Electric vehicle charging scheduling method and system considering network security

Abstract

The invention provides an electric vehicle charging scheduling method considering network security, and the method comprises the steps: firstly determining a partition time-of-use electricity price based on line loss, then constructing an electric vehicle charging scheduling model with minimization of electric vehicle charging cost as a target based on the partition time-of-use electricity price, and finally solving the electric vehicle charging scheduling model. And obtaining an optimal scheduling scheme of the electric vehicle. On the premise of considering the safety of the power distribution network, the traveling electric vehicle is guided to be charged, the stability of the power distribution network can be effectively improved, and the charging cost of an electric vehicle user is reduced.

Description

Technical Field

[0001] The present invention belongs to the technical field of smart grids, and in particular relates to an electric vehicle charging scheduling method and system taking network security into consideration. Background Art

[0002] As terminal energy in the transportation sector becomes increasingly electrified, the charging load of electric vehicles will become one of the largest loads in the future power grid.

[0003] It has become a trend to connect electric vehicles to the distribution network on a large scale, and the on-board charging technology of electric vehicles has also been widely used. As an important bridge connecting the power generation end and the load end, the access of a large number of charging loads to the distribution network will have a negative impact on the safe and stable operation of the urban distribution system, and the charging and discharging of electric vehicles is random and uncertain in time and space. Therefore, how to guide electric vehicle users to participate in the optimization and dispatching of the distribution system will play a positive role in the safe operation of the distribution network, and it is also a difficult problem that needs to be solved urgently. Summary of the invention

[0004] The purpose of the present invention is to provide an electric vehicle charging scheduling method and system taking network security into consideration in view of the above-mentioned problems existing in the prior art.

[0005] To achieve the above objectives, the technical solution of the present invention is as follows:

[0006] In a first aspect, the present invention proposes a method for scheduling charging of electric vehicles taking network security into consideration, comprising:

[0007] S1. Determine the electricity price by region and time based on line loss;

[0008] S2. Based on the time-based electricity prices, an electric vehicle charging scheduling model is constructed to minimize the charging cost of electric vehicles;

[0009] S3. Solve the electric vehicle charging scheduling model to obtain the optimal scheduling plan for electric vehicles.

[0010] S1 uses the following formula to determine the time-based electricity price:

[0011] C j,t =C base,j,t +C base,j,t θ l P loss,j

[0012] P loss,j =3I j 2 R j ×10 -3

[0013] In the above formula, C j,t, C base,j,t are the charging price and base price of charging station j at time t, θ l is the electricity price adjustment coefficient, P loss,j is the line loss from the substation to the charging station j, I j , R j are the line current and resistance from substation to charging station j respectively.

[0014] In S2, the objective function of the electric vehicle charging scheduling model includes:

[0015]

[0016] In the above formula, C j,t is the charging price of charging station j in time period t, P j is the charging power of charging station j, T ij,t is the time that electric vehicle i charges at charging station j during period t, x ij The decision variables for selecting charging station j for electric vehicle i to charge.

[0017] The constraints of the electric vehicle charging scheduling model include:

[0018]

[0019]

[0020]

[0021] In the above formula, M and N are the number of charging stations and electric vehicles respectively, and J is the set of charging stations. is the initial charge of electric vehicle i, D ij is the distance between electric vehicle i and charging station j, E c It is the energy consumed by electric vehicles per unit distance.

[0022] The S3 uses a genetic algorithm to solve the electric vehicle charging scheduling model.

[0023] In a second aspect, the present invention proposes an electric vehicle charging scheduling system considering network security, including an electricity price calculation module, a scheduling model construction module, and a scheduling model solving module;

[0024] The electricity price calculation module is used to determine the electricity price by area and time based on line loss;

[0025] The scheduling model building module is used to build an electric vehicle charging scheduling model with the goal of minimizing the electric vehicle charging cost based on the zoned and time-based electricity prices;

[0026] The scheduling model solving module is used to solve the electric vehicle charging scheduling model to obtain the optimal scheduling solution for the electric vehicle.

[0027] The electricity price calculation module uses the following formula to determine the electricity price by zone and time:

[0028] C j,t =C base,j,t +C base,j,t θ l P loss,j

[0029] P loss,j =3I j 2 R j ×10 -3

[0030] In the above formula, C j,t , C base,j,t are the charging price and base price of charging station j at time t, θ l is the electricity price adjustment coefficient, P loss,j is the line loss from the substation to the charging station j, I j , R j are the line current and resistance from substation to charging station j respectively.

