A Multiobjective Reinforcement Learning Framework for Microgrid Energy
  Management
M. Vivienne Liu Patrick M. Reed David Gold Garret Quist C. Lindsay Anderson
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.