基于双层离散粒子群优化的智能小区车辆与家庭互动调度策略

A Dispatching Strategy for V2H of Intelligent Community Based on Bilayer Discrete Particle Swarm Optimization

综合考虑电动汽车的行驶特性、电池特性等约束条件,建立了计及峰谷差、日负荷率、负荷均方差和用户电费的多目标智能小区车辆与家庭互动(vehicle to home,V2H)调度策略。为求解该策略,提出双层离散粒子群算法优化V2H调度模型,以解决智能优化算法难以求解含等式约束方程的问题。第1层优化通过离散粒子群算法求解满足所有约束条件的单辆电动汽车充放电计划可行解,第2层优化采用基本粒子群算法迭代优化V2H调度模型。对无序充电、有序充电调度、V2H调度模式以及不同用户响应度的V2H调度策略进行仿真分析,结果表明:V2H调度在减小峰谷差、负荷波动、提升日负荷率方面的作用最显著,与无序充电相比用户电费下降1/3以上;随着用户对V2H调度策略响应度提高,负荷特性改善越明显,但是V2H调度的车均电费会增加。

Considering driving characteristics and battery characteristics, this paper proposed a dispatching strategy taking account of valley-peak difference, daily load factor, mean-square deviation of load and electricity of users. Since intelligent optimization algorithms can＇t appropriately solve problem with equation constraints, a bilayer discrete particle swarm optimization is proposed. On the first layer, charging and discharging plan per electric vehicle satisfying all constrains is made with discrete particle swarm optimization; on the second layer, basic discrete particle swarm optimization is applied to optimize model of vehicle to home （V2H） dispatching strategy. Daily load and electricity are analyzed in uncoordinated and coordinated charging modes, V2H mode under different responsiveness of users to V2H dispatching strategy. It is concluded that V2H dispatching strategy could in most extent reduce valley-peak difference, load fluctuations and improve daily load factor. Electricity of V2H mode falls more than 1/3 than that of uncoordinated charging mode; and the higher responsiveness of users to V2H dispatching strategy is, the better load characteristics will be, but the fare per vehicle in V2H dispatching will be higher.