CN115345451A - Electric vehicle charging guiding method based on charging station recommendation strategy - Google Patents
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Abstract
The invention provides an electric vehicle charging guiding method based on a charging station recommendation strategy, which comprises the following steps: acquiring real-time related data when a user generates a charging demand and conventional data required by a system; a charging station recommendation model is built, and a charging strategy ranking based on time cost and economic cost synthesis is provided for a user; acquiring a charging decision of all charging demand users in a certain time period based on a charging station recommendation strategy, and counting to obtain the charging load of each charging station in the time period; and (3) constructing a real-time electricity price guide model, taking the charging load of each charging station as the model input of the real-time electricity price guide model, solving the objective function of the real-time electricity price guide model by adopting a simulated annealing algorithm, obtaining the ideal charging load of each charging station in the next time period, and outputting the electricity price of each charging station in the next time period. The invention can accurately guide the charging behavior of the user, realize load curve peak clipping and valley filling of the charging station and improve the utilization rate of the charging equipment, and provide effective basis for site selection of the electric vehicle charging station.
Description
Technical Field
The invention relates to the technical field of electric vehicle charging scheduling, in particular to an electric vehicle charging guiding method based on a charging station recommendation strategy.
Background
The electric automobile is an effective way for solving the problems of energy crisis and environmental pollution at present, and has wide market prospect. However, a large number of electric vehicles rush into the road, which not only brings great pressure to the urban traffic system, but also puts new requirements on the economy and safety of the power grid. Under the background, it is necessary to research an electric vehicle charging guidance strategy to provide a charging station recommendation service for a charging demand user, so as to minimize the time cost and the money cost of user charging by aiming at improving the satisfaction degree of user charging behaviors, and by guiding related electric vehicle charging services, the load pressure of a power grid is relieved by peak clipping and valley filling of a charging load, and the utilization rate of charging equipment is improved, thereby forming overall coordination and optimization of power grid-user benefits.
Currently, there are many related researches on charging guidance strategies, which can be divided into two categories according to different guidance modes: one type of direct charging guidance based on a guidance strategy and the other type of indirect charging guidance through a time-of-use electricity price mode. The former is as described in patent application No.: CN202010027578.1, name: a multi-factor and multi-scene electric vehicle charging station recommendation method is provided, which gives a recommendation scheme to a user intelligently by collecting SOC and position information of a to-be-charged electric vehicle and considering various targets of shortest charging time, lowest charging cost, most balanced power grid load and the like of the user. The patent comprehensively considers multi-party interests to provide charging station selection for users, however, users start from the interests of the users, and the method cannot reflect charging selection from the perspective of the users; patent application No.: CN202110154195.5, name: the method comprises the steps of constructing a user integral charging time-consuming calculation model by acquiring charging pile attributes and the current state in an area to be analyzed, solving the calculation model and calculating a user charging pile selection effect, so as to charge and guide vehicles in the area to be analyzed. The patent can effectively reduce the charging time of the electric automobile, but the benefit of a user is considered independently, the influence of the network access of the charging automobile on the load of a power grid is not considered, and the relationship of mutual coordination and mutual restriction among the benefit of the user, the charging station operator and the power grid is ignored.
The latter as in patent application No.: CN201711021969.7, name: a peak-valley time-of-use price-based electric vehicle ordered charging control method is characterized in that a two-stage optimization model aiming at the lowest total charging cost of an electric vehicle and the lowest load peak-valley difference of a power distribution network is established based on the peak-valley time-of-use price, so that the stability of a power grid is ensured, but the optimization aiming at the lowest total charging cost of the electric vehicle cannot represent the benefit of each user, and a good guiding effect cannot be achieved. Patent application No.: CN201910910606, name: a road-network-vehicle-related electric vehicle charging guiding method provides a concept of equivalent road length, comprehensively considers factors such as road traffic conditions and overall time consumption in a charging process, and introduces a microscopic traffic distribution model to describe charging requirements and travel rules of electric vehicle users. In addition, the patent adopts a method of time-division and zone-division electricity price formulation to guide users to charge, so that the electricity purchasing cost of the charging station is remarkably reduced, and the safety and the economy of the operation of the power system are improved. The related characteristic quantity cannot well simulate the driving behavior of an electric automobile user, so that accurate charging demand space-time distribution cannot be obtained.
