Demand response based industrial energy management with focus on consumption of renewable energy: a deep reinforcement learning approach
Atit Bashyal, Hani Alnahas, Tina Boroukhian, Hendro Wicaksono
Abstract
Integrating Renewable Energy Resources (RESs) into power grids requires effective Demand Response (DR) programs. Despite high DR potential in industrial sectors, adoption lags behind that of residential and commercial sectors due to diverse operations and production continuity requirements. This paper explores a reinforcement learning (RL)-based DR scheme for energy-intensive industries, promoting the consumption of distributed Renewable Energy (RE) generation. Our approach introduces modifications to the existing Markov Decision Process (MDP) framework. It proposes a flexible reward structure that provides flexibility in balancing production requirements and promotes the consumption of RE. This study addresses the gap in industrial DR literature, emphasizing tailored DR solutions for industrial settings. The key highlight of our RL-based DR solution is its ability to facilitate a price-based DR scheme while promoting the integration of RE into the smart grid.