A research team has introduced an enhanced optimization algorithm for designing hybrid microgrid systems in remote or isolated locations. The improved particle swarm optimization (IPSO) method addresses a critical challenge in autonomous energy system design: balancing technical performance with economic viability while accounting for real-world variability in solar radiation, wind speed, and electrical demand throughout the year.
The IPSO algorithm incorporates adaptive mutation and chaos-based initialization strategies to overcome a common limitation of standard optimization techniques—premature convergence and entrapment in suboptimal solutions. This enables the algorithm to explore a wider solution space and identify genuinely optimal configurations rather than local minima.
For the Shlateen site in Egypt, researchers evaluated four hybrid configurations integrating photovoltaic panels, wind turbines, diesel generators, and battery energy storage units. The optimization process simultaneously minimized three competing objectives: cost of energy, loss of power supply probability, and renewable energy utilization. This multi-objective approach reflects real-world engineering constraints where designers must satisfy both financial and reliability requirements.
Results showed the IPSO method outperformed established algorithms including Multi-Objective Differential Evolution and the original Particle Swarm Optimization approach. The recommended configuration—photovoltaic arrays paired with diesel backup and battery storage—delivered a levelized cost of energy of $0.272 per kilowatt-hour while maintaining loss of power supply probability below 0.52 percent, indicating excellent system dependability.
The work underscores how computational intelligence and advanced algorithms improve decision-making in microgrid planning. For power engineers and utility planners designing systems in remote regions, island communities, or areas with weak grid infrastructure, this approach provides a systematic methodology for determining optimal equipment sizing and component selection under real operating conditions.
