Reactive power reserve-constrained optimal reactive power dispatch for enhanced voltage stability

Maintaining adequate reactive power reserve is essential for preventing voltage instability in modern transmission networks, particularly under high-load or contingency scenarios. Existing optimal reactive power dispatch (ORPD) methods often minimize losses or improve voltage profiles without explicitly enforcing reserve constraints, potentially compromising system security. This study proposes a reactive power reserve-constrained ORPD formulation that optimizes generator voltages, transformer tap settings, and shunt compensation while preserving a predefined reserve margin. The optimization is performed using the Grey Wolf Optimizer, a population-based metaheuristic capable of navigating complex non-linear search spaces, with the algorithm parameters tuned for robust convergence. The method is evaluated on IEEE 30-bus, IEEE 57-bus, and Nordic 44-bus transmission test systems under base and varied load conditions. Results show up to 25.8 % reduction in active power loss and a 32.8 % improvement in the voltage stability index compared with competing algorithms such as CHIO, PSO, BAT, and ABC, while consistently maintaining higher emergency reactive reserve values. Performance under load variations from 80 % to 130 % of base load confirms the method’s robustness, with lower voltage instability indicators across all scenarios. The findings demonstrate that integrating reserve constraints into ORPD improves both operational security and voltage stability, offering a scalable approach for large transmission systems and providing practical guidance for grid planning and real-time operation. • Enforces reactive power reserve as a challenging constraint in optimal reactive power dispatch • Improves voltage stability index on standard IEEE and Nordic test systems • Maintains robust performance under wide load variation (80–130 % of base load) • Scalable to large transmission networks for real-world application • Offers practical insights for grid operators and future smart grid planning.