CN113962742B - A dynamic pricing method for electric vehicle charging considering user urgency - Google Patents
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Abstract
本发明公开一种考虑用户紧迫度的电动汽车充电动态定价方法。该方法为：把用户的急迫度分为M个等级，每个等级分别对应一个充电功率；构建充电站‑电动汽车用户收益模型；构建所述模型的目标函数；设置约束条件；制定一天内充电站不同功率的总充电价格策略；制定均衡状态下急迫度等级为m时电动汽车最优充电价格策略。本发明通过充电站与用户的动态博弈给出一个合理的不同充电功率电价来引导用户选择相应的充电模式，从而在最大化充电站和电动汽车用户收益同时降低电网的负荷冲击。
The present invention discloses a dynamic pricing method for electric vehicle charging that takes into account the user's urgency. The method comprises: dividing the user's urgency into M levels, each level corresponding to a charging power; constructing a charging station-electric vehicle user benefit model; constructing the objective function of the model; setting constraints; formulating a total charging price strategy for different powers of the charging station within a day; and formulating an optimal charging price strategy for the electric vehicle when the urgency level is m in an equilibrium state. The present invention provides a reasonable electricity price for different charging powers through a dynamic game between the charging station and the user to guide the user to select the corresponding charging mode, thereby maximizing the benefits of the charging station and the electric vehicle user while reducing the load impact of the power grid.
Description
Technical Field
The invention relates to an electric vehicle charging dynamic pricing method and a charging station dynamic power distribution method adopting the pricing method, in particular to an electric vehicle charging dynamic pricing method considering user urgency and a charging station dynamic power distribution method adopting the pricing method.
Background
In recent years, electric vehicles have been rapidly developed. The sales of new energy automobiles in China can be expected to reach 40% -50% of the total sales of automobiles by 2030, and the sales of new energy automobiles in China can be more than 50% in 2035, and the sales of new energy automobiles can be maintained to be about 1 hundred million. In the continuous large-scale development process of the electric automobile, the disordered charging of the electric automobile can bring great impact to a power grid. And electricity price is an important form of user demand response, and the electricity price can be used as a signal to guide or change the charging mode of the user. The reasonable electricity price mechanism can motivate users to select reasonable time to charge and discharge, so that the user income is increased on one hand, and the impact on the power grid is reduced on the other hand.
In terms of charging station power distribution, a user selects variable charging power according to his own needs, and then the charging station performs power distribution under the limited power given by the power grid. This strategy is based on time-of-use electricity prices, dividing charging power into several levels, each power assuming a charging price, with dynamic charge and user charging time as objective functions. But this strategy does not take into account the user's demand response and does not give a reasonable charge price.
In the aspect of electric vehicle charging pricing, the user response degree and the peak shaving incentive given to the charging station by the power grid are considered, and the charging station and the electric vehicle user form two game parties. Wherein the charging station is the leading person of price establishment, the user is the follower of price establishment, then carries out dynamic game around price and user response degree. Targeting charge station revenue maximization and user charge cost minimization. However, this strategy ignores the different charging demands of the user and is not practical.
Disclosure of Invention
In order to solve the power distribution of the charging station, the charging requirement of a user can be met. On the basis of fully considering the two strategies, the invention provides an electric vehicle charging dynamic pricing method considering the urgency of a user and a charging station dynamic power distribution method adopting the pricing method.
The invention adopts the following technical scheme that the electric automobile charging dynamic pricing method considering the urgency of a user comprises the following steps:
1. the urgency of the user is divided into M grades, each grade corresponds to one charging power,
2. Constructing a charging station-electric vehicle user profit model, wherein the model comprises a power grid, a charging station and at least one user, wherein between the charging station and each user, the charging station gives a charging price c m(i),cm (i) of the user, which is represented as a charging price with an urgent level M in the ith period, the M takes on the value 1,2, and M, the user responds according to the charging price, x n(i),xn (i) which represents a charging decision of the user of the electric vehicle in the ith period, and N takes on the value 1,2, and N, N is the number of electric vehicles, wherein when x n (i) =1, the charging of the electric vehicle in the ith period is started, x n (i) =0, which represents that the electric vehicle in the nth period does not participate in the charging, and the power grid gives an economic incentive alpha of the charging station between the charging station and the power grid;
3. Constructing an objective function of the model, namely, maximum income max (f) of the charging station and minimum charging cost min (f 4) of the user, wherein the income f=f 1+f2+f3,f1+f2+f3 of the charging station is the selling electricity profit of the charging station, the economic incentive of the power grid and the parking cost of the electric automobile respectively, and f 4 is the charging cost of the user;
