CN112183882A - Intelligent charging station charging optimization method based on electric vehicle quick charging requirement - Google Patents
Intelligent charging station charging optimization method based on electric vehicle quick charging requirement Download PDFInfo
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Abstract
The invention discloses a charging optimization method of intelligent charging stations based on electric vehicle quick charging requirements. Secondly, the comprehensive path planning model of the electric automobile is established based on the charging time cost and the charging economic cost of the electric automobile. And solving the model by using a genetic algorithm to obtain an optimal operation strategy for optimizing the charging comprehensive cost of the electric automobile and the comprehensive benefit of the intelligent charging station. The strategy provided by the invention can effectively reduce the comprehensive charging cost of the electric vehicle and improve the comprehensive benefit of the intelligent charging station.
Description
Technical Field
The invention relates to the technical field of electric vehicle dispatching, in particular to a charging optimization method of an intelligent charging station based on the quick charging requirement of an electric vehicle.
Background
With the increase in environmental pollution, users who have consciously chosen to use electric vehicles have increased year by year. The huge electric automobile group makes the electric automobile charging demand increasingly growing.
The charging requirements of the electric automobile can be divided into slow charging requirements and fast charging requirements according to the charging mode. Some studies utilize the time-shifting characteristic of the slow charging demand of electric vehicles to achieve target optimization. The research realizes system optimization by reasonably scheduling the electric automobile time sequence, but the research does not relate to the characteristics of the electric automobile on space transfer.
The fast charging requirement is obviously different from the slow charging requirement in that the electric automobile with the fast charging requirement can move in the regional space, so that a charging path needs to be reasonably planned for the electric automobile with the fast charging requirement. In part of researches, reasonable charging paths such as economy, time, distance, energy consumption and the like are planned for the electric automobile with the quick charging requirement by analyzing some influence factors of the quick charging of the electric automobile. However, most of the documents only plan the optimal charging station and route from the perspective of the electric vehicle, and do not consider the benefits of other charging participants. Therefore, the technical problem of poor charging scheduling effect exists in the prior art.
Disclosure of Invention
In view of the above, the present invention provides a charging optimization method for an intelligent charging station based on a fast charging requirement of an electric vehicle, so as to solve or at least partially solve the technical problem of poor charging scheduling effect in the prior art.
In order to solve the technical problem, the invention provides a charging optimization method of an intelligent charging station based on the quick charging requirement of an electric vehicle, which comprises the following steps:
s1: adjusting the service price of the intelligent charging station according to the comprehensive load information and the traffic flow information of the intelligent charging station;
s2: calculating the charging time cost of the electric automobile, calculating the charging economic cost of the electric automobile according to the service price of the intelligent charging station, and constructing an electric automobile comprehensive path planning model by taking the charging time cost of the automobile and the charging economic cost of the electric automobile as targets, wherein the electric automobile comprehensive path planning model is used for selecting a corresponding intelligent charging station for charging for an electric automobile user when the electric automobile generates a quick charging demand;
s3: based on the fast charging load and the traffic flow brought by the decision of the electric vehicle comprehensive path planning model, the intelligent charging station optimization model is established by taking the economic benefit of the intelligent charging station, the power grid safety target and the road network utilization rate target as comprehensive targets, and the energy storage equipment in the intelligent charging station is scheduled through the intelligent charging station optimization model to obtain a charging scheduling result.
In one embodiment, S1 specifically includes:
s1.1: evaluating the influence of the comprehensive load of the intelligent charging station on the service price of the intelligent charging station,
the comprehensive load of the intelligent charging station is as follows:
wherein the content of the first and second substances,
respectively integrating the load, the basic load and the new energy output of the intelligent charging station k in the t-th time period;
obtaining a comprehensive load mean value according to the comprehensive load, and expressing the influence rate of the comprehensive load on the service price of the intelligent charging station as
Wherein the content of the first and second substances,
in order to integrate the influence rate of the load on the service price of the intelligent charging station k,
synthesizing a load mean value for the intelligent charging station k, wherein T represents a moment;
s1.2: evaluating the influence of the traffic flow of the road network nodes of the intelligent charging station on the service price of the intelligent charging station,
the road health vehicle flow range is [0.4C ]jk,0.7Cjk]Therefore, the road section unbalance quantity formed by the road network nodes connected with the intelligent charging station nodes is obtained:
wherein the content of the first and second substances,
express that wisdom charging station k node arrives road network node jkThe unbalance amount of the road section in the time period t; j. the design is a squarekA road network node set connected with the intelligent charging station k is formed; j is a function ofkRepresents the nodes of the road network connected with the intelligent charging station k, jk∈Jk；
Show intelligent charging station k and road network node jkThe traffic flow of the connected road section at the time period t;
show intelligent charging station k and road network node jkThe traffic capacity of the connected road sections;
according to the amount of unbalance of the traffic flow of each road section, the influence rate of the traffic flow on the service price of the intelligent charging station is calculated
Expressed as:
wherein the content of the first and second substances,
rate of influence of traffic flow on intelligent charging station k service price;
s1.3: according to the influence of the comprehensive load of the intelligent charging station and the road network node traffic flow of the intelligent charging station on the service price of the intelligent charging station, the service price of the intelligent charging station is adjusted.
