Energy Prosumer for Local Energy Marketplace

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1 Citation (Scopus)

Abstract

This paper describes the creation of a new role - Energy Prosumer. It considers instances when generated energy is distributed via commercial PV and home usage. The authors explore how a blockchain system can improve the process of electricity exchange and show a prediction of the households' power profiles with the blockchain process. An original multi-agent system was designed and tested with Transactive Energy (TE) and Distributed Energy Resources (DER). The prediction process of blockchain-based LEMs is usually accepted as given, but it needs to be reliable for effective LEMs. Therefore, an LSTM forecasting model is proposed for reliable prediction results, and this model was implemented in the blockchain system. Results show that the proposed LEMs could deliver excellent and stable predictions and efficient blockchain architecture. Generally, this study has demonstrated how blockchain can improve electricity exchange and the advantages of reliable short-term forecasts. The outcomes could provide an effective way to manage electricity exchange and production.11Some of the material discussed in this paper is the advanced results of work previously described in [2] and [11].

Original languageEnglish
Title of host publication2023 IEEE International Conference on Industrial Technology, ICIT 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798350336504
DOIs
Publication statusPublished - 2023
Event2023 IEEE International Conference on Industrial Technology, ICIT 2023 - Orlando, United States
Duration: 4 Apr 20236 Apr 2023

Publication series

NameProceedings of the IEEE International Conference on Industrial Technology
Volume2023-April

Conference

Conference2023 IEEE International Conference on Industrial Technology, ICIT 2023
Country/TerritoryUnited States
CityOrlando
Period4/04/236/04/23

Keywords

  • Blockchain
  • Short-term energy forecasting
  • long short-term memory (LSTM)
  • market mechanism
  • market simulation

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