A novel framework for enhancing energy efficiencies and sustainability using Algorand blockchain-based competitive Negawatt Model in Virtual Power Plants

The rapid growth of renewable energy sources, distributed energy resources (DERs), microgrids, smart grids, energy storage, and controllable loads has increased the complexity of grid coordination, making centralized power systems inefficient and vulnerable to scalability, transparency, and trust limitations. Virtual Power Plants (VPP) offer a promising and viable framework for coordinating underlying microgrids, smart grids, and DERs, however, enabling secure market participation, incentivizing user engagement, and ensuring fair settlement remains challenging. In this research work a blockchain-enabled competitive negawatt trading framework integrated with a Distributed Alternating Direction Method of Multiplier (ADMM) optimization algorithm is proposed to support decentralized energy management within a VPP ecosystem. The prime optimization problem on-site or locally at each smart meter, while the dual updates, energy trading and settlement are executed on-chain using Algorand’s Pure Proof of Stake (PPoS) smart contracts. In this study the energy Decentralized Application (dApp) built on the Algorand blockchain that seamlessly combines VPP energy management and tokenization of energy credits is implemented using Algorand Standard Asset (ASA) and box storage to ensure low-cost, secure and transparent accounting reward of negawatts. Simulation results across various varying node scenarios demonstrate significant improvements, including up to 38% peak load reduction, 1750 KWh/day combined energy trading, with convergence achieved in an average of 15-19 ADMM iterations and also there will be a significantly improvement in user annual revenue. The findings validate the feasibility of using blockchain-driven economic incentives alongside distributed optimization to enhance participation, stability, and sustainability for setting up future Net-Zero Energy Grids (NZEGs).