Balancing Cost and Dissatisfaction in Online EV Charging under Real-time Pricing
Refereed conference paper presented and published in conference proceedings


摘要We consider an increasingly popular demand response scenario where a 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 decision 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 best possible 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 under small and large values of alpha suggest the fundamental difference of the problems with and without considering user dissatisfaction. 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 its theoretical worst-case guarantee. Moreover, our algorithms achieve large perfiffmance gains as compared to conceivable alternatives. The results also suggest that increasing EV charging rate limit decreases overall cost almost linearly.
著者Hanling Yi, Qiulin Lin, Minghua Chen
會議名稱IEEE Conference on Computer Communications (IEEE INFOCOM)
會議地點Paris, France
會議論文集題名IEEE Conference on Computer Communications (IEEE INFOCOM)
頁次1801 - 1809

上次更新時間 2022-08-01 於 23:49