计及不确定性的大型商场能源优化运行模型

Energy operation optimization model for large-scale shopping malls considering uncertainties

近年来,随着国民用电需求的持续增长和环境污染的日益严重,旨在通过改善用能结构、提高综合能效,进而减少用户能耗、降低用能成本的需求侧能源管理开始受到广泛关注。大型商场由于具有用电设备多、运行时间长、整体能耗高等特点,节能降耗潜力较大,有必要对其开展能源优化运行研究。在此背景下,以大型商场为研究对象,对中央空调、屋顶光伏、电动汽车充电服务的联合优化控制,计及光伏出力等因素的不确定性,考虑舒适度、功率平衡等约束,以运行成本最小为优化目标,建立大型商场能源优化运行模型。采用拉丁超立方采样嵌套遗传算法的方法求解该模型。以某实际大型商场为例的仿真结果表明,所提优化模型能够减少运行成本,为商场带来经济效益。

With the continuous growth of demand for national electricity and increasing environmental pollution,demand side energy management which designed to improve energy structure,upgrade energy efficiency,reduce power consumption,and decrease operation costs,has begun to be focused on.Large-scale shopping malls have great potential of power saving and consumption reduction,due to large number of power-devices,long operation hours,high power consumption and so on.An energy optimization operation model is established for the large-scale shopping mall by coordinated optimization controlling of central air conditions,PVs and EV charging services.This model takes the minimum operation cost as goal,considering temperature comfort,power balance and other constraints,taking uncertainties of factors such as the power of electric vehicles into account.The genetic algorithm with Latin Hypercube sampling is taken to solve the problem.Simulation results of a large-scale shopping mall demonstrate that the proposed optimization model could reduce operating costs and bring economic benefits.