This paper deals with the Optimal location and control of a unified power flow controller (UPFC) along with transformer taps are tuned to simultaneously optimize the voltage stability limit (VSL) and real power losses of a mesh power system network. This problem is dealt as a nonlinear equality and inequality constrained optimization problem with an objective function incorporating both the real power loss and VSL. An evolutionary algorithm known as shuffled leap is applied for solving the UPFC location, its injected series voltage, and also the tap positions of transformers as the variables. The then obtained results of SFC algorithm are compared with the results of Bacteria Foraging-Based Algorithm using IEEE 14 bus. Index Terms: Shuffled leap, continuation power flow, linearprogramming, optimal power flow (OPF). I. Introduction: The OPF methods are conventional and intelligent and solved by varieties of methods such as successive linear programming, the Newton-based nonlinear programming method, and with varieties of recently proposed interior point methods. The Optimal Power Flow solution is used to optimize a selected objective function such as fuel cost with optimal adjustment of the power system control variables, at the same time satisfying various equity and inequality constraints. The drawback of the OPF is solved from different perspectives, like analyzing the effects of load on voltage stability/power flow solvability, generation rescheduling for cost minimization of power generation, controls such as taps, shunts, and other modern VAR sources adjustments to minimize real power losses in the system. The advent of Flexible ac transmission systems (FACTS) system made the possibility for optimizing the power flow without the restoration of generation rescheduling or changes to topology. Unified power flow controller (UPFC) is the advanced in the controllers family and can provide the OPF with significant flexibility by injecting compensation in series and shunt in controlled manner. The UPFC can provide simultaneous control of all basic power system parameters (transmission voltage, impedance and phase angle). The controller can fulfil functions of reactive shunt compensation, series compensation and phase shifting meeting multiple control objectives. From a functional perspective, the objectives are met by applying a boosting transformer injected voltage and a exciting transformer reactive current. The injected voltage is inserted by a series transformer. The continuation power flow (CPF) method is robust; however has some weakness in large electric power system considering generators reactive power limits and gives information regarding how much percentage overloading the system can withstand before a possible voltage collapse. The CPF problem is incorporated into an OPF problem so that both the issues can be addressed simultaneously. In this paper, the voltage stability limit is defined as the maximum percentage overloading (λmax) the system can withstand and incorporated along with the objective of real power loss minimization. The classical techniques of OPF solution has the disadvantage that they are sensitive to starting points and leading to non-monotonic solution. To eliminate this problem evolutionary techniques have been applied in solving the OPF problem (10), (11) like particle swarm optimization (PSO) to the problem of OPF. In this paper a new evolutionary algorithm known as Shuffled Frog Leap algorithm (SFLA) is used to solve the combined problem of CPF-OPF for real power loss minimization and VSL maximization of the system. The algorithm has been inspired from memetic evolution of a group of frogs when seeking for food. In this method, a solution to a given problem is presented in the form of a string, called frog which has been considered as a control vector in this paper. The main objective is transformer taps optimization, location of UPFC and its series injected voltage for the single objective of real power loss minimization and then for the multiple objectives of loss minimization and VSL maximization. Finally, a cost analysis for installation of UPFC is carried out to establish the investment in putting a UPFC for the cause. Test results indicate that SFLA method can obtain better results than the simple heuristic search method on the 14-bus radial distribution systems.
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