Details

Autor(en) / Beteiligte

• Zhang, Tingjun
• Yu, Liang
• Yue, Dong
• Zhang, Dingfan

Titel

Hierarchical Multi-Agent Deep Reinforcement Learning for Coordinated Voltage Regulation in Active Distribution Networks with Hybrid Devices

Ist Teil von

• 2023 42nd Chinese Control Conference (CCC), 2023, p.7219-7224

Ort / Verlag

Technical Committee on Control Theory, Chinese Association of Automation

Erscheinungsjahr

2023

Quelle

IEEE Electronic Library (IEL)

Beschreibungen/Notizen

• Voltage regulation is indispensable to the secure operation of active distribution networks (ADNs) with high penetrated renewables. In this paper, we investigate an optimal two-timescale voltage regulation problem of ADNs with hybrid devices. Specifically, we intend to minimize the total power loss of the whole ADNs while maintaining all bus voltages within a safe range. Due to the existence of uncertain parameters, temporal couplings, multiple timescales, mixed decision variables, and unknown system models, it is challenging to solve the above optimization problem. To this end, we propose a coordinated voltage regulation algorithm based on hierarchical multi-agent attention-based deep reinforcement learning. The proposed algorithm can support flexible collaboration among hybrid devices. Simulation results based on real-world traces show the effectiveness of the proposed algorithm.