The conventional research is limited to the fact that the charging self-decision-making behaviors of electric vehicle users cannot be accurately described on one hand, and on the other hand, the charging mode of guiding the users at the time-of-use price depends on day-ahead data and cannot cope with frequent and abnormal fluctuation of loads, so that the charging guiding and scheduling of the users are almost impossible to achieve global optimization.
Disclosure of Invention
The invention aims to overcome the defects of the prior art, provides an electric vehicle charging guiding method based on a charging station recommendation strategy, and provides high-quality charging station recommendation for users with charging requirements by comprehensively considering time cost and economic cost from the perspective of users; in addition, compared with a load prediction method, the charging behavior of the user can be guided more accurately based on the charging station recommendation system by taking the load data of the charging station counted in real time as the basis for making the charging electricity price in the next time period, so that the load curve of the charging station is clipped to fill the valley, and the utilization rate of the charging equipment is improved. The method gives consideration to the demands of users and the power grid, and forms the overall coordination and optimization of power grid-user benefits.
The technical scheme adopted by the invention is as follows:
an electric vehicle charging guiding method based on a charging station recommendation strategy comprises the following steps:
A. acquiring data, including real-time related data when a user generates a charging demand and conventional data required by a system;
B. establishing a charging station recommendation strategy at a user visual angle, establishing a charging station comprehensive evaluation index through the charging station recommendation strategy, and providing charging station recommendation based on comprehensive time cost and economic cost for a user;
C. acquiring a charging decision of all charging demand users in a certain time period based on a charging station recommendation strategy, and further counting to obtain the charging load of each charging station in the time period;
D. and C, constructing a real-time electricity price guide model, inputting the charging load of each charging station counted in the step C as the model of the real-time electricity price guide model, solving an objective function of the real-time electricity price guide model by adopting a simulated annealing algorithm to obtain the ideal charging load of each charging station in the next time period, and outputting the electricity price of each charging station in the next time period based on a strategy made by real-time electricity price.
Further, acquiring real-time related data when a user generates a charging demand and conventional data required by a system in the step A, wherein the real-time related data when the user generates the charging demand comprises position information of the electric automobile generating the charging demand, SOC data and a charging standard of the electric automobile, the remaining mileage of the electric automobile, the charging electric quantity set by an automobile owner and the guiding distance maximally tolerated by the user; the conventional data required by the system comprises the geographical position data of each charging station in the district, the model number, the number and the running state of each charging pile in the station, the time that a charging station operator is willing to reserve the applied charging pile for a user, and the historical traffic information of a road.
Further, a charging recommendation strategy under the user view angle is constructed in the step B, the charging recommendation strategy takes the data acquired in the step A as index parameter input, the comprehensive evaluation index of the feasible charging station is calculated, the evaluation index is ranked and then returned to the user, so that the user can make the optimal charging selection based on the time cost and the economic cost, and the method specifically comprises the following steps:
firstly, screening out a feasible charging station set { S) according to constraint conditions 1 ,S 2 ,…,S n The method comprises the following steps:
judging whether each charging station is idle and available according to the charging pile model and the running state of each charging station;
judging whether a charging station exists in the reachable range or not according to the current position of the electric vehicle, the remaining mileage and the guiding distance maximally tolerated by the user;
the charging station operator is willing to reserve for the user whether the time of the applied charging pile is not less than the time consumed by the user to arrive at the charging station;
secondly, establishing a comprehensive evaluation index of the feasible charging station, wherein the expression is as follows:
wherein σ i Comprehensive evaluation finger for charging station i in districtThe mark is that,
the cost evaluation index of the time used when the charging station i charges the electric automobile to be charged is selected for the user,
wherein t is o Reserving the time of the applied charging pile for the user for the charging station operator; t is t p,k For the time consumed by the distance l from the position where the charging demand is generated to the charging station i for the vehicle k to be charged k And a running speed v k Related to; t is t c,k The charging time from the current electric quantity to the electric quantity required by the user for the automobile to be charged and the charging electric quantity Q k Charging power P i,k And charging efficiency e k Related to; t is t w The method comprises the steps that the waiting time from the arrival of a vehicle to be charged to the charging station to the start of charging is taken as the minimum value of the residual charging time of all charging piles of the charging station;
wherein s is c Charging fee for the vehicle to be charged from the current electric quantity to the electric quantity required by the user, and charging time t c,k And real-time electricity prices ρ i,j (ii) related; s is t For the distance from the location of the charging demand to the location of the charging station i, s p For the extra parking fee of the vehicle to be charged when the charging station is charged, the value of theta is as follows, and is related to the time of staying at the charging station:
λ 1 、λ 2 respectively corresponding weight coefficients of each evaluation index;
and finally, calculating comprehensive evaluation indexes of all feasible charging stations in the district, sorting the comprehensive evaluation indexes according to the numerical values of the index calculation results, and taking the sorted results as the recommended ranks of the charging stations and returning the ranked results to the user.
