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Regulating the Collective Charging Load of Electric Taxi Fleet via Real-Time Pricing

This paper investigates the problem of how to regulate the collective charging load of a large fleet of plug-in electric taxies (PETs) in a metropolis from the viewpoint of utility company. Because the charging load of PET is much higher and less predictable compared with common private electric vehicle, the unregulated charging load of PET fleet can potentially bring large and unpredictable peaks to the distribution system and cause severe damage. To address this problem, this paper proposes a real-time pricing mechanism that can successfully regulate the collective charging load to track a given load profile. The mechanism design consists of three steps. First, we propose two aggregated models for PET fleet to characterize, separately, the relationship between charging decisions of the fleet and real time prices, and the relationship between energy dynamics and charging decisions of the fleet. Then, an optimization problem is designed to calculate the optimal charging load profile that can be accomplished by the fleet. Finally, an efficient online method is launched to calculate proper real time prices, such that the collective charging load of fleet can track the desired value as the response to the prices. Simulation results validate the effectiveness of the proposed mechanism.