Sizing a hybrid hydrogen production plant including life cycle assessment indicators by combining NSGA-III and principal component analysis (PCA)

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Sizing a hybrid hydrogen production plant including life cycle assessment indicators by combining NSGA-III and principal component analysis (PCA)

Authors: Hemant Sharma, Guillaume Mandil, Élise Monnier, Emmanuelle Cor, Peggy Zwolinski,

Published in: Energy Conversion and Management: X

Volumen: 18 | Pages: 13 | Year of Publication: 2021 | License: CC BY 4.0

Paper Access: https://doi.org/10.1016/j.ecmx.2023.100361

Keywords: Energy systems, Life cycle assessment, Many-objective optimization, NSGA-III, Objective reduction

What is the objective?

The study aims to determine the size of each technological component of a hybrid hydrogen production plant while considering the technical, economic, and environmental indicators.

Our Short Summary.

For the design of a hybrid hydrogen production plant, the authors considered the assessment of the environmental performance of each component using an LCA model in Brightway2 (Python). An evolutionary algorithm NSGA-III was used to evaluate the performance of 13 midpoint indicators selected from IMPACT World+. The study found that the following impact categories should be considered to avoid impact transfer: climate change, freshwater ecotoxicity, land acidification, water scarcity, ozone depletion, ionising radiation, and fossil and nuclear energy use.

Why you should read it!

The study allows researchers and designers of energy systems to identify mechanisms to avoid impact transfer of a high number of LCA indicators during the many-objective search for Pareto optimal solutions.

Original Abstract

In the design and synthesis of energy systems, mathematical tools such as optimization algorithms are often used. While using them, environmental indicators are increasingly used as optimization criteria to exploit the possible environmental benefits of these systems. The problem is that the high number of environmental indicators poses a problem for optimization algorithms in terms of convergence, computational time and visualization. In this paper, this problem is addressed through a many-objective search of solutions using a state-of-the-art evolutionary algorithm, NSGA-III. Furthermore, the performance of this algorithm is tested using different settings in the PCA-based objective reduction framework. The original 14 indicators are reduced to seven, four, three and two, which reveal important insights about the use of NSGA-III, objective reduction and a combination of the two. It was found that by using objective reduction, the performance of NSGA-III can be further improved in terms of the quality of solutions and computational time. However, beyond a certain point, further objective reduction leads to a trade-off between solution quality and computational time. For this case study, the best quality solutions were obtained in the PCA reduction procedure when the CUT value was maintained at 99.99% without additional reduction in the last step, using a correlation matrix. The algorithms were applied to a real-life sizing case study involving hydrogen production from polymer-electrolyte-membrane (PEM) water electrolysis, for which the demand is furnished by the electricity spot market, solar photovoltaics (PV) or wind turbine in Marseille, France. These results will be useful for future applications of many-objective optimization and objective reduction. They will also be practical for including environmental indicators in the many-objective search for solutions.

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