This paper provides a comprehensive review of hybrid energy systems (HESs), focusing on their challenges, optimization techniques, and control strategies to enhance performance, reliability, and sustainability across various applications, such as microgrids (MGs), commercial buildings, healthcare facilities, and cruise ships. The integration of renewable energy sources (RESs), including solar photovoltaics (PVs), with enabling technologies such as fuel cells (FCs), batteries (BTs), and energy storage systems (ESSs) plays a critical role in improving energy management, reducing emissions, and increasing economic viability. This review highlights advancements in multi-objective optimization techniques, real-time energy management, and sophisticated control strategies that have significantly contributed to reducing fuel consumption, operational costs, and environmental impact. However, key challenges remain, including the scalability of optimization techniques, sensitivity to system parameter variations, and limited incorporation of user behavior, grid dynamics, and life cycle carbon emissions. The review underlines the need for robust, adaptable control strategies capable of accommodating rapidly changing energy environments, as well as the importance of life cycle assessments to ensure the long-term sustainability of RES technologies. Future research directions emphasize the integration of variable RESs, advanced scheduling, and the application of emerging technologies such as artificial intelligence and blockchain to improve system resilience and efficiency. This paper introduces a novel classification framework, distinct from existing taxonomies, addressing gaps in prior reviews by incorporating emerging technologies and focusing on the dynamic nature of energy management in hybrid systems. It also advocates for bridging the gap between theoretical advancements and real-world implementation to promote the development of more sustainable and reliable HESs.

The energy sector is undergoing a profound transformation to tackle climate change issues rapidly and efficiently. The development of decentralized renewable energy and the electrification of the transport sector are two major aspects affecting electrical grids. This paper presents a novel energy management strategy that supervises controllable loads like electric vehicles and electric water heaters in a distribution grid to reduce energy transmission costs and promote the local consumption of renewable energy. The proposed strategy uses day-ahead forecasts of energy production and consumption to schedule future load profiles. It also considers potential grid constraints using a real-time co-simulation approach. The estimated constraints are mitigated by modifying the controllable loads' dispatch or adjusting their power set-points. The system's performance is finally evaluated on a real distribution grid model during days that present the most voltage constraints in the year.

In recent years, the cost of photovoltaic panels has dropped considerably, and the difference between the manufacturing cost of monofacial and bifacial is shrinking. Consequently, the choice of bifacial modules becomes very advantageous, especially in areas with high albedo. In this paper, we investigate the power production of bifacial modules using two models: Pvfactors and Bifacial_Radiance; that enable us to determine the amount of irradiance received on both the front and back surfaces of the module. Additionally, an empirical model is used to estimate the total power production. The results of the study show that in summer, the bifacial gain can reach well over 10\%, while in winter, the gain is twice as high (20\%); the reason is that in winter, we have snowfall which increases the ground albedo value. The purpose of this study is to investigate the accuracy and time performance of the proposed power model of bifacial panels under severe weather conditions. For this reason, we performed an experimental validation, and we found that the NRMSE (Normalized Root Mean Squared Error ) varies between [0.28\%-1.39\%] using Bifacial_Radiance and [0.98\%-1.45\%] using PVfactors for the first three seasons of the year (spring, summer and fall). However, in winter we notice higher values of the NRMSE, in fact, the model with PVfactors has 6.67\%, and the model with Bifacial_Radiance has 7.8\%. The snow cover can explain these high error values. Indeed, the module is partially covered with snow throughout the winter, which significantly reduces the generated power. As a result, the presented model is accurate and allows us to estimate the power production of bifacial modules when no snow accumulates on the PV modules.