Computational methods of Gaussian Particle Swarm Optimization (GPSO) and Lagrange Multiplier on economic dispatch issues (case study on electrical system of Java-Bali IV area)

Abstract

Abstract. The objective in this paper is about economic dispatch problem of electric power generation where scheduling the committed generating units outputs so as to meet the required load demand at minimum operating cost, while satisfying all units and system equality and inequality constraint. In the operating of electric power system, an economic planning problem is one of variables that its must be considered since economically planning will give more efficiency in operational cost. In this paper the economic dispatch problem which has non linear cost function solved by using swarm intelligent method is Gaussian Particle Swarm Optimization (GPSO) and Lagrange Multiplier. GPSO is a population-based stochastic algorithms which their moving inspired by swarm intelligent and probabilities theories. To analize its accuracy, the economic dispatch solution by GPSO method will be compared with Lagrange multiplier method. From the running test result the GPSO method give economically planning calculation which it better than Lagrange multiplier method and the GPSO method faster to getting error convergence. Therefore the GPSO method have better performance to getting global best solution than the Lagrange method.

A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

“…The optimization tool was a MOPSO variant that used heuristic selection and assignment of leaders or guides for efficient identification of nondominated solutions. Komsiyah [222] used Gaussian PSO and Lagrange multiplier to solve the EDP of electric power generation, scheduling the committed generating units outputs so as to meet the required load demand at minimum operating cost, while satisfying all units and system equality and inequality constraint. Feng et al [223] employed orthogonal signal correction and PSO in order to detect wound infection by and improve the performance of electronic nose.…”

“…Ganguly et al [221], Komsiyah [222], Feng et al [223], Pekşen et al [224], Yang et al [225], de Mendonça et al [226], Liu et al [227], Aich and Banerjee [228], Chou et al [229], Lee et al [230], Thakral and Bakhshi [231], Fister et al [232], Aghaei et al [233], Selakov et al [234], Shirvany et al [235], and Tungadio et al [236] Automatic control Cai and Yang [237], Kolomvatsos and Hadjieftymiades [238], Pandey et al [239],Štimac et al [240], Nedic et al [241], Chang and Chen [242], Xiang et al [243], Danapalasingam [244], Mahmoodabadi et al [245], Zhong et al [246], Perng et al [247], Huang and Li [248], and Nisha and Pillai [249] Communication Yousefi et al [250], Sun et al [251], Yongqiang et al [252], Chiu et al [253], Zubair and Moinuddin [255], Kim and Lee [256], Yazgan and Hakki Cavdar [257], Rabady and Ababneh [258], Das et al [259], Scott-Hayward and Garcia-Palacios [260], Omidvar and Mohammadi [261], and Kuila and Jana [262] Operations Liu and Wang…”

Constrained Multiobjective Equilibrium Optimizer Algorithm for Solving Combined Economic Emission Dispatch Problem

“…In this approach, a specified target is assigned for each objective to be achieved and then aims to minimize the deviation from the desired targets to the objective functions. e second direction is to solve the single objective CEEDP by any single objective meta-heuristics algorithm such as artificial bee colony (ABC) [9], gravitational search algorithm (GSA) [10], and Gaussian particle swarm optimization (GPSO) [11] or by any hybrid single objective meta-heuristics algorithms such as hybrid particle swarm optimization (PSO) algorithm and firefly algorithm (FA) [12], hybrid ABC algorithm and simulated annealing algorithm (SA) [13], and PSO-GSA algorithm [14]. e third direction is to handle both objectives of CEEDP simultaneously, by using meta-heuristics-based MOO techniques, as competing objective functions instead of transforming the MOP formulation to a single objective problem, as dynamic random neighborhood PSO (DRN-PSO) [15], nondominated sorting genetic algorithm (NSGA) [16], niched Pareto genetic algorithm (NPGA) [17], fuzzy clustering-based particle swarm (FCPSO) [18], modified shuffled frog leaping algorithm (MSFLA) [19], real coded genetic algorithm (RCGA) [20], and strength Pareto evolutionary algorithm (SPEA) [21].…”

A novel particle swarm optimization algorithm for maximum power point tracking based photovoltaic system

“…To tackle the issue of slow convergence of PSO and Gaussian PSO, CPSO is proposed and implemented. It is an improved version of Gaussian PSO [GPSO] [20] with enhanced convergence speed. Other advantages are reduction in the number of tuning parameters, speedy convergence rate and faster output settling time.…”