Household-Aware Community Battery Coordination: A Multi-Agent Reinforcement Learning Approach

This poster presents a multi-agent reinforcement learning framework for coordinating household and community battery energy storage systems. The proposed approach considers household heterogeneity, multi-level objectives, internal pricing, and coordinated battery operation within a shared community energy system.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-09-21
DOI
https://doi.org/10.5281/zenodo.22868160
Primary Topic
Smart Grid Energy Management
Type
article
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article

Household-Aware Community Battery Coordination: A Multi-Agent Reinforcement Learning Approach

Chun Ouyang, Cao Tram Anh Hoang
Zenodo (CERN European Organization for Nuclear Research)
Smart Grid Energy Management
article

Household-Aware Community Battery Coordination: A Multi-Agent Reinforcement Learning Approach

Chun Ouyang, Cao Tram Anh Hoang
article en

Abstract

This poster presents a multi-agent reinforcement learning framework for coordinating household and community battery energy storage systems. The proposed approach considers household heterogeneity, multi-level objectives, internal pricing, and coordinated battery operation within a shared community energy system.

Zenodo (CERN European Organization for Nuclear Research)
Queensland University of Technology (AU)
Affordable and clean energy
Openalex Percentile: Top 21%
Smart Grid Energy Management
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Household-Aware Community Battery Coordination: A Multi-Agent Reinforcement Learning Approach — Chun Ouyang, Cao Tram Anh Hoang · Zenodo (CERN European Organization for Nuclear Research) (2026) | TGRS Research Map | TGRS