Hwa-Seok Lee

Economic load dispatch using evolutionary algorithms

This paper presents evolutionary algorithms to solve the problems of economic load dispatch (ELD) with quadratic cost functions and piecewise quadratic cost functions. Genetic algorithms (GAs), evolutionary programming (EP) and evolution strategies (ESs) are applied to the ELD problems. To improve these methods in nonlinear optimization problems, two hybrid optimization methods exploiting the advantages of each of evolutionary algorithms are developed. Optimization methods, combining GA with ES and EP with ES, are tested in the ELD problem with piecewise quadratic cost functions. Case studies illustrate the superiority of the proposed methods to existing numerical methods.

A Modified Particle Swarm Optimization for Optimal Power Flow

The optimal power flow (OPF) problem was introduced by Carpentier in 1962 as a network constrained economic dispatch problem. Since then, it has been intensively studied and widely used in power system operation and planning. In the past few decades, many stochastic optimization methods such as Genetic Algorithm (GA), Evolutionary Programming (EP), and Particle Swarm Optimization (PSO) have been applied to solve the OPF problem. In particular, PSO is a newly proposed population based stochastic optimization algorithm. The main idea behind it is based on the food-searching behavior of birds and fish. Compared with other stochastic optimization methods, PSO has comparable or even superior search performance for some hard optimization problems in real power systems. Nowadays, some modifications such as breeding and selection operators are considered to make the PSO superior and robust. In this paper, we propose the Modified PSO (MPSO), in which the mutation operator of GA is incorporated into the conventional PSO to improve the search performance. To verify the optimal solution searching ability, the proposed approach has been evaluated on an IEEE 30-bus test system. The results showed that performance of the proposed approach is better than that of the standard PSO.

Application of parallel particle swarm optimization on power system state estimation

In power system operations, state estimation plays an important role in security control. For the state estimation problem, the weighted least squares (WLS) method is widely used at present. However, these algorithms can converge to local optimal solutions. Recently, modern heuristic optimization methods such as Particle Swarm Optimization (PSO) have been introduced to overcome the disadvantage of the classical optimization problem. However, heuristic optimization methods based on populations require a lengthy computing time to find an optimal solution. In this paper, we used particle swarm optimization (PSO) to search for the optimal solution of state estimation in power systems. To overcome the shortcoming of heuristic optimization methods, we proposed parallel processing of the PSO algorithm based on the PC cluster system. The proposed approach was tested with the IEEE-118 bus systems. From the simulation results, we found that the parallel PSO based on the PC cluster system can be applicable for power system state estimation.

Multiple Bad Data Identification Using Binary Particle Swarm Optimization

The identification of multiple bad data, especially when mutually interacting, may be difficult to handle, since the well known procedures based on the normalized or weighted residuals may become faulty. In such a case, successive elimination of the measurement with the largest normalized residual may result in the suppression of correct measurements instead of the bad data. Then the problem of identifying bad data is considered as a combinatorial decision procedure. In this paper, binary PSO is used for the identification of multiple bad data in the power system state estimation. The proposed binary PSO based procedures behave satisfactorily in the identifying multiple bad data. The test is carried out with reference to the IEEE-14 bus system.

PC Cluster based Parallel Adaptive Evolutionary Algorithm for Service Restoration of Distribution Systems

This paper presents an application of the parallel Adaptive Evolutionary Algorithm (AEA) to search an optimal solution of the service restoration in electric power distribution systems, which is a discrete optimization problem. The main objective of service restoration is, when a fault or overload occurs, to restore as much load as possible by transferring the de-energized load in the out of service area via network reconfiguration to the appropriate adjacent feeders at minimum operational cost without violating operating constraints. This problem has many constraints and it is very difficult to find the optimal solution because of its numerous local minima. In this investigation, a parallel ABA was developed for the service restoration of the distribution systems. In parallel AEA, a genetic algorithm (GA) and an evolution strategy (ES) in an adaptive manner are used in order to combine the merits of two different evolutionary algorithms: the global search capability of the GA and the local search capability of the ES. In the reproduction procedure, proportions of the population by GA and ES are adaptively modulated according to the fitness. After ABA operations, the best solutions of AEA processors are transferred to the neighboring processors. For parallel computing, a PC cluster system consisting of 8 PCs was developed. Each PC employs the 2 ㎓ Pentium Ⅳ CPU and is connected with others through switch based fast Ethernet. To show the validity of the proposed method, the developed algorithm has been tested with a practical distribution system in Korea. From the simulation results, the proposed method found the optimal service restoration strategy. The obtained results were the same as that of the explicit exhaustive search method. Also, it is found that the proposed algorithm is efficient and robust for service restoration of distribution systems in terms of solution quality, speedup, efficiency, and computation time.

SERVICE RESTORATION IN DISTRIBUTION AUTOMATION SYSTEM USING PARALLEL HYBRID GENETIC ALGORITHM-TABU SEARCH

Abstract This paper presents an application of parallel hybrid Genetic Algorithm-Tabu Search (GA-TS) in searching the optimal service restoration solution in an electric power distribution system, a discrete optimization problem. In this investigation, parallel hybrid GA-TS was developed for the service restoration of a distribution system. For parallel computing, a PC-cluster system consisting of 8 PCs was developed. Each PC employed a 2 GHz Pentium IV CPU, and was connected with others through Ethernet-switch-based fast Ethernet. The newly developed algorithm was tested with a practical distribution system in Korea. From the simulation results, the proposed method found the optimal fault restoration strategy. The obtained results were the same as those of the explicit exhaustive search method (Wu, Tomsovic, and Chen, 1991). Also, it was found that the proposed algorithm is efficient and robust for distribution system service restoration in the solution quality, speed-up, efficiency, and computation time.