charging power of multiple target passing locations by considering various factors in the actual charging process (such as the charging capacity of the target passing location, the state of charge of the battery).

[0097] Among them, the corrected vehicle number is involved. The corrected vehicle number is the number of vehicles after the number of passing vehicles at the corresponding target passing location is corrected considering the demand correction parameter.

[0098] In the steps involved in this embodiment, first, the demand correction parameters corresponding to multiple target passing locations are determined according to the charging facility parameters corresponding to multiple target passing locations respectively. Then, the corrected vehicle numbers corresponding to multiple target passing locations are determined according to the demand correction parameters corresponding to multiple target passing locations respectively and the number of passing vehicles. Finally, the power consumption parameters corresponding to multiple target passing locations are determined according to the charging demand parameters corresponding to multiple target passing locations respectively and the corrected vehicle numbers.

[0099] Through the demand correction parameter, a comprehensive consideration of the influence of various factors in the actual charging process on the charging demand is realized, so that the problem of more accurately reflecting the actual power consumption demand can be solved, providing more reliable data for the charging facility scheduling. By determining the corrected vehicle number, the number of vehicles actually in need of charging services is more accurately determined to achieve an accurate matching of the supply and demand of charging services and avoid over-scheduling or resource waste.

[0100] Based on the above embodiments and alternative embodiments, an alternative implementation manner is provided, which is specifically described below.

[0101] In the related art, using a fixed charging pile to charge an electric vehicle can meet the conventional charging needs to a certain extent. However, in situations where the charging demand for electric vehicles surges, such as during peak travel periods, it is difficult to quickly adjust the layout of fixed charging piles to cope with this short-term demand change. As a result, when facing a sudden increase in the charging demand for electric vehicles in a short period, there is a technical problem of high scheduling costs for electric vehicle charging facilities.

[0102] For the above problems, no effective solution has been proposed yet.

[0103] In view of this, in an alternative embodiment of the present invention, there is provided a method for optimizing the deployment of highway modular and energy storage mobile charging facilities, which can effectively solve the technical problem of high scheduling costs for electric vehicle charging facilities in the related art when facing a sudden increase in the charging demand for electric vehicles in a short period.

[0104] Figure 2 It is a framework flowchart of the charging facility scheduling method in an alternative embodiment of the present invention. Figure 3 It is a detailed flowchart of the charging facility scheduling method in an alternative embodiment of the present invention. Figure 4 It is a flowchart for solving using the simulated annealing algorithm in an alternative embodiment of the present invention. As Figure 2 , Figure 3 and Figure 4 shown, a detailed introduction will be given below.

[0105] S1. Receive a charging facility scheduling request corresponding to a target area;

[0106] S2. In response to the charging facility scheduling request, determine multiple target passing places within the target area;

[0107] Specifically, S2 further includes:

[0108] S21. Determine multiple predetermined starting places within the target area, and predetermined destinations respectively corresponding to the multiple predetermined starting places;

[0109] S22. According to the multiple predetermined starting places and the predetermined destinations respectively corresponding to the multiple predetermined starting places, determine multiple target travel routes, where the multiple target travel routes correspond one-to-one to the multiple predetermined starting places;

[0110] S23. According to the multiple target travel routes, determine multiple target passing places within the target area.

[0111] S3. Determine the electric vehicle parameters and charging facility parameters respectively corresponding to the multiple target passing places, where the corresponding electric vehicle parameters are the charging demand parameters of the electric vehicles passing through the corresponding target passing places within a predetermined time period and the corresponding number of passing vehicles;

[0112] S4. Determine the power consumption parameters corresponding to multiple target passing locations respectively based on the charging facility parameters corresponding to the multiple target passing locations respectively, the charging demand parameters of the electric vehicles at the corresponding target passing locations, and the corresponding number of passing vehicles.

[0113] Specifically, S4 further includes:

[0114] S41. Determine the demand correction parameters corresponding to multiple target passing locations respectively based on the charging facility parameters corresponding to the multiple target passing locations respectively.

[0115] S42. Determine the corrected vehicle numbers corresponding to multiple target passing locations respectively based on the demand correction parameters corresponding to the multiple target passing locations respectively and the number of passing vehicles.

[0116] S43. Determine the power consumption parameters corresponding to multiple target passing locations respectively based on the charging demand parameters corresponding to the multiple target passing locations respectively and the corrected vehicle numbers.

