TY - GEN
T1 - Economic load dispatch optimation of thermal power plant based on merit order and bat algorithm
AU - Hanafi, Ikhsan Fahri
AU - Dalimi, Ir Rinaldy
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/6
Y1 - 2019/6
N2 - Fuel cost optimization is generally done using an approach of deterministic and undeterministic methods. This study compares the application of deterministic merit order algorithms with the application of undeterministic bat algorithms. The issue of economic load dispatch has complex equality and inequality constraints, so it is difficult to determine the optimum value using a conventional approach. In determining the optimum value it is necessary to schedule generator units to divide the generated power in meeting system requirements so the optimum fuel costs are obtained. Merit orders are arranged based on the amount of hourly fuel costs per unit operating at its maximum output, while the bat algorithm is based on echolocation characteristics of microbats simulated on a computer program from the position, velocity and frequency of bats. The researched data are the actual data of thermal power plants which amount to 6 (six) plants in the peak loads condition in 2018. By using 2 (two) different method, namely merit order and bat algorithm, the results of different production costs are obtained. The merit order can reduce production costs by 14.67% or 291640 of the actual cost, while the bat algorithm produces an efficiency of 15.66% or 311405 of the actual cost. From the results of this calculation it can be concluded that the use of bat algorithm can produce a more efficient (smaller) generation costs that is equal to 19765 or 0.99% smaller than the merit order method. This can occur because of the bat algorithm manages to create a loading combination of more efficient power plants.
AB - Fuel cost optimization is generally done using an approach of deterministic and undeterministic methods. This study compares the application of deterministic merit order algorithms with the application of undeterministic bat algorithms. The issue of economic load dispatch has complex equality and inequality constraints, so it is difficult to determine the optimum value using a conventional approach. In determining the optimum value it is necessary to schedule generator units to divide the generated power in meeting system requirements so the optimum fuel costs are obtained. Merit orders are arranged based on the amount of hourly fuel costs per unit operating at its maximum output, while the bat algorithm is based on echolocation characteristics of microbats simulated on a computer program from the position, velocity and frequency of bats. The researched data are the actual data of thermal power plants which amount to 6 (six) plants in the peak loads condition in 2018. By using 2 (two) different method, namely merit order and bat algorithm, the results of different production costs are obtained. The merit order can reduce production costs by 14.67% or 291640 of the actual cost, while the bat algorithm produces an efficiency of 15.66% or 311405 of the actual cost. From the results of this calculation it can be concluded that the use of bat algorithm can produce a more efficient (smaller) generation costs that is equal to 19765 or 0.99% smaller than the merit order method. This can occur because of the bat algorithm manages to create a loading combination of more efficient power plants.
KW - Bat Algorithm
KW - Economic Load Dispatch
KW - Fuel Cost Optimization
KW - Generator Scheduling
KW - Merit Order
UR - http://www.scopus.com/inward/record.url?scp=85084554359&partnerID=8YFLogxK
U2 - 10.1109/ICIRD47319.2019.9074734
DO - 10.1109/ICIRD47319.2019.9074734
M3 - Conference contribution
AN - SCOPUS:85084554359
T3 - 2nd IEEE International Conference on Innovative Research and Development, ICIRD 2019
BT - 2nd IEEE International Conference on Innovative Research and Development, ICIRD 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2nd IEEE International Conference on Innovative Research and Development, ICIRD 2019
Y2 - 28 June 2019 through 30 June 2019
ER -