Multi-objective robust transmission expansion planning using information-gap decision theory and augmented ɛ-constraint method

• Published in: IET generation, transmission & distribution transmission & distribution, 2014-05, Vol.8 (5), p.828-840
• Main Authors: Dehghan, Shahab, Kazemi, Ahad, Amjady, Nima
• Format: Article
• Language: English
• Subjects: augmented ε‐constraint method; decision theory; information gap decision theory; integer programming; Latin hypercube sampling based post optimisation procedure; linear programming; multiobjective optimisation problem; multiobjective robust transmission expansion planning; multiyear transmission expansion planning; planning uncertainty; power transmission economics; power transmission planning; sampling methods; tractable mixed integer‐linear programming model; uncertain capital cost
• ISSN: 1751-8687; 1751-8695; 1751-8695
• DOI: 10.1049/iet-gtd.2013.0427

This study presents a novel tractable mixed-integer linear programming model for multiyear transmission expansion planning (TEP) problem coping with the uncertain capital costs and uncertain electricity demands using the information-gap decision theory (IGDT). As the uncertain capital costs and electricity demands compete to occupy the permissible uncertainty budget, the proposed IGDT-based TEP (IGDT-TEP) framework employs the augmented ɛ-constraint method to solve a multi-objective optimisation problem maximising the robust regions against the uncertain variables (i.e. capital costs and electricity demands) centred on their forecasted values. This framework enables the system's planner to control the immunisation level of the optimal expansion plan regarding the enforced planning uncertainties using a certain uncertainty budget. Also, a Latin hypercube sampling-based post-optimisation procedure is introduced to evaluate the robustness of an expansion plan obtained from the proposed IGDT-TEP framework. Simulation results demonstrate the effectiveness of the IGDT-TEP model to handle the uncertain nature of capital costs and electricity demands.