Solution of Energy Management Strategy in Microgrid Operation and Uses of Battery Energy Storage Under Uncertain Parametric Variations

Abstract

The modern energy landscape is witnessing a paradigm shift towards decentralized and cleaner energy alternatives. Microgrids (MGs), as localized energy systems, have emerged as a promising solution to integrate Distributed Energy Resources (DERs), including Battery Energy Storage Systems (BESS). However, the intermittent and uncertain nature of Renewable Energy Sources (RES) power output, combined with stochastic variations in energy demand, imposes significant challenges in planning and operating MG. In such a context, intelligent computing-based approaches have become essential tools for planning optimal energy management. These approaches begin by translating real-world operational challenges into mathematical models, considering system constraints, variables, and objectives. However, due to the unique operational behavior of every MG system, selecting appropriate scheduling strategies remains a crucial task. Additionally, the inclusion of diverse and uncertain load profiles, such as thermal and electric vehicle (EV) demands, has introduced new dimensions of complexity into MG operation. This complexity is further increased by the practical limitations of BESS operation, particularly under varying load and generation scenarios, often resulting in suboptimal scheduling. Improper management of the State of Charge (SOC) can lead to significant operational drawbacks, including reduced efficiency, increased energy costs, and faster degradation of battery life. Accordingly, the development of an effective Stochastic Energy Management Strategy (SEMS) that can dynamically respond to uncertainties while ensuring economic and operational efficiency has become a major research concern. This thesis is motivated by this need and presents a comprehensive framework for SEMS design that incorporates advanced forecasting, uncertainty modeling, and intelligent optimization.