A dynamic pricing method for electric vehicle charging considering user urgency

Abstract

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

[0001] The present invention relates to a dynamic pricing method for electric vehicle charging and a dynamic power allocation method for a charging station using the pricing method, and in particular to a dynamic pricing method for electric vehicle charging considering user urgency and a dynamic power allocation method for a charging station using the pricing method. Background Art

[0002] In recent years, electric vehicles have developed rapidly. By 2030, China's new energy vehicle sales are expected to reach 40%-50% of total vehicle sales; by 2035, it will exceed 50%, with a total of about 100 million vehicles. In the process of continuous large-scale development of electric vehicles, the disorderly charging of electric vehicles will have a great impact on the power grid. As an important form of user demand response, electricity prices can be used as signals to guide or change users' charging methods. A reasonable electricity price mechanism can encourage users to choose a reasonable time to charge and discharge, which will increase user benefits on the one hand and reduce the impact on the power grid on the other.

[0003] In terms of power allocation at charging stations, users choose variable charging power according to their own needs, and then the charging station allocates power under the limited power given by the power grid. This strategy is based on time-of-use electricity prices, which divides charging power into several levels, assumes a charging price for each power, and takes dynamic costs and user charging time as the objective function. However, this strategy does not take into account the user's demand response and cannot give a reasonable charging price.

[0004] In terms of electric vehicle charging pricing, the user response level and the peak load incentives given by the power grid to charging stations are taken into consideration, with charging stations and electric vehicle users as the two parties in the game. The charging station is the leader in setting electricity prices, and the user is the follower of electricity price setting. Then a dynamic game is conducted around electricity prices and user response level. The goal is to maximize the benefits of charging stations and minimize the charging costs of users. However, this strategy ignores the different charging needs of users and does not conform to the actual situation. Summary of the invention

[0005] In order to solve the power allocation of charging stations and meet the charging needs of users, based on the above two strategies, the present invention provides a dynamic pricing method for electric vehicle charging that considers the urgency of users and a dynamic power allocation method for charging stations using the pricing method.

[0006] The present invention is implemented by the following technical solution: a dynamic pricing method for electric vehicle charging considering user urgency, comprising the following steps:

[0007] 1. Divide the user's urgency into M levels, each level corresponds to a charging power.

[0008] 2. Construct a charging station-electric vehicle user benefit model, which includes a power grid, a charging station and at least one user; between the charging station and each user, the charging station gives the user a charging price c m (i), c m (i) represents the charging price of the urgency level m in the i-th period, where m is 1, 2, …, M; the user responds x according to the charging price n (i), x n (i) represents the charging decision of the nth electric vehicle user in the i-th period, n is 1, 2, ..., N, N is the number of electric vehicles, and when x n When (i) = 1, it means that the nth electric car starts charging in the i-th period; n (i) = 0, indicating that the nth electric vehicle does not participate in charging in the i-th period; between the charging station and the power grid, the power grid gives the charging station an economic incentive α;

[0009] 3. Construct the objective function of the model: the maximum revenue of the charging station max(f), the minimum charging cost of the user min(f4), where the revenue of the charging station f = f1+f2+f3, f1+f2+f3 are the profit of the charging station from selling electricity, the economic incentive of the power grid, and the parking fee of the electric vehicle; f4 is the charging cost of the user;

[0010] 4. Set constraints: electric vehicle charging power constraints per unit time, battery state of charge constraints, and electric vehicle charging demand constraints;

[0011] 5. Formulate the total charging price strategy c for different power of charging stations within one day:

[0012] c={c(1),c(2),…,c(μ)}

[0013] Where c(1) is the total charging price strategy of different powers of the charging station in the first period, c(1) = {c1(1), c2(1), …, c m (1)}, c1(1) is the charging price for the first period with an urgency level of 1, c2(1) is the charging price for the first period with an urgency level of 2, c m (1) is the charging price for the first period with urgency level m;

[0014] c(2) is the total charging price strategy of different powers of charging stations in the second period, c(2) = {c1(2), c2(2), …, c m (2)}, c1(2) is the charging price for the urgency level 1 in the second period, c2(2) is the charging price for the urgency level 2 in the second period, c m (2) is the charging price for the urgency level m in the second period;

[0015] c(μ) is the total charging price strategy of different powers of charging stations in the μth period, c(μ)={c1(μ),c2(μ),…,c m (μ)}, c1(μ) is the charging price of the μth time period with an urgency level of 1, c2(μ) is the charging price of the μth time period with an urgency level of 2, c m (μ) is the charging price for the μth time period with urgency level m;

[0016] Then, the total load response strategy x of the user in one day is:

[0017] x={x(1),x(2),...x(μ)}

[0018] Among them, x(1) is the total load response strategy of the user in the first period, x(1) = {x1(1), x2(1), …, x n (1)}, x1(1) is the charging decision of the first electric vehicle user in period 1, x2(1) is the charging decision of the second electric vehicle user in period 1, x n (1) is the charging decision of the nth electric vehicle user in period 1;

