Multi-Objective Optimal Power Flow Using Metaheuristic Optimization Algorithms With Unified Power Flow Controller to Enhance the Power System Performance

Introduction

Due to economic growth the demand of electric power has been increased drastically which needs optimal operation in Power systems. Optimal power flow is the one strategy for minimizing the real power generation cost and losses in transmission lines. The transmission line losses can be further reduced by installing FACTS controllers. The variables and parameter of the transmission line, which include line reactance, voltage magnitude, and phase angle are able to be controlled using FACTS controllers in a fast and effective way. Controlling power flows is the main function of FACTS controllers (Acha et al., 2004; Jirapong, 2013). According to the IEEE definition, FACTS is defined as “The Flexible AC Transmission System(FACTS) is a new technology based on power electronic devices which offers an opportunity to enhance controllability, stability and power transfer capability of AC Transmission Systems” (Hingorani, 2000; Edris, 2000).

The Flexible AC Transmission System (FACTS) is a transmission system which use reliable high-speed thyristor based controllable elements such as SVC, TCSC, and UPFC etc. are designed based on state of the art developments in power semiconductor devices. Issues include increased utilization of existing facilities such as secure system operation at higher power transfers across existing transmission lines which are limited by stability constraints, the development of control designs for FACTS devices, and determination of functional performance requirements for FACTS components. The reactive power compensation of AC transmission systems using fixed series or shunt capacitors can solve some of the problems associated with AC networks. However the slow nature of control using mechanical switches (circuit breaker) and limits on the frequency of switching imply that faster dynamic controls are required to overcome the above mentioned problems. (Hammons and Lim, 1997) presented a review literature, which addresses the application of FACTS, concepts for the improvement of power system utilization and performance. Recent developments involving deregulation and restructuring of the power industry is feasible only if the operation of AC transmission systems is made flexible by introducing FACTS devices. In view of increased power demand, power engineers are looking for ways to better utilize their existing transmission systems. The system loadability and stability margins also need to be improved. In recent years, advances in the power electronic devices, have led to the development of controllers that provide controllability and flexibility for power transmission. Flexible AC Transmission System controllers have been developed and their usage in controlling power transmission is seen to be increasing now a days. Among FACTS controllers, UPFC is a versatile device that plays the vital function of controlling all power system parameters simultaneously. The primary function of the UPFC device is to control the load flow of the power system.

Optimal power flow (OPF) is an important tool for power system operation, control and planning. It was first introduced by (Dommel and Tinney, 1968). The main purpose of an OPF program is calculating the optimal operating point of a power system and setting the variables that optimize a certain objective function while satisfying power flow equations, inequality and equality constraints. The optimal power flow (OPF) is a power flow problem in which certain variables are adjusted to minimize an objective function such as cost of the active power generation and the real power losses explained in (Tinney, 1968). Over the last three decades, many successful OPF techniques have been developed such as, the generalized reduced gradient method (Stagg, 1968), linear programming solution, quadratic programming, the Newton method (Carpentier, 1979), the Interior Point Method (IPM), Genetic Algorithm (GA), Evolutionary Programming, Differential Evolutionary algorithm etc. After obtaining the OPF solution (Sun, 1984 ; Kumar Roy, 2013), the implementation of the optimal control variables will bring the system to the “optimum” state (Kundur P,1993 ; Chung, T. S., & Li, Y. Z, 2001).