Optimizing Decentralized Local Energy Trading: Enhancing Scalability and Storage Efficiency through DAG Pruning and Smart Contract Optimization

Abstract

The energy sector is experiencing a paradigm shift toward decentralized, renewable sources, necessitating efficient energy and data management solutions. In this paper, we propose methods to improve both scalability and storage efficiency within a Distributed Ledger Technology (DLT) based local energy trading framework. Our contributions include the improved use of directed acyclic graphs (DAGs) to manage Smart Contracts (SCs) separately, reducing the data retention burden on individual nodes, and implementing time based data pruning to enhance storage efficiency. We also demonstrate the scalability of our energy trading platform. Our results indicate significant improvements in storage usage and scalability, thereby supporting the long term viability and scalability of decentralized energy trading platforms in prosumer communities.