CN112183882B - 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 quick charging requirements of electric vehicles. 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 a 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 first and the second end of the pipe are connected with each other,
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 first and the second end of the pipe are connected with each other,
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 ,0.7C jk ]Therefore, the unbalance of the road section formed by the road network nodes connected with the intelligent charging station nodes is obtained:
wherein the content of the first and second substances,
show wisdom charging stationk node to road network node j k The unbalance amount of the road section in the time period t; j. the design is a square k A road network node set connected with the intelligent charging station k is formed; j is a function of k Represents the nodes of the road network connected with the intelligent charging station k, j k ∈J k ；
Show intelligent charging station k and road network node j k The traffic flow of the connected road section at the time t;
show wisdom charging station k and road network node j k The 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 C s Standard 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, T dr The 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 is a unit of ij Is the travel time for road segment ij; i. j is a node in the road network;
2) Waiting time of the electric automobile:
wherein, T w Charging the electric vehicle for a waiting time; n is w The number of vehicles in the front waiting queue; n is c The number of vehicles which are being charged by using the charging pile; n is p The total number of the charging piles in the intelligent charging station; t is i,k (k＝1,2…,n c ) Charging the ith batchk remaining charging times of vehicles;
3) Charging time of the electric automobile:
wherein, T ch Charging time for the electric automobile; p ch Charging power for the charging pile; SOC max Is the battery maximum state of charge; SOC w Charging state of charge when prompting for a vehicle; c is the battery capacity; EC (EC) dr Energy consumption of a path for the electric automobile to go to the intelligent charging station; e.g. of the type t Maintaining 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:
t c ＝t 0 +T dr +T w
wherein, C in The method comprises the following steps of (1) initially charging the electric automobile, namely, prompting the economic cost from the vehicle to charge to full charge; t is t 0 Generating a moment for charging the electric automobile, namely a vehicle prompt charging moment; t is t c Charging time for users;
is t c Smart charging station k service price at the time period:
2) Electric automobile goes to wisdom charging station route power energy consumption cost:
wherein, C pc The cost of power consumption in a path for the electric vehicle to go to the intelligent charging station; EC (EC) ij Energy 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, C npc The 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:
C sy ＝min{(C in +C pc +C npc )+λ·(T dr +T w +T ch )}
wherein, C sy The 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:
(SOC max -SOC w )·C-EC dr -e t ·T dr ＞SOC min ·C
therein, SOC min Minimum 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, C ij The traffic capacity of the road section ij is obtained, 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 located currently; j is the next alternative node; x is the number of ij The variable is 0-1 and indicates whether the electric automobile selects to pass through the road section ij or not; s. the T For the electric automobile to access the road network node set, i belongs to S T Representing that the current node is classified into the visited road network node set; n is a radical of i A 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, F 1 Earnings for intelligent charging station k;
rapidly charging 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; c grid (t) the price of electricity purchased by the power grid in the period of t;
energy storage cost for the intelligent charging station k; k ES A charge-discharge cost coefficient for the energy storage device;
the charging and discharging power of the energy storage device is respectively t time period; eta c 、η d Respectively the charge and discharge efficiency of the energy storage device;
2) Grid security objectives: the tie line power fluctuation is minimal;
wherein, F 2 The tie line power for the intelligent charging station k fluctuates;
3) Road network utilization ratio target: the node overall unbalance rate is minimum;
wherein, F 3 The 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 the maximum and minimum values of the grid utilization rate target; c. C 1 、c 2 Converting coefficients for the target benefit;
s3.2: the constraint conditions of the intelligent charging station optimization model are constructed, 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) The intelligent charging station tie line power constraint is that 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 upper limit and the lower limit of the charging service fee of the intelligent charging station are constrained:
wherein, C max 、C min Respectively serving an upper limit and a lower limit of the price for the intelligent charging station;
4) Energy storage device power and energy constraint:
S min ≤S k (t)≤S max
wherein S is max 、S min Respectively representing the upper limit and the lower limit of the charge state of the energy storage device; s k (t) the state of charge of the energy storage device of the intelligent charging station k in a time period t;
and the maximum value of the charging and discharging power of the energy storage device is 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 an overall flowchart of a charging optimization method for an intelligent charging station based on a quick 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 intelligent 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 obtained by a person skilled in the art without making any creative effort based on the embodiments in the present invention, belong to 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 electric 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 mainly proposes a daily service price formulation strategy for the intelligent charging station, and adjusts the service price of the intelligent charging station in a floating manner according to the comprehensive load information and traffic flow information of the intelligent charging station.
And S2, establishing an electric vehicle comprehensive path planning model, and planning an optimal charging path and selecting an optimal intelligent charging station 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.
And S3, on the basis of considering the fast charging load and the traffic flow brought by the decision of the electric vehicle comprehensive path planning model, constructing a smart charging station optimization model by taking the economic benefit, the power grid safety target and the road network utilization rate target of the smart charging station as comprehensive targets, and obtaining a better charging scheduling scheme through the smart charging station optimization model.
