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Microgrid Energy Management with Reinforcement Learning

integration_instructionsResearch softwarekeyboard_double_arrow_right Software 04 Mar 2026Publisher:Zenodo
Authors: Çetinkaya, Berker;

doi: 10.5281/zenodo.18859128 , 10.5281/zenodo.18859278 , 10.5281/zenodo.18859129

Microgrid Energy Management with Reinforcement Learning

Abstract

A reinforcement learning framework for optimal energy management in residential microgrids with battery energy storage systems. Implements SAC, PPO, and Robust SAC agents benchmarked against a Linear Programming optimal solution, with comprehensive robustness analysis under forecast uncertainty.

Keywords

microgrid, reinforcement learning, battery energy storage, energy management, demand response, proximal policy optimization, linear programming, smart grid, soft actor-critic

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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