Co-scheduling of HVAC control, EV charging and battery usage for building energy efficiency

Tianshu Wei, Qidong Zhu, Mehdi Maasoumy
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引用次数: 36

Abstract

Building stock consumes 40% of primary energy consumption in the United States. Among various types of energy loads in buildings, HVAC (heating, ventilation, and air conditioning) and EV (electric vehicle) charging are two of the most important ones and have distinct characteristics. HVAC system accounts for 50% of the building energy consumption and typically operates throughout the day, while EV charging is an emerging major energy load that is hard to predict and may cause spikes in energy demand. To maximize building energy efficiency and grid stability, it is important to address both types of energy loads in a holistic framework. Furthermore, on the supply side, the utilization of multiple energy sources such as grid electricity, solar, wind, and battery storage provides more opportunities for energy efficiency, and should be considered together with the scheduling of energy loads. In this paper, we present a novel model predictive control (MPC) based algorithm to co-schedule HVAC control, EV scheduling and battery usage for reducing the total building energy consumption and the peak energy demand, while maintaining the temperature within the comfort zone for building occupants and meeting the deadlines for EV charging. Experiment results demonstrate the effectiveness of our approach under a variety of demand, supply and environment constraints.
协同调度暖通空调控制、电动汽车充电和电池使用,以提高建筑能效
在美国,建筑消耗了40%的一次能源消耗。在建筑各类能源负荷中,暖通空调(HVAC)和电动汽车(EV)充电是最重要的两种,具有鲜明的特点。暖通空调系统占建筑能耗的50%,通常全天运行,而电动汽车充电是一个新兴的主要能源负荷,很难预测,可能会导致能源需求激增。为了最大限度地提高建筑能源效率和电网稳定性,在一个整体框架中解决这两种类型的能源负荷是很重要的。此外,在供给侧,电网电力、太阳能、风能和电池储能等多种能源的利用为能源效率提供了更多的机会,应与能源负荷调度一起考虑。本文提出了一种基于模型预测控制(MPC)的新算法,该算法将空调控制、电动汽车调度和电池使用协同调度,以降低建筑总能耗和峰值能源需求,同时将建筑居民的温度保持在舒适区域内,并满足电动汽车充电的截止日期。实验结果证明了我们的方法在各种需求、供应和环境约束下的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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