Learning technique for real-time congestion management in an active distribution networks

M.S. Babar, A.N.M.M. Haque, H.P. Nguyen, V. Cuk, I.G. Kamphuis, J.G. Slootweg, M. Bongaerts

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/CongresprocedureConferentiebijdrageAcademicpeer review

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During the last few decades, the concept of demand response (DR) in the energy sector has gained substantial momentum. Research has led to a range of DR solutions. These solutions mostly differ in their applications, the hosting power system, the energy market etc. Moreover, as per the EU directive, DR aggregators should be allowed to trade DR alongside supply in both day-ahead and real time electricity markets. Meanwhile, independent aggregators do not consider physical limitations of a network, thus setting up new a challenges for network operation. In this paper, an active learning technique for real-time congestion management is proposed to tackle this challenge. This enables distributed system operator (DSO) to incenticize independent aggregators efficiently in order to use DR for overloading mitigation. Lastly, a case study is simulated which verifies the performance of a new approach for congestion management.

Originele taal-2Engels
Titel2017 IEEE Manchester PowerTech, 18-22 June 2017, Manchester, United Kingdom
Plaats van productiePiscataway
UitgeverijInstitute of Electrical and Electronics Engineers
ISBN van elektronische versie978-1-5090-4237-1
ISBN van geprinte versie978-1-5090-4238-8
StatusGepubliceerd - 18 jun 2017
Evenement12th IEEE PES PowerTech Conference - University of Manchester, Manchester, Verenigd Koninkrijk
Duur: 18 jun 201722 jun 2017
Congresnummer: 12


Congres12th IEEE PES PowerTech Conference
Verkorte titelPowerTech 2017
LandVerenigd Koninkrijk
Internet adres

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