期刊
ENERGIES
卷 15, 期 1, 页码 -出版社
MDPI
DOI: 10.3390/en15010027
关键词
electric vehicle; Monte Carlo; smart scheduling; particle swarm optimization
资金
- PT.PLN (Persero)
A novel charge scheduling strategy is proposed in this paper to address the needs of vertically structured power systems without relying on time-of-use pricing. By providing a decision-making framework that considers the considerations of transmission and distribution network operators and allowing for dynamically changing charging loads through timely forecast updates, the strategy demonstrates its effectiveness in a case study.
Charge scheduling can mitigate against issues arising from excessive electric vehicle (EV) charging loads and is commonly implemented using time-of-use pricing. A charge scheduling strategy to suit vertically structured power systems without relying on time-of-use pricing has not yet been reported, despite being needed by industry. Therefore, a novel charge scheduling strategy to meet this need is proposed in this paper. Key aspects include the provision of a decision-making framework that accommodates for the considerations of transmission and distribution network operators, and the allowance for dynamically changing charging loads through timely forecast updates with reduced communication requirements. A case study based on the Indonesian Java-Bali power system is undertaken to demonstrate the strategy's effectiveness. Different and realistic EV uptake scenarios are considered, using probabilistic modeling, survey work, and a Monte Carlo modeling approach. Even under slow EV charging conditions case study results show assets are overloaded and high electricity production costs are incurred. These are alleviated through adopting the proposed strategy.
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