Sizing a Hybrid Hydrogen Production Plant Including Life Cycle Assessment Indicators by Combining Nsgaiii and Principal Component Analysis (Pca)

In the design and synthesis of energy systems, mathematical tools such as optimisation algorithms are often used. While using them, environmental indicators are increasingly used as optimisation criteria in order to exploit possible environmental benefits. However, here the problem is that environmental indicators are numerous (sometimes 15+) which pose a problem for optimisation algorithms. In this paper, this is addressed through multi-objective search of solutions using NSGA-III. The performance of this algorithm is improved using PCA objective-reduction. Different settings in the PCA procedure are changed which reveal not-yet known insights about NSGAIII, objective reduction and combination of the two. These algorithms were applied on a real life sizing case study involving hydrogen production from polymer-electrolyte-membrane water electrolysis for which the demand is furnished by electricity-spot-market, solar-PV or wind turbine. It was found that the performance of NSGA-III can be further improved using correlations in terms of solution quality and reduction in computational time. However, beyond a certain point, objective reduction leads to a trade-off between solution quality and computational time. These results will be useful for the future applications of multi-objective optimisation and objective reduction. They will also be practical for including environmental indicators in the multi-objective search of solutions