Hybrid renewable energy systems (HRES) combining photovoltaic, wind, fuel cell, and energy storage technologies are becoming established as viable options for reliable, environmentally friendly distributed electricity generation. In this review, we examine the key architectures, monitoring and forecast approaches, and control systems that improve the efficiency of HRES and facilitate the just-energy transition to low-carbon power generation systems. The main optimization and decision-aware approaches, particularly the evolutionary generation algorithms and machine learning-based prediction models, are addressed with a focus on improving energy allocation, cost minimization, and increased use of clean renewable energy sources. Technical, economic, and environmental performance indicators, such as the levelized cost of energy (LCOE), net present cost (NPC), renewable fraction (RF), and CO2 emissions reduction, have been compared to demonstrate the feasibility of various system scenarios. This paper evaluates and summarizes recent case studies from around the world and presents the best practices and the challenges they encounter, including resource availability, governance, and economic drivers. The balance of the paper demonstrates that smart forecasting with advanced energy management approaches is crucial for developing sustainable and resilient hybrid distributed power systems for the future.
Ouederni et al. (2026) studied this question.