A Hybrid EP and SQP for Dynamic Economic Dispatch with Nonsmooth Fuel Cost Function

Attaviriyanupap, Pathom; Kita, H.; Tanaka, Eiichi; Hasegawa, Jun

doi:10.1109/mper.2002.4312139

Cited by 354 publications

(125 citation statements)

References 17 publications

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“…In order to obtain a high quality solution , the first part, EP is applied to obtain a near global solution. After the specified termination criteria for EP is reached, EPSO is applied in the second part by using the solution from EP as an initial starting point and searches by using a gradient information to obtain the final optimal solution [2].…”

Section: Non-smooth Cost Function With Valve-pointmentioning

confidence: 99%

“…velocity of individual of iteration at k ω weight parameter C 1 , C 2 acceleration factors rand 1 rand 2 random numbers between 0 and 1. X i k Position of individual i at iteration k Pbest best position of group throughout iteration k Each individual moves from the current position to the next one by the using the following equation:…”

Section: Implementation Of Pso For Ed Problemsmentioning

confidence: 99%

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Solving Economic Load Dispatch Problem Considering Transmission Losses by a Hybrid EP-EPSO Algorithm for Solving both Smooth and Non-Smooth Cost FunctionSolving Economic Load Dispatch Problem Considering Transmission Losses by a Hybrid EP-EPSO Algorithm for Solving both Smooth and Non-Smooth Cost Function

Pandian¹,

Thanushkodi²

2010

IJCEE

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Abstract-An Evolutionary Programming (EP) and Efficient Particle Swarm Optimization (EPSO) techniques are employed to solve Economic Dispatch (ED) problems including transmission losses in power system is presented in this paper. This paper is clearly justified with the results separately obtained for the above two techniques and also provided with the results by applying both the algorithms separately. With practical consideration, ED will have non-smooth cost functions with equality and inequality constraints that make the problem, a large-scale highly constrained nonlinear optimization problem. The proposed method expands the original PSO to handle a different approach for satisfying those constraints. In this paper, an Efficient Particle Swarm Optimization (EPSO) technique is employed so that optimized results are obtained, and by applying EP, faster convergence is obtained . To demonstrate the effectiveness of the proposed method it is being applied to test ED problems, one with smooth and other with non smooth cost functions considering valve-point loading effects. Comparison with other optimization and hybrid algorithm techniques showed the superiority of the proposed EP-EPSO approach and confirmed its potential for solving nonlinear economic load dispatch problems with losses.

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Section: Non-smooth Cost Function With Valve-pointmentioning

confidence: 99%

Section: Implementation Of Pso For Ed Problemsmentioning

confidence: 99%

Solving Economic Load Dispatch Problem Considering Transmission Losses by a Hybrid EP-EPSO Algorithm for Solving both Smooth and Non-Smooth Cost FunctionSolving Economic Load Dispatch Problem Considering Transmission Losses by a Hybrid EP-EPSO Algorithm for Solving both Smooth and Non-Smooth Cost Function

Pandian¹,

Thanushkodi²

2010

IJCEE

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“…Memetic Algorithms have been proved to be orders of magnitude faster and more accurate than evolutionary algorithms for different classes of problems. As reported in the literature, hybrid methods combining probabilistic methods and deterministic methods have found success in solving complex optimization problems [4][5][6].…”

Section: Hybrid Approaches Incorporating Local Search and Othersmentioning

confidence: 99%

Hybrid Evolutionary Algorithms: Methodologies, Architectures, and Reviews

Groşan

Abraham

2007

Studies in Computational Intelligence

150

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Summary. Evolutionary computation has become an important problem solving methodology among many researchers. The population-based collective learning process, selfadaptation, and robustness are some of the key features of evolutionary algorithms when compared to other global optimization techniques. Even though evolutionary computation has been widely accepted for solving several important practical applications in engineering, business, commerce, etc., yet in practice sometimes they deliver only marginal performance. Inappropriate selection of various parameters, representation, etc. are frequently blamed. There is little reason to expect that one can find a uniformly best algorithm for solving all optimization problems. This is in accordance with the No Free Lunch theorem, which explains that for any algorithm, any elevated performance over one class of problems is exactly paid for in performance over another class. Evolutionary algorithm behavior is determined by the exploitation and exploration relationship kept throughout the run. All these clearly illustrates the need for hybrid evolutionary approaches where the main task is to optimize the performance of the direct evolutionary approach. Recently, hybridization of evolutionary algorithms is getting popular due to their capabilities in handling several real world problems involving complexity, noisy environment, imprecision, uncertainty, and vagueness. In this chapter, first we emphasize the need for hybrid evolutionary algorithms and then we illustrate the various possibilities for hybridization of an evolutionary algorithm and also present some of the generic hybrid evolutionary architectures that has evolved during the last couple of decades. We also provide a review of some of the interesting hybrid frameworks reported in the literature.

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“…Recently, hybrid optimization techniques which combine different approaches receive widespread concerns. In [21], the authors presented a hybrid EP and sequential quadratic programming (SQP) for solving the ED problem with nonsmooth fuel cost function. A hybrid self-tuning DE was proposed to solve the ED problem with kinds of constraints in [22].…”

Section: Introductionmentioning

confidence: 99%

Sequential quadratic programming particle swarm optimization for wind power system operations considering emissions

Zhang

Yao

et al. 2013

J. Mod. Power Syst. Clean Energy

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In this paper, a computation framework for addressing combined economic and emission dispatch (CEED) problem with valve-point effects as well as stochastic wind power considering unit commitment (UC) using a hybrid approach connecting sequential quadratic programming (SQP) and particle swarm optimization (PSO) is proposed. The CEED problem aims to minimize the scheduling cost and greenhouse gases (GHGs) emission cost. Here the GHGs include carbon dioxide (CO 2 ), nitrogen dioxide (NO 2 ), and sulphur oxides (SO x ). A dispatch model including both thermal generators and wind farms is developed. The probability of stochastic wind power based on the Weibull distribution is included in the CEED model. The model is tested on a standard system involving six thermal units and two wind farms. A set of numerical case studies are reported. The performance of the hybrid computational method is validated by comparing with other solvers on the test system.

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A Hybrid EP and SQP for Dynamic Economic Dispatch with Nonsmooth Fuel Cost Function

Cited by 354 publications

References 17 publications

Hybrid Evolutionary Algorithms: Methodologies, Architectures, and Reviews

Sequential quadratic programming particle swarm optimization for wind power system operations considering emissions

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