Framework planning considering coordinated charging of electric vehicles in active distribution networks
Journal of Electric Power Science and Technology, ISSN: 1673-9140, Vol: 35, Issue: 3, Page: 83-91
2020
- 8Citations
- 18Usage
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Citations8
- Citation Indexes8
- Usage18
- Downloads17
- Abstract Views1
Article Description
Under the circumstances of development for scaled electric vehicles (EVs), it is of greal significance to rationally control and optimize the EV charging load and carry out the strueture planning of the active distribution net-work. This paper establishes a bi-level model for strueture planning of active distribution networks based on the regu-lation of the EV charging load. Firstly, taking the uncertainties of distributed generation and load into consideration, the main process framework for EV charging load forecasting is construeted, and then the dispatching method of the charging load is described and the mathematical model of strueture planning for active distribution networks is build up. In such a mathematical model, the planning layer aims at the lowest comprehensive economic cost for kilowatt-hour of electricity and the operational layer is targeted at minimizing the load curve variance. Secondly, in the process of model solving. the Prim algorithm is utilized to improve the population generation process of particle swarm opti-mization (PSO) and constraints are processed by a penalty funetion so as to improve the efficiency and precision of algorithm. Finally, the rationality and validity of the model are verified by an actual distribution network.
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