Virtual Generation Alliance Automatic Generation Control Based on Deep Reinforcement Learning

This article proposes a distributed hierarchical automatic generation control (AGC) framework with multiple regulation units in the performance-based frequency regulation market, named virtual generation alliance automatic generation control (VGA-AGC), aiming to achieve the coordination of control a...

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Vydáno v:IEEE access Ročník 8; s. 182204 - 182217
Hlavní autoři: Li, Jiawen, Yu, Tao
Médium: Journal Article
Jazyk:angličtina
Vydáno: Piscataway IEEE 2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Algorithms
Automatic control
Automatic generation control
centralized-decentralized autonomy
Classification algorithms
Control algorithms
Control theory
Cooperative control
Electric power grids
Frequency control
multi agent distributed multiple improved deep deterministic policy gradient
Multiagent systems
Optimization
Performance-based frequency regulation market
Power grids
Real-time systems
regulation mileage payment
Training
virtual generation alliance automatic generation control (VGA-AGC)
Wind turbines
ISSN:2169-3536, 2169-3536
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Shrnutí:This article proposes a distributed hierarchical automatic generation control (AGC) framework with multiple regulation units in the performance-based frequency regulation market, named virtual generation alliance automatic generation control (VGA-AGC), aiming to achieve the coordination of control algorithm and AGC dispatch algorithm and adapt to the development trend of AGC from centralized framework to centralized-decentralized framework. The framework also involves a multi agent distributed multiple improved deep deterministic policy gradient (MADMI-TD3) algorithm that is characterized by excellent global search capability and optimizing speed. The algorithm can help create an optimal AGC strategy in a randomization environment so as to obtain an optimal cooperative control of AGC. According to a simulation verification on the LFC model for an interconnected power grid of a province, the algorithm is superior to the current algorithms and conventional engineering methods in terms of control performance and economic benefits. In other words, the algorithm can improve control performance and reduce the regulation mileage payment.
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ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2020.3029189