Reliability‐based model for generation and transmission expansion planning. Issue 2 (1st January 2017)

• Record Type: Journal Article
• Authors: Ahmadi, Abdollah; Mavalizadeh, Hani; Zobaa, Ahmed F.; Shayanfar, Heidar Ali
• Is Part Of: IET generation, transmission & distribution. Volume 11:Issue 2(2017)
• Page Start: 504
• Page End: 511
• Publication Date: 2017-01-01
• Subjects: power transmission planning -- power transmission reliability -- integer programming -- linear programming -- decision theory -- power generation planning -- power generation reliability; reliability‐based model -- transmission expansion planning -- mixed integer linear multiobjective model -- information gap decision theory -- coordinated multiyear generation -- fuel transportation price -- gaseous emission -- fuel availability -- multiobjective method -- ε‐constraint method -- investment costs -- six‐bus Garver test system -- 24‐bus IEEE test system; Electric power production -- Periodicals; Electric power transmission -- Periodicals; Electric power distribution -- Periodicals; 621.3105
• DOI: 10.1049/iet-gtd.2016.1058
• Languages: English
• ISSNs: 1751-8687

Abstract

Abstract : This study presents a mixed integer linear multi‐objective model based on information gap decision theory, which is used to solve coordinated multiyear generation and transmission expansion planning problems. The model maximises the robustness of each uncertain parameter while a maximum allowable budget range is set. Fuel transportation price is considered. The results provide a numerical tool for system planner to help him adjust the appropriate level of robustness for each uncertain parameter of the problem. Extra limits on security, gaseous emission and fuel availability are considered. A multi‐objective method called the ɛ ‐constraint method is used here to maximise the robust region of load and investment costs simultaneously. The model is implemented on a six‐bus Garver test system and 24‐bus IEEE test system. The numerical results show the good performance of the model.