Further, in the step C, the user makes a charging decision based on the charging station recommendation strategy in the step B, and the charging decision behaviors of the user are represented as a set
Wherein the elements
For selection of charging stations, elements
For selection of the moment of start of charging, element
For selection of routes to charging stations, elements
For selection of departure time, elements
Selecting the charging capacity;
obtaining charging decisions of all charging demand users in the time period j based on a charging station recommendation strategy, and counting charging loads L generated by charging of electric vehicles in a charging station i in the region of the time period j i,j Wherein the time period is divided into 24 time periods according to 1 hour for 1 day, and j =1 represents 0:00-1: and in the time period of 00, analogizing, and charging load L of charging station i in the district of the time period j i,j The statistical method specifically comprises the following steps:
first, according to the elements
Element(s)
Predicting the moment when the user arrives at the charging station and starts charging, according to the elements
Element(s)
Predicting a charging duration of the vehicle;
second, the expected start of charge time is compared to the element
The charging start time selected in (1) determines whether the user can arrive at the charging station and start charging before the selected charging time starts. If yes, updating element by taking arrival time as charging start time
Otherwise holding the element
The selected charge start time;
finally, according to the element
Element(s)
And the charging load L generated by the charging of the electric vehicle in the charging station i in the region of the estimated charging time period j i,j The calculation method comprises the following steps:
wherein m is opening within a time period jNumber of electric vehicles charged across time period from start of charging, t k,j Charging time period t of electric vehicle k in time period j k,j+1 For the charging duration, P, of the electric vehicle k in the time period j +1 i,k And charging power of the electric vehicle k at a charging station i.
Further, the real-time electricity price guiding model in the step D is used for reducing peak-valley difference of power grid load and improving utilization rate of power grid equipment, and the output of the real-time electricity price guiding model is the electricity price ρ rho of each charging station in the next time period i,j+1 The model objective function expression is:
min F＝η 1 ε 1 +η 2 ε 2
wherein epsilon 1 For evaluating the index of the difference of the charging pile utilization rates of the charging stations, epsilon 1 Is as follows, wherein C i For charging station i fill electric pile quantity:
ε 2 for evaluating the smooth daily charge load curve index, epsilon, of all charging stations in the jurisdiction 2 Is as follows, wherein L i，j Charging load for charging station i in time period j:
η 1 、η 2 respectively corresponding weight coefficients;
solving the problem that the optimal value of the model objective function is actually an n-element function to solve the minimum value in the independent variable definition domain, solving the objective function of the real-time electricity price guide model by adopting a simulated annealing algorithm, taking the optimal solution of the objective function as the ideal charging load of each charging station in the next time period, and solving the objective function of the real-time electricity price guide model by adopting the simulated annealing algorithm comprises the following steps:
step1: inputting each charging station to count load L in real time i,j ；
Step2: setting algorithm parameters, respectively, annealing initiation T b Termination temperature T e Cooling speed r, maximum iteration number n, probability coefficient S of receiving differential solution 1 ；
Step3: randomly generating an initial solution ω (L) 1,j+1 ,L 2,j+1 ,…,L n,j+1 ) Calculating an objective function F (omega);
step4: the perturbation produces a new solution ω '(L' 1,j+1 ,L′ 2,j+1 ,…,L′ n,j+1 ) Calculating an objective function F (ω ') and a metric value Δ F = F (ω') -F (ω);
step5: judging whether the new solution is accepted or not according to a Metropolis criterion, wherein if delta F is less than 0, the omega 'is accepted as a new current solution omega, and otherwise, the omega' is accepted as a new current solution S according to the probability exp (-delta F/T);
step6: and (4) judging termination conditions: the current temperature T is less than the minimum temperature T e Or the number of iterations is less than 0;
step7: if the end condition is met, taking the current solution omega as an optimal solution, and outputting the ideal charging load L of the charging station i in the next time period i,j+1 Ending the program;
after the simulated annealing algorithm solves the objective function, the optimal solution omega is the ideal charging load L of the charging station i in the next time period i,j+1 The power grid operator can adjust the electricity price rho of the next time period i,j+1 To achieve the desired value of the charging load: real-time electricity prices ρ in step B i,j+1 The influence on the comprehensive evaluation index of the charging station is embodied in the economic cost evaluation index
In the above, that is, the floating of the real-time electricity prices of the charging stations may affect the recommendation ranking of the charging stations, so that the update of the recommendation ranking of the charging stations plays a guiding role for users who have a charging demand in the next time period, and the policy of making the real-time electricity prices of the charging stations i is as follows:
ρ i,j for the time-of-use price, ρ, of the charging station i in the time period j i,cs For the service charge of the charging station i,
is the adjustment factor for charging station i.
