The load scheduling of resilient communities in the islanded mode is subject to many uncertainties such as weather forecast errors and occupant behavior. In this paper, we investigated how occupants鈥� thermal preference would affect the optimal load scheduling performance in resilient communities. We adopted Monte Carlo simulations and chance constraints for the development of the stochastic scheduler and tested it against a deterministic scheduler on our Net-Zero Energy Communities (NZEC) virtual testbed. Results showed occupants鈥� thermal preferences need to be considered in optimal scheduling problems.
This article has been published under the title of 鈥淥ccupant Preference-Aware Load Scheduling for Resilient Communities鈥� in the journal of Energy and Buildings. For more information about this paper please visit this link.
The first author of this paper, Jing Wang, is a Ph.D. candidate at the SBS lab, where her research focuses on resilient energy systems, building energy system modeling and control and building-to-grid integration. Congratulations to Jing on her recent paper publication!
