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.