[0019] x(2) is the total load response strategy of the users in the second period, x(2) = {x1(2), x2(2), …, x n (2)}, x1(2) is the charging decision of the first electric vehicle user in the 2nd period, x2(2) is the charging decision of the second electric vehicle user in the 2nd period, x n (2) The charging decision of the nth electric vehicle user in period 2;

[0020] x(μ) is the total load response strategy of the user in the μth period, x(μ)={x1(μ),x2(μ),…,x n (μ)}, x1(μ) is the charging decision of the first electric vehicle user in the μ period, x2(2) is the charging decision of the second electric vehicle user in the μ period, x n (μ) is the charging decision of the nth electric vehicle user in the μ period;

[0021] 6. In the game process, the charging station first gives a charging price c for the urgency level m in the μth time period. m , the user makes a response strategy x according to the charging price m , and then calculate the benefits of the charging station and the user in this round, that is, f(c m ,x m ) and f4(c m ,x m ); In the next round of the game, the charging station gives a charging price based on the user's response strategy, and then the user makes a response strategy x based on the charging pricem ; The profit f(c m ,x m ) and f4(c m ,x m ) is the maximum value in all game rounds. After k rounds of games, k groups of c will be generated. m 、x m 、f(c m ,x m )、f4(c m ,x m ); when the kth group {f k (c m ,x m ), f 4k (c m ,x m )} is equal to the k-1th group {f k-1 (c m ,x m ), f 4k-1 (c m ,x m )}, that is, both parties can no longer obtain more benefits, then the game reaches equilibrium; at this time, the game strategy of the charging station and the electric vehicle user (c m * ,x * m ) is the best;

[0022] Among them, c m * is the optimal charging price strategy for electric vehicles when the urgency level is m in the equilibrium state; It is the optimal load response strategy for electric vehicle users when the urgency level is m in the equilibrium state.

[0023] The present invention provides a reasonable electricity price with different charging powers through dynamic game between charging stations and users to guide users to select corresponding charging modes, thereby maximizing the benefits of charging stations and electric vehicle users while reducing the load impact of the power grid.

[0024] As a further improvement of the above scheme, the economic incentive f1 of the power grid is:

[0025]

[0026] Among them, D(P sta ) is the evaluation index of peak load regulation effect of charging station, D nor D(P sta ) is the normalization factor.

[0027] Furthermore, D(P sta ) is designed as:

[0028]

[0029] Among them, P grid (i) is the system load power in the i-th period; is the average grid load power of the day; P sta (i) is the load power of the charging station in the i-th period; is the average load power of the charging station in one day; P sta is the set of charging station load powers in μ time periods.

[0030] Furthermore, D nor Designed for:

[0031]

[0032] Among them, P grid is the set of system load powers in μ time periods.

[0033] Furthermore, the 24 hours of a day are divided into μ time periods, and i takes the value of 1, 2, …, μ; then the profit f2 of the charging station from selling electricity is:

[0034]

[0035] Among them, P EV (n) is the charging power of the nth electric vehicle, c g (i) is the electricity purchase price in the i-th period.

[0036] Furthermore, the 24 hours of a day are divided into μ time periods, and i takes the values ​​of 1, 2, …, μ; then the parking fees f3 of electric vehicles at the charging station are:

[0037]

[0038] Among them, T lea (n) is the time when the nth electric vehicle leaves the charging station, T end (n) is the charging completion time of the nth electric vehicle, c p One hour of parking at the charging station.

[0039] Furthermore, the user's charging fee f4 is:

[0040]

[0041] Furthermore, (1) the electric vehicle charging power constraint per unit time is:

[0042]

[0043] Among them, T char Charging time for electric vehicles,

[0044] (2) Battery state of charge constraints:

[0045]

[0046] Among them, SOC max (n), SOC min (n) are the upper and lower limits of the state of charge of the battery of the nth electric vehicle; SOC i (n) The state of charge of the nth electric vehicle in the i-th time period;

[0047] (3) Constraints on electric vehicle charging demand:

[0048] (SOC end -SOC sta )·E=P EV ·T char ·η char

[0049] Among them, SOC sta , SOC end They are the starting state of charge and the ending state of charge of the electric vehicle respectively; P EV is the charging power selected by the user, η char Charging efficiency for electric vehicles.

