This study presents proposed pseudo-inspired chaotic bat algorithm (PI-CBA) for solving economic dispatch (ED) problem in the power system. The ED problem is modelled as a complex mathematical function that takes cost...
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This study presents proposed pseudo-inspired chaotic bat algorithm (PI-CBA) for solving economic dispatch (ED) problem in the power system. The ED problem is modelled as a complex mathematical function that takes cost coefficients of all the possible fuel options as well as effects of valve-pointloading including transmission losses. Problem involves number of equality and inequality constraints such as power balance, prohibited operating zone and ramp rate limits. Combination of CBA along with a pseudo-code-based algorithm is utilised to solve above problem. The feasibility of the proposed methodology is demonstrated for three different tests comprising 6, 10, 15, 40 and 160 thermal units. A qualitative comparison of the obtained result with other standard population-based meta-heuristic techniques manifests proposed technique's superiority.
Optimization of fuel cost function of large-scale thermal generating units under several constraints in smart power grid is a challenging problem. Because of these constraints, the fuel cost function becomes multimoda...
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Optimization of fuel cost function of large-scale thermal generating units under several constraints in smart power grid is a challenging problem. Because of these constraints, the fuel cost function becomes multimodal, discontinuous and non-convex. Although the global particle swarm optimization with inertia weight (GPSO-w) algorithm is a popular optimization technique, it is not capable of solving such complex problems satisfactory. In this paper, a novel multi-gradient PSO (MG-PSO) algorithm is proposed to solve such a challenging problem. In MG-PSO algorithm, two phases, called Exploration phase and Exploitation phase, are used. In the Exploration phase, the m particles are called Explorers and undergo multiple episodes. In each episode, the Explorers use a different negative gradient to explore new neighbourhood whereas in the Exploitation phase, the m particles are called Exploiters and they use one negative gradient that is less than that of the Exploration phase, to exploit a best neighborhood. This diversity in negative gradients provides a balance between global search and local search. The effectiveness of the MG-PSO algorithm is demonstrated using four (medium and large) power generation systems. Superior performance of the MG-PSO algorithm over several PSO variants in terms of several performance measures has been shown. (C) 2017 Elsevier Ltd. All rights reserved.
To address the problem of combined heat and power economic emission dispatch (CHPEED), a two-stage approach is proposed by combining multi-objective optimization (MOO) with integrated decision making (IDM). First, a p...
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To address the problem of combined heat and power economic emission dispatch (CHPEED), a two-stage approach is proposed by combining multi-objective optimization (MOO) with integrated decision making (IDM). First, a practical CHPEED model is built by taking into account power transmission losses and the valve-point loading effects. To solve this model, a two-stage methodology is thereafter proposed. The first stage of this approach relies on the use of a powerful multi-objective evolutionary algorithm, called theta-dominance based evolutionary algorithm (theta-DEA), to find multiple Pareto-optimal solutions of the model. Through fuzzy c-means (FCM) clustering, the second stage separates the obtained Pareto-optimal solutions into different clusters and thereupon identifies the best compromise solutions (BCSs) by assessing the relative projections of the solutions belonging to the same cluster using grey relation projection (GRP). The novelty of this work is in the incorporation of an IDM technique FCM-GRP into CHPEED to automatically determine the BCSs that represent decision makers' different, even conflicting, preferences. The simulation results on three test cases with varied complexity levels verify the effectiveness and superiority of the proposed approach. (C) 2018 Elsevier Ltd. All rights reserved.
This paper presents backtracking search algorithm (BSA) for solving economic dispatch (ED) problems with considering valve-point loading effects, prohibited operating zones, and multiple fuel options. The proposed met...
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This paper presents backtracking search algorithm (BSA) for solving economic dispatch (ED) problems with considering valve-point loading effects, prohibited operating zones, and multiple fuel options. The proposed method is an evolutionary technique of optimization with simple structure and single control parameter to solve numerical optimization problems. It is a powerful method for effectively exploring the search space of an optimization problem to find the optimal solution within a low computation time. Different test systems with up to 160 generating units have been used to show the performance of BSA to solve ED problems with high nonlinearities. The results are compared with several methods of optimization to verify the high performance of BSA for solving the ED problems. Statistical analysis of the results among 50 independent runs has been carried out to validate the BSA as a highly robust method. (C) 2016 Elsevier Ltd. All rights reserved.
This paper presents the design and application of an efficient hybrid heuristic search method to solve the practical economic dispatch problem considering many nonlinear characteristics of power generators, and their ...
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This paper presents the design and application of an efficient hybrid heuristic search method to solve the practical economic dispatch problem considering many nonlinear characteristics of power generators, and their operational constraints, such as transmission losses, valve-pointeffects, multi-fuel options, prohibited operating zones, ramp rate limits and spinning reserve. These practical operation constraints which can usually be found at the same time in realistic power system operations make the economic load dispatch problem a nonsmooth optimization problem having complex and nonconvex features with heavy equality and inequality constraints. The proposed approach combines in the most effective way the properties of two of the most popular evolutionary optimization techniques now in use for power system optimization, the Differential Evolution (DE) and Particle Swarm Optimization (PSO) algorithms. To improve the global optimization property of DE, the PSO procedure is integrated as additional mutation operator. The effectiveness of the proposed algorithm (termed DEPSO) is demonstrated by solving four kinds of ELD problems with nonsmooth and nonconvex solution spaces. The comparative results with some of the most recently published methods confirm the effectiveness of the proposed strategy to find accurate and feasible optimal solutions for practical ELD problems. (C) 2012 Elsevier B. V. All rights reserved.
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