The invention has the following beneficial effects:
compared with the disordered charging, the charging station has the advantages that the peak-valley difference value of the total load curve is smaller, the load in each time period is more balanced, and the user waiting phenomenon in the disordered charging is relieved. The electric vehicle charging guidance system can provide effective basis for further charging related services such as electric vehicle charging station address selection and the like
Drawings
FIG. 1 is a flowchart of one embodiment of a charging station recommendation strategy based electric vehicle charging guidance method according to the present invention;
FIG. 2 is a detailed flow chart of an embodiment of the present invention;
FIG. 3 is a flow chart of the present invention for model objective function solution using simulated annealing algorithm;
FIG. 4 is a road network diagram of a jurisdiction;
FIG. 5 is a graph of the daily average load of each charging station before and after the present invention is used to guide;
FIG. 6 is a graph of the total load of a lead front and rear charging station using the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, are within the scope of the present invention.
As shown in fig. 1 and fig. 2, an embodiment of the present invention provides an electric vehicle charging guidance method based on a charging station recommendation policy, including the following steps:
A. acquiring data, including real-time related data when a user generates a charging demand and conventional data required by a system;
B. a charging station recommendation strategy under a user visual angle is established, a charging station comprehensive evaluation index is established through the charging station recommendation strategy, and charging station recommendation based on comprehensive time cost and economic cost is provided for a user;
C. acquiring a charging decision of all charging demand users in a certain time period based on a charging station recommendation strategy, and further counting to obtain the charging load of each charging station in the time period;
D. and C, constructing a real-time electricity price guide model, inputting the charging loads of all the charging stations counted in the step C as the model of the real-time electricity price guide model, solving an objective function of the real-time electricity price guide model by adopting a simulated annealing algorithm to obtain ideal charging loads of all the charging stations in the next time period, and outputting the electricity prices of all the charging stations in the next time period based on a formulation strategy of the real-time electricity prices.
Step A, acquiring real-time related data when a user generates a charging demand and conventional data required by a system, wherein the real-time related data when the user generates the charging demand comprises position information of the electric automobile generating the charging demand, SOC data and a charging standard of the electric automobile, the remaining mileage of the electric automobile, the charging electric quantity set by an automobile owner and the guide distance maximally tolerated by the user; the conventional data required by the system comprises the geographic position data of each charging station in the district, the model, the quantity and the running state of each charging pile in the station, the time that a charging station operator is willing to reserve the applied charging pile for a user and the historical traffic information of a road.
