A data-driven electric water heater scheduling and control system
A data-driven electric water heater scheduling and control system
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DOI：
10.1016/j.enbuild.2021.110924
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发表时间：
2021-04-17
影响因子：
6.7
通讯作者：
Zhang, K. Max
中科院分区：
文献类型：
--
作者：
Shen, Gulai;Lee, Zachary E.;Zhang, K. Max
生活热水（DHW）加热占平均家庭能源使用的30%。与燃气热水器相比，电热水器（EWH）可以由可再生能源提供动力，从而使其成为一种潜在的可再生能源加热选择。此外，随着对能源储存的需求不断增长，可再生资源的整合以及全球范围内的举措，预计生活热水供暖的电气化将继续快速增长。然而，许多具有监测和警报功能的商业EWH产品缺乏优化和执行数据预测控制的智能;另一方面，具有精细模型和模拟的研究在结合实时数据和在现实世界设置的不确定性下提供鲁棒的最优控制方面不足。本文提出了一个EWH智能调度和控制系统，使用数据驱动的扰动预测在一个强大的模型预测控制（MPC），以实现各种需求侧管理目标。测试与真实世界的EWH数据集和两个状态的EWH模型，强大的MPC模拟进行中央EWH供应生活热水的多单元公寓楼量化的预测不确定性。结果表明，该系统是能够预测生活热水的需求与不确定性区间覆盖高达97%的实际需求，在测试日，降低电力成本高达33.2%，以及保持所需的生活热水温度，而不影响用户的舒适度。此外，该系统的灵活性，以改变不同的需求响应（DR）计划下的负载配置文件被证明。可以实现电力和水消耗的减少。所提出的系统可以创建预测生活热水使用和优化控制的可实施的解决方案，作为真实世界设置中的鲁棒和可靠的建筑物能源管理和控制系统的一部分。(C)2021爱思唯尔有限公司版权所有。
Domestic hot water (DHW) heating accounts for up to 30% of average household energy use. Compared to gas fired water heaters, electric water heaters (EWH) can be powered by renewable generation resources, thus making it a potential renewable heating option. Furthermore, with the growing need for energy storage, incorporation of renewable resources, and initiatives worldwide, the electrification of DHW heating is expected to continue the rapid growth. However, many commercial EWH products with monitoring and alerting functionalities lack the intelligence to optimize and perform predictive control with data; on the other hand, research studies with refined models and simulations come short in incorporating real-time data and providing robust optimal controls under uncertainties in real-world settings. This paper presents a EWH Smart Scheduling and Control System using data-driven disturbance forecasts in a robust Model Predictive Control (MPC) to accomplish various demand side management objectives. Testing with a real-world EWH dataset and a two-state EWH model, robust MPC simulations are conducted on a central EWH supplying DHW for a multi-unit apartment building with quantified prediction uncertainty. Results show that the proposed system is capable of anticipating DHW demand with an uncertainty interval covering up to 97% of the actual demand during the test days and reducing electricity cost up to 33.2% as well as maintaining a desired DHW temperature without affecting user comfort. Further, the flexibility of the system to alter load profiles under different Demand Response (DR) programs is demonstrated. Reductions in both power and water consumption can be accomplished. The proposed system can create an implementable solution of forecasting DHW usage and optimizing controls as a part of a robust and reliable building energy management and control system in real-world settings. (C) 2021 Elsevier B.V. All rights reserved.