[0050] The present invention also provides a dynamic pricing device for electric vehicle charging that takes into account user urgency, comprising:

[0051] A user urgency level setting module, which is used to implement step 1 of outputting any of the above-mentioned electric vehicle charging dynamic pricing methods considering user urgency;

[0052] A model building module for implementing step 2 of the output of any of the above-mentioned methods for dynamic pricing of electric vehicle charging considering user urgency;

[0053] An objective function building module for implementing step three of the output of any of the above-mentioned methods for dynamic pricing of electric vehicle charging considering user urgency;

[0054] A constraint condition setting module, which is used to implement step 4 of outputting any of the above-mentioned electric vehicle charging dynamic pricing methods considering user urgency;

[0055] A total price strategy formulation module, which is used to implement step five of the output of any of the above-mentioned electric vehicle charging dynamic pricing methods considering user urgency;

[0056] The optimal charging price strategy formulation module is used to implement step 6 of the output of any of the above-mentioned electric vehicle charging dynamic pricing methods considering user urgency. The present invention also provides a charging station dynamic power allocation method, which includes the following steps:

[0057] Read the total power allocated by the power grid;

[0058] Obtaining the maximum charging power of the electric vehicle and the initial value of the state of charge of the battery of the electric vehicle according to the total power;

[0059] The charging power of all users of the charging station is accumulated to obtain the total charging power, wherein the user selects the corresponding charging power according to the charging price of each power, and the charging price is the optimal charging price strategy c of the electric vehicle when the urgency level is m in the equilibrium state output by any of the above-mentioned electric vehicle charging dynamic pricing methods considering the user's urgency m * ;

[0060] Determining whether the total charging power exceeds the total power allocated by the power grid;

[0061] If the total charging power does not exceed the total power allocated by the power grid, it is determined whether the charging parking space is full; if the parking space at the charging station is not full, the electric vehicle is charged, and then the charging end time and the time of leaving the charging station are recorded; if the parking space at the charging station is full, the shortest waiting time is calculated, and if the waiting time is accepted, the waiting vehicle is added, and then the process of determining whether the charging parking space is full is returned, and if the waiting time is not accepted, the electric vehicle leaves directly;

[0062] If the total charging power exceeds the total power allocated by the power grid, the process returns to the step of calculating the shortest waiting time.

[0063] In order to solve the power distribution of charging stations and meet the charging needs of users, the present invention fully considers the above two strategies and gives a reasonable different charging power price through dynamic game between charging stations and users to guide users to choose the corresponding charging mode. This maximizes the benefits of charging stations and electric vehicle users while reducing the load impact of the power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] Figure 1 This is a charging station-electric vehicle user benefit model diagram of the present invention;

[0065] Figure 2 This is a flow chart of the dynamic game between the charging station and the user in the present invention;

[0066] Figure 3 This is a flow chart of the dynamic power allocation method for a charging station according to the present invention. DETAILED DESCRIPTION

[0067] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0068] Example 1

[0069] The electric vehicle charging dynamic pricing method of the present invention takes into account the user's urgency and provides a reasonable electricity price of different charging powers through dynamic game between the charging station and the user to guide the user to select a corresponding charging mode.

[0070] The dynamic pricing method for electric vehicle charging mainly includes the following steps.

[0071] 1. Divide the user's urgency into M levels, and each level corresponds to a charging power.

[0072] 2. Construct a charging station-electric vehicle user benefit model.

[0073] See also Figure 1 , the model includes a power grid, a charging station and at least one user.

[0074] Between the charging station and each user, the charging station gives the user a charging price c m (i) c. m (i) represents the charging price of the urgency level m in the i-th period, where m is 1, 2, …, M. The user responds to the charging price x n (i), x n (i) represents the charging decision of the nth electric car user in the i-th period, n is 1, 2, ..., N, and N is the number of electric cars. n When (i) = 1, it means that the nth electric car starts charging in the i-th period; n (i)=0, indicating that the nth electric vehicle does not participate in charging in the i-th period.

[0075] Between the charging station and the power grid, the power grid provides an economic incentive α for the charging station.

[0076] 3. Construct the objective function of the model.

[0077] The maximum revenue of the charging station is max(f), and the minimum charging cost of the user is min(f4), where the revenue of the charging station f=f1+f2+f3, f1+f2+f3 are the profit of the charging station from electricity sales, the economic incentive of the power grid, and the parking fee of the electric vehicle respectively; f4 is the charging cost of the user.

[0078] Assume that the economic incentive f1 of the power grid is:

[0079]

[0080] Among them, D(P sta ) is the evaluation index of peak load regulation effect of charging station, D nor D(P sta ) is the normalization factor.

[0081] D(P sta ) is designed as:

[0082]

[0083] Among them, P grid (i) is the system load power in the i-th period; is the average grid load power of the day; P sta (i) is the load power of the charging station in the i-th period; is the average load power of the charging station in one day; P sta is the set of charging station load powers in μ time periods.

[0084] D nor Designed for:

[0085]

[0086] Among them, P grid is the set of system load powers in μ time periods.

[0087] Divide 24 hours a day into μ time periods, and i takes the value of 1, 2, …, μ; then the electricity sales profit f2 of the charging station and the parking fee f3 of the electric vehicle are:

[0088]

[0089] Among them, P EV (n) is the charging power of the nth electric vehicle, c g (i) is the electricity purchase price in the i-th period.

