In a competitive power market, the task of an independent system operator (ISO) is to ensure full dispatches of the contracted power are carried out reliably. However, if it threatens the system security then ISO makes decision on the re-dispatch of the contracted power i.e., Congestion Management (CM). This paper proposes an optimal congestion management approach in a deregulated electricity market with optimal location of TCSC under Combined Economic Emission Dispatch Environment (CEED) using Particle Swarm optimization with Time Varying Acceleration Co-efficient (PSO-TVAC). Sensitivity factors are used to find the optimal location TCSC. After placing TCSC the investment cost of TCSC and generator rescheduling cost is minimized using Particle Swarm Optimization (PSO) and PSO-TVAC. Numerical results on test system, IEEE 30 bus and IEEE 118 bus systems are presented for illustration purpose and the results are compared with Particle swarm optimization (PSO) in terms of solution quality. The comprehensive experimental results prove that the PSO-TVAC is one among the challenging optimization methods which is indeed capable of obtaining higher quality solutions for the proposed problem.

This paper proposes GENCOs' profit maximization using Binary Article Bee Colony Optimization based on global best parameters (GbBABC). The optimal rival bidding strategy is employed to maximize GENCOs' profit. Monte Carlo (MC) simulation has been used to predict the bidding behavior of the rivals. In this paper, a bi-level optimization problem has been proposed to obtain the optimal bidding strategy of a supplier in which lower level problem represents the market clearing process of the system operator (SO) and the upper level optimization problem represents the supplier's profit maximization function, which is a non-linear function. In the proposed algorithm, global best parameter was incorporated into BABC algorithm, which makes the exploitation capacity improved and convergence speed quickened. At the same time, in order to maintain the population diversity the bit mutation operator is also performed. The feasibility of the proposed approach is analyzed on IEEE 30-bus system and IEEE-57 bus system. Results obtained using the GbBABC algorithm have been compared with those obtained using standard Artificial Bee Colony (ABC) optimization, global best guided ABC (GbABC) and global best distance guided ABC (GbdABC).

This paper proposes an optimal congestion management approach under hybrid electricity market using Self organizing hierarchical particle swarm optimization with Time Varying Acceleration Coefficients (SPSO-TVAC). The aim of the proposed work is to minimize deviations from preferred transaction schedules and hence the congestion cost under hybrid electricity market. The values of Transmission Congestion Distribution factors (TCDFs) are used to select redispatch of generators. Generator reactive power support is considered to lower the congestion cost. Numerical results on IEEE 57 bus system is presented for illustration purpose and the results are compared with Particle swarm optimization (PSO) in terms of solution quality. The comprehensive experimental results prove that the SPSO-TVAC is one among the challenging optimization methods which is indeed capable of obtaining higher quality solutions for the proposed problem.