[0117] S5. Determine the target scheduling amounts of the facilities to be scheduled corresponding to multiple target passing locations respectively based on the power consumption parameters corresponding to the multiple target passing locations respectively.

[0118] Specifically, S5 further includes:

[0119] S51. Determine the facility type parameters corresponding to multiple target passing locations respectively based on the multiple target passing locations, where the facility type parameters are the parameters indicating the types of the facilities to be scheduled.

[0120] S52. Determine the selection preference parameters corresponding to multiple target passing locations respectively based on the electric vehicle parameters corresponding to the multiple target passing locations and the facility type parameters, where the selection preference parameters are the preference degrees of the electric vehicles corresponding to the multiple target passing locations for the facilities to be scheduled corresponding to the facility type parameters.

[0121] Specifically, S52 further includes:

[0122] S521. Determine the number of charging vehicles corresponding to the scheduled type of scheduling facilities corresponding to multiple target passing locations respectively based on the number of passing vehicles corresponding to the multiple target passing locations and the facility type parameters.

[0123] S522. Determine the waiting time parameters corresponding to the scheduled type of scheduling facilities corresponding to multiple target passing locations respectively.

[0124] S523. Determine the charging time parameters corresponding to the scheduled type of scheduling facilities corresponding to multiple target passing locations respectively based on the number of charging vehicles and the charging power index of the scheduled type of scheduling facilities corresponding to the multiple target passing locations respectively.

[0125] S524. Determine the charging price parameters of the pre - determined type of dispatching facilities corresponding to multiple target passing - through locations respectively;

[0126] S525. Determine the sub - preference parameters of the pre - determined type of dispatching facilities corresponding to multiple target passing - through locations respectively according to the charging time parameters, waiting time parameters and charging price parameters of the pre - determined type of dispatching facilities corresponding to multiple target passing - through locations respectively;

[0127] S526. Determine the selection preference parameters corresponding to multiple target passing - through locations respectively according to the sub - preference parameters of the pre - determined type of dispatching facilities corresponding to multiple target passing - through locations respectively.

[0128] S53. Determine the charging power index of the pre - determined type of dispatching facilities corresponding to multiple target passing - through locations respectively according to the facility type parameters corresponding to multiple target passing - through locations;

[0129] S54. Determine the target dispatching volume of the facilities to be dispatched corresponding to multiple target passing - through locations respectively according to the charging power index of the pre - determined type of dispatching facilities corresponding to multiple target passing - through locations respectively, the power consumption parameters corresponding to multiple target passing - through locations respectively and the selection preference parameters.

[0130] Specifically, S5 further includes:

[0131] S501. Determine the dispatching cost parameters corresponding to multiple target passing - through locations respectively according to the power consumption parameters corresponding to multiple target passing - through locations respectively;

[0132] S502. Determine the facility type parameters of the facilities to be dispatched corresponding to multiple target passing - through locations respectively according to multiple target passing - through locations;

[0133] S503. Determine the charging income parameters corresponding to multiple target passing - through locations respectively according to the facility type parameters of the facilities to be dispatched corresponding to multiple target passing - through locations;

[0134] S504. Determine the target dispatching volume of the facilities to be dispatched corresponding to multiple target passing - through locations respectively according to the dispatching cost parameters and charging income parameters corresponding to multiple target passing - through locations.

[0135] S6. Dispatch the facilities to be dispatched according to the target dispatching volume of the facilities to be dispatched corresponding to multiple target passing - through locations respectively.

[0136] According to the above steps, a specific example is described in detail below, which mainly includes two parts: the preparation stage and the optimization stage.

[0137] A1. Preparation stage:

[0138] In the preparation stage (corresponding to the demand calculation layer), based on the origin-destination (OD) matrix of electric vehicle trips during the peak travel time obtained from travel prediction, the optimal path (same as the above-mentioned target travel route) for each origin-destination pair (OD pair, same as the above-mentioned multiple predetermined origins and the corresponding predetermined destinations for the multiple predetermined origins) is calculated through the Floyd algorithm, and the arrival rate of electric vehicles at each station along the path (same as the above-mentioned number of passing vehicles) is calculated through the average inbound probability; in addition, based on the known probability distribution of electric vehicle battery capacity and the distribution of the state of charge (SoC), the average charging demand power (same as the above-mentioned charging demand parameter) and the demand correction term used in calculating the user selection probability later (same as the above-mentioned demand correction parameter) are calculated.