And B, constructing a charging recommendation strategy under the user view angle, wherein the charging recommendation strategy takes the data acquired in the step A as index parameters to be input, and returns the evaluation indexes to the user after calculating the comprehensive evaluation indexes of the feasible charging stations and ranking the evaluation indexes so that the user can make the optimal charging selection based on the time cost and the economic cost, and the method specifically comprises the following steps of:
firstly, screening out a feasible charging station set { S) according to constraint conditions 1 ,S 2 ,…,S n The method comprises the following steps:
1. judging whether the charging pile is available or not according to the model and the running state of the charging pile of each charging station;
2. judging whether a charging station exists in the reachable range or not according to the current position of the electric vehicle, the remaining mileage and the guiding distance maximally tolerated by the user;
3. the charging station operator is willing to reserve whether the time of the applied charging pile is not less than the time consumed by the user to arrive at the charging station or not for the user;
secondly, establishing a comprehensive evaluation index of the feasible charging station, wherein the expression is as follows:
wherein σ i For the comprehensive evaluation index of the charging station i in the jurisdiction,
the user is given a choice of a cost evaluation index of the time used when charging station i charges the electric vehicle to be charged,
wherein t is o Reserving the time of the applied charging pile for the user for the charging station operator; t is t p,k The distance l between the vehicle k to be charged and the charging station i is the time consumed for the distance from the location where the charging request is generated to the charging station i k And a running speed v k (ii) related; t is t c,k The charging time and the charging electric quantity Q for the vehicle to be charged from the current electric quantity to the electric quantity required by the user k Charging power P i,k And charging efficiency e k (ii) related; t is t w The method comprises the steps that the waiting time from the arrival of a vehicle to be charged to the charging station to the start of charging is taken as the minimum value of the residual charging time of all charging piles of the charging station;
wherein s is c Charging fee for the vehicle to be charged from the current electric quantity to the electric quantity required by the user, and charging time t c,k And real-time electricity prices ρ i,j (ii) related; s t For the distance of the vehicle to be charged from the location where the charging request is generated to the location of the charging station i, s p For the extra parking fee of the vehicle to be charged when the charging station is charged, the value of theta is as follows, and is related to the time of staying at the charging station:
λ 1 、λ 2 respectively corresponding weight coefficients of each evaluation index;
and finally, calculating comprehensive evaluation indexes of all feasible charging stations in the district, sorting the comprehensive evaluation indexes according to the numerical values of the index calculation results, and returning the sorted results as the recommended ranks of the charging stations to the user. And the user refers to the charging station recommendation ranking to reserve the charging station and arrives at the charging station for charging within the appointed time.
C, the user makes a charging decision based on the charging station recommendation strategy in the step B, and the charging decision behaviors of the user are represented as a set
Wherein the elements
For selection of charging stations, elements
For selection of the moment of start of charging, element
For selection of routes to charging stations, elements
For selection of departure time, elements
Selecting the charging electric quantity;
acquiring charging decisions of all charging demand users in the time period j based on a charging station recommendation strategy, and counting charging loads L of charging stations i in the region of the time period j, which are generated due to charging of electric vehicles i,j Wherein the time period is divided into 24 time periods according to 1 hour for 1 day, and j =1 represents 0:00-1: and in the time period of 00, analogizing, and charging load L of charging station i in the district of the time period j i,j The statistical method specifically comprises the following steps:
first, according to the elements
Element(s)
Predicting the moment when the user arrives at the charging station and starts charging, according to the elements
Element(s)
Predicting a charging period of the vehicle;
second, the expected start of charge time is compared to the element
The charging start time selected in (1) determines whether the user can arrive at the charging station and start charging before the selected charging time starts. If yes, updating element by taking arrival time as charging start time
Otherwise holding the element
The selected charge start time;
finally, according to the element
Element(s)
And the charging load L generated by the charging of the electric vehicle in the charging station i in the region of the estimated charging time period j i,j The calculation method comprises the following steps:
wherein m is the number of electric vehicles charged across the time period for starting charging in the time period j, t k,j Charging time period t of electric vehicle k in time period j k,j+1 For the charging duration, P, of the electric vehicle k in the time period j +1 i,k Charging power of the electric vehicle k at a charging station i.
Charging load L of each charging station in step D based on statistics in step C ij And constructing a real-time electricity price guide model, wherein the model aims to reduce the peak-valley difference of the load of the power grid and improve the utilization rate of the power grid equipment. The output is the price rho of each charging station in the next time period i,j+1 The model objective function expression is:
min F＝η 1 ε 1 +η 2 ε 2
wherein epsilon 1 For evaluating the index of the difference of the charging pile utilization rates of the charging stations, epsilon 1 Is as follows, wherein C i For charging station i fill electric pile quantity:
ε 2 for evaluating the smooth daily charge load curve index, epsilon, of all charging stations in the jurisdiction 2 Is as follows, wherein L i，j Charging load for charging station i in time period j:
η 1 、η 2 respectively corresponding weight coefficients.
