The increasing penetration of renewable energy resources is central to global sustainability and decarbonisation goals, yet it introduces intermittency and voltage instability in modern distribution networks. Ensuring stable operation while maximising renewable utilisation is critical for achieving long-term energy sustainability, reduced carbon emissions, and efficient grid performance. This study proposes a sustainability-oriented, Reinforcement Learning (RL)-driven voltage control framework that enables reliable and energy-efficient operation of wind-integrated distribution systems. A Deep Q-Network (DQN) agent formulates voltage regulation as a Markov Decision Process (MDP) and autonomously learns optimal control policies for on-load tap changers (OLTCs) and capacitor banks under highly variable wind and load conditions. Using the IEEE 33-bus test system with realistic stochastic wind and ZIP-load models, the results show that the proposed controller maintains voltages within statutory limits, reduces total active power losses by up to 18%, and enhances the network’s capacity to host renewable energy. These improvements translate to increased energy efficiency, reduced technical losses, and greater operational resilience, key enablers of sustainable energy distribution. The findings demonstrate that intelligent RL-based frameworks offer a scalable and model-free tool for advancing sustainable, low-carbon, and resilient power systems.
Behara et al. (Fri,) studied this question.