A Contingency-Aware Sensitivity-Based Framework for Sustainable Shunt Compensation Planning in Transmission Systems Under N–1 Security Constraints

This paper proposes a contingency-aware, sensitivity-based criterion for the optimal placement of shunt compensation in transmission power systems under N–1 security constraints. Conventional approaches typically rely on post-contingency voltage severity or heuristic optimization techniques, which may fail to capture the system-wide impact of reactive power support during the planning stage. The proposed method integrates contingency severity assessment with a system-wide sensitivity index to support structured and physically interpretable planning decisions. First, a global contingency index is used to identify the most critical operating condition under N–1 scenarios. Based on this condition, a reduced set of candidate buses is selected according to post-contingency voltage magnitudes. These candidates are then ranked using a sensitivity metric defined as the derivative of the contingency index with respect to reactive power injection (𝜕J/𝜕Qk), which quantifies the global effect of local reactive support on system performance. The selected compensation locations are validated through AC optimal power flow simulations, enabling the evaluation of voltage profiles and active power losses under both normal and contingency conditions. The methodology is tested on the IEEE 14-, 30-, and 57-bus transmission systems to assess its scalability and consistency across networks of different sizes. Results show that the bus with the lowest post-contingency voltage is not necessarily the optimal compensation location. Instead, the proposed sensitivity-based criterion identifies buses that provide greater system-wide benefits, including reductions in active power losses and improved voltage recovery. The approach provides a transparent and reproducible planning-oriented decision criterion, supporting improved operational efficiency and aligning with sustainability-oriented objectives in modern power systems. The proposed method provides a reproducible and planning-oriented decision criterion that complements conventional optimization-based approaches.