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An Efficient Stochastic Reconfiguration Model for Distribution Systems With Uncertain Loads
Sao Paulo State Univ, Bioenergy Res Inst IPBEN, Associated Lab, Campus Ilha Solteira, BR-15385000 Ilha Solteira, Brazil..ORCID iD: 0000-0002-0454-5484
Univ Coll Dublin, Sch Elect & Elect Engn, Dublin D04 V1W8, Ireland..
KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electric Power and Energy Systems.ORCID iD: 0000-0002-9998-9773
2022 (English)In: IEEE Access, E-ISSN 2169-3536, Vol. 10, p. 10640-10652Article in journal (Refereed) Published
Abstract [en]

Active power losses of distribution systems are higher than transmission ones, in which these losses affect the distribution operational costs directly. One of the efficient and effective methods for power losses reduction is distribution system reconfiguration (DSR). In this way, the network configuration is changed based on a specific power demand that has been already predicted by load forecasting techniques. The ohmic loss level in distribution system is affected by energy demand level, this is while an error in load forecasting can influence losses. Accordingly, including load uncertainty in DSR formulation is essential but this issue should not lead to change of the reconfiguration results significantly (i.e. the model should be robust). This paper presents a robust and efficient model for considering load uncertainty in network reconfiguration that is simple enough to implement in available commercial software packages and it is precise enough to find accurate solutions with low computational time. The analysis of results shows high efficiency and robustness of the proposed model for reconfiguration of distribution systems under demand uncertainty.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE) , 2022. Vol. 10, p. 10640-10652
Keywords [en]
Uncertainty, Load modeling, Computational modeling, Distribution networks, Mathematical models, Probability density function, Load flow analysis, Distribution network reconfiguration, power losses reduction, robust model, uncertainty in demand
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:kth:diva-308652DOI: 10.1109/ACCESS.2022.3144665ISI: 000748262500001Scopus ID: 2-s2.0-85123347443OAI: oai:DiVA.org:kth-308652DiVA, id: diva2:1637077
Note

QC 20220211

Available from: 2022-02-11 Created: 2022-02-11 Last updated: 2022-06-25Bibliographically approved

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Hesamzadeh, Mohammad Reza

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