Abstract
Modern power systems rely on renewable energy sources and distributed generation systems more than ever before. The combination of those two along with the advanced energy storage systems contributed widely to the development of microgrids (MGs). This paper considers a microgrid system that consists of two distributed generators, which are diesel synchronous generator, and photovoltaic power system integrated with energy storage devices. The voltage and frequency at the point of common coupling need to be maintained at steady state conditions. Hence, an energy storage-based PI controller is designed by using a differential evolution optimization technique. The controller parameters (Kp and Ki) have been optimized under several operating conditions. The obtained inputs and outputs patterns used to train the neural networks (NN) to restore the system frequency and voltage effectively subjected to unknown disturbances. Finally, the NN has been tested, and the simulation results show a superior robustness performance of the proposed controller.
| Original language | English |
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| Title of host publication | 2020 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference, ISGT 2020 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| ISBN (Electronic) | 9781728131030 |
| DOIs | |
| State | Published - Feb 2020 |
Publication series
| Name | 2020 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference, ISGT 2020 |
|---|
Bibliographical note
Publisher Copyright:© 2020 IEEE.
Keywords
- Differential evolution optimization
- Distributed renewable generation
- Energy storage devices
- Microgrid
- Neural network
- Proportional-integral controller
ASJC Scopus subject areas
- Artificial Intelligence
- Energy Engineering and Power Technology
- Electrical and Electronic Engineering
- Safety, Risk, Reliability and Quality
- Control and Optimization