4. setting constraint conditions, namely constraint on charging power of the electric automobile, constraint on state of charge of a battery and constraint on charging requirement of the electric automobile in unit time;
5. the method comprises the following steps of formulating a total charging price strategy c of different powers of a charging station in one day:
c={c(1),c(2),...,c(μ)}
wherein c (1) is a total charging price policy of different powers of the charging station in the 1 st period, c (1) = { c 1(1),c2(1),…,cm(1)},c1 (1) is a charging price of the 1 st period urgent grade 1, c 2 (1) is a charging price of the 1 st period urgent grade 2, and c m (1) is a charging price of the 1 st period urgent grade m;
c (2) is a total charging price policy of different powers of the charging station in the 2 nd period, c (2) = { c 1(2),c2(2),…,cm(2)},c1 (2) is a charging price of the 2 nd period urgent grade 1, c 2 (2) is a charging price of the 2 nd period urgent grade 2, and c m (2) is a charging price of the 2 nd period urgent grade m;
c (μ) is a total charging price policy of charging stations of different powers in the μ -th period, c (μ) = { c 1(μ),c2(μ),…,cm(μ)},c1 (μ) is a charging price of urgent rank 1 in the μ -th period, c 2 (μ) is a charging price of urgent rank 2 in the μ -th period, and c m (μ) is a charging price of urgent rank m in the μ -th period;
then the total load response policy x for the user during the day is:
x={x(1),x(2),...x(μ)}
Wherein x (1) is a total load response policy of the 1 st time period user, x (1) = { x 1(1),x2(1),…,xn(1)},x1 (1) is a charging decision of the 1 st electric vehicle user in the 1 st time period, x 2 (1) is a charging decision of the 2 nd electric vehicle user in the 1 st time period, and x n (1) is a charging decision of the n th electric vehicle user in the 1 st time period;
x (2) is a total load response policy of the user in the 2 nd period, x (2) = { x 1(2),x2(2),…,xn(2)},x1 (2) is a charging decision of the user of the 1 st electric automobile in the 2 nd period, x 2 (2) is a charging decision of the user of the 2 nd electric automobile in the 2 nd period, and x n (2) is a charging decision of the user of the n-th electric automobile in the 2 nd period;
x (μ) is a total load response policy of the user in the μ -th period, x (μ) = { x 1(μ),x2(μ),…,xn(μ)},x1 (μ) is a charging decision of the user of the 1 st electric vehicle in the μ -th period, x 2 (2) is a charging decision of the user of the 2 nd electric vehicle in the μ -th period, and x n (μ) is a charging decision of the user of the n-th electric vehicle in the μ -th period;
6. In the game process, a charging station gives a charging price c m with an urgency level of m in a mu period, a user makes a response strategy x m according to the charging price, then benefits of the charging station and the user in the round, namely f (c m,xm) and f 4(cm,xm) are calculated, in the next game round, the charging station gives a charging price according to the response strategy of the user, then the user makes a response strategy x m according to the charging price, benefits f (c m,xm) and f 4(cm,xm obtained in the last game are the maximum value in all game rounds, k groups c m、xm、f(cm,xm)、f4(cm,xm are generated after k games are conducted, when the k groups { f k(cm,xm),f4k(cm,xm) are equal to the k-1 groups { f k-1(cm,xm),f4k-1(cm,xm) }, namely the two parties can not obtain more benefits, balance is achieved, and the obtained charging station and the strategy (c m *,x* m) of the electric automobile user are optimal;
Wherein c m * is an optimal charging price strategy of the electric automobile when the urgency level is m in an equilibrium state; and the optimal load response strategy is an optimal load response strategy for the electric automobile user when the urgency level is m in the balanced state.
According to the invention, a reasonable price of different charging power is given through dynamic games of the charging station and the user to guide the user to select a corresponding charging mode, so that the benefits of the charging station and the electric automobile user are maximized and the load impact of a power grid is reduced.
As a further improvement of the above solution, the economic incentive f 1 of the grid is:
Wherein D (P sta) is a charging station peak shaving effect evaluation index, and D nor is a normalization factor of D (P sta).
Further, D (P sta) is designed to:
Wherein P grid (i) is the i-th period system load power; P sta (i) is the charging station load power of the ith period; average charging station load power for one day, and P sta is the set of charging station load power for μ time periods.
Still further, D nor is designed to:
Where P grid is the set of system load powers for μ time periods.
Further, 24 hours a day is divided into mu time periods, i takes the value of 1,2, mu, and then the electricity selling profit f 2 of the charging station is:
Wherein, P EV (n) is the charging power of the nth electric automobile, and c g (i) is the electricity purchase price of the ith period.
Further, the 24 hours a day is divided into mu time periods, i takes the value of 1,2, mu, and the parking cost f 3 of the electric vehicle at the charging station is respectively:
Wherein, T lea (n) is the time when the nth electric vehicle leaves the charging station, T end (n) is the charging end time of the nth electric vehicle, and c p is the parking fee of one hour of the charging station.
