Performance comparison using firefly and PSO algorithms on congestion management of deregulated power market involving renewable energy sources - Farzana, D. Fathema; Mahadevan, K (Soft computing (Berlin, Germany), 2020)
The power industry across the globe is subjected to a sweeping change in its business as well as in an operating model where the monopoly utilities are being liberalized and opened up for competition with private players. As an outcome of this, the transmission corridors evacuating the power of inexpensive generators would be burdened if all such transactions are admitted. One of the most proficient techniques for congestion management is rescheduling the generators. This research paper suggests a framework to regulate the power flows of the transmission lines within the stipulated limit in a deregulated electricity market environment through rescheduling with and without renewable energy sources (RES). The problem of rescheduling is framed with the intention of lessening the congestion cost. Unlike the traditional method, the best location for the placement of RES is established utilizing a novel weighted locational marginal price (LMP)-based method. The firefly algorithm (FA) and particle swarm optimization (PSO) algorithm are employed in order to get optimized results. The realistic cases are considered, and the results obtained with and without RES using FA and PSO are compared to prove the research study. The efficacy of the method is explored with IEEE 30-bus system.