Operational planning for multi-building portfolio in an uncertain energy market

• Published in: Energy and buildings Vol. 103; pp. 271 - 283
• Main Authors: Farzan, Farbod, Vaghefi, Seyed A., Mahani, Khashayar, Jafari, Mohsen A., Gong, Jie
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 15.09.2015
• Subjects: Demand response; Energy market; Hybrid end-use models; Multi-building portfolio; Uncertain market trend; Demand response; Energy market; Multi-building portfolio; Uncertain market trend; Hybrid end-use models
• ISSN: 0378-7788
• DOI: 10.1016/j.enbuild.2015.06.060

•We propose a framework for operational planning of a multi-building portfolio.•Physics-based and statistical models for HVAC, lighting and equipment are developed.•The proposed pricing scheme reflect the trend of energy market uncertainty.•The proposed framework can lead to achievement of load management strategies. In this study, we propose an optimization framework for day-ahead operational planning of a multi-building portfolio under market uncertainty. The portfolio of interest consists of two groups of buildings: controllable and uncontrollable. In the proposed framework, first, physics-based and statistical models are developed for estimation and prediction of end-use consumptions including Heating, Ventilation Air Conditioning (HVAC), lighting, and equipment in controllable buildings. In addition, calculation of hourly load distributions in uncontrollable buildings is developed using a non-parametric bootstrapping method. Then a multi-objective mathematical programming is formulated to minimize the energy expenditure given utility price signals while satisfying the occupants’ comfort. The proposed pricing scheme considers the differences between the day-ahead and real-time prices to reflect the trend of energy market uncertainty. It is demonstrated that this pricing scheme results in better performance, in terms of achieving to demand management goals, than the current scheme. Current pricing scheme is solely based upon the day-ahead forecasted price. The performance of the proposed framework is explored using real energy market data.