Further, the charging fee f 4 of the user is:
Further, (1) constraint of charging power of electric automobile in unit time:
Wherein T char is the charging time of the electric automobile,
(2) Battery state of charge constraints:
The SOC max(n)、SOCmin (n) is the upper limit value and the lower limit value of the charge state of the battery of the nth electric automobile of the electric automobiles respectively, and the charge state of the nth electric automobile of the SOC i (n) in the ith period;
(3) Electric automobile charging demand constraint:
(SOCend-SOCsta)·E＝PEV·Tchar·ηchar
The SOC sta、SOCend is the initial charge state and the final charge state of the electric vehicle, P EV is the charging power selected by the user, and η char is the charging efficiency of the electric vehicle.
The invention also provides an electric automobile charging dynamic pricing device considering the urgency of a user, which comprises:
The user urgency level setting module is used for realizing the step one of outputting the electric vehicle charging dynamic pricing method taking the urgency of the user into consideration;
the model building module is used for realizing the step two of outputting the electric vehicle charging dynamic pricing method taking the user urgency into consideration;
the objective function construction module is used for realizing the step three of outputting the electric vehicle charging dynamic pricing method taking the user urgency into consideration;
the constraint condition setting module is used for realizing the step four of outputting the electric vehicle charging dynamic pricing method taking the urgency of the user into consideration;
The total price policy making module is used for realizing the step five of outputting the electric vehicle charging dynamic pricing method taking the urgency of the user into consideration;
And the optimal charging price strategy making module is used for realizing the step six of outputting the electric vehicle charging dynamic pricing method taking the urgency of the user into consideration. The invention also provides a charging station dynamic power distribution method, which comprises the following steps:
Reading the total power distributed by a power grid;
acquiring the maximum charging power of the electric automobile and the initial value of the state of charge of the battery of the electric automobile according to the total power;
Accumulating charging power of all users of the charging station to obtain total charging power, wherein the users select corresponding charging power according to charging price of each power, and the charging price adopts an electric vehicle charging optimal electricity price strategy c m * when the urgency level is m in an equilibrium state output by the electric vehicle charging dynamic pricing method taking urgency of the users into consideration;
Judging whether the total charging power exceeds the total power distributed by a power grid;
if the charging total power does not exceed the total power distributed by the power grid, judging whether the charging parking space is full, if the charging station parking space is not full, charging the electric vehicle, and then recording the charging ending time and the time of leaving the charging station;
and if the total charging power exceeds the total power distributed by the power grid, returning to the calculation step of the shortest waiting time.
The invention aims to solve the problem of power distribution of the charging station and can meet the charging requirement of a user. Based on the two strategies, a reasonable price of different charging power and electricity is given through dynamic games of the charging station and the user to guide the user to select a corresponding charging mode. Thereby reducing the load impact of the power grid while maximizing charging station and electric vehicle user benefits.
Drawings
FIG. 1 is a diagram of a charging station-electric vehicle user benefit model of the present invention;
FIG. 2 is a flow chart of the charging station and user response dynamic gaming of the present invention;
fig. 3 is a flow chart of a dynamic power distribution method of the charging station.
Detailed Description
The present invention will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
Example 1
The charging dynamic pricing method of the electric automobile considers the urgency of the user, and gives a reasonable price of different charging power through dynamic games of the charging station and the user to guide the user to select a corresponding charging mode.
The dynamic pricing method for the electric automobile charging mainly comprises the following steps.
1. The urgency of the user is divided into M grades, and each grade corresponds to one charging power.
2. And constructing a charging station-electric vehicle user profit model.
Referring to fig. 1, the model includes a power grid, a charging station, and at least one user.
Between the charging station and each user, the charging station gives a user charging price c m(i).cm (i) denoted as an i-th period urgent charging price of class M, value M1, 2. The user responds x n(i),xn (i) according to the charging price to represent a charging decision of the nth electric vehicle user in the period i, N takes on the values 1, 2. When x n (i) =1, it indicates that the nth electric vehicle starts charging in the ith period, and x n (i) =0, it indicates that the nth electric vehicle does not participate in charging in the ith period.
Between the charging station and the power grid, the power grid gives the charging station economic incentive α.
3. An objective function of the model is constructed.
Maximum income max (f) of the charging station and minimum charging cost min (f 4) of the user, wherein income f=f 1+f2+f3,f1+f2+f3 of the charging station is sales profit of the charging station, economic incentive of a power grid and parking cost of an electric vehicle, and f 4 is charging cost of the user.
Assume that the economic incentive f 1 for the grid is:
Wherein D (P sta) is a charging station peak shaving effect evaluation index, and D nor is a normalization factor of D (P sta).
