Optimizing renewable energy systems with hybrid action space reinforcement learning: A case study on achieving net zero energy in Japan

Abstract

This research introduces a reinforcement learning optimization framework for renewable energy systems, aimed at advancing Net-Zero Energy Buildings integrated with solar photovoltaic, biomass power generation, and battery storage. To address the challenges posed by mixed action spaces in the deployment of reinforcement learning, an algorithm utilizing a parameterized action space has been employed. This study is capable of managing the operational scheduling of various renewable energy sources without incurring additional computational load, thereby achieving Net-Zero Energy Buildings. The proposed model has been case-analyzed based on actual measurement data from existing energy systems. The study’s findings indicate that the reinforcement learning algorithm with a parameterized action space, compared to the baseline model, can enhance off-grid operational performance by 4 %, offering a more promising route towards achieving Net-Zero Energy Buildings. Simultaneously, the time the battery operates within the safe range has increased by 90 % compared to the baseline model, enhancing the system’s energy flexibility. While achieving these objectives, there has been no additional computational burden on the reinforcement learning algorithm. This provides a feasible approach for the zero-carbon operation of office buildings and offers guidance and reference for stakeholders looking to develop similar carbon-neutral structures.