In one embodiment, S1.3 specifically includes:
when the influence rate of the comprehensive load and the traffic flow of the intelligent charging station on the service price of the intelligent charging station is more than or equal to 1 or less than or equal to 1, the service price of each time period of the intelligent charging station is expressed as follows:
in the formula:
for the intelligent charging station k in the time period t, the service price CsStandard electricity prices for intelligent charging stations;
when the influence rate of the comprehensive load and the traffic flow of the intelligent charging station on the service price of the intelligent charging station is more than or equal to 1 and less than or equal to 1, the service price of each time period of the intelligent charging station is expressed as follows:
in one embodiment, S2 specifically includes:
s2.1: calculating the cost of the charging time of the electric automobile, wherein the total charging time of the electric automobile mainly comprises the driving time of the electric automobile, the waiting time of the electric automobile and the charging time of the electric automobile,
1) the electric vehicle travel time is expressed as:
wherein, TdrThe driving time of the electric vehicle from the starting point to the intelligent charging station is obtained; u is a feasible path for the electric vehicle to reach the intelligent charging station; t isijIs the travel time for road segment ij; i. j is a node in the road network;
2) waiting time of the electric automobile:
wherein, TwCharging the electric vehicle for a waiting time; n iswThe number of vehicles in the front waiting queue; n iscThe number of vehicles which are being charged by using the charging pile; n ispThe total number of charging piles in the intelligent charging station; t isi,k(k＝1,2…,nc) Charging time remaining for the kth vehicle in charging of the ith batch;
3) charging time of the electric automobile:
wherein, TchCharging time for the electric vehicle; pchCharging power for the charging pile; SOCmaxIs the maximum state of charge of the battery; SOCwCharging state of charge when prompting for a vehicle; c is the battery capacity; EC (EC)drEnergy consumption of a path for the electric automobile to go to the intelligent charging station; e.g. of the typetMaintaining constant-temperature non-power in the vehicle for the electric vehicle;
s2.2: calculating the charging economic cost of the electric automobile according to the service price of the intelligent charging station, wherein the charging economic cost of the electric automobile mainly comprises the initial charging economic cost of the electric automobile, the power energy consumption cost of the electric automobile for going to the intelligent charging station and the non-power energy consumption cost of the electric automobile for going to the intelligent charging station;
1) initial charging economic cost of the electric vehicle:
tc＝t0+Tdr+Tw
wherein, CinThe method comprises the following steps of (1) initially charging the electric automobile with economic cost, namely the economic cost from vehicle prompt charging to full charge; t is t0Generating a moment for charging the electric automobile, namely a vehicle prompt charging moment; t is tcCharging time for users;
is tcSmart charging station k service price at the time period:
2) electric automobile goes to wisdom charging station route power energy consumption cost:
wherein, CpcThe cost of power consumption in a path for the electric vehicle to go to the intelligent charging station; EC (EC)ijEnergy consumption is consumed for the electric automobile to run on the road section ij;
3) electric automobile goes to the non-power energy consumption cost of wisdom charging station:
wherein, CnpcThe non-power energy consumption cost for the electric vehicle to go to the intelligent charging station; Δ t is the period length;
s2.3: constructing an electric automobile comprehensive path planning model by taking automobile charging time cost and electric automobile charging economic cost as targets, and expressing as follows:
Csy＝min{(Cin+Cpc+Cnpc)+λ·(Tdr+Tw+Tch)}
wherein, CsyThe comprehensive cost of charging the electric automobile is saved; λ is a time cost conversion factor.
In one embodiment, the constraints of the electric vehicle comprehensive path planning model in step S2 include:
1) electric automobile residual capacity can satisfy electric automobile and reach selected wisdom charging station:
(SOCmax-SOCw)·C-ECdr-et·Tdr＞SOCmin·C
therein, SOCminMinimum state of charge to maintain battery life;
2) traffic flow of each road section of a road network node is restricted, and the traffic flow of each road section cannot exceed the traffic capacity of the road section:
wherein the content of the first and second substances,
traffic flow for section ij at time t, CijThe traffic capacity of the road section ij is shown, and N is a road network node set;
3) the road network intermediate node selection constraint is expressed as:
wherein i is a node where the vehicle is currently located; j is the next candidate node; x is the number ofijThe variable is 0-1 and indicates whether the electric automobile selects to pass through the road section ij or not; sTFor the electric automobile to access the road network node set, i belongs to STRepresenting that the current node is classified into the visited road network node set; n is a radical ofiA road network node set connected with the i node is obtained;
indicating that the candidate node does not belong to the set of visited nodes.
In one embodiment, S3 specifically includes:
s3.1: constructing an objective function of an intelligent charging station optimization model,
1) wisdom charging station economic objective: the intelligent charging station benefits are maximized;
wherein, F1Earnings for intelligent charging station k;
fast charging for the intelligent charging station;
for the intelligent charging station to purchase electric power, i.e. tie-line power,
the intelligent charging station purchases electricity from the power grid,
the intelligent charging station sells electricity to the power grid; cgrid(t) the price of electricity purchased by the power grid in the period of t;
energy storage cost for the intelligent charging station k; kESA charge-discharge cost coefficient for the energy storage device;
the charging and discharging power of the energy storage device is respectively t time period; etac、ηdRespectively the charge and discharge efficiency of the energy storage device;
2) grid security objectives: the tie line power fluctuation is minimal;
wherein, F2The tie line power for the intelligent charging station k fluctuates;
3) road network utilization ratio target: the node overall unbalance rate is minimum;
wherein, F3The total unbalance rate of k road network nodes of the intelligent charging station is obtained;
according to the economic target of the intelligent charging station, the safety target of the power grid and the utilization rate target of the power grid, constructing an objective function of the intelligent charging station:
in the formula: f is the comprehensive benefit of the intelligent charging station k;
respectively representing the maximum and minimum values of the safety target of the power grid;
respectively representing the maximum and minimum values of the grid utilization rate target; c. C1、c2Converting coefficients for the target benefit;
s3.2: constructing constraint conditions of an intelligent charging station optimization model, wherein the constraint conditions of the intelligent charging station optimization model comprise:
1) fast charge power balance constraint:
wherein the content of the first and second substances,
in order to intelligently charge the power of the energy storage device,
the energy storage device is discharged and,
the energy storage device is charged;
2) binding the intelligent charging station tie line power, wherein the tie line power of each intelligent charging station node must be within a certain range;
wherein the content of the first and second substances,
the minimum and maximum power of the grid node where the intelligent charging station k is located are respectively obtained;
3) the charging service cost upper and lower limits of the intelligent charging station are restricted:
wherein, Cmax、CminRespectively serving an upper limit and a lower limit of the price for the intelligent charging station;
4) energy storage device power and energy constraint:
Smin≤Sk(t)≤Smax
wherein S ismax、SminRespectively representing the upper limit and the lower limit of the charge state of the energy storage device; sk(t) the state of charge of the energy storage device of the intelligent charging station k in a time period t;
and the maximum values of the charging and discharging power of the energy storage device are respectively. One or more technical solutions in the embodiments of the present application have at least one or more of the following technical effects:
the invention provides a charging optimization method of an intelligent charging station based on the quick charging demand of an electric vehicle, which comprises the steps of firstly, adjusting the service price of the intelligent charging station according to the comprehensive load information and the traffic flow information of the intelligent charging station; then calculating the charging time cost of the electric automobile, calculating the charging economic cost of the electric automobile according to the service price of the intelligent charging station, and constructing an electric automobile comprehensive path planning model by taking the charging time cost of the automobile and the charging economic cost of the electric automobile as targets; and then, based on the fast charging load and the traffic flow brought by the decision of the electric vehicle comprehensive path planning model, establishing a smart charging station optimization model by taking the economic benefit of the smart charging station, a power grid safety target and a road network utilization rate target as comprehensive targets, and scheduling energy storage equipment in the smart charging station through the smart charging station optimization model to obtain a charging scheduling result.
