Power optimization for a hydrocarbon industrial plant using a genetic algorithm

In this paper, a genetic algorithm (GA) is considered for optimizing electrical power loss for a real hydrocarbon industrial plant as a single objective problem. The subject plant electrical system consists of 275 buses, two gas turbine generators, two steam turbine generators, large synchronous motors, and other rotational and static loads. The minimization of power losses (J1) objective is used to guide the optimization process, and, consequently, the injected power into the grid (PRInject) is increased. The results obtained demonstrate the potential and effectiveness of the proposed approach to optimize the power consumption. Also, in this paper a cost appraisal for the potential daily, monthly and annual cost saving will be addressed

Optimal design of power-system stabilizers using particle swarm optimization

In this paper, a novel evolutionary algorithm-based approach to optimal design of multimachine power-system stabilizers (PSSs) is proposed. The proposed approach employs a particle-swarm-optimization (PSO) technique to search for optimal settings of PSS parameters. Two eigenvalue-based objective functions to enhance system damping of electromechanical modes are considered. The robustness of the proposed approach to the initial guess is demonstrated. The performance of the proposed PSO-based PSS (PSOPSS) under different disturbances, loading conditions, and system configurations is tested and examined for different multimachine power systems. Eigenvalue analysis and nonlinear simulation results show the effectiveness of the proposed PSOPSSs to damp out the local and interarea modes of oscillations and work effectively over a wide range of loading conditions and system configurations. In addition, the potential and superiority of the proposed approach over the conventional approaches is demonstrated

A new multiobjective evolutionary algorithm forenvironmental/economic power dispatch

In this paper, a new multiobjective evolutionary algorithm for environmental/economic power dispatch (EED) optimization problem is presented. The EED problem is formulated as a nonlinear constrained multiobjective optimization problem with both equality and inequality constraints. A new nondominated sorting genetic algorithm (NSGA) based approach is proposed to handle the problem as a true multiobjective optimization problem with competing and noncommensurable objectives. The proposed approach employs a diversity-preserving technique to overcome the premature convergence and search bias problems and produce a well-distributed Pareto-optimal set of nondominated solutions. A hierarchical clustering technique is also imposed to provide the decision maker with a representative and manageable Pareto-optimal set. Several optimization runs of the proposed approach are carried out on a standard IEEE test system. The results demonstrate the capabilities of the proposed NSGA based approach to generate the true Pareto-optimal set of nondominated solutions of the multiobjective EED problem in one single run. Simulation results with the proposed approach have been compared to those reported in the literature. The comparison shows the superiority of the proposed NSGA based approach and confirms its potential to solve the multiobjective EED proble

Particle swarm optimization for multimachine power systemstabilizer design

In this paper, a novel evolutionary algorithm based approach to optimal design of multimachine power system stabilizers (PSSs) is proposed. The proposed approach develops and employs particle swarm optimization (PSO) technique to search for optimal settings of PSS parameters. Two eigenvalue-based objective functions to enhance system damping of electromechanical modes are considered. The robustness of the proposed approach to the initial guess is demonstrated. The performance of the proposed PSO based PSS (PSOPSS) under different disturbances and loading conditions is tested and examined. Eigenvalue analysis and nonlinear simulation results show the effectiveness of the proposed PSOPSSs to damp out the electromechanical oscillations and work effectively over a wide range of loading condition

Genetic-based TCSC damping controller design for power systemstability enhancement

A genetic-based damping controller for a thyristor-controlled series capacitor (GCSC) is presented in this paper. Minimizing the real part of the system eigenvalue associated with low frequency oscillation mode is proposed as the objective function of the design problem. The proposed controller has been examined on a weakly connected power system with different disturbances and loading conditions. Eigenvalue analysis and nonlinear simulation results show that the performance of the proposed GCSC outperforms that of conventional power system stabilizer (CPSS). It is also observed that the proposed GCSC improves greatly the voltage profile of the system under severe disturbance

Simultaneous design of damping controllers and internal controllers of a unified power flow controller

In this paper, the use of a supplementary controller of a unified power flow controller (UPFC) to damp low frequency oscillations is investigated. A new technique to design UPFC damping controllers simultaneously with UPFC internal controllers is proposed. An optimization problem to search for the optimal controller settings is formulated so as to optimize a time-domain based objective function that considers all the controllers simultaneously. The effectiveness of the proposed controllers in damping low frequency oscillations is verified through eigenvalue analysis and non-linear time simulation. A comparison with a sequential design of the controllers under study is also .included

Robust design of multimachine power system stabilizers usingsimulated annealing

Robust design of multimachine power system stabilizers (PSSs) using simulated annealing (SA) optimization technique is presented in this paper. The proposed approach employs SA to search for optimal parameter settings of a widely used conventional fixed-structure lead-lag PSS (CPSS). The parameters of the proposed simulated annealing based power system stabilizer (SAPSS) are optimized in order to shift the system electromechanical modes at different loading conditions and system configurations simultaneously to the left in the s-plane. Incorporation of SA as a derivative-free optimization technique in PSS design significantly reduces the computational burden. One of the main advantages of the proposed approach is its robustness to the initial parameter settings. In addition, the quality of the optimal solution does not rely on the initial guess. The performance of the proposed SAPSS under different disturbances and loading conditions is investigated for two multimachine power systems. The eigenvalue analysis and the nonlinear simulation results show the effectiveness of the proposed SAPSS's to damp out the local as well as the interarea modes and enhance greatly the system stability over a wide range of loading conditions and system configuration