This work presents an alternative to the conventional photovoltaic maximum power point tracking (MPPT) methods, by using an opposition-based learning firefly algorithm (OFA) that improves the performance of the Photovoltaic (PV) system both in the uniform irradiance changes and in partial shading conditions. The firefly algorithm is based on fireflies’ search for food, according to which individuals emit progressively more intense glows as they approach the objective, attracting the other fireflies. Therefore, the simulation of this behavior can be conducted by solving the objective function that is directly proportional to the distance from the desired result. To implement this algorithm in case of partial shading conditions, it was necessary to adjust the Firefly Algorithm (FA) parameters to fit the MPPT application. These parameters have been extensively tested, converging satisfactorily and guaranteeing to extract the global maximum power point (GMPP) in the cases of normal and partial shading conditions analyzed. The precise adjustment of the coefficients was made possible by visualizing the movement of the particles during the convergence process, while opposition-based learning (OBL) was used with FA to accelerate the convergence process by allowing the particle to move in the opposite direction. The proposed algorithm was simulated in the closest possible way to authentic operating conditions, and variable irradiance and partial shading conditions were implemented experimentally for a 60 [W] PV system. A two-stage PV grid-connected system was designed and deployed to validate the proposed algorithm. In addition, a comparison between the performance of the Perturbation and Observation (P&O) method and the proposed method was carried out to prove the effectiveness of this method over the conventional methods in tracking the GMPP.

In recent times, the significance of renewable energy generation has increased and photovoltaic‐thermoelectric (PV‐TE) technologies have emerged as a promising solution. However, the incorporation of these technologies still faces difficulties in energy storage and optimization. This review paper addresses these challenges by providing a comprehensive overview of the latest advancements in PV‐TE technologies. The paper emphasizes the integration of phase change materials (PCMs) for thermal energy storage, also buttressing the use of encapsulated PCM for thermal storage and efficiency, and the use of hybrid PCM to enhance overall performance. Furthermore, reviews on the use of machine learning techniques for efficient optimization and the integration of thermoelectric modules into tandem perovskite silicon solar cells have been comprehensively analyzed. The advancements in photovoltaic‐thermoelectric systems, as reviewed in this article, signify significant progress in attaining sustainable and effective energy production and storage. This review comprehensively addresses the 4Es, underlining their importance. It not only consolidates recent developments but also charts a path for future research in the field of PV‐TE technologies, offering precise insights to guide upcoming studies and innovations.

PEMFCs, or proton exchange membrane fuel cells, have enormous potential for clean energy and environmentally friendly transportation. PEMFCs’ cost, performance, and durability, however, continue to be major obstacles to their mainstream deployment. This study examines recent developments in PEMFC technology with an emphasis on novel oxygen reduction reaction catalysts, creative flow field designs, methods for reducing degradation processes, and system-level optimization and integration. The results show that innovative studies in these fields have significantly increased the performance and longevity of PEMFCs while lowering expenses. For PEMFC technology to evolve further, be successfully implemented in a variety of applications, and contribute to a more sustainable future, more research and development must be put forward.

The increasing peak electricity demand and the growth of renewable energy sources with high variability underscore the need for effective electrical energy storage (EES). While conventional systems like hydropower storage remain crucial, innovative technologies such as lithium batteries are gaining traction due to falling costs. This paper examines the diverse applications of energy storage, spanning from grid connectivity to end-user solutions, and emphasizes large-scale energy recovery and system stability. The integration of EES with various energy infrastructures and consumer strategies is explored, highlighting the use of tariffs and peak pricing systems for energy cost savings. Country-specific priorities shape EES deployment, with the U.S focusing on grid stability, Japan on emergency power, and South Korea, still in the demonstration phase, prioritizing peak demand reduction. Our analysis of the UK, U.S., and South Korea reveals the pivotal role of energy storage in achieving flexible and efficient energy systems. The industry shows promising growth, with significant commercial expansion expected around 2035, presenting profound policy and deployment implications for the future.