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

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 ε-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.