[0090]

[0091] Among them, T lea (n) is the time when the nth electric vehicle leaves the charging station, T end (n) is the charging completion time of the nth electric vehicle, c p One hour of parking at the charging station.

[0092] The user's charging fee f4 is:

[0093]

[0094] 4. Set constraints: electric vehicle charging power constraints per unit time, battery charge state constraints, and electric vehicle charging demand constraints.

[0095] (1) Constraints on electric vehicle charging power per unit time

[0096]

[0097] Among them, T char Charging time for electric vehicles.

[0098] (2) Battery state of charge constraints

[0099]

[0100] Among them, SOC max (n), SOC min (n) are the upper and lower limits of the state of charge of the battery of the nth electric vehicle; SOC i (n) The state of charge of the nth electric vehicle at the ith time period.

[0101] (3) Constraints on electric vehicle charging demand

[0102] (SOC end -SOC sta )·E=P EV ·T char ·η char

[0103] Among them, SOC sta , SOC end They are the starting state of charge and the ending state of charge of the electric vehicle respectively; P EV is the charging power selected by the user, η char Charging efficiency for electric vehicles.

[0104] 5. Assume that the total charging price strategy c of different power of charging stations in one day is:

[0105] c={c(1),c(2),…,c(μ)}

[0106] Where c(1) is the total charging price strategy of different powers of the charging station in the first period, c(1) = {c1(1), c2(1), …, c m (1)}, c1(1) is the charging price for the first period with an urgency level of 1, c2(1) is the charging price for the first period with an urgency level of 2, c m (1) is the charging price for the first period with urgency level m.

[0107] c(2) is the total charging price strategy of different powers of charging stations in the second period, c(2) = {c1(2), c2(2), …, c m (2)}, c1(2) is the charging price for the urgency level 1 in the second period, c2(2) is the charging price for the urgency level 2 in the second period, c m (2) is the charging price for the urgency level m in the second period.

[0108] c(μ) is the total charging price strategy of different powers of charging stations in the μth period, c(μ)={c1(μ),c2(μ),…,c m (μ)}, c1(μ) is the charging price of the μth time period with an urgency level of 1, c2(μ) is the charging price of the μth time period with an urgency level of 2, c m (μ) is the charging price for the μth time period with urgency level m.

[0109] Then, the total load response strategy x of the user in one day is:

[0110] x={x(1),x(2),...x(μ)}

[0111] Among them, x(1) is the total load response strategy of the user in the first period, x(1) = {x1(1), x2(1), …, x n (1)}, x1(1) is the charging decision of the first electric vehicle user in period 1, x2(1) is the charging decision of the second electric vehicle user in period 1, x n (1) is the charging decision of the nth electric vehicle user in period 1.

[0112] x(2) is the total load response strategy of the users in the second period, x(2) = {x1(2), x2(2), …, x n (2)}, x1(2) is the charging decision of the first electric vehicle user in the 2nd period, x2(2) is the charging decision of the second electric vehicle user in the 2nd period, x n (2) is the charging decision of the nth electric vehicle user in period 2.

[0113] x(μ) is the total load response strategy of the user in the μth period, x(μ)={x1(μ),x2(μ),…,x n (μ)}, x1(μ) is the charging decision of the first electric vehicle user in the μ period, x2(2) is the charging decision of the second electric vehicle user in the μ period, x n (μ) is the charging decision of the nth electric vehicle user in the μ period.

[0114] 6. In the game process, the charging station first gives a charging price c for the urgency level m in the μth time period. m, the user makes a response strategy x according to the charging price m , and then calculate the benefits of the charging station and the user in this round, that is, f(c m ,x m ) and f4(c m ,x m ); In the next round of the game, the charging station gives a charging price based on the user's response strategy, and then the user makes a response strategy x based on the charging price m ; The profit f(c m ,x m ) and f4(c m ,x m ) is the maximum value in all game rounds. After k rounds of games, k groups of c will be generated. m 、x m 、f(c m ,x m )、f4(c m ,x m ); when the kth group {f k (c m ,x m ), f 4k (c m ,x m )} is equal to the k-1th group {f k-1 (c m ,x m ), f 4k-1 (c m ,x m )}, that is, both parties can no longer obtain more benefits, then the game reaches equilibrium; at this time, the game strategy of the charging station and the electric vehicle user (c m * ,x * m ) is the best;

[0115] Among them, c m * is the optimal charging price strategy for electric vehicles when the urgency level is m in the equilibrium state; It is the optimal load response strategy for electric vehicle users when the urgency level is m in the equilibrium state.

[0116] The electric vehicle charging dynamic pricing method of this implementation can be designed as a separate software module before application, such as an electric vehicle charging dynamic pricing device that considers user urgency. The electric vehicle charging dynamic pricing device includes a user urgency level setting module, a model building module, an objective function building module, a constraint condition setting module, a total price strategy formulation module, and an optimal charging price strategy formulation module.

