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  1. Optimal incentive plans for plug-in electric vehicles
Optimal incentive plans for plug-in electric vehicles
449 viewed

Optimal incentive plans for plug-in electric vehicles

Rahmani Andebili, M

Optimal incentive plans for plug-in electric vehicles

Rahmani Andebili, M ; Sharif University of Technology | 2018

449 Viewed
  1. Type of Document: Article
  2. DOI: 10.1007/978-981-10-7056-3_11
  3. Publisher: Springer Verlag , 2018
  4. Abstract:
  5. This chapter investigates implementation of some parking lots for a plug-in electric vehicle (PEV) aggregator to participate in energy market. Herein, behaviors of the PEVs’ drivers regarding their cooperation with the aggregator with respect to the introduced incentive (value of discount on charging fee of PEVs) are modeled. The considered incentive includes the value of discount on the charging fee of PEVs’ batteries. In addition, the capability of parking lots for transacting electrical energy is modeled based on the hourly arrival/departure time of PEVs to/from the parking lots and the hourly state of charge (SOC) of PEVs’ batteries. Also, the degradation of PEVs’ batteries is modeled based on the effective ampere-hours throughput of the PEVs’ batteries due to vehicle-to-grid (V2G). Moreover, the economic factors such as inflation and interest rates and the technical factors including the PEVs’ batteries power limit, the depth of discharge (DOD) constraint of PEVs’ batteries, the yearly maintenance of parking lot, and the yearly replacement rate of the conventional vehicles with the PEVs are taken into consideration in the problem over the definite planning horizon. Furthermore, due to variability and uncertainties involved with the energy market prices and the PEVs’ drivers’ behavior, the planning problem is solved stochastically. © Springer Nature Singapore Pte Ltd. 2018
  6. Keywords:
  7. Modeling behavior of PEVs’ drivers ; Modeling capability of parking lot for energy transaction ; Modeling life loss of PEVs’ batteries ; Optimal incentive plans ; Stochastic optimization
  8. Source: Power Systems ; Issue 9789811070556 , 2018 , Pages 299-320 ; 16121287 (ISSN)
  9. URL: https://link.springer.com/chapter/10.1007%2F978-981-10-7056-3_11