Rescheduling-based congestion management scheme using particle swarm optimization with distributed acceleration constants

Rescheduling-based congestion management schemes are prominent solutions for secure and reliable power flow under deregulated environment. Since the rescheduling process exhibits multimodal behavior by nature, the role of heuristic methods has become crucial. Despite numerous heuristic search algorithms are reported in the literature to address the challenge, this paper attempts to improve Particle Swarm Optimization (PSO), which is a renowned swarm intelligence-based optimization algorithm. Our improved version of PSO intends to determine adaptive acceleration constants based on the particle position and the evaluation it has undergone till the current iteration. Due to the distributed nature of acceleration constant, this paper calls the proposed PSO as PSO with distributed acceleration constant (PSODAC). PSODAC attempts to solve the rescheduling problem in a hybrid electricity market so that congestion is aimed to minimize at best rescheduling cost. An experimental investigation is carried out in IEEE14 bus system under single point as well as multipoint congestion scenarios. Subsequently, the dynamics of the particles are also investigated. The experimental results show that PSODAC is better than PSO in terms of cost-effective congestion mitigation as well as exhibiting high particle dynamics.