	US 12,391,141 B1
	Large-scale electric vehicle cluster coordinated charging optimization method based on greedy repair genetic algorithm
Shuhua Gao, Jinan (CN); Jing Wang, Jinan (CN); Zhengfang Wang, Jinan (CN); Ruipeng Cui, Jinan (CN); Junhui Kou, Jinan (CN); Shuo Wang, Jinan (CN); Lei Jia, Jinan (CN); and Qingmei Sui, Jinan (CN)
Assigned to SHANDONG UNIVERSITY, Jinan (CN)
Filed by SHANDONG UNIVERSITY, Jinan (CN)
Filed on Jan. 7, 2025, as Appl. No. 19/011,682.
Claims priority of application No. 202411053069.0 (CN), filed on Aug. 2, 2024.
Int. Cl. B60L 53/64 (2019.01); B60L 53/62 (2019.01); B60L 53/63 (2019.01); B60L 53/67 (2019.01); G06Q 10/0631 (2023.01); G06Q 50/06 (2012.01)

CPC B60L 53/64 (2019.02) [B60L 53/62 (2019.02); B60L 53/63 (2019.02); B60L 53/67 (2019.02); G06Q 10/06314 (2013.01); G06Q 50/06 (2013.01)]

9 Claims

1. A large-scale electric vehicle cluster coordinated charging optimization method based on greedy repair genetic algorithm, comprising the following steps:

S1, establishing a task model: establishing a task model of electric vehicle cluster coordinated charging in large charging stations according to task characteristics of electric vehicle charging; specifically comprising:

S11, setting a discrete-time system: adopting a discrete-time system setting, defining a sampling period length as Δ_t, comprising 5 minutes, 10 minutes, 15 minutes, half an hour and one hour, indexing each time period with a positive integer t≥1, regarding a basic load, a connection state of an electric vehicle and a charging pile, and a charging power and an electricity price of the electric vehicle as constant values under the discrete-time system setting and in each time period with a length Δ_t;

S12, determining a scheduling interval: starting a scheduling interval for electric vehicle coordinated charging from a current time step t and lasting until a time step t+T;

S13, defining a charging task: defining a charging task d_vfor the electric vehicle v when the electric vehicle v enters a garage and is connected to the charging pile, expressed as:

d_v=(v,t_v^a,t_v^d,s_v^a,s_v^d,E_v,P_v,η_v,i_v,Φ_v);

wherein t_v^ais a plug-in time when the electric vehicle v arrives at the garage to connect the charging pile and get ready for charging, and t_v^dis a plug-out time of the electric vehicle v; s_v^ais an initial SOC of a battery when the electric vehicle v arrives at the garage, s_v^dis an expected SOC when the electric vehicle v is plugged out; E_vis a battery capacity of the electric vehicle v, P_vis the charging power of the electric vehicle v, η_v∈[0,1] is a charging efficiency of the battery of the electric vehicle v, and i_vis the number of the charging pile connected to the electric vehicle v, and Φ_v∈{A, B, C} is a charging load phase of the electric vehicle v;

S14, defining decision variables: defining a decision variable matrix X for V vehicles when the V vehicles enter the garage and are connected to charging piles, expressed as:

wherein V is the number of rows in the decision variable matrix X, and T is the number of columns in the decision variable matrix X, x_v,t∈{0,1} indicates an on-off state of the charging pile i_vat the current time step t; and

S15, defining a coordinated charging time domain: defining T_tas a set of time steps comprised in a finite time domain of coordinated charging from the current time step t, expressed as:

T_t={t,t+1, . . . ,t+T_t−1}

S2, constructing constraint conditions: constructing constraint conditions for a coordinated charging optimization problem according to principles of guaranteeing stable operation of a power distribution network and meeting a charging requirement of the electric vehicle, wherein the constructing the constraint conditions in the S2 comprises:

S21, charging task constraints: meeting relevant constraints of a electric vehicle charging task, comprising a charging time constraint and a battery state of charge (SOC) constraint; and

S22, distribution network stability constraints: meeting relevant constraints of a safe and stable operation of the power distribution network, comprising an each-phase total load constraint and a three-phase load balance degree constraint of a distribution transformer;

S3, constructing a constrained optimization problem: designing an objective function with a minimum charging cost, performing linear mathematical transformation on non-linear constraint conditions, and constructing a 0/1 integer linear programming problem;

S4, solving the constrained optimization problem: carrying out genetic coding on a decision model of a charging station to obtain a coded decision model of the charging station, and specifically designing a genetic algorithm with greedy repair operators to solve the constructed constrained optimization problem; and

S5, controlling, based on the constraint conditions for the coordinated charging optimization problem and the coded decision model of the charging station, the charging station to charge for a target electric vehicle cluster, thereby reasonably scheduling a charging time and an order of the target electric vehicle cluster and improving performance of the charging station.