A Multi-Objective Approach with Modified Particle Swarm Optimization and Hybrid Energy Systems

Abstract

Designing a photovoltaic (PV) power grid involves intricate considerations, focusing on sizing the PV system and strategically optimizing its placement. Intelligent multi-objective optimization techniques are crucial for addressing the complexity of this task, seeking an optimal solution that balances various objectives such as maximizing energy production, minimizing costs, and ensuring system reliability. In this research, we have selected Modified Particle Swarm Optimization (MPSO) as a suitable multi-objective optimization technique. The primary objective of this optimization is to maximize the energy generated by the PV system, involving the minimization of installation costs, including expenses associated with solar panels, batteries, and related equipment. The optimization technique aims to determine the capacity of the PV system, considering factors such as energy demand, available space, and budget constraints. The ultimate goal is to achieve maximal energy production while adhering to specified budget and space limitations. Optimizing the placement of solar panels is crucial for maximizing energy production. This optimization process takes into account various factors, including shading, panel orientation, tilt angle, and spacing between panels. Utilizing optimization algorithms, the aim is to identify the most effective configuration that ensures the highest energy production. The final step involves implementing the selected PV system design, considering practical installation considerations and regulatory requirements. This comprehensive approach ensures that the designed PV power grid not only meets energy production goals but also considers real-world constraints and compliance with relevant regulations. Through the use of a Hybrid Energy System (HES) with a 15 kW PV scheme and a modest bank, maximum investments for the user and a reduction in carbon influence of more than half can be achieved. This outcome was observed across all four sites evaluated in this research, involving two building types.

Keywords

• energy production optimization
• hybrid energy system (HES)
• modified particle swarm optimization (MPSO)
• multi-objective optimization
• photovoltaic power grid