# A Multiobjective Reinforcement Learning Framework for Microgrid Energy Management

arXiv: https://arxiv.org/abs/2307.08692 · HTML: https://mlsanity.com/paper/2307.08692/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 47 references that are in the map.
- First posted on arXiv 17 Jul 2023.

## Builds on (most cited first, 4 of 4)

- [Deep Reinforcement Learning: An Overview](https://mlsanity.com/paper/1701.07274/) (2017)
- [Reinforcement Learning for Selective Key Applications in Power Systems: Recent Advances and Future Challenges](https://mlsanity.com/paper/2102.01168/) (2021)
- [A Learning-based Power Management for Networked Microgrids Under Incomplete Information](https://mlsanity.com/paper/1810.01758/) (2018)
- [Application of Microgrids in Supporting Distribution Grid Flexibility](https://mlsanity.com/paper/1612.00570/) (2016)

## Built on by (most cited first, 1 of 1)

- [A Meta-Learning Approach for Multi-Objective Reinforcement Learning in Sustainable Home Environments](https://mlsanity.com/paper/2407.11489/) (2024)

## Abstract (arXiv)

The emergence of microgrids (MGs) has provided a promising solution for decarbonizing and decentralizing the power grid, mitigating the challenges posed by climate change. However, MG operations often involve considering multiple objectives that represent the interests of different stakeholders, leading to potentially complex conflicts. To tackle this issue, we propose a novel multi-objective reinforcement learning framework that explores the high-dimensional objective space and uncovers the tradeoffs between conflicting objectives. This framework leverages exogenous information and capitalizes on the data-driven nature of reinforcement learning, enabling the training of a parametric policy without the need for long-term forecasts or knowledge of the underlying uncertainty distribution. The trained policies exhibit diverse, adaptive, and coordinative behaviors with the added benefit of providing interpretable insights on the dynamics of their information use. We employ this framework on the Cornell University MG (CU-MG), which is a combined heat and power MG, to evaluate its effectiveness. The results demonstrate performance improvements in all objectives considered compared to the status quo operations and offer more flexibility in navigating complex operational tradeoffs.