Elham Akhavan-Rezai

Uncoordinated charging impacts of electric vehicles on electric distribution grids: Normal and fast charging comparison

Plug-in electric vehicles (PEVs) have uncertain penetration in electric grids due to uncertainties in charging and discharging patterns. This uncertainty together with various driving habits makes it difficult to accurately assess the effects on local distribution network. Extra electrical loads due uncoordinated charging of electric vehicles have different impacts on the local distribution grid. This paper proposes a method to evaluate the impacts of uncoordinated PEVs charging on the distribution grid during peak period. Two PEVs charging scenarios are studied, including normal and fast charging. The impact analysis is evaluated in terms of voltage violations, power losses and line loading, which is implemented on a real distribution system in Canada. The results of the analysis indicate that there are significant impacts on distribution networks due to PEVs charging, which limits the accommodation of desired penetration levels of PEVs.

Managing Demand for Plug-in Electric Vehicles in Unbalanced LV Systems With Photovoltaics

Although the future impact of plug-in electric vehicles (PEVs) on distribution grids is disputed, all parties agree that mass operation of PEVs will greatly affect load profiles and grid assets. The large-scale penetration of domestic energy storage, such as with photovoltaics (PVs), into the edges of low-voltage grids is increasing the amount of customer-generated electricity. Distribution grids, which are inherently unbalanced, tend to become even more so with the uneven spread of PVs and PEVs. In combination, PEVs and local generation could provide voltage support for distribution networks, and support increased penetration. This paper develops an interactive energy management system for incorporating PEVs in demand response (DR). Using this system, owners can immediately choose whether they want to discharge their PEV battery back into the grid. The system not only provides owners with a flexible scheme for contributing to DR but also ensures that, through real-time collaboration of PEVs and PVs, the three-phase grid operates within acceptable voltage unbalance. An extensive performance evaluation using MATLAB/GAMS simulation of the 123-bus test system verifies the effectiveness of the proposed approach.

Demand responce through interactive incorporation of plug-in electric vehicles

PEV coordination introduces a significant challenge in demand response programs (DR). In one hand, there is a serious challenge due to uncertainty and dynamics associated with PEVs to be devoted to DR. In another hand, with proper charging and communication infrastructure, PEVs may play a dual role in smart grids; they may eventually either turn into Interruptible Loads (IL) when plugged in for charging or act as grid-able storage responding to the pricing commands. This paper aims to provide an approach that realises DR using aggregated PEVs in parking lots. This approach includes a real-time interaction between the aggregator and the PEV owner, where the aggregator suggests different offers and, accordingly, the owner responds based on his/her preference. A multi-stage optimization solution is proposed here to accommodate properly different offers to the PEV owners, while it is benefiting from an ANN-based forecast model to incorporate the effect of the future PEV arrivals in the decision actions. Implementation results on the 38-bus test system indicate how effectively the proposed solution could help future smart parking lots in DR contribution.

New EMS to Incorporate Smart Parking Lots Into Demand Response

Demand response (DR) seeks to involve end-use customers in modifying their electricity usage and to offer incentive payments to encourage lower electricity use at times of high prices. This paper provides an approach that realizes DR by developing an energy management system for incorporating aggregated plug-in electric vehicles (PEVs) in parking lots. This approach includes real-time interaction between the aggregator and PEV owners, whereby the aggregator proposes a number of offers and the owner responds based on his/her preference. The optimization problem is defined as mixed integer nonlinear programming. An extensive performance evaluation using MATLAB/GAMS simulation of the 38-bus test system verifies the success and effectiveness of the proposed method.