Robust dynamic network expansion planning considering load uncertainty

•Proposing a new scheme for local prediction of maximum post-contingency deviation of power system frequency.•Application of Artificial Neural Network (ANN) and Support Vector Regression (SVR) for power system frequency prediction.•Adjusting the sampling of frequency trajectory based on the modal analysis of the power system.

This paper presents a dynamic transmission expansion planning framework with considering load uncertainty based on Information-Gap Decision Theory. Dynamic transmission planning process is carried out to obtain the minimum total social cost over the planning horizon. Robustness of the decisions against under-estimated load predictions is modeled using a robustness function. Furthermore, an opportunistic model is proposed for risk-seeker decision making. The proposed IGDT-based dynamic network expansion planning is formulated as a stochastic mixed integer non-linear problem and is solved using an improved standard branch and bound technique. The performance of the proposed scheme is verified over two test cases including the 24-bus IEEE RTS system and Iran national 400-kV transmission network.