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

arXiv: https://arxiv.org/abs/2005.08006 · HTML: https://mlsanity.com/paper/2005.08006/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 May 2020.

## Built on by (most cited first, 4 of 4)

- [Data-driven battery operation for energy arbitrage using rainbow deep reinforcement learning](https://mlsanity.com/paper/2106.06061/) (2021)
- [Review of Metrics to Measure the Stability, Robustness and Resilience of Reinforcement Learning](https://mlsanity.com/paper/2203.12048/) (2022)
- [Renewable energy management in smart home environment via forecast embedded scheduling based on Recurrent Trend Predictive Neural Network](https://mlsanity.com/paper/2307.01622/) (2023)
- [Optimal Planning of Hybrid Energy Storage Systems using Curtailed Renewable Energy through Deep Reinforcement Learning](https://mlsanity.com/paper/2212.05662/) (2022)

## Abstract (arXiv)

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.