Demand side management of electric vehicles in smart grids: A survey on strategies, challenges, modeling, and optimization
Energy Reports, ISSN: 2352-4847, Vol: 8, Page: 12466-12490
2022
- 100Citations
- 264Usage
- 241Captures
- 3Mentions
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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
- Citations100
- Citation Indexes100
- 100
- CrossRef59
- Usage264
- Downloads212
- Abstract Views52
- Captures241
- Readers241
- 241
- Mentions3
- News Mentions3
- 3
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Article Description
The shift of transportation technology from internal combustion engine (ICE) based vehicles to electric vehicles (EVs) in recent times due to their lower emissions, fuel costs, and greater efficiency has brought EV technology to the forefront of the electric power distribution systems due to their ability to interact with the grid through vehicle-to-grid (V2G) infrastructure. The greater adoption of EVs presents an ideal use-case scenario of EVs acting as power dispatch, storage, and ancillary service-providing units. This EV aspect can be utilized more in the current smart grid (SG) scenario by incorporating demand-side management (DSM) through EV integration. The integration of EVs with DSM techniques is hurdled with various issues and challenges addressed throughout this literature review. The various research conducted on EV-DSM programs has been surveyed. This review article focuses on the issues, solutions, and challenges, with suggestions on modeling the charging infrastructure to suit DSM applications, and optimization aspects of EV-DSM are addressed separately to enhance the EV-DSM operation. Gaps in current research and possible research directions have been discussed extensively to present a comprehensive insight into the current status of DSM programs employed with EV integration. This extensive review of EV-DSM will facilitate all the researchers to initiate research for superior and efficient energy management and EV scheduling strategies and mitigate the issues faced by system uncertainty modeling, variations, and constraints.
Bibliographic Details
http://www.sciencedirect.com/science/article/pii/S2352484722017462; http://dx.doi.org/10.1016/j.egyr.2022.09.023; http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85139015022&origin=inward; https://linkinghub.elsevier.com/retrieve/pii/S2352484722017462; https://arrow.tudublin.ie/engscheleart2/321; https://arrow.tudublin.ie/cgi/viewcontent.cgi?article=1349&context=engscheleart2; https://arrow.tudublin.ie/engscheleart2/322; https://arrow.tudublin.ie/cgi/viewcontent.cgi?article=1350&context=engscheleart2; https://dx.doi.org/10.1016/j.egyr.2022.09.023
Elsevier BV
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