Resilience Microgrid as Power System Integrity Protection Scheme Element With Reinforcement Learning Based Management

The microgrid is a solution for integrating renewable energy resources into the power system. However, overcoming the randomness of these nature-based resources requires a robust control system. Moreover, electricity market participation and ancillary service provision for the utility grid are other...

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Bibliographic Details
Published in:IEEE access 2021, Vol.9, p.83963-83975
Main Authors: Tightiz, Lilia, Yang, Hyosik
Format: Article
Language:English
Subjects:
Ancillary services
Batteries
deep deterministic policy gradient
Distributed generation
Electric power systems
Electricity supply industry
Energy management
Energy management system
Energy management systems
Energy policy
Energy sources
Integrity
Learning
Microgrids
Power generation
Randomness
Robust control
soft actor-critic
system integrity protection schema
Uncertainty
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Summary:The microgrid is a solution for integrating renewable energy resources into the power system. However, overcoming the randomness of these nature-based resources requires a robust control system. Moreover, electricity market participation and ancillary service provision for the utility grid are other aspects, although intensify microgrid penetration makes its environment interactions more complex. Reinforcement learning is a technique vastly applied to such an intricate environment. Hence, in this paper, we deployed deep deterministic policy gradient and soft-actor critic methods to solve the high-dimensional, continuous, and stochastic problem of the microgrid's energy management system and compared the performance of two methods. Additionally, we developed the microgrid interactions with the utility grid as a participant of system integrity protection schema responding promptly to the utility grid protection requirements based on its reliable available resources. Moreover, we applied actual data of Gasa Island microgrid in Korea to prove the efficiency of proposed method.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2021.3087491