D (P sta) is designed as:
Wherein P grid (i) is the i-th period system load power; P sta (i) is the charging station load power of the ith period; average charging station load power for one day, and P sta is the set of charging station load power for μ time periods.
D nor is designed as:
Where P grid is the set of system load powers for μ time periods.
The 24 hours a day is divided into mu time periods, i takes the values of 1,2, and mu, and then the electricity selling profits f 2 of the charging station and the parking cost f 3 of the electric automobile are respectively as follows:
Wherein, P EV (n) is the charging power of the nth electric automobile, and c g (i) is the electricity purchase price of the ith period.
Wherein, T lea (n) is the time when the nth electric vehicle leaves the charging station, T end (n) is the charging end time of the nth electric vehicle, and c p is the parking fee of one hour of the charging station.
The charge fee f 4 of the user is:
4. and setting constraint conditions, namely constraint on charging power of the electric automobile, constraint on state of charge of a battery and constraint on charging requirement of the electric automobile in unit time.
(1) Electric automobile charging power constraint in unit time
Wherein T char is the charging time of the electric automobile.
(2) Battery state of charge constraints
The SOC max(n)、SOCmin (n) is the upper limit value and the lower limit value of the charge state of the battery of the nth electric automobile of the electric automobiles, and the charge state of the nth electric automobile of the SOC i (n) in the ith period.
(3) Electric automobile charging demand constraint
(SOCend-SOCsta)·E＝PEV·Tchar·ηchar
The SOC sta、SOCend is the initial charge state and the final charge state of the electric vehicle, P EV is the charging power selected by the user, and η char is the charging efficiency of the electric vehicle.
5. Assume that the total charging price policy c for charging stations of different power during a day is:
c={c(1),c(2),...,c(μ)}
Wherein c (1) is a total charging price policy of different powers of the charging station in the 1 st period, c (1) = { c 1(1),c2(1),…,cm(1)},c1 (1) is a charging price of the 1 st period urgent grade 1, c 2 (1) is a charging price of the 1 st period urgent grade 2, and c m (1) is a charging price of the 1 st period urgent grade m.
C (2) is a total charging price policy of different powers of the charging station in the 2 nd period, c (2) = { c 1(2),c2(2),…,cm(2)},c1 (2) is a charging price of the 2 nd period urgent grade 1, c 2 (2) is a charging price of the 2 nd period urgent grade 2, and c m (2) is a charging price of the 2 nd period urgent grade m.
C (μ) is a total charging price policy of charging stations of different powers in the μ -th period, c (μ) = { c 1(μ),c2(μ),…,cm(μ)},c1 (μ) is a charging price of urgent rank 1 in the μ -th period, c 2 (μ) is a charging price of urgent rank 2 in the μ -th period, and c m (μ) is a charging price of urgent rank m in the μ -th period.
Then the total load response policy x for the user during the day is:
x={x(1),x(2),...x(μ)}
Wherein x (1) is a total load response policy of the 1 st time period user, x (1) = { x 1(1),x2(1),…,xn(1)},x1 (1) is a charging decision of the 1 st electric vehicle user in the 1 st time period, x 2 (1) is a charging decision of the 2 nd electric vehicle user in the 1 st time period, and x n (1) is a charging decision of the n th electric vehicle user in the 1 st time period.
X (2) is the total load response strategy of the user in the 2 nd period, x (2) = { x 1(2),x2(2),…,xn(2)},x1 (2) is the charging decision of the user of the 1 st electric automobile in the 2 nd period, x 2 (2) is the charging decision of the user of the 2 nd electric automobile in the 2 nd period, and x n (2) is the charging decision of the user of the n-th electric automobile in the 2 nd period.
X (μ) is a total load response policy of the user in the μ -th period, x (μ) = { x 1(μ),x2(μ),…,xn(μ)},x1 (μ) is a charging decision of the user of the 1 st electric vehicle in the μ -th period, x 2 (2) is a charging decision of the user of the 2 nd electric vehicle in the μ -th period, and x n (μ) is a charging decision of the user of the n-th electric vehicle in the μ -th period.
6. In the game process, a charging station gives a charging price c m with an urgency level of m in a mu period, a user makes a response strategy x m according to the charging price, then benefits of the charging station and the user in the round, namely f (c m,xm) and f 4(cm,xm) are calculated, in the next game round, the charging station gives a charging price according to the response strategy of the user, then the user makes a response strategy x m according to the charging price, benefits f (c m,xm) and f 4(cm,xm obtained in the last game are the maximum value in all game rounds, k groups c m、xm、f(cm,xm)、f4(cm,xm are generated after k games are conducted, when the k groups { f k(cm,xm),f4k(cm,xm) are equal to the k-1 groups { f k-1(cm,xm),f4k-1(cm,xm) }, namely the two parties can not obtain more benefits, balance is achieved, and the obtained charging station and the strategy (c m *,x* m) of the electric automobile user are optimal;
Wherein c m * is an optimal charging price strategy of the electric automobile when the urgency level is m in an equilibrium state; and the optimal load response strategy is an optimal load response strategy for the electric automobile user when the urgency level is m in the balanced state.
