Multi-area Environmental Economic Dispatch with Reserve Constraints Using Enhanced Particle Swarm Optimization

Jadoun, Vinay Kumar; Gupta, Nikhil; Niazi, K. R.; Swarnkar, Anil; Bansal, R.C.

doi:10.1080/15325008.2015.1044052

Cited by 14 publications

(7 citation statements)

References 41 publications

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“…An enhanced PSO (EPSO) was developed to solve the MAEED problem with reserve constraints [20]. The PSO parameters were adaptively varied to preserve the balance among cognitive and social conduct of the swarm.…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Squirrel Search Algorithm for Multi-Area Economic Environmental Dispatch With Multiple Fuels and Valve Point Effects

Sakthivel¹,

Goh²,

Subramanian³

et al. 2021

IEEE Access

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The essential goal of multi-area economic environmental dispatch (MAEED) is to determine the optimum power generation schedule of each unit and power transfer between the areas in order to minimize fuel costs and pollutant emissions, when the generation, power balance and tie-line limits are satisfied. This paper focuses on developing multi-objective squirrel search algorithm (MOSSA) to solve the MAEED problem, of which the goal is to simultaneously minimize the total fuel cost and emission considering valve point effects and multi-fuel options. The proposed MOSSA combines squirrel search algorithm along with Pareto-dominance theory to generate non-dominated solutions. It uses an external elitist depository mechanism with crowding distance sorting to preserve the distribution diversity of Paretooptimal solutions as the evolution continues. In addition, a fuzzy decision maker is used to select the best compromised solution from the obtained Pareto frontiers. Furthermore, the MAEED problem is unraveled by squirrel search algorithm based weighted sum approach with price penalty factors, artificial bee colony and exchange market algorithm. Different case studies are performed on 10-unit with three-area system, 40unit with four-area system and 140-unit real Korean power system considering valve point effects and multi-fuel options which testify the supremacy of the suggested approach. The comparisons with state-ofthe-art approaches suggest that MOSSA can generate more competitive trade-off solutions for solving the MAEED problems.INDEX TERMS Fuzzy decision maker, multi-area economic and environmental dispatch, multiple fuels, Pareto optimal front, squirrel search algorithm.

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Section: Introductionmentioning

confidence: 99%

Multi-Objective Squirrel Search Algorithm for Multi-Area Economic Environmental Dispatch With Multiple Fuels and Valve Point Effects

Sakthivel¹,

Goh²,

Subramanian³

et al. 2021

IEEE Access

View full text Add to dashboard Cite

show abstract

“…The research papers [9], [12]- [19] used heuristic methods to solve the MAED problem. The paper [12], presents a decomposition approach to multiarea generation scheduling problem using expert system.…”

Section: Introductionmentioning

confidence: 99%

“…The existing situation is that most of the conventional gradient and heuristic methods are time consuming and still use a sequential method to solve the MACEED problem [14]- [17], [26], and [27]. The traditional heuristic methods for MACEED problem do not always guarantee global best solutions; they often achieve a fast and near global optimal solution [1], [2], [14]- [19]. Researches have constantly observed that all these methods very quickly find a good local solution but get stuck there for a number of iterations without further improvement, sometimes causing premature convergence.…”

Section: Introductionmentioning

confidence: 99%

Decomposition-Coordinating Method for Parallel Solution of a Multi-area Combined Economic Emission Dispatch Problem

Krishnamurthy

Tzoneva

2016

IJECE

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<p>Multi-area Combined Economic Emission Dispatch (MACEED) problem is an optimization task in power system operation for allocating the amount of generation to the committed units within the system areas. Its objective is to minimize the fuel cost and the quantity of emissions subject to the power balance, generator limits, transmission line and tie-line constraints. The solutions of the MACEED problem in the conditions of deregulation are difficult, due to the model size, nonlinearities, and the big number of interconnections, and require intensive computations in real-time. High-Performance Computing (HPC) gives possibilities for the reduction of the problem complexity and the time for calculation by the use of parallel processing techniques for running advanced application programs efficiently, reliably and quickly. These applications are considered as very new in the power system control centers because there are not available optimization methods and software based on them that can solve the MACEED problem in parallel, paying attention to the existence of the power system areas and the tie-lines between them. A decomposition-coordinating method based on Lagrange’s function is developed in this paper. Investigations of the performance of the method are done using IEEE benchmark power system models.</p>

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“…The traditional heuristic methods for MACEED problem do not always guarantee global best solutions; they often achieve a fast and near global optimal solution [1], [2],[14]- [19]. Researches have constantly observed that all these methods very quickly find a good local solution but get stuck there for a number of iterations without further improvement, sometimes causing premature convergence.…”

mentioning

confidence: 99%

“…MACEED and SACEED problem is solved using PSO method in [19] and [20] respectively. SACEED problem with valve point is considered in [21].…”

mentioning

confidence: 99%

Decomposition-Coordinating Method for Parallel Solution of a Multi-area Combined Economic Emission Dispatch Problem

Krishnamurthy

Tzoneva

2016

IJECE

View full text Add to dashboard Cite

Multi-area Combined Economic Emission Dispatch (MACEED) problem is an optimization task in power system operation for allocating the amount of generation to the committed units within the system areas. Its objective is to minimize the fuel cost and the quantity of emissions subject to the power balance, generator limits, transmission line and tie-line constraints. The solutions of the MACEED problem in the conditions of deregulation are difficult, due to the model size, nonlinearities, and the big number of interconnections, and require intensive computations in real-time. HighPerformance Computing (HPC) gives possibilities for the reduction of the problem complexity and the time for calculation by the use of parallel processing techniques for running advanced application programs efficiently, reliably and quickly. These applications are considered as very new in the power system control centers because there are not available optimization methods and software based on them that can solve the MACEED problem in parallel, paying attention to the existence of the power system areas and the tie-lines between them. A decomposition-coordinating method based on Lagrange's function is developed in this paper. Investigations of the performance of the method are done using IEEE benchmark power system models.

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Multi-area Environmental Economic Dispatch with Reserve Constraints Using Enhanced Particle Swarm Optimization

Cited by 14 publications

References 41 publications

Multi-Objective Squirrel Search Algorithm for Multi-Area Economic Environmental Dispatch With Multiple Fuels and Valve Point Effects

Multi-Objective Squirrel Search Algorithm for Multi-Area Economic Environmental Dispatch With Multiple Fuels and Valve Point Effects

Decomposition-Coordinating Method for Parallel Solution of a Multi-area Combined Economic Emission Dispatch Problem

Decomposition-Coordinating Method for Parallel Solution of a Multi-area Combined Economic Emission Dispatch Problem

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