Abstract

California has set the goal to source 100% of its electricity from renewable energy by 2045, up from 32% in 2017. Meanwhile, the growing mismatch between the renewable energy generation and peak demand is raising concerns on the electric grid's stability and efficiency. At the same time, electric vehicles are expected to reach 2.3 million in sales by 2050 in the US. The escalating load of electric vehicles amplifies load peaks and may exacerbate the existing supply-demand discrepancy. However, electric vehicle drivers are typically flexible in choosing the time to charge their vehicle batteries. This flexibility allows the electric vehicle drivers to shift their charging time and power to match the supply with little sacrifice or discomfort. As a result, incentives can be utilized to guide electric vehicle user behaviors to mitigate the grid's demand-supply discrepancy. Today, most existing solutions for the demand-supply disparity directly control the time and power of every load in the grid. But the forced control is difficult to implement for electric vehicles due to the lack of hardware support and commercial viability. Instead, incentives can motivate voluntary user behavior changes. This approach is more practical for large-scale deployments.This dissertation presents a smart electric vehicle charging system that incentivizes users to shift their charging time to match the renewable energy generation. The system applies non-monetary and monetary incentives by assigning charging priorities and awarding in-system virtual currency to users based on their consumption of renewable energy. Consequently, users with higher renewable energy consumption receive more power and faster service from the charging system. The effectiveness of the non-monetary incentives is demonstrated through two experiments conducted on the UCLA campus for 15 and 14 months respectively. The first experiment with four charging plugs and one solar panel measures users' willingness to manually change their consumption schedules to match the renewable energy generation. The results indicate that the solar consumption ratio has grown by 37% since the incentives were implemented. The second scaled-up experiment with 28 charging plugs and 2 solar panels introduced system algorithms to automatically control users' charging schedules when they choose so. The results reveal that more than 23% of the participants use the automatic programs regularly to improve their renewable energy usages.The incentive design and data analysis in this dissertation provide comprehensive insights on utilizing electric vehicles to mitigate demand-supply disparity through user behavior changes. Additionally, the experiment and system implementations provide practical experience on building effective interactions between the system and users.