Intelligent optimization of a hybrid renewable energy system using an improved flower pollination algorithm

Renewable energy is an emerging trend to replace fossil fuels as a primary energy source. However, the intermittency of sources and high investment costs inhibit the full-scale adoption of renewable energy as the principal energy producer. This study presented a stand-alone hybrid renewable energy system, comprising solar panels and wind turbines as the primary energy source, with batteries and a diesel engine integrated as a backup system. Attempting to minimize the annualized total cost of investment and carbon emission, this study applied a new optimization algorithm, specifically the improved flower pollination algorithm, to acquire a techno-economically feasible design of a stand-alone hybrid renewable energy system. Performance comparison with the flower pollination algorithm showed that the proposed improved flower pollination algorithm could converge faster to the optimal solution in single-objective optimization problems. While minimizing both annualized total cost and carbon emission, the configurations of improved flower pollination algorithm were more dominant and evenly distributed than flower pollination algorithm. Lastly, the sensitivity analysis indicated that the annualized total cost of the hybrid renewable energy system was highly dependent on solar radiation, but not on wind speed.