Assessing Impact of Large-Scale Distributed Residential HVAC Control Optimization on Electricity Grid Operation and Renewable Energy Integration

Charles Corbin

MPRA Paper from University Library of Munich, Germany

Abstract: Demand management is an important component of the emerging Smart Grid, and a potential solution to the supply-demand imbalance occurring increasingly as intermittent renewable electricity is added to the generation mix. Model predictive control (MPC) has shown great promise for controlling HVAC demand in commercial buildings, making it an ideal solution to this problem. MPC is believed to hold similar promise for residential applications, yet very few examples exist in the literature despite a growing interest in residential demand management. This work explores the potential for residential buildings to shape electric demand at the distribution feeder level in order to reduce peak demand, reduce system ramping, and increase load factor using detailed sub-hourly simulations of thousands of buildings coupled to distribution power ow software. More generally, this work develops a methodology for the optimization of residential HVAC operation using a distributed but directed MPC scheme that can be applied today's programmable thermostat technologies to address the increasing variability in electric supply and demand. Case studies incorporating varying levels of renewable energy generation demonstrate the approach and highlight important considerations for large-scale residential model predictive control.

Keywords: Energy Systems Engineering; clean energy; HVAC; smart grid; demand side management; mathematical modeling (search for similar items in EconPapers)
JEL-codes: C6 C61 C63 C65 (search for similar items in EconPapers)
Date: 2014-05-14
New Economics Papers: this item is included in nep-ene
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Citations: View citations in EconPapers (1)

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