An investigation of optimal control of passive building thermal storage with real time pricing

Abstract

The cost savings potential of optimal passive thermal storage control were examined for day-ahead real time electricity rate structures. The operational strategies of three office building models were optimized in four US cities (Chicago, New York, Houston and Los Angeles) using price and weather data for the summer of 2008. Optimization of building thermal mass was conducted using a predictive optimal controller to define supervisory strategies in terms of building global cooling temperature setpoints. A global minimization algorithm determined optimal setpoint trajectories for each day divided into four distinct time periods. Cost savings were found to range from 0 to 14% depending on the building, climate, and characteristics of the rate signal. The best cost savings occurred for price spikes or cool nighttime temperatures. Moreover, it was found that low internal gains favoured a more flexible precooling strategy, while high internal gains coupled with low thermal mass resulted in poor precooling performance.

Keywords:

• optimal control
• building thermal mass
• real time pricing
• thermal energy storage
• load shifting

Acknowledgement

This work has been sponsored through a research contract with Clean Urban Energy, Inc., for which the authors express their sincere gratitude. Moreover, G.P. Henze discloses his role as technology advisor and co-founder of Clean Urban Energy, Inc.