Journal of Modern Power Systems and Clean Energy

ISSN 2196-5625 CN 32-1884/TK

Dynamic Nonlinear Droop-based Fast Frequency Regulation for Power Systems with Flexible Resources Using Meta-reinforcement Learning Approach
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Affiliation:

1.Department of Electrical Engineering, Tsinghua University, Beijing 100084, China;2.State Key Laboratory of Internet of Things for Smart City, University of Macau, Macau, China

Fund Project:

This work was supported by the Key Research and Development Program of Inner Mongolia, China (No. 2021ZD0039).

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    Abstract:

    The increasing penetration of renewable energy resources and reduced system inertia pose risks to frequency security of power systems, necessitating the development of fast frequency regulation (FFR) methods using flexible resources. However, developing effective FFR policies is challenging because different power system operating conditions require distinct regulation logics. Traditional fixed-coefficient linear droop-based control methods are suboptimal for managing the diverse conditions encountered. This paper proposes a dynamic nonlinear P-f droop-based FFR method using a newly established meta-reinforcement learning (meta-RL) approach to enhance control adaptability while ensuring grid stability. First, we model the optimal FFR problem under various operating conditions as a set of Markov decision processes and accordingly formulate the frequency stability-constrained meta-RL problem. To address this, we then construct a novel hierarchical neural network (HNN) structure that incorporates a theoretical frequency stability guarantee, thereby converting the constrained meta-RL problem into a more tractable form. Finally, we propose a two-stage algorithm that leverages the inherent characteristics of the problem, achieving enhanced optimality in solving the HNN-based meta-RL problem. Simulations validate that the proposed FFR method shows superior adaptability across different operating conditions, and achieves better trade-offs between regulation performance and cost than benchmarks.

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History
  • Received:January 17,2024
  • Revised:May 17,2024
  • Online: March 26,2025