A UNIFIED FRAMEWORK FOR MULTI-LEVEL CONTROL AND MULTIOBJECTIVE OPTIMIZATION OF HYBRID CAMPUS MICROGRIDS: INTEGRATING ADAPTIVE MPC, MODIFIED FIREFLY ALGORITHM, AND HOMER SIMULATIONS

Abstract

Hybrid microgrids have become an essential solution in integrating renewable energy sources into campus energy systems, addressing sustainability, reliability, and energy efficiency challenges. In this dissertation, I explore a comprehensive framework for controlling and optimizing hybrid microgrids, specifically focusing on the combination of solar PV, wind turbines, Combined Heat and Power (CHP) systems, and Battery Storage Systems (BSS) in a university campus setting. Traditional control strategies often fall short when dealing with the dynamic and intermittent nature of these renewable sources, necessitating advanced optimization and control techniques.To address these limitations, this research first examines the design and operation of hybrid renewable energy systems through a case study at Oakland University, utilizing HOMER software for simulations. This case study demonstrates the effectiveness in balancing key performance indicators, such as Net Present Cost (NPC), Levelized Cost of Energy (LCOE), and environmental impact. The optimized system configuration illustrates how hybrid microgrids can significantly reduce energy costs while enhancing sustainability in campus applications.Furthermore, I propose a Modified Firefly Algorithm (MFA) to optimize hybrid microgrid operations by solving multi-objective problems, including cost minimization and greenhouse gas emissions reduction. The MFA was specifically adapted to improve the efficiency of energy management systems (EMS) in microgrids by dynamically adjusting the optimization parameters. The algorithm outperformed traditional optimization techniques, offering superior results for complex multi-objective problems in hybrid microgrids.Additionally, this dissertation develops a multi-level control framework for hybrid microgrids, incorporating Adaptive Model Predictive Control (MPC) communication. The Adaptive MPC leverages predictive modeling to dynamically adjust control actions based on real-time data, optimizing power dispatch across the various energy sources and storage systems. This integration ensures reliable and efficient communication between distributed energy resources, improving system stability and performance under fluctuating conditions.This research provides valuable insights into the optimization and control of hybrid microgrids, demonstrated through real-world case studies and modified algorithmic approaches. The proposed methodologies cover more efficient, reliable, and sustainable energy systems in campus microgrids, offering a robust framework for future developments in smart grid technologies.