After the electric vehicle generates a quick charging demand, an intelligent charging station which is more beneficial to the user can be selected for charging through the electric vehicle comprehensive path planning model. And the road network traffic flow can be changed when the user of the electric vehicle goes to the intelligent charging station, and the influence caused by the charging of the electric vehicle can be optimized by the intelligent charging station through reasonably scheduling the energy storage capacity through the method provided by the invention. The intelligent charging station charging optimization strategy based on the electric vehicle quick charging requirement can reduce the electric vehicle charging comprehensive cost, improve the comprehensive benefit of the intelligent charging station, optimize the scheduling effect and meet the urgent need of future large-scale electric vehicle quick charging.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
FIG. 1 is a flowchart illustrating an overall method for optimizing charging of a smart charging station based on a fast charging requirement of an electric vehicle according to an embodiment;
fig. 2 is a charging optimization strategy architecture of a smart charging station in an embodiment;
FIG. 3 is a schematic diagram of a network architecture in accordance with an embodiment;
FIG. 4 is a schematic diagram of the base load and renewable energy output curves of an embodiment;
fig. 5 is a schematic diagram of electricity prices at each time period of the smart charging station according to an embodiment;
FIG. 6 is a schematic diagram of smart charging station tie line power in an embodiment
FIG. 7 is a schematic view of a smart charging station traffic flow in accordance with an embodiment;
fig. 8 is a schematic diagram of the comprehensive benefits of the intelligent charging station in the embodiment.
Detailed Description
The inventor of the application finds out through a great deal of research and practice that: as an energy supplement link of the electric vehicle, along with the technical development, the functions of the charging station are gradually developed towards integration and integration. Some research has been conducted on rapid charging station design criteria that integrate photovoltaic and energy storage devices, but only consider power flow between different systems, and do not consider benefit optimization of charging stations and electric vehicles.
It can be seen that most of the current research focuses on achieving system optimization by reasonably scheduling the space-time order of the electric vehicle, and relatively few researches are conducted on intelligent charging stations with comprehensive energy complementation. Compared with a conventional charging station, the intelligent charging station is provided with a wind power and photovoltaic power generation system, and also needs to be provided with an energy storage system with a certain capacity in consideration of fluctuation and intermittent characteristics of the wind power and the photovoltaic power generation. Therefore, the key for ensuring the stable and economic operation of the whole system is to realize the energy control and scheduling optimization of the intelligent charging station.
Aiming at the defects and optimization requirements of the existing research, the invention provides an intelligent charging station charging optimization method based on the quick charging requirement of an electric vehicle, so that the aim of improving the charging scheduling effect is fulfilled.
In order to achieve the above technical effects, the present invention has the following general inventive concept:
through an electricity price incentive means, different fast charging loads and traffic flows are brought according to decision results of the electric vehicles, and each intelligent charging station builds an optimization model taking an economic target, a power grid safety target and a road network utilization rate target of the intelligent charging station as a comprehensive target by scheduling energy storage of the charging station. Secondly, the comprehensive path planning model of the electric automobile is established based on the charging time cost and the charging economic cost of the electric automobile. And solving the model by using a genetic algorithm to obtain an optimal operation strategy for optimizing the charging comprehensive cost of the electric automobile and the comprehensive benefit of the intelligent charging station. The strategy provided by the invention can effectively reduce the comprehensive charging cost of the electric vehicle and improve the comprehensive benefit of the intelligent charging station.
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 derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1 to 8, the present embodiment provides a charging optimization method for an intelligent charging station based on a fast charging requirement of an electric vehicle, including:
s1: adjusting the service price of the intelligent charging station according to the comprehensive load information and the traffic flow information of the intelligent charging station;
s2: calculating the charging time cost of the electric automobile, calculating the charging economic cost of the electric automobile according to the service price of the intelligent charging station, and constructing an electric automobile comprehensive path planning model by taking the charging time cost of the automobile and the charging economic cost of the electric automobile as targets, wherein the electric automobile comprehensive path planning model is used for selecting a corresponding intelligent charging station for charging for an electric automobile user when the electric automobile generates a quick charging demand;
s3: based on the fast charging load and the traffic flow brought by the decision of the electric vehicle comprehensive path planning model, the intelligent charging station optimization model is established by taking the economic benefit of the intelligent charging station, the power grid safety target and the road network utilization rate target as comprehensive targets, and the energy storage equipment in the intelligent charging station is scheduled through the intelligent charging station optimization model to obtain a charging scheduling result.
Specifically, step S1 is to provide a strategy for making the daily service price of the intelligent charging station, and float and adjust the service price of the intelligent charging station according to the comprehensive load information and traffic flow information of the intelligent charging station.
Step S2, an electric vehicle comprehensive path planning model is established, and an optimal charging path is planned and an optimal intelligent charging station is selected for the electric vehicle by taking the comprehensive cost formed by the electric vehicle charging economic cost and the charging time cost as a target.
Step S3 is to construct an intelligent charging station optimization model by taking the economic benefit, the grid security objective, and the grid utilization objective of the intelligent charging station as the comprehensive objectives, and obtain a better charging scheduling scheme through the intelligent charging station optimization model, on the basis of considering the fast charging load and the traffic flow brought by the electric vehicle comprehensive path planning model decision.
Please refer to fig. 1, which is a flowchart illustrating a charging optimization method for an intelligent charging station based on a fast charging requirement of an electric vehicle.
