Environmental/economic power dispatch using a Hybrid Big Bang–Big Crunch optimization algorithm

Labbi, Yacine; Attous, Djilani Ben

doi:10.1007/s13198-013-0210-5

Cited by 10 publications

(3 citation statements)

References 20 publications

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“…For solving the CEED problem with consideration of the valve-point effect, the authors in Jiang et al (2014) presented a hybrid approach including PSO and GSA techniques with the weighted sum method. Similarly, in Labbi and Ben Attous (2014), both objective functions, cost and emission, are summed based on the weighted sum method, and the associated problem is solved by Hybrid Big Bang-Big Crunch optimization algorithm.…”

Section: Literature Reviewmentioning

confidence: 99%

An efficient bi-objective approach for dynamic economic emission dispatch of renewable-integrated microgrids

Gholami

Abbasi

Azizivahed

et al. 2022

J Ambient Intell Human Comput

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To overcome the challenges of conventional power systems, such as increasing power demand, requirements of stability and reliability, and increasing integration of renewable energy sources, the concept of microgrids was introduced and is currently one of the most important solutions for solving the mentioned problems. Generally, microgrids have two operating modes, namely grid-connected and islanded modes. Based on the literature and its unique characteristics, the islanded mode is more challenging than the other one. In this paper, a new self-adaptive comprehensive differential evolution (SACDE) algorithm is proposed for solving economic load dispatch (ELD) and combined economic emission dispatch (CEED) problems, achieving optimal power consumption in isolated microgrids. Initially, SACDE is employed for solving the ELD problem as a single-objective function, meaning that the operational cost is just considered as the objective function, and thereby, the resources are scheduled accordingly. Then, a multi-objective platform based on SACDE is also proposed to solve the CEED problem. It means two objective functions, including operational cost and emission, are simultaneously optimized. For evaluating the performance of the proposed method, three different scenarios under various cases are considered. According to the results, when SACDE is employed to solve the single objective function (cost minimization) problem, it has better performance than other methods. In terms of the bi-objective scheme (cost and emission minimization), SACDE is significantly superior to the price penalty factor technique which is frequently used in previous studies.

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Section: Literature Reviewmentioning

confidence: 99%

An efficient bi-objective approach for dynamic economic emission dispatch of renewable-integrated microgrids

Gholami

Abbasi

Azizivahed

et al. 2022

J Ambient Intell Human Comput

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“…There have also been individual attempts to make changes in the main algorithm, such as PSO segmentation, which divides the PSO into sub-algorithms, and the big-bang crunch algorithm, which depends on the redistribution of the particles when one or more particles is left out of the initial input range. 16,[32][33][34] Inspired by super high dense solutions in the big-bang crunch theory of the universe creation, an algorithm simulating this phenomenon was introduced and investigated by Erol and Eksin. 35 The algorithm depends on neglecting the small values of mass and create the new galaxies for high values of mass, to solve many known benchmark functions.…”

Section: Introductionmentioning

confidence: 99%

Garra Rufa‐inspired optimization technique

Jaber¹,

Abdulbari

Shalash³

et al. 2020

Int J Intell Syst

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Natural selection has inspired researchers to develop and apply several intelligent optimization techniques in the past few decades. Generally, in artificial intelligence optimization, the particles follow a local or global best particle until finding an acceptable solution. In well‐ developed optimization techniques, such as swarm optimization (PSO) and the firefly algorithm (FA), getting around the initial optimal value of the group and randomly checking the effect of the surrounding points may lead to a better solution than the initial optimal value. The present work was inspired by the fascinating movement of Garra Rufa fish between two immersed legs during a regular “fish massage session.” A new optimization approach is proposed and modeled based on the movements of Garra Rufa fish, in which the particles are separated into groups, and the best optimal value leads each group for the group. Also, some of these particles are allowed to change groups depending on the fitness of the leaders of the groups. The suggested strategy is then compared with PSO and FA using multiple test optimization functions, such as the Ackley, Hartmann, Michalewicz, Shubert, Easom, Bohachevsky, and Rastrigin functions. Also, a multiobjective real issue in power system is tested using the proposed methods where the objectives were cumulative voltage deviation and power losses of three weight sets during the selection allocation of distribution generators. The results show that the proposed method provides good data and greater convergence to the optimal point compared with the classical methods for most of the functions tested.

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“…However, when power companies generate electricity, they produce harmful pollutants. Thus, these companies have to reduce the emissions of these harmful pollutants, such as CO 2 , CO and NOx [8]. Therefore, the economic and emission dispatch (EED) problem is a highly critical problem for power companies.…”

Section: Introductionmentioning

confidence: 99%

Economic and Emission Dispatch Using Ensemble Multi-Objective Differential Evolution Algorithm

et al. 2018

Sustainability

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Abstract:In the past two decades, China's manufacturing industry has achieved great success. However, pollution and environmental impacts have become more serious while this industry has grown. The economic and emission dispatch (EED) problem is a typical multi-objective optimization problem with conflicting fuel costs and pollution emission objectives. An ensemble multi-objective differential evolution (EMODE) is proposed to tackle the EED problem. First, the equality constraints of the problem have been transformed into inequality constraints. Next, two mutation strategies DE/rand/1 and DE/current-to-rand/1 have been implemented to improve the conventional DE. The performance of the proposed algorithm is evaluated on six test functions and the numerical results have indicated that the proposed algorithm is effective. The proposed algorithm EMODE is used to solve a series of six generators and eleven generators in the EED problem. The experimental results obtained are compared with those reported using single optimization algorithms and multi-objective evolutionary algorithms (MOEAs). The results have revealed that the proposed algorithm EMODE either matches or outperforms those algorithms. The proposed algorithm is an effective candidate to optimize the manufacturing industry of China.

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Environmental/economic power dispatch using a Hybrid Big Bang–Big Crunch optimization algorithm

Cited by 10 publications

References 20 publications

An efficient bi-objective approach for dynamic economic emission dispatch of renewable-integrated microgrids

An efficient bi-objective approach for dynamic economic emission dispatch of renewable-integrated microgrids

Garra Rufa‐inspired optimization technique

Economic and Emission Dispatch Using Ensemble Multi-Objective Differential Evolution Algorithm

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