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 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 ,0.7C jk ]Therefore, the unbalance of the road section formed by the road network nodes connected with the intelligent charging station nodes is obtained:
wherein the content of the first and second substances,
show that wisdom charging station k node arrives road network node j k The unbalance amount of the road section in the time period t; j. the design is a square k A road network node set connected with the intelligent charging station k is formed; j is a function of k Represents the nodes of the road network connected with the intelligent charging station k, j k ∈J k ；
Show intelligent charging station k and road network node j k The traffic flow of the connected road section at the time period t;
show wisdom charging station k and road network node j k The traffic capacity of the connected road sections;
according to the amount of the traffic flow unbalance 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 section in the road network where congestion is most likely to occur is the road section 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 unbalance amount of the road section 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 C s Standard 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, T dr The 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 is ij Is the travel time for road segment ij; i. j is a node in the road network;
2) Waiting time of the electric automobile:
wherein, T w Charging the electric vehicle for a waiting time; n is w The number of vehicles in the front waiting queue; n is a radical of an alkyl radical c The number of vehicles which are being charged by using the charging pile; n is p The total number of charging piles in the intelligent charging station; t is i,k (k＝1,2…,n c ) Charging time remaining for the kth vehicle in charging of the ith batch;
3) Charging time of the electric automobile:
wherein, T ch Charging time for the electric vehicle; p ch Charging power for the charging pile; SOC max Is the maximum state of charge of the battery; SOC w Charging state of charge when prompting for a vehicle; c is the battery capacity; EC (EC) dr Energy consumption is carried out on a path for the electric automobile to go to the intelligent charging station; e.g. of the type t Maintaining 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:
t c ＝t 0 +T dr +T w
wherein, C in The 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 t 0 Generating a moment for charging the electric automobile, namely a vehicle prompt charging moment; t is t c Charging time for users;
is t c Intelligent charging station k service price in located time periodGrid:
2) Electric automobile goes to wisdom charging station route power energy consumption cost:
wherein, C pc The cost of power consumption in a path for the electric vehicle to go to the intelligent charging station; EC (EC) ij Energy 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, C npc The 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 the automobile charging time cost and the electric automobile charging economic cost as targets, and expressing the models as follows:
C sy ＝min{(C in +C pc +C npc )+λ·(T dr +T w +T ch )}
wherein, C sy The comprehensive cost of charging the electric automobile is saved; λ is a time cost reduction factor, for example, λ =35 yen/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 P ch The charging time of an electric vehicle is mainly related to the state of charge (SOC) of a battery, namely, the energy consumption of the vehicle, and the lower the SOC of the battery, the charged electric vehicleThe higher the quantity is, the longer the charging time is, so that a calculation formula of the charging time of the electric automobile is obtained. Concrete implementation of Lizhong, T ch Charging time for the electric automobile; p ch Charging power for charging piles, P ch ＝60kW；SOC max To the maximum state of charge, SOC, of the battery max ＝0.9；SOC w To indicate the state of charge, SOC, of a vehicle w =0.3; c is battery capacity, C =24kWh; EC (EC) dr Energy consumption of a path for the electric automobile to go to the intelligent charging station; e.g. of a cylinder t The 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:
(SOC max -SOC w )·C-EC dr -e t ·T dr ＞SOC min ·C
therein, SOC min To maintain a minimum state of charge for the life of the battery, e.g. SOC min ＝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 first and the second end of the pipe are connected with each other,
traffic flow for section ij at time t, C ij The 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 a radical of a fluorine atom ij The variable is 0-1 and indicates whether the electric automobile selects to pass through the road section ij or not; s T For the electric automobile to access the road network node set, i belongs to S T Representing that the current node is classified into the visited road network node set; n is a radical of i A 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, F 1 Earnings for intelligent charging station k;
for making an intelligenceThe intelligent charging station quickly charges;
for the intelligent charging station to purchase and sell electric power, namely tie-line power,
the intelligent charging station purchases electricity from the power grid,
the intelligent charging station sells electricity to the power grid; c grid (t) buying and selling electricity price for the electric network in t time period;
energy storage cost for the intelligent charging station k; k ES For charging and discharging the energy storage device by a cost factor, e.g. K ES =0.19 yuan/kWh;
the charging and discharging power of the energy storage device at the time t is respectively; eta c 、η d Respectively for charging and discharging efficiency of energy storage devices, e.g. eta c ＝0.95，η d ＝0.95。
2) Grid security objectives: the tie line power fluctuation is minimal;
wherein, F 2 The tie line power for the intelligent charging station k fluctuates;
3) Road network utilization ratio target: the node overall unbalance rate is minimum;
wherein, F 3 The 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 the maximum and minimum values of the safety targets of the power grid;
respectively representing the maximum and minimum values of the grid utilization rate target; c. C 1 、c 2 Converting coefficients for target benefit, e.g. c 1 =10000 yuan, c 2 =10000 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 first and the second end of the pipe are connected with each other,
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, C max 、C min Serving upper and lower price limits for intelligent charging stations, e.g. C max =2 yuan/kWh, C min =0.2 yuan/kWh;
4) Energy storage device power and energy constraint:
S min ≤S k (t)≤S max
wherein S is max 、S min Respectively representing the upper limit and the lower limit of the charge state of the energy storage device; s. the k (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 the road network traffic volume change in the in-process of going to this wisdom charging station, and the process of charging at wisdom charging station can make the fast charge increase of this wisdom charging station finally. 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.