[0139] In this step, based on the electric vehicle travel OD matrix, the shortest path (same as the above-mentioned target travel route) for each OD pair is calculated using the Floyd algorithm, and then the traffic flow of each path is calculated. Assume that each electric vehicle enters any charging station along the driving direction with a fixed probability α arr The expected number of arrivals N at each charging station j,d,t (same as the above-mentioned number of passing vehicles) is calculated as follows:

[0140]

[0141] where j, d, and t represent the charging station number, day number, and time respectively, is the set containing all charging stations j passed through, represents the OD matrix, and od represents the origin-destination pair passing through charging station j. The arrival situation of electric vehicles in each time period can be modeled using the Poisson process.

[0142] The overall arrival rate at each station can be estimated using the following formula:

[0143]

[0144] where T is the length of each time period.

[0145] Assume that the demand for each charging is independent of the arrival time and arrival rate of the electric vehicle. The random variable Cap i represents the electric vehicle battery capacity, and SOC ch,i represents the charging demand measured by SoC for each charging, and i is the number of the charging behavior. Cap i and SOC ch,iA set of random variables that are all independently and identically distributed. Therefore, in the subsequent description, the subscript i is omitted for simplicity. Assume that the probability density functions (PDFs) of these two random variables are known. Due to independence, the expected electricity demand for each charging behavior (same as the above charging demand parameter) can be calculated according to the following formula:

[0146] M0 = Cap × SOC ch

[0147]

[0148] where M0 is a random variable representing the electricity demand for each charging, and the operator E() represents taking the expectation of the random variable.

[0149] A2. Optimization stage:

[0150] In the optimization stage, corresponding to Figure 3 the optimization model layer and the objective function calculation layer in. In this stage, based on the calculation results of the preparation stage and the existing distribution of fixed charging facilities, an optimization model is established with the goal of maximizing the profit of the mobile charging operator. To calculate the above objective function, it is necessary to model the selection behavior of electric vehicle charging users based on queuing theory and the mixed multinomial logit model, construct and solve a closed-loop fixed-point equation for the user selection probability. This optimization model is solved using the simulated annealing algorithm.

[0151] Specifically, the user selection model, corresponding to Figure 3 the objective function calculation layer in, and the technical details are as follows.

[0152] Assume that the charging demand is random and independently selects one of the component-based mobile charging piles (BLMC), the energy storage-based mobile charging piles (BIMC), or the fixed mobile charging piles (FC) with probability to obtain charging services. Note that the above three probabilities are also random variables themselves, but only one estimate of Z is used in the model. The first-order origin moment is used to estimate Z in the following formula:

[0153]

[0154] where represents the expected probability value of selecting a certain type (same as the above sub-preference parameter). The component-based mobile charging facility refers to an electric vehicle mobile charging facility without an energy storage battery, mainly including power electronic devices that can achieve grid-vehicle energy transfer and can be dynamically dispatched to locations with specific interfaces pre-installed to provide charging services; the energy storage-based mobile charging facility refers to an electric vehicle mobile charging facility equipped with an energy storage battery, which can be dispatched to locations with peak charging demand and needs to charge and replenish its own energy storage battery after the charging task is completed.

[0155] According to the splitting property of the Poisson process, the arrival process of each type of charging pile remains an independent Poisson process, and the arrival rate of each type of charging pile can be calculated by the following formula:

[0156]

[0157] Based on the arrival rates of each type of pile, the M / M / c / ∞ / ∞ queuing system is used to evaluate the queuing time (same as the above charging time parameter) of each type of pile (same as the above predetermined type of scheduling facility) under this selection probability. The formula is as follows:

[0158]

[0159] where, represents the average queue length, is the expected queuing duration of site j at time t and date d.

[0160]

[0161] where, represents the service intensity, that is, the ratio of the arrival rate to the service rate. is the installation quantity of each type of charging pile at site j. represents the corrected service intensity, represents the probability that there are no electric vehicles queuing for charging in the system at charging station j on date d and time t.

[0162]

[0163] where, represents the probability that there is at least one electric vehicle queuing for charging in the system at charging station j on date d and time t.

