Collaborative Research: CISE-MSI: DP: IIS RI: Research Capacity Expansion via Development of AI Based Algorithms for Optimal Management of Electric Vehicle Transactions with Grid

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Pressing challenges such as climate change and the necessity to reduce carbon emissions require the transition from gasoline-powered vehicles to electric vehicles. The Federal Government has set a goal to make half of all new vehicles sold in the U.S. in 2030 zero-emissions vehicles. It is projected that there will be 26.4 million electric vehicles on U.S. roads in 2030. One concern regarding the adoption of electric vehicles is the ability of power systems to accommodate their high-power demand. Another concern is the present high costs of electric vehicles, which make them unaffordable for most of the country’s population. This project contributes a solution to address both the concerns. First, it contributes to developing advanced intelligent demand response programs, which have been recognized as being effective in shaving peak demand of power systems (including the demand by electric vehicles), thereby reducing the system operation cost and cutting costs by deferring equipment upgrade and investment. Such intelligent demand response programs can potentially save billions of dollars annually. Second, the project develops intelligent algorithms that enable transactions between electric vehicles and power grids, where the vehicle owners can make considerable additional income by charging during off-peak hours and selling (i.e., discharging) power back to the power system during peak hours. The owners can earn thousands of dollars per year, thereby offsetting the high costs of electric vehicles and making them more affordable. Furthermore, the project supports underrepresented minorities and female students participating in high-level and high-quality research. Its overall outcomes increase sustainable development and economic competitiveness of the United States.The emphasis of this project is to advance artificial intelligence and machine learning algorithms for optimal management of electric vehicles interactions with the electric power grid. First, a hierarchical forecasting framework that is scalable and distributable is developed using cellular computational networks. Electric vehicle charging (Grid-to-Vehicle) and discharging (Vehicle-to-Grid) potential transactions are forecasted. Secondly, a hierarchical architecture-based methodology for scalable demand response with electric vehicles is developed. The hierarchical demand response architecture overlaying the physical hierarchy of the power system allows for decomposing the demand response to tackle the electric vehicle’s problem and solve it in a distributed manner. The computational time required to solve this optimization problem using this framework is only dependent on the number of levels in the hierarchical architecture. Thirdly, an adaptive critic design approach based on combined concepts of approximate dynamic programming and reinforcement learning is created for utilizing the capabilities of the electric vehicle battery systems for optimal reactive power compensation and voltage control on the distribution system. This is essential to maintain grid security and reliability as the number of electric vehicles penetrating the electric power distribution system rapidly grows to millions over the next few decades.This project is jointly funded by the CISE MSI Research Expansion and the Established Program to Stimulate Competitive Research (EPSCoR).This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.