AIMS Energy, 2018, 6(1): 170-186. doi: 10.3934/energy.2018.1.170.

Research article

Export file:

Format

  • RIS(for EndNote,Reference Manager,ProCite)
  • BibTex
  • Text

Content

  • Citation Only
  • Citation and Abstract

Composite reliability evaluation for transmission network planning

1 School of Electrical and Electronic Engineering, Universiti Sains Malaysia (USM); Malaysia
2 Department of Vehicle Engineering, National Taipei University of Technology, 1, Sec. 3, Chung-Hsiao E. Road, Taipei 106, Taiwan, R.O.C
3 Department of Information and Communication Engineering, Chaoyang University of Technology, Taichung 41349, Taiwan, R.O.C

As the penetration of wind power into the power system increases, the ability to assess the reliability impact of such interaction becomes more important. The composite reliability evaluations involving wind energy provide ample opportunities for assessing the benefits of different wind farm connection points. A connection to the weak area of the transmission network will require network reinforcement for absorbing the additional wind energy. Traditionally, the reinforcements are performed by constructing new transmission corridors. However, a new state-of-art technology such as the dynamic thermal rating (DTR) system, provides new reinforcement strategy and this requires new reliability assessment method. This paper demonstrates a methodology for assessing the cost and the reliability of network reinforcement strategies by considering the DTR systems when large scale wind farms are connected to the existing power network. Sequential Monte Carlo simulations were performed and all DTRs and wind speed were simulated using the auto-regressive moving average (ARMA) model. Various reinforcement strategies were assessed from their cost and reliability aspects. Practical industrial standards are used as guidelines when assessing costs. Due to this, the proposed methodology in this paper is able to determine the optimal reinforcement strategies when both the cost and reliability requirements are considered.
  Figure/Table
  Supplementary
  Article Metrics

Keywords power system; transmission network planning; DTR system; reliability; Monte Carlo; ARMA model

Citation: Jiashen Teh, Ching-Ming Lai, Yu-Huei Cheng. Composite reliability evaluation for transmission network planning. AIMS Energy, 2018, 6(1): 170-186. doi: 10.3934/energy.2018.1.170