[0164]

[0165] where, μ type is the service rate, that is, the service rate of the charging facility of a specific type (BLMC, BIMC or FC, same as the above facility type parameter). It can be understood as the number of electric vehicles that each type of charging facility can serve per unit time (same as the above number of charging vehicles). The condition is a necessary condition for the queuing system to remain stable. If this condition is not satisfied in practice, a linear approximation can be used to estimate the queuing duration.

[0166] μ type is calculated according to the following formula:

[0167]

[0168] where, Indicates the rated charging power of various types of charging piles (same as the above charging power index).

[0169] After calculating the expected queuing time, the unnormalized choice probability at the current queuing time can be calculated according to the mixed multinomial logit model (same as the above sub-preference parameter):

[0170]

[0171] Among them, is the charging physical cost of each type of charging pile, VOT is the average time value of electric vehicle users (same as the above waiting time parameter), and W q,j,d,t represents the expected queuing time at site j on date d and time t, and π se,type are the electricity price and charging service fee respectively (same as the above charging price parameter), represents the charging efficiency. The reciprocal of the total charging cost is used as the utility function in the choice model, and M0 represents a random variable. is a constant provided by the demand calculation layer. When a certain type of pile is not installed at site j, its is always equal to 0.

[0172] Normalize the choice probability to obtain the target choice probability (same as the above sub-preference parameter), and the formula is as follows:[[]]

[0173]

[0174] Thus, a closed-loop fixed-point equation about the choice probability is established as follows:[[]]

[0175]

[0176] The solution of this equation can be directly used to calculate the objective function value in the optimization model. For the convenience of narration, the calculation process will be described together with other parts in the optimization model layer.

[0177] Specifically, the optimization model corresponds to Figure 3 the optimization model layer in, and the specific details are as follows.

[0178] The optimization placement goal of the two types of mobile charging facilities is to maximize the total profit F of the mobile charging operator:[[]]

[0179] maxF = R – C

[0180] Among them, R is the total revenue (same as the above charging revenue parameter), and C is the total cost (same as the above scheduling cost parameter).

[0181] Since the optimization time scale is several days, which is much less than one year, the costs of each item and the total cost can be calculated by linear amortization. The formula is as follows:

[0182]

[0183] Among them, N J represents the total number of charging stations in the target area, and respectively represent the land use cost, fixed cost of transportation vehicles, manufacturing cost of mobile charging equipment, labor cost and maintenance cost amortized to each charging pile and to day D. c bt and respectively represent the cost of the energy storage battery per charging pile and the capacity of the energy storage battery per charging pile amortized to D days. τ type is the average number of vehicles required for transporting each charging pile. D 0,j is the round-trip distance from station j to the storage location, is the average energy consumption rate per unit time of the charging facility under the condition of no power input, e tk is the power consumption per kilometer of the transportation vehicle.

[0184] Since VOT is much greater than the influence of M0 on is very small. Therefore, M0 and can be regarded as approximately independent. Thus, for each charging behavior, the expected demand of each type of charging pile is calculated by the following formula:

[0185]

[0186] Furthermore, the total revenue in the objective function can be calculated as follows:

[0187] R = R BLMC + R BIMC

[0188]

[0189] Among them, R BLMC is the total revenue of the component-based mobile charging facility, R BIMC is the total revenue of the energy storage-based mobile charging facility. π se,blmc is the service fee of the component-based mobile charging facility, π se,bimc is the service fee of the energy storage-based mobile charging facility, are the charging efficiency of the component-based mobile charging facility, the charging efficiency of the energy storage battery of the energy storage-based mobile charging facility and the charging efficiency for electric vehicles respectively. The calculation of revenue takes into account both the charging demand on the user side and the losses generated during the energy transmission process.

[0190] The constraints in the optimization model are as follows:

[0191]

[0192] Among them, is the upper limit of the number of component-based (BLMC) / energy storage-based (BIMC) charging piles at each site. The upper limit of the number of component-based mobile charging piles is determined by the remaining transformer capacity of each site. represents the state of charge SOC of the energy storage-based mobile charging facility at site j at date d and time t. SOC thresh is the defining threshold for determining whether the battery SOC of the energy storage-based mobile charging facility is sufficient to provide charging services. is the minimum value of the total number of two types of mobile charging piles deployed at site j, which is calculated according to the following formula:

[0193]

[0194] Among them, is ρ in the queuing model q at the maximum value of each time period throughout the day and the average value of a weighted average, β max and β ave are the corresponding weights. Here, ρ q is calculated using the overall arrival rate, which represents the ratio of the traffic flow (or arrival rate) of a specific site to the service rate of the charging facilities at that site.