[0031] The objective function of the electric vehicle charging scheduling model includes:

[0032]

[0033] In the above formula, C j,t is the charging price of charging station j in time period t, P j is the charging power of charging station j, T ij,t is the time that electric vehicle i charges at charging station j during period t, x ij The decision variables for selecting charging station j for electric vehicle i to charge.

[0034] The constraints of the electric vehicle charging scheduling model include:

[0035]

[0036]

[0037]

[0038] In the above formula, M and N are the number of charging stations and electric vehicles respectively, and J is the set of charging stations. is the initial charge of electric vehicle i, D ij is the distance between electric vehicle i and charging station j, E cIt is the energy consumed by electric vehicles per unit distance.

[0039] The scheduling model solving module uses a genetic algorithm to solve the electric vehicle charging scheduling model.

[0040] Compared with the prior art, the present invention has the following beneficial effects:

[0041] The present invention provides an electric vehicle charging scheduling method considering network security. The method first determines the time-based electricity price based on line loss, then constructs an electric vehicle charging scheduling model with the goal of minimizing the electric vehicle charging cost based on the time-based electricity price, and finally solves the electric vehicle charging scheduling model to obtain the optimal scheduling plan for the electric vehicle. The method guides the charging of electric vehicles on the road under the premise of considering the safety of the distribution network, which can effectively improve the stability of the distribution network and reduce the charging cost of electric vehicle users. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 This is a flowchart of the method described in Example 1.

[0043] Figure 2 The structure diagram of the system of the present invention is shown in FIG. DETAILED DESCRIPTION

[0044] The present invention is further described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0045] The present invention proposes an electric vehicle charging scheduling method considering network security. The method formulates a zoned and timed electricity price based on line loss and basic electricity price, and guides the charging behavior of electric vehicles based on the formulated zoned and timed electricity price.

[0046] Embodiment 1:

[0047] An electric vehicle charging scheduling method considering network security, such as Figure 1 As shown, the specific steps are as follows:

[0048] 1. According to the line branch current and resistance, the following line loss model is established:

[0049] P loss,j =3I j 2 R j ×10 -3

[0050] In the above formula, P loss,j is the line loss from the substation to the charging station j, I j , R j are the line current and resistance from substation to charging station j respectively.

[0051] 2. Based on line loss and basic electricity price, formulate zoned and time-based electricity prices:

[0052] C j,t =C base,j,t +C base,j,t θ l P loss,j

[0053] In the above formula, C j,t , C base,j,t are the charging price and base price of charging station j at time t, θ l is the electricity price adjustment factor.

[0054] 3. Based on the remaining power, initial location, expected charging capacity and other information uploaded by the electric vehicle, the intelligent networking system combines the zoned and time-based electricity prices to build the following electric vehicle charging scheduling model with the goal of minimizing the electric vehicle charging cost:

[0055]

[0056]

[0057]

[0058]

[0059] In the above formula, C j,t is the charging price of charging station j in time period t, P j is the charging power of charging station j, T ij,t is the time that electric vehicle i charges at charging station j during period t, x ij is the decision variable for electric vehicle i to choose charging station j for charging. When electric vehicle i chooses charging station j for charging, x ij =1, otherwise x ij =0, M, N are the number of charging stations and electric vehicles respectively, J is the set of charging stations, is the initial charge of electric vehicle i, D ij is the distance between electric vehicle i and charging station j. This parameter is related to x in the objective function. ij Related, through x ij After choosing to charge at charging station j, you can get D ij , E c It is the energy consumed by electric vehicles per unit distance.

[0060] 4. Use genetic algorithm to solve the electric vehicle charging scheduling model and obtain the optimal scheduling plan for electric vehicles.

[0061] Embodiment 2:

[0062] An electric vehicle charging scheduling system considering network security, such as Figure 2 As shown, it includes an electricity price calculation module, a scheduling model construction module, and a scheduling model solving module.

[0063] The electricity price calculation module is used to determine the electricity price by region and time based on line loss:

[0064] C j,t =C base,j,t +C base,j,t θ l P loss,j

[0065] P loss,j =3I j 2 R j ×10 -3

[0066] In the above formula, C j,t , C base,j,t are the charging price and base price of charging station j at time t, θ l is the electricity price adjustment coefficient, P loss,j is the line loss from the substation to the charging station j, I j , R j are the line current and resistance from substation to charging station j respectively.