Before application, the electric vehicle charging dynamic pricing method can be designed into an independent software module, such as an electric vehicle charging dynamic pricing device considering the urgency of a user. The electric automobile charging dynamic pricing device comprises a user urgency level setting module, a model construction module, an objective function construction module, a constraint condition setting module, a total price strategy making module and an optimal charging price strategy making module.
The method comprises a step I of realizing the output of the electric vehicle charging dynamic pricing method considering the user urgency, a step II of realizing the output of the electric vehicle charging dynamic pricing method considering the user urgency by a model building module, a step III of realizing the output of the electric vehicle charging dynamic pricing method considering the user urgency by a target function building module, a step IV of realizing the output of the electric vehicle charging dynamic pricing method considering the user urgency by a constraint condition setting module, a step V of realizing the output of the electric vehicle charging dynamic pricing method considering the user urgency by a total price policy making module and a step six of realizing the output of the electric vehicle charging dynamic pricing method considering the user urgency by an optimal charging price policy making module. When the electric vehicle charging dynamic pricing method is applied, the method can be applied to a charging station dynamic power distribution method, and the charging station dynamic power distribution method comprises the following steps.
Acquiring the maximum charging power of the electric automobile and the initial value of the state of charge of the battery of the electric automobile according to the total power;
accumulating charging power of all users of the charging station to obtain total charging power, wherein the users select corresponding charging power according to charging price of each power, and the charging price adopts an optimal charging price strategy c m * of the electric vehicle when the urgency level is m under the balanced state output by the dynamic charging pricing method of the electric vehicle considering the urgency of the users;
Judging whether the total charging power exceeds the total power distributed by a power grid;
if the charging total power does not exceed the total power distributed by the power grid, judging whether the charging parking space is full, if the charging station parking space is not full, charging the electric vehicle, and then recording the charging ending time and the time of leaving the charging station;
and if the total charging power exceeds the total power distributed by the power grid, returning to the calculation step of the shortest waiting time.
Example 2
The urgency of the user is considered, and the urgency is divided into M grades, and each grade corresponds to one charging power. The charging station may be priced based on the user's responsiveness to each charging mode price. Thus, the charging process can be seen as a dynamic gaming process with respect to the charging station and the user. The charging station aims at achieving the maximum benefit of the charging station, and the user aims at minimum charging cost. Both parties game around the charging price and the response degree, and the higher the charging price (c), the lower the response degree (x) of the user. In the game process, when any party of the electric vehicle charging station and the electric vehicle user changes the strategy, more benefits cannot be obtained, the game is balanced. And then calculating optimal population individuals, namely charging price and user response degree in an equilibrium state by using an optimization algorithm, such as a genetic algorithm, a particle swarm optimization algorithm, a nonlinear programming algorithm and the like. After obtaining the charging price corresponding to each power, executing the power distribution strategy of the charging station. The following is a specific flow.
(1) A charging station-electric vehicle user benefit model is constructed as shown in fig. 1. The model comprises three parts of a power grid, a charging station and a user. Between the charging station and the user, the charging station gives the user a charging price c m (i) from which the user responds x n (i). Between the charging station and the power grid, the power grid gives the charging station an economic incentive α according to the peak shaving effect of the charging station. The maximum benefit max (f) of the charging station and the minimum charging cost min (f 4) of the user are model objective functions. It is therefore necessary to calculate the charging station benefit f and the user charging fee f 4.
(2) For electric vehicle charging stations, the benefits mainly come from three aspects, namely the sales profits of the charging station, the economic incentives of the electric network and the parking costs of the electric vehicle. Assuming that the revenues in these three aspects are f 1、f2、f3, respectively, the total revenue fee is f=f 1+f2+f3.
The method comprises the steps of dividing 24 hours a day into mu time periods, assuming that N is the number of electric vehicles, c m (i) is the charging price with the urgent grade of M in the ith time period, the value of M is 1,2,. M, x n (i) is the charging decision of an nth electric vehicle user in the ith time period, when x n (i) =1, the nth electric vehicle starts to charge in the ith time period, x n (i) =0, the nth electric vehicle does not participate in charging in the ith time period, the correlation degree of an electric vehicle charging station load curve and a power grid load curve shows the charging station peak regulation effect, and the pearson correlation coefficient is considered to reflect the correlation between different charging stations, so that the pearson correlation coefficient is utilized to establish the peak regulation effect evaluation index. The specific expression of the charging station peak regulation effect evaluation index D (P sta) is as follows:
wherein P grid (i) is the system load power in the ith period; P sta (i) is the charging station load power of the ith period; average charging station load power for one day, and P sta is the set of charging station load power for μ time periods. Economic incentives for grid companies:
Where α is the incentive rate of the grid company, D (P sta)/Dnor represents the charging station equivalent peak shaving capacity), D nor is the normalization factor of D (P sta), and its expression is:
the selling profits of the charging station are:
wherein P EV represents the charging power of the nth electric automobile, and c g (i) is the electricity purchase price of the charging station in the ith period.
