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  1. Transactive-based day-ahead electric vehicles charging scheduling
Transactive-based day-ahead electric vehicles charging scheduling
148 viewed

Transactive-based day-ahead electric vehicles charging scheduling

Kabiri Renani, Y

Transactive-based day-ahead electric vehicles charging scheduling

Kabiri Renani, Y ; Sharif University of Technology | 2023

148 Viewed
  1. Type of Document: Article
  2. DOI: 10.1109/TTE.2023.3348490
  3. Publisher: Institute of Electrical and Electronics Engineers Inc , 2023
  4. Abstract:
  5. In this paper, a transactive-based scheduling approach is proposed to optimize EVs charging/discharging scheduling taking into account technical requirements of EVs with different State-Of-Charge (SOC) levels and EV owners’ preferences. In the proposed approach, EV aggregator (EVA) solves an optimization problem to determine the charging/discharging schedule of each individual EV in the EV Parking Lot (PL) in which the response curves of individual EVs are used to consider the EV owners’ charging/discharging preferences. Then, the EVAs provide their optimum day-ahead bids to the corresponding DSO based on calculated Distribution Locational Marginal Prices (DLMPs). The DSO’s transactive market-clearing procedure is simulated to iteratively calculate DLMPs in the local distribution area (LDA) nodes. The Monte Carlo (MC) scenarios are used to model the uncertainties associated with the EVs’ parameters and the driving behavior of the EV owners. Also, the robust optimization method is used to model the uncertainties associated with LMPs of the Transmission Network (TN) bus, Distributed Renewable Energy Resources (DRERs), and load demand. The proposed model is implemented on the modified IEEE-33 node distribution system and effectiveness of the model is investigated and presented. IEEE
  6. Keywords:
  7. Costs ; Distributed renewable energy resources ; DLMP ; DSO ; Electric vehicle charging ; EVs charging ; Indexes ; Power system dynamics ; Renewable energy sources ; Schedules ; Smart grid ; Transactive market ; Uncertainty
  8. Source: IEEE Transactions on Transportation Electrification ; 2023 , Pages 1-1 ; 23327782 (ISSN)
  9. URL: https://ieeexplore.ieee.org/document/10378662