SE PhD Prospectus Defense: Erhan Ozcan

SE PhD Prospectus Defense: Erhan Ozcan

TITLE: Distributed Solution Techniques To Regulate The Load Consumption Of A Residential Neighborhood

ADVISOR: Ioannis Paschalidis ECE, SE, BME

COMMITTEE: Alex Olshevsky ECE, SE; Christos Cassandras ECE, SE; Michael Caramanis ME, SE

ABSTRACT: Maintaining a balance between electricity demand and supply is extremely important to ensure the reliability of power grids, and the main challenge utility companies are facing is to meet the electricity demand during peak hours. The residential dwellings account for a significant percentage of overall load consumption, with space heating and cooling, water heating, electric vehicle charging, and routine appliances making up the bulk of the electricity use in households. Therefore, demand response programs can reduce the peak load in residential neighborhoods and facilitate matching supply with demand by controlling these loads. However, the continuous participation of users is a key factor in the success of demand response programs, and methods failing to maintain the user comfort may not realize their full potential.

This dissertation formulates a novel optimization problem seeking to maintain the overall load consumption of a residential community close to some target load level while ensuring consumer comfort across various appliances. However, as in other multi-agent systems, the proposed formulation suffers from two major drawbacks that might limit its practical use in real-world scenarios. First, the optimization becomes computationally intractable as the number of participating homes increases. Second, the proposed formulation requires participating homes to share their sensitive personal data with a central coordination agent at each time interval to maintain the user comfort. .

To address the aforementioned problems, we develop two distributed solution strategies. The first algorithm employs a gradient-based distributed optimization approach, while the second utilizes a Dantzig-Wolfe decomposition technique to solve the problem in a distributed manner. Our research demonstrates the effectiveness of our distributed solution strategies in solving the formulated problem within a simulated environment, compared to a commercial solver attempting to solve the centralized version of the problem.