Joong-Rin Shin

Bio: Joong-Rin Shin is an academic researcher from Konkuk University. The author has contributed to research in topics: Fault (power engineering) & Economic dispatch. The author has an hindex of 15, co-authored 45 publications receiving 2757 citations.

A particle swarm optimization for economic dispatch with nonsmooth cost functions

Abstract: This work presents a new approach to economic dispatch (ED) problems with nonsmooth cost functions using a particle swarm optimization (PSO) technique. The practical ED problems have nonsmooth cost functions with equality and inequality constraints that make the problem of finding the global optimum difficult using any mathematical approaches. A modified PSO (MPSO) mechanism is suggested to deal with the equality and inequality constraints in the ED problems. A constraint treatment mechanism is devised in such a way that the dynamic process inherent in the conventional PSO is preserved. Moreover, a dynamic search-space reduction strategy is devised to accelerate the optimization process. To show its efficiency and effectiveness, the proposed MPSO is applied to test ED problems, one with smooth cost functions and others with nonsmooth cost functions considering valve-point effects and multi-fuel problems. The results of the MPSO are compared with the results of conventional numerical methods, Tabu search method, evolutionary programming approaches, genetic algorithm, and modified Hopfield neural network approaches.

An Improved Particle Swarm Optimization for Nonconvex Economic Dispatch Problems

Abstract: This paper presents an efficient approach for solving economic dispatch (ED) problems with nonconvex cost functions using an improved particle swarm optimization (IPSO). Although the particle swarm optimization (PSO) approaches have several advantages suitable to heavily constrained nonconvex optimization problems, they still can have the drawbacks such as local optimal trapping due to premature convergence (i.e., exploration problem), insufficient capability to find nearby extreme points (i.e., exploitation problem), and lack of efficient mechanism to treat the constraints (i.e., constraint handling problem). This paper proposes an improved PSO framework employing chaotic sequences combined with the conventional linearly decreasing inertia weights and adopting a crossover operation scheme to increase both exploration and exploitation capability of the PSO. In addition, an effective constraint handling framework is employed for considering equality and inequality constraints. The proposed IPSO is applied to three different nonconvex ED problems with valve-point effects, prohibited operating zones with ramp rate limits as well as transmission network losses, and multi-fuels with valve-point effects. Additionally, it is applied to the large-scale power system of Korea. Also, the results are compared with those of the state-of-the-art methods.

An efficient simulated annealing algorithm for network reconfiguration in large-scale distribution systems

Abstract: This paper presents an efficient algorithm for loss minimization by using an automatic switching operation in large-scale distribution systems. Simulated annealing is particularly well suited for a large combinatorial optimization problem since it can avoid local minima by accepting improvements in cost. However, it often requires meaningful cooling schedule and a special strategy, which makes use of the property of distribution systems in finding the optimal solution. In this paper, we augment the cost function with the operation condition of distribution systems, improve the perturbation mechanism with system topology, and utilize the polynomial-time cooling schedule, which is based on the statistical calculation during the search. The validity and effectiveness of the proposed methodology is demonstrated in the Korea Electric Power Corporation's distribution system.

Closure to discussion of “An improved particle swarm optimization for nonconvex economic dispatch problems”

Abstract: In this paper, the authors proposed a heuristic constraint-handing technique for considering equality and inequality constraints of economic dispatch (ED) problems. The proposed constraint-handling technique is able to improve the solution quality even if it might spend more CPU time than any penalty factor approaches.

New Digital Algorithm for Adaptive Reclosing Based on the Calculation of the Faulted Phase Voltage Total Harmonic Distortion Factor

Abstract: This paper presents a new numerical algorithm suitable for determining adaptive dead time, and blocking automatic reclosing during permanent faults on overhead lines. It is based on terminal voltage input data processing. The decision if it is safe or not to reclose is determined by the voltage signal of faulted and tripped line phase using the total harmonic distortion factor calculated by discrete Fourier transform. The algorithm was successfully tested using signals recorded on the real power system. The tests demonstrate the ability of the presented algorithm to determine the secondary arc extinction time and to block unsuccessful automatic reclosing of high-voltage lines with permanent fault

Particle Swarm Optimization: Basic Concepts, Variants and Applications in Power Systems

Abstract: Many areas in power systems require solving one or more nonlinear optimization problems. While analytical methods might suffer from slow convergence and the curse of dimensionality, heuristics-based swarm intelligence can be an efficient alternative. Particle swarm optimization (PSO), part of the swarm intelligence family, is known to effectively solve large-scale nonlinear optimization problems. This paper presents a detailed overview of the basic concepts of PSO and its variants. Also, it provides a comprehensive survey on the power system applications that have benefited from the powerful nature of PSO as an optimization technique. For each application, technical details that are required for applying PSO, such as its type, particle formulation (solution representation), and the most efficient fitness functions are also discussed.

Particle swarm optimization algorithm: an overview

Abstract: Particle swarm optimization (PSO) is a population-based stochastic optimization algorithm motivated by intelligent collective behavior of some animals such as flocks of birds or schools of fish. Since presented in 1995, it has experienced a multitude of enhancements. As researchers have learned about the technique, they derived new versions aiming to different demands, developed new applications in a host of areas, published theoretical studies of the effects of the various parameters and proposed many variants of the algorithm. This paper introduces its origin and background and carries out the theory analysis of the PSO. Then, we analyze its present situation of research and application in algorithm structure, parameter selection, topology structure, discrete PSO algorithm and parallel PSO algorithm, multi-objective optimization PSO and its engineering applications. Finally, the existing problems are analyzed and future research directions are presented.

Electricity price forecasting: A review of the state-of-the-art with a look into the future

Abstract: A variety of methods and ideas have been tried for electricity price forecasting (EPF) over the last 15 years, with varying degrees of success. This review article aims at explaining the complexity of available solutions, their strengths and weaknesses, and the opportunities and treats that the forecasting tools offer or that may be encountered. The paper also looks ahead and speculates on the directions EPF will or should take in the next decade or so. In particular, it postulates the need for objective comparative EPF studies involving (i) the same datasets, (ii) the same robust error evaluation procedures and (iii) statistical testing of the significance of the outperformance of one model by another.

A New Particle Swarm Optimization Solution to Nonconvex Economic Dispatch Problems

Abstract: This paper proposes a new version of the classical particle swarm optimization (PSO), namely, new PSO (NPSO), to solve nonconvex economic dispatch problems. In the classical PSO, the movement of a particle is governed by three behaviors, namely, inertial, cognitive, and social. The cognitive behavior helps the particle to remember its previously visited best position. This paper proposes a split-up in the cognitive behavior. That is, the particle is made to remember its worst position also. This modification helps to explore the search space very effectively. In order to well exploit the promising solution region, a simple local random search (LRS) procedure is integrated with NPSO. The resultant NPSO-LRS algorithm is very effective in solving the nonconvex economic dispatch problems. To validate the proposed NPSO-LRS method, it is applied to three test systems having nonconvex solution spaces, and better results are obtained when compared with previous approaches