A Decentralised Coordination Framework for Demand-Side Solar-Storage and Its Electricity Market Participation
DOI:
https://doi.org/10.4108/eetsis.12071Keywords:
Distributed energy resources, Multi-agent reinforcement learning, Decentralised coordination, Electricity market participationAbstract
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.
References
[1] Clean Energy Council (2025), Rooftop solar and storage report, july to december 2025, https: //cleanenergycouncil.org.au/news-resources/ rooftop-solar-and-storage-report-july-to-dec-2025.
[2] Australian Government Department of Climate Change, Energy, the Environment and Water (2025), Cheaper home batteries program, Available: https://www.dcceew.gov.au/energy/programs/ cheaper-home-batteries.
[3] Gorman, W., Barbose, G., Carvallo, J.P., Baik, S., Miller, C.A., White, P. and Praprost, M. (2023) County-level assessment of behind-the-meter solar and storage to mitigate long duration power interruptions for residential customers. Applied Energy 342: 121166. doi:10.1016/j.apenergy.2023.121166.
[4] Huseinović, A., Mrdović, S., Bicakci, K. and Uludag, S. (2020) A survey of denial-of-service attacks and solutions in the smart grid. IEEE Access 8: 177447– 177470.
[5] Australian Renewable Energy Agency (2022), Dynamic operating envelopes workstream, Distributed Energy Integration Program (DEIP). https://arena.gov.au/knowledge-innovation/ distributed-energy-integration-program/ dynamic-operating-envelopes-workstream/.
[6] Kataray, T., Nitesh, B., Yarram, B., Sinha, S., Cuce, E., Shaik, S., Vigneshwaran, P. et al. (2023) Integration of smart grid with renewable energy sources: Opportunities and challenges–a comprehensive review. Sustainable Energy Technologies and Assessments 58: 103363.
[7] Etherden, N., Vyatkin, V. and Bollen, M.H.J. (2016) Virtual power plant for grid services using IEC 61850. IEEE Transactions on Industrial Informatics 12(1): 437– 447. doi:10.1109/TII.2015.2414354.
[8] Katiraei, F., Morovati, S., Chuangpishit, S. and Ghorashi, S.A. (2023) Virtual power plant empowerment in the next generation of data centers: Outlining the challenges. IEEE Electrification Magazine 11(3): 35–44.
[9] Mota, B., Faria, P. and Vale, Z. (2024) Energy cost optimization through load shifting in a photovoltaic energy-sharing household community. Renewable Energy 221: 119812. doi:10.1016/j.renene.2023.119812.
[10] Bassey, K.E., Rajput, S.A. and Oyewale, K. (2024) Peer-to-peer energy trading: Innovations, regulatory challenges, and the future of decentralized energy systems. World J. Adv. Res. Rev 24(2): 172–186.
[11] Fan, J. and Zhou, X. (2023) Optimization of a hybrid solar/wind/storage system with bio-generator for a household by emerging metaheuristic optimization algorithm. Journal of Energy Storage 73: 108967.
[12] Abualigah, L., Zitar, R.A., Almotairi, K.H., Hussein, A.M., Abd Elaziz, M., Nikoo, M.R. and Gandomi, A.H. (2022) Wind, solar, and photovoltaic renewable energy systems with and without energy storage optimization: A survey of advanced machine learning and deep learning techniques. Energies 15(2): 578.
[13] Li, J., Wang, C. and Wang, H. (2024) Attentive convolutional deep reinforcement learning for optimizing solarstorage systems in real-time electricity markets. IEEE Transactions on Industrial Informatics 20(5): 7205–7215. doi:10.1109/TII.2024.3352229.
[14] Gul, E., Baldinelli, G., Wang, J., Bartocci, P. and Shamim, T. (2025) Artificial intelligence based forecasting and optimization model for concentrated solar power system with thermal energy storage. AppliedEnergy 382: 125210.
[15] He, Z., Liu, C., Wang, Y., Wang, X. and Man, Y. (2023) Optimal operation of wind-solar-thermal collaborative power system considering carbon trading and energy storage. Applied Energy 352: 121993.
[16] Qiu, Y., Li, Q., Ai, Y., Wang, T., Chen, W., Bai, H., Benbouzid, M. et al. (2024) Optimal scheduling for microgrids considering long-term and short-term energy storage. Journal of Energy Storage 93: 112137. doi:10.1016/j.est.2024.112137.
[17] Li, B., He, Q., Chen, F., Dai, H., Jin, H., Xiang, Y. and Yang, Y. (2021) Cooperative assurance of cache data integrity for mobile edge computing. IEEE Transactions on Information Forensics and Security 16: 4648–4662.
