Abstract
Abstract Due to the increasing penetration of electric vehicles (EVs) in the distribution grid, coordinated control of their charging is required to maintain a proper grid operation. Many EV charging strategies assume that the EV can charge at any rate up to a maximum value. Furthermore, many strategies use detailed predictions of uncertain data such as uncontrollable loads as input. However, in practice, charging can often be done only at a few discrete charging rates and obtaining detailed predictions of the uncertain data is difficult. Therefore, this paper presents an online EV scheduling approach based on discrete charging rates that does not require detailed predictions of this uncertain data. Instead, the approach requires only a prediction of a single value that characterizes an optimal offline EV schedule. Simulation results show that this approach is robust against prediction errors in this characterizing value and that this value can be easily predicted. Moreover, the results indicate that incorporating practical limitations such as discrete charging rates and uncertainty in uncontrollable loads can be done in an efficient and effective way.
Highlights
The penetration of electric vehicles (EVs) is rapidly increasing and coordination of the charging of these EVs is required in order to reduce the wear of grid assets and prevent blackouts [1]
This paper presented an online approach for electric vehicle (EV) scheduling with discrete charging rates that does not require predictions of uncertain power consumption and production
This approach requires only the prediction of a single value that characterizes the optimal solution to a relaxation of the original EV scheduling problem at the start of the planning
Summary
The penetration of electric vehicles (EVs) is rapidly increasing and coordination of the charging of these EVs is required in order to reduce the wear of grid assets and prevent blackouts [1]. To the discrete nature of charging rates, a second important aspect of EV scheduling problems concerns the quality of an EV schedule This quality is often evaluated by means of a cost function that depends on data such as electricity prices, uncontrollable power consumption (base load), or domestic power production from, e.g., solar panels. With regard to addressing the discrete nature of the charging power, the approach in this paper handles arbitrary sets of discrete charging rates This is in contrast to the mentioned works [13,14,15], where only three rates are possible that correspond to ‘‘no charging’’, ‘‘positive charging’’, and ‘‘negative charging’’, but where the corresponding charging levels are fixed.
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