Electrical and Computer Engineering ETDs

Publication Date

Summer 7-25-2024

Abstract

Community-driven energy initiatives have become crucial for effective energy management, particularly in trading and management. The rise of Distributed Energy Resources in smart grids demands a redesign of traditional Demand Response Management (DRM) models to account for prosumers' dynamic behavior in energy markets. Decentralization, including peer-to-peer (P2P) energy trading, is vital for resilience and sustainability. This thesis introduces two coalitional DRM models: one based on hedonic community formation games, and the other combining matching theory with coalition games. These models empower prosumers to autonomously select energy trading communities using partially available data. To optimize energy consumption, two additional models are proposed—one using log-linear reinforcement learning, and the other a distributed game-theoretic approach. Furthermore, a non-cooperative pricing model for P2P markets and a reinforcement learning mechanism for EV energy trading are developed. Simulation results validate the effectiveness and scalability of these models, demonstrating their superior performance.

Keywords

Game Theory, Reinforcement Learning, Demand Response Management, Distributed Energy Resources, Peer-to-Peer Trading

Document Type

Dissertation

Language

English

Degree Name

Computer Engineering

Level of Degree

Doctoral

Department Name

Electrical and Computer Engineering

First Committee Member (Chair)

Eirini Eleni Tsiropoulou

Second Committee Member

Jim Plusquellic

Third Committee Member

Aris Leivadeas

Fourth Committee Member

Payman Zarkesh-Ha

Comments

Removed blank page between Approval page and Title page; added "UNM" to previous degrees after degree name and before year on both title page and Abstract page; fixed spacing between previous degrees; edited abstract to be <150 words.

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