Non-dominated sorting genetic algorithm-II for robust multi-objective optimal reactive power dispatch

Li, Zhihuan; Li, Yinhong; Duan, Xianzhong

doi:10.1049/iet-gtd.2010.0105

Cited by 68 publications

(40 citation statements)

References 18 publications

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“…In the area of energy, the NSGA-II algorithm has been widely used in the reactive power planning problem [15,16,17,18,19], the optimal power flow problem [20,21,22] and the GEP problem [6,23].…”

Section: Methodsmentioning

confidence: 99%

A multi-step multi-objective generation expansion planning model-A case study in Mexico

Palácios¹,

Valdés²,

Batres³

2018

SGCE

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Planning in the energy sector implies multiple and conflicting objectives. Multi-objective models allow the analysis of the inter-relationships and trade-off solutions to be obtained. This paper presents a mixed integer linear model for multi-step multi-objective generation expansion planning (MMGEP). The MMGEP problem is defined as the problem of determining the answers to the following questions: What types of generation technologies are to be added to the grid? What is the capacity of each new generation plant? Where will the plant be located? When will the plant be located? The MMGEP objectives are to minimize the global cost of the system, minimize the environmental impact and maximize the social profits. The proposed model is based on a real power system in Mexico for the planning period between 2017 and 2037. The problem was solved using the NSGA-II algorithm.

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Section: Methodsmentioning

confidence: 99%

A multi-step multi-objective generation expansion planning model-A case study in Mexico

Palácios¹,

Valdés²,

Batres³

2018

SGCE

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show abstract

“…5.2052 6.4149 Several studies have been done around these topics up to now and different multi objective optimization algorithms used in order to find better solutions [30][31][32][33]. But the disadvantage of these algorithms such as NSGA II is that they do not cover entire space.…”

Section: Bulletin Of Eeimentioning

confidence: 99%

New Hybrid Non-Dominated Sorting Differential Evolutionary Algorithm

Bakhshipour¹,

Namdari²,

Bahador³

2016

Bulletin EEI

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“…Strength Pareto evolutionary algorithm (SPEA) [21], multiple evolutionary algorithms with adaptive selection strategy (MEAASS) [22], and modified DE (MDE) [23] have been applied to solve a multiobjective ORPD model minimizing network real power loss and voltage deviation of the system, simultaneously. In [24,25], voltage stability index and real power loss were considered as two objective functions and NSGA-II was utilized as an optimization technique. A multiobjective teaching learning algorithm based on decomposition (MOTLA/D) [26] was also utilized to solve the same biobjective problem.…”

Section: Journal Of Electrical and Computer Engineeringmentioning

confidence: 99%

Application of Hybrid MOPSO Algorithm to Optimal Reactive Power Dispatch Problem Considering Voltage Stability

Zeng

Sun

2014

Journal of Electrical and Computer Engineering

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This study presents a novel hybrid multiobjective particle swarm optimization (HMOPSO) algorithm to solve the optimal reactive power dispatch (ORPD) problem. This problem is formulated as a challenging nonlinear constrained multiobjective optimization problem considering three objectives, that is, power losses minimization, voltage profile improvement, and voltage stability enhancement simultaneously. In order to attain better convergence and diversity, this work presents the use of combing the classical MOPSO with Gaussian probability distribution, chaotic sequences, dynamic crowding distance, and self-adaptive mutation operator. Moreover, multiple effective strategies, such as mixed-variable handling approach, constraint handling technique, and stopping criteria, are employed. The effectiveness of the proposed algorithm for solving the ORPD problem is validated on the standard IEEE 30-bus and IEEE 118-bus systems under nominal and contingency states. The obtained results are compared with classical MOPSO, nondominated sorting genetic algorithm (NSGA-II), multiobjective evolutionary algorithm based on decomposition (MOEA/D), and other methods recently reported in the literature from the point of view of Pareto fronts, extreme, solutions and multiobjective performance metrics. The numerical results demonstrate the superiority of the proposed HMOPSO in solving the ORPD problem while strictly satisfying all the constraints.

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Non-dominated sorting genetic algorithm-II for robust multi-objective optimal reactive power dispatch

Cited by 68 publications

References 18 publications

A multi-step multi-objective generation expansion planning model-A case study in Mexico

A multi-step multi-objective generation expansion planning model-A case study in Mexico

New Hybrid Non-Dominated Sorting Differential Evolutionary Algorithm

Application of Hybrid MOPSO Algorithm to Optimal Reactive Power Dispatch Problem Considering Voltage Stability

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