[18] Yuan, L., He, Q., Tan, S., Li, B., Yu, J., Chen, F. and Yang, Y. (2022) Coopedge+: Enabling decentralized, secure and cooperative multi-access edge computing based on blockchain. IEEE Transactions on Parallel and Distributed Systems 34(3): 894–908.
[19] Koc, C.K., Ozdemir, F. and Ozger, Z.O. (2021) Partially homomorphic encryption.
[20] Kebede, A.A., Kalogiannis, T., Van Mierlo, J. and Berecibar, M. (2022) A comprehensive review of stationary energy storage devices for large scale renewable energy sources grid integration. Renewable and Sustainable Energy Reviews 159: 112213. doi:10.1016/j.rser.2022.112213.
[21] Yuan, L., He, Q., Tan, S., Li, B., Yu, J., Chen, F., Jin, H. et al. (2021) Coopedge: A decentralized blockchainbased platform for cooperative edge computing. In Proceedings of the Web Conference 2021: 2245–2257.
[22] Li, Y., Wang, R. and Yang, Z. (2022) Optimal scheduling of isolated microgrids using automated reinforcement learning-based multi-period forecasting. IEEE Transactions on Sustainable Energy 13(1): 159–169. doi:10.1109/TSTE.2021.3105529.
[23] Lin, W., Wu, D. and Boulet, B. (2021) Spatial-temporal residential short-term load forecasting via graph neural networks. IEEE Transactions on Smart Grid 12(6): 5373– 5384. doi:10.1109/TSG.2021.3093515.
[24] Liao, W., Wang, S., Yang, D., Yang, Z., Fang, J., Rehtanz, C. and Porte-Agel, F. (2025) TimeGPT in load forecasting: A large time series model perspective. Applied Energy 379: 124973. doi:10.1016/j.apenergy.2024.124973.
[25] Jiang, B., Yang, H., Wang, Y., Liu, Y., Geng, H., Zeng, H. and Ding, J. (2024) Dynamic temporal dependency model for multiple steps ahead short-term load forecasting of power system. IEEE Transactions on Industry Applications 60(4): 5244–5254.
[26] Pentsos, V., Tragoudas, S., Wibbenmeyer, J. and Khdeer, N. (2025) A hybrid LSTM-transformer model for power load forecasting. IEEE Transactions on Smart Grid 16(3): 2624 – 2634.
[27] Huang, Y., Zhu, Y., Lei, G., Wang, A. and Zhu, J. (2025) LLM-enhanced short-term electricity price forecasting method for australian electricity market. Applied Sciences 16(1): 200.
[28] Zhao, L., Li, B., Zhou, J., Chen, C., Xiao, F. and Yang, Y. (2026) Maximizing revenue for reliability-aware edge application deployment. IEEE Transactions on Industrial Informatics .
[29] Xia, X., Chen, F., He, Q., Grundy, J., Abdelrazek, M. and Jin, H. (2020) Online collaborative data caching in edge computing. IEEE Transactions on Parallel and Distributed Systems 32(2): 281–294.
[30] Zhao, L., Zhou, J., Li, B., Xu, X., Dong, X., Xiao, F. and Yang, Y. (2025) Utility oriented edge service provision via penalized multi-armed bandit. IEEE Transactions on Services Computing .
[31] Ge, Y.F., Wang, H., Bertino, E., Cao, J., Zhang, Y. and Zheng, Z. (2025) Distributed bandit-based cooperative coevolution for large-scale multi-objective data publishing. IEEE Transactions on Services Computing .
[32] Ge, Y.F., Orlowska, M., Cao, J., Wang, H. and Zhang, Y. (2021) Knowledge transfer-based distributed differential evolution for dynamic database fragmentation. Knowledge-Based Systems 229: 107325. doi:10.1016/j.knosys.2021.107325.
[33] You, M., Ge, Y.F., Wang, K., Wang, H., Cao, J. and Kambourakis, G. (2024) Hierarchical adaptive evolution framework for privacy-preserving data publishing. World Wide Web 27(4). doi:10.1007/s11280-024-01286- z.
[34] You, M., Ge, Y.F., Yin, J., Wang, K., Zheng, Z., Zhang, Y. and Wang, H. (2025) Akief: Adaptive knowledge inheritance evolutionary framework for dynamic privacy-preserving data publishing. ACM Transactions on the Web doi:10.1145/3779413.
[35] Jendoubi, I. and Bouffard, F. (2023) Multi-agent hierarchical reinforcement learning for energy management. Applied Energy 332: 120500.
[36] Zhao, L., Wu, Z., Zhou, J., Cai, H., Li, B. and Xiao, F. (2025) EdgePro: Adaptive edge service provision via safe deep reinforcement learning. In 2025 IEEE International Conference on Web Services (ICWS) (IEEE): 742–752.
