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.

Foresight of geothermal energy installation is valuable for energy decision-makers, allowing them to readily identify new capacity units, improve existing energy policies and plans, expand future infrastructure, and fulfill consumer load needs. Therefore, in this paper, an improved grey prediction model (IGM (1,1)) was applied to perform the annual geothermal energy installation capacity prediction for the top 10 countries based on installed power generation capacity evaluated at the end of 2021, namely the United States, Indonesia, Philippines, Turkey, New Zealand, Mexico, Italy, Kenya, Iceland, and Japan, for the next nine years for the period from 2022 through 2030. These data can be used by future researchers in the field. Separately, datasets from 2000 to 2021 were collected for each country’s geothermal energy installation capacity to build a model which can accurately predict the annually geothermal energy installation capacity by 2030. The IGM (1,1) model used a small dataset of 22 data points, with one point denoting one year (i.e., 22 years), to predict the capacity of geothermal energy installations for the next nine years. Following that, the model was implemented for each dataset in MATLAB, where appropriate, and the model accuracy was evaluated. Ten separate geothermal energy installation capacity datasets were used to validate the improved model, and these datasets further demonstrated the overall improved model’s accuracy. The results prove that the prediction accuracy of the IGM (1,1) model outperforms the benchmark conventional GM (1,1) model, thereby enhancing the overall accuracy of the GM (1,1) model. The IGM (1,1) model ensures error reduction, suggesting that it is an effective and promising tool for accurate short-term prediction. The results reveal the 2030 geothermal energy installation capacity rankings.

This paper presents a mathematical model of heat transfer behavior between the liquid inside vertical underground geothermal pipes and the surrounding ground for heating (in the winter) and cooling (in the summer) modes in a ground heat exchanger (GHE) that can optimize its output temperature. The GHE’s output temperature reaches the appropriate value when the water velocity is lowered enough. Subsequently, the proposed model was applied to a case study of a 400-ton geothermal heat pump system (GHPS) at Oakland University, in both the heating and cooling modes, to assess its validity and improve the GHE’s performance. The model was implemented in MATLAB using an ordinary differential equation (ODE) solver. Four different water velocities were used to demonstrate the significant effect of velocity on the loop exit temperature. Model predictive control (MPC) was designed to optimize the GHE’s output temperature by controlling the water velocity, which could reduce the energy consumption used for heat and water circulating pumps. The results reveal that the acceptable range of the water velocity for Oakland University’s GHE was between 0.35 and 0.45 $\mathrm{m}/\mathrm{s}$, which ensured that the heat pump system delivered the proper temperature to provide the Human Health Building (HHB) with a comfortable temperature regardless of the season. The suggested water velocity ranges in vertical single U-tube pipes with diameters of De 25 $\mathrm{mm}$, De 32 $\mathrm{mm}$, and De 40 $\mathrm{mm}$ are between 0.33 and 0.43 $\mathrm{m}/\mathrm{s}$, 0.35 to 0.45 $\mathrm{m}/\mathrm{s}$, and 0.38 to 0.48 $\mathrm{m}/\mathrm{s}$, respectively.

This paper presents the Improved Grey Prediction Model, also called IGM (1,1) model, to increase the prediction accuracy of the Grey Prediction Model (GM) model that performs the GHPS output temperature prediction. This was based on correcting the current predicted value by subtracting the error between the previous predicted value and the previous immediate mean of the measured value. Subsequently, the IGM (1,1) model was applied to predict the output temperature of the GHPSs at Oklahoma University, the University Politècnica de València, and Oakland University, respectively. For each GHPS, the model uses a small dataset of 24 data points (i.e., 24 h) for training to predict the output temperature eight hours in advance. The proposed model was verified using three different output temperature datasets; these datasets were also used to validate the power efficiency of the proposed model. In addition, the empirical results show that the proposed IGM (1,1) model significantly improves the simulation (in-sample) and the prediction (out-of-sample) of the output temperature of the GHPS through error reduction, thereby enhancing the GM (1,1) model’s overall accuracy. As a result, the prediction accuracies were compared, and the improved model was found to be more accurate than the GM (1,1) model in both simulation and prediction results for all datasets used.

This study reviews heat transfer fluids (HTFs) and their velocity effects on the thermal behavior of ground heat exchangers (GHEs) within geothermal heat pump (GHP) applications. It examines the classification, thermophysical properties, and operational behavior of standard working fluids, including water–glycol mixtures, as well as emerging nanofluids. Fundamental heat exchange mechanisms are discussed, with emphasis on how conductivity, viscosity, and heat capacity interact with fluid velocity to influence energy transfer performance, hydraulic resistance, and system reliability. Special attention is given to nanofluids, whose enhanced thermal behavior depends on nanoparticle type, concentration, dispersion stability, and flow conditions. The review analyzes stabilization strategies, including surfactants, functionalization, and pH control, for maintaining long-term performance. It also highlights the role of velocity optimization in balancing convective benefits with pumping energy demands, providing velocity ranges suited to different GHE configurations. Drawing from recent experimental and numerical studies, the review offers practical guidelines for integrating nanofluid formulation with engineered operating conditions to maximize energy efficiency and extend system lifespan.