Optimal vehicle-to-grid control for supplementary frequency regulation using deep reinforcement learning
نویسندگان
چکیده
The expanding Electric Vehicle (EV) market presents a new opportunity for electric vehicles to deliver wide range of valuable grid services. Indeed, the emerging Vehicle-to-Grid (V2G) technology with bi-directional flow power provides access mobile energy storage demand response, frequency regulation and balancing local distribution system. This reduces electricity costs at peak hours can be profitable customers, network operators retailers. In this paper, an optimal V2G control strategy using Deep Reinforcement Learning (DRL) is proposed simultaneously maximise benefits EV owners aggregators while fulfilling driving needs owners. DRL-based strategy, Deterministic Policy Gradient (DDPG) agent used dynamically adjust scheduling satisfy users perform tasks. scheme tested on two-area system undergoing deviations. results showed that leads better deviation reduction improved Area Control Error (ACE), satisfying charging demands EVs as compared other strategies.
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ژورنال
عنوان ژورنال: Electric Power Systems Research
سال: 2023
ISSN: ['1873-2046', '0378-7796']
DOI: https://doi.org/10.1016/j.epsr.2022.108949