Wednesday, October 14, 2015 — 11:00 AM EDT

Candidate

Nafeesa Mehboob

Title

Smart charging of plug-in electric vehicles considering uncertainties

Supervisors

Claudio Canizares and Catherine Rosenberg

Abstract

The integration of Plug-in Electric Vehicles (PEVs) into the existing distribution system, without significant infrastructure upgrades, will be possible only through smart charging of these loads, considering their uncertainty associated with their arrival and departure times, their quantity at any given time, and their initial battery State-of-Charge (SOC), which pose a challenge for the optimal operation of the grid. This paper presents a novel two-step approach for the smart charging of PEVs in a primary distribution feeder, accounting for the uncertainty associated with PEVs, considering the perspectives of both the Local Distribution Company (LDC) and the PEV owner. In the first step of the proposed approach, the average daily feeder peak demand and the corresponding average-based hourly feeder control schedules, such as taps and switched capacitor setpoints, are determined using a nonparametric Bootstrap technique, an alternative to the conventional Monte Carlo Simulations (MCS) approach, accounting for variations in the arrival and departure times, and the initial battery SOC of PEVs. In the second step, the maximum possible power that can be given to the charging PEVs at each node is computed every few minutes, maintaining the peak demand value and corresponding feeder dispatch schedules defined in the first step. Studies are carried out for the proposed optimization approach using a realistic three-phase unbalanced primary distribution feeder, comparing it with a popular sensitivity-based heuristic approach to show the advantages and feasibility of the presented technique. The results show that the proposed approach could be implemented in practice due to its reasonable computational burden, and is able to charge the PEV loads better than a heuristic method, while satisfying feeder limits and without exceeding an optimal feeder peak demand value.

Location 
EIT building
Room 3142

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