Decentralized Energy Marketplace (DEM) via NFTs and AI-based Agents

Overview

This project introduces an advanced Decentralized Energy Marketplace (DEM) integrating blockchain technology and artificial intelligence to manage energy exchanges among smart homes with energy storage systems. Using Non-Fungible Tokens (NFTs) to represent unique energy profiles, the system fosters a transparent and secure trading environment. The core innovation lies in leveraging Federated Deep Reinforcement Learning (FDRL) for collaborative and adaptive energy management strategies while maintaining user privacy.

Key Features

• Blockchain Integration: NFTs for individual energy profiles on a smart contract-based marketplace.
• AI in Energy Systems: Federated Deep Reinforcement Learning (FDRL) for collaborative energy management.
• Virtual Power Plants (VPP): Combining AI agents with household batteries.
• Oracle Integration: Utilizing Chainlink for real-time data integration with smart contracts.

System Model

• Smart Homes with Energy Storage: Equipped with AI for energy use monitoring and management.
• DEM Platform: A decentralized application (DApp) for secure and transparent energy exchange.
• FDRL Framework: Enables smart buildings to refine energy strategies collaboratively.
• VPP and Grid Operators: Integration of DEM, smart homes, and FDRL agents to create a VPP.

Blockchain-enabled Smart Contracts

• EnergyProfile Contract: Manages participants' energy profiles.
• EnergyPool Contract: Handles energy commitments and aggregates supply-demand.
• EnergyMarket Contract: Core operational link among contracts, managing transactions and settlements.

AI-based Agents

• FDRL Framework: Combines FL and DRL for decentralized energy management.
• Local Training Algorithm: Utilizes SAC technique for model training.

Performance Evaluation

• Deployment and Testing: Using Brownie and Ganache with Chainlink mock contracts.
• Training of AI Agents: Dataset analysis and model training results.

Future Work

Integration of off-chain AI agent execution using the Zero-Knowledge Machine Learning (ZK-ML) stack to fortify security and reduce blockchain computational demands.

How to Cite

If you find this work useful for your research, please consider citing: insert bibtex here

link to arxiv: https://arxiv.org/abs/2311.10406

@misc{nikbakht2023decentralized, title={Decentralized Energy Marketplace via NFTs and AI-based Agents}, author={Rasoul Nikbakht and Farhana Javed and Farhad Rezazadeh and Nikolaos Bartzoudis and Josep Mangues-Bafalluy}, year={2023}, eprint={2311.10406}, archivePrefix={arXiv}, primaryClass={cs.NI} }

Contributions

This work is a collaborative effort of researchers at the Centre Tecnològic Telecomunicacions Catalunya (CTTC/CERCA).

License

This project is open-source and available under LICENSE.

Acknowledgments

Supported by the Spanish Government (MICCIN & NextGenEU program), ECSEL Joint Undertaking (JU), and the Catalan government. Additional funding from Generalitat de Catalunya and MCIN/AEI.