Social welfare maximization with fuzzy based genetic algorithm by TCSC and SSSC in double-sided auction market

This paper presents a fuzzy-based genetic algorithm to maximize total system social welfare bybest the placement and sizing of tcsc and sssc devices, considering their investment cost in a double-sided auction market. to introduce more accurate modeling, the valve loading effects are incorporatedinto the conventional quadratic smooth generator cost curves. in addition, quadratic consumer benefitfunctions are integrated into the objective function to guarantee that locational marginal prices chargedat the demand buses are less than, or equal to, the discos benefit, earned by selling the power to retailcustomers. the proposed approach utilizes fuzzy-based genetic algorithms for optimal scheduling ofgencos and discos, as well as optimal placement and sizing of sssc and tcsc units. in addition, thenewtonraphson approach is used to minimize the mismatch of the power flow equation. simulationresults on the modified ieee 14-bus and ieee 30-bus test systems (with/without line flow constraints,before and after the compensation) are used to examine the impact of sssc and tcsc on total system socialwelfare improvement versus their cost. to validate the accuracy of the proposed method, several casestudies are presented and simulation results are compared with those generated by genetic and sequentialquadratic programming (sqp) approaches.