Chaotic darwinian particle swarm optimization for real-time hierarchical congestion management of power system integrated with renewable energy sources

Abstract

The increasing penetration of renewable energy sources that have intermittent outputs challenge the Independent System Operator while managing transmission congestion. A new Real-Time Hierarchical Congestion Management (RHCM) technique is proposed that reschedules generators in two stages based on Available Congestion Clearing Time (ACCT) of the transmission lines in presence of renewable energy sources. Chaotic Darwinian Particle Swarm Optimization (CDPSO) is used for determining the optimal schedules of demand response loads and reschedules of conventional generators to mitigate congestion. The solar and wind energy sources are modelled using Rayleigh and Beta probability density functions; Latin Hyper cube sampling is adopted for sampling. IEEE 39 bus system is simulated for cases of overloads and contingencies and RHCM using CDPSO is tested to ensure the security of the system. The benefit of RHCM incorporating demand response is presented in terms of reduced congestion relieving costs and decreased power loss.