Deep Neural Network-Based Optimal Power Flow for Active Distribution Systems with High Photovoltaic Penetration

Peng Y. Lak, Jin-Woo Lim and Soon-Ryul Nam ()
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Peng Y. Lak: Department of Electrical Engineering, Myongji University, Yongin 17058, Republic of Korea
Jin-Woo Lim: Department of Electrical Engineering, Myongji University, Yongin 17058, Republic of Korea
Soon-Ryul Nam: Department of Electrical Engineering, Myongji University, Yongin 17058, Republic of Korea

Energies, 2025, vol. 18, issue 17, 1-17

Abstract: The integration of photovoltaic (PV) generation into distribution systems supports decarbonization and cost reduction but introduces challenges for secure and efficient operation due to voltage fluctuations and power flow variability. Traditional centralized optimal power flow (OPF) methods require full system observability and significant computational resources, limiting their real-time applicability in active distribution systems. This paper proposes a deep neural network (DNN)-based OPF control framework designed for active distribution systems with high PV penetration under limited measurement availability. The proposed method leverages offline convex chance-constrained OPF (convex-CCOPF) solutions, generated through iterative simulations across a wide range of PV and load conditions, to train the DNN to approximate optimal control actions, including on-load tap changer (OLTC) positions and inverter reactive power dispatch. To address observability constraints, the DNN is trained using a reduced set of strategically selected measurement points, making it suitable for real-world deployment in distribution systems with sparse sensing infrastructure. The effectiveness of the proposed framework is validated on the IEEE 33-bus test system under varying operating conditions. The simulation results demonstrate that the DNN achieves near-optimal performance with a significantly reduced computation time compared to conventional OPF solvers while maintaining voltage profiles within permissible limits and minimizing power losses.

Keywords: chance constraint; convex optimal power flow; deep neural network; distribution system; photovoltaic; onload tap changer (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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