ANFIS‐based non‐dominated sorting genetic algorithm II for scenario‐based joint energy and reserves market clearing considering TCSC device

SUMMARY This paper presents a multi-objective mathematical programming (MMP) model to day-ahead market clearing of joint energy and reserves auctions by integrating a thyristor-controlled series compensator (TCSC) device with the optimization problem developed under normal and contingency cases. The proposed market clearing framework includes minimization of energy and reserves offer cost, congestion rent, TCSC cost, expected interruption cost, line overload, voltage deviation, and loadability limit. A new index called congestion efficiencyindexisproposedforthebestplacementofTCSCundernetworkcontingencyconditions.Traditional MMP methods such as direction scalarization and e-constraint methods scalarize the objective vector into a single objective. Those cases are time-consuming and require a number of runs equal to the number of desired efficient solutions. In this paper, the non-dominated sorting genetic algorithm II (NSGA-II), which is integrated with an adaptive neuro-fuzzy inference system (ANFIS), is proposed to find the solution of the optimal schedule of the units energy and reserves by which the parameters of NSGA-II (probabilities of crossover and mutation) are dynamically set, according to a training process. The proposed methodology is developed on the IEEE 30-bus test system, and the results are compared with fuzzy-based and ordinary NSGA-II methods. These comparisons confirm the efficiency of the developed method. Copyright © 2014 John Wiley & Sons, Ltd.

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