@Article{Karandikar_2021, author = {Nikita Karandikar and Rockey Abhishek and Nishant Saurabh and Zhiming Zhao and Alexander Lercher and Ninoslav Marina and Radu Prodan and Chunming Rong and Antorweep Chakravorty}, journal = {Blockchain: Research and Applications}, title = {Blockchain-based prosumer incentivization for peak mitigation through temporal aggregation and contextual clustering.1}, year = {2021}, issn = {2096-7209}, month = jun, pages = {1--35}, abstract = {Peak mitigation is of interest to power companies as peak periods may require the operator to over provision supply in order to meet the peak demand. Flattening the usage curve can result in cost savings, both for the power companies and the end users. Integration of renewable energy into the energy infrastructure presents an opportunity to use excess renewable generation to supplement supply and alleviate peaks. In addition, demand side management can shift the usage from peak to off peak times and reduce the magnitude of peaks. In this work, we present a data driven approach for incentive based peak mitigation. Understanding user energy profiles is an essential step in this process. We begin by analysing a popular energy research dataset published by the Ausgrid corporation. Extracting aggregated user energy behavior in temporal contexts and semantic linking and contextual clustering give us insight into consumption and rooftop solar generation patterns. We implement, and performance test a blockchain based prosumer incentivization system. The smart contract logic is based on our analysis of the Ausgrid dataset. Our implementation is capable of supporting 792,540 customers with a reasonably low infrastructure footprint.}, doi = {10.1016/j.bcra.2021.100016}, keywords = {Peak shaving, aggregation analysis, contextual clustering, blockchain, incentivization}, publisher = {Elsevier (BV)}, url = {https://www.sciencedirect.com/science/article/pii/S2096720921000117?via=ihub} }