The parking cost of the electric automobile is as follows:
Wherein T lea (n) is the time when the nth electric vehicle leaves the charging station, T end (n) is the charging end time of the nth electric vehicle, and c p is the parking cost of the charging station for one hour.
(3) For the electric automobile user, on the basis of meeting self charging requirements, according to a charging price c m (i) formulated by a charging station, a corresponding charging plan is determined, and a decision variable of the charging plan is a charging decision x n (i) of each period, so that the charging cost of all electric automobiles in the day is:
(4) The model can be established under certain constraint conditions, and the three constraint conditions are electric vehicle charging power constraint, battery charge state constraint and electric vehicle charging requirement constraint respectively.
1> Electric automobile charging Power constraint
The charging power constraint of the electric automobile in unit time is as follows:
2> State of charge constraints for batteries
In the formula, the SOC min and the SOC max are respectively an upper limit value and a lower limit value of the charge state of the battery of the electric automobile, and the SOC i (n) is the charge state of the nth electric automobile in the ith period.
3> Electric automobile charging demand constraint
The actual charge amount of the electric vehicle should be equal to the charge amount required by the user, that is:
(SOCend-SOCsta)·E＝PEV·Tchar·ηchar (9)
In the formula, SOC sta and SOC end are respectively the initial charge state of the electric vehicle, E is the rated capacity of the battery, P EV is the charging power selected by a user, T char is the charging duration of the electric vehicle, and eta char is the charging efficiency of the electric vehicle.
(5) When the charging station establishes the charging price, a game process exists between the charging station and the user, wherein the process is shown in fig. 2, the price strategies of different powers of the charging station in the i-th period can be expressed as c (i) = { c 1(i),c2(i),...c6 (i) }, the price strategies of the charging station in the mu-th period can be expressed as c= { c (1), c (2),. The c (mu) }, the load response strategies of the user in the i-th period are the charging decisions of n electric automobile users and can be expressed as x (i) = { x 1(i),x2(i),...xn (i) }, and the load response strategies of the user in the mu-th period are expressed as x= { x (1), x (2),. X (mu) }. In the game process, the charging station gives a charging price c m with the urgency level m in the mu-th period, the user makes a response strategy x m according to the charging price, and then the benefits of the charging station and the user in the round are calculated, namely f (c m,xm) and f 4(cm,xm). In the next game round, the charging station gives a charging price according to the response strategy of the user, and then the user makes a response strategy x m according to the charging price. The revenues f (c m,xm) and f 4(cm,xm from the last game are the maximum of all game rounds, and after k games, k sets of c m、xm、f(cm,xm)、f4(cm,xm are generated. When the kth set { f k(cm,xm),f4k(cm,xm) } is equal to the kth-1 set { f k-1(cm,xm),f4k-1(cm,xm) }, i.e., both parties cannot receive more revenue, the game reaches equilibrium. Game strategy for obtaining charging station and electric automobile userI.e.Wherein c m * is an optimal charging price strategy of the electric automobile when the urgency level is m in an equilibrium state; and the optimal load response strategy is an optimal load response strategy for the electric automobile user when the urgency level is m in the balanced state. And finally, abstracting the game strategies of the electric vehicle charging station and the electric vehicle user into population individuals in an algorithm, and optimizing the game strategies of the two parties by using an optimization algorithm.
(6) After the optimal charging prices corresponding to different urgency levels are obtained through calculation, a charging station dynamic power distribution strategy is adopted, and a specific flow is shown in fig. 3.
(7) Firstly, the charging station reads the total power distributed by the power grid, and acquires the maximum charging power and the initial SOC value of the electric automobile.
(8) The user selects corresponding charging power according to the charging price of each power, and accumulates charging total power of the charging station.
(9) If the charging station parking space is not full, the electric vehicle charges, and then the charging ending time and the leaving time of the charging station are recorded. If the parking space is full, the shortest waiting time is calculated, and if the waiting is accepted, the waiting vehicle is added, and then whether the parking space is full is judged. If not, leaving directly.
(10) If the total charging station charging power exceeds the total power allocated by the power grid, the shortest waiting time is calculated, and the flow refers to the step (9).
