TY - GEN
T1 - A Two-Timescale Voltage Control Framework for Distribution Networks under Weather-Driven PV Variability
AU - Gao, Chenxuan
AU - Zhu, Yidian
AU - Zhang, Xian
AU - Wang, Guibin
N1 - Publisher Copyright:
© 2026 IEEE.
PY - 2026
Y1 - 2026
N2 - With the growing penetration of distributed photovoltaics (PV), voltage regulation in active distribution networks becomes more challenging due to weather-driven variability of PV output. Rapid changes in solar irradiance can induce significant intra-hour fluctuations that are difficult to handle by conventional slow-response devices. To address this issue, this paper proposes a two-timescale weather-driven voltage control framework. In the slow timescale, weather evolution is explicitly modeled to construct PV output intervals, and a robust optimal power flow model is formulated to coordinate on-load tap changers (OLTCs) and capacitor banks (CBs). In the fast timescale, a multi-agent soft actor-critic (MASAC) algorithm is employed to provide real-time reactive power support from PV inverters, compensating for intra-hour PV fluctuations. Simulation results on a modified IEEE 33-bus system demonstrate that the proposed method effectively improves voltage profiles under various weather scenarios. In particular, under highly unstable conditions, the average voltage deviation is reduced by over 50%, highlighting the effectiveness of the proposed framework in handling fast PV variability.
AB - With the growing penetration of distributed photovoltaics (PV), voltage regulation in active distribution networks becomes more challenging due to weather-driven variability of PV output. Rapid changes in solar irradiance can induce significant intra-hour fluctuations that are difficult to handle by conventional slow-response devices. To address this issue, this paper proposes a two-timescale weather-driven voltage control framework. In the slow timescale, weather evolution is explicitly modeled to construct PV output intervals, and a robust optimal power flow model is formulated to coordinate on-load tap changers (OLTCs) and capacitor banks (CBs). In the fast timescale, a multi-agent soft actor-critic (MASAC) algorithm is employed to provide real-time reactive power support from PV inverters, compensating for intra-hour PV fluctuations. Simulation results on a modified IEEE 33-bus system demonstrate that the proposed method effectively improves voltage profiles under various weather scenarios. In particular, under highly unstable conditions, the average voltage deviation is reduced by over 50%, highlighting the effectiveness of the proposed framework in handling fast PV variability.
KW - multi-agent reinforcement learning
KW - reactive power control
KW - robust dispatch
KW - voltage control
KW - weather uncertainty
UR - https://www.scopus.com/pages/publications/105045538423
U2 - 10.1109/EPSIC70071.2026.11590723
DO - 10.1109/EPSIC70071.2026.11590723
M3 - 会议稿件
AN - SCOPUS:105045538423
T3 - 2026 IEEE 3rd International Conference on Electrical Power Systems and Intelligent Control, EPSIC 2026
BT - 2026 IEEE 3rd International Conference on Electrical Power Systems and Intelligent Control, EPSIC 2026
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 3rd IEEE International Conference on Electrical Power Systems and Intelligent Control, EPSIC 2026
Y2 - 22 May 2026 through 24 May 2026
ER -