Analyzing the effects of comfort relaxation on energy demand flexibility of buildings: A multiobjective optimization approach

Journal Article · Mon Dec 01 04:00:00 EST 2014 · Energy and Buildings

DOI:https://doi.org/10.1016/j.enbuild.2014.09.040· OSTI ID:1396027

Morales-Valdés, Pilar; Flores-Tlacuahuac, Antonio; Zavala, Victor M.

We present a multiobjective optimization framework to evaluate the effects of comfort relaxation on the energy demands of buildings. This work is motivated by recent interest in understanding demand elasticity available for real-time electricity market operations and demand response events. We analyze the flexibility provided by an economics-based control architecture that directly minimizes total energy and by a traditional tracking control system that minimizes deviations from reference temperature and relative humidity set-points. Our study provides the following insights: (i) using percentage mean vote (PMV) and predicted percentage dissatisfied (PPD) constraints within an economics-based system consistently gives the most flexibility as comfort is relaxed, (ii) using PMV and PPD penalization objectives results in high comfort volatility, (iii) using temperature and relative humidity bounds severely overestimates flexibility, and (iv) tracking control offers limited flexibility even if used with optimal set-back conditions. We present a strategy to approximate nonlinear comfort regions using linear polyhedral regions, and we demonstrate that this reduces the computational complexity of optimal control formulations. (C) 2014 Published by Elsevier B.V.

• Research Organization: Argonne National Laboratory (ANL)
• Sponsoring Organization: USDOE Office of Science
• DOE Contract Number: AC02-06CH11357
• OSTI ID: 1396027
• Journal Information: Energy and Buildings, Journal Name: Energy and Buildings Vol. 85; ISSN 0378-7788
• Publisher: Elsevier
• Country of Publication: United States
• Language: English

Related Subjects

HVAC systems
comfort relaxation
energy flexibility
multiobjective optimization
optimal control