Ortalama diferansiyel gelişim algoritmasının kayıplı sabit düşülü kısa dönem hidrotermal koordinasyon problemine uygulanması

Öz

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.

Anahtar Kelimeler

• Hidroelektrik-termal güç üretimi
• Newton yöntemi
• Güç dağıtımı
• Güç üretim dağıtımı
• Evrimsel hesaplama.

Application of Average Differential Evolution Algorithm to Lossy Fixed Head Short-Term Hydrothermal Coordination Problem

Öz

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.

Anahtar Kelimeler

• Hydroelectric‐thermal power generation
• Newton method
• Power distribution
• Power generation dispatch
• Evolutionary computation.

Kaynakça

1. [1] S. A. Papazis, G. C. Bakos, “Generalized Model of Economic Dispatch Optimization as an Educational Tool for Management of Energy Systems,” Advances in Electrical and Computer Engineering, vol.21, no.2, pp.75-86, 2021. doi:10.4316/aece.2021.02009
2. [2] C. Yaşar, S. Özyön, “A Modified Incremental Gravitational Search Algorithm for Short-Term Hydrothermal Scheduling with Variable Head,” Engineering Applications of Artificial Intelligence, vol.95 (103845), pp.1-17, 2020. doi:10.1016/j.engappai.2020.103845
3. [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. [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
5. [5] J. Sasikala, M. Ramaswamy, “Optimal Gamma based Fixed Head Hydrothermal Scheduling using Genetic Algorithm,” Expert Systems with Applications, vol.37, no.4, pp.3352-3357, 2009. doi:10.1016/j.eswa.2009.10.015
6. [6] V. S. Kumar, M. R. Mohan, “A Genetic Algorithm Solution to the Optimal Short-term Hydrothermal Scheduling,” International Journal of Electrical Power & Energy Systems, vol.33, no.4, pp.827-835, 2010. doi:10.1016/j.ijepes.2010.11.008
7. [7] S. Özyön, C. Yaşar, Y. Aslan, H. Temurtaş, “Solution to Environmental Economic Power Dispatch Problems in Hydrothermal Power Systems by Using Genetic Algorithm,” in 6th International Conference on Electrical and Electronics Engineering (ELECO’09), Bursa, TÜRKİYE, 2009, pp.387-392.
8. [8] S. Özyön, C. Yaşar, “Gravitational Search Algorithm Applied to Fixed Head Hydrothermal Power System with Transmission Line Security Constraints,” Energy, vol.155, pp.392-407, 2018. doi:10.1016/j.energy.2018.04.172

9. [9] M. S. Fakhar, S. A. R. Kashif, S. Liaquat, A. Rasool, S. Padmanaban, M. A. Iqbal, M. A. Baig, B. Khan, “Implementation of APSO and Improved APSO on Non-cascaded and Cascaded Short-term Hydrothermal Scheduling,” IEEE Access, vol.9, pp.77784-77797, 2021. doi:10.1109/access.2021.3083528
10. [10] S. Fadıl, C. Yaşar, “A Pseudo Spot Price Algorithm Applied to Short-term Hydrothermal Scheduling Problem,” Electric Power Components and Systems, vol.29, no.11, pp.112-119, 2010. doi:10.1080/153250001753239202
11. [11] A. E. Nezhad, S. Jowkar, T. T. Sabour, E. Rahimi, F. Ghanavati, F. Esmaeilnezhad, “A Short-term Wind-Hydrothermal Operational Framework in the Presence of Pumped-Hydro Storage,” e-Prime - Advances in Electrical Engineering, Electronics and Energy, vol.8 (100577), pp.1-16, 2024. doi:10.1016/j.prime.2024.100577
12. [12] M. Basu, “A Simulated Annealing-based Goal-Attainment Method for Economic Emission Load Dispatch of Fixed Head Hydrothermal Power Systems,” International Journal of Electrical Power & Energy Systems, vol.27, no.2, pp.147-153, 2004. doi:10.1016/j.ijepes.2004.09.004
13. [13] M. Basu, “Economic Environmental Dispatch of Fixed Head Hydrothermal Power Systems using Nondominated Sorting Genetic Algorithm-II,” Applied Soft Computing, vol.11, no.3, pp.3046-3055, 2010. doi:10.1016/j.asoc.2010.12.005
14. [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. [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
16. [16] T. T. Nguyen, D. N. Vo, “Modified Cuckoo Search Algorithm for Short-term Hydrothermal Scheduling,” International Journal of Electrical Power & Energy Systems, vol.65, pp.271-281, 2014. doi:10.1016/j.ijepes.2014.10.004
17. [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. [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
19. [19] N. Gouthamkumar, V. Sharma, R. Naresh, “Disruption-based Gravitational Search Algorithm for Short-term Hydrothermal Scheduling,” Expert Systems with Applications, vol.42, no.20, pp.7000-7011, 2015. doi:10.1016/j.eswa.2015.05.017
20. [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. [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
22. [22] M.S. Fakhar, S. Liaquat, S.A.R. Kashif, A. Rasool, M. Khizer, M.A. Iqbal, S. Padmanaban, “Conventional and metaheuristic optimization algorithms for solving short term hydrothermal scheduling problem: A review.” IEEE Access, vol.9, pp.25993-26025, 2021. Doi:10.1109/access.2021.3055292
23. [23] S. Özyön, “Optimal Short-term Operation of Pumped-storage Power Plants with Differential Evolution Algorithm,” Energy, vol.194(116866), pp.1-13, 2019. doi:10.1016/j.energy.2019.116866
24. [24] A.J. Wood, B.F. Wollenberg, G.B. Sheble, Power Generation Operation and Control, IEEE & Wiley, 2013, p.656.
25. [25] D. P. Kothari, J. S. Dhillon, Power System Optimization, PHI Learning Private Limited, 2007, p.732.
26. [26] S. Özyön, “Optimal Aktif Güç Dağıtımı için Karşıt Öğrenme Tabanlı Diferansiyel Gelişim Algoritmasi.” Uludağ Üniversitesi Mühendislik Fakültesi Dergisi, vol.25, no.1, pp.231-246, 2020. doi:10.17482/uumfd.635957
27. [27] B. Durmuş, “Optimal Components Selection for Active Filter Design with Average Differential Evolution Algorithm,” AEU - International Journal of Electronics and Communications, vol.94, pp.293-302, 2018. doi:10.1016/j.aeue.2018.07.021
28. [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. [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.