Minimization of Transmission Congestion Cost using P-OPF based LSF, PSO and ANN Techniques

The inclusion of renewable energy sources (RES) into a stabilized network has opened gates to numerous optimization problems. Optimization of RES generation might not imply merely the myth of high availability but it is furthermore fenced by parameters such as transmission line power flow patterns, having the optimized location, bus nodal pricing (LMP), congestion scenario, congestion cost and reliability margins of the system (ATC,TRM). Integration of RES also drags towards the congestion episode within the transmission system. The uncertain behavior of renewable energy initiates uncertainty into the system from generation perspective. These uncertainties lead to congestion which alters the linear sensitivity factors (LSF), LMP and reliability margins of the network. The congestion scenario may jeopardize the security of transmission network hampering the limits of transmission lines. The change in marginal values reflects the occurrence of congestion with additional congestion cost. Different optimization tools have been introduced in order to optimize various objectives like optimization of generation, rescheduling generators, load curtailment etc. In this work we have presented optimization of congestion cost (i.e. Congestion Management in terms of economics) post inclusion of uncertain RES (here wind and solar source are considered) into the system. The optimization problem is resolved using Probability Optimal Power Flow (P-OPF) based Particle Swarm Optimization (PSO) technique in MATLAB-MATPOWER software with Area Based Congestion Management (ABCM).