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Lookup NU author(s): Dr Tanuj Rawat
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© 2024 Elsevier LtdExcessive penetration of renewables in distribution system can create several operational issues. One of the technologies that can maximize accommodation of renewables is electric vehicles (EVs). However, along with EVs additional measures are required while maximizing hosting capacity (HC) to avoid negative impact on system and account for uncertain parameters. In this regard, this paper proposes a two-stage stochastic optimization approach for planning of renewables in distribution system integrated with EVs. In the first stage, capacity of renewables is determined while maximizing HC whereas in the second stage operating strategy of EVs, renewables and grid power is evaluated to minimize expected energy losses. The proposed framework includes uncertainties in load demand, renewables and EVs characteristics through stochastic programming approach. The optimization problem is formulated as second order cone programming problem. The proposed model is tested on modified IEEE-33 bus and real-life 108-bus Indian distribution systems with different charging/discharging patterns and penetration rates of EVs. Moreover, an economic based cost analysis to compare planning cost and EV charging cost is also provided. Simulation results reveal that in 33-bus system with 500 EVs, the HC with vehicle-to-grid capability (V2G) is higher as compared to uncoordinated and coordinated charging by 29.86% and 6.36% respectively, while in 108-bus system with 1000 EVs, it is higher by 18.51% and 5.69% respectively. Further, the energy losses with V2G are 27.54% and 1.36% less in 33-bus system and 7.31% and 3.22% less in 108-bus system as compared to uncoordinated and coordinated charging respectively.
Author(s): Rawat T, Singh J, Pandey VC, Sharma S
Publication type: Article
Publication status: Published
Journal: Sustainable Energy, Grids and Networks
Year: 2024
Volume: 39
Print publication date: 01/09/2024
Online publication date: 10/06/2024
Acceptance date: 08/06/2024
ISSN (electronic): 2352-4677
Publisher: Elsevier Ltd
URL: https://doi.org/10.1016/j.segan.2024.101445
DOI: 10.1016/j.segan.2024.101445
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