Research Area:  Machine Learning

Abstract:

The increasingly complex energy systems are turning the attention towards model-free control approaches such as reinforcement learning (RL). This work proposes novel RL-based energy management approaches for scheduling the operation of controllable devices within an electric network. The proposed approaches provide a tool for efficiently solving multi-dimensional, multi-objective and partially observable power system problems. The novelty in this work is threefold: We implement a hierarchical RL-based control strategy to solve a typical energy scheduling problem. Second, multi-agent reinforcement learning (MARL) is put forward to efficiently coordinate different units with no communication burden. Third, a control strategy that merges hierarchical RL and MARL theory is proposed for a robust control framework that can handle complex power system problems. A comparative performance evaluation of various RL-based and model-based control approaches is also presented. Experimental results of three typical energy dispatch scenarios show the effectiveness of the proposed control framework.

Keywords:  

Author(s) Name:  Imen Jendoubi , François Bouffard

Publisher name:  ScienceDirect

DOI:  10.1016/j.apenergy.2022.120500

Volume Information:  Volume 332, (2023)

Paper Link:   https://www.sciencedirect.com/science/article/abs/pii/S0306261922017573