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sis 26(8):

Research Article

A Decentralised Coordination Framework for Demand-Side Solar-Storage and Its Electricity Market Participation

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  • @ARTICLE{10.4108/eetsis.12071,
        author={Bo Li and Yong-Feng Ge and Yuan Miao and Ruoxuan Cui and Xin Li and Kate Wang and Hua Wang},
        title={A Decentralised Coordination Framework for Demand-Side Solar-Storage and Its Electricity Market Participation},
        journal={EAI Endorsed Transactions on Scalable Information Systems},
        volume={12},
        number={8},
        publisher={EAI},
        journal_a={SIS},
        year={2026},
        month={3},
        keywords={Distributed energy resources, Multi-agent reinforcement learning, Decentralised coordination, Electricity market participation},
        doi={10.4108/eetsis.12071}
    }
    
  • Bo Li
    Yong-Feng Ge
    Yuan Miao
    Ruoxuan Cui
    Xin Li
    Kate Wang
    Hua Wang
    Year: 2026
    A Decentralised Coordination Framework for Demand-Side Solar-Storage and Its Electricity Market Participation
    SIS
    EAI
    DOI: 10.4108/eetsis.12071
Bo Li1,*, Yong-Feng Ge1, Yuan Miao1, Ruoxuan Cui2, Xin Li3, Kate Wang4, Hua Wang1
  • 1: Victoria University
  • 2: Hebei University
  • 3: YSTC Energy
  • 4: RMIT University
*Contact email: bo.li@vu.edu.au

Abstract

The rapid proliferation of rooftop photovoltaics and behind-the-meter battery storage is creating systemic risks in distribution networks. This includes local network constraint violations, insufficient outage resilience, and enlarged cybersecurity attack surfaces, leaving millions of demand-side distributed energy resources (DERs) to operate without coordinated oversight. Existing solutions (dynamic operating envelopes, virtual power plants, peer-to-peer trading, and single-household energy management systems) each address only partial aspects of this challenge. Based on multi-agent reinforcement learning, this paper proposes a Decentralised Coordination Framework (DCF) that organises the energy dispatch into a three-tier hierarchical architecture. Specifically, it has a user layer executing Proximal Policy Optimisation (PPO) for local dispatch, a feeder layer in which a dynamically elected L1 leader applies MADDPG to coordinate community flexibility via a Virtual Aggregation Unit (VAU); and a cross-feeder layer where an L2 leader manages inter-community balancing and market interfaces. It integrates a directed acyclic graph (DAG)-based verifiable execution ledger, Paillier homomorphic encryption, and LLM-based anomaly detection to enhance security. Potential market participation pathway and revenue distribution mechanism are proposed to align with the Australian National Electricity Market. The DCF provides a scalable, market-ready foundation for commercial demandside DER deployment under high renewable penetration.

Keywords
Distributed energy resources, Multi-agent reinforcement learning, Decentralised coordination, Electricity market participation
Received
2026-03-03
Accepted
2026-03-12
Published
2026-03-19
Publisher
EAI
http://dx.doi.org/10.4108/eetsis.12071

Copyright © 2026 Bo Li et al., licensed to EAI. This is an open access article distributed under the terms of the CC BY-NCSA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.

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