سال انتشار:
2020
عنوان انگلیسی مقاله:
Modified deep learning and reinforcement learning for an incentive-based demand response model
ترجمه فارسی عنوان مقاله:
یادگیری عمیق اصلاح شده و یادگیری تقویتی برای یک مدل پاسخ تقاضای مبتنی بر انگیزه
منبع:
Sciencedirect - Elsevier - Energy, 205 (2020) 118019. doi:10.1016/j.energy.2020.118019
نویسنده:
Lulu Wen a, b, c, Kaile Zhou a, b, *, Jun Li d, Shanyong Wang e
چکیده انگلیسی:
Incentive-based demand response (DR) program can induce end users (EUs) to reduce electricity demand
during peak period through rewards. In this study, an incentive-based DR program with modified deep
learning and reinforcement learning is proposed. A modified deep learning model based on recurrent
neural network (MDL-RNN) was first proposed to identify the future uncertainties of environment by
forecasting day-ahead wholesale electricity price, photovoltaic (PV) power output, and power load. Then,
reinforcement learning (RL) was utilized to explore the optimal incentive rates at each hour which can
maximize the profits of both energy service providers (ESPs) and EUs. The results showed that the
proposed modified deep learning model can achieve more accurate forecasting results compared with
some other methods. It can support the development of incentive-based DR programs under uncertain
environment. Meanwhile, the optimized incentive rate can increase the total profits of ESPs and EUs
while reducing the peak electricity demand. A short-term DR program was developed for peak electricity
demand period, and the experimental results show that peak electricity demand can be reduced by 17%.
This contributes to mitigating the supply-demand imbalance and enhancing power system security.
Keywords: Demand response | Modified deep learning | Reinforcement learning | Smart grid
قیمت: رایگان
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