Journal article
Optimized Energy-Storage Method Based on Deep-Learning Adaptive-Dynamic Programming
Journal of energy engineering, Vol.146(3), 04020011
06/2020
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Abstract
Renewable energy has the characteristics of fluctuation and intermittence due to environmental conditions; the fluctuation of output power affects the operation of the power grid. To combat these fluctuations, a control strategy for the battery energy-storage system using a deep-learning adaptive-dynamic algorithm is proposed in this work. First, power-fluctuation rate feedback control is used to suppress the power fluctuation from the renewable energy source. Second, by introducing an adaptive-dynamic algorithm based on a deep belief network, the charging and discharging power of the battery energy-storage system with secondary regulation is achieved. Finally, the validity of the methods is verified by a series of examples using available data for wind and photovoltaic sources. It was found the proposed control strategy is highly effective for suppression of unwanted fluctuations.
Details
- Title
- Optimized Energy-Storage Method Based on Deep-Learning Adaptive-Dynamic Programming
- Creators
- Lei Miao - University of Science and Technology BeijingYongjun Zhang - University of Science and Technology BeijingChaonan Tong - University of Science and Technology BeijingQiang Guo - University of Science and Technology BeijingJiawei Zhang - University of SydneyTanju Yildirim - Australian National University
- Publication Details
- Journal of energy engineering, Vol.146(3), 04020011
- Publisher
- American Society of Civil Engineers
- Identifiers
- 991013160981802368
- Academic Unit
- Faculty of Science and Engineering
- Language
- English
- Resource Type
- Journal article