TY - JOUR
T1 - Optimal Economic Dispatch Using Mayfly Optimization Algorithm in Sulbagsel Electricity System with Integrated Renewable Energy Sources
AU - Hasanah, Rini Nur
AU - Robandi, Imam
AU - Syafaruddin,
AU - lystianingrum, Vita
AU - Guntur, Harus Laksana
AU - Djalal, Muhammad Ruswandi
AU - Prakasa, Mohamad Almas
AU - Himawari, Waseda
N1 - Publisher Copyright:
© (2025), (Intelligent Network and Systems Society). All rights reserved.
PY - 2025
Y1 - 2025
N2 - This study focuses on optimizing generation costs for thermal power plants in the Southern Sulawesi (Sulbagsel) electricity system by incorporating Renewable Energy Sources (RESs). The Improved Mayfly Algorithm (IMA), inspired by the mating and flight behaviors of adult mayflies and enhanced with Exponent Decreasing Inertia Weight (EDIW) to adjust inertia variations, is applied to minimize generation costs. The effectiveness of the proposed IMA is evaluated through comparisons with other methods, such as the Quadratic Time Optimization (QTO) and the standard MA. Statistical analysis of the benchmarking results demonstrates that IMA outperforms comparable other algorithms. For the first case, mid-day peak load, the optimization results show that QTO reduces costs by 24.24%, MA by 24.25%, and the proposed IMA by 24.28%. In the second case, nighttime peak load, the cost reductions achieved are 25.96% for QTO, 26.28% for MA, and 26.72% for IMA.
AB - This study focuses on optimizing generation costs for thermal power plants in the Southern Sulawesi (Sulbagsel) electricity system by incorporating Renewable Energy Sources (RESs). The Improved Mayfly Algorithm (IMA), inspired by the mating and flight behaviors of adult mayflies and enhanced with Exponent Decreasing Inertia Weight (EDIW) to adjust inertia variations, is applied to minimize generation costs. The effectiveness of the proposed IMA is evaluated through comparisons with other methods, such as the Quadratic Time Optimization (QTO) and the standard MA. Statistical analysis of the benchmarking results demonstrates that IMA outperforms comparable other algorithms. For the first case, mid-day peak load, the optimization results show that QTO reduces costs by 24.24%, MA by 24.25%, and the proposed IMA by 24.28%. In the second case, nighttime peak load, the cost reductions achieved are 25.96% for QTO, 26.28% for MA, and 26.72% for IMA.
KW - Cost
KW - Economic dispatch
KW - Improved mayfly algorithm
KW - Sulbagsel system
KW - Swarm intelligence
UR - http://www.scopus.com/inward/record.url?scp=85214257674&partnerID=8YFLogxK
U2 - 10.22266/ijies2025.0229.63
DO - 10.22266/ijies2025.0229.63
M3 - Article
AN - SCOPUS:85214257674
SN - 2185-310X
VL - 18
SP - 894
EP - 903
JO - International Journal of Intelligent Engineering and Systems
JF - International Journal of Intelligent Engineering and Systems
IS - 1
ER -