Quantum-Enhanced PPO for Demand Follower Model in Stochastic Industrial Smart Grid Environment

The increasing penetration of renewable energy sources and the integration of distributed energy resources (DERs) have significantly increased the stochastic nature of modern industrial smart grids (ISG). Efficient demand-side management requires a robust and adaptive approach to dynamically respond to uncertainties in the energy supply and demand. This study introduces a Demand Follower Model (DFM) tailored for stochastic ISG environments, leveraging a Quantum-Enhanced Proximal Policy Optimization (Q-PPO) algorithm on a simulated, noise-free ibmq_qasm_simulator environment to achieve optimal energy management. Unlike conventional demand response mechanisms, the proposed model operates in real-time and, continuously adapts to volatile network conditions and stochastic load variations. The Q-PPO algorithm improves the learning efficiency and decision-making by integrating quantum-inspired principles, leading to a fast convergence rate and reduced computational complexity. The proposed model was tested on various stochastic grid scenarios, considering network uncertainties, dynamic pricing, and fluctuating generation. The experimental results demonstrated that Q-PPO significantly outperforms classical reinforcement learning (RL) methods in achieving efficient supply-demand variations control, voltage profile enhancement, frequency control, minimizing energy costs, and maintaining grid stability. This study provides a novel framework for next-generation ISGs, enabling intelligent and efficient energy utilization in highly uncertain environments.