Abstract
In the smart grid environment, the penetration of electric vehicle (EV) is increasing, and dynamic pricing and vehicle-to-grid technologies are being introduced. Consequently, automatic charging and discharging scheduling responding to electricity prices that change over time is required to reduce the charging cost of EVs, while increasing the grid reliability by moving charging loads from on-peak to off-peak periods. Hence, this study proposes a deep reinforcement learning-based, real-time EV charging and discharging algorithm. The proposed method utilizes kernel density estimation, particularly the nonparametric density function estimation method, to model the usage pattern of a specific charger at a specific location. Subsequently, the estimated density function is used to sample variables related to charger usage pattern so that the variables can be cast in the training process of a reinforcement learning agent. This ensures that the agent optimally learns the characteristics of the target charger. We analyzed the effectiveness of the proposed algorithm from two perspectives, i.e., charging cost and load shifting effect. Simulation results show that the proposed method outperforms the benchmarks that simply model usage pattern through general assumptions in terms of charging cost and load shifting effect. This means that when a reinforcement learning-based charging/discharging algorithm is deployed in a specific location, it is better to use data-driven approach to reflect the characteristics of the location, so that the charging cost reduction and the effect of load flattening are obtained.
Highlights
As environmental problems, such as climate change caused by global warming and air pollution, have become global issues, electric vehicle (EV) are considered as an alternative to vehicles that use fossil fuels
We evaluate the effectiveness of the proposed method, i.e., DRL trained with charger usage patterns that were sampled from the probability distribution modeled by kernel density estimation (KDE)
We first present the results of the KDE (Section 4.1) and discuss the results of a charging/discharging scheduling performed by DRL agent (Section 4.2)
Summary
As environmental problems, such as climate change caused by global warming and air pollution, have become global issues, electric vehicle (EV) are considered as an alternative to vehicles that use fossil fuels. The distribution of EVs and charging stations is increasing rapidly with government investments [1]. This increasing number of alternative vehicles will change the existing load profile, and it may affect the grid in terms of power loss and voltage deviation. The most common one is time-of-use (ToU) [4], under which electricity price varies depending on whether the current time zone is on-peak or off-peak. Another scheme is Energies 2020, 13, 1950; doi:10.3390/en13081950 www.mdpi.com/journal/energies
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