Occupant preference-aware load scheduling for resilient communities

• Published in: Energy and buildings Vol. 252; p. 111399
• Main Authors: Wang, Jing, Huang, Sen, Zuo, Wangda, Vrabie, Draguna
• Format: Journal Article
• Language: English
• Published: Lausanne Elsevier B.V 01.12.2021; Elsevier BV; Elsevier
• Subjects: ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION; Microgrid; Model predictive control; Occupant behavior; Optimal load scheduling; Predictive control; Preferences; Resilience; Resilient community; Room temperature; Scheduling; Stochasticity; Uncertainty; Weather forecasting; Model predictive control; Resilient community; Uncertainty; Occupant behavior; Microgrid; Optimal load scheduling
• ISSN: 0378-7788; 1872-6178
• DOI: 10.1016/j.enbuild.2021.111399

•Propose an occupant thermal preference-aware load scheduler for resilient communities.•Quantify the impact of occupant thermostat changing behavior on resilient community optimal scheduling through selected key resilience indicators.•Adopt the chance-constrained method for addressing the occupant behavior uncertainty in the community optimal control problem.•Provide a case study for controller performance evaluation on a virtual community tesebed. The load scheduling of resilient communities in the islanded mode is subject to many uncertainties such as weather forecast errors and occupant behavior stochasticity. To date, it remains unclear how occupant preferences affect the effectiveness of the load scheduling of resilient communities. This paper proposes an occupant preference-aware load scheduler for resilient communities operating in the islanded mode. The load scheduling framework is formulated as a model predictive control problem. Based on this framework, a deterministic load scheduler is adopted as the baseline. Then, a chance-constrained scheduler is proposed to address the occupant-induced uncertainty in room temperature setpoints. Key resilience indicators are selected to quantify the impacts of the uncertainties on community load scheduling. Finally, the proposed preference-aware scheduler is compared with the deterministic scheduler on a virtual testbed based on a real-world net-zero energy community in Florida, USA. Results show that the proposed scheduler performs better in terms of serving the occupants’ thermal preference and reducing the required battery size, given the presence of the assumed stochastic occupant behavior. This work indicates that it is necessary to consider the stochasticity of occupant behavior when designing optimal load schedulers for resilient communities.