Power System Reliability Assessment Considering Impacts of Climate Change

Year 2022, Volume: 8 Issue: 1, 19 - 25, 29.06.2022

Mohammadreza Gholami , Parvaneh Esmaili

Abstract

Power system reliability, as one of the most significant issues in power system studies, is affected remarkably by load demand changes. On other hand, climate changes and global warming lead to increasing the electricity demand of a power system. In this paper, the impacts of global change on generation power system reliability indices have been investigated. Loss of Load Probability and Expected Energy Not Supply are considered as power system reliability indices. In addition, a Particle Swarm Optimization method is used to assess these reliability indices. IEEE_79 Reliability test system is selected as a standard test system. The results show that reliability indices are affected noticeably by temperature rising and climate change.

Keywords

Climate Change, Power System Reliability Indices, Loss of Load Probability, Expected Energy Not Supplied, Load demand, Particle Swarm Optimization

Supporting Institution

Near East University - The paper belongs to NRSEM-2021

Thanks

NRSEM-2021

References

• 1. N. X. Tung, N. Q. Dat, T. N. Thang, V. K. Solanki and N. T. N. Anh, "Analysis of temperature-sensitive on short-term electricity load forecasting," 2020 IEEE-HYDCON, 2020, pp. 1-7, doi: 10.1109/HYDCON48903.2020.9242910.
• 2. Parkpoom, Suchao & Harrison, Gareth. (2008). Analyzing the Impact of Climate Change on Future Electricity Demand in Thailand. Power Systems, IEEE Transactions on. 23. 1441 - 1448. 10.1109/TPWRS.2008.922254.
• 3. Pandey, G.P.; Shrestha, A.; Mali, B.; Singh, A.; Jha, A.K. Performance enhancement of radial distribution system via network reconfiguration: A case study of an urban city in nepal. J. Renew. Energy Electr. Comput. Eng. 2021, 1, 1–11.
• 4. M. Victoria Gasca, M. Bueno-López, F. Ibáñez and D. Pozo, "Ambient Temperature Impact on the Aggregated Demand Response Flexibility in Microgrids," 2021 IEEE Madrid PowerTech, 2021, pp. 1-6, doi: 10.1109/PowerTech46648.2021.9494915.
• 5. N. Samaan and C. Singh, .Using of genetic algorithms to evaluate frequency and duration indices for generation system reliability. Presented at the Proc. 11th Intell. Syst. Appl. to Power Syst. Conf. (ISAP 2001), Budapest, Hungry, pp. 251-256, June 2001.
• 6. N. Samaan and C. Singh, .An improved genetic algorithm based method for reliability assessment of generation system, Presented at the Proc. 8th Int.Middle-East Power Syst. Conf. (MEPCON 2001), Cairo, Egypt, pp. 235-242, Dec. 2001
• 7. N. Samaan and C. Singh, .Adequacy assessment of power system generation using a modified simple genetic algorithm, IEEE Trans. Power Syst., vol. 17, pp. 974-981, Nov. 2002.
• 8. N. Samaan and C. Singh, .New method for composite system annualized reliability indices based on genetic algorithms, Presented at the Proc. IEEE PES Summer Meeting, Chicago,pp. 850-855, July 2002.
• 9. N. Samaan and C. Singh, .Using genetic algorithms for composite system reliability indices considering chronological load curves, Presented at the Proc. 12th Intell. Syst. Appl. to Power Syst. Conf. (ISAP 2003), Lemnos, Greece, Aug.2003.
• 10. N. Samaan and C. Singh, .Genetic algorithms approach for the evaluation of composite generation-transmission systems reliability worth, Presented at the Proc. of IEEE PES Transm. And Dist. Conference, Dallas, Sep. 2003.
• 11. A N. Samaan and C. Singh, .Genetic algorithms approach for the assessment of composite power system reliability considering multi-state components, presented at the Proc. of Int. Conf. on Probability Methods Applied to Power Systems (PMAPS 2004), Ames, Iowa, Sept. 2004.
