Distributed quantum multiagent deep meta reinforcement learning for area autonomy energy management of a multiarea microgrid

Jiawen Li, Tao Zhou, He Keke, Hengwen Yu, Hongwei Du, Shuangyu Liu and Haoyang Cui

Applied Energy, 2023, vol. 343, issue C, No S0306261923005457

Abstract: This paper presents a distributed area autonomy load frequency control (DAA-LFC) method capable of balancing the interests of different grid operators and achieving fast frequency recovery. The method treats each area controller in a multiarea microgrid as an agent. During offline training, the agents enter into gameplay with each other to obtain a global optimization policy. The agents involved in this method are capable of independent decision-making and need not communicate with each other during online operation. In addition, this paper presents a distributed quantum multiagent deep meta-deterministic policy gradient (DQMA-DMDPG) algorithm, which employs both large-scale learning and meta-learning to achieve collaborative multitask learning by setting reasonable exploration parameters under different tasks. A quantum method is used to set the exploration action noise as a set of superposition states to obtain richer samples. These innovations deliver better performance in terms of frequency deviation and total generation cost, thus satisfying the requirements of different grid operators. A simulation based on a four-area microgrid of the China Southern Grid (CSG) demonstrates that the proposed method can simultaneously reduce the frequency deviation and power generation costs and balance the interests of multiple operators.

Keywords: Load frequency control; Islanded microgrid; Distributed quantum multiagent deep meta-deterministic policy gradient; Quantum method; Autonomy load frequency control (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (1)

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DOI: 10.1016/j.apenergy.2023.121181

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