Scalable unit commitment by memory-bounded ant colony optimization with local search

Saber, Ahmed Yousuf; Alshareef, Abdulaziz

doi:10.1016/j.ijepes.2008.01.001

Cited by 31 publications

(13 citation statements)

References 40 publications

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“…NACO utilization also needed to be modified because there were differences in objective functions in its utilization. One modification that had been made was altering visibility values by using best cost per produced unit stated in [6]. These NACO simulation results were then used as final results in the study.…”

Section: ) Outage Replacement Rate (Orr)mentioning

confidence: 99%

“…Best cost per produced [6] utilization in NACO in this study is used to substitute fuel cost value used as visibility. By entering fuel cost coefficient from each generator into (14), value of the best cost per produced for each generator is obtained.…”

Section: Best Cost Per Produced Utilization As a Substitute For Fumentioning

confidence: 99%

“…The utilized NACO concept in this study remains the same as NACO which comes from [5]. The difference is that NACO utilized in this paper adopts the best cost per produced unit function instead of fuel cost visibility value [6].…”

Section: Introductionmentioning

confidence: 99%

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Ant Colony Optimization for Resolving Unit Commitment Issues by Considering Reliability Constraints

Afifi

Sarjiya

Wijoyo

2019

IJITEE

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Unit Commitment or generator scheduling is one of complex combination issues aiming to obtain the cheapest generating power total costs. Ant Colony Optimization is proposed as a method to solve Unit Commitment issues because it has a better result convergence according to one of journals that reviews methods to solve Unit Commitment issues. Ant Colony Optimization modification into Nodal Ant Colony Optimization as well as addition of several elements are also conducted to overcome Ant Colony Optimization limitations in resolving Unit Commitment issues. Nodal Ant Colony Optimization simulations are then compared with Genetic Algorithm and Simulated Annealing methods which previously has similar simulations. Reliability index combination in a form of Loss of Load Probability and Expected Unserved Energy are also added as reliability constraints in the system. Comparison of three methods shows that Nodal Ant Colony Optimization is able to provide better results up to 0.08% cheaper than Genetic Algorithm or Simulated Annealing methods.

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Section: ) Outage Replacement Rate (Orr)mentioning

confidence: 99%

Section: Best Cost Per Produced Utilization As a Substitute For Fumentioning

confidence: 99%

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Ant Colony Optimization for Resolving Unit Commitment Issues by Considering Reliability Constraints

Afifi

Sarjiya

Wijoyo

2019

IJITEE

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“…In time, more ant colony‐based search algorithms were developed in solving the ED problem or other related problems (like the unit commitment (UC) problem or combined heat and power ED ). To solve the ED problem, Song and Chou developed a new encoding technique to overcome the difficulties in applying the ant colony search algorithm in a continuous search space .…”

Section: Introductionmentioning

confidence: 99%

“…In , an evolved ACO method for solving the UC problem is proposed, where the ACO parameters set are optimized. Other algorithms combine ACO with other local searching techniques for solving the UC problem .…”

Section: Introductionmentioning

confidence: 99%

A method based on the ant colony optimization algorithm for dynamic economic dispatch with valve-point effects

Secui

2013

Int. Trans. Electr. Energ. Syst.

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Summary This article proposes the use of a heuristic method based on the ant colony optimization algorithm (MACO) to solve the dynamic economic dispatch (DED) problem with taking into consideration the valve‐point effects. The suggested method takes into account both static and dynamic constraints of generator operation, the transmission losses and non‐smooth cost functions that shape the valve‐point effects. Also, a strategy for the DED imposed equality constraint handling is suggested. The proposed method's feasibility is proven for three different systems that have 10, 13 and 30 thermal generating units. The simulation results show that the suggested approach is very efficient having a capacity to obtain quality solutions in solving the DED problem with valve‐points effects. The numerical results obtained through the suggested method are superior to other results recently reported in literature, for the same systems. Copyright © 2013 John Wiley & Sons, Ltd.

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Application of Artificial Intelligence-Based Solution Methodology in Generation Scheduling Problem

Tiwari¹,

Bhadoria²,

Dwivedi

2020

Advances in Intelligent Systems and Computing

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Scalable unit commitment by memory-bounded ant colony optimization with local search

Cited by 31 publications

References 40 publications

Ant Colony Optimization for Resolving Unit Commitment Issues by Considering Reliability Constraints

Ant Colony Optimization for Resolving Unit Commitment Issues by Considering Reliability Constraints

A method based on the ant colony optimization algorithm for dynamic economic dispatch with valve-point effects

Application of Artificial Intelligence-Based Solution Methodology in Generation Scheduling Problem

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