[0195] The above constraints ensure that ρ in the queuing model q will not be broken too severely and ensure that when the battery SoC of the energy storage-based mobile charging facility at site j is depleted at time t, the probability that an electric vehicle selects the energy storage-based mobile charging facility is always 0. ift<T START ort>T END indicates that the energy storage-based mobile charging facility can only provide services during the time period of [T START ,T END every day, and charges and replenishes energy for itself in place during the remaining time periods.

[0196] The embodiment of this application uses the simulated annealing algorithm to solve the above optimization model, and the solution flow chart is as shown in Figure 4 In Figure 4 , the probability p that the Metropolis process accepts a worse solution Metro is:

[0197]

[0198] Among them, the "energy" U of simulated annealing is the negative objective function, and a "base energy" value is introduced to normalize it to improve the algorithm performance. U new represents the energy value of the new solution (i.e., the newly proposed facility scheduling strategy) in the simulated annealing algorithm. Here, the "energy" usually refers to the objective function value of the optimization problem, that is, the total cost brought by the new scheduling strategy. U current represents the energy value of the current solution in the simulated annealing algorithm, indicating the total cost of the current scheduling strategy. U base represents a "base energy" value introduced in the simulated annealing algorithm.

[0199] Through the above optional implementation manners, at least the following beneficial effects can be achieved:

[0200] (1) Compared with the related technologies, the present invention determines multiple target passing places in the target area according to the corresponding charging facility scheduling requests, which helps to subsequently analyze the charging demands corresponding to the multiple target passing places respectively. By determining the electric vehicle parameters and charging facility parameters corresponding to the multiple target passing places respectively, it helps to accurately determine the specific power to be provided corresponding to the multiple target passing places subsequently. By accurately quantifying the power to be provided for each target passing place, that is, the power consumption parameter, it helps to accurately adjust the charging facilities subsequently, avoiding unnecessary scheduling of charging facilities. By combining the power consumption parameters corresponding to the multiple target passing places respectively, the target scheduling amounts of the facilities to be scheduled corresponding to the multiple target passing places are determined, so as to ensure that the charging facilities can be efficiently and accurately scheduled to the corresponding target passing places during the peak charging demand period (such as the peak travel period). By scheduling the facilities to be scheduled according to the target scheduling amounts of the facilities to be scheduled corresponding to the multiple target passing places respectively, it effectively avoids the mismatch between the power supply amount and the demand amount caused by the over-concentration or dispersion of the charging facilities, thus avoiding unnecessary scheduling of charging facilities, effectively reducing the scheduling cost during the charging facility scheduling process, and further solving the technical problem of high scheduling cost of electric vehicle charging facilities in the related technologies when facing the short-term surge in electric vehicle charging demands.

[0201] (2) Compared with the related technologies, the present invention determines multiple target travel routes according to multiple predetermined starting places and the predetermined destinations corresponding to the multiple predetermined starting places respectively, which can more accurately predict the distribution of electric vehicle charging demands, so as to predict which passing places will have a surge in charging demands. Thus, by determining multiple target passing places in the target area according to the multiple target travel routes, the uncertainty in the facility scheduling process can be reduced, the pertinence and efficiency of the charging facility scheduling can be improved, the unnecessary scheduling cost can be reduced, and at the same time, the user charging experience can be enhanced.

[0202] (3) Compared with the related art, the present invention can identify the charging demand characteristics of different target transit points in the target area by determining the facility type parameters corresponding to multiple target transit points, thereby helping to determine which types of charging facilities are more suitable for arrangement in the corresponding target transit points, avoiding the increase in scheduling costs caused by inappropriate facility types. By determining the selection preference parameters corresponding to multiple target transit points, the user's preference for different types of charging facilities at different target transit points can be quantified. By adjusting the type and number of charging facilities in different target transit points to match user preferences, unnecessary charging facilities can be dispatched, thereby helping to further reduce scheduling costs. Comprehensively considering the electricity demand, user preferences and facility charging capacity of the transit points ensures that while meeting the charging demand, unnecessary scheduling operations are reduced, thereby reducing the scheduling cost of charging facilities.

