Decentralized Energy Management for Rural Communities: A Blockchain-Based Virtual Power Plant with AI-Driven Forecasting

Abstract

This paper presents the design and evaluation of RuralVPP, a decentralized Virtual Power Plant (VPP) architecture designed for rural energy communities. The system integrates ten semi-autonomous municipalities into a coordinated structure based on a dual-layer market framework, consisting of Local Energy Markets (LEMs) and a Supra-Municipal Market. Energy transactions within and between communities are managed through smart contracts implemented on a permissioned blockchain platform using Hyperledger Fabric, ensuring secure, transparent, and auditable settlements. The communication infrastructure is based on the IEC 61850 standard, enabling interoperability among distributed energy resources (DERs), smart meters, and flexible loads. To support efficient market operation and grid management, the system incorporates advanced forecasting techniques using deep learning models, including Long Short-Term Memory (LSTM) networks and Transformer architectures. These models are trained on real energy generation and demand data collected from the participating communities. Results show high forecasting accuracy, effective automation of energy trades, and enhanced local energy utilization. The proposed solution improves energy resilience, lowers operational costs, and provides a scalable reference model for decentralized rural energy systems based on blockchain and artificial intelligence.

This paper presents the design and evaluation of RuralVPP, a decentralized Virtual Power Plant (VPP) architecture designed for rural energy communities. The system integrates ten semi-autonomous municipalities into a coordinated structure based on a dual-layer market framework, consisting of Local Energy Markets (LEMs) and a Supra-Municipal Market. Energy transactions within and between communities are managed through smart contracts implemented on a permissioned blockchain platform using Hyperledger Fabric, ensuring secure, transparent, and auditable settlements. The communication infrastructure is based on the IEC 61850 standard, enabling interoperability among distributed energy resources (DERs), smart meters, and flexible loads. To support efficient market operation and grid management, the system incorporates advanced forecasting techniques using deep learning models, including Long Short-Term Memory (LSTM) networks and Transformer architectures. These models are trained on real energy generation and demand data collected from the participating communities. Results show high forecasting accuracy, effective automation of energy trades, and enhanced local energy utilization. The proposed solution improves energy resilience, lowers operational costs, and provides a scalable reference model for decentralized rural energy systems based on blockchain and artificial intelligence.