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Reliability-based Probabilistic Wind Power Planning Considering Correlation of Load and Wind | ||
International Journal of Industrial Electronics Control and Optimization | ||
دوره 5، شماره 4، اسفند 2022، صفحه 304-315 اصل مقاله (771.01 K) | ||
نوع مقاله: Research Articles | ||
شناسه دیجیتال (DOI): 10.22111/ieco.2022.41531.1414 | ||
نویسندگان | ||
Morteza Jadidoleslam* ؛ Morteza Ghaseminejad | ||
Department of Electrical Engineering, Sirjan University of Technology, Sirjan, Iran | ||
چکیده | ||
Wind power has been considered a future alternative to fossil energy resources. However, due to its stochastic nature, the integration of wind power plants (WPPs) into power systems poses some reliability problems such as a mismatch between load profile and efficient wind power generation. This issue can be alleviated by considering the correlation between hourly load and wind speed variations in the planning phase. To this end, a reliability-based wind power planning procedure is proposed and formulated as a stochastic programming problem. The objective function is the minimization of total costs, including capital investment, operating and maintenance, and customer energy not served costs. A new hybrid method that combines features of the load-duration curve and the K-means clustering algorithm is proposed to model the uncertainty of the input data. A shuffled frog-leaping algorithm is used to solve the proposed model. The simulation results indicate that the amount of adaptation between hours with high loads and those with high wind speeds markedly affects the selection of wind sites as optimal locations for WPP installation. Considering this issue can also improve power system reliability in the presence of WPPs. | ||
کلیدواژهها | ||
Power system reliability؛ Shuffled frog leaping algorithm؛ Uncertainty modeling؛ Wind power planning | ||
مراجع | ||
[1] E. Jafari, "Coordinated Operation of Wind Farms, Cascaded Hydro, Photo-voltaic, and Pump-storage Considering WTANN- ICA Hybrid Prediction Method," International Journal of Industrial Electronics, Control and Optimization, vol. 4, no. 1, pp. 127-139, 2021.
[2] M. Jadidoleslam, A. Ebrahimi, and M. A. Latify, "Probabilistic transmission expansion planning to maximize the integration of wind power," Renewable Energy, vol. 114, pp. 866-878, 2017. [3] R. Billinton and Y. Gao, "Multistate Wind Energy Conversion System Models for Adequacy Assessment of Generating Systems Incorporating Wind Energy," IEEE T Energy Conver, vol. 23, pp. 163-170, 2008. [4] M. Haji-Bashi and A. Ebrahimi, "Markovian Approach Applied to Reliability Modeling of a Wind Farm," Turk J Electr Eng Co, vol. 22, pp. 287-301, 2014. [5] S. Eryilmaz, İ. Bulanık, and Y. Devrim, "Reliability based modeling of hybrid solar/wind power system for long term performance assessment," Reliability Engineering & System Safety, vol. 209, pp. 1074-78, 2021. [6] A. Heshmati and H. R. Najafi, "Wind farm incorporation in reliability assessment of power systems from the viewpoint of reactive power management," Journal of Energy Management and Technology, vol. 4, no. 4, pp. 57-67, 2020. [7] S. Eryilmaz and J. Navarro, "A decision theoretic framework for reliability-based optimal wind turbine selection," Reliability Engineering & System Safety, vol. 221, p. 108291, 2022. [8] S. Rebennack, "Generation Expansion Planning under Uncertainty with Emissions Quotas," Electr Pow Syst Res vol. 114, pp. 78-85, 2014. [9] M. Jadidoleslam and A. Ebrahimi, "Reliability Constrained Generation Expansion Planning by a Modified Shuffled Frog Leaping Algorithm," Electr Pow Energy Syst, vol. 64, pp. 743-751, 2015. [10] O. H. Abdalla, L. Smieee, M. A. A. Adma, and A. S. Ahmed, "Two-stage robust generation expansion planning considering long- and short-term uncertainties of high share wind energy," Electric Power Systems Research, vol. 189, p.106618, 2020. [11] S. Mokhtari and K. K. Yen, "Impact of large-scale wind power penetration on incentive of individual investors, a supply function equilibrium approach," Electric Power Systems Research, vol. 194, p. 107014, 2021. [12] L. Baringo and A. J. Conejo, "Risk-constrained multi-stage wind power investment," IEEE Trans Power Syst, vol. 28, pp. 401-411, 2013. [13] A. Naderipour et al., "Optimal design of hybrid gridconnected photovoltaic/wind/battery sustainable energy system improving reliability, cost and emission," Energy, vol.257, p. 124679, 2022 2022. [14] S. M. H. Baygi and J. Farzaneh, "Application of Artificial intelligence techniques for optimum design of hybrid gridindependent PV/WT/battery power system," International Journal of Industrial Electronics, Control and Optimization, vol. 3, no. 3, pp. 275-290, 2020. [15] R. Krishnakumar and C. S. Ravichandran, "Reliability and Cost Minimization of Renewable Power System with Tunicate Swarm Optimization Approach Based on the Design of PV/Wind/FC System," Renewable Energy Focus, vol. 42, pp. 266-276, 2022. [16] T. Cai, M. Dong, H. Liu, and S. Nojavan, "Integration of hydrogen storage system and wind generation in power systems under demand response program: A novel p-robust stochastic programming," International Journal of Hydrogen Energy, vol. 47, no. 1, pp. 443-458, 2022. [17] R. Li, S. Guo, Y. Yang, and D. Liu, "Optimal sizing of wind/ concentrated solar plant/ electric heater hybrid renewable energy system based on two-stage stochastic programming," Energy, vol. 209, p. 118472, 2020. [18] S. Wharton and J. K. Lundquist, "Atmospheric Stability Affects Wind Turbine Power Collection," Environ Res Lett, vol. 7, pp. 1-9, 2012. [19] R. Xu and D. Wunsch, "Survey of Clustering Algorithms," IEEE Trans Neural Net, vol. 16, pp. 645-678, 2005. [20] R. Billinton and A. A. Chowdhury, "Incorporation of wind energy conversion systems in conventional generating capacity adequacy assessment," IEE Proc. C vol. 139, pp. 47-56, 1992. [21] R. Billinton and R. N. Allan, Reliability Evaluation of Power Systems, 2nd ed. Plenum, 1996. [22] H. Seifi and M. S. Sepasian, Electric Power System Planning: Issues, Algorithms and Solutions. Springer 2011. [23] R.Billinton and W.Wangdee, " Predicting Bulk Electricity System Reliability Performance Indices Using Sequential Monte Carlo Simulation," IEEE T Power Deliver, vol. 21, pp. 909-917, 2006. [24] M. M. Eusuff and K. Lansey, "Optimization of Water Distribution Network Design Using the Frog Leaping Algorithm," J Water Res Planning Manage, vol. 129, pp. 21-25, 2003. [25] M. Wang, Q. Zhang, H. Chen, A. A. Heidari, M. Mafarja, and H. Turabieh, "Evaluation of constraint in photovoltaic cells using ensemble multi-strategy shuffled frog leading algorithms," Energy Conversion and Management, vol. 244, p. 114484, 2021. [26] R. Billinton et al., "A Reliability Test System for Educational Purposes-Basic Data," IEEE Power Engineering Review, vol. 9, pp. 67-68, 1989. [27] Reliability Test System Task Force, "The IEEE reliability Test System-1996," IEEE Trans Power Syst, vol. 14, pp.1010-1020, 1999. [28] Renewable Energy Organization of Iran. (2016). Wind Energy Office Data. Available: http://www.suna.org. | ||
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