Lifelong Control of Off-grid Microgrid with Model Based Reinforcement Learning
This addresses the operational planning challenge for rural electrification microgrids, but it is incremental as it builds on existing reinforcement learning methods for a specific domain.
The paper tackles the lifelong control problem of an off-grid microgrid by developing a model-based reinforcement learning algorithm to handle progressive and abrupt changes in system dynamics, and the results show it outperforms rule-based and model predictive control benchmarks in this setting.
The lifelong control problem of an off-grid microgrid is composed of two tasks, namely estimation of the condition of the microgrid devices and operational planning accounting for the uncertainties by forecasting the future consumption and the renewable production. The main challenge for the effective control arises from the various changes that take place over time. In this paper, we present an open-source reinforcement framework for the modeling of an off-grid microgrid for rural electrification. The lifelong control problem of an isolated microgrid is formulated as a Markov Decision Process (MDP). We categorize the set of changes that can occur in progressive and abrupt changes. We propose a novel model based reinforcement learning algorithm that is able to address both types of changes. In particular the proposed algorithm demonstrates generalisation properties, transfer capabilities and better robustness in case of fast-changing system dynamics. The proposed algorithm is compared against a rule-based policy and a model predictive controller with look-ahead. The results show that the trained agent is able to outperform both benchmarks in the lifelong setting where the system dynamics are changing over time.