References

  • 1. Davis MW (1977) A new thermal rating approach: The real time thermal rating system for strategic overhead conductor transmission lines-Part I: General description and justification of the real time thermal rating system. IEEE T Power Apparatus Syst 96: 810–825.    
  • 2. Guidelines for increased utilization of existing overhead transmission lines. CIGRE Working Group B2.13 2008. Available from: https://e-cigre.org/publication/353-guidelines-for-increased-utilization-of-existing-overhead-transmission-lines.
  • 3. IEEE (2007) IEEE Standard for Calculating the Current-Temperature of Bare Overhead Conductors 2: c1-59.
  • 4. Seppa O, Clements M, Damsgaard-Mikkelsen S, et al. (2000) Application of real time thermal ratings for optimizing transmission line investment and operating decisions. CIGRE Paper, 22–301.
  • 5. Douglass DA, Edris A, Pritchard GA (1997) Field application of a dynamic thermal circuit rating method. IEEE T Power Deliver 12: 823–831.    
  • 6. Douglass DA, Edris A (1996) Real-time monitoring and dynamic thermal rating of power transmission circuits. IEEE T Power Deliver 11: 1407–1418.    
  • 7. Sun WQ, Zhang Y, Wang CM, et al. (2013) Flexible load shedding strategy considering real-time dynamic thermal line rating. IET Gener Transm Dis 7: 130–137.    
  • 8. Shaker H, Zareipour H, Fotuhi-Firuzabad M (2013) Reliability modeling of dynamic thermal rating. IEEE T Power Deliver 28: 1600–1609.    
  • 9. Teh J, Cotton I (2015) Risk informed design modification of dynamic thermal rating system. IET Gener Transm Dis 9: 2697–2704.    
  • 10. Teh J, Cotton I (2015) Critical span identification model for dynamic thermal rating system placement. IET Gener Transm Dis 9: 2644–2652.    
  • 11. Jerrell JW, Black WZ, Parker TJ (1988) Critical span analysis of overhead conductors. IEEE T Power Deliver 3: 1942–1950.    
  • 12. Billinton R, Chen H, Ghajar R (1996) Time-series models for reliability evaluation of power systems including wind energy. Microelectron Reliab 36: 1253–1261.    
  • 13. Kazerooni AK, Mutale J, Perry M, et al. (2011) Dynamic thermal rating application to facilitate wind energy integration. PowerTech, 2011 IEEE Trondheim. IEEE, 1–7.
  • 14. Heckenbergerova J, Hosek J (2012) Dynamic thermal rating of power transmission lines related to wind energy integration. International Conference on Environment and Electrical Engineering. IEEE, 798–801.
  • 15. Ringelband T, Lange M, Dietrich M, et al. (2009) Potential of improved wind integration by dynamic thermal rating of overhead lines. PowerTech, 2009 IEEE Bucharest. IEEE, 1–5.
  • 16. Teh J, Cotton I (2015) Reliability impact of dynamic thermal rating system in wind power integrated network. IEEE T Reliab 65: 1081–1089.
  • 17. Bevrani H, Ghosh A, Ledwich G (2010) Renewable energy sources and frequency regulation: survey and new perspectives. IET Renew Power Gen 4: 438–457.    
  • 18. Haddad G, Sandborn PA, Pecht MG (2012) An options approach for decision support of systems with prognostic capabilities. IEEE T Reliab 61: 872–883.    
  • 19. Byon E, Ntaimo L, Ding Y (2010) Optimal maintenance strategies for wind turbine systems under stochastic weather conditions. IEEE T Reliab 59: 393–404.    
  • 20. Boutsika T, Santoso S (2012) Quantifying short-term wind power variability using the conditional range metric. IEEE T Sustain Energ 3: 369–378.    
  • 21. Sulaeman S, Benidris M, Mitra J, et al. (2016) A wind farm reliability model considering both wind variability and turbine forced outages. IEEE T Sustain Energ 99: 1–1.
  • 22. Ou TC, Hong CM (2014) Dynamic operation and control of microgrid hybrid power systems. Energy 66: 314–323.    
  • 23. Silva LD, Manso LAF, Flavio SA, et al. (2013) Composite Reliability Assessment of Power Systems with Large Penetration of Renewable Sources. In: Billinton R, Karki R, Verma A (eds.) India: Springer. Available from: https://link.springer.com/chapter/10.1007/978-81-322-0987-4_8
  • 24. Jaeseok C, Tran T, El-Keib AA, et al. (2005) A method for transmission system expansion planning considering probabilistic reliability criteria. IEEE T Power Syst 20: 1606–1615.    
  • 25. Billinton R, Allan RN, Snaith ER (1984) Book review of â reliability evaluation of power systems â. Safe Reliab 4: 16–16.
  • 26. Giorsetto P, Utsurogi KF (1983) Development of a new procedure for reliability modeling of wind turbine generators. IEEE T Power Apparatus Syst PAS-102: 134–143.    
  • 27. Gao Q, Liu C, Xie B, et al. (2013) Evaluation of the mainstream wind turbine concepts considering their reliabilities. IET Renew Power Gen 6: 348–357.
  • 28. Broersen PMT (2002) Automatic spectral analysis with time series models. IEEE T Instrum Meas 51: 211–216.    
  • 29. ISO/RTO Council Planning Committee (2006) ISO/RTO Electric System Planning Current Practices, Expansion Plans and Planning Issues. Available from: http://nescoe.com/resource-center/t-planning-comparison-feb2016/.
  • 30. Sanghvi AP, Balu NJ, Lauby MG (1991) Power system reliability planning practice in North America. IEEE T Power Syst 6: 1485–1492.    
  • 31. Kirschen D, Strbac G (2006) Fundamentals of power system economics. John Wiley Sons 4: 76–78.
  • 32. Li W (2005) Risk assessment of power systems: models, methods, and applications. Wiley-IEEE Press.
  • 33. Billinton R, Allan RN (1987) Reliability evaluation of engineering systems: concepts and techniques. Harlow: Longman Scientific & Technical.
  • 34. Zimmerman RD, Murillo-Sanchez CE (2014) MATPOWER 5.0 User's Manual. Available from: http://www.pserc.cornell.edu/matpower/MATPOWER-manual.pdf.
  • 35. IEEE Reliability Test System Force Subcommittee (1999) The IEEE reliability test system-1996. IEEE T Power Syst 14: 1010–1020.    
  • 36. British Atmospheric Data Centre (BADC). Available from: http://badc.nerc.ac.uk/home/.
  • 37. Counting the cost: the economic and social costs of electricity shortfalls in the UK. Royal Academy of Engineering, November 2014. Available from: https://www.raeng.org.uk/publications/reports/counting-the-cost.

 

This article has been cited by

  • 1. Hamza Abunima, Jiashen Teh, Ching-Ming Lai, Hussein Jabir, A Systematic Review of Reliability Studies on Composite Power Systems: A Coherent Taxonomy Motivations, Open Challenges, Recommendations, and New Research Directions, Energies, 2018, 11, 9, 2417, 10.3390/en11092417
  • 2. Wook-Won Kim, Jong-Keun Park, Yong-Tae Yoon, Mun-Kyeom Kim, Transmission Expansion Planning under Uncertainty for Investment Options with Various Lead-Times, Energies, 2018, 11, 9, 2429, 10.3390/en11092429

Reader Comments

your name: *   your email: *  

© 2018 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution Licese (http://creativecommons.org/licenses/by/4.0)

Download full text in PDF

Export Citation

Copyright © AIMS Press All Rights Reserved