Researcher(s)
- Devin Roskoph, Mechanical Engineering, Worcester Polytechnic Institute
- Henry Herman, Electrical Engineering, University of Alabama
Faculty Mentor(s)
- Mark Nejad, Department of Civil and Environmental Engineering, University of Delaware
Abstract
With the widespread adoption of electric vehicles (EVs) continuing to rise, significant strain on the electricity grid is becoming a larger point of concern. Sourcing electricity from renewable sources is preferred, however renewable energy (RE) such as solar and wind is inherently intermittent and currently lacks sufficient large-scale storage solutions. Some pre-existing research looks into load balancing algorithms between the electricity grid and renewable sources, while other research focuses on satisfying the needs of the self-interested parties involved in EV charging. The proposed system provides a solution that serves to balance the priorities of EV owners and electricity producers and optimize sourcing from the grid and RE sources. In our solution, we make use of an aggregation system to store electricity data from both grid and RE sources including wind, solar, and coal, to efficiently process consumer requests in near real-time. Our system leverages a multi-step strategy to determine the best electricity source to fulfill each specific user request and creates a charging schedule accordingly. Based on the study’s constraints, we showed our proposed mechanism to be feasible by running a programmed iteration of the system, making use of simulated producer and consumer data. Our results show schedules created using only first-ranked sources, suggesting a balanced distribution between all sources studied without the need for second-ranked source utilization. Further implications would include testing more extensive EV consumer data to show incorporation of second and third ranked sources when first-ranked sources are no longer available, along with implementing a full system to allow for direct consumer interaction.