Modified particle swarm optimization for economic-emission load dispatch of power system operation

Abdullah, Mohd Noor; Bakar, Ab Halim Abu; Rahim, Nasrudin Abd; Mokhlis, Hazlie

doi:10.3906/elk-1307-204

Cited by 12 publications

(8 citation statements)

References 34 publications

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“…PSO and imperialist competitive algorithm (ICA) were chosen for comparison. PSO was chosen because it is a very common and widely used algorithm in various applications since the past [32,33]. ICA is another recent optimisation method which was introduced in 2007 [34].…”

Section: Results Comparison With Other Optimisation Techniques and Pamentioning

confidence: 99%

Hybrid feature selection–artificial intelligence–gravitational search algorithm technique for automated transformer fault determination based on dissolved gas analysis

Illias

Chan

Mokhlis

2020

IET Generation, Transmission & Distribution

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Section: Results Comparison With Other Optimisation Techniques and Pamentioning

confidence: 99%

Hybrid feature selection–artificial intelligence–gravitational search algorithm technique for automated transformer fault determination based on dissolved gas analysis

Illias

Chan

Mokhlis

2020

IET Generation, Transmission & Distribution

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“…In the second part of the simulation studies, in order to compare the performance of the proposed G-NHGWO with that of GWO in solving a real-world engineering problem, they were used for solving the economic load dispatch (ELD) problem for 6-, 20-, and 40-generator test power systems [14][15][16]. The power transmission losses are considered in 6-and 20-generator test systems and the valve points effects are considered in 20-, and 40-generator test power systems [14][15][16]. The maximum number of objective function evaluations and the penalty factor for violating the power balance equation caused by generation deficit were set to 50,000 and 1 × 10 8 for all the studied test systems, respectively.…”

Section: Greedy Non-hierarchical Gwo (G-nhgwo)mentioning

confidence: 99%

A greedy non‐hierarchical grey wolf optimizer for real‐world optimization

Akbari

Rahimnejad

Gadsden

2021

Electronics Letters

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Grey wolf optimization (GWO) algorithm is a new emerging algorithm that is based on the social hierarchy of grey wolves as well as their hunting and cooperation strategies. Introduced in 2014, this algorithm has been used by a large number of researchers and designers, such that the number of citations to the original paper exceeded many other algorithms. In a recent study by Niu et al., one of the main drawbacks of this algorithm for optimizing real-world problems was introduced. In summary, they showed that GWO's performance degrades as the optimal solution of the problem diverges from 0. In this paper, by introducing a straightforward modification to the original GWO algorithm, that is, neglecting its social hierarchy, the authors were able to largely eliminate this defect and open a new perspective for future use of this algorithm. The efficiency of the proposed method was validated by applying it to benchmark and real-world engineering problems.

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“…They reveal that GSA outperforms the biogeography-based optimization in terms of solution quality and computational efficiency. The authors in (Abdullah et al 2015), propose a modified particle swarm optimization with time-varying acceleration coefficients (MPSO-TVAC) for economic emission load dispatch optimization. Similarly, a combination of PPF (Zhang, Gong and Ding.…”

Section: Introductionmentioning

confidence: 99%

Solving Multi-Objective Energy Management of a DC Microgrid using Multi-Objective Multiverse Optimization

Lagouir

Badri

Sayouti

2021

Int. J. Renew. Energy Dev.

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This paper deals with the multi-objective optimization dispatch (MOOD) problem in a DC microgrid. The aim is to formulate the MOOD to simultaneously minimize the operating cost, pollutant emission level of (NOx, SO2 and CO2) and the power loss of conversion devices. Taking into account the equality and inequality constraints of the system. Two approaches have been adopted to solve the MOOD issue. The scalarization approach is first introduced, which combines the weighted sum method with price penalty factor to aggregate objective functions and obtain Pareto optimal solutions. Whilst, the Pareto approach is based on the implementation of evolutionary multi-objective optimization solution. Single and multi-objective versions of multi-verse optimizer algorithm are, respectively, employed in both approaches to handle the MOOD. For each time step, a fuzzy set theory is selected to find the best compromise solution in the Pareto optimal set. The simulation results reveal that the Pareto approach achieves the best performances with a considerable decrease of 28.96 $/day in the daily operating cost, a slight reduction in the power loss of conversion devices from 419.79 kWh to 419.29 kWh, and in less computational time. While, it is noticing a small increment in the pollutant emission level from 11.54 kg/day to 12.21 kg/day, for the daily microgrid operation. This deviation can be fully covered when comparing the cost related to the treatment of these pollutants, which is only 5.55 $/day, to the significant reduction in the operating cost obtained using the Pareto approach.

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Modified particle swarm optimization for economic-emission load dispatch of power system operation

Cited by 12 publications

References 34 publications

Hybrid feature selection–artificial intelligence–gravitational search algorithm technique for automated transformer fault determination based on dissolved gas analysis

Hybrid feature selection–artificial intelligence–gravitational search algorithm technique for automated transformer fault determination based on dissolved gas analysis

A greedy non‐hierarchical grey wolf optimizer for real‐world optimization

Solving Multi-Objective Energy Management of a DC Microgrid using Multi-Objective Multiverse Optimization

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