A blockchain-enabled multi-agent deep reinforcement learning framework for real-time demand response in renewable energy grids

Kód výsledku v 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>

Nalezeny alternativní kódy

RIV/61989100:27730/25:10258683

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

A blockchain-enabled multi-agent deep reinforcement learning framework for real-time demand response in renewable energy grids

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

A blockchain-enabled multi-agent deep reinforcement learning framework for real-time demand response in renewable energy grids

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20200 - Electrical engineering, Electronic engineering, Information engineering

Projekt

<a href="/cs/project/TN02000025" target="_blank" >TN02000025: Národní centrum pro energetiku II</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Energy Strategy Reviews

ISSN

2211-467X

e-ISSN

2211-4688

Svazek periodika

62

Číslo periodika v rámci svazku

Volume 62

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

19

Strana od-do

nestránkováno

Kód UT WoS článku

001587271200001

EID výsledku v databázi Scopus

—