A blockchain-enabled multi-agent deep reinforcement learning framework for real-time demand response in renewable energy grids
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F25%3A10258683" target="_blank" >RIV/61989100:27240/25:10258683 - isvavai.cz</a>
Alternative codes found
RIV/61989100:27730/25:10258683
Result on the web
<a href="https://www.sciencedirect.com/science/article/pii/S2211467X25002688" target="_blank" >https://www.sciencedirect.com/science/article/pii/S2211467X25002688</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.esr.2025.101905" target="_blank" >10.1016/j.esr.2025.101905</a>
Alternative languages
Result language
angličtina
Original language name
A blockchain-enabled multi-agent deep reinforcement learning framework for real-time demand response in renewable energy grids
Original language description
The increasing integration of renewable energy into smart grids introduces challenges of demand-supply imbalance, peak load stress, and cyber-physical vulnerabilities. Existing demand response (DR) frameworks often lack scalability, privacy-preserving data sharing, and secure transaction mechanisms, which limit user participation and grid resilience. To address these challenges, this study proposes GridSyncNet, a blockchainenabled multi-agent deep reinforcement learning framework for real-time demand response. The framework integrates federated learning to enhance decentralized forecasting accuracy, blockchain consensus to ensure transparent and tamper-proof energy trading, and actor-critic based DRL agents to dynamically optimize load scheduling and energy dispatch across prosumers. Extensive simulations demonstrate that GridSyncNet outperforms benchmark models such as OD-CNN, D-FCAS, and USTCF. Specifically, it achieves a 98.2 % demand response efficiency, 30.6 % reduction in carbon emissions, and 97.4 % forecasting accuracy. Comparative analysis with multi-agent DRL (MADRL) approaches further confirms that GridSyncNet provides superior scalability, privacy, and security in decentralized environments. The proposed framework contributes to the design of secure, resilient, and sustainable energy management systems, offering practical insights for accelerating the transition toward net-zero energy communities. By combining blockchain, federated learning, and multi-agent reinforcement learning, GridSyncNet establishes a comprehensive pathway for trustworthy and adaptive smart grid operations.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
20200 - Electrical engineering, Electronic engineering, Information engineering
Result continuities
Project
<a href="/en/project/TN02000025" target="_blank" >TN02000025: National Centre for Energy II</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Energy Strategy Reviews
ISSN
2211-467X
e-ISSN
2211-4688
Volume of the periodical
62
Issue of the periodical within the volume
Volume 62
Country of publishing house
NL - THE KINGDOM OF THE NETHERLANDS
Number of pages
19
Pages from-to
nestránkováno
UT code for WoS article
001587271200001
EID of the result in the Scopus database
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