The advent of electric and flying vehicles (EnFVs) has brought significant advancements to the transportation industry, offering improved sustainability, reduced congestion, and enhanced mobility. However, the efficient routing of messages in EnFVs presents unique challenges that demand specialized algorithms to address their specific constraints and objectives. This study analyzes several case studies that investigate the effectiveness of genetic algorithms (GAs) in optimizing routing for EnFVs. The major contributions of this research lie in demonstrating the capability of GAs to handle complex optimization problems with multiple objectives, enabling the simultaneous consideration of factors like energy efficiency, travel time, and vehicle utilization. Moreover, GAs offer a flexible and adaptive approach to finding near-optimal solutions in dynamic transportation systems, making them suitable for real-world EnFV networks. While GAs show promise, there are also limitations, such as computational complexity, difficulty in capturing real-world constraints, and potential sub-optimal solutions. Addressing these challenges, the study highlights several future research directions, including the integration of real-time data and dynamic routing updates, hybrid approaches with other optimization techniques, consideration of uncertainty and risk management, scalability for large-scale routing problems, and enhancing energy efficiency and sustainability in routing. By exploring these avenues, researchers can further improve the efficiency and effectiveness of routing algorithms for EnFVs, paving the way for their seamless integration into modern transportation systems.

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.

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.

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.

Amidst a growing global focus on sustainable energy, this study investigates the underutilization of renewable resources in the southern region of Saudi Arabia, with a specific emphasis on the Najran Secondary Industrial Institute (NSII). This research presents an in-depth analysis of installing a hybrid microgrid (MG) system on the roofs of NSII buildings, exploring six cases with varying tilt and azimuth angles. The study innovatively integrates architectural design and system administration, a novel approach for this location, and benchmarks the optimal angles against Hybrid Optimization of Multiple Energy Resources (HOMER) software defaults. The proposed system consists of solar photovoltaic (PV) panels, a battery storage system (BSS), a converter, a diesel generator (DG), and a grid. The selected model balances technological and economic viability with environmental considerations, ensuring a reliable power supply within the NSII’s roof area constraints. An extensive sensitivity analysis evaluates the system’s resilience across different scenarios. The current system, which is grid-only, has an estimated Net Present Cost (NPC) of about USD 7.02M and emits 1.81M kg/yr of CO₂. The findings point to installing a microgrid with a 20.97° tilt and 50° azimuth angle as optimal, demonstrating 54.69% lower NPC and 92% lower CO₂ emissions, along with zero kWh/year unmet electrical load when applying the resilience assessments. This outcome highlights Saudi Arabia’s southern region’s renewable energy potential, aligning with national mega-projects and energy initiatives.

As a nature-inspired search algorithm, the Firefly algorithm (being a naturally outstanding search algorithm with few control parameters) may have a considerable influential performance. In this paper, we present a new firefly algorithm to address the parameter selection and adaptation strategy in the standard firefly algorithm. The proposed firefly algorithm introduces a modified exploration and exploitation mechanism, with adaptive randomness and absorption coefficients. The proposed method employs the adaptation of the randomness and absorption coefficients to be a function of time/iterations. Moreover, gray relational analysis advancing fireflies is used to allocate different information from appealing ones effectively. Standard benchmark functions are applied to verify the effects of these improvements and it is illustrated that, in most situations, the performance of the proposed firefly algorithm is superior to (or at least highly competitive with) the standard firefly algorithm, and state-of-the-art approaches in terms of performance.