Compared with the prior art, the charging system has the beneficial effects that a reasonable charging electricity price problem cannot be given in terms of charging station power distribution, and the problem of neglecting different requirements of users in terms of electric automobile charging pricing is solved. The invention combines charging station power distribution with dynamic pricing, taking into account the urgency demands of different users. Reasonable electricity price is utilized to guide a user to select a reasonable charging mode and charging time, so that the benefits of the charging station and the electric automobile user are maximum, and the impact of electric automobile charging on a power grid is reduced.
Claims (9)
1. The electric automobile charging dynamic pricing method considering the urgency of the user is characterized by comprising the following steps of:
1. Dividing the urgency of the user into M grades, wherein each grade corresponds to one charging power respectively;
2. Constructing a charging station-electric vehicle user profit model, wherein the model comprises a power grid, a charging station and at least one user, wherein between the charging station and each user, the charging station gives a user charging price c m(i),cm (i) which is expressed as a charging price with an urgent level of M in an ith period, M takes on values of 1,2, and M, the user responds to the charging price x n(i),xn (i) to represent a charging decision of the user of the nth electric vehicle in the ith period, N takes on values of 1,2, and N, N are the number of electric vehicles, and when x n (i) =1, the charging of the nth electric vehicle is started in the ith period, x n (i) =0 to represent that the nth electric vehicle does not participate in the charging in the ith period, and the power grid gives an economic incentive f 1 to the power grid between the charging station and the power grid;
3. Constructing an objective function of the model, namely, maximum income max (f) of the charging station and minimum charging cost min (f 4) of the user, wherein the income f=f 1+f2+f3,f1、f2、f3 of the charging station is respectively the economic incentive of a power grid, the electricity selling profit of the charging station and the parking cost of the electric automobile, and f 4 is the charging cost of the user;
4. setting constraint conditions, namely constraint on charging power of the electric automobile, constraint on state of charge of a battery and constraint on charging requirement of the electric automobile in unit time;
5. Assume that the total charging price policy c for charging stations of different power during a day is:
c={c(1),c(2),...,c(μ)}
wherein c (1) is a total charging price policy of different powers of the charging station in the 1 st period, c (1) = { c 1(1),c2(1),…,cm(1)},c1 (1) is a charging price of the 1 st period urgent grade 1, c 2 (1) is a charging price of the 1 st period urgent grade 2, and c m (1) is a charging price of the 1 st period urgent grade m;
c (2) is a total charging price policy of different powers of the charging station in the 2 nd period, c (2) = { c 1(2),c2(2),…,cm(2)},c1 (2) is a charging price of the 2 nd period urgent grade 1, c 2 (2) is a charging price of the 2 nd period urgent grade 2, and c m (2) is a charging price of the 2 nd period urgent grade m;
c (μ) is a total charging price policy of charging stations of different powers in the μ -th period, c (μ) = { c 1(μ),c2(μ),…,cm(μ)},c1 (μ) is a charging price of urgent rank 1 in the μ -th period, c 2 (μ) is a charging price of urgent rank 2 in the μ -th period, and c m (μ) is a charging price of urgent rank m in the μ -th period;
then the total load response policy x for the user during the day is:
x={x(1),x(2),...x(u)}
Wherein x (1) is a total load response policy of the 1 st time period user, x (1) = { x 1(1),x2(1),…,xn(1)},x1 (1) is a charging decision of the 1 st electric vehicle user in the 1 st time period, x 2 (1) is a charging decision of the 2 nd electric vehicle user in the 1 st time period, and x n (1) is a charging decision of the n th electric vehicle user in the 1 st time period;
x (2) is a total load response policy of the user in the 2 nd period, x (2) = { x 1(2),x2(2),…,xn(2)},x1 (2) is a charging decision of the user of the 1 st electric automobile in the 2 nd period, x 2 (2) is a charging decision of the user of the 2 nd electric automobile in the 2 nd period, and x n (2) is a charging decision of the user of the n-th electric automobile in the 2 nd period;
x (μ) is a total load response policy of the user in the μ -th period, x (μ) = { x 1(μ),x2(μ),…,xn(μ)},x1 (μ) is a charging decision of the user of the 1 st electric vehicle in the μ -th period, x 2 (2) is a charging decision of the user of the 2 nd electric vehicle in the μ -th period, and x n (μ) is a charging decision of the user of the n-th electric vehicle in the μ -th period;
6. in the game process, firstly, a charging station gives a charging price c m with an urgency level of m in a mu period, a user makes a response strategy x m according to the charging price, then benefits of the charging station and the user in the round, namely f (c m,xm) and f 4(cm,xm) are calculated, in the next game round, the charging station gives a charging price according to the response strategy of the user, then the user makes a response strategy x m according to the charging price, benefits f (c m,xm) and f 4(cm,xm obtained in the last game are the maximum value in all game rounds, k groups c m、xm、f(cm,xm)、f4(cm,xm are generated after k games are performed, when the k groups { f k(cm,xm),f4k(cm,xm) are equal to the k-1 groups { f k-1(cm,xm),f4k-1(cm,xm }, namely, more benefits can not be obtained by both parties, balance is achieved, and the obtained charging station and the electric vehicle user's strategy (c m, x m) are optimal;
Wherein c m * is an optimal charging price strategy of the electric automobile when the urgency level is m in an equilibrium state; An optimal load response strategy for the electric automobile user when the urgency level is m in the balanced state;
wherein, (1) the electric automobile charging power constraint in unit time:
Wherein T char is the charging time of the electric automobile,
(2) Battery state of charge constraints:
The SOC max(n)、SOCmin (n) is the upper limit value and the lower limit value of the charge state of the battery of the nth electric automobile of the electric automobiles respectively, and the charge state of the nth electric automobile of the SOC i (n) in the ith period;
(3) Electric automobile charging demand constraint:
(SOCend-SOCsta)·E＝PEV·Tchar·ηchar
The SOC sta、SOCend is the initial charge state and the final charge state of the electric vehicle, P EV is the charging power selected by the user, and η char is the charging efficiency of the electric vehicle.
