面向居住者的需求响应与多区域建筑热控制
Occupant-Oriented Demand Response with Multi-Zone Thermal Building Control
AI总结：
提出新型多区域控制器PSC，综合考虑居住者热满意度、储能和能源价格，无需建筑模型或预测，在多区域供暖场景中成本降低22%，适用于未来能源系统HVAC控制。
摘要：
在未来具有高比例可再生能源的能源系统中，为了维持稳定性，建筑的电力需求必须对波动的电力发电做出响应。由于建筑消耗了全球三分之一的能源，且其中近一半用于供暖、通风和空调（HVAC）系统，HVAC 适合在时间上转移其电力消耗。为此，需要智能控制策略，因为 HVAC 的常规控制并未针对居住者的实际需求和电网当前状况进行优化。在本文中，我们提出了一种新型多区域控制器 Price Storage Control (PSC)，它不仅考虑了房间个体的居住者热满意度 (OTS)，还考虑了可用的储能和能源价格。PSC 的主要特点是，它不需要建筑模型或未来需求预测即可推导建筑内多个房间的控制动作。作为对比，我们使用了一种理想的、无误差的模型预测控制 (MPC)、一种不考虑储能的简化变体 (PC)，以及一种传统的基于滞环的两点控制。我们在多区域环境中评估了这四种控制器，用于冬季建筑供暖，并考虑了两种不同场景，其区别在于允许温度的变化幅度。此外，我们比较了高热容和低热容模型参数的影响。结果表明，PSC 在两种场景和两种参数下均大幅优于传统控制方法。在高热容情况下，它实现了 22% 的成本降低，而理想 MPC 实现了超过 39% 的成本降低。考虑到 PSC 与 MPC 不同，不需要任何建筑模型或预测，结果支持了我们开发的控制策略适用于未来能源系统中 HVAC 系统控制的结论。
英文摘要：
In future energy systems with high shares of renewable energy sources, the electricity demand of buildings has to react to the fluctuating electricity generation in view of stability. As buildings consume one-third of global energy and almost half of this energy accounts for Heating, Ventilation, and Air Conditioning (HVAC) systems, HVAC are suitable for shifting their electricity consumption in time. To this end, intelligent control strategies are necessary as the conventional control of HVAC is not optimized for the actual demand of occupants and the current situation in the electricity grid. In this paper, we present the novel multi-zone controller Price Storage Control (PSC) that not only considers room-individual Occupants' Thermal Satisfaction (OTS), but also the available energy storage, and energy prices. The main feature of PSC is that it does not need a building model or forecasts of future demands to derive the control actions for multiple rooms in a building. For comparison, we use an ideal, error-free Model Predictive Control (MPC), a simplified variant without storage consideration (PC), and a conventional hysteresis-based two-point control. We evaluate the four controllers in a multi-zone environment for heating a building in winter and consider two different scenarios that differ in how much the permitted temperatures vary. In addition, we compare the impact of model parameters with high and low thermal capacitance. The results show that PSC strongly outperforms the conventional control approach in both scenarios and for both parameters. For high capacitance, it leads to 22 % costs reduction while the ideal MPC achieves cost reductions of more than 39 %. Considering that PSC does not need any building model or forecast, as opposed to MPC, the results support the suitability of our developed control strategy for controlling HVAC systems in future energy systems.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
