Optimal utilization of automated distributed generation in smart grid using genetic algorithm
Distributed generation (DG) is an essential attributor in smart grid to fulfill the uncontrollable increase in the demand for energy. Artificial intelligent optimization techniques are widely used within automation systems for guarantee the optimal operation and utilization of DG allocation on the d...
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| Format: | EJournal Article |
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Institute of Advanced Engineering and Science,
2019-10-01.
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| Online Access: | Get fulltext |
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| LEADER | 02518 am a22003133u 4500 | ||
|---|---|---|---|
| 001 | ijeecs18017_12917 | ||
| 042 | |a dc | ||
| 100 | 1 | 0 | |a Hoballah, Ayman |e author |
| 100 | 1 | 0 | |a Taif University |e contributor |
| 700 | 1 | 0 | |a Ahmad, Yasser |e author |
| 700 | 1 | 0 | |a Shoush, Kamel A |e author |
| 245 | 0 | 0 | |a Optimal utilization of automated distributed generation in smart grid using genetic algorithm |
| 260 | |b Institute of Advanced Engineering and Science, |c 2019-10-01. | ||
| 500 | |a https://ijeecs.iaescore.com/index.php/IJEECS/article/view/18017 | ||
| 520 | |a Distributed generation (DG) is an essential attributor in smart grid to fulfill the uncontrollable increase in the demand for energy. Artificial intelligent optimization techniques are widely used within automation systems for guarantee the optimal operation and utilization of DG allocation on the day-ahead power scheduling. In this paper, the genetic algorithm technique used for obtaining the optimal utilization of the automated operation of distributed generation for power losses and total cost minimization as well as user comfort maximization considering all operating constraints technique. Distributed generation represented by fuel cells to supply part of the daily demand in the power system. The target is to apply decision-making strategy of smart operation for economical and reliable operation of power system. Concentrated fuel cell units considered representing the available DG at the load centers. The methodology applied to the 11-bus test system. The simulation results have demonstrated that the GA capability for full automation of DGs in a smart manner within the power system for economic and safe operation | ||
| 540 | |a Copyright (c) 2019 Institute of Advanced Engineering and Science | ||
| 540 | |a http://creativecommons.org/licenses/by-nc/4.0 | ||
| 546 | |a eng | ||
| 690 | |a Distributed Generation (DG); Genetic Algorithm | ||
| 690 | |a Distributed Generation (DG); Genetic Algorithm (GA); Fuel Cell (FC); Renewable energy. | ||
| 655 | 7 | |a info:eu-repo/semantics/article |2 local | |
| 655 | 7 | |a info:eu-repo/semantics/publishedVersion |2 local | |
| 655 | 7 | |2 local | |
| 786 | 0 | |n Indonesian Journal of Electrical Engineering and Computer Science; Vol 16, No 1: October 2019; 82-91 | |
| 786 | 0 | |n 2502-4760 | |
| 786 | 0 | |n 2502-4752 | |
| 786 | 0 | |n 10.11591/ijeecs.v16.i1 | |
| 787 | 0 | |n https://ijeecs.iaescore.com/index.php/IJEECS/article/view/18017/12917 | |
| 856 | 4 | 1 | |u https://ijeecs.iaescore.com/index.php/IJEECS/article/view/18017/12917 |z Get fulltext |