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Blockchain-based peer-to-peer energy trading method

Blockchain-enabled peer-to-peer energy trading provides a method for neighbours and communities to trade energy generated from local and distributed renewable energy sources. Effective matching can facilitate greater energy efficiency during transmission, increases user welfare through preference and improves power quality. The proposed algorithm builds upon work to develop a system of scoring an energy transaction. It uses a McAfee-priced double auction, and scores based upon preference of price, locality, and energy generation type, alongside the quantity of energy being traded. The algorithm pre-evaluates transactions to determine the optimal transactional pathway. The transaction carried out is that leading to the greatest cumulative score. Simulated over a range of scenarios, the proposed algorithm provides an average increase in user welfare of 75. Commercially, the algorithm may be deployed in small to large settlements whilst remaining stable. By reducing power loss, the algorithm allows consumers to save 25 on their cost of energy, whilst providing a 50 increase in the revenue earned by prosumers.
- Northumbria University United Kingdom
- Durham University United Kingdom
- Northumbria University United Kingdom
Technology, T, Physics, QC1-999, G900, 006
Technology, T, Physics, QC1-999, G900, 006
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).9 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Top 10% influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Average impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Top 10%
