Electric Vehicle Trip Chain Information-Based Hierarchical Stochastic Energy Management With Multiple Uncertainties

Sumit Rathor, Dipti Saxena, Vinod Khadkikar

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


This paper proposes a vehicle trip chain information-based hierarchical stochastic energy management system (Hi-SEMS) for a microgrid integrated with highly uncertain plug-in electric vehicles (PEVs) fleet, renewable energy sources, and loads. The two-layer Hi-SEMS scheme aims at optimal scheduling of PEV fleet to counteract the operational uncertainties encountered in real-time operation and minimizing the microgrid daily operating cost comprising the fuel cost and the emission cost. In the proposed work, the realistic behavior of PEVs fleet is modeled using a stochastic trip chain information to accurately forecast the charge/discharge demand taking into account the driver's behavior as foundation. Realistic statistical trip data, multi-location charge/discharge of PEVs fleet to include coupled spatial-temporal dynamics of heterogeneous PEVs fleet is considered for intelligent charge/discharge scheduling. The effectiveness of the proposed strategy is verified on modified LV CIGRE and modified IEEE 33 bus radial distribution test networks and is compared with four different cases on each of the two test systems. The simulation results exhibit that the proposed Hi-SEMS outperforms the other four cases in terms of daily operating cost, the incentive to the PEVs owner, and robustness to multiple uncertainties.

Original languageBritish English
Pages (from-to)18492-18501
Number of pages10
JournalIEEE Transactions on Intelligent Transportation Systems
Issue number10
StatePublished - 1 Oct 2022


  • Plug-in electric vehicles
  • stochastic modeling
  • transportation
  • trip chain information


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