In one embodiment, S1 specifically includes:
s1.1: evaluating the influence of the comprehensive load of the intelligent charging station on the service price of the intelligent charging station,
the comprehensive load of the intelligent charging station is as follows:
wherein the content of the first and second substances,
respectively synthesizes the load, the basic load and the new energy at the t-th time period for the intelligent charging station kSource output;
obtaining a comprehensive load mean value according to the comprehensive load, and expressing the influence rate of the comprehensive load on the service price of the intelligent charging station as
Wherein the content of the first and second substances,
in order to integrate the influence rate of the load on the service price of the intelligent charging station k,
synthesizing a load mean value for the intelligent charging station k, wherein T represents a moment;
s1.2: evaluating the influence of the traffic flow of the road network nodes of the intelligent charging station on the service price of the intelligent charging station,
the road health vehicle flow range is [0.4C ]jk,0.7Cjk]Therefore, the road section unbalance quantity formed by the road network nodes connected with the intelligent charging station nodes is obtained:
wherein the content of the first and second substances,
express that wisdom charging station k node arrives road network node jkThe unbalance amount of the road section in the time period t; j. the design is a squarekA road network node set connected with the intelligent charging station k is formed; j is a function ofkRepresents the nodes of the road network connected with the intelligent charging station k, jk∈Jk；
Show intelligent charging station k and road network node jkThe traffic flow of the connected road section at the time period t;
show intelligent charging station k and road network node jkThe traffic capacity of the connected road sections;
according to the amount of unbalance of the traffic flow of each road section, the influence rate of the traffic flow on the service price of the intelligent charging station is calculated
Expressed as:
wherein the content of the first and second substances,
rate of influence of traffic flow on intelligent charging station k service price;
s1.3: according to the influence of the comprehensive load of the intelligent charging station and the road network node traffic flow of the intelligent charging station on the service price of the intelligent charging station, the service price of the intelligent charging station is adjusted.
Specifically, in step S1.2, since the road segment most likely to be congested in the road network is the road segment formed by the road network nodes connected to the intelligent charging station nodes, the healthy traffic flow range of the road is defined first, and then the imbalance of the road segment formed by the road network nodes connected to the intelligent charging station nodes is obtained, so as to obtain the influence rate of the traffic flow on the service price of the intelligent charging station.
Fig. 3 is a schematic diagram of a road network structure in an embodiment, and fig. 4 is a schematic diagram of a basic load and renewable energy output curve in an embodiment. Fig. 5 is a schematic diagram of electricity prices of the intelligent charging station in each time period according to an embodiment.
In one embodiment, S1.3 specifically includes:
when the influence rate of the comprehensive load and the traffic flow of the intelligent charging station on the service price of the intelligent charging station is more than or equal to 1 or less than or equal to 1, the service price of each time period of the intelligent charging station is expressed as follows:
in the formula:
for the intelligent charging station k in the time period t, the service price CsStandard electricity prices for intelligent charging stations;
when the influence rate of the comprehensive load and the traffic flow of the intelligent charging station on the service price of the intelligent charging station is more than or equal to 1 and less than or equal to 1, the service price of each time period of the intelligent charging station is expressed as follows:
specifically, when the influence rates of the comprehensive load and the traffic flow on the service prices of the intelligent charging stations are both greater than or equal to 1 or both less than or equal to 1, the corresponding service prices are necessarily greater than the standard electricity prices of the intelligent charging stations or less than the standard electricity prices, and the power grid and the road network have the same effect on the service prices of the intelligent charging stations. Therefore, a first time-interval service pricing scheme of the intelligent charging station is obtained.
When the influence rates of the comprehensive load and the traffic flow on the service price of the intelligent charging station are respectively more than or equal to 1 and less than or equal to 1, the influence of the power grid and the road network on the service price of the intelligent charging station is contradictory, the electric vehicle may be caused to respond to the power grid requirement to aggravate the unbalance rate of the road network, and the electric vehicle may also be caused to respond to the road network requirement to aggravate the fluctuation of the power grid load. Therefore, the two needs to be alleviated to obtain the service pricing scheme of the second intelligent charging station in each time period.
In one embodiment, S2 specifically includes:
s2.1: calculating the cost of the charging time of the electric automobile, wherein the total charging time of the electric automobile mainly comprises the driving time of the electric automobile, the waiting time of the electric automobile and the charging time of the electric automobile,
1) the electric vehicle travel time is expressed as:
wherein, TdrThe driving time of the electric vehicle from the starting point to the intelligent charging station is obtained; u is a feasible path for the electric vehicle to reach the intelligent charging station; t isijIs the travel time for road segment ij; i. j is a node in the road network;
2) waiting time of the electric automobile:
wherein, TwCharging the electric vehicle for a waiting time; n iswThe number of vehicles in the front waiting queue; n iscThe number of vehicles which are being charged by using the charging pile; n ispThe total number of charging piles in the intelligent charging station; t isi,k(k＝1,2…,nc) Charging time remaining for the kth vehicle in charging of the ith batch;
3) charging time of the electric automobile:
wherein, TchCharging time for the electric vehicle; pchCharging power for the charging pile; SOCmaxIs the maximum state of charge of the battery; SOCwCharging state of charge when prompting for a vehicle; c is the battery capacity; EC (EC)drEnergy consumption of a path for the electric automobile to go to the intelligent charging station; e.g. of the typetMaintaining constant-temperature non-power in the vehicle for the electric vehicle;
s2.2: calculating the charging economic cost of the electric automobile according to the service price of the intelligent charging station, wherein the charging economic cost of the electric automobile mainly comprises the initial charging economic cost of the electric automobile, the power energy consumption cost of the electric automobile for going to the intelligent charging station and the non-power energy consumption cost of the electric automobile for going to the intelligent charging station;
1) initial charging economic cost of the electric vehicle:
tc＝t0+Tdr+Tw
wherein, CinThe method comprises the following steps of (1) initially charging the electric automobile with economic cost, namely the economic cost from vehicle prompt charging to full charge; t is t0Generating a moment for charging the electric automobile, namely a vehicle prompt charging moment; t is tcCharging time for users;
is tcSmart charging station k service price at the time period:
2) electric automobile goes to wisdom charging station route power energy consumption cost:
wherein, CpcThe cost of power consumption in a path for the electric vehicle to go to the intelligent charging station; EC (EC)ijEnergy consumption is consumed for the electric automobile to run on the road section ij;
3) electric automobile goes to the non-power energy consumption cost of wisdom charging station:
wherein, CnpcThe non-power energy consumption cost for the electric vehicle to go to the intelligent charging station; Δ t is the period length;
s2.3: constructing an electric automobile comprehensive path planning model by taking automobile charging time cost and electric automobile charging economic cost as targets, and expressing as follows:
Csy＝min{(Cin+Cpc+Cnpc)+λ·(Tdr+Tw+Tch)}
wherein, CsyThe comprehensive cost of charging the electric automobile is saved; λ is a time cost conversion factor, for example, λ is 35 yuan/hour.
