Multiobjective scatter search approach with new combination scheme applied to solve environmental/economic dispatch problem

Silva, Marsil de Athayde Costa e; Klein, Carlos Eduardo; Mariani, Viviana Cocco; Coelho, Leandro dos Santos

doi:10.1016/j.energy.2013.02.045

Cited by 56 publications

(13 citation statements)

References 48 publications

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“…The Pareto optimal curve for the combined economic emission dispatch problems is obtained using multiobjective algorithms such as fuzzy dominance based bacterial foraging algorithm in [23], multiobjective scatter search approach in [24], multiobjective quasi-oppositional teaching-learning based optimization in [25], the multiobjective backtracking search algorithm in [26], a robust multiobjective opposition based greedy heuristic search with adaptive parameters in [27], Nondominated Sorting Genetic Algorithm II (NSGA II) and modified NSGA II in [28], multiobjective particle swarm optimization (MOPSO) in [29], multiobjective differential evolution MODE in [30], multiobjective harmony search in [31], and multiobjective bat algorithm in [32]. The holistic review on solution strategies for CEED problem is available in [33].…”

Section: A Literature Reviewmentioning

confidence: 99%

Combined Heat and Power Economic Emission Dispatch Using Hybrid NSGA II-MOPSO Algorithm Incorporating an Effective Constraint Handling Mechanism

Sundaram

2020

IEEE Access

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This research work proposes a synergistic hybrid metaheuristic algorithm a merger of Nondominated Sorting Genetic Algorithm II and Multiobjective Particle Swarm Optimization algorithm for solving the highly complicated combined heat and power economic emission dispatch problem to operate the power system economically and to reduce the impact of environmental pollution. During the iteration, based on ranking, the population is divided into two halves. The exploration is carried out by Nondominated Sorting Genetic Algorithm II using the upper half of the population. The modification of Multiobjective Particle Swarm Optimization to effectively exploit the lower half of the population is done by increasing the personal learning coefficient, decreasing the global learning coefficient and by using an adaptive mutation operator. To satisfy the linear, nonlinear constraints, and to ensure the populations always lie in the Feasible Operating Region of the cogeneration plant, an effective constraint handling mechanism is developed. The proposed hybrid algorithm with an effective constraint handling mechanism enhances the searching capability by effective information interchange. The algorithm is applied to standard test functions and test systems while considering the valve point effects of the thermal plants, transmission power losses, bounds of the units and feasible operating region of the cogeneration units. The hybrid algorithm can obtain a well spread and diverse Pareto optimal solution and also can converge to the actual Pareto optimal front faster than some of the existing algorithms. The statistical analysis reveals that the proposed hybrid algorithm is a viable alternative to solve this complicated and vital problem.

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Section: A Literature Reviewmentioning

confidence: 99%

Combined Heat and Power Economic Emission Dispatch Using Hybrid NSGA II-MOPSO Algorithm Incorporating an Effective Constraint Handling Mechanism

Sundaram

2020

IEEE Access

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“…Case Study 1 considered IEEE 30-bus 6-unit system with the power demand of 2.834 p.u. The input data of this case study are taken from [17] and the data are provided in per unit (p.u). The best optimal cost and emission solution produced by ABC are tabulated in Table 1.…”

Section: Case Study 1: Ieee 30-bus 6-unit Systemmentioning

confidence: 99%

Combined Economic and Emission Dispatch Solution using Artificial Bee Colony Algorithm with Fuzzy Approach

Abdullah¹,

Sim²,

Azmi³

et al. 2018

IJET

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The cost and emission minimization in power system operation become important issue in power dispatch due to increase of environmental pollution and fossil fuel price. Therefore, combined economic and emission dispatch (CEED) must be considered in generation scheduling in order to provide balanced solution for optimal cost and emissions level of power generation. In this paper, an Artificial Bee Colony (ABC) algorithm with Fuzzy best compromise solution is proposed to determine the optimal cost and emission level by converting the multi-objective (cost and emission) into single objective problem using weighted sum method approach. The best compromise solution among Pareto front solution was determined by fuzzy approach. The effectiveness of ABC algorithm has been validated in terms of the best solution, convergence behaviour and consistency for power system benchmark such as IEEE 30-bus 6-unit system and 10-unit system. The comparison study shows that ABC algorithm capable to obtain a better performance of minimizing the cost and emission level in power generation.

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“…These non-dominated solutions portray the tradeoff between fuel cost and emission objectives of CEED problem. Modern meta-heuristic optimization algorithms like Genetic Algorithm [4,5], Biogeography Based Optimization [6], Particle Swarm Optimization [7], Bacterial Foraging Algorithm [8], Scatter Search [9], Teaching Learning Based Optimization [10], Differential Evolution [11] and Harmony Search Algorithm [12] have been developed and successfully implemented to solve this complex, highly nonlinear, non-convex CEED problem.…”

Section: Introductionmentioning

confidence: 99%

Solution of Combined Economic Emission Dispatch Problem with Valve-Point Effect Using Hybrid NSGA II-MOPSO

A¹

2018

Particle Swarm Optimization With Applications

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This chapter formulates a multi-objective optimization problem to simultaneously minimize the objectives of fuel cost and emissions from the power plants to meet the power demand subject to linear and nonlinear system constraints. These conflicting objectives are formulated as a combined economic emission dispatch (CEED) problem. Various metaheuristic optimization algorithms have been developed and successfully implemented to solve this complex, highly nonlinear, non-convex problem. To overcome the shortcomings of the evolutionary multi-objective algorithms like slow convergence to Pareto-optimal front, premature convergence, local trapping, it is very natural to think of integrating various algorithms to overcome the shortcomings. This chapter proposes a hybrid evolutionary multi-objective optimization framework using Non-Dominated Sorting Genetic Algorithm II and Multi-Objective Particle Swarm Optimization to solve the CEED problem. The hybrid method along with the proposed constraint handling mechanism is able to balance the exploration and exploitation tasks. This hybrid method is tested on IEEE 30 bus system with quadratic cost function considering transmission loss and valve point effect. The Pareto front obtained using hybrid approach demonstrates that the approach converges to the true Pareto front, finds the diverse set of solutions along the Pareto front and confirms its potential to solve the CEED problem.

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Multiobjective scatter search approach with new combination scheme applied to solve environmental/economic dispatch problem

Cited by 56 publications

References 48 publications

Combined Heat and Power Economic Emission Dispatch Using Hybrid NSGA II-MOPSO Algorithm Incorporating an Effective Constraint Handling Mechanism

Combined Heat and Power Economic Emission Dispatch Using Hybrid NSGA II-MOPSO Algorithm Incorporating an Effective Constraint Handling Mechanism

Combined Economic and Emission Dispatch Solution using Artificial Bee Colony Algorithm with Fuzzy Approach

Solution of Combined Economic Emission Dispatch Problem with Valve-Point Effect Using Hybrid NSGA II-MOPSO

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