Unit Commitment Risk Evaluation Considering Load Uncertainty and Wind Power

Document Type : Original Article


1 Electrical engineering Department, faculty of engineering, Razi university, kermanshah, iran

2 Department of Electrical Engineering, Faculty of Electrical and Computer Engineering, University of Razi, Kermanshah, Iran



Given the importance of unit commitment risk (UCR) assessment in determining the probability of meeting load demand in the ahead short-term operation period, in this paper, a new analytical model for UCR assessment is presented. In the proposed model to consider the impact of wind power participation in the entire short-term period of operation of the system, using the developed risk area concept, a new model for UCR assessment is presented. Furthermore, uncertainties of wind power and load demand are considered simultaneously. Unlike the models presented in previous research for UCR assessment, which consider the share of wind power in the last period of operation, the proposed model for UCR assessment considers the share of wind power in all periods of operation. The proposed model was tested and evaluated on the RBTS system with a wind farm. Moreover, the results obtained from the simulation were reported. According to the results, in all cases, the value of UCR in the proposed model is lower than the modified PGM (M-PGM) method. The effectiveness of this innovative approach in evaluating the UCR of the power system despite wind power and load uncertainties was confirmed based on the results.


Main Subjects

1. M. R. Patel, Wind and solar power systems: design, analysis, and operation. CRC press, 2005.
2. X. Zhu, Z. Yu, and X. Liu, “Security constrained unit commitment with extreme wind scenarios,” Journal of Modern Power Systems and Clean Energy, vol. 8, no. 3, pp. 464–472, 2020.
3. A. Tuohy, P. Meibom, E. Denny, and M. O’Malley, “Unit commitment for systems with significant wind penetration,” IEEE
Transactions on power systems, vol. 24, no. 2, pp. 592–601, 2009.
4. S. Wang, Y. Ding, X. Han, P. Wang, L. Goel, and J. Ma, “Shortterm reliability evaluation of integrated electricity and gas systems considering dynamics of gas flow,” IET Generation, Transmission & Distribution, vol. 15, no. 20, pp. 2857–2871, 2021.
5. R. Billinton, R. N. Allan, R. Billinton, and R. N. Allan, “Applications of monte carlo simulation,” Reliability Evaluation of Power Systems, pp. 400–442, 1996.
6. J. Wen, Y. Zheng, and F. Donghan, “A review on reliability assessment for wind power,” Renewable and sustainable energy reviews, vol. 13, no. 9, pp. 2485–2494, 2009.
7. D. Ming and W. Yichun, “Optimal expansion planning of winddiesel energy system,” in 2006 International Conference on Power System Technology, pp. 1–6, IEEE, 2006.
8. F. Vallee, J. Lobry, and O. Deblecker, “System reliability assessment method for wind power integration,” IEEE Transactions on Power Systems, vol. 23, no. 3, pp. 1288–1297, 2008.
9. A. Colantoni, M. Villarini, D. Monarca, M. Carlini, E. M. Mosconi, E. Bocci, and S. R. Hamedani, “Economic analysis and risk assessment of biomass gasification chp systems of different sizes through monte carlo simulation,” Energy Reports, vol. 7, pp. 1954–1961, 2021.
10. S. Thapa, R. Karki, and R. Billinton, “Utilization of the area risk concept for operational reliability evaluation of a wind-integrated power system,” IEEE Transactions on Power Systems, vol. 28, no. 4, pp. 4771–4779, 2013.
11. R. McCullough, M. Weisdorf, J.-C. Ende, and A. Absar, “Exactly how inefficient is the pjm capacity market?,” The Electricity Journal, vol. 33, no. 8, p. 106819, 2020.
12. I. Abdou and M. Tkiouat, “Unit commitment problem in electrical power system: A literature review.,” International Journal of Electrical & Computer Engineering (2088-8708), vol. 8, no. 3, 2018.
13. R. Billinton, B. Karki, R. Karki, and G. Ramakrishna, “Unit commitment risk analysis of wind integrated power systems,” IEEE Transactions on power systems, vol. 24, no. 2, pp. 930–939, 2009.
14. X. Lou, C. Feng, W. Chen, and C. Guo, “Risk-based coordination of maintenance scheduling and unit commitment in power systems,” IEEE Access, vol. 8, pp. 58788–58799, 2020.
15. C. Samudio-Carter, A. Vargas, R. Albarracín-Sánchez, and J. Lin, “Mitigation of price spike in unit commitment: A probabilistic approach,” Energy Economics, vol. 80, pp. 1041–1049, 2019.
16. Y. Wang, S. Zhao, Z. Zhou, A. Botterud, Y. Xu, and R. Chen, “Risk adjustable day-ahead unit commitment with wind power based on chance constrained goal programming,” IEEE Transactions on Sustainable Energy, vol. 8, no. 2, pp. 530–541, 2016.
17. M. S. Pinto, V. Miranda, and O. R. Saavedra, “Risk and unit commitment decisions in scenarios of wind power uncertainty,” Renewable Energy, vol. 97, pp. 550–558, 2016.
18. N. Kazemzadeh, S. M. Ryan, and M. Hamzeei, “Robust optimization vs. stochastic programming incorporating risk measures for unit commitment with uncertain variable renewable generation,” Energy Systems, vol. 10, pp. 517–541, 2019.
19. S. Abedi, M. He, and D. Obadina, “Congestion risk-aware unit commitment with significant wind power generation,” IEEE Transactions on Power Systems, vol. 33, no. 6, pp. 6861–6869, 2018.
20. R. Karki, P. Hu, and R. Billinton, “A simplified wind power generation model for reliability evaluation,” IEEE transactions on Energy conversion, vol. 21, no. 2, pp. 533–540, 2006.
21. R. Billinton, H. Chen, and R. Ghajar, “Time-series models for reliability evaluation of power systems including wind energy,” Microelectronics Reliability, vol. 36, no. 9, pp. 1253–1261, 1996.
22. P. Giorsetto and K. F. Utsurogi, “Development of a new procedure for reliability modeling of wind turbine generators,” IEEE transactions on power apparatus and systems, no. 1, pp. 134–143, 1983.
23. A. Zakariazadeh, S. Jadid, and P. Siano, “Economic-environmental energy and reserve scheduling of smart distribution systems: A multiobjective mathematical programming approach,” Energy Conversion and Management, vol. 78, pp. 151–164, 2014.
24. S. S. Reddy, P. Bijwe, and A. R. Abhyankar, “Joint energy and spinning reserve market clearing incorporating wind power and load forecast uncertainties,” IEEE Systems Journal, vol. 9, no. 1, pp. 152–164, 2013.
25. R. Billinton and A. V. Jain, “The effect of rapid start and hot reserve units in spinning reserve studies,” IEEE Transactions on Power Apparatus and Systems, no. 2, pp. 511–516, 1972.
26. R. Billinton, S. Kumar, N. Chowdhury, K. Chu, K. Debnath, L. Goel, E. Khan, P. Kos, G. Nourbakhsh, and J. Oteng-Adjei, “A reliability test system for educational purposes-basic data,” IEEE Transactions on Power Systems, vol. 4, no. 3, pp. 1238–1244, 1989.