Specifically, in S2.1, the driving time of the electric vehicle, i.e., the driving time of the electric vehicle from the departure point to the intelligent charging station; and after the electric automobile arrives at the intelligent charging station, queuing according to the single queue. The waiting time of the electric vehicle from arriving at the intelligent charging station to starting charging depends on the charging demand time of the vehicles in the front waiting queue and the residual charging time of the vehicles in the charging state, so that a calculation formula of the waiting time of the electric vehicle is obtained. Assuming that the charger charge remains constant, i.e. the charging power is a constant value PchThe charging time of the electric vehicle is mainly related to the state of charge (SOC) of the battery, that is, the energy consumption of the vehicle, and the lower the SOC of the battery, the higher the charged amount of the electric vehicle is, and the longer the charging time is, so as to obtain a calculation formula of the charging time of the electric vehicle. Concrete implementation of Lizhong, TchCharging time for the electric vehicle; pchCharging power for charging piles, Pch＝60kW；SOCmaxTo the maximum state of charge, SOC, of the batterymax＝0.9；SOCwTo indicate the state of charge, SOC, of a vehiclew0.3; c is the battery capacity, and C is 24 kWh; EC (EC)drEnergy consumption of a path for the electric automobile to go to the intelligent charging station; e.g. of the typetThe constant-temperature non-power in the vehicle is maintained for the electric vehicle.
In S2.2, the power energy loss of the electric vehicle is related to a plurality of factors, and is mainly determined by factors such as path length, driving speed, driving time, vehicle parameters, gradient and the like, so that a calculation formula of the power energy consumption cost of the electric vehicle from a starting point to the intelligent charging station can be obtained. The non-power energy consumption of the electric automobile is mainly the energy consumption of the vehicle-mounted air conditioner, so that the non-power energy consumption is assumed to be the energy consumption of the vehicle-mounted air conditioner, and a calculation formula of the non-power energy consumption cost of the electric automobile going to the intelligent charging station can be obtained.
In one embodiment, the constraints of the electric vehicle comprehensive path planning model in step S2 include:
1) electric automobile residual capacity can satisfy electric automobile and reach selected wisdom charging station:
(SOCmax-SOCw)·C-ECdr-et·Tdr＞SOCmin·C
therein, SOCminTo maintain a minimum state of charge for the life of the battery, e.g. SOCmin＝0.1；
2) Traffic flow of each road section of a road network node is restricted, and the traffic flow of each road section cannot exceed the traffic capacity of the road section:
wherein the content of the first and second substances,
traffic flow for section ij at time t, CijThe traffic capacity of the road section ij is shown, and N is a road network node set;
3) the road network intermediate node selection constraint is expressed as:
wherein i is a node where the vehicle is currently located; j is the next candidate node; x is the number ofijThe variable is 0-1 and indicates whether the electric automobile selects to pass through the road section ij or not; sTFor the electric automobile to access the road network node set, i belongs to STRepresenting that the current node is classified into the visited road network node set; n is a radical ofiA road network node set connected with the i node is obtained;
indicating that the candidate node does not belong to the set of visited nodes.
Specifically, in the road network intermediate node selection constraint, the selectable node after the road network intermediate node request is necessarily the node connected with the node, and the electric vehicle does not select to return to the previous node.
In one embodiment, S3 specifically includes:
s3.1: constructing an objective function of an intelligent charging station optimization model,
1) wisdom charging station economic objective: the intelligent charging station benefits are maximized;
wherein, F1Earnings for intelligent charging station k;
fast charging for the intelligent charging station;
for the intelligent charging station to purchase electric power, i.e. tie-line power,
the intelligent charging station purchases electricity from the power grid,
the intelligent charging station sells electricity to the power grid; cgrid(t) the price of electricity purchased by the power grid in the period of t;
energy storage cost for the intelligent charging station k; kESFor charging or discharging energy storage devices by cost factor, e.g. KES0.19 yuan/kWh;
charging and discharging energy storage device respectively in t periodPower; etac、ηdRespectively charge-discharge efficiency of energy storage devices, e.g. etac＝0.95，ηd＝0.95。
2) Grid security objectives: the tie line power fluctuation is minimal;
wherein, F2The tie line power for the intelligent charging station k fluctuates;
3) road network utilization ratio target: the node overall unbalance rate is minimum;
wherein, F3The total unbalance rate of k road network nodes of the intelligent charging station is obtained;
according to the economic target of the intelligent charging station, the safety target of the power grid and the utilization rate target of the power grid, constructing an objective function of the intelligent charging station:
in the formula: f is the comprehensive benefit of the intelligent charging station k;
respectively representing the maximum and minimum values of the safety target of the power grid;
respectively representing the maximum and minimum values of the grid utilization rate target; c. C1、c2Converting coefficients for target benefit, e.g. c110000 yuan, c210000 yuan.
