2014/07/11 by Yourim Yoon, Yoon, Yourim, Yong-Hyuk Kim +1
Engineering · #Electric Vehicles and Infrastructure #FOS: Computer and information sciences #Microgrid Control and Optimization #Neural and Evolutionary Computing (cs.NE) #Smart Grid Energy Management
paper · pdf · doi:10.48550/arxiv.1407.3077
openalex publication_date 2014/07/11 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
A real-coded genetic algorithm is used to schedule the charging of an energy storage system (ESS), operated in tandem with renewable power by an electricity consumer who is subject to time-of-use pricing and a demand charge. Simulations based on load and generation profiles of typical residential customers show that an ESS scheduled by our algorithm can reduce electricity costs by approximately 17%, compared to a system without an ESS, and by 8% compared to a scheduling algorithm based on net power.