[0117] The user urgency level setting module is used to implement step one of the output of the above-mentioned dynamic pricing method for charging electric vehicles taking into account user urgency; the model construction module is used to implement step two of the output of the above-mentioned dynamic pricing method for charging electric vehicles taking into account user urgency; the objective function construction module is used to implement step three of the output of the above-mentioned dynamic pricing method for charging electric vehicles taking into account user urgency; the constraint condition setting module is used to implement step four of the output of the above-mentioned dynamic pricing method for charging electric vehicles taking into account user urgency; the total price strategy formulation module is used to implement step five of the output of the above-mentioned dynamic pricing method for charging electric vehicles taking into account user urgency; the optimal charging price strategy formulation module is used to implement step six of the output of the above-mentioned dynamic pricing method for charging electric vehicles taking into account user urgency. When applied, the dynamic pricing method for charging electric vehicles of this implementation can be applied to a dynamic power allocation method for charging stations, and the dynamic power allocation method for charging stations includes the following steps.

[0118] Obtaining the maximum charging power of the electric vehicle and the initial value of the state of charge of the battery of the electric vehicle according to the total power;

[0119] The charging power of all users of the charging station is accumulated to obtain the total charging power, wherein the user selects the corresponding charging power according to the charging price of each power, and the charging price is the optimal charging price strategy c of the electric vehicle when the urgency level is m in the equilibrium state output by the dynamic pricing method for charging electric vehicles considering the urgency of users m * ;

[0120] Determining whether the total charging power exceeds the total power allocated by the power grid;

[0121] If the total charging power does not exceed the total power allocated by the power grid, it is determined whether the charging parking space is full; if the parking space at the charging station is not full, the electric vehicle is charged, and then the charging end time and the time of leaving the charging station are recorded; if the parking space at the charging station is full, the shortest waiting time is calculated, and if the waiting time is accepted, the waiting vehicle is added, and then the process of determining whether the charging parking space is full is returned, and if the waiting time is not accepted, the electric vehicle leaves directly;

[0122] If the total charging power exceeds the total power allocated by the power grid, the process returns to the step of calculating the shortest waiting time.

[0123] Example 2

[0124] Considering the user's urgency, the urgency is divided into M levels, and each level corresponds to a charging power. The charging station can set prices according to the user's response to the price of each charging mode. Therefore, the charging process can be regarded as a dynamic game process between the charging station and the user. The charging station aims to maximize its own benefits, and the user aims to minimize the charging cost. The two parties in the game play around the charging price and the degree of response. The higher the charging price (c), the lower the user's response (x). In the game process, when either the electric vehicle charging station or the electric vehicle user can't get more benefits by changing the strategy, the game reaches equilibrium. Then use optimization algorithms, such as genetic algorithms, particle swarm optimization algorithms, nonlinear programming algorithms, etc. to calculate the optimal population individuals, that is, the charging price and user response in the equilibrium state. After obtaining the charging price corresponding to each power, the power allocation strategy of the charging station is executed. The following is the specific process.

[0125] (1) Construct a charging station-electric vehicle user benefit model, such as Figure 1 The model includes three parts: power grid, charging station and user. Between the charging station and the user, the charging station gives the user a charging price c m (i), users respond based on charging price x n (i). Between the charging station and the power grid, the power grid gives the charging station an economic incentive α based on the peak load regulation effect of the charging station. The maximum revenue of the charging station max(f) and the minimum charging cost of the user min(f4) are the model objective functions. Therefore, it is necessary to calculate the charging station revenue f and the user charging cost f4.

[0126] (2) For electric vehicle charging stations, their revenue mainly comes from three aspects: the profit from electricity sales of the charging station, the economic incentives of the power grid and the parking fees of electric vehicles. Assuming that the income from these three aspects is f1, f2, and f3 respectively, the total revenue cost is f = f1 + f2 + f3.

[0127] First, divide the 24 hours of a day into μ time periods; assume that N is the number of electric vehicles; c m (i) is the charging price of the urgency level m in the i-th period, and the value of m is 1, 2, ...M; x n (i) is the charging decision of the nth electric car user in period i. n When (i) = 1, it means that the nth electric car starts charging in the i-th period; n (i) = 0, indicating that the nth electric vehicle does not participate in charging in the i-th period. The correlation between the load curve of the electric vehicle charging station and the load curve of the power grid reflects the peak-shaving effect of the charging station. Considering that the Pearson correlation coefficient can reflect the correlation between different load curves, the Pearson correlation coefficient is used to establish the peak-shaving effect evaluation index of the charging station. The peak-shaving effect evaluation index of the charging station D(Psta ) The specific expression is:

[0128]

[0129] Where P grid (i) is the system load power in the i-th period; is the average grid load power of the day; P sta (i) is the load power of the charging station in the i-th period; is the average load power of the charging station in one day; P sta is the set of charging station load powers in μ time periods. The economic incentives of the power grid company are:

[0130]

[0131] Where α is the incentive rate of the power grid company, D(P sta ) / D nor represents the equivalent peak load capacity of the charging station, D nor D(P sta ), which is expressed as:

[0132]

[0133] The profit of electricity sales at the charging station is:

[0134]

[0135] Where P EV represents the charging power of the nth electric car; c g (i) is the electricity purchase price of the charging station in the i-th period.