The optimization model is actually an n-ary function solving the problem of the minimum value in the argument definition domain. According to the embodiment of the invention, a simulated annealing algorithm is introduced to solve the objective function of the model, and the optimal solution of the objective function is used as the ideal charging load of each charging station in the next time period. The simulated annealing algorithm has high operation efficiency and strong robustness in solving a complex nonlinear optimization problem, and the main solving steps are as follows (as shown in fig. 3):
step1, inputting real-time statistical load L of each charging station i,j ；
Step2 sets algorithm parameters, namely annealing starting T b End temperature T e Cooling speed r, maximum iteration number n, probability coefficient S of receiving differential solution 1 ；
Step3 randomly generates an initial solution ω (L) 1,j+1 ,L 2,j+1 ,…,L n,j+1 ) Calculating an objective function F (omega);
step4 perturbation generates a new solution ω '(L' 1,j+1 ,L′ 2,j+1 ,…,L′ n,j+1 ) Calculating an objective function F (ω ') and a metric value Δ F = F (ω') -F (ω);
step5, judging whether the new solution is accepted or not according to a Metropolis criterion, wherein if delta F is less than 0, omega 'is accepted as a new current solution omega, and otherwise, omega' is accepted as a new current solution S according to probability exp (-delta F/T);
and Step6, judging termination conditions: the current temperature T is less than the minimum temperature T e Or the number of iterations is less than 0;
step7, if the end condition is met, taking the current solution omega as the optimal solution, and outputting the ideal load L of the charging station i in the next time period i,j+1 And the routine is ended.
After the simulated annealing algorithm solves the objective function, the optimal solution omega is the ideal charging load L of the charging station i in the next time period i,j+1 The power grid operator can adjust the electricity price rho of the next time period i,j+1 To achieve the desired value of the load: electricity price ρ in step B i,j+1 The influence on the comprehensive evaluation index of the charging station is embodied in the economic cost evaluation index
In the above way, that is, the fluctuation of the real-time electricity prices of the charging stations can affect the recommendation ranking of the charging stations, so that the update of the recommendation ranking of the charging stations plays a role of guiding the users who generate the charging demands in the next time period. The real-time electricity price formulation strategy of the charging station i is as follows:
ρ i,j for the time-of-use price, ρ, of charging station i in time period j i,cs For the service charge of the charging station i,
is the adjustment factor for charging station i.
When the electric vehicle charging guiding method based on the charging station recommendation strategy is simulated, taking a certain jurisdiction as an example, a traffic road network diagram is shown in fig. 4, and the following parameters are given: (1) The simulation time is 0; (2) The number of charging stations in the district is 5, each charging station is provided with 10 quick charging piles, the position of each charging station is shown in the figure, and the charging price of each charging station J is shown in the table 1. 1000 electric automobiles are provided, the charging modes are constant-power quick charging, and the charging power is 60KW; (3) The time when the electric vehicle user generates the charging requirement accords with uniform distribution, the positions of the electric vehicle generating the charging requirement are randomly selected, and the user selects the charging station with the highest comprehensive evaluation index for charging; (4) The remaining distance of the electric vehicle user when the charging demand is generated is larger than the distance between the electric vehicle user and any charging station in the jurisdiction, and the electric vehicle user is chargedThe length is 1 hour; (5) The charging cost is only related to the real-time electricity price, and the journey time is only related to the journey length; (6) Weight coefficient ε 1 Take 0.6, ε 2 Take 0.4, weight coefficient lambda 1 Take 0.6, λ 2 Take 0.4.
TABLE 1 charging tariff at each charging station J moment
To embody the guiding effect of the present invention, the result of the charge guiding is compared with the result of the disordered charging. As can be seen from fig. 5, under the charging guidance of the present invention, the daily average load of charging stations No. 1, 3, and 4 is reduced, and the daily average load of charging stations No. 2 and 5 is increased, as compared with the disordered charging. Because the number of vehicles in the district is fixed, the charging guiding method guides the vehicles originally charged in the charging stations 1, 3 and 4 to the charging stations 2 or 5, so that the loads borne by the charging stations with the same load capacity are more balanced, and the utilization rate of the whole charging pile is improved. .
As can be seen from fig. 6, under the charging guidance of the present invention, compared with the disordered charging, the charging station has smaller peak-to-valley difference of the total load curve, and the loads in each time period are more balanced, thereby slowing down the user waiting phenomenon in the disordered charging.
