KaydedilenlerDerginizi ekleyin

Dergiler / Istanbul University Journal of Electrical and Electronics Engineering / 2014 / Cilt: 14 - Sayı: 2

IMPLEMENTATION OF A NEW STOCHASTIC ALGORITHM OF NETWORK RECONFIGURATION IN DISTRIBUTION SYSTEMS FOR LOSSES AND COSTS REDUCTION

Mohammad Bagher HADDADI
Mahdi HAYATDAVUDI
Mojtaba SAEEDIMOGHADAM
Reza SEDAGHATI
Sayfa
1817–1823
Yayın Tarihi
2022-10-18
DOI
—
🏛️Bu makalenin bibliyografik verileri ve tam metin bağlantısı DergiPark üzerinden sağlanmıştır.
DergiPark Kaydı ↗
✨

Yapay Zeka Hızlı Bakış & Çıkarımlar

AI Insight
⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

The feasibility of the MFO algorithm and the proposed DFR is demonstrated and compared with the solutions obtained by other approaches and evolutionary methods

📌 3 Maddede Bu Makale

🎯
Amaç & Hipotez:In many countries the power systems are going to move toward creating a competitive structure for selling and buying electrical energy.
📊
Temel Bulgular & Veri:The effectiveness of the proposed algorithm is demonstrated through IEEE 32 bus standard test systems.
💡
Sonuç & Katkı:The feasibility of the MFO algorithm and the proposed DFR is demonstrated and compared with the solutions obtained by other approaches and evolutionary methods
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Alan Parametreleri

Abstract

In many countries the power systems are going to move toward creating a competitive structure for selling and buying electrical energy. This paper presents a new method based on Modified Firefly Optimization (MFO) algorithm to Distribution Feeder Reconfiguration (DFR) problems at the distribution networks considering Wind Turbines (WTs). The objectives consist of minimization of costs and losses of distributed system. The effectiveness of the proposed algorithm is demonstrated through IEEE 32 bus standard test systems. Also, regarding the uncertainties of the new complicated power systems such as the active and reactive loads in addition to the wind speed variations effectively, in this paper for the first time, the DFR problem is investigated in a stochastic environment by the use of probabilistic load flow technique based on Point Estimate Method (PEM). The feasibility of the MFO algorithm and the proposed DFR is demonstrated and compared with the solutions obtained by other approaches and evolutionary methods

🔖

Bu Makaleye Atıf Yap

HADDADI, M. B., HAYATDAVUDI, M., SAEEDIMOGHADAM, M., SEDAGHATI, R. (2022). IMPLEMENTATION OF A NEW STOCHASTIC ALGORITHM OF NETWORK RECONFIGURATION IN DISTRIBUTION SYSTEMS FOR LOSSES AND COSTS REDUCTION. Istanbul University Journal of Electrical and Electronics Engineering, Cilt: 14 - Sayı: 2, 1817–1823. https://ajindex.com/articles/543650

📄 Tam Metin PDF Okuyucu

📄

PDF görüntüleyici hazırlanıyor...

Kaynakça (13)

  1. T. Ackerman, G. Anderson, L. Soder, “Distributed generation: a definition,” Elsevier Sci. 195–204, 2003
  2. D. Zhu, R.P. Broadwater, K. Tam, R. Seguin, H. Asgeirsson, “ Impact of DG placement on reliability and efficiency with time-varying loads,” IEEE Transactions on Power Systems 21 (1), 419–427, 2006. [3] “Distributed Energy Resources Guide,” The California Energy Commission, 2003. [Online]. Available: http://www.energy.ca.gov/. [4] “Wind power systems,” in Encyclopedia of Physical Science and Technology, 3rd ed. New York: Academic, 2006
  3. Y.T. Hsiao, C.Y. Chien, “Multiobjective optimal feeder reconfiguration,” IEE Proceedings Generation, Transmission and Distribution, 148(4):333–336, 2001
  4. H. Merlin, “Search for a minimal-loss operating spanning tree configuration in an urban power distribution system,” in: Proceedings of the Fifth Power System Computation Conference, Cambridge, UK, pp. 1–18, 2008
  5. S. Civanlar, J.J. Grainger, H. Yin, S.S.H. Lee, “Distribution feeder reconfiguration for loss reduction,” IEEE Trans Power Deliver; 3(4):1217–23, 2010
  6. K. Aoki, H. Kawabara, T. Satoh, M. Kanezashi, “An efficient algorithm for load balancing of transformers and feeders,” IEEE Trans Power Deliver; 3(4):1865–72, 2002
  7. G. Celli, E. Ghiani, S. Mocci, F. and Pilo, F., “A multiobjective evolutionary algorithm for the sizing and siting of distributed generation,” IEEE Trans. Power Syst., vol. 20, no. 2, pp. 750–757, May 2009
  8. T. Taylor, D. Lubkeman, “Implementation of heuristic search strategies for distribution feeder reconfiguration,” IEEE Trans. Power Del, vol. 5, pp. 239 – 245, 1990
  9. P. Agalgaonkar, S.V. Kulkarni, S.A. Khaparde, S.A. Soman, “Placement and penetration of distributed generation under standard market design,” Int J Emerg Electr Power Syst;1(1), 2004
  10. J. Olamaie, T. Niknam, G. Gharehpetion, “Application of particle swarm optimization for distribution feeder reconfiguration considering distributed generators,” Appl Math Comput;20(1):575– 86, 2008
  11. J.M. Morales, J. Perez-Ruiz, “Point estimate schemes to solve the probabilistic power flow,” IEEE Transactions on Power System, vol. 22, pp. 1594-1601,.2007
  12. R. Billinton, and P. Wang, “Teaching Distribution System Reliability Evaluation Using Monte Carlo Simulation,” IEEE Transactions on Power Systems, vol. 14, no. 2, pp. 397-403, May 1999
  13. A. Soroudi, M. Ehsan, R. Caire, N. Hadjsaid, “Possibilistic evaluation of distributed generations Impacts on di