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Over the past few decades, many universities have turned to using microgrid systems because of their dependability, security, flexibility, and less reliance on the primary grid. Microgrids on campuses face challenges in the instability of power production due to meteorological conditions, as the output of renewable sources such as solar and wind power relies entirely on the weather and determining the optimal size of microgrids. Therefore, this paper comprehensively reviews the university campuses’ microgrids. Some renewable energy sources, such as geothermal (GE), wind turbine (WT), and photovoltaic (PV), are compared in terms of installation costs, availability, weather conditions, efficiency, environmental impact, and maintenance. Furthermore, a description of microgrid systems and their components, including distributed generation (DG), energy storage system (ESS), and microgrid load, is presented. As a result, the most common optimization models for analyzing the performance of campus microgrids are discussed. Hybrid microgrid system configurations are introduced and compared to find the optimal configuration in terms of energy production and flexibility. Therefore, configuration A (Hybrid PV- grid-connected) is the most common configuration compared to the others due to its simplicity and free-charge operation.

This research paper presents a comprehensive study on the optimal planning and design of hybrid renewable energy systems for microgrid (MG) applications at Oakland University. The HOMER Pro platform analyzes the technical, economic, and environmental aspects of integrating renewable energy technologies. The research also focuses on the importance of addressing unmet load in the MG system design to ensure the university’s electricity demand is always met. By optimizing the integration of various renewable energy technologies, such as solar photovoltaic (PV), energy storage system (ESS), combined heat and power (CHP), and wind turbine energy (WT), the study aims to fulfill the energy requirements while reducing reliance on traditional grid sources and achieving significant reductions in greenhouse gas emissions. The proposed MG configurations are designed to be scalable and flexible, accommodating future expansions, load demands changes, and technological advancements without costly modifications or disruptions. By conducting a comprehensive analysis of technical, economic, and environmental factors and addressing unmet load, this research contributes to advancing renewable energy integration within MG systems. It offers a complete guide for Oakland University and other institutions to effectively plan, design, and implement hybrid renewable energy solutions, fostering a greener and more resilient campus environment. The findings demonstrate the potential for cost-effective and sustainable energy solutions, providing valuable guidance for Oakland University’s search for energy resilience and environmental surveillance, which has a total peak load of 9.958 MW. The HOMER simulation results indicate that utilizing all renewable resources, the estimated net present cost (NPC) is a minimum of USD 30 M, with a levelized energy cost (LCOE) of 0.00274 USD/kWh. In addition, the minimum desired load will be unmetered on some days in September.

Ground heat pump systems (GHPSs) are esteemed for their high efficiency within renewable energy technologies, providing effective solutions for heating and cooling requirements. These GHPSs operate by utilizing the relatively constant temperature of the Earth’s subsurface as a thermal source or sink. This feature allows them to perform greater energy transfer than traditional heating and cooling systems (i.e., heating, ventilation, and air conditioning (HVAC)). The GHPSs represent a sustainable and cost-effective temperature-regulating solution in diverse applications. The ground heat exchanger (GHE) technology is well known, with extensive research and development conducted in recent decades significantly advancing its applications. Improving GHE performance factors is vital for enhancing heat transfer efficiency and overall GHPS performance. Therefore, this paper provides a comprehensive review of research on various factors affecting GHE performance, such as soil thermal properties, backfill material properties, borehole depth, spacing, U-tube pipe properties, and heat carrier fluid type and velocity. It also discusses their impact on heat transfer efficiency and proposes optimal solutions for improving GHE performance.

Geothermal heat pump systems (GHPSs) offer a sustainable and energy-efficient solution for heating and cooling buildings. Ground heat exchanger (GHE) design and configuration significantly impact on the overall performance and installation expenses of geothermal heat pump systems. This paper presents a comprehensive analysis of GHPSs, focusing on their advantages, disadvantages, key components, types, and particularly the various closed-loop GHE configurations. Detailed comparisons highlight how different designs affect thermal performance and installation costs. The findings reveal that helical GHEs offer superior thermal efficiency with reduced drilling requirements and cost savings, while coaxial GHEs, especially those using steel tubes, enhance heat transfer and enable shorter boreholes. Cost-effective options like W-type GHEs provide performance comparable to more complex systems. Additionally, triple U-tube and spiral configurations balance high efficiency with economic feasibility. The single and double U-tube remain the most common borehole geometry, though coaxial designs present distinct advantages in targeted scenarios. These insights support the optimization of vertical GHEs, advancing system performance, cost-effectiveness, and long-term sustainability in GHPS applications.

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.

This paper designs two DC-DC converter configurations integrated with solar PV renewable energy resource. Its focuses on comparing two converter topologies: the conventional boost converter and the switched capacitor boost converter. The Perturb and Observe (P&O), Incremental Conductance (INC), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO) algorithms are employed to dynamically enhance the Maximum Power Point Tracking (MPPT) performance for both converters. The simulation results demonstrate that both converter topologies, when integrated with appropriate MPPT algorithms, can effectively harvest maximum power from the solar PV. However, the switched capacitor topology converter exhibits advantages in terms of current capabilities and voltage performance. In addition, combing the switched capacitor boost converter with the GA-MPPT algorithm improved the output voltage profile. The switched capacitor topology demonstrates distinct advantages by exhibiting enhanced current control, enabling improved handling of dynamic load changes and varying irradiance conditions. It shows voltage regulation, resulting in reduced output voltage fluctuations and enhanced stability, thereby optimizing energy extraction. The GA-MPPT simulation demonstrates a substantial increase in maximized output current for the switched capacitor boost configuration (70 A) when compared to the conventional type (10 A). The validation and implementation of the system models are carried out using MATLAB/Simulink.