Power Line Monitoring-Based Consensus Algorithm for Performance Enhancement of Energy Blockchain Applications in Smart Grid 2.0
Energy blockchain applications are becoming inevitable with the transformation of electricity distribution networks into the decentralized Smart Grid 2.0 architecture. The scalability of the blockchain platform plays a key role in catering to the increasing number of nodes connected due to consumer-turned-prosumers being integrated into the distribution grid in a distributed manner. Hence, this study aims to optimize blockchain utilization for Smart Grid 2.0 applications through a novel consensus mechanism, which eliminates the requirement for performing additional complex computations to mine a new block. The algorithm utilizes the grid monitoring process through the existing smart meters, and thus has been capable of reducing the energy footprint for block mining to a fraction of that of the legacy Proof-of-Work algorithm, and reducing the block creation time by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\sim \lt 60\%$ </tex-math></inline-formula>. The proposed Power Line Monitoring-based Consensus Mechanism (PLMC) algorithm is validated using the Process Analysis Toolkit (PAT). In addition, data collected while monitoring the network for block mining is utilized for power quality measurement purposes.
Authors
- Uditha Wijewardhana (ORCID: https://orcid.org/0000-0002-1208-530X)
- Janaka Ekanayake (ORCID: https://orcid.org/0000-0003-0362-3767)
- Madhusanka Liyanage (ORCID: https://orcid.org/0000-0003-4786-030X)
- Chamitha de Alwis (ORCID: https://orcid.org/0000-0002-2219-6142)
- Charithri Yapa (ORCID: https://orcid.org/0000-0002-2766-8399)
Institutions
- University College Dublin (IE)
- University of Peradeniya (LK)
- University of Bedfordshire (GB)
- University of Sri Jayewardenepura (LK)
- Cardiff University (GB)
Publication Details
- Journal
- IEEE Transactions on Smart Grid
- Published
- 2024-08-21
- DOI
- https://doi.org/10.1109/tsg.2024.3445659
- Citations
- 5
- Primary Topic
- Traffic Prediction and Management Techniques
- Type
- article
- Field-Weighted Citation Impact
- 1.33
Funders
- Science Foundation Ireland
- Division of Human Resource Development