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ANN-Based Binary Backtracking Search Algorithm for VPP Optimal Scheduling and Cost-Effective Evaluation

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dc.citedby4
dc.contributor.authorHannan M.A.en_US
dc.contributor.authorAbdolrasol M.G.en_US
dc.contributor.authorMohamed R.en_US
dc.contributor.authorAl-Shetwi A.en_US
dc.contributor.authorKer P.en_US
dc.contributor.authorBegum R.en_US
dc.contributor.authorMuttaqi K.en_US
dc.contributor.authorid7103014445en_US
dc.contributor.authorid35796848700en_US
dc.contributor.authorid7005169066en_US
dc.contributor.authorid57004922700en_US
dc.contributor.authorid37461740800en_US
dc.contributor.authorid14007780000en_US
dc.contributor.authorid55582332500en_US
dc.date.accessioned2023-05-29T09:11:20Z
dc.date.available2023-05-29T09:11:20Z
dc.date.issued2021
dc.descriptionCost effectiveness; Cost reduction; Electric power transmission networks; Learning algorithms; Neural networks; Renewable energy resources; Scheduling; Wind; Backtracking search algorithms; Charging/discharging; Correlation coefficient; Mean absolute error; Optimal scheduling; Optimization algorithms; Renewable energy source; Virtual power plants (VPP); Electric power system controlen_US
dc.description.abstractThis article reports an artificial neural network (ANN)-based binary backtracking search algorithm (BBSA) for optimal scheduling controller applied on IEEE 14-bus system for controlling microgrids (MGs) formed virtual power plant (VPP) toward sustainable renewable energy sources (RESs) integration. The model of VPP was simulated and validated based on actual parameters and load data reported in Perlis, Malaysia. BBSA optimization algorithm offers the best binary fitness function to find the best cell. It creates the optimum scheduling using the actual data for wind speed, solar radiation, fuel conditions, battery charging/discharging, and specific hour demand. The developed ANN-based BBSA search for the optimal ANN parameters architecture, e.g., (the number of neurons and learning rate) that enhanced the ANN controller to predict the optimal schedules to regulate power-sharing via prioritizing the utilization of RES in place of the national grid purchases. The results of the optimal on/off status prediction of the 25 DGs showed that the ANN-BBSA gives a mean absolute error (MAE) of 6.2 � 10-3 with a unity correlation coefficient. The results showed a significant reduction in the cost and emission by 41.88% and 40.7%, respectively. Thus, the developed algorithms reduced the energy cost while delivered reliable power toward grid decarbonization. � 1972-2012 IEEE.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1109/TIA.2021.3100321
dc.identifier.epage5613
dc.identifier.issue6
dc.identifier.scopus2-s2.0-85111564673
dc.identifier.spage5603
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85111564673&doi=10.1109%2fTIA.2021.3100321&partnerID=40&md5=6b183b7e5bf72b836ccce67f2918de25
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/26511
dc.identifier.volume57
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.sourceScopus
dc.sourcetitleIEEE Transactions on Industry Applications
dc.titleANN-Based Binary Backtracking Search Algorithm for VPP Optimal Scheduling and Cost-Effective Evaluationen_US
dc.typeArticleen_US
dspace.entity.typePublication
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