[0203] (4) Compared with the related art, the present invention uses demand correction parameters to comprehensively consider the various factors that affect the charging demand during the actual charging process, thereby more accurately reflecting the actual electricity demand and providing more reliable data for the scheduling of charging facilities. By determining the number of corrected vehicles, the number of vehicles that actually need charging services is more accurately determined to achieve accurate matching of supply and demand of charging services and avoid over-scheduling or waste of resources.

[0204] (5) Compared with the related art, the present invention comprehensively considers two types of mobile charging facilities, component type and energy storage type, combines multiple constraints, and calculates the objective function based on the user selection model. It can not only effectively characterize user behavior and grasp the characteristics of charging demand, but also explore the comparative advantages of the two types of mobile charging facilities through optimization combination, reduce costs, and increase the revenue of charging operators. The optimized deployment of component type and energy storage type mobile charging facilities on highways during peak travel time can effectively alleviate the charging queue phenomenon and improve the long-distance travel experience of electric vehicles without increasing the investment and construction costs of fixed charging facilities.

[0205] It should be noted that, for the above-mentioned method embodiments, for the sake of simplicity, they are all described as a series of action combinations, but those skilled in the art should know that the present invention is not limited by the described action sequence, because according to the present invention, certain steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present invention.

[0206] Through the description of the above embodiments, those skilled in the art can clearly understand that the method according to the above embodiments can be implemented by means of software plus a necessary general hardware platform. Of course, it can also be implemented by hardware, but in many cases, the former is a better implementation. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions for causing a terminal device (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods of the various embodiments of the present invention.

[0207] Embodiment 2

[0208] According to an embodiment of the present invention, there is also provided a device for implementing the above charging facility scheduling method. Figure 5 It is a structural block diagram of the charging facility scheduling device according to an embodiment of the present invention, as Figure 5 shown. The device includes: a receiving module 502, a response module 504, a first determination module 506, a second determination module 508, a third determination module 510, and a fourth determination module 512. The device will be described in detail below.

[0209] The receiving module 502 is configured to receive a charging facility scheduling request corresponding to a target area; the response module 504 is connected to the above receiving module 502 and is configured to determine a plurality of target passing places in the target area in response to the charging facility scheduling request; the first determination module 506 is connected to the above response module 504 and is configured to determine an electric vehicle parameter and a charging facility parameter corresponding to each of the plurality of target passing places. Among them, the corresponding electric vehicle parameter is the parameter of the electric vehicle passing through the corresponding target passing place within a predetermined time period, including the corresponding charging demand parameter and the corresponding number of passing vehicles; the second determination module 508 is connected to the above first determination module 506 and is configured to determine an electricity consumption parameter corresponding to each of the plurality of target passing places according to the charging facility parameters corresponding to the plurality of target passing places, as well as the charging demand parameter and the corresponding number of passing vehicles of the electric vehicle at the corresponding target passing place; the third determination module 510 is connected to the above second determination module 508 and is configured to determine a target scheduling amount of the facility to be scheduled corresponding to each of the plurality of target passing places according to the electricity consumption parameters corresponding to the plurality of target passing places; the fourth determination module 512 is connected to the above third determination module 510 and is configured to schedule the facility to be scheduled according to the target scheduling amount of the facility to be scheduled corresponding to each of the plurality of target passing places.

[0210] It should be noted here that the above receiving module 502, response module 504, first determination module 506, second determination module 508, third determination module 510, and fourth determination module 512 correspond to steps S102 to S112 in the implementation of the charging facility scheduling method. The instances and application scenarios implemented by the multiple modules and the corresponding steps are the same, but are not limited to the content disclosed in the above-mentioned Embodiment 1.

[0211] Embodiment 3

[0212] According to another aspect of the embodiments of the present invention, there is also provided an electronic device, including: a processor; a memory for storing processor-executable instructions, wherein the processor is configured to execute the instructions to implement the charging facility scheduling method of any one of the above.

[0213] Embodiment 4

[0214] According to another aspect of the embodiments of the present invention, there is also provided a computer-readable storage medium. When the instructions in the computer-readable storage medium are executed by the processor of the electronic device, the electronic device can execute the charging facility scheduling method of any one of the above.

