Genetic Algorithm and Newton Raphson (NR) based approach to Optimal Power Flow problem has been presented. Both algorithms were tested on a 14-bus IEEE test system. The Genetic Algorithm Optimization (GAs) is very efficient in solving the OPF problem.The traditional concepts and practices of power systems are superimposed by economic market management. So OPF has become complex, and classical optimization methods were used to solve OPF effectively. But, in recent years, Artificial Intelligence methods (GA, etc.) have emerged that can solve highly complex OPF problems. In this work two algorithms, were used for the solution of dynamic optimal power flow (OPF) problem taking the transmission losses and the cost of generation as the main constraints. Both algorithms were tested on a 14-bus IEEE test system. The contingency analysis was considered in the application of the algorithms. Additionally, a comparison was made between the two algorithms. The obtained results showed the effectiveness of the GA algorithm over the traditional algorithm.
The aim of this work is to solve the unit commitment (UC) problem in power systems by calculating minimum production cost for the power generation and finding the best distribution of the generation among the units (units scheduling) using binary grey wolf optimizer based on particle swarm optimization (BGWOPSO) algorithm. The minimum production cost calculating is based on using the quadratic programming method and represents the global solution that must be arriving by the BGWOPSO algorithm then appearing units status (on or off). The suggested method was applied on “39 bus IEEE test systems”, the simulation results show the effectiveness of the suggested method over other algorithms in terms of minimizing of production cost and suggesting excellent scheduling of units.
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