Minimizing Cost-Plus-Dissatisfaction in Online EV Charging Under Real-Time Pricing

We consider an increasingly popular demand-response scenario where a home user schedules the flexible electric vehicle (EV) charging load in response to real-time electricity prices. The objective is to minimize the total charging cost with user dissatisfaction taken into account. We focus on the online setting where neither accurate prediction nor distribution of future real-time prices is available to the user when making irrevocable charging decisions in each time slot. The emphasis on considering user dissatisfaction and achieving optimal competitive ratio differentiates our work from existing ones and makes our study uniquely challenging. Our key contribution is two simple online algorithms with the optimal competitive ratio among all deterministic algorithms. The optimal competitive ratio is upper-bounded by $min{root{alpha/p_{min}},p_{max}/p_{min}} $ and the bound is asymptotically tight with respect to alpha, where $p_{max}$ and $p_{min}$ are the upper and lower bounds of real-time prices and $alpha >= p_{min}$ captures the consideration of user dissatisfaction. The bounds with respect to small and large values of alpha suggest the fundamental difference of the problems with and without considering user dissatisfaction. revFWe also extend the algorithms to take minimum charging requirement and short-term prediction into account. Simulation results based on real-world traces corroborate our theoretical findings and show that the empirical performance of our algorithms can be substantially better than the theoretical worst-case guarantees. Our algorithms also achieve notable performance gains under diverse settings as compared to conceivable alternatives.

Automated summary

This study focuses on an online algorithm for scheduling electric vehicle charging to minimize cost while considering user dissatisfaction, achieving optimal competitive ratio. Simulation results confirm the effectiveness of the algorithms and their notable performance gains in diverse settings.