The above description is only an embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (5)
1. An electric vehicle charging guiding method based on a charging station recommendation strategy is characterized by comprising the following steps: the method comprises the following steps:
A. acquiring data, including real-time related data when a user generates a charging demand and conventional data required by a system;
B. establishing a charging station recommendation strategy at a user visual angle, establishing a charging station comprehensive evaluation index through the charging station recommendation strategy, and providing charging station recommendation based on comprehensive time cost and economic cost for a user;
C. acquiring a charging decision of all charging demand users in a certain time period based on a charging station recommendation strategy, and further counting to obtain the charging load of each charging station in the time period;
D. and C, constructing a real-time electricity price guide model, inputting the charging load of each charging station counted in the step C as the model of the real-time electricity price guide model, solving an objective function of the real-time electricity price guide model by adopting a simulated annealing algorithm to obtain the ideal charging load of each charging station in the next time period, and outputting the electricity price of each charging station in the next time period based on a strategy made by real-time electricity price.
2. The method of claim 1, wherein the method comprises: step A, acquiring real-time related data when a user generates a charging demand and conventional data required by a system, wherein the real-time related data when the user generates the charging demand comprises position information of the electric automobile generating the charging demand, SOC data and a charging standard of the electric automobile, the remaining mileage of the electric automobile, the charging electric quantity set by an automobile owner and the guide distance maximally tolerated by the user; the conventional data required by the system comprises the geographic position data of each charging station in the district, the model, the quantity and the running state of each charging pile in the station, the time that a charging station operator is willing to reserve the applied charging pile for a user and the historical traffic information of a road.
3. The method of claim 1, wherein the method comprises: and B, establishing a charging recommendation strategy under the user perspective, inputting the data acquired in the step A as index parameters in the charging recommendation strategy, calculating a comprehensive evaluation index of the feasible charging station, ranking the evaluation index and returning the evaluation index to the user so that the user can make an optimal charging selection based on time cost and economic cost, wherein the method specifically comprises the following steps of:
firstly, screening out a feasible charging station set { S) according to constraint conditions 1 ,S 2 ,…,S n The method comprises the following steps:
judging whether each charging station is idle and available according to the charging pile model and the running state of each charging station;
judging whether a charging station exists in the reachable range or not according to the current position of the electric vehicle, the remaining mileage and the guiding distance maximally tolerated by the user;
the charging station operator is willing to reserve whether the time of the applied charging pile is not less than the time consumed by the user to arrive at the charging station or not for the user;
secondly, establishing a comprehensive evaluation index of the feasible charging station, wherein the expression is as follows:
wherein σ i Is a comprehensive evaluation index of a charging station i in the district,
the cost evaluation index of the time used when the charging station i charges the electric automobile to be charged is selected for the user,
wherein t is o Reserving the time of the applied charging pile for the user for the charging station operator; t is t p,k For the time consumed by the distance l from the position where the charging demand is generated to the charging station i for the vehicle k to be charged k And a running speed v k Related to; t is t c,k The charging time from the current electric quantity to the electric quantity required by the user for the automobile to be charged and the charging electric quantity Q k Charging power P i,k And charging efficiency e k (ii) related; t is t w Waiting time from arrival at charging station to start of charging for vehicle to be chargedThe minimum value of the residual charging time of all the charging piles of the charging station is obtained;
wherein s is c Charging fee for the vehicle to be charged from the current electric quantity to the electric quantity required by the user, and charging time t c,k And real-time electricity prices ρ i,j (ii) related; s t For the distance from the location of the charging demand to the location of the charging station i, s p For the extra parking fee of the vehicle to be charged when the charging station is charged, the value of theta is related to the staying time at the charging station, and the value of theta is as follows:
λ 1 、λ 2 respectively corresponding weight coefficients of each evaluation index;
and finally, calculating comprehensive evaluation indexes of all feasible charging stations in the district, sorting the comprehensive evaluation indexes according to the numerical values of the index calculation results, and taking the sorted results as the recommended ranks of the charging stations and returning the ranked results to the user.
