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ESTD Year: 2017 | Impact Factor (2026): 8.7
DOI Prefix: 10.47001/IRJIET
ESTD Year: 2017 | Impact Factor (2026): 8.7
DOI Prefix: 10.47001/IRJIET
Vol 10 No 5 (2026): Volume 10, Issue 5, May 2026 | Pages: 736-743
International Research Journal of Innovations in Engineering and Technology
OPEN ACCESS | Research Article | Published Date: 31-05-2026
The increasing integration of photovoltaic (PV) systems into distribution networks has introduced significant challenges related to voltage regulation, reverse power flow, and power quality. Traditional voltage control methods, which rely on rule-based logic or centralized optimization, often fail to address the dynamic and stochastic nature of PV generation. This paper proposes an adaptive deep reinforcement learning (DRL) framework for coordinated voltage control in PV-rich distribution grids. The proposed method employs a Deep Deterministic Policy Gradient (DDPG) algorithm that learns optimal control policies by interacting with the distribution network environment. The DRL agent coordinates multiple voltage regulation devices, including smart inverters, on-load tap changers (OLTCs), and capacitor banks, to maintain voltage within acceptable limits while minimizing network losses and control actions. The framework incorporates an adaptive mechanism that adjusts the learning process based on changing network conditions, seasonal variations, and PV output patterns. Simulation results on a modified IEEE 33-bus distribution system demonstrate that the proposed approach outperforms conventional methods in terms of voltage profile improvement, loss reduction, and computational efficiency.
Deep reinforcement learning, voltage control, photovoltaic systems, distribution networks, smart inverters, DDPG algorithm.
Tarun Kumar Modi, Naresh Sapate, & Shailendra Turker. (2026). Impact of Exogenous Variables on Short-Term Electricity Price Forecasting Accuracy: A Deep Learning Approach Using CNN-LSTM Networks in the Indian Energy Exchange. International Research Journal of Innovations in Engineering and Technology - IRJIET, 10(5), 736-743.
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