Blockchain Driven Platform for Energy Distribution in a Microgrid

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Abstract

Ever since the Digital Revolution, the World population has been growing at an ever-increasing rate, with the last billion being amassed in only 9 years. The increase in population only exacerbates the current challenge of meeting peak-energy demands while accommodating an increasing amount of distributed energy resources such as solar panels. Utilities must learn how to encourage energy-efficient practices to reduce demand, as well as how to effectively coordinate interaction between consumers, renewable-energy sources, and the grid. With earnings currently communicated on monthly billing cycles, existing solutions such as net-metering, feed-in-tariffs and demand response are neither transparent nor resolute enough. They are also not flexible enough to accommodate community shared energy resources. This paper aims to bridge this gap by developing a secure energy-distribution model for multi-family apartment buildings and neighborhoods that share a solar panel array. EOSIO smart contracts will be used to transparently and dynamically distribute energy using tokens. At each time-step, the residents are allocated tokens worth a fair portion of total energy production minus their unit’s consumption. Excess energy is sold to neighbors at just below the retail rate, instead of back to the grid at much lower cogeneration rates, thus constantly incentivizing efficiency and energy sharing. This will pave the way towards community scale solar and even communal food and water resources where residents can equitably share in both costs and profits. The model presented in the paper was validated through the award winning entries in the 2019 EOSIO Hackathon as well as the Virginia Tech’s grand prize winning entry in the 2019 Solar Decathlon Design Challenge.

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APA

Choudhry, A., Dimobi, I., & Isaac Gould, Z. M. (2019). Blockchain Driven Platform for Energy Distribution in a Microgrid. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11737 LNCS, pp. 271–288). Springer. https://doi.org/10.1007/978-3-030-31500-9_18

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