Environment-Friendly Power Scheduling Based on Deep Contextual Reinforcement Learning

A novel approach to power scheduling is introduced, focusing on minimizing both economic and environmental impacts. This method utilizes deep contextual reinforcement learning (RL) within an agent-based simulation environment. Each generating unit treated as independent, heterogeneous agent, the dynamics are formulated Markov decision processes (MDPs). The MDPs then used train a RL model determine optimal schedules. performance of this evaluated across various systems, including small-scale large-scale systems with up 100 units. results demonstrate that proposed exhibits superior scalability in handling larger number