[0215] The above serial numbers of the embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0216] In the above embodiments of the present invention, the descriptions of the respective embodiments have their own emphases. For parts not detailed in a certain embodiment, reference may be made to the relevant descriptions of other embodiments.

[0217] In the several embodiments provided by the present application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only illustrative. For example, the division of the units can be a logical function division. In actual implementation, there can be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces. The indirect couplings or communication connections of the units or modules can be in an electrical or other form.

[0218] The units described as separate components may or may not be physically separated. The components displayed as units may or may not be physical units, that is, they can be located in one place or distributed to multiple units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0219] In addition, in each embodiment of the present invention, each functional unit can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above integrated unit can be implemented in the form of hardware or in the form of a software functional unit.

[0220] If the above integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in each embodiment of the present invention. The foregoing storage medium includes: various media such as USB flash drives, read-only memories (ROMs), random access memories (RAMs), mobile hard disks, magnetic disks, or optical discs that can store program codes.

[0221] The above are only the preferred embodiments of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the principle of the present invention, several improvements and refinements can be made, and these improvements and refinements should also be regarded as the protection scope of the present invention.

Claims

1. A charging facility scheduling method, characterized in that: include: receiving a charging facility dispatch request corresponding to a target area; In response to the charging facility dispatch request, determining a plurality of target waypoints within the target area; Determine electric vehicle parameters and charging facility parameters corresponding to the plurality of target passing places, respectively, wherein the corresponding electric vehicle parameters are charging demand parameters of electric vehicles passing through the corresponding target passing places within a predetermined time period and the corresponding number of passing vehicles; Determine the power usage parameters corresponding to the multiple target passing places respectively according to the charging facility parameters corresponding to the multiple target passing places respectively, as well as the charging demand parameters of the electric vehicles at the corresponding target passing places and the corresponding number of passing vehicles; Determine the target dispatching amount of the to-be-dispatched facilities corresponding to the multiple target passing places respectively according to the power consumption parameters corresponding to the multiple target passing places respectively; The facilities to be scheduled are scheduled according to the target scheduling quantities of the facilities to be scheduled corresponding to the multiple target transit points respectively.

2. The method according to claim 1, characterized in that: The determining of a plurality of target passing points within the target area comprises: Determining a plurality of predetermined starting points within the target area, and predetermined destinations corresponding to the plurality of predetermined starting points respectively; Determining a plurality of target travel routes according to a plurality of predetermined starting points and predetermined destinations respectively corresponding to the plurality of predetermined starting points, wherein the plurality of target travel routes correspond one-to-one to the plurality of predetermined starting points; Based on the multiple target travel routes, multiple target passing places in the target area are determined.

3. The method according to claim 1, characterized in that The step of determining target dispatching quantities of the facilities to be dispatched corresponding to the plurality of target passing places respectively according to the power consumption parameters corresponding to the plurality of target passing places respectively comprises: Determining, based on the multiple target waypoints, facility type parameters corresponding to the multiple target waypoints, wherein the facility type parameter is a parameter representing the type of the facility to be scheduled; Determining the selection preference parameters corresponding to the multiple target waypoints respectively according to the electric vehicle parameters corresponding to the multiple target waypoints and the facility type parameters, wherein the selection preference parameters represent the preference degree of the electric vehicles corresponding to the multiple target waypoints for the facilities to be dispatched corresponding to the facility type parameters; Determining charging power indexes of predetermined types of dispatching facilities corresponding to the plurality of target waypoints, respectively, according to facility type parameters corresponding to the plurality of target waypoints; Based on the charging power index of the predetermined type of dispatching facilities corresponding to the multiple target transit points, the power consumption parameters corresponding to the multiple target transit points and the selection preference parameters, the target dispatching quantity of the facilities to be dispatched corresponding to the multiple target transit points is determined.

4. The method according to claim 3, characterized in that The step of determining the selection preference parameters corresponding to the plurality of target passing places respectively according to the electric vehicle parameters corresponding to the plurality of target passing places and the facility type parameters comprises: Determine the number of charging vehicles corresponding to the predetermined type of dispatching facilities respectively corresponding to the multiple target passing places according to the number of passing vehicles corresponding to the multiple target passing places and the facility type parameter; Determine waiting time parameters corresponding to the predetermined types of dispatching facilities corresponding to the plurality of target waypoints respectively; Determining sub-preference parameters of the predetermined type of dispatching facilities corresponding to the plurality of target passing places respectively according to the number of charging vehicles and waiting time parameters of the predetermined type of dispatching facilities corresponding to the plurality of target passing places respectively; Based on the sub-preference parameters of the predetermined type of scheduling facilities corresponding to the multiple target transit points, the selection preference parameters corresponding to the multiple target transit points are determined.

