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Renewable sources can provide a clean and smart solution to the increased demands. Thus, Photovoltaic (PV) system and Wind Turbine (WT) are considered here as sources of Distributed Generation (DG). Allocation and sizing of DG have greatly affected on the system losses. This paper aims to propose Ant Lion Optimization Algorithm (ALOA) for optimal allocation and sizing of renewable DG sources in various distribution networks. First the most candidate buses for installing DG are suggested using Loss Sensitivity Factors (LSFs). Then the proposed ALOA is employed to deduce the locations of DG and their sizing from the elected buses. The proposed algorithm is tested on 33 and 69 bus radial distribution systems. The obtained results via the proposed algorithm are compared with others to highlight its benefits in reducing total power losses and consequently maximizing the net saving. Moreover, the results are introduced to verify the superiority of the proposed algorithm to improve the voltage profiles for various loading conditions. Also, the Wilcoxon test is applied to confirm the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

A new metaheuristic method, the Cuckoo Search (CS) algorithm, based on the life of a bird family is proposed in this paper for optimal design of static synchronous compensator (STATCOM) in a multimachine environment. PV curves are illustrated to determine the best location of STATCOM. The STATCOM parameter tuning problem is converted to an optimization problem which is solved by CS Algorithm. The performance of the proposed CS based STATCOM (CSSTATCOM) is compared with Genetic Algorithm (GA) based STATCOM (GASTATCOM) and open loop STATCOM under various operating conditions and disturbances. The superiority of the proposed technique in damping oscillations and enhancing voltage profiles is confirmed through eigenvalues and time domain simulation results over the GA and open loop one. [ABSTRACT FROM AUTHOR]

Cuckoo Search (CS) algorithm is introduced in this paper for optimal Power System Stabilizers (PSSs) design in a multimachine power system. The PSSs parameter tuning problem is formulated as an optimization problem which is solved by CS Algorithm. An eigenvalues based objective function involving the damping ratio, and the damping factor of the lightly damped electromechanical modes is considered for the PSSs design problem. The performance of the proposed CS based PSSs (CSPSS) has been compared with Genetic Algorithm (GA) based PSSs (GAPSS) and the Conventional PSSs (CPSS) under different operating conditions and disturbances. The results of the developed CSPSS are verified through time domain analysis, eigenvalues and performance indices. Also, the effectiveness of the proposed algorithm in providing good damping characteristics is confirmed. [ABSTRACT FROM AUTHOR]

Abstract: This paper develops a novel algorithm for simultaneous coordinated designing of power system stabilizers (PSSs) and static var compensator (SVC) in a multimachine power system. The coordinated design problem of PSS and SVC over a wide range of loading conditions is formulated as an optimization problem. The Bacterial Foraging Optimization Algorithm (BFOA) is employed to search for optimal controllers parameters. By minimizing the proposed objective function, in which the speed deviations between generators are involved; stability performance of the system is improved. To compare the capability of PSS and SVC, both are designed independently, and then in a coordinated manner. Simultaneous tuning of the bacterial foraging based coordinated controller gives robust damping performance over wide range of operating conditions and large disturbance in compare to optimized PSS controller based on BFOA (BFPSS) and optimized SVC controller based on BFOA (BFSVC). Moreover, a statistical T test is performed to ensure the effectiveness of coordinated controller versus uncoordinated one. [Copyright &y& Elsevier]

Abstract: This paper develops a novel algorithm for simultaneous coordinated designing of power system stabilizers (PSSs) and thyristor controlled series capacitor (TCSC) in a multimachine power system. The coordinated design problem of PSS and TCSC over a wide range of loading conditions is formulated as an optimization problem. The bacterial swarm optimization (BSO) algorithm is employed to search for optimal controllers parameters. By minimizing the proposed objective function, in which the speed deviations between generators are involved; stability performance of the system is improved. To compare the capability of PSS and TCSC, both are designed independently, and then in a coordinated manner. Simultaneous tuning of the bacterial swarm based coordinated controllers gives robust damping performance over wide range of operating conditions, and different disturbances in compare to optimized PSS controller based on BSO (BSOPSS) and optimized TCSC controller based on BSO (BSOTCSC). Moreover, the results are compared to the results obtained using the bacteria foraging (BF) and particle swarm optimization (PSO) to show the effectiveness of using BSO to attain a global optimal solution of the proposed coordinated design problem. [Copyright &y& Elsevier]

Abstract: Social foraging behavior of Escherichia coli bacteria has recently been explored to develop a novel algorithm for distributed optimization and control. The Bacterial Foraging Optimization Algorithm (BFOA), as it is called now, is currently gaining popularity in the community of researchers, for its effectiveness in solving certain difficult real world optimization problems. This paper proposes BFOA based Load Frequency Control (LFC) for the suppression of oscillations in power system. A two area non-reheat thermal system is considered to be equipped with proportional plus integral (PI) controllers. BFOA is employed to search for optimal controller parameters by minimizing the time domain objective function. The performance of the proposed controller has been evaluated with the performance of the conventional PI controller and PI controller tuned by genetic algorithm (GA) in order to demonstrate the superior efficiency of the proposed BFOA in tuning PI controller. Simulation results emphasis on the better performance of the optimized PI controller based on BFOA in compare to optimized PI controller based on GA and conventional one over wide range of operating conditions, and system parameters variations. [Copyright &y& Elsevier]

Economic Load Dispatch (ELD) is the process of allocating the required load between the available generation units such that the cost of operation is minimized. The ELD problem is formulated as a nonlinear constrained optimization problem with both equality and inequality constraints. The dual-objective Combined Economic Emission Dispatch (CEED) problem is considering the environmental impacts that accumulated from emission of gaseous pollutants of fossil-fueled power plants. In this paper, an implementation of Flower Pollination Algorithm (FPA) to solve ELD and CEED problems in power systems is discussed. A comparison of the simulated results using the proposed FPA is carried out to confirm its effectiveness against other swarm intelligent algorithms for six various power systems. The superiority of the proposed FPA compared with other algorithms is demonstrated even for large scale power system considering valve point loading effect. [ABSTRACT FROM AUTHOR]