Date Approved

4-28-2025

Embargo Period

4-28-2025

Document Type

Dissertation

Degree Name

Doctor of Philosophy (Ph.D.) Engineering

Department

Electrical and Computer Engineering

College

Henry M. Rowan College of Engineering

Advisor

Jie Li, Ph.D.

Committee Member 1

Ben Wu, Ph.D.

Committee Member 2

Huaxia Wang, Ph.D.

Committee Member 3

Mohammad Jalayer, Ph.D.

Committee Member 4

Ning Wang, Ph.D.

Keywords

Artificial intelligence;Distributed Energy Resources;Electricity Market;Microgrid;Power Systems;Reinforcement learning

Abstract

Electric power systems are undergoing a major transition from a centralized to a decentralized operation paradigm, driven by the rapid adoption of Distributed Energy Resources (DERs). While increased DER penetration contributes to consumer empowerment, energy security, and system flexibility, it also introduces challenges involving grid stability, load variability, and infrastructure constraints. This dissertation addresses these challenges by exploring advanced Energy Management System (EMS) solutions including model-based and model-free optimization methodologies: first, a model-based optimization strategy is proposed to coordinate multi-energy sources, while balancing economic, emission, and operational goals for sustainable campus microgrid operations; second, a distributed multi-agent deep reinforcement learning framework is proposed for community energy management, enabling decentralized control while addressing critical concerns related to data privacy, solution convergence and scalability; third, advanced market strategies are formulated for prosumers collaborating with DER aggregators, leveraging both single and multi-agent reinforcement learning algorithms to support prosumers’ engagement, strategic bidding, and active wholesale market participation. This research demonstrates improved economic viability of DERs in future electric grids, advances EMS design through customized AI methods, and underscores the potential for ongoing innovation in an evolving energy landscape.

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