A Cooperative Learning Approach for Decentralized Peer-to-Peer Energy Trading Markets and its Structural Robustness Against Cyberattacks

Peer-to-peer (P2P) energy trading has recently emerged as a promising paradigm for integrating renewable and distributed energy resources into local energy grids with the presence of active prosumers. However, prosumers often have different preferences on energy trading price and amount. Therefore,...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autor principal:	Dinh Hoa Nguyen
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	P2P energy trading systems cooperative learning inverse optimization interval analysis cybersecurity <italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">r</italic>-robustness Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/00473a09d89b43598b1b8a5a56923a7e
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:00473a09d89b43598b1b8a5a56923a7e
record_format	dspace
spelling	oai:doaj.org-article:00473a09d89b43598b1b8a5a56923a7e2021-11-18T00:05:14ZA Cooperative Learning Approach for Decentralized Peer-to-Peer Energy Trading Markets and its Structural Robustness Against Cyberattacks2169-353610.1109/ACCESS.2021.3125031https://doaj.org/article/00473a09d89b43598b1b8a5a56923a7e2021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9599658/https://doaj.org/toc/2169-3536Peer-to-peer (P2P) energy trading has recently emerged as a promising paradigm for integrating renewable and distributed energy resources into local energy grids with the presence of active prosumers. However, prosumers often have different preferences on energy trading price and amount. Therefore, in decentralized P2P energy markets, a negotiation between prosumers is needed to obtain a commonly satisfactory set of preferences, i.e., a market-clearing solution. To achieve that, this paper proposes a novel approach in which a decentralized inverse optimization problem is solved by prosumers to cooperatively learn to set their objective function parameters, given their preferential intervals of energy prices and amounts. As such, prosumers’ parameters can be determined in specific intervals computed analytically from the lower and upper bounds of their preferential intervals, if a certain learning condition is satisfied. Next, the structural robustness of prosumer’s cooperative learning against the malicious and Byzantine models of cyberattacks is studied with the weighted-mean-subsequence-reduced (WMSR) resilient consensus algorithm. A novel sufficient robustness condition is then derived. Finally, case studies are conducted on the IEEE European Low Voltage Test Feeder system to validate the effectiveness of the proposed theoretical results.Dinh Hoa NguyenIEEEarticleP2P energy trading systemscooperative learninginverse optimizationinterval analysiscybersecurity<italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">r</italic>-robustnessElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 148862-148872 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	P2P energy trading systems cooperative learning inverse optimization interval analysis cybersecurity <italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">r</italic>-robustness Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	P2P energy trading systems cooperative learning inverse optimization interval analysis cybersecurity <italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">r</italic>-robustness Electrical engineering. Electronics. Nuclear engineering TK1-9971 Dinh Hoa Nguyen A Cooperative Learning Approach for Decentralized Peer-to-Peer Energy Trading Markets and its Structural Robustness Against Cyberattacks
description	Peer-to-peer (P2P) energy trading has recently emerged as a promising paradigm for integrating renewable and distributed energy resources into local energy grids with the presence of active prosumers. However, prosumers often have different preferences on energy trading price and amount. Therefore, in decentralized P2P energy markets, a negotiation between prosumers is needed to obtain a commonly satisfactory set of preferences, i.e., a market-clearing solution. To achieve that, this paper proposes a novel approach in which a decentralized inverse optimization problem is solved by prosumers to cooperatively learn to set their objective function parameters, given their preferential intervals of energy prices and amounts. As such, prosumers’ parameters can be determined in specific intervals computed analytically from the lower and upper bounds of their preferential intervals, if a certain learning condition is satisfied. Next, the structural robustness of prosumer’s cooperative learning against the malicious and Byzantine models of cyberattacks is studied with the weighted-mean-subsequence-reduced (WMSR) resilient consensus algorithm. A novel sufficient robustness condition is then derived. Finally, case studies are conducted on the IEEE European Low Voltage Test Feeder system to validate the effectiveness of the proposed theoretical results.
format	article
author	Dinh Hoa Nguyen
author_facet	Dinh Hoa Nguyen
author_sort	Dinh Hoa Nguyen
title	A Cooperative Learning Approach for Decentralized Peer-to-Peer Energy Trading Markets and its Structural Robustness Against Cyberattacks
title_short	A Cooperative Learning Approach for Decentralized Peer-to-Peer Energy Trading Markets and its Structural Robustness Against Cyberattacks
title_full	A Cooperative Learning Approach for Decentralized Peer-to-Peer Energy Trading Markets and its Structural Robustness Against Cyberattacks
title_fullStr	A Cooperative Learning Approach for Decentralized Peer-to-Peer Energy Trading Markets and its Structural Robustness Against Cyberattacks
title_full_unstemmed	A Cooperative Learning Approach for Decentralized Peer-to-Peer Energy Trading Markets and its Structural Robustness Against Cyberattacks
title_sort	cooperative learning approach for decentralized peer-to-peer energy trading markets and its structural robustness against cyberattacks
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/00473a09d89b43598b1b8a5a56923a7e
work_keys_str_mv	AT dinhhoanguyen acooperativelearningapproachfordecentralizedpeertopeerenergytradingmarketsanditsstructuralrobustnessagainstcyberattacks AT dinhhoanguyen cooperativelearningapproachfordecentralizedpeertopeerenergytradingmarketsanditsstructuralrobustnessagainstcyberattacks
_version_	1718425202355863552

A Cooperative Learning Approach for Decentralized Peer-to-Peer Energy Trading Markets and its Structural Robustness Against Cyberattacks

Ejemplares similares