An IGDT-based robust model for service restoration of distribution networks by scheduling of multiple resources

•Introducing a comprehensive service restoration approach for electrical distribution grids incorporating multiple resources, including mobile/fixed power resources and repair crew groups.•Employ a risk-averse information gap decision theory (RA-IGDT) robust optimization approach to identify a relia...

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Vydáno v:Electric power systems research Ročník 252; s. 112351
Hlavní autoři: Asadi, Qasem, Falaghi, Hamid, Ramezani, Maryam
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier B.V 01.01.2026
Témata:
Information gap decision theory
Mixed-integer quadratically constrained programming
Mobile power source
Repair crew
Service restoration
Traffic
Repair crew
Information gap decision theory
Mobile power source
Service restoration
Mixed-integer quadratically constrained programming
Traffic
ISSN:0378-7796
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Shrnutí:•Introducing a comprehensive service restoration approach for electrical distribution grids incorporating multiple resources, including mobile/fixed power resources and repair crew groups.•Employ a risk-averse information gap decision theory (RA-IGDT) robust optimization approach to identify a reliable and secure bound for the uncertainty radius of network parameters.•Considering multiple objectives for the service restoration problem and solving it mathematically by modeling it as a mixed integer quadratically constrained program (MIQCP). The service restoration (SR) problem has always been one of the most challenging issues in the operation of urban electrical distribution grids. In this paper, a new method is proposed to solve the SR problem. Multiple resource including mobile power sources (MPSs), local dispatchable generators and repair crews (RCs) are included in the proposed model and were scheduled simultaneously. In order to guarantee the effectiveness of the solutions, the SR problem discussed in this paper was formulated and addressed as a mixed-integer quadratically constrained programming (MIQCP) problem. Also, to handling the uncertainty related to load demand and transportation network traffic, we used an Information gap decision theory (IGDT) based model. Finally, the proposed method was tested on a 33-bus test network and its efficiency was proved.
ISSN:0378-7796
DOI:10.1016/j.epsr.2025.112351