OPTIMAL POWER FLOW AND CONGESTION REDUCTION USING EVOLUTIONARY ALGORITHMS

Abstract

Power system congestion control is essential, particularly in deregulated electricity markets where power flow is constrained by transmission limitations. This study uses an optimization strategy based on Genetic Algorithms (GA) in conjunction with N-1 contingency analysis to handle congestion in IEEE 14-bus and 9-bus systems. In order to identify overloaded transmission lines under various loading scenarios, the investigation starts with power flow analysis and contingency assessment using Pandapower. In order to minimize congestion and preserve system stability, GA is used in the following step to optimize generator rescheduling. Selection, crossover, and mutation processes are used in the optimization process to modify generator outputs in order to iteratively increase performance. The goal function improves load balance throughout the system and reduces congestion fines. According to simulation studies, GA successfully lowers traffic, boosts grid stability, and increases transmission efficiency. The goal function improves load balance throughout the system and reduces congestion fines. According to simulation studies, GA successfully lowers traffic, boosts grid stability, and increases transmission efficiency. For contemporary power systems dealing with rising energy demands and greater integration of renewable sources, this strategy offers a scalable and flexible solution