考虑大规模风电和电动汽车入网的协同优化调度

Synergistic Optimization Scheduling Considering Large-scale Wind Power and Electric Vehicles Integration

为了实现发电机组的经济调度,同时减少污染气体的排放和等效负荷的波动,结合电动汽车与电力系统之间能量双向流动的特点,考虑风力发电的间歇性和电动汽车充放电的随机性对电网的影响,以发电成本、排污成本和等效负荷波动方差最小为多目标函数,构建了计及风电和插入式混合电动汽车(Plug-in Hybrid Electric Vehicles,PHEVs)的协同优化调度模型。并应用基于ε占优的多目标粒子群算法进行求解,该方法能保证外部存档非劣解的收敛性和分布性,克服了粒子群算法(Particle Swarm Optimization,PSO)求解多目标优化问题极易收敛到伪pareto前沿和收敛速度慢的缺陷。最后,通过仿真实验,验证了所构建多目标协同优化调度模型与单目标优化调度模型相比,能有效平抑可再生能源发电出力波动,降低发电总成本和排污成本。

In order to achieve an economic dispatch of the turbines, reduced pollution and the equivalent load fluc-tuation, we have used a two-way flow between EV.Considering the impact of wind power intermittent and the ran-domness of electric vehicle charging and discharging on the grid, a mathematical model of economic dispatch of large-scale wind power and electric vehicles integration is built.It minimizes power generation cost, exhaust emis-sion cost and the equivalent load fluctuation variance as its multi-objective function.The epsilon dominant multi-objective particle swarm optimization algorithm is used to solve the model.This method guarantees external archive the pareto solutions in convergence and distribution, and overcomes the defects of the particle swarm optimization （ PSO） algorithm in solving multi-objective optimization problems.So it is easy to converge to pseudo pareto front and slow convergence speed.Finally, the simulation experiment shows that the synergistic optimization scheduling model compared with the single objective optimization scheduling model, can effectively restrain the renewable en-ergy power generation output fluctuations and reduce power generation cost and exhaust emission cost.