The Power of Diversity: Data-Driven Robust Predictive Control for Energy-Efficient Buildings and Districts
The Power of Diversity: Data-Driven Robust Predictive Control for Energy-Efficient Buildings and Districts
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DOI：
10.1109/tcst.2017.2765625
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发表时间：
2019-01-01
影响因子：
4.8
通讯作者：
Lygeros, John
中科院分区：
文献类型：
--
作者：
Darivianakis, Georgios;Georghiou, Angelos;Lygeros, John
集合式建筑的协同能源管理由于具有巨大的节能潜力，近年来受到人们的极大关注。这些收益主要通过两种方式获得：1)利用合作建筑的负荷转移能力；2)利用建筑界共同使用的昂贵但节能的设备(例如热泵、电池和光伏)。在文献中已经提出了几种努力实现这些节省的确定性和随机控制方案。所有这些方法的一个共同困难是整合关于影响系统的干扰的知识。在这种情况下，基于历史数据的潜在扰动分布往往没有得到很好的描述。在本文中，我们通过利用历史数据来构造分布族来解决这个问题，这些分布族以高置信度包含这些潜在的分布。然后，我们使用数据驱动稳健优化的工具来建立一个多阶段随机优化问题，该问题可以用一个有限维线性规划来近似。我们在数值研究中证明了它的有效性，在能源成本节约和违反约束方面，它的表现优于文献中已有的求解技术。我们通过展示通过合作管理具有不同特征的建筑集合而获得的显著的能源收益来总结这篇文章。
The cooperative energy management of aggregated buildings has recently received a great deal of interest due to substantial potential energy savings. These gains are mainly obtained in two ways: 1) exploiting the load shifting capabilities of the cooperative buildings and 2) utilizing the expensive but energy-efficient equipment that is commonly shared by the building community (e.g., heat pumps, batteries, and photovoltaics). Several deterministic and stochastic control schemes that strive to realize these savings have been proposed in the literature. A common difficulty with all these methods is integrating knowledge about the disturbances affecting the system. In this context, the underlying disturbance distributions are often poorly characterized based on historical data. In this paper, we address this issue by exploiting the historical data to construct families of distributions, which contain these underlying distributions with high confidence. We then employ tools from data-driven robust optimization to formulate a multistage stochastic optimization problem, which can be approximated by a finite-dimensional linear program. We demonstrate its efficacy in a numerical study, in which it is shown to outperform, in terms of energy cost savings and constraint violations, established solution techniques from the literature. We conclude this paper by showing the significant energy gains that are obtained by cooperatively managing a collection of buildings with heterogeneous characteristics.