[0136] Parking fees for electric vehicles are:

[0137]

[0138] Where T lea (n) is the time when the nth electric vehicle leaves the charging station; T end (n) is the charging completion time of the nth electric vehicle; c p One hour of parking at the charging station is charged.

[0139] (3) For electric vehicle users, on the basis of meeting their own charging needs, they can charge the electric vehicle according to the charging price set by the charging station. m (i) Determine the corresponding charging plan, whose decision variables are the charging decision x in each period n (i) The charging cost of all electric vehicles in a day is:

[0140]

[0141] (4) This model can only be established under certain constraints. There are three constraints: electric vehicle charging power constraint, battery state of charge constraint, and electric vehicle charging demand constraint.

[0142] 1>Electric vehicle charging power constraints

[0143] The charging power constraint of electric vehicles per unit time is:

[0144]

[0145] 2>Battery state of charge constraints

[0146]

[0147] In the formula, SOC min and SOC max They are the upper and lower limits of the state of charge of the electric vehicle battery; SOC i (n) is the charge state of the nth electric vehicle in the i-th time period.

[0148] 3> Electric vehicle charging demand constraints

[0149] The actual charging amount of an electric vehicle should be equal to the charging amount required by the user, that is:

[0150] (SOC end -SOC sta )·E=P EV ·T char ·η char (9)

[0151] In the formula, SOC sta and SOC end They are the initial state of charge of the electric vehicle; E is the rated capacity of the battery; P EV The charging power selected by the user; T char Charging time for electric vehicles; η char Charging efficiency for electric vehicles.

[0152] (5) When charging stations set charging prices, there is a bargaining process between them and users. The process is as follows: Figure 2 As shown in the figure, the price strategy of the charging station with different powers in the i-th period can be expressed as c(i) = {c1(i), c2(i), ...c6(i)}; the price strategy of the charging station in μ periods can be expressed as c = {c(1), c(2), ...c(μ)}; the load response strategy of the user in the i-th period is the charging decision of n electric vehicle users, which can be expressed as x(i) = {x1(i), x2(i), ...x n(i)}; then the user's load response strategy in the μ period is expressed as x = {x(1), x(2), ... x(μ)}. In the game process, the charging station first gives a charging price c with an urgency level m in the μ period. m , the user makes a response strategy x according to the charging price m , and then calculate the benefits of the charging station and the user in this round, that is, f(c m , x m ) and f4(c m , x m ). In the next round of the game, the charging station gives a charging price based on the user's response strategy, and then the user makes a response strategy x based on the charging price. m The profit f(c m ,x m ) and f4(c m ,x m ) is the maximum value in all game rounds. After k rounds of games, k groups of c will be generated. m 、x m 、f(c m ,x m )、f4(c m ,x m ). When the kth group {f k (c m ,x m ),f 4k (c m ,x m )} is equal to the k-1th group {f k-1 (c m ,x m ),f 4k-1 (c m ,x m )}, that is, both parties can no longer obtain more benefits, and the game reaches equilibrium. The game strategy of charging stations and electric vehicle users is obtained Right now Among them, c m * is the optimal charging price strategy for electric vehicles when the urgency level is m in the equilibrium state; is the optimal load response strategy of electric vehicle users when the urgency level is m in the equilibrium state. Finally, the game strategies of electric vehicle charging stations and electric vehicle users are abstracted as population individuals in the algorithm, and the game strategies of both parties are optimized using the optimization algorithm.

[0153] (6) After obtaining the optimal charging price corresponding to different urgency levels from the above calculation, the dynamic power allocation strategy of the charging station is adopted. The specific process is as follows Figure 3 shown.

[0154] (7) First, the charging station reads the total power allocated by the power grid and obtains the maximum charging power and initial SOC value of the electric vehicle.

[0155] (8) The user selects the corresponding charging power according to the charging price of each power and accumulates the total charging power of the charging station.

[0156] (9) If the total charging power of the charging station does not exceed the total power allocated by the power grid, then determine whether the charging parking space is full; if the charging station parking space is not full, the electric vehicle will charge, and then record the end time of charging and the time of leaving the charging station. If the parking space is full, calculate the shortest waiting time; if you accept the waiting time, add the waiting vehicles, and then determine whether the parking space is full. If you do not accept the waiting time, leave directly.

[0157] (10) If the total charging power of the charging station exceeds the total power allocated by the power grid, the shortest waiting time is calculated and the process refers to step (9).

