A modeling framework for optimization-based control of a residential building thermostat for time-of-use pricing. (15th May 2019)

• Record Type: Journal Article
• Authors: Tabares-Velasco, Paulo Cesar; Speake, Andrew; Harris, Maxwell; Newman, Alexandra; Vincent, Tyrone; Lanahan, Michael
• Is Part Of: Applied energy. Volume 242(2019)
• Page Start: 1346
• Page End: 1357
• Publication Date: 2019-05-15
• Subjects: Model predictive control simulation framework -- Single-zone building -- Co-simulation environment -- Applied optimization -- Linear programming; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2019.01.241
• Languages: English
• ISSNs: 0306-2619

Abstract

Highlights: We analyze time-of-use rates with respect to model predictive controls. We show that thermal storage potential can vary greatly for differing climates. We build a model predictive control framework for indoor temperature setpoints. We analyze of the impacts of different variable electric rates. The framework is capable of reducing cooling electricity costs by 30%. Abstract: Heating, ventilation and air conditioning for residential and commercial buildings requires a substantial share of electric energy, and ultimately drives summer peak demand in the United States. Variable electric rates are becoming more common in the residential market, as utilities try to encourage users to shift their energy demand. Model predictive controls, one method of reducing energy usage, employ an optimization model to minimize peak demand, energy usage, or electricity costs. This paper details the development of a co-simulation framework to rapidly model and simulate building energy use and optimize cooling setpoint controls. The framework integrates commercially available software to: (i) simulate all energy interactions between the building, internal gains, outdoor environment, and heating and cooling systems via a building energy simulation program (EnergyPlus), (ii) algebraically formulate an optimization problem (with AMPL) using a black-box, reduced-order model for rapid calculations, (iii) employ Simulink as the environment that links calls to EnergyPlus and AMPL, and (iv) solve the optimization model (with CPLEX) to minimize electricity costs and user discomfort. Variable electric time-of-use rates are analyzed in the context of total cooling electricity costs, thermal comfort of users, and peak demand shedding. The framework uses a model predictive control formulation capable of reducing cooling electricity costs by up to 30%; however, cost savings and peak demand shedding are highly dependent on the time-of-use electricity rate schedule.