S3.2: constructing constraint conditions of an intelligent charging station optimization model, wherein the constraint conditions of the intelligent charging station optimization model comprise:
1) fast charge power balance constraint:
wherein the content of the first and second substances,
in order to intelligently charge the power of the energy storage device,
the energy storage device is discharged and,
the energy storage device is charged;
2) binding the intelligent charging station tie line power, wherein the tie line power of each intelligent charging station node must be within a certain range;
wherein the content of the first and second substances,
the minimum and maximum power of the grid node where the intelligent charging station k is located are respectively obtained; for example,
3) the charging service cost upper and lower limits of the intelligent charging station are restricted:
wherein, Cmax、CminServing upper and lower price limits for intelligent charging stations, e.g. Cmax2-membered/kWh, Cmin0.2 yuan/kWh;
4) energy storage device power and energy constraint:
Smin≤Sk(t)≤Smax
wherein S ismax、SminRespectively representing the upper limit and the lower limit of the charge state of the energy storage device; sk(t) the state of charge of the energy storage device of the intelligent charging station k in a time period t;
the maximum charge and discharge power of the energy storage device, for example,
specifically, after the electric vehicle generates a quick charging demand, a user of the electric vehicle selects a smart charging station which is more beneficial to the user to charge according to the electric vehicle comprehensive path planning model. And the electric automobile user can make road network traffic flow change at the in-process of going to this wisdom charging station, and the process of finally charging at wisdom charging station can make the fast load increase of this wisdom charging station. Therefore, the intelligent charging station optimization model mainly aims to optimize income of the intelligent charging station, optimize power fluctuation of power grid nodes and optimize traffic flow of a road network, so that the traffic flow is kept in a healthy interval range, and the road network is smoother. Thus, the intelligent charging station optimization model sets an intelligent charging station economic objective, a grid security objective, and a grid utilization objective.
The main purpose of the intelligent charging station operator is that the comprehensive benefit of the intelligent charging station is the maximum, so the potential benefits of minimizing the fluctuation of the intelligent charging station connecting lines and minimizing the total unbalance rate of the connecting nodes must be converted into actual benefits, and therefore, it is assumed that the power grid operation and the connecting departments give certain scheduling subsidies to the intelligent charging station, and the benefit maximization of the intelligent charging station is realized. And then, obtaining the target function of the intelligent charging station.
Please refer to fig. 2, which is a structure diagram of a charging optimization strategy of a smart charging station in an embodiment, please refer to fig. 6 to 8, wherein fig. 6 is a schematic diagram of power of a smart charging station tie line in an embodiment, and fig. 7 is a schematic diagram of traffic flow of a smart charging station in an embodiment; fig. 8 is a schematic diagram of the comprehensive benefits of the intelligent charging station in the embodiment.
The invention has the beneficial effects that: the user of the electric automobile can select the intelligent charging station to charge with more excellent charging comprehensive cost, and the influence caused by charging of the electric automobile can be optimized by the intelligent charging station through reasonably scheduling energy storage capacity. The intelligent charging station charging optimization strategy based on the electric vehicle quick charging requirement can reduce the electric vehicle charging comprehensive cost, improve the comprehensive benefit of the intelligent charging station, and meet the urgent need of future large-scale electric vehicle quick charging.
It should be understood that the above description of the preferred embodiments is given for clarity and not for any purpose of limitation, and that various changes, substitutions and alterations can be made herein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (6)
1. The utility model provides an wisdom charging station optimization method that charges based on electric automobile fills demand soon which characterized in that includes:
s1: adjusting the service price of the intelligent charging station according to the comprehensive load information and the traffic flow information of the intelligent charging station;
s2: calculating the charging time cost of the electric automobile, calculating the charging economic cost of the electric automobile according to the service price of the intelligent charging station, and constructing an electric automobile comprehensive path planning model by taking the charging time cost of the automobile and the charging economic cost of the electric automobile as targets, wherein the electric automobile comprehensive path planning model is used for selecting a corresponding intelligent charging station for charging for an electric automobile user when the electric automobile generates a quick charging demand;
s3: based on the fast charging load and the traffic flow brought by the decision of the electric vehicle comprehensive path planning model, the intelligent charging station optimization model is established by taking the economic benefit of the intelligent charging station, the power grid safety target and the road network utilization rate target as comprehensive targets, and the energy storage equipment in the intelligent charging station is scheduled through the intelligent charging station optimization model to obtain a charging scheduling result.
2. The method of claim 1, wherein S1 specifically comprises:
s1.1: evaluating the influence of the comprehensive load of the intelligent charging station on the service price of the intelligent charging station,
the comprehensive load of the intelligent charging station is as follows:
wherein the content of the first and second substances,
respectively integrating the load, the basic load and the new energy output of the intelligent charging station k in the t-th time period;
obtaining a comprehensive load mean value according to the comprehensive load, and expressing the influence rate of the comprehensive load on the service price of the intelligent charging station as
Wherein the content of the first and second substances,
in order to integrate the influence rate of the load on the service price of the intelligent charging station k,
synthesizing a load mean value for the intelligent charging station k, wherein T represents a moment;
s1.2: evaluating the influence of the traffic flow of the road network nodes of the intelligent charging station on the service price of the intelligent charging station,
the road health vehicle flow range is
Therefore, the road section unbalance quantity formed by the road network nodes connected with the intelligent charging station nodes is obtained:
wherein the content of the first and second substances,
express that wisdom charging station k node arrives road network node jkThe unbalance amount of the road section in the time period t; j. the design is a squarekA road network node set connected with the intelligent charging station k is formed; j is a function ofkRepresents the nodes of the road network connected with the intelligent charging station k, jk∈Jk；
Show intelligent charging station k and road network node jkThe traffic flow of the connected road section at the time period t;
show intelligent charging station k and road network node jkThe traffic capacity of the connected road sections;
according to the amount of unbalance of the traffic flow of each road section, the influence rate of the traffic flow on the service price of the intelligent charging station is calculated
Expressed as:
wherein the content of the first and second substances,
rate of influence of traffic flow on intelligent charging station k service price;
s1.3: according to the influence of the comprehensive load of the intelligent charging station and the road network node traffic flow of the intelligent charging station on the service price of the intelligent charging station, the service price of the intelligent charging station is adjusted.
