A probabilistic risk-averse approach for energy storage sizing in all-electric ship. (1st November 2022)

• Record Type: Journal Article
• Authors: Hein, Kyaw; Xu, Yan; Aditya, Venkataraman; Gupta, Amit Kumar
• Is Part Of: Journal of energy storage. Volume 55:Part A(2022)
• Publication Date: 2022-11-01
• Subjects: All-electric ship microgrid -- Energy dispatch -- Energy storage sizing -- Information-gap decision -- Multi-objective -- Risk-averse; Energy storage -- Periodicals; Energy storage -- Research -- Periodicals; 621.3126
• DOI: 10.1016/j.est.2022.105392
• Languages: English
• ISSNs: 2352-152X

Abstract

Abstract: In recent years, energy storage systems (ESS) are becoming an integral part of modern all-electric ships (AES). The topic of optimal ESS sizing is important as it determines the cost and effectiveness of the vessel operation. Conventional ESS sizing only considers the investment stage and ignores the operation stage and uncertainties. This research work adopts a risk-averse approach by coordinating investment and operation stages. The proposed two-stage method ensures optimal operation by reducing cost and emission while addressing the stochasticity present in voyage planning. Firstly, multi-objective ESS sizing and energy management scheduling are jointly optimized by considering the operation and sizing objectives with the probabilistic hydrodynamic data-oriented constraints from the voyage scenarios. In the second stage, the information-gap decision (IGD) decides the number of modules and configuration of the ESS to achieve power and energy requirements. Various ESS technologies and numerous scenarios are used to verify the effectiveness of the proposed strategy. The results indicate the validity and requirement of the proposed method. Highlights: Coordinate investment and operation stages in a single optimization framework Accounted for task-dependency, stochasticity, weights, payload, and ESS properties. Deployed efficient scenario generation, reduction and data-oriented KDE methods. Scenario-based multi-objective problem with information-gap decision for uncertainty. Case studies and sensitivity analysis of operating scenarios in decision-making.