Optimal design of a photovoltaic/diesel/battery hybrid system: A multidimensional sustainability assessment and sensitivity analysis using an innovative collaborative hybrid meta-heuristic framework

This study aims to design and optimize a Photovoltaic/Diesel/Battery hybrid energy system to provide a stable and sustainable electricity supply for the University Institute of Technology Fotso Victor in Bandjoun, Cameroon, which currently suffers from frequent power outages. The main objective is to ensure reliable and cost-effective energy access for the university campus. Specifically, the study seeks to (i) determine the optimal configuration of the hybrid system, (ii) assess its techno-economic and environmental performance, and (iii) evaluate its social acceptance and impact on human development. To achieve these goals, a novel hybrid metaheuristic optimization framework was developed by integrating four algorithms: Particle Swarm Optimization, Differential Evolution, Genetic Algorithm, and Cuckoo Search Algorithm. Each algorithm plays a distinct role in the optimization process: Particle Swarm Optimization explores the solution space, Differential Evolution refines the best candidates, Genetic Algorithm introduces diversity through crossover and mutation, and Cuckoo Search Algorithm fine-tunes the best local solutions. This cooperative scheme, implemented in a parallel computing environment using MATLAB, improves computational efficiency and ensures a thorough exploration of the solution space. Unlike traditional metaheuristic methods, which often suffer from issues such as stochastic result variability, premature convergence to local optima, high computational costs, and sensitivity to parameter tuning, the proposed hybrid framework addresses these limitations through algorithmic synergy and parallelization. By leveraging the strengths of each algorithm, the approach achieves greater solution stability, better global exploration, and enhanced adaptability to complex hybrid energy system designs. A comprehensive techno-economic and environmental analysis was conducted to minimize overall system costs while ensuring performance and sustainability. The optimization results indicated 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 Loss of Power Supply Probability was evaluated to guarantee energy reliability. The hybrid system is also projected to reduce annual CO2 emissions by 96,480 kg compared to conventional diesel-based supply. In addition, a structured survey was conducted involving 100 participants from the University Institute of Technology Fotso Victor, including students (40 %), administrative staff (25 %), technical staff (15 %), and faculty members (20 %), selected to reflect diverse perspectives within the campus community. The social assessment revealed that 89 % of respondents supported the adoption of the proposed hybrid energy system, citing anticipated benefits such as job creation, particularly in the solar and battery sectors, and a projected improvement in the Human Development Index ranging from 0.002 to 0.007 points. A sensitivity analysis demonstrated that variations in fuel price (from USD 1.15 to USD 1.33 per liter) significantly affect system costs, highlighting the importance of fuel price volatility in energy planning. Despite requiring slightly longer computation time, the hybrid optimization method outperformed the individual algorithms by achieving the best trade-off between cost, efficiency, and sustainability. It thus offers a robust and replicable solution for improving energy resilience in academic institutions and similar settings.