3. The method according to claim 2, wherein S1.3 specifically comprises:
when the influence rate of the comprehensive load and the traffic flow of the intelligent charging station on the service price of the intelligent charging station is more than or equal to 1 or less than or equal to 1, the service price of each time period of the intelligent charging station is expressed as follows:
in the formula:
for the intelligent charging station k in the time period t, the service price CsStandard electricity prices for intelligent charging stations;
when the influence rate of the comprehensive load and the traffic flow of the intelligent charging station on the service price of the intelligent charging station is more than or equal to 1 and less than or equal to 1, the service price of each time period of the intelligent charging station is expressed as follows:
4. the method of claim 1, wherein S2 specifically comprises:
s2.1: calculating the cost of the charging time of the electric automobile, wherein the total charging time of the electric automobile mainly comprises the driving time of the electric automobile, the waiting time of the electric automobile and the charging time of the electric automobile,
1) the electric vehicle travel time is expressed as:
wherein, TdrThe driving time of the electric vehicle from the starting point to the intelligent charging station is obtained; u is a feasible path for the electric vehicle to reach the intelligent charging station; t isijIs the travel time for road segment ij; i. j is a node in the road network;
2) waiting time of the electric automobile:
wherein, TwCharging the electric vehicle for a waiting time; n iswThe number of vehicles in the front waiting queue; n iscThe number of vehicles which are being charged by using the charging pile; n ispThe total number of charging piles in the intelligent charging station; t isi,k(k＝1,2…,nc) Charging time remaining for the kth vehicle in charging of the ith batch;
3) charging time of the electric automobile:
wherein, TchCharging time for the electric vehicle; pchCharging power for the charging pile; SOCmaxIs the maximum state of charge of the battery; SOCwCharging state of charge when prompting for a vehicle; c is the battery capacity; EC (EC)drEnergy consumption of a path for the electric automobile to go to the intelligent charging station; e.g. of the typetMaintaining constant-temperature non-power in the vehicle for the electric vehicle;
s2.2: calculating the charging economic cost of the electric automobile according to the service price of the intelligent charging station, wherein the charging economic cost of the electric automobile mainly comprises the initial charging economic cost of the electric automobile, the power energy consumption cost of the electric automobile for going to the intelligent charging station and the non-power energy consumption cost of the electric automobile for going to the intelligent charging station;
1) initial charging economic cost of the electric vehicle:
tc＝t0+Tdr+Tw
wherein, CinThe method comprises the following steps of (1) initially charging the electric automobile with economic cost, namely the economic cost from vehicle prompt charging to full charge; t is t0Generating a moment for charging the electric automobile, namely a vehicle prompt charging moment; t is tcCharging time for users;
is tcSmart charging station k service price at the time period:
2) electric automobile goes to wisdom charging station route power energy consumption cost:
wherein, CpcThe cost of power consumption in a path for the electric vehicle to go to the intelligent charging station; EC (EC)ijEnergy consumption is consumed for the electric automobile to run on the road section ij;
3) electric automobile goes to the non-power energy consumption cost of wisdom charging station:
wherein, CnpcThe non-power energy consumption cost for the electric vehicle to go to the intelligent charging station; Δ t is the period length;
s2.3: constructing an electric automobile comprehensive path planning model by taking automobile charging time cost and electric automobile charging economic cost as targets, and expressing as follows:
Csy＝min{(Cin+Cpc+Cnpc)+λ·(Tdr+Tw+Tch)}
wherein, CsyThe comprehensive cost of charging the electric automobile is saved; λ is a time cost conversion factor.
5. The method of claim 4, wherein the constraints of the electric vehicle comprehensive path planning model in step S2 include:
1) electric automobile residual capacity can satisfy electric automobile and reach selected wisdom charging station:
(SOCmax-SOCw)·C-ECdr-et·Tdr＞SOCmin·C
therein, SOCminMinimum state of charge to maintain battery life;
2) traffic flow of each road section of a road network node is restricted, and the traffic flow of each road section cannot exceed the traffic capacity of the road section:
wherein the content of the first and second substances,
traffic flow for section ij at time t, CijThe traffic capacity of the road section ij is shown, and N is a road network node set;
3) the road network intermediate node selection constraint is expressed as:
wherein i is a node where the vehicle is currently located; j is the next candidate node; x is the number ofijThe variable is 0-1 and indicates whether the electric automobile selects to pass through the road section ij or not; sTFor the electric automobile to access the road network node set, i belongs to STRepresenting that the current node is classified into the visited road network node set; n is a radical ofiA road network node set connected with the i node is obtained;
indicating that the candidate node does not belong to the set of visited nodes.
6. The method of claim 1, wherein S3 specifically comprises:
s3.1: constructing an objective function of an intelligent charging station optimization model,
1) wisdom charging station economic objective: the intelligent charging station benefits are maximized;
wherein, F1Earnings for intelligent charging station k;
fast charging for the intelligent charging station;
for the intelligent charging station to purchase electric power, i.e. tie-line power,
the intelligent charging station purchases electricity from the power grid,
the intelligent charging station sells electricity to the power grid; cgrid(t) the price of electricity purchased by the power grid in the period of t;
energy storage cost for the intelligent charging station k; kESA charge-discharge cost coefficient for the energy storage device;
the charging and discharging power of the energy storage device is respectively t time period; etac、ηdRespectively the charge and discharge efficiency of the energy storage device;
2) grid security objectives: the tie line power fluctuation is minimal;
wherein, F2The tie line power for the intelligent charging station k fluctuates;
3) road network utilization ratio target: the node overall unbalance rate is minimum;
wherein, F3The total unbalance rate of k road network nodes of the intelligent charging station is obtained;
according to the economic target of the intelligent charging station, the safety target of the power grid and the utilization rate target of the power grid, constructing an objective function of the intelligent charging station:
