IEEE Open Access Journal of Power and Energy | |
Safe Reinforcement Learning-Based Resilient Proactive Scheduling for a Commercial Building Considering Correlated Demand Response | |
Zheming Liang1  Wencong Su1  Bin Wang2  Nan Duan3  Vaibhav Donde3  Can Huang3  Xianbo Zhao4  | |
[1] Department of Electrical and Computer Engineering, University of Michigan-Dearborn, Dearborn, MI, USA;Lawrence Berkeley National Laboratory, Berkeley, CA, USA;Lawrence Livermore National Laboratory, Livermore, CA, USA;School of Engineering and Technology, Central Queensland University, Sydney, NSW, Australia; | |
关键词: Safe reinforcement learning; deep Q-network; conditional-value-at-risk; resilience; proactive scheduling; commercial building; | |
DOI : 10.1109/OAJPE.2021.3064319 | |
来源: DOAJ |
【 摘 要 】
It is a crucial yet challenging task to ensure commercial load resilience during high-impact, low-frequency extreme events. In this paper, a novel safe reinforcement learning (SRL)-based resilient proactive scheduling strategy is proposed for commercial buildings (CBs) subject to extreme weather events. It deploys the correlation between different CB components with demand response capabilities to maximize the customer comfort levels while minimizing the energy reserve cost. It also develops an SRL-based algorithm by combining deep-Q-network and conditional-value-at-risk methods to handle the uncertainties in the extreme weather events such that the impact from extreme epochs in the learning process is greatly mitigated. As a result, an optimum control decision can be derived that targets proactive scheduling goals, where exploration and exploitation are considered simultaneously. Extensive simulation results show that the proposed SRL-based proactive scheduling decisions can ensure the resilience of a commercial building while maintaining comprehensive comfort levels for the occupants.
【 授权许可】
Unknown