Abstract
This paper proposes a multiagent (MA) smart generation control (SGC) scheme for the coordination of automatic generation control (AGC) in power grids with system uncertainties. Under the control performance standards, SGC will undergo a non-Markov random process, of which the optimal solution can be resolved online by the reinforcement learning. Therefore, an MA decentralized correlated equilibrium Q(λ)-learning algorithm, and an MA stochastic dynamic game-based SGC simulation platform (SGC-SP) have been proposed for its implementation, which can achieve AGC coordination in a highly uncertain environment resulting from the increasing penetration of renewable energy. Single-agent Q-learning, Q(λ)-learning, R(λ)-learning, and proportional integral control are implemented and embedded in SGC-SP for the control performance analysis. Two case studies on both a two-area power system and the China Southern Power Grid model have been done, which verify its effectiveness and scalability.
Original language | English |
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Article number | 04015012 |
Journal | Journal of Energy Engineering |
Volume | 142 |
Issue number | 1 |
DOIs | |
Publication status | Published - 1 Mar 2016 |
Keywords
- Automatic generation control (AGC)
- Multiagent
- Reinforcement learning
- Smart generation control
ASJC Scopus subject areas
- Civil and Structural Engineering
- Renewable Energy, Sustainability and the Environment
- Nuclear Energy and Engineering
- Energy Engineering and Power Technology
- Waste Management and Disposal