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The main challenge utility companies face is to meet the electricity demand during peak hours, and residential dwellings significantly contribute to peak load, with space heating and cooling, water heating, electric vehicle charging, and the use of other routine appliances. Demand Response (DR) programs aim to control these loads to facilitate matching load demand with available supply, and various demand response strategies have been proposed for residential communities. However, their use in real-world scenarios can be limited due to computational challenges and privacy concerns. In order to overcome these limitations, developing distributed solution techniques is as important as designing demand response strategies. This dissertation focuses on DR strategies for residential communities and designs distributed solutions techniques.
The continuous participation of users is a key factor to the success of demand response programs, and methods failing to maintain user comfort may not realize their full potential. Hence, the first part of the dissertation proposes two residential demand response strategies based on model predictive control algorithms to ensure user comfort. 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.
Deep Reinforcement Learning (RL) has shown its potential to solve complex sequential decision-making problems, and it is a prominent tool for DR-related applications due to its model-free nature. However, RL algorithms can suffer from sample inefficiency issues, which limit their use in real-world applications. To address this issue, the second part of the dissertation proposes utilizing expert data to improve the performance of RL algorithms. In addition to proposing a general framework that improves the efficiency of RL algorithms by leveraging expert data, we investigate the effect of utilizing expert data in a demand response related RL algorithm.
