Analysis of Meta-Heuristic Algorithms to Optimal Sizing of Grid-Connected Hybrid Renewable Power Systems

This dataset is used to analyze optimal sizing strategies for a grid-connected hybrid renewable energy system integrating photovoltaic and biogas generators with energy storage. It helped investigate several optimization techniques to determine the optimal size. The data are input into the Non-dominated Sorting Whale Optimization Algorithm (NSWOA), the Multi-Objective Gray Wolf Optimizer (MOGWO), the Multi-Objective Grasshopper Optimization Algorithm (MOGOA), the Multi-Objective Salp Swarm Algorithm (MSSA), and the Non-dominated Sorting Genetic Algorithm II (NSGA-II) for this purpose. Real-time evaluation data integrated with meteorological information from Debre Markos in Ethiopia are also the input for simulation. Results show that the cost of energy (COE) values for MOPSO, NSWOA, MOGWO, MOGOA, MSSA and NSGA-II are 0.091 €/kWh, 0.08625 €/kWh, 0.092 €/kWh, 0.097 €/kWh, 0.089 € / kWh and 0.087 €/kWh, respectively. Meanwhile, the net present cost (NPC) values for these methods are 3.54 × 10⁶ €, 3.15 × 10⁶ €, 3.25 × 10⁶ €, 3.95 × 10⁶ €, 3.39 × 10⁶ €, and 3.19 × 10⁶ €. Based on the simulated results, the NSWOA technique is the best for the correct system size.