4. The electric vehicle charging guidance method based on the charging station recommendation strategy according to claim 1, characterized in that: c, the user makes a charging decision based on the charging station recommendation strategy in the step B, and the charging decision behaviors of the user are represented as a set
Wherein the elements
For selection of charging stations, elements
For selection of the moment of start of charging, element
For selection of routes to charging stations, elements
For selection of departure time, elements
Selecting the charging capacity;
acquiring charging decisions of all charging demand users in the time period j based on a charging station recommendation strategy, and counting charging loads L of charging stations i in the region of the time period j, which are generated due to charging of electric vehicles i,j Wherein the time period is divided into 24 time periods according to 1 hour for 1 day, and j =1 represents 0:00-1: and in the time period of 00, analogizing the time period, and charging load L of the charging station i in the district of the time period j i,j The statistical method specifically comprises the following steps:
first, according to the elements
Element(s)
Predicting the moment when the user arrives at the charging station and starts charging, according to the elements
Element(s)
Predicting a charging duration of the vehicle;
secondly, compareCounting the time and elements of starting charging
The selected charging start time in (1) determines whether the user can reach the charging station and start charging before the selected charging time starts. If yes, updating element by taking arrival time as charging start time
Otherwise holding the element
The selected charge start time;
finally, according to the element
Element(s)
And the charging load L generated by the charging of the electric vehicle in the charging station i in the region of the estimated charging time period j i,j The calculation method comprises the following steps:
wherein m is the number of electric vehicles charged across the time period for starting charging in the time period j, t k,j Charging time period t of electric vehicle k in time period j k,j+1 For the charging duration, P, of the electric vehicle k in the time period j +1 i,k And charging power of the electric vehicle k at a charging station i.
5. The method according to claim 3, wherein the charging guidance method comprises: the real-time electricity price guide model in the step D is used for reducing the peak-valley difference of the power grid load and improving the utilization rate of power grid equipment, and the output of the real-time electricity price guide model isThe electricity price rho of each charging station in the next time period i,j+1 The model objective function expression is:
min F＝η 1 ε 1 +η 2 ε 2
wherein epsilon 1 For evaluating the index of the difference of the charging pile utilization rate of each charging station, epsilon 1 Is as follows, wherein C i For charging station i fill electric pile quantity:
ε 2 for evaluating the smooth daily charge load curve index, epsilon, of all charging stations in the jurisdiction 2 Is as follows, wherein L i，j Charging load for charging station i in time period j:
η 1 、η 2 respectively corresponding weight coefficients;
the problem that the optimal value of the model objective function is actually solved as the minimum value of an n-element function in the independent variable definition domain is solved, the objective function of the real-time electricity price guide model is solved by adopting a simulated annealing algorithm, the optimal solution of the objective function is taken as the ideal charging load of each charging station in the next time period, and the steps of solving the objective function of the real-time electricity price guide model by adopting the simulated annealing algorithm are as follows:
step1: inputting each charging station to count load L in real time i,j ；
Step2: setting algorithm parameters, respectively, annealing initiation T b End temperature T e Cooling rate r, maximum iteration number n, probability coefficient S of receiving differential solution 1 ；
Step3: randomly generating an initial solution ω (L) 1,j+1 ,L 2,j+1 ,…,L n,j+1 ) Calculating an objective function F (omega);
step4: the perturbation produces a new solution ω '(L' 1,j+1 ,L′ 2,j+1 ,…,L′ n,j+1 ) Calculating an objective function F (ω ') and a metric value Δ F = F (ω') -F (ω);
step5: judging whether the new solution is accepted or not according to a Metropolis criterion, wherein if delta F is less than 0, the omega 'is accepted as a new current solution omega, and otherwise, the omega' is accepted as a new current solution S according to the probability exp (-delta F/T);
step6: and (4) judging termination conditions: the current temperature T is less than the minimum temperature T e Or the number of iterations is less than 0;
step7: if the end condition is met, taking the current solution omega as an optimal solution, and outputting the ideal charging load L of the charging station i in the next time period i,j+1 Ending the program;
after the simulated annealing algorithm solves the objective function, the optimal solution omega is the ideal charging load L of the charging station i in the next time period i,j+1 The power grid operator can adjust the electricity price rho of the next time period i,j+1 To achieve the desired value of the charging load: real-time electricity prices ρ in step B i,j+1 The influence on the comprehensive evaluation index of the charging station is embodied in the economic cost evaluation index
On, the floating of each charging station real-time electricity price can influence charging station recommendation rank promptly to the update of charging station recommendation rank plays the effect of guide to the user that next time slot produced the demand of charging, and the policy of making of charging station i's real-time electricity price is:
ρ i,j for the time-of-use price, ρ, of the charging station i in the time period j i,cs For the service charge of the charging station i,
is the adjustment factor for charging station i.
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| CN115489378A (en) * | 2022-11-16 | 2022-12-20 | 国网浙江省电力有限公司宁波供电公司 | Electric vehicle charging prediction method, device and system and readable storage medium |
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| CN115577863A (en) * | 2022-12-07 | 2023-01-06 | 南方科技大学 | Electric vehicle charging field station recommendation method, system, equipment and medium |
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