Elitist Genetic Algorithm (EGA)-based Energy Storage System (ESS)-facilitated Heavy Good Electric Vehicle (HGEV) charging station designer
收藏DataCite Commons2024-01-12 更新2024-07-13 收录
下载链接:
https://researchdata.reading.ac.uk/id/eprint/526
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资源简介:
This solution leverages an Elitist Genetic Algorithm (EGA) to tackle interconnected challenges in the Energy Storage System (ESS) design and demand-side management, particularly focusing on battery scheduling. It addresses these concerns simultaneously for diverse styles of Heavy Goods Electrical Vehicle (HGEV) Depots and on-route charging stations. The ESS design encompasses considerations like battery capacity and power electronic board rating power, while demand-side management involves a series of battery charging/discharging power scheduling within the evaluation time window.
提供机构:
University of Reading
创建时间:
2024-01-11



