Please use this identifier to cite or link to this item:
https://knowledgecommons.lakeheadu.ca/handle/2453/5169
Title: | Blockchain and artificial intelligence enabled peer-to-peer energy trading in smart grids |
Authors: | Moniruzzaman, Md. |
Keywords: | Peer-to-peer (P2P) energy trading;Blockchain technology;Artificial intelligence (AI) in energy trading;Coalition formation mechanism |
Issue Date: | 2023 |
Abstract: | Peer-to-peer (P2P) energy trading allows smart grid-connected parties to trade renewable energy with each other. It is widely considered a scheme to mitigate the supplydemand imbalances during peak-hour. In a P2P energy trading system, users (e.g., prosumers, Electric Vehicles (EV)) increase their utility by trading energy securely with each other at a lower price than that of the main grid. However, three challenges hinder the development of secured P2P energy trading systems. First, there is a lack of implicit trust and transparency between trading participants because they do not know each other. Second, P2P energy trading systems cannot offer an intelligent trading strategy that could maximize users’ (agents’) utility. This is because the agents may lack previous trading experience data that enable them to select an optimal trading strategy. Third, the current energy trading platforms are mainly centralized, which makes them vulnerable to malicious attacks and Single point of failure (SPOF). This may interrupt the transaction validation mechanism when the system is compromised, and the central database is unavailable. [...] |
URI: | https://knowledgecommons.lakeheadu.ca/handle/2453/5169 |
metadata.etd.degree.discipline: | Engineering : Electrical & Computer |
metadata.etd.degree.name: | Doctor of Philosophy |
metadata.etd.degree.level: | Doctoral |
metadata.dc.contributor.advisor: | Yassine, Abdulsalam Benlamri, Rachid |
Appears in Collections: | Electronic Theses and Dissertations from 2009 |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
MoniruzzamanM2023d-1a.pdf | 4.65 MB | Adobe PDF | View/Open |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.