Algorithms and Simulation Framework for Residential Demand Response

An electric power system is a complex network consisting of a large number of power generators and consumers interconnected by transmission and distribution lines. One remarkable thing about the electric grid is that there has to be a continuous balance between the amount of electricity generated and consumed at all times. Maintaining this balance is critical for the stable operation of the grid and this task is achieved in the long term, short term and real-time by operating a three-tier wholesale electricity market consisting of the capacity market, the energy market and the ancillary services market respectively. For a demand resource to participate in the energy and the capacity markets, it needs to be able to reduce the power consumption on-demand, whereas to participate in the ancillary services market, the power consumption of the demand resource needs to be varied continuously following the regulation signal sent by the grid operator. This act of changing the demand to help maintain energy balance is called demand response (DR). The dissertation presents novel algorithms and tools to enable residential buildings to participate as demand resources on such markets to provide DR.

Residential sector consumes 37% of the total U.S. electricity consumption and a recent consumer survey showed that 88% of consumers are either eager or supportive of advanced technologies for energy efficiency, including demand response. This indicates that residential sector is a very good target for DR.

Two broad solutions for residential DR are presented. The first is a set of efficient algorithms that intelligently controls the customers' heating, ventilating and air conditioning (HVAC) devices for providing DR services to the grid. The second solution is an extensible residential demand response simulation framework that can help evaluate and experiment with different residential demand response algorithms.

One of the algorithms presented in this dissertation is to reduce the aggregated demand of a set of HVACs during a DR event while respecting the customers' comfort requirements. The algorithm is shown to be efficient, simple to implement and is proven to be optimal. The second algorithm helps provide the regulation DR while honoring customer comfort requirements. The algorithm is efficient, simple to implement and is shown to perform well in a range of real-world situations. A case study is presented estimating the monetary benefit that can be obtained by implementing the algorithm in a cluster of 100 typical homes and shows promising result.

Finally, the dissertation presents the design of a python-based object-oriented residential DR simulation framework which is easy to extend as needed. The framework supports simulation of thermal dynamics of a residential building and supports house hold appliances such as HVAC, water heater, clothes washer/dryer and dish washer. A case study showing the application of the simulation framework for various DR implementation is presented, which shows that the simulation framework performs well and can be a useful tool for future research in residential DR.