A genetic algorithm for battery-based energy storage transportation using railway

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Authors
Abdallah, Khaled S.
Taha, Raghda B.
Advisors
Issue Date
2019-12
Type
Article
Keywords
Battery Based Energy Storage System , Renewable resources , Genetic algorithm , Energy distribution systems
Research Projects
Organizational Units
Journal Issue
Citation
Abdallah, K. S., & Taha, R. B. (2019). A genetic algorithm for battery-based energy storage transportation using railway. Journal of Management & Engineering Integration, 12(2), 27-41. https://doi.org/10.62704/10057/24256
Abstract

The use of renewable energy sources has increased significantly in the past few years. Due to the intermittent nature of the renewable energy sources, planning and distribution of this energy is considered a challenging process. Energy storage systems have a great potential towards these challenges as it can store energy from different sources and then distribute it to regions with high demand such as in the case of Battery Based Energy Storage System. In this paper, the impact of railway Battery Based Energy Storage System on the power grid is considered. A genetic algorithm is proposed for solving the dispatching of rechargeable battery-based energy storage train vehicles to satisfy the charging/discharging requirements of rural areas not directly connected to the power grid due to being temporary locations such as rural cities under construction and military rural campuses. A multi-objective model is proposed that has the following objectives: 1) to minimize the transportation cost associated with the train route; and 2) to minimize the number of Battery Storage vehicles. A Genetic Algorithm is developed and tested using numerical examples. The results show the effectiveness of the proposed algorithm in providing good solutions using the minimum number of Battery storage vehicles in a cost-effective energy distribution system.

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Description
Published in SOAR: Shocker Open Access Repository by Wichita State University Libraries Technical Services, November 2022.
Publisher
Association for Industry, Engineering and Management Systems (AIEMS)
Journal
Book Title
Series
Journal of Management & Engineering Integration
v.12 no.2
PubMed ID
ISSN
1939-7984
EISSN