2. The method for dynamically pricing electric vehicle charging with consideration of customer urgency as recited in claim 1, wherein the economic incentive f 1 of the power grid is:
Wherein, alpha is the incentive rate of the power grid company, D (P sta) is the evaluation index of the peak shaving effect of the charging station, and D nor is the normalization factor of D (P sta).
3. The method for dynamic pricing of electric vehicle charging taking into account user urgency of claim 2, wherein D (P sta) is designed to:
Wherein P grid (i) is the i-th period system load power; P sta (i) is the charging station load power of the ith period; average charging station load power for one day, and P sta is the set of charging station load power for μ time periods.
4. The method for dynamic pricing of electric vehicle charging taking into account user urgency of claim 3, wherein D nor is designed to:
Where P grid is the set of system load powers for μ time periods.
5. The method for dynamically pricing electric vehicle charging with consideration of user urgency according to claim 2, wherein 24 hours a day is divided into μ time periods, i is 1,2,..μ, and then the electricity sales profit f 2 of the charging station is:
Wherein, P EV (n) is the charging power of the nth electric automobile, and c g (i) is the electricity purchase price of the ith period.
6. The method for dynamically pricing electric vehicle charging with consideration of user urgency according to claim 2, wherein 24 hours a day is divided into μ time periods, i is 1,2,..μ, and parking fee f 3 of electric vehicle at charging station is:
Wherein, T lea (n) is the time when the nth electric vehicle leaves the charging station, T end (n) is the charging end time of the nth electric vehicle, and c p is the parking fee of one hour of the charging station.
7. The method for dynamically pricing electric vehicle charging taking into account user urgency as recited in claim 2, wherein,
The charge fee f 4 of the user is:
8. an electric vehicle charging dynamic pricing device considering user urgency, characterized in that it comprises:
A user urgency level setting module for implementing step one of the electric vehicle charging dynamic pricing method output taking into account user urgency as defined in any one of claims 1 to 7;
a model building module for implementing the step two of outputting the electric vehicle charging dynamic pricing method taking into account user urgency according to any one of claims 1 to 7;
An objective function construction module, configured to implement step three of outputting the electric vehicle charging dynamic pricing method taking into account user urgency according to any one of claims 1 to 7;
A constraint condition setting module, configured to implement step four of outputting the electric vehicle charging dynamic pricing method taking into account user urgency according to any one of claims 1 to 7;
A total price policy formulation module for implementing step five of the electric vehicle charging dynamic pricing method output taking into account user urgency as defined in any one of claims 1 to 7;
An optimal charging price policy formulation module, configured to implement step six of outputting the electric vehicle charging dynamic pricing method considering user urgency according to any one of claims 1 to 7.
9. A charging station dynamic power distribution method, characterized in that it comprises the following steps:
Reading the total power distributed by a power grid;
acquiring the maximum charging power of the electric automobile and the initial value of the state of charge of the battery of the electric automobile according to the total power;
Accumulating charging power of all users of the charging station to obtain total charging power, wherein the users select corresponding charging power according to charging price of each power, and the charging price adopts an electric vehicle charging optimal electricity price strategy c m * when the urgency level is m under the balanced state output by the electric vehicle charging dynamic pricing method considering the urgency of the users according to any one of claims 1 to 7;
Judging whether the total charging power exceeds the total power distributed by a power grid;
if the charging total power does not exceed the total power distributed by the power grid, judging whether the charging parking space is full, if the charging station parking space is not full, charging the electric vehicle, and then recording the charging ending time and the time of leaving the charging station;
and if the total charging power exceeds the total power distributed by the power grid, returning to the calculation step of the shortest waiting time.
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