5. The method according to claim 4, characterized in that The determining of sub-preference parameters of the predetermined type of dispatching facilities corresponding to the plurality of target passing places respectively according to the number of charging vehicles and waiting time parameters of the predetermined type of dispatching facilities corresponding to the plurality of target passing places respectively includes: Determining charging time parameters of the predetermined type of dispatching facilities respectively corresponding to the plurality of target passing places according to the number of charging vehicles and the charging power index of the predetermined type of dispatching facilities respectively corresponding to the plurality of target passing places; Determining charging price parameters of predetermined types of dispatching facilities corresponding to the plurality of target passing places respectively; Based on the charging time parameters, waiting time parameters and charging price parameters of the scheduled type of dispatching facilities corresponding to the multiple target transit points respectively, the sub-preference parameters of the scheduled type of dispatching facilities corresponding to the multiple target transit points respectively are determined.

6. The method according to claim 1, characterized in that The step of determining target dispatching quantities of the facilities to be dispatched corresponding to the plurality of target passing places respectively according to the power consumption parameters corresponding to the plurality of target passing places respectively comprises: Determine the dispatching cost parameters corresponding to the multiple target passing places according to the power consumption parameters corresponding to the multiple target passing places respectively; Determining, based on the multiple target transit points, facility type parameters of the to-be-scheduled facilities corresponding to the multiple target transit points; Determining charging income parameters corresponding to the multiple target passing places respectively according to facility type parameters of the to-be-dispatched facilities corresponding to the multiple target passing places; Based on the dispatching cost parameters and charging income parameters corresponding to the multiple target transit points, the target dispatching quantities of the facilities to be dispatched corresponding to the multiple target transit points are determined.

7. The method according to any one of claims 1 to 6, characterized in that: The method of determining the power usage parameters corresponding to the plurality of target passing places respectively according to the charging facility parameters corresponding to the plurality of target passing places respectively, and the charging demand parameters of the electric vehicles at the corresponding target passing places and the corresponding number of passing vehicles, comprises: Determining demand correction parameters corresponding to the plurality of target passing places respectively according to charging facility parameters corresponding to the plurality of target passing places respectively; Determine the corrected number of vehicles corresponding to each of the plurality of target passing places according to the demand correction parameters and the number of passing vehicles corresponding to each of the plurality of target passing places; Based on the charging demand parameters and the corrected number of vehicles respectively corresponding to the multiple target passing points, the power consumption parameters respectively corresponding to the multiple target passing points are determined.

8. A charging facility scheduling device, characterized in that: include: A receiving module, configured to receive a charging facility dispatch request corresponding to a target area; A response module, configured to determine a plurality of target passing points within the target area in response to the charging facility dispatch request; A first determination module is used to determine electric vehicle parameters and charging facility parameters corresponding to the plurality of target passing places, wherein the corresponding electric vehicle parameters are parameters of electric vehicles passing the corresponding target passing places within a predetermined time period, including corresponding charging demand parameters and corresponding number of passing vehicles; A second determination module is used to determine the power usage parameters corresponding to the multiple target passing places respectively according to the charging facility parameters corresponding to the multiple target passing places respectively, and the charging demand parameters of the electric vehicles at the corresponding target passing places and the corresponding number of passing vehicles; A third determination module is used to determine the target dispatching amount of the to-be-dispatched facilities corresponding to the multiple target passing places respectively according to the power consumption parameters corresponding to the multiple target passing places respectively; The fourth determination module is used to schedule the facilities to be scheduled according to the target scheduling quantities of the facilities to be scheduled corresponding to the multiple target transit points respectively.

9. An electronic device, characterized in that: include: processor; a memory for storing instructions executable by the processor; The processor is configured to execute the instructions to implement the charging facility scheduling method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: When the instructions in the computer-readable storage medium are executed by a processor of an electronic device, the electronic device is enabled to execute the charging facility scheduling method as described in any one of claims 1 to 7.

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