pymops: A multi-agent simulation-based optimization package for power scheduling

Abstract

In response to the NP-hard power scheduling problem, the pymops package is developed as a robust solution. The package operates within a multi-agent simulation environment, where power-generating units are represented as reinforcement learning (RL) agents. The environment is designed to account for a comprehensive range of constraints. It also accommodates thermal valve point effects (VPEs) within cost and emissions functions. Moreover, in cases of constraint violations, the environment makes real-time contextual adjustments. Within the environment, the power scheduling problem is broken down into sequential Markov decision processes (MDPs), which serve as inputs for training a deep RL model aimed at solving the optimization problem.