Uncontrolled Charging Scenarios Research Articles

Leveraging owners’ flexibility in smart charge/discharge scheduling of electric vehicles to support renewable energy integration

High integration of intermittent renewable energy sources (RES), predominantly wind power, has created complexities in power system operations. In addition, large fleets of Electric Vehicles (EVs) increase the uncertainty on electricity consumption significantly. Thus, an uncoordinated charging process will further complicate the grid scheduling. In this work, we propose a coordinated scheduling algorithm for charge/discharge of aggregated EV fleets to maximize the integration of wind generation and minimize the charging cost for EV owners in a vehicle-to-grid (V2G) setup. Challenges for people participation in V2G, such as battery degradation and insecurity about unexpected events, are also addressed.The real-time charge/discharge scheduling problem is formulated as a multi-objective mixed-integer quadratic programming problem that the aggregator takes advantage of real-time communication in smart grid to frequently update the EV schedule with updated real-time information about EV availabilities, wind generation forecast, and electricity price. Simulations demonstrate significant increase in wind utilization and reduction in charging cost and battery degradation compared to the uncontrolled charging scenario.

Controlled Charging of Electric Vehicles to Minimize Energy Losses in Distribution Systems

Abstract: Controlled electric vehicle (EV) charging scenarios are proposed, each characterized with an algorithm and associated computational and communication requirements, to be adopted by an EV aggregator or system operator. The proposed scenarios include uniform, random, conditional-random, and valley-filling charging scenarios. Different from previous studies, this paper focuses on easy-to-implement charging scenarios. Further, a modeling framework is presented to investigate the impact of the proposed charging scenarios on energy losses in distribution systems, as compared to an uncontrolled charging scenario and a reference scenario with no EVs. The modeling framework considers uncertainties involved in the behavior of low voltage customers and EV charging loads. It is applied to a distribution system for various case studies, including different penetrations and combinations of EVs with various characteristics. As seen by the results, although the valley-filling charging algorithm represents the optimal solution from the perspective of energy losses, the uniform charging scenario emerges as a quasi-optimal solution, having lower computational and communication requirements, which makes it easier to be adopted by EV aggregators or system operators. Further, with appropriately selected coefficients, the conditional-random charging algorithm can also exhibit a performance comparable to that of the uniform charging algorithm.

Models for optimal charging scheduling for electric vehicles in smart grid

Electric vehicles have experienced high demand in the market, which will introduce a substantial new load to the current electricity grid. Furthermore, uncontrolled electric vehicle charging from residential users will exacerbate the existing peak load during evening hours. In this paper, we propose optimisation models to alleviate the impact of extra load from electric vehicles on the power grid. Particularly, two centralised models allow the controller to minimise the total electricity costs for all users. Consequently, load levelling of the entire power grid is achieved. On the other hand, a decentralised model allows each user to minimise his/her own electricity cost and studies the resulting system-wide load levelling. Our numerical results indicate significant improvements in the total cost and peak-to-average ratio by both the centralised and decentralised optimal charging scheduling models, when compared to the uncontrolled charging scenarios.

Network Impacts and Cost Savings of Controlled EV Charging

This paper investigates the distribution system impacts of electric vehicle (EV) charging. The analysis is based on a large number of operational distribution networks in The Netherlands. Future load profiles have been constructed by adding different EV charging profiles to household loads and solving the power flows to assess the network impacts on various network levels. The results indicate that controlled charging of EVs leads to significant reduction of overloaded network components that have to be replaced, but the impact varies per network level. Overall, in the uncontrolled charging scenarios roughly two times more replacements are needed compared to the controlled charging scenario. Furthermore, it was shown that for the controlled charging scenario the overall reduction in net present value due to energy losses and the replacement of overloaded network components is approximately 20% in comparison with the uncontrolled charging scenario. The results suggest that the deployment of a flexible and intelligent distribution network is a cost-beneficial way to accommodate large penetrations of EVs.

Electric Energy and Power Consumption by Light-Duty Plug-In Electric Vehicles

This paper proposes methodologies to estimate the electric energy and power consumption by light-duty plug-in electric vehicles (PEVs). Using the travel patterns of light-duty vehicles in the U.S. obtained from the 2009 National Household Travel Survey, the PEVs' energy and power consumption are estimated for two uncontrolled charging scenarios.