Economic Dispatch Solution Using Moth-Flame Optimization Algorithm

Sulaiman, Mohd Herwan; Mustaffa, Zuriani; Rashid, Mamunur; Daniyal, Hamdan

doi:10.1051/matecconf/201821403007

Cited by 12 publications

(6 citation statements)

References 14 publications

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“…With these features, for example, compared with other metaheuristic methods, MFO yielded better results in economic dispatch problems [29], [30], software fault prediction datasets [31], and energy efficient modeling for assembly sequence planning [32]. Moreover, MFO was modified and improved for gaining better performances applied to solve problems as discussed in [33]- [40].…”

Section: Introductionmentioning

confidence: 99%

Short-Term Operational Scheduling of Unit Commitment Using Binary Alternative Moth-Flame Optimization

Kigsirisin

Miyauchi

2021

IEEE Access

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This study proposes a novel binary optimization method for solving unit commitment (UC) problems. Developed from moth-flame optimization (MFO), the proposed method is called binary alternative MFO (BAMFO). Originally, each MFO moth is restricted to fly toward a predetermined flame (solution), causing it to be trapped in the local optimum of that flame and reducing its performance. To address these drawbacks, BAMFO represents four alternative characteristics for moths to discover better flames all over the search space. The exploration and exploitation performances of BAMFO via these characteristics are balanced by a random parameter competing with the criteria of each characteristic. A repair strategy working with the priority list is created to adjust any unit status from the BAMFO simulation to satisfy all UC constraints. Then, Lambda-iteration method is adopted to solve economic dispatch problems. The proficiency of BAMFO is verified in various scales of unit systems for short-term operational scheduling. The results show that BAMFO yields better solutions than do other methods. The Wilcoxon signed rank test was conducted to prove the predominance of BAMFO.

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Section: Introductionmentioning

confidence: 99%

Short-Term Operational Scheduling of Unit Commitment Using Binary Alternative Moth-Flame Optimization

Kigsirisin

Miyauchi

2021

IEEE Access

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“…The values of the proper fitness values corresponding to all moths; can be assumed and stored as below [41], [43]:…”

Section: Moth-flame Optimizermentioning

confidence: 99%

“…The initialization function I randomly generates a population of moths with their fitness values as expressed in the following When the initialization process is finished, the P function starts operation until the termination function T is fulfilled. The logarithmic spiral function is considered as the main mechanism for updating the position of moths in accordance with their corresponding flame [43].…”

Section: Moth-flame Optimizermentioning

confidence: 99%

Application of Different Optimization Algorithms for Optimal Sizing of PV/Wind/Diesel/Battery Storage Stand-Alone Hybrid Microgrid

et al. 2019

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In this paper, a simulation model describing the operation of a PV/wind/diesel hybrid microgrid system with battery bank storage has been proposed. Optimal sizing of the proposed system has been presented to minimize the cost of energy (COE) supplied by the system while increasing the reliability and efficiency of the system presented by the loss of power supply probability (LPSP). Novel optimization algorithms of Whale Optimization Algorithm (WOA), Water Cycle Algorithm (WCA), Moth-Flame Optimizer (MFO), and Hybrid particle swarm-gravitational search algorithm (PSOGSA) have been applied for designing the optimized microgrid. Moreover, a comprehensive comparison has been accomplished between the proposed optimization techniques. The optimal sizing of the system components has been carried out using real-time meteorological data of Abu-Monqar village located in the Western Desert of Egypt for the first time for developing this promising remote area. Statistical study for determining the capability of the optimization algorithm in finding the optimal solution has been presented. Simulation results confirmed the promising performance of the hybrid WOA over the other algorithms. INDEX TERMS Isolated microgrids, cost of energy (COE), loss of power supply probability (LPSP), optimization.

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“…Traditional methods, i.e., lambda iteration [7], have failed to solve such a problem. The ED problem has been solved using nonconventional ways, such as artificial bee colony algorithms (ABC) [8], hybrid grey wolf optimizer algorithms (GWO) [9], genetic algorithms (GA) [10], particle swarm optimization (PSO) [11], moth flame optimization, algorithms (MFA) [12], chameleon swarm algorithm (CSA) [13], firefly algorithms (FA) [14]. Also, the woodpecker mating algorithm (WMA) [15], bald eagle search (BES) algorithm [16], whale optimization algorithm (WOA) [17], and bat-inspired algorithm [18] were also explored to solve the ED problems.…”

Section: Introductionmentioning

confidence: 99%

Generalized Normal Distribution Optimization Algorithm for Economic Dispatch with Renewable Resources Integration

Hoque,

Rashidul Islam,

Shafiullah

et al. 2023

J Energ Power Technol

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In an electric power system operation, the main goal of economic dispatch (ED) is to schedule the power outputs of committed generating units efficiently. This involves consideration of relevant system equality and inequality constraints to meet the required power demand at the lowest possible operational cost. This is a challenging optimization problem for power system operators that can be dealt with efficient meta-heuristic algorithms. This article uses a recent meta-heuristic approach named the generalized normal distribution optimization (GNDO) algorithm to achieve near-optimal solutions. The efficacy of the proposed GNDO algorithm is validated through experimentation on three distinct test power system networks: one with three thermal units, the second one with six thermal-unit, and the third one with ten thermal units. The algorithm's performance is also assessed on a power network with renewable energy sources. All analyses of the four test cases are conducted on the MATLAB/SIMULINK platform. Finally, this article also compares the obtained results with other literature-reported strategies, genetic algorithm (GA), particle swarm optimization (PSO), whale optimization algorithm (WOA), flower pollination algorithm (FPA), and bald eagle search (BES) algorithm. It is evident from the simulated cases that the employed GNDO algorithm exhibits superior performance for two cases and competitive performance for the remaining cases in achieving the lowest operation costs and power losses.

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Economic Dispatch Solution Using Moth-Flame Optimization Algorithm

Cited by 12 publications

References 14 publications

Short-Term Operational Scheduling of Unit Commitment Using Binary Alternative Moth-Flame Optimization

Short-Term Operational Scheduling of Unit Commitment Using Binary Alternative Moth-Flame Optimization

Application of Different Optimization Algorithms for Optimal Sizing of PV/Wind/Diesel/Battery Storage Stand-Alone Hybrid Microgrid

Generalized Normal Distribution Optimization Algorithm for Economic Dispatch with Renewable Resources Integration

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