This paper presents a novel approach, the Gaussian Mixture Method-enhanced Cuckoo Optimization Algorithm (GMMCOA), designed to optimize power flow decision parameters, with a specific focus on minimizing fuel cost, em...
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This paper presents a novel approach, the Gaussian Mixture Method-enhanced Cuckoo Optimization Algorithm (GMMCOA), designed to optimize power flow decision parameters, with a specific focus on minimizing fuel cost, emissions, network loss, and voltage deviation. GMMCOA integrates the strengths of COA and GMM while mitigating their individual limitations. While COA offers robust search capabilities, it suffers from initial parameter dependency and the risk of getting trapped in local optima. Conversely, GMM delivers high-speed performance but requires guidance to identify the best solution. By combining these methods, GMMCOA achieves an intelligent approach characterized by reduced parameter dependence and enhanced convergence speed. The effectiveness of GMMCOA is demonstrated through extensive testing on both the IEEE 30-bus and the large-scale 118-bus test systems. Notably, for the 118-bus test system, GMMCOA achieved a minimum cost of $129,534.7529 per hour and $103,382.9225 per hour in cases with and without the consideration of renewable energies, respectively, surpassing outcomes produced by alternative algorithms. Furthermore, the proposed method is benchmarked against the CEC 2017 test functions. Comparative analysis with state-of-the-art algorithms, under consistent conditions, highlights the superior performance of GMMCOA across various optimization functions. Remarkably, GMMCOA consistently outperforms its competitors, as evidenced by simulation results and Friedman examination outcomes. With its remarkable performance across diverse functions, GMMCOA emerges as the preferred choice for solving optimization problems, emphasizing its potential for real-world applications.
Economic dispatch aims to make the minimal operating cost of power plant by determining the optimal power produced by each generating unit under constrained circumstances. At the present time, power utilities have stu...
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Economic dispatch aims to make the minimal operating cost of power plant by determining the optimal power produced by each generating unit under constrained circumstances. At the present time, power utilities have stumble upon a fresh dispatch problem, because of crucial concern over fuel shortages. Fuel suppliers increase constraints in their fuel supply contracts that force the utilities to schedule the generation on the basis of fuel availability. With the ever increasing proportion of the fuel budget in the total operating costs, the fuel restricted economic dispatch problem has popped up. A new methodology based on a teaching learning-based optimization algorithm is proposed for solving fuel restricted economic dispatch problems. The potential of the proposed method is tested with standard test systems which include different cost characteristics. The obtained results are compared to other algorithms surfaced in the recent state-of-the-art literature, confirming the effectiveness of the developed methodology.
Economic Dispatch is one of the power systems management tools. It is used to allocate an amount of power generation to the generating units to meet the active load demands. The Economic Dispatch problem is a large-sc...
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ISBN:
(纸本)9781479938667
Economic Dispatch is one of the power systems management tools. It is used to allocate an amount of power generation to the generating units to meet the active load demands. The Economic Dispatch problem is a large-scale nonlinear constrained optimization problem. In this paper, two novel techniques are developed to solve the non-convex Economic Dispatch problem. Firstly, a novel approximation of the non-convex generation cost function is developed to solve non-convex Economic Dispatch problem with the transmission losses. This approximation enables the use of gradient and Newton techniques to solve the non-convex Economic Dispatch problem. Secondly, Q-Learning with eligibility traces technique is adopted to solve the nonconvex Economic Dispatch problem with valve point loading effects, multiple fuel options, and power transmission losses. The eligibility traces are used to speed up the Q-Learning process. This technique showed superior results compared to other heuristic techniques.
In literature, economic power dispatch problems are generally categorized as convex and non-convex optimization problems. In this study, incremental artificial bee colony (IABC) and incremental artificial bee colony w...
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In literature, economic power dispatch problems are generally categorized as convex and non-convex optimization problems. In this study, incremental artificial bee colony (IABC) and incremental artificial bee colony with local search (IABC-LS) have been used for the solution of the economic dispatch problem with valve point effect. In these kind of problems, fuel cost curve increases as sinusoidal oscillations. In the solution of the problem B loss matrix has been used for the calculation of the line losses. Total fuel cost has been minimized under electrical constraints. IABC and IABC-LS methods have been applied to four different test systems one with 6 buses 3 generators, the other with 14 buses 5 generators (IEEE), the third one with 30 buses 6 generators (IEEE) and the last one is 40-generator system. The obtained best values have been compared with different methods in literature and the results of them have been discussed. (C) 2012 Elsevier B. V. All rights reserved.
In this paper it is intended to solve an Economical Dispatch (ED) problem with a new tool, named Sensing Cloud Optimization (SCO). It is a technique based on clouds of particles which allow a dynamic change in search ...
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ISBN:
(纸本)9781479902248
In this paper it is intended to solve an Economical Dispatch (ED) problem with a new tool, named Sensing Cloud Optimization (SCO). It is a technique based on clouds of particles which allow a dynamic change in search space. It has appropriate heuristic characteristic to solve not convex, not differentiable and highly constrained optimisation problems. It is provided with a statistical analysis which determines the cloud's dimension with dynamic adjustments in search space in order to accelerate the convergence and to avoid to get trapped in local minima. Two case studies are presented in which SCO demonstrated good performances reaching lower cost values where compared with other techniques.
In this paper it is intended to solve an Economical Dispatch (ED) problem with a new tool, named Sensing Cloud Optimization (SCO). It is a technique based on clouds of particles which allow a dynamic change in search ...
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ISBN:
(纸本)9781479902255
In this paper it is intended to solve an Economical Dispatch (ED) problem with a new tool, named Sensing Cloud Optimization (SCO). It is a technique based on clouds of particles which allow a dynamic change in search space. It has appropriate heuristic characteristic to solve not convex, not differentiable and highly constrained optimisation problems. It is provided with a statistical analysis which determines the cloud's dimension with dynamic adjustments in search space in order to accelerate the convergence and to avoid to get trapped in local minima. Two case studies are presented in which SCO demonstrated good performances reaching lower cost values where compared with other techniques.
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