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We consider an increasingly popular demand-response scenario where a userschedules the flexible electric vehicle (EV) charging load in response toreal-time electricity prices. The objective is to minimize the total chargingcost with user dissatisfaction taken into account. We focus on the onlinesetting where neither accurate prediction nor distribution of future real-timeprices is available to the user when making irrevocable charging decision ineach time slot. The emphasis on considering user dissatisfaction and achievingoptimal competitive ratio differentiates our work from existing ones and makesour study uniquely challenging. Our key contribution is two simple onlinealgorithms with the best possible competitive ratio among all deterministicalgorithms. The optimal competitive ratio is upper-bounded by \min\left\{\sqrt{\alpha/p_{\min}},p_{\max}/p_{\min}\right\} and the bound isasymptotically tight with respect to \alpha, where p_{\max} and p_{\min} arethe upper and lower bounds of real-time prices and \alpha\geq p_{\min} capturesthe consideration of user dissatisfaction. The bound under small and largevalues of \alpha suggests the fundamental difference of the problems withconsidering user dissatisfaction (\alpha takes small values) and without(\alpha takes large values). Along the way we also develop a general techniquefor designing online algorithms from an idea hinted in One-Way-Trading Problem,which can be of independent interest. Simulation results based on real-worldtraces corroborate our theoretical findings and show that our algorithmsachieve substantial performance gain as compared to conceivable alternatives.The results also suggest that increasing EV charging rate limit decreasesoverall cost almost linearly.

Balancing Cost and Dissatisfaction in Online EV Charging under Real-time Pricing