سال انتشار:

2020

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عنوان انگلیسی مقاله:

Control of battery charging based on reinforcement learning and long short-term memory networks

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ترجمه فارسی عنوان مقاله:

کنترل شارژ باتری بر اساس یادگیری تقویت کننده و شبکه های حافظه کوتاه مدت

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منبع:

Sciencedirect - Elsevier - Computers and Electrical Engineering, 85 (2020) 106670. doi:10.1016/j.compeleceng.2020.106670

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نویسنده:

Fangyuan Chang a , Tao Chen a , Wencong Su a , ∗, Qais Alsafasfeh b

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چکیده انگلیسی:

In an electricity market with time-varying pricing, uncontrolled charging of energy storage systems (ESSs) may increase charging costs. A novel battery charging control methodology based on reinforcement-learning (RL) is proposed in this paper to minimize the charg- ing costs. A significant characteristic of this method is that it is model-free, with no need for a high-accuracy battery/ESS model. Therefore, it overcomes the challenges brought by limited types of battery models and non-ignorable parametric uncertainties in reality. Ad- ditionally, since an accurate prediction of fluctuating electricity prices can promote the control performance, a long short-term memory (LSTM) network is leveraged to improve the prediction precision. The final control objective is to seek an optimal charging portfo- lio to minimize charging costs. Moreover, the presented control algorithm provides a basic framework for a more complicated electricity market where various types of ESSs, genera- tors, and loads exist.
Keywords: Energy storage battery/system | Smart charging control | Long short-term memory (LSTM) | Reinforcement learning (RL) | Electric vehicle (EV)

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قیمت: رایگان

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توضیحات اضافی:

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