Date of Award

6-10-2021

Document Type

Thesis

Publisher

Santa Clara : Santa Clara University, 2021.

Degree Name

Master of Science (MS)

Department

Mechanical Engineering

First Advisor

Hohyun Lee

Abstract

The consumption and production of energy are more dynamic as distributed energy resources (DER) such as solar photovoltaic (PV) are deployed within the electric distribution system. The traditional energy price at a predetermined rate cannot accommodate these dynamics and can lead to wasted energy and higher costs for both utility companies and consumers. Commercial building and residential energy management systems are usually on a fixed schedule and are not able to respond to changes in energy price instantaneously. There is a need for a real-time pricing structure that can accommodate the fluctuating cost of energy based on supply and demand, and a need for an energy management system that is able to respond to the dynamic utility rate. As such, there is a need for a robust energy management control strategy and methodology to validate new approaches. To address this gap, a strategy to control HVAC systems in a residential house was developed along with a validation methodology. A model of predictive control was implemented to optimize the thermostat setpoints and minimize energy cost for an individual residential house while maintaining thermal comfort of users. Using the dynamic pricing, current indoor temperature, and predicted outdoor temperature and solar radiation, the control algorithm optimizes the energy consumption by adjusting the temperature setpoint on an hourly basis. This model was integrated with EnergyPlus simulation via an open source co-simulation platform previously developed at the U.S. National Institute of Standards and Technology (NIST). Total energy consumption and cost for consumers were compared between four energy control cases: fixed setpoint, fixed comfort zone with optimization, adaptive comfort zone control, and adaptive comfort zone control with optimization. Control strategies with optimization were found to reduce the total cost compared to those without optimization. Adaptive comfort zone control with optimization resulted in the most significant cost reduction. The simple dynamic pricing model used in simulations was proportional to the demand of energy at that time of day. This work will contribute to the development of utility dynamic pricing models and residential control strategies for grid-interactive buildings and homes. The simulation strategy enables the utility pricing models and control strategies to be tested independently so that a wide range of options can be considered. The outcome of this research can be expanded to different building models or locations in future work.

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