Inclusion of renewable energy in power grid at micro and macro level has imposed numerous challenges in the recent years. Occurrence and managing congestion in the power transmission line due to unpredictable and stochastic nature of Renewable Energy Source (RES) integration has become a challenging task to the grid operators. Transmission lines operate at bottlenecks during a congestion episode adding to the extra congestion cost and risk in grid stability which becomes burden to the generation as well as end users. Different methodologies are applied to detect and manage the congestion to eliminate the congestion cost factor and maintain grid stability. The presented analysis compares conventional methodology Linear Sensitivity Factors (LSF) and Heuristic methodology Particle Swarm Optimization (PSO) and Artificial Neural Network (ANN) methods for congestion management in power transmission lines with integrated RES (wind and solar) system. For comparative analysis, a modified standard IEEE 30 Bus system is chosen which is integrated with real time RES generation. Optimal locations of RES generation with minimized congestion cost and percentage of RES curtailment are chosen as objective function for comparison of the methodologies.

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).

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).