Journal
IEEE TRANSACTIONS ON POWER SYSTEMS
Volume 35, Issue 6, Pages 4599-4608Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TPWRS.2020.2999890
Keywords
Power systems; Frequency control; Reinforcement learning; Turbines; Generators; Mathematical model; Load modeling; Load frequency control; multi-agent deep reinforcement learning; deep deterministic policy gradient (DDPG); multi-area power systems
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Funding
- Ministry of Education (MOE), Republic of Singapore [AcRF TIER 1 2019-T1-001-069 (RG75/19)]
- Nanyang Assistant Professorship from Nanyang Technological University, Singapore
- Singtel Cognitive and Artificial Intelligence Lab for Enterprises (SCALE@NTU)
- Singapore Government
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This paper proposes a data-driven cooperative method for load frequency control (LFC) of the multi-area power system based on multi-agent deep reinforcement learning (MA-DRL) in continuous action domain. The proposed method can nonlinearly and adaptively derive the optimal coordinated control strategies for multiple LFC controllers through centralized learning and decentralized implementation. The centralized learning is achieved by MA-DRL based on a global action-value function to quantify overall LFC performance of the power system. To solve the MA-DRL problem, multi-agent deep deterministic policy gradient (DDPG) is derived to adjust control agents parameters considering the nonlinear generator behaviors. For implementation, each individual controller only needs local information in its control area to deliver optimal control signals. Numerical simulations on a three-area power system and the fully-modeled New-England 39-bus system demonstrate that the proposed method can effectively minimize control errors against stochastic frequency variations caused by load and renewable power fluctuations.
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