Power management and sizing optimisation of renewable energy hydrogen production systems

Abstract

This paper presents the combined sizing and power management optimisation methodology for a small-scale stand-alone hybrid renewable energy (RE) hydrogen production system. System cost is argued to be dependent on efficiency and reliability. The optimisation strategy developed, implements a strength Pareto evolutionary algorithm (SPEA) and a single objective genetic algorithm (GA) in cascade. The objectives for optimisation are: system efficiency, cost and reliability. Three different geographic sites with different wind and solar renewable energy input potentials are optimised. Results are presented as three-dimensional Pareto surfaces as well as two-dimensional scatter plots. Relationships between objectives are illustrated as well as important correlations between objectives and design variables. The expected conflicting relationship between cost and efficiency is clearly observed from the Pareto curves. The methodology developed highlights the importance of considering the simultaneous optimisation of sizing and power management. It is concluded that the optimisation methodology developed is useful in evaluating these and similar hybrid RE systems and provides insight into the design trade-offs for multiple conflicting objectives.