Abstract
In recent years, the importance of electric mobility has increased in response to climate change. The fast-growing deployment of electric vehicles (EVs) worldwide is expected to decrease transportation-related CO2 emissions, facilitate the integration of renewables, and support the grid through demand–response services. Simultaneously, inadequate EV charging patterns can lead to undesirable effects in grid operation, such as high peak-loads or low self-consumption of solar electricity, thus calling for novel methods of control. This work focuses on applying deep reinforcement learning (RL) to the EV charging control problem with the objectives to increase photovoltaic self-consumption and EV state of charge at departure. Particularly, we propose mathematical formulations of environments with discrete, continuous, and parametrized action spaces and respective deep RL algorithms to resolve them. The benchmarking of the deep RL control against naive, rule-based, deterministic optimization, and model-predictive control demonstrates that the suggested methodology can produce consistent and employable EV charging strategies, while its performance holds a great promise for real-time implementations.
Highlights
IntroductionElectric vehicles (EVs) are regarded as an effective way to reach emissions reduction targets and alleviate the current energy crisis
We focus on a simple energy system composed of a utility grid, building load, PV generation, and a single electric vehicles (EVs)
We have demonstrated the application of reinforcement learning to the EV charging control problem, focusing on a simple energy system composed of a utility grid, building load, PV generation, and a single EV
Summary
Electric vehicles (EVs) are regarded as an effective way to reach emissions reduction targets and alleviate the current energy crisis. EVs are unlikely to drastically drive up the overall electricity demand, the increase in peak-loads can impose significant threats on the secure and stable operation of power systems due to capacity issues of grid infrastructures. Efficient control strategies are required to manage the charging processes of EVs to avoid significant grid investments and guarantee the stability of the electricity supply. As driving patterns demonstrate, the EVs are parked more than 80% of the time [3], which gives the potential to intelligently shift the charging load, deploying smart energy management techniques. Using EVs can reduce energy costs, contribute to peak shaving, improve system balancing, and integrate a larger share of renewables into power production. The trade-off is to combine demand–response with seamless availability of EVs, such as the primary purpose of enabling mobility is served in a reliable and timely manner
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