• 12. Ii RCG, Member S, Wang L, Alam M. Intelligent State Space Pruning Using Multi- Objective PSO for Reliability Analysis of Composite Power Systems: Observations, Analyses, and Impacts, in IEEE Conference, pp. 1–8; 2011.
• 13. Benidris M, Elsaiah S, Mitra J. Power system reliability evaluation using a state space classification technique and particle swarm optimisation search method. IET Gener Transm Distrib 2015;9(14):1865–73.
• 14. Ii RCG, Member S, Wang L, Alam M. State Space Pruning for Reliability Evaluation Using Binary Particle Swarm Optimization, in IEEE PES power System Conference and Exposition, pp. 1–7; 2011.
• 15. Benidris M, Member S, Mitra J, Member S. Composite Power System Reliability Assessment Using Maximum Capacity Flow and Directed Binary Particle Swarm Optimization, in North American Power Symposium, pp. 1–6; 2013.
• 16. Mohamadreza Gholami, Reza Sharifi, Hamid Radmanesh, “Development of Composite Power System Effective Load Duration Curves by Using a New Optimization Method for Assessment Composite Generation and Transmission Reliability”, International Journal of Power and Energy Research, Vol. 1, No. 1, April 2017 https://dx.doi.org/10.22606/ijper.2017.11004
• 17. Gholami, Mohammadreza, HOSEINI, SEIED HADI, MOHAMAD TAHERI, MEISAM, “Assessment of Power Composite System Annualized Reliability Indices Based on Improved Particle Swarm Optimization and Comparative Study between the Behaviour of GA and PSO”, 2nd IEEE International Conference on Power and Energy (PECon 08), December 1-3, 2008, Johor Baharu, Malaysia
• 18. R Billinton and A. Sankarakrishnan, .A comparison of Monte Carlo simulation techniques for composite power system reliability assessment, Presented at the Proc. IEEE Comm., Power and Comp. Conf. (WESCANEX 95), Winnipeg, Canada, vol. 1, pp. 145-150, May 1995.
• 19. L.W.R. Billinton, Reliability Assessment of Electric power systems using Monte Carlo Methods.pdf., 1994.
• 20. Melo ACG, Silva AML. “A conditional probabiuty approach to the calculation of frequency and duration indices in composite reuabilily evaluation”, IEEE Trans Power Syst 1993;8(3):1118–25.
• 21. Pereira MVF, Balu NJ, Objectives A, System P. Composite Generation /Transmission Reliability Evaluation. Proc IEEE 1992;80(4):470–91.
• 22. Bhuiyan RNAMR, “Modelling multistate problems in sequential simulation of power system reliability studies”. IEEE Proceed Gener Transm Distrib 1995;142(4):343–9.
• 23. Billinton R, Sankarakrishnan A. Effective techniques for reliability Worth assessment in composite power system networks using Monte Carlo simulation. IEEE Trans Power Syst 1996;11(3):1255–61.
• 24. Billinton RGR, Chen H, “A Sequential Simulation Technique for Adequacy Evaluation of Generating Systems Including Wind Energy”. IEEE Trans Energy Convers 1996;11(4):728–34.
• 25. Jayatheertha HJ, “Evaluation of composite electric system performance indices using sequential Monte Carlo simulation”. Int J Adv Eng Res Stud 2012;vol. EISSN2249: 4–7.
• 26. Mello JCO, da Silva AML, Pereira MVF. Efficient loss-of-load cost evaluation by combined pseudo-sequential and state transition simulation [in]. IEEE Proc Gener, Transm Distrib 1997;144(2):147–54.
• 27. X. Li and D.J. Sailor, “Electricity use sensitivity to climate and climate change”, Energy Planning and Policy, vol. 7, no. 3, pp. 334-346, 1995.
• 28. K. P. Linder, “National impacts of climate change on electric utilities”, in: The Potential Effects of Global Warming on the United States, J. B. Smith and D. A. Tirpak, Eds., Washington, D.C.: Environmental Protection Agency, 1990.
• 29. C.-L. Hor, S. J. Watson and S. Majithia, “Analyzing the impact of weather variables on monthly electricity demand”, IEEE Trans. Power Syst., vol. 20, no. 4, 2078 – 2085, Nov. 2005.