in the formula: f is the comprehensive benefit of the intelligent charging station k;
respectively representing the maximum and minimum values of the safety target of the power grid;
respectively representing the maximum and minimum values of the grid utilization rate target; c. C1、c2Converting coefficients for the target benefit;
s3.2: constructing constraint conditions of an intelligent charging station optimization model, wherein the constraint conditions of the intelligent charging station optimization model comprise:
1) fast charge power balance constraint:
wherein the content of the first and second substances,
in order to intelligently charge the power of the energy storage device,
the energy storage device is discharged and,
the energy storage device is charged;
2) binding the intelligent charging station tie line power, wherein the tie line power of each intelligent charging station node must be within a certain range;
wherein the content of the first and second substances,
the minimum and maximum power of the grid node where the intelligent charging station k is located are respectively obtained;
3) the charging service cost upper and lower limits of the intelligent charging station are restricted:
wherein, Cmax、CminRespectively serving prices for intelligent charging stationsUpper and lower limits;
4) energy storage device power and energy constraint:
Smin≤Sk(t)≤Smax
wherein S ismax、SminRespectively representing the upper limit and the lower limit of the charge state of the energy storage device; sk(t) the state of charge of the energy storage device of the intelligent charging station k in a time period t;
and the maximum values of the charging and discharging power of the energy storage device are respectively.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112907153A (en) * | 2021-01-15 | 2021-06-04 | 中原工学院 | Electric vehicle dispatching method considering various requirements of user in mixed scene |
| CN114037177A (en) * | 2021-11-22 | 2022-02-11 | 山东德佑电气股份有限公司 | Bus charging load optimization method in crowded traffic state based on train number chain |
| CN114485702A (en) * | 2021-12-30 | 2022-05-13 | 国网江苏省电力有限公司连云港供电分公司 | Electric vehicle charging path planning method and system |
| CN117553816A (en) * | 2023-11-02 | 2024-02-13 | 浙江大学 | Electric vehicle path planning and charging and discharging strategy combined optimization method considering preference and demand of vehicle owners |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103793758A (en) * | 2014-01-23 | 2014-05-14 | 华北电力大学 | Multi-objective optimization scheduling method for electric vehicle charging station including photovoltaic power generation system |
| CN105844432A (en) * | 2016-05-01 | 2016-08-10 | 上海大学 | VANET based electric automobile charge scheduling system and method |
| CN109242163A (en) * | 2018-08-21 | 2019-01-18 | 国网山东省电力公司电力科学研究院 | A kind of coordination optimizing method of wind-powered electricity generation quotient and electric automobile charging station based on leader-followers games |
| CN109301852A (en) * | 2018-11-23 | 2019-02-01 | 武汉理工大学 | A kind of micro-capacitance sensor classification united economic load dispatching method of multiple target |
| CN109523051A (en) * | 2018-09-18 | 2019-03-26 | 国网浙江省电力有限公司经济技术研究院 | A kind of electric car charging Real time optimal dispatch method |
| CN109840624A (en) * | 2019-01-08 | 2019-06-04 | 浙江工业大学 | A kind of electric car charging schedule optimization method based on Dijkstra algorithm |
| CN110271448A (en) * | 2019-06-10 | 2019-09-24 | 南方科技大学 | Charging schedule method, charging schedule system and the charging station of charging station |
| CN110458332A (en) * | 2019-07-18 | 2019-11-15 | 天津大学 | A kind of electric vehicle rapid charging demand dispatch method based on load space transfer |
| CN111652405A (en) * | 2020-02-20 | 2020-09-11 | 贵州电网有限责任公司 | Double-layer optimization method for electric vehicle charging and discharging strategy and power grid side-sharing electricity price |
-
2020
- 2020-10-19 CN CN202011118693.6A patent/CN112183882B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103793758A (en) * | 2014-01-23 | 2014-05-14 | 华北电力大学 | Multi-objective optimization scheduling method for electric vehicle charging station including photovoltaic power generation system |
| CN105844432A (en) * | 2016-05-01 | 2016-08-10 | 上海大学 | VANET based electric automobile charge scheduling system and method |
| CN109242163A (en) * | 2018-08-21 | 2019-01-18 | 国网山东省电力公司电力科学研究院 | A kind of coordination optimizing method of wind-powered electricity generation quotient and electric automobile charging station based on leader-followers games |
| CN109523051A (en) * | 2018-09-18 | 2019-03-26 | 国网浙江省电力有限公司经济技术研究院 | A kind of electric car charging Real time optimal dispatch method |
| CN109301852A (en) * | 2018-11-23 | 2019-02-01 | 武汉理工大学 | A kind of micro-capacitance sensor classification united economic load dispatching method of multiple target |
| CN109840624A (en) * | 2019-01-08 | 2019-06-04 | 浙江工业大学 | A kind of electric car charging schedule optimization method based on Dijkstra algorithm |
| CN110271448A (en) * | 2019-06-10 | 2019-09-24 | 南方科技大学 | Charging schedule method, charging schedule system and the charging station of charging station |
| CN110458332A (en) * | 2019-07-18 | 2019-11-15 | 天津大学 | A kind of electric vehicle rapid charging demand dispatch method based on load space transfer |
| CN111652405A (en) * | 2020-02-20 | 2020-09-11 | 贵州电网有限责任公司 | Double-layer optimization method for electric vehicle charging and discharging strategy and power grid side-sharing electricity price |
Non-Patent Citations (6)
| Title |
|---|
| XU P: "Dynamic pricing at electric vehicle charging stations for queueing delay reduction", 《2017 IEEE 37TH INTERNATIONAL CONFERENCE ON DISTRIBUTED COMPUTING SYSTEMS》 * |
| YU L: "Online energy management for a sustainable smart home with an HVAC load and random occupancy", 《IEEE TRANSACTIONS ON SMART GRID》 * |
| 严弈遥: "融合电网和交通网信息的电动车辆最优充电路径推荐策略", 《中国电机工程学报》 * |
| 徐鹏: "动态定价策略在电动汽车公共充电网络的应用问题研究", 《中国优秀硕士学位论文全文数据库 (基础科学辑)》 * |
| 杨洪明: "利用实时交通信息感知的电动汽车路径选择和充电导航策略", 《电力系统自动化》 * |
| 陈炜: "含电动汽车储能与分布式风力发电的虚拟发电厂优化运行", 《电力自动化设备》 * |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112907153A (en) * | 2021-01-15 | 2021-06-04 | 中原工学院 | Electric vehicle dispatching method considering various requirements of user in mixed scene |
| CN114037177A (en) * | 2021-11-22 | 2022-02-11 | 山东德佑电气股份有限公司 | Bus charging load optimization method in crowded traffic state based on train number chain |
| CN114037177B (en) * | 2021-11-22 | 2024-05-14 | 山东德佑电气股份有限公司 | Bus charging load optimization method based on train number chain in crowded traffic state |
| CN114485702A (en) * | 2021-12-30 | 2022-05-13 | 国网江苏省电力有限公司连云港供电分公司 | Electric vehicle charging path planning method and system |
| CN117553816A (en) * | 2023-11-02 | 2024-02-13 | 浙江大学 | Electric vehicle path planning and charging and discharging strategy combined optimization method considering preference and demand of vehicle owners |
| CN117553816B (en) * | 2023-11-02 | 2024-05-24 | 浙江大学 | Electric vehicle path planning and charging and discharging strategy combined optimization method considering preference and demand of vehicle owners |
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