[0158] Compared with the existing technology, the beneficial effects of the invention are reflected in: the problem of being unable to give a reasonable charging electricity price in terms of charging station power allocation and the problem of ignoring different user needs in electric vehicle charging pricing. The present invention takes into account the urgency needs of different users and combines charging station power allocation with dynamic pricing. It uses reasonable electricity prices to guide users to choose reasonable charging modes and charging times, so that the benefits of charging stations and electric vehicle users are maximized, and the impact of electric vehicle charging on the power grid is reduced.

Claims

1. A dynamic pricing method for electric vehicle charging considering user urgency, characterized in that: It includes the following steps:

1. Divide the user's urgency into M levels, and each level corresponds to a charging power; 2. Construct a charging station-electric vehicle user benefit model, which includes a power grid, a charging station and at least one user; between the charging station and each user, the charging station gives the user a charging price c m (i), c m (i) represents the charging price of the urgency level m in the i-th period, where m is 1, 2, …, M; the user responds x according to the charging price n (i), x n (i) represents the charging decision of the nth electric vehicle user in the i-th period, n is 1, 2, ..., N, N is the number of electric vehicles, and when x n When (i) = 1, it means that the nth electric car starts charging in the i-th period; n (i) = 0, indicating that the nth electric vehicle does not participate in charging in the i-th period; between the charging station and the power grid, the power grid gives the charging station an economic incentive f1; 3. Construct the objective function of the model: the maximum revenue of the charging station max(f), the minimum charging cost of the user min(f4), where the revenue of the charging station f = f1+f2+f3, f1, f2, f3 are the economic incentive of the power grid, the profit of the charging station from selling electricity, and the parking fee of the electric vehicle respectively; f4 is the charging fee of the user; 4. Set constraints: electric vehicle charging power constraints per unit time, battery state of charge constraints, and electric vehicle charging demand constraints; 5. Assume that the total charging price strategy c of different power of charging stations in one day is: c={c(1),c(2),…,c(μ)} Where c(1) is the total charging price strategy of different powers of the charging station in the first period, c(1) = {c1(1), c2(1), …, c m (1)}, c1(1) is the charging price for the first period with an urgency level of 1, c2(1) is the charging price for the first period with an urgency level of 2, c m (1) is the charging price for the first period with urgency level m; c(2) is the total charging price strategy of different powers of charging stations in the second period, c(2) = {c1(2), c2(2), …, c m (2)}, c1(2) is the charging price for the urgency level 1 in the second period, c2(2) is the charging price for the urgency level 2 in the second period, c m (2) is the charging price for the urgency level m in the second period; c(μ) is the total charging price strategy of different powers of charging stations in the μth period, c(μ)={c1(μ),c2(μ),…,c m (μ)}, c1(μ) is the charging price of the μth time period with an urgency level of 1, c2(μ) is the charging price of the μth time period with an urgency level of 2, c m (μ) is the charging price for the μth time period with urgency level m; Then, the total load response strategy x of the user in one day is: x={x(1),x(2),…x(u)} Among them, x(1) is the total load response strategy of the user in the first period, x(1) = {x1(1), x2(1), …, x n (1)}, x1(1) is the charging decision of the first electric vehicle user in period 1, x2(1) is the charging decision of the second electric vehicle user in period 1, x n (1) is the charging decision of the nth electric vehicle user in period 1; x(2) is the total load response strategy of the users in the second period, x(2) = {x1(2), x2(2), …, x n (2)}, x1(2) is the charging decision of the first electric vehicle user in the 2nd period, x2(2) is the charging decision of the second electric vehicle user in the 2nd period, x n (2) The charging decision of the nth electric vehicle user in period 2; x(μ) is the total load response strategy of the user in the μth period, x(μ)={x1(μ),x2(μ),…,x n (μ)}, x1(μ) is the charging decision of the first electric vehicle user in the μ period, x2(2) is the charging decision of the second electric vehicle user in the μ period, x n (μ) is the charging decision of the nth electric vehicle user in the μ period; 6. In the game process, the charging station first gives a charging price c for the urgency level m in the μth time period. m , the user makes a response strategy x according to the charging price m , and then calculate the benefits of the charging station and the user in this round, that is, f(c m ,x m ) and f4(c m ,x m ); In the next round of the game, the charging station gives a charging price based on the user's response strategy, and then the user makes a response strategy x based on the charging price m ; The profit f(c m ,x m ) and f4(c m ,x m ) is the maximum value in all game rounds. After k rounds of games, k groups of c will be generated. m 、x m 、f(c m ,x m )、f4(c m ,x m ); when the kth group {f k (c m ,x m ), f 4k (c m ,x m )} is equal to the k-1th group {f k-1 (c m ,x m ), f 4k-1 (c m ,x m )}, that is, both parties can no longer obtain more benefits, then the game reaches equilibrium; at this time, the game strategy of the charging station and the electric vehicle user (c m *,x*m) is the best; Among them, c m * is the optimal charging price strategy for electric vehicles when the urgency level is m in the equilibrium state; is the optimal load response strategy for electric vehicle users when the urgency level is m in the equilibrium state; Among them, (1) the charging power constraint of electric vehicles per unit time is: Among them, T char Charging time for electric vehicles, (2) Battery state of charge constraints: Among them, SOC max (n), SOC min (n) are the upper and lower limits of the state of charge of the battery of the nth electric vehicle; SOC i (n) the state of charge of the nth electric vehicle in the i-th time period; (3) Constraints on electric vehicle charging demand: (SOC end -SOC sta )·E=P EV ·T char ·η char Among them, SOC sta , SOC end They are the starting state of charge and the ending state of charge of the electric vehicle respectively; P EV is the charging power selected by the user, η char Charging efficiency for electric vehicles.

