Application of Average Differential Evolution Algorithm to Lossy Fixed Head Short-Term Hydrothermal Coordination Problem
Year 2025,
Volume: 13 Issue: 2, 230 - 242
Serdar Özyön
,
Hasan Temurtaş
,
Burhanettin Durmuş
,
Celal Yaşar
Abstract
Short-term hydrothermal coordination problems (STHCP) include power systems with thermal and hydraulic production units. Suppose the reservoirs of the hydraulic production units in the system are vast. In that case, it is assumed that the water in the reservoirs stays mostly the same during the operation period. Short-term hydrothermal coordination problems with hydraulic production units having this feature are called constant-head STHCP. Constant-head STHCP includes both electrical and hydraulic constraints. Variables such as the amount of water entering and leaving the reservoir of each hydraulic production unit, the reservoir capacity, and the amount of water stored in the reservoir are known as hydraulic constraints. The average differential evolution (ADE) algorithm, one of the newly developed meta-heuristic algorithms, is applied to solve the STHCP with a fixed head. Transmission line losses of the power system are calculated using the Newton-Raphson load flow method. In this study, the lossy STHCP with fixed head is solved for two cases where the input and output characteristics of the thermal generation units have both convex and non-convex characteristics. The results obtained from the solutions to both cases' problems are discussed.
Supporting Institution
Kütahya Dumlupınar Üniversitesi Bilimsel Araştırmalar Koordinatörlüğü
Thanks
Kütahya Dumlupınar Üniversitesi Akıllı Sistemler Tasarım Uygulama ve Araştırma Merkezi
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Ortalama diferansiyel gelişim algoritmasının kayıplı sabit düşülü kısa dönem hidrotermal koordinasyon problemine uygulanması
Year 2025,
Volume: 13 Issue: 2, 230 - 242
Serdar Özyön
,
Hasan Temurtaş
,
Burhanettin Durmuş
,
Celal Yaşar
Abstract
Kısa dönem hidrotermal koordinasyon problemleri (STHCP), termal ve hidrolik üretim birimlerine sahip güç sistemlerini içerir. Sistemdeki hidrolik üretim birimlerinin rezervuarlarının geniş olduğunu ve bu durumda, rezervuarlardaki suyun işletme süresi boyunca çoğunlukla aynı kaldığı varsayılır. Bu özelliğe sahip hidrolik üretim birimleri ile kısa dönem hidrotermal koordinasyon problemleri sabit düşülü STHCP olarak adlandırılır. Sabit düşülü STHCP hem elektriksel hem de hidrolik kısıtları içerir. Her bir hidrolik üretim biriminin rezervuarına giren ve çıkan su miktarı, rezervuar kapasitesi ve rezervuarda depolanan su miktarı gibi değişkenler hidrolik kısıtlar olarak bilinir. Yeni geliştirilen meta sezgisel algoritmalardan biri olan ortalama diferansiyel gelişim (ADE) algoritması, STHCP'yi sabit bir düşü ile çözmek için kullanılmıştır. Güç sisteminin iletim hattı kayıpları Newton-Raphson yük akışı yöntemi kullanılarak hesaplanmıştır. Bu çalışmada, sabit düşülü kayıplı STHCP termik üretim birimlerinin giriş ve çıkış karakteristiklerinin hem konveks hem de konveks olmayan karakteristiklere sahip olduğu iki durum için çözülmüştür. Her iki durumdaki problemlerin çözümlerinden elde edilen sonuçlar tartışılmıştır.
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- [3] M. Basu, “Hopfield Neural Networks for Optimal Scheduling of Fixed Head Hydrothermal Power Systems,” Electric Power Systems Research, vol.64, no.1, pp.11-15, 2023. doi:10.1016/S0378-7796(02)00118-9
- [4] C. E. Zoumas, A. G. Bakirtzis, J. B. Theocharis, V. Petridis, “A Genetic Algorithm Solution Approach to the Hydrothermal Coordination Problem,” IEEE Transactions on Power Systems, vol.19, no.2, pp.1356-1364, 2004. doi:10.1109/TPWRS.2004.825896
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- [14] M. Basu, “Artificial Immune System for Fixed Head Hydrothermal Power System,” Energy, vol.36, no.1, pp.606-612, 2010. doi:10.1016/j.energy.2010.09.057
- [15] T. T. Nguyen, D. N. Vo, A. V. Truong, “Cuckoo Search Algorithm for Short-term Hydrothermal Scheduling,” Applied Energy, vol.132, no.1, pp.276-287, 2014. doi:10.1016/j.apenergy.2014.07.017
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- [17] N. Narang, J. S. Dhillon, D. P. Kothari, “Scheduling Short-term Hydrothermal Generation using Predator-prey Optimization Technique,” Applied Soft Computing, vol.21, pp.298-308, 2014. doi:10.1016/j.asoc.2014.03.029
- [18] A. Rasoulzadeh-akhijahani, B. Mohammadi-ivatloo, “Short-term Hydrothermal Generation Scheduling by a Modified Dynamic Neighborhood Learning based Particle Swarm Optimization,” International Journal of Electrical Power & Energy Systems, vol.67, pp.350-367, 2014. doi:10.1016/j.ijepes.2014.12.011
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- [20] G. Chen, M. Gao, Z. Zhang, S. Li, “Hybridization of Chaotic Grey Wolf Optimizer and Dragonfly Algorithm for Short-term Hydrothermal Scheduling,” IEEE Access, vol.8, pp.142996-143020, 2020. doi:10.1109/access.2020.3014114
- [21] M.A. Almubaidin, A.N. Ahmed, L.M. Sidek, K.A.H. AL-Assifeh, A. El-Shafie, “Deriving Optimal Operation Rule for Reservoir System Using Enhanced Optimization Algorithms. Water Resources Management, vol.38, no.4, pp.1207-1223, 2024. doi:10.1007/s11269-010-9712-6
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- [25] D. P. Kothari, J. S. Dhillon, Power System Optimization, PHI Learning Private Limited, 2007, p.732.
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- [28] B. Durmuş, H. Temurtaş, S. Özyön, “The Design of Multiple Feedback Topology Chebyshev Low-pass Active Filter with Average Differential Evolution Algorithm,” Neural Computing & Applications, vol.32, no.22, pp.17097-17113, 2020. doi:10.1007/s00521-020-04922-7
- [29] S. Özyön, “The Solution of the Short-term Hydrothermal Coordination Problem by Improved Incremental Gravitational Search Algorithm,” Electrical-Electronics Engineering, Ph.D. Thesis, Kütahya Dumlupınar University, Institute of Science and Technology, 2008.