Lifelong Control of Off-grid Microgrid with Model Based Reinforcement Learning

TL;DR

This paper introduces a reinforcement learning framework for lifelong control of off-grid microgrids, effectively handling both gradual and sudden changes in system dynamics to improve operational performance.

Contribution

It presents a novel model-based reinforcement learning algorithm capable of managing both progressive and abrupt changes in microgrid conditions, outperforming traditional methods.

Findings

The proposed algorithm demonstrates strong generalization and transfer capabilities.

It outperforms rule-based and model predictive control benchmarks in dynamic settings.

The framework is open-source and tailored for rural electrification microgrids.

Abstract

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.