Quantum-Inspired Deep Reinforcement Learning for Adaptive Frequency Control of Low Carbon Park Island Microgrid Considering Renewable Energy Sources Provisionally Accepted
- 1Measurement Center, Yunnan Power Grid Co. Ltd, China
- 2Electric Power Research Institute of CSG, Guangzhou, China; Guangdong Provincial Key Laboratory of Intelligent Measurement and Advanced Metering of Power Grid, Guangzhou, China, China
- 3Metrology Center of Guangdong Power Grid Co. Ltd,, China
The low carbon park islanded microgrid faces operational challenges due to the high variability and uncertainty of distributed renewable energy sources. These sources cause severe random disturbances that impair the frequency control performance and increase the regulation cost of the islanded microgrid, jeopardizing its safety and stability. This paper presents a data-driven intelligent load frequency control (DDI-LFC) method to address this problem. The method replaces the conventional LFC controller with an intelligent agent based on a deep reinforcement learning algorithm. To adapt to the complex islanded microgrid environment and achieve adaptive multi-objective optimal frequency control, this paper proposes the quantum-inspired maximum entropy actor-critic (QIS-MEAC) algorithm, which incorporates the quantum-inspired principle and the maximum entropy exploration strategy into the actor-critic algorithm. The algorithm transforms the experience into a quantum state and leverages the quantum features to improve the deep reinforcement learning's experience replay mechanism, enhancing the data efficiency and robustness of the algorithm and thus the quality of DDI-LFC. The validation on the Yongxing Island isolated microgrid model of China Southern Grid (CSG) demonstrates that the proposed method utilizes the frequency regulation potential of distributed generation, and reduces the frequency deviation and generation cost.
Keywords: Load frequency control, deep meta-reinforcement learning, Islanded microgrid, maximum entropy exploration, Quantum-inspired
Received: 05 Jan 2024;
Accepted: 28 Mar 2024.
Copyright: © 2024 Shen, Tang, Pan, Qian and Zhao. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Mx. Jianlin Tang, Electric Power Research Institute of CSG, Guangzhou, China; Guangdong Provincial Key Laboratory of Intelligent Measurement and Advanced Metering of Power Grid, Guangzhou, China, Guangzhou, China