Quantifying demand flexibility of building energy systems under uncertainty

Abstract

Quantification of demand flexibility is of key importance to decision makers at the planning level, when designing demand response programs and determining capacity requirements for new battery storage installations, and, at the operation level, when assessing the demand-side potential for providing grid services. While different frameworks have been proposed for demand flexibility quantification, the literature still lacks a framework that considers uncertainty sources, affecting building energy control problems, when directly quantifying the demand flexibility potential. We propose a novel framework based on stochastic model predictive control for direct demand flexibility quantification in a bottom-up manner that can account for uncertainty. The proposed framework is utilized to quantify the flexibility potential of a building under uncertainty arising from weather forecasts and the available reduced-order model. The results are compared with those obtained using an existing deterministic approach. We demonstrate that the deterministic approach tends to overestimate the flexibility potential on account of ignoring uncertainty, and the occupants’ thermal comfort may be jeopardized if the grid asks for the flexibility estimated with the deterministic approach. The study accentuates the importance of developing an accurate model for model-based demand flexibility quantification owing to the significant effect of the modeling uncertainty.