Fig. 2 is a schematic diagram of a charging optimization strategy of a smart charging station in an embodiment, fig. 6 to 8 are shown, where fig. 6 is a schematic diagram of a tie line power of a smart charging station in an embodiment, and fig. 7 is a schematic diagram of a traffic volume 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 illustrative, and not restrictive, and that various changes and modifications may be made therein by those skilled in the art without departing from the scope of the invention as defined in the appended claims.
Claims (4)
1. The utility model provides a 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 electric 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, 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 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;
wherein, 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 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, T dr The 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 is ij Is the travel time for road segment ij; i. j is a node in the road network;
2) Waiting time of the electric automobile:
wherein, T w Charging the electric vehicle for a waiting time; n is w The number of vehicles in the front waiting queue; n is c The number of vehicles which are being charged by using the charging pile; n is p The total number of charging piles in the intelligent charging station; t is l,h For the h vehicle charging the first batch, h =1,2 …, n c ；
3) Charging time of the electric automobile:
wherein, T ch Charging time for the electric vehicle; p ch Charging power for the charging pile; SOC max Is the maximum state of charge of the battery; SOC w Charging state of charge when prompting for a vehicle; c is the battery capacity; EC (EC) dr Energy consumption of a path for the electric automobile to go to the intelligent charging station; e.g. of the type t Maintaining 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 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:
t c ＝t 0 +T dr +T w
wherein, C in Initial charging of electric vehicles for economic cost, i.e. prompting from vehicleThe economic cost of charging to full charge; t is t 0 Generating a moment for charging the electric automobile, namely a vehicle prompt charging moment; t is t c Charging time for users;
is t c The service price of the intelligent charging station k in the time period;
2) Electric automobile goes to wisdom charging station route power energy consumption cost:
wherein, C pc The cost of power consumption in a path for the electric vehicle to go to the intelligent charging station; EC (EC) ij Energy 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, C npc The non-power energy consumption cost for the electric vehicle to go to the intelligent charging station; delta t is the time period length of the electric automobile going to the intelligent charging station;
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:
C sy ＝min{(C in +C pc +C npc )+λ·(T dr +T w +T ch )}
wherein, C sy The comprehensive cost of charging the electric automobile is saved; λ is a time cost conversion coefficient;
s3 specifically comprises the following steps:
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, F 1 A benefit for the 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; c grid (t) the price of electricity purchased by the power grid in the period of t;
energy storage cost for the intelligent charging station k; k ES A charge-discharge cost coefficient for the energy storage device;
the charging and discharging power of the energy storage device is respectively t time period; eta c 、η d Respectively representing the charge and discharge efficiency of the energy storage device, wherein delta t' is the unit time interval length in the charge and discharge process;
2) Grid security objectives: the tie line power fluctuation is minimum;
wherein, F 2 The tie line power for the intelligent charging station k fluctuates;
3) Road network utilization ratio target: the total unbalance rate of the nodes is minimum;
wherein, F 3 The 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; f 3 max 、F 3 min Respectively representing the maximum and minimum values of the grid utilization rate target; c. C 1 、c 2 Converting coefficients for the target benefit;
s3.2: the constraint conditions of the intelligent charging station optimization model are constructed, wherein the constraint conditions of the intelligent charging station optimization model comprise:
1) Fast charge power balance constraint:
wherein, the first and the second end of the pipe are connected with each other,
in order to provide the power of the energy storage device for the intelligent charging station,
the energy storage device is discharged and,
the energy storage device is charged;
2) The intelligent charging station tie line power constraint is that 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, C max 、C min Respectively serving an upper limit and a lower limit of the price for the intelligent charging station;
4) Energy storage device power and energy constraint:
S min ≤S k (t)≤S max
wherein S is max 、S min Respectively representing the upper limit and the lower limit of the charge state of the energy storage device; s k (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.
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 first and the second end of the pipe are connected with each other,
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 first and the second end of the pipe are connected with each other,
in order to integrate the influence rate of the load on the service price of the intelligent charging station k,
synthesizing a mean value of loads for the intelligent charging station k, wherein T represents the number of time periods;
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 j k The unbalance amount of the road section in the time period t; j is a unit of k A road network node set connected with the intelligent charging station k is formed; j is a function of k Represents the nodes of the road network connected with the intelligent charging station k, j k ∈J k ；
Show intelligent charging station k and road network node j k The traffic flow of the connected road section at the time period t;
show intelligent charging station k and road network node j k The 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 first and the second end of the pipe are connected with each other,
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 C s Standard 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 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:
(SOC max -SOC w )·C-EC dr -e t ·T dr >SOC min ·C
therein, SOC min Minimum 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, C ij The 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 of ij The variable is 0-1 and indicates whether the electric automobile selects to pass through the road section ij or not; s. the T For the electric automobile to access the road network node set, i belongs to S T Representing that the current node is classified into the visited road network node set; n is a radical of i A 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.
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