Real Coded Genetic Algorithm based dynamic Congestion Management in open power markets

Abstract

The first real time task of ISO focuses on Static Congestion Management (SCM), i.e. the congestion caused by the thermal and voltage limits. When power systems undergoes discrete changes in system configuration due to outage and contingencies, the system dynamic performance will be affected and the system stability might be threatened. In this respect, the second real time task of ISO focuses on the utilization of available resources to maintain system security and reliability. This process is called Dynamic Congestion Management (DCM). The DCM refers to the process that secures stability of the post fault power system in an economic manner. The objective function of the DCM model used in this paper has been formulated as a constrained nonlinear optimization problem. This proposes the application of Real Coded Genetic Algorithm (RCGA) for solving the constrained nonlinear DCM model to assess the generation re-scheduling for minimizing the objective function. In the proposed RCGA method, owing to the adaptive capability, Simulated Binary Crossover (SBX) and Tournament selection is used as selection mechanism in order to avoid premature convergence. To establish the linear inequality of transmission limit constraints and transient stability constraints, generator shift factor and trajectory sensitivities are calculated. The algorithm's performance has been examined over 3-machine, 9-bus WSCC system.