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 by best 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 incorporated into the conventional quadratic smooth generator cost curves. In addition, quadratic consumer benefit functions are integrated into the objective function to guarantee that locational marginal prices charged at the demand buses are less than, or equal to, the DisCos benefit, earned by selling the power to retail customers. The proposed approach utilizes fuzzy-based genetic algorithms for optimal scheduling of GenCos and DisCos, as well as optimal placement and sizing of SSSC and TCSC units. In addition, the Newton–Raphson approach is used to minimize the mismatch of the power flow equation. Simulation results 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 social welfare improvement versus their cost. To validate the accuracy of the proposed method, several case studies are presented and simulation results are compared with those generated by genetic and Sequential Quadratic Programming (SQP) approaches

Optimal location of FACTS devices to enhance power system security

This paper compares three heuristic methods (SA, TS and GA) applied to the optimal location of FACTS devices in a power system. The optimizations are made on three parameters: the location of the devices, their types and their sizes. The FACTS devices are located in order to enhance the system security. Five types of FACTS controllers are modeled for steady-state studies: TCSC, TCVR, TCPST, SVC and UPFC. Simulations are performed on an IEEE 118-bus power system for several numbers of devices. Results show that the three algorithms converge to similar optimal solutions. The security margin of the system may be increased with the use of FACTS devices, but some limitations are observed. The locations of the devices and their influence areas are analyze

Optimal location of multi-type FACTS devices in a power system by means of genetic algorithms

This paper presents a genetic algorithm to seek the optimal location of multi-type FACTS devices in a power system. The optimizations are performed on three parameters: the location of the devices, their types and their values. The system loadability is applied as a measure of power system performance. Four different kinds of FACTS controllers are used and modeled for steady-state studies: TCSC, TCPST, TCVR and SVC. Simulations are done on a 118-bus power system for several numbers of devices. Results show the difference of efficiency of the devices used in this context. They also show that the simultaneous use of several kinds of controllers is the most efficient solution to increase the loadability of the system. In all the cases (single- and multi-type FACTS devices), we observe a maximum number of devices beyond which this loadability cannot be improve