Bacterial Foraging Particle Swarm Optimization based Wheeling Pricing Allocation through AF-mile and RF-mile approach

The research and control of wholesale pricing across each generator and consumer is one of the most important and difficult tasks in the deregulated electricity market. Wheeling price management involves calculating, lowering fees, and allocating benefits between users (generators and consumers) and the owner of transmission. Flow of power control and benefit dividing amongst all trading partners may be modified as the best strategy to manage wheeling prices and generate money. Because of efficient utilization and the fact that users are responsible for paying reverse-flow charges, the majority of owner of transmissions chose an absolute method for pricing computation. However, if the flow of powers are optimized throughout the process and the advantages of the reverse flow are shared by the owner of transmission and users using the benefit dividing factor determined by the governing body, then all trading parties can benefit. Wheeling pricing analysis is a challenging combinational optimization issue that involves nonlinear functions with several local minima and nonlinear discontinuity restrictions. The total price of transmission in flow of power-based approaches depends on flow of power. Here, the management of wheeling prices is described using an optimum flow of power problem with nonlinear constraints. In this study, population-based methods for particle swarm optimization (PSO) and bacterial foraging particle swarm optimization (BF-PSO) were used to examine wheeling pricing, compute reverse flow charges, and optimize power. In this paper wheeling prices analyzed and managed to calculate reverse flow charges and power optimization using particle swarm optimization and BF-PSO which are population based algorithms. On an IEEE-30 bus system, PSO and BF-PSO have been shown to be effective.