[0067] The scheduling model building module is used to build the following electric vehicle charging scheduling model based on the zoned and time-based electricity prices with the goal of minimizing the electric vehicle charging cost:

[0068]

[0069]

[0070]

[0071]

[0072] In the above formula, C j,t is the charging price of charging station j in time period t, P j is the charging power of charging station j, T ij,t is the time that electric vehicle i charges at charging station j during period t, x ij is the decision variable for electric vehicle i to select charging station j for charging, M and N are the number of charging stations and electric vehicles respectively, and J is the set of charging stations. is the initial charge of electric vehicle i, D ij is the distance between electric vehicle i and charging station j, E c It is the energy consumed by electric vehicles per unit distance.

[0073] The scheduling model solving module is used to solve the electric vehicle charging scheduling model by using a genetic algorithm to obtain an optimal scheduling solution for the electric vehicle.

Claims

1. A method for scheduling electric vehicle charging considering network security, characterized in that: The method comprises: S1. Determine the electricity price by region and time based on line loss; S2. Based on the time-based electricity prices, an electric vehicle charging scheduling model is constructed to minimize the charging cost of electric vehicles; S3. Solve the electric vehicle charging scheduling model to obtain the optimal scheduling plan for electric vehicles.

2. The electric vehicle charging scheduling method considering network security according to claim 1 is characterized in that: S1 uses the following formula to determine the time-based electricity price: C j,t =C base,j,t +C base,j,t θ l P loss,j P loss,j =3I j 2 R j ×10 -3 In the above formula, C j,t , C base,j,t are the charging price and base price of charging station j at time t, θ l is the electricity price adjustment coefficient, P loss,j is the line loss from the substation to the charging station j, I j , R j are the line current and resistance from substation to charging station j respectively.

3. The electric vehicle charging scheduling method considering network security according to claim 1 or 2, characterized in that: In S2, the objective function of the electric vehicle charging scheduling model includes: In the above formula, C j,t is the charging price of charging station j in time period t, P j is the charging power of charging station j, T ij,t is the time that electric vehicle i charges at charging station j during period t, x ij The decision variables for selecting charging station j for electric vehicle i to charge.

4. The electric vehicle charging scheduling method considering network security according to claim 3 is characterized in that: The constraints of the electric vehicle charging scheduling model include: In the above formula, M and N are the number of charging stations and electric vehicles respectively, and J is the set of charging stations. is the initial charge of electric vehicle i, D ij is the distance between electric vehicle i and charging station j, E c It is the energy consumed by electric vehicles per unit distance.

5. The electric vehicle charging scheduling method considering network security according to claim 1 or 2, characterized in that: The S3 uses a genetic algorithm to solve the electric vehicle charging scheduling model.

6. An electric vehicle charging scheduling system considering network security, characterized in that: The system includes an electricity price calculation module, a scheduling model construction module, and a scheduling model solution module; The electricity price calculation module is used to determine the electricity price by area and time based on line loss; The scheduling model building module is used to build an electric vehicle charging scheduling model with the goal of minimizing the electric vehicle charging cost based on the zoned and time-based electricity prices; The scheduling model solving module is used to solve the electric vehicle charging scheduling model to obtain the optimal scheduling solution for the electric vehicle.

7. The electric vehicle charging scheduling system considering network security according to claim 6 is characterized in that: The electricity price calculation module uses the following formula to determine the electricity price by zone and time: C j,t =C base,j,t +C base,j,t θ l P loss,j P loss,j =3I j 2 R j ×10 -3 In the above formula, C j,t , C base,j,t are the charging price and base price of charging station j at time t, θ l is the electricity price adjustment coefficient, P loss,j is the line loss from the substation to the charging station j, I j , R j are the line current and resistance from substation to charging station j respectively.

8. The electric vehicle charging scheduling system considering network security according to claim 6 is characterized in that: The objective function of the electric vehicle charging scheduling model includes: In the above formula, C j,t is the charging price of charging station j in time period t, P j is the charging power of charging station j, T ij,t is the time that electric vehicle i charges at charging station j during period t, x ij The decision variables for selecting charging station j for electric vehicle i to charge.

9. The electric vehicle charging scheduling system considering network security according to claim 8 is characterized in that: The constraints of the electric vehicle charging scheduling model include: In the above formula, M and N are the number of charging stations and electric vehicles respectively, and J is the set of charging stations. is the initial charge of electric vehicle i, D ij is the distance between electric vehicle i and charging station j, E c It is the energy consumed by electric vehicles per unit distance.

10. An electric vehicle charging scheduling system considering network security according to claim 6 or 7, characterized in that: The scheduling model solving module uses a genetic algorithm to solve the electric vehicle charging scheduling model.

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