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
efficiency index is proposed for the best placement of TCSC under network contingency conditions. Traditional
MMP methods such as direction scalarization and ε-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 andmutation) are
dynamically set, according to a training process. The proposedmethodology 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.