[37] Li, B., He, Q., Yuan, L., Chen, F., Lyu, L. and Yang, Y. (2022) Edgewatch: Collaborative investigation of data integrity at the edge based on blockchain. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining: 3208–3218.
[38] Zhao, Y., Qu, Y., Li, B., Zhao, L., Chen, F., Xiang, Y. and Gao, L. (2025) Collusion-resistant and time-aware co-verification for edge data integrity. IEEE Transactions on Dependable and Secure Computing.
[39] Ge, Y.F., Orlowska, M., Cao, J., Wang, H. and Zhang, Y. (2022) Mdde: multitasking distributed differential evolution for privacy-preserving database fragmentation. The VLDB Journal 31(5): 957–975. doi:10.1007/s00778-021-00718-w.
[40] Ge, Y.F., Wang, H., Cao, J., Zhang, Y. and Jiang, X. (2024) Privacy-preserving data publishing: an information driven distributed genetic algorithm. World Wide Web 27(1): 1.
[41] Jahan, S., Ge, Y.F., Wang, H. and Kabir, E. (2025) Adaptive-parameter memetic algorithm for privacy preserving trajectory data publishing: A multiobjective optimization approach. Computing 107(7). doi:10.1007/s00607-025-01504-0.
[42] Jahan, S., Ge, Y., Bertino, E., Kabir, E., Mahmud, H., Zheng, Z. and Wang, H. (2026) Systematic literature review on differential privacy in machine learning. ACM Computing Surveys doi:10.1145/3800684.
[43] Lin, X. and Lu, B. (2026) EBTM: Web vulnerability attack behavior recognition method based on feature fusion. EAI Endorsed Transactions on Scalable Information Systems 12(7).
[44] Vaghela, G. (2024) A review on ddos attack in controller environment of software defined network. EAI Endorsed Transactions on Scalable Information Systems 12(1).
[45] Le, X. and Zeng, H. (2025) An integrated cybersecurity defense framework for attack intelligence analysis, counteraction, and traceability in complex network architectures. EAI Endorsed Transactions on Scalable Information Systems 12(6).
[46] Xie, Z. and Zheng, L. (2026) Optimization algorithm for blockchain data storage and retrieval based on dht. EAI Endorsed Transactions on Scalable Information Systems 12(7).
[47] Ju, P., Ghosh, A. and Shroff, N. (2023) Achieving fairness in multi-agent mdp using reinforcement learning. In The Twelfth International Conference on Learning Representations.
[48] Schulman, J., Wolski, F., Dhariwal, P., Radford, A. and Klimov, O. (2017) Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 .
[49] Du, J., Kong, Z., Sun, A., Kang, J., Niyato, D., Chu, X. and Yu, F.R. (2023) MADDPG-based joint service placement and task offloading in MEC empowered air– ground integrated networks. IEEE Internet of Things Journal 11(6): 10600–10615.
[50] Ge, Y.F., Bertino, E., Wang, H., Cao, J. and Zhang, Y. (2023) Distributed cooperative coevolution of data publishing privacy and transparency. ACM Transactions on Knowledge Discovery from Data 18(1): 1–23. doi:10.1145/3613962.
[51] Ge, Y.F., Wang, H., Cao, J., Zhang, Y. and Kambourakis, G. (2024) Federated genetic algorithm: Two-layer privacy-preserving trajectory data publishing. In Proceedings of the genetic and evolutionary computation conference: 749–758.
[52] Jahan, S., Ge, Y.F., Kabir, E. and Wang, K. (2025) Analysis and multi-objective protection of public medical datasets from privacy and utility perspectives. Data Science and Engineering 10(3): 362–375. doi:10.1007/s41019-025-00283-0.
[53] Ge, Y.F., Wang, H., Bertino, E., Cao, J. and Zhang, Y. (2025) Multiobjective privacy-preserving task assignment in spatial crowdsourcing. IEEE Transactions on Cybernetics 55(8): 3584–3597. doi:10.1109/tcyb.2025.3573292.
[54] Yu, Z., Wen, M., Guo, X. and Jin, H. (2024) Maltracker: A fine-grained npm malware tracker copiloted by llm enhanced dataset. In Proceedings of the 33rd ACM SIGSOFT International Symposium on Software Testing and Analysis: 1759–1771.
[55] Australian Energy Market Operator (2022), AESCSF framework and resources, https://aemo.com.au/initiatives/major-programs/cyber-security/aescsf-framework-and-resources.
[56] Ge, Y.F., Wang, H., Bertino, E., Zhan, Z.H., Cao, J., Zhang, Y. et al. (2024) Evolutionary dynamic database partitioning optimization for privacy and utility. IEEE Transactions on Dependable and Secure Computing 21(4): 2296–2311. doi:10.1109/TDSC.2023.3302284.
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