| Title |
Active Reconfigurable Operation with Long Short-Term Memory Prediction for Smart City Microgrids |
| ID_Doc |
36805 |
| Authors |
Lin, HW; Kai, S; Tan, ZH; Guerrero, JM |
| Title |
Active Reconfigurable Operation with Long Short-Term Memory Prediction for Smart City Microgrids |
| Year |
2019 |
| Published |
|
| DOI |
10.1109/egrid48402.2019.9092691 |
| Abstract |
This paper proposes an optimal operation methodology for active reconfiguration in a DC-AC hybrid energy system in the smart city context. In a clear-sharp contrast to the conventional operation of traditional microgrids, the proposed active reconfiguration capability is able to control the power supply and provide various controllable ancillary services such as reactive power compensation, harmonic suppression, and so forth, making this new concept suitable for smart-city microgrids. The system operation of active reconfiguration is treated as a unit commitment problem through optimizing the total operational cost of the system. State recognition and prediction are performed by Long Short-Term Memory Recurrent Neural Network for the smart operation. The effectiveness of the active reconfiguration operation is validated experimentally over an electrical system model. Furthermore, the methodology of the active reconfigurable operation in microgrids presents an energy construction scheme for the future "green smart cities". |
| Author Keywords |
Active reconfiguration; energy management; machine learning; microgrid; optimal operation; power electronics; reconfigurable operation; smart grid; smart city; smart infrastructures |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Conference Proceedings Citation Index - Science (CPCI-S) |
| EID |
WOS:000627598200012 |
| WoS Category |
Energy & Fuels; Engineering, Electrical & Electronic |
| Research Area |
Energy & Fuels; Engineering |
| PDF |
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