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access icon openaccess Methodology of risk assessment and decomposition in power grid applications

This is an open access article published by the IET under the Creative Commons Attribution -NonCommercial License (http://creativecommons.org/licenses/by-nc/3.0/)
Received 02/11/2017, Accepted 30/04/2018, Revised 26/01/2018, Published 21/05/2018

The most common approach to risk assessment for power systems is based on the principle. Nevertheless, the economic rationale suggests its relaxation in cases where the consequences are relatively minor and exacerbation when they are large. This study addresses this need by proposing an alternative operational risk assessment methodology that is based on both probabilities and costs of possible contingencies. The foundations are built on listing all possible contingencies that may be considered by the transmission system operator (TSO). As only a subset of these contingencies can be examined in reasonable time, the upper and lower risk boundaries are introduced to quantify the risk underestimation. The ratio of those limits is used as an accuracy indicator, which – according to the desired level – may help the TSO to identify the required number of contingencies that have to be analysed. Furthermore, several approaches to improve the reduction of the number of simulated contingencies are discussed and the results obtained basing on the dynamic IEEE39 model are presented.

Inspec keywords: risk management; power transmission economics; power grids

Other keywords: economic rationale; transmission system operator; accuracy indicator; dynamic IEEE39 model; power grid applications; power systems; risk underestimation; TSO; risk assessment methodology; N−1 principle; risk boundaries; risk decomposition

Subjects: Power system management, operation and economics; Power transmission, distribution and supply

References

    1. 1)
      • 14. Wang, Q., Yang, A., Wen, F., et al: ‘Risk-based security-constrained economic dispatch in power systems’, J. Mod. Power Syst. Clean Energy, 2013, 1, (2), pp. 142–149.
    2. 2)
      • 16. ENTSO-E: ‘Supporting document for the network code on operational security’ (ENTSO-E, Brussels, Belgium, 2013), p. 252.
    3. 3)
      • 22. Pavella, M., Ernst, D., Ruiz-Vega, D.: ‘Transient stability of power systems: a unified approach to assessment and control’ (Springer Science & Business Media, New York, NY, USA, 2012).
    4. 4)
      • 9. Vrakopoulou, M., Margellos, K., Lygeros, J., et al: ‘Probabilistic guarantees for the N − 1 security of systems with wind power generation’. Proc. of PMAPS'2012, Turkey, June 2012.
    5. 5)
      • 1. ENTSO-E: ‘P3 – policy 3: operational security’, in ‘Continental Europe operational handbook’ (ENTSO-E / UCTE, Brussels, Belgium, 2004), p. 21.
    6. 6)
      • 7. Bell, K.R.W., Kirschen, D.S., Allan, R.N., et al: ‘Efficient Monte Carlo assessment of the value of security’. Proc. of 13th Power System Computation Conf., Trondheim, Norway, June 1999, pp. 81–87.
    7. 7)
      • 3. Rios, M.A., Kirschen, D.S., Jayaweera, D., et al: ‘Value of security: modeling time-dependent phenomena and weather conditions’, IEEE Trans. Power Syst., 2002, 17, pp. 543–548.
    8. 8)
      • 2. PSE S.A.: ‘Instructions for power transport in transmission network’ (PSE S. A. – Polish Transmission System Operator, Konstancin-Jeziorna, Poland, 2010).
    9. 9)
      • 21. Billinton, R., Bollinger, K.E.: ‘Transmission system reliability evaluation using Markov processes’, IEEE Trans. Power Appar. Syst., 1968, 2, pp. 538–547.
    10. 10)
      • 13. Dissanayaka, A., Annakkage, U.D., Jayasekara, B., et al: ‘Risk-based dynamic security assessment’, IEEE Trans. Power Syst., 2011, 26, (3), pp. 1302–1308.
    11. 11)
      • 6. Kirschen, D.S., Rios, M.A., Bell, K.R.W., et al: ‘Selection of operating scenarios based on the value of security’. Proc. PMAPS'2000 – 6th Int. Conf. on Probabilistic Methods Applied to Power Systems, Madeira, Portugal, September 2000.
    12. 12)
      • 5. Kirschen, D.S., Bell, K.R., Nedic, D.P., et al: ‘Computing the value of security’, Proc. IEEE Gener. Transm. Distrib., 2003, 150, (6), pp. 673–678.
    13. 13)
      • 17. Wang, J., Xiong, X., Zhou, N., et al: ‘Time-varying failure rate simulation model of transmission lines and its application in power system risk assessment considering seasonal alternating meteorological disasters’, IET Gener. Transm. Distrib., 2016, 10, (7), pp. 1582–1588.
    14. 14)
      • 20. Ozdemir, A., Bagriyanik, M., Kaypmaz, A., et al: ‘Impact of adverse weather conditions on outage statistics of 154 kV power transmission system’. IEEE 2015 Int. Conf. In Electronics, Communications and Computers (CONIELECOMP), Cholula, Mexico, February 2015, pp. 29–34.
    15. 15)
      • 19. PSE S.A. data, http://www.pse.pl/index.php?dzid=180&did=1364#t8_1, accessed 7 October 2015.
    16. 16)
      • 11. Lebrevelec, C., Peligry, L., Bulot, M., et al: ‘The value of the cost/security trade off’. Proc. CIGRE Session, Paris, France, August 27 – Spetember 2, 2000, pp. 37–102.
    17. 17)
      • 18. Achilles, S., Schramm, S., Bebic, J.: ‘Transmission system performance analysis for high-penetration photovoltaics’ (GE Global Research, Niskayuna, New York, 2008).
    18. 18)
      • 4. Stott, B., Alsac, O., Monticelli, A.J.: ‘Security analysis and optimization’, Proc. IEEE, 1987, 75, (12), pp. 1623–1644.
    19. 19)
      • 15. Datta, S., Vittal, V.: ‘Operational risk metric for dynamic security assessment of renewable generation’, IEEE Trans. Power Syst., 2017, 32, (2), pp. 1389–1399.
    20. 20)
      • 12. Wan, H., McCalley, J.D., Vittal, V.: ‘Risk-based voltage security assessment’, IEEE Trans. Power Syst., 2000, 15, pp. 1247–1254.
    21. 21)
      • 10. McCalley, J.D., Fouad, A.A., Agrawal, B.L., et al: ‘A risk-based security index for determining operating limits in stability limited electric power systems’, IEEE Trans. Power Syst., 1997, 12, pp. 1210–1217.
    22. 22)
      • 8. Jayaweera, D., Islam, S.: ‘Security assessment in active distribution networks with change in weather patterns’. Proc. 2014 Int. Conf. on Probabilistic Methods Applied to Power Systems, Durham, UK, July 2014, pp. 1–6.
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