2. The electric vehicle charging dynamic pricing method considering user urgency as claimed in claim 1, characterized in that: The economic incentive f1 of the power grid is: Among them, α is the incentive rate of the power grid company, D(P sta ) is the peak load effect evaluation index of the charging station, D nor D(P sta ) is the normalization factor.

3. The electric vehicle charging dynamic pricing method considering user urgency as claimed in claim 2, characterized in that: D(P sta ) is designed as: Among them, P grid (i) is the system load power in the i-th period; is the average grid load power of the day; P sta (i) is the load power of the charging station in the i-th period; is the average load power of the charging station in one day; P sta is the set of charging station load powers in μ time periods.

4. The electric vehicle charging dynamic pricing method considering user urgency as claimed in claim 3, characterized in that: D nor Designed for: Among them, P grid is the set of system load powers in μ time periods.

5. The electric vehicle charging dynamic pricing method considering user urgency as claimed in claim 2, characterized in that: Divide 24 hours a day into μ time periods, and i takes the value of 1, 2, …, μ; then the profit f2 of the charging station from selling electricity is: Among them, P EV (n) is the charging power of the nth electric vehicle, c g (i) is the electricity purchase price in the i-th period.

6. The electric vehicle charging dynamic pricing method considering user urgency as claimed in claim 2, characterized in that: Divide 24 hours a day into μ time periods, i takes the value of 1, 2, …, μ; then the parking fee f3 of the electric vehicle at the charging station is: Among them, T lea (n) is the time when the nth electric vehicle leaves the charging station, T end (n) is the charging completion time of the nth electric vehicle, c p One hour of parking at the charging station is charged.

7. The electric vehicle charging dynamic pricing method considering user urgency as claimed in claim 2, characterized in that: The user's charging fee f4 is:

8. A dynamic pricing device for electric vehicle charging that takes into account user urgency, characterized in that: It includes: A user urgency level setting module, which is used to implement step 1 of outputting the method for dynamic pricing of electric vehicle charging considering user urgency as claimed in any one of claims 1 to 7; A model building module for implementing step 2 of outputting the method for dynamic pricing of electric vehicle charging considering user urgency as claimed in any one of claims 1 to 7; An objective function building module, which is used to implement step three of outputting the method for dynamic pricing of electric vehicle charging considering user urgency as described in any one of claims 1 to 7; A constraint condition setting module, which is used to implement step 4 of outputting the method for dynamic pricing of electric vehicle charging considering user urgency as described in any one of claims 1 to 7; A total price strategy formulation module, which is used to implement step 5 of outputting the method for dynamic pricing of electric vehicle charging considering user urgency as described in any one of claims 1 to 7; An optimal charging price strategy formulation module is used to implement step six of the output of the electric vehicle charging dynamic pricing method considering user urgency as described in any one of claims 1 to 7.

9. A method for dynamic power allocation of a charging station, characterized in that: It includes the following steps: Read the total power allocated by the power grid; Obtaining the maximum charging power of the electric vehicle and the initial value of the state of charge of the battery of the electric vehicle according to the total power; The charging powers of all users of the charging station are accumulated to obtain the total charging power, wherein the user selects the corresponding charging power according to the charging price of each power, and the charging price is the optimal charging price strategy c of the electric vehicle when the urgency level is m in the equilibrium state output by the dynamic pricing method for charging electric vehicles considering the urgency of users as claimed in any one of claims 1 to 7. m * ; Determining whether the total charging power exceeds the total power allocated by the power grid; If the total charging power does not exceed the total power allocated by the power grid, it is determined whether the charging parking space is full; if the parking space at the charging station is not full, the electric vehicle is charged, and then the charging end time and the time of leaving the charging station are recorded; if the parking space at the charging station is full, the shortest waiting time is calculated, and if the waiting time is accepted, the waiting vehicle is added, and then the process returns to the step of determining whether the charging parking space is full, and if the waiting time is not accepted, the electric vehicle leaves directly; If the total charging power exceeds the total power allocated by the power grid, the process returns to the step of calculating the shortest waiting time.

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