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|Multiobjective Optimal Power Flow
|Jain, Sanjay Kumar
|Optimal Power Flow;Multiobjective Optimization;NSGA-II;Genetic Algorithm
|The optimization is one of the challenging problems in power system. The optimization sometimes is mainly restricted to the minimization of the operating cost. However, the operation of power plants, mostly thermal units, results into various types of emissions like SOx, NOx and COx etc. The environmental concern dictates the minimization of the emissions by the thermal plant. Individually, if one objective is optimized, other is compromised. The objectives like minimization of cost, losses and emission may be conflicting and thus the decision has to be based on robust multi-objective optimization. The Optimal power flow (OPF) is used widely for the decision making by various power system operators. The OPF can provide the solution (decision variables) by optimizing various objectives namely generation cost, transmission losses etc. The objectives may be conflicting and the robust multi-objective formulation will help the decision making process. The optimal power flow using genetic algorithm has been considered for both single objective optimization and for multi-objective optimization. Different objectives considered are minimization of generation cost, minimization of transmission losses and minimization of emission. The multi-objective optimization problem is formulated for simultaneous minimization of fuel cost and losses, losses and emission and finally fuel cost and emission to obtain a Pareto optimal front. The optimization is carried out using Elitist Non Dominating sorting Genetic Algorithm for standard IEEE-30 bus system.
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