A machine learning-based decision support framework for energy storage selection

Energy storage systems (ESS) are becoming more prevalent and indispensable in modern electrical infrastructure. The process of choosing the proper type of ESS technology for the application is the first step in designing the best performing ESS. However, the selection process involves a variety of factors, and currently there lacks a sophisticated and sys-tematic framework for convenient energy storage selection. This paper develops a data-driven optimization framework for selecting energy storage systems for general energy system applications. In the framework, a supervised classification machine learning method is proposed for the first time to quantify the technical suitability of energy storage technologies for different applications. The DOE Global Energy Storage Database provided the basic information for machine learning, and the Random Forest Classifier had the best prediction performance for this dataset. The probability of technical suitability can be predicted and further incorporated in a multi-objective optimization for technology recommendation based on integrated technical, economic, and environmental criteria. As a demonstration of the methodology, the prediction of technical suitability and recommendation of technology selection were conducted for eleven common energy storage applications. The result showed that the prediction of technical suitability and the multi-objective optimization yielded reasonable recommendations compared with the literature. In conclusion, the proposed data-driven decision-making approach can be used as a convenient tool to help decision-makers make informed decisions when choosing the energy storage system for general applications. (c) 2022 Institution of Chemical Engineers. Published by Elsevier Ltd. All rights reserved.