Privacy-Preserving Energy Scheduling for ESCOs Based on Energy Blockchain Network

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ID: 42485
2019
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Abstract
Capable of aggregating multiple energy resources, the energy service company (ESCO) has been regarded as a promising alternative for improving power system flexibility and facilitate the consumption of renewable resources in the energy market. However, the issues have become significantly more serious related to the privacy and security of the data in consumption and trading. In this paper, we address the problem by proposing a privacy-preserving energy scheduling (PPES) model based on energy blockchain network. A Lagrangian relaxation method is applied to decompose the model into several individual optimal scheduling problems, and the individual scheduling problems are solved by consensus algorithm and smart contracts in energy blockchain network. The performance of the proposed model and method is evaluated with several case studies based on multiple energy nodes. Simulation results show the rationality and validity of the proposed method, and the model is conducive to the protection of environment and transparent scheduling of energy service companies (ESCOs). In addition, it can reflect the information of energy demand and supply to improve the privacy and security of data.
Reference Key
tan2019privacypreservingenergies Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Tan, Shengmin;Wang, Xu;Jiang, Chuanwen;
Journal energies
Year 2019
DOI
DOI not found
URL
https://www.mdpi.com/1996-1073/12/8/1530
Keywords Keywords not found

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