In the heart of Cameroon, a groundbreaking study is setting a new standard for sustainable energy solutions in academic institutions and beyond. Led by Pascalin Tiam Kapen from the Department of Renewable Energy at the University Institute of Technology Fotso Victor, the research focuses on designing an optimal photovoltaic/diesel/battery hybrid system to combat frequent power outages at the university campus. The study, published in the journal *Energy Strategy Reviews*, offers a multidimensional approach to energy sustainability, combining techno-economic, environmental, and social analyses to pave the way for a more resilient energy future.
The research team developed an innovative collaborative hybrid meta-heuristic framework that integrates four distinct algorithms: Particle Swarm Optimization, Differential Evolution, Genetic Algorithm, and Cuckoo Search Algorithm. Each algorithm plays a unique role in the optimization process, ensuring a thorough exploration of the solution space and addressing common issues like stochastic result variability and premature convergence. “By leveraging the strengths of each algorithm, our approach achieves greater solution stability, better global exploration, and enhanced adaptability to complex hybrid energy system designs,” Kapen explains.
The optimization results are promising, with a net present cost of USD 3,107,000 and a levelized cost of energy (COE) as low as USD 0.0741 per kilowatt-hour. The hybrid system is projected to reduce annual CO2 emissions by 96,480 kg compared to conventional diesel-based supply, demonstrating significant environmental benefits. Moreover, a structured survey involving 100 participants from the university community revealed that 89% of respondents supported the adoption of the proposed hybrid energy system, citing anticipated benefits such as job creation and an improvement in the Human Development Index.
The study also highlights the importance of fuel price volatility in energy planning. A sensitivity analysis demonstrated that variations in fuel price significantly affect system costs, emphasizing the need for careful consideration of fuel price fluctuations in future energy projects. Despite requiring slightly longer computation time, the hybrid optimization method outperformed individual algorithms by achieving the best trade-off between cost, efficiency, and sustainability.
This research has significant implications for the energy sector, particularly in regions prone to power outages and high fuel price volatility. By providing a robust and replicable solution for improving energy resilience, the study offers a blueprint for academic institutions and similar settings to transition towards more sustainable and cost-effective energy systems. As the world continues to grapple with climate change and energy security challenges, innovative approaches like this one will be crucial in shaping the future of the energy landscape.