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Reliability based planning and operation planning of thermal-wind units | ||
| International Journal of Nonlinear Analysis and Applications | ||
| مقاله 3، دوره 16، شماره 9، آذر 2025، صفحه 15-26 اصل مقاله (2.34 M) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22075/ijnaa.2024.34298.5119 | ||
| نویسندگان | ||
| Farzad Arefi؛ Hassan Meyar-Naimi* ؛ Ahmad Ghaderi Shamim | ||
| Department of Electrical Engineering, Hamedan Branch, Islamic Azad University, Hamedan, Iran | ||
| تاریخ دریافت: 17 فروردین 1403، تاریخ پذیرش: 06 تیر 1403 | ||
| چکیده | ||
| Today, the issue of planning and operation planning of thermal units (TUs) based on generation system reliability has become very important due to the restructuring process in power systems, load increment and distributed generation penetration on the demand side. This paper proposes a two-step approach to solve the aforementioned issue in the presence of wind farm renewable energy resources in the electricity market environment. In the first step, the optimal installation capacity of TUs is determined by the goal of providing annual peak load and being at the desired level of generation system risk using the loss of load probability analysis. Their economic dispatch and spinning reserve are determined in the second step. Expected energy not served is used for generation system reliability evaluation in the operation planning phase. Single contingencies of TUs are defined as system uncertainty. It is assumed that wind farm has constant capacity in the planning phase, and it generates active power (negative injection) as a function of wind speed at the installation region in the operation planning phase. Auto Recursive and Moving Average time series model is applied for wind speed estimation at different time intervals in the operation planning phase. The genetic algorithm has been used to solve this optimization problem. To validate the effectiveness of the proposed model, numerical studies and simulations are performed on the standard test generation system with 32 TUs and 1 wind farm. Finally, conceptual results have been expressed. | ||
| کلیدواژهها | ||
| Thermal-wind Units؛ Operating Reserve؛ Generation System Reliability؛ Well-Being Analysis (WBA)؛ Auto Recursive and Moving Average (ARMA) | ||
| مراجع | ||
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[1] R. Bagen and R. Billinton, Incorporating well-being considerations in generating systems using energy storage, Trans. Energy Conver. 20 (2005), no. 1, 225–230. [2] F. Bavafa, T. Niknam, R. Azizipanah-Abarghooee, and V. Terzija, A new bi-objective probabilistic risk based wind-thermal unit commitment using heuristic techniques, IEEE Trans. Ind. Inf. 13 (2017), no. 1, 115–124. [3] R. Billinton and R.N. Allan, Reliability Evaluation of Power Systems, 2nd eds. Plenum, New York, NY, USA, 1996. [4] R. Billinton, M. Fotuhi–Firuzabad, and R. Karki, A new approach to adequacy assessment of small isolated power generating systems, Int. Conf. Energy Manag. Power Deliv. 2 (1998). [5] R. Billinton and Y. Gao, Multistate wind energy conversion system models for adequacy assessment of generating systems incorporating wind energy, IEEE Trans. Energy Conver. 23 (2008), no. 1, 163–170. [6] R. Billinton and B. Karki, Well-being analysis of wind integrated power systems, Trans. Power Syst. 26 (2011), no. 4, 2101–2108. [7] R. Billinton, B. Karki, R. Karki, and G. Ramakrishna, Unit commitment risk analysis of wind integrated power systems, Trans. Power Syst. 24 (2009), no. 2, 930–939. [8] Y. Ding, C. Singh, L. Goel, J. Ostergaard, and P. Wang, Short-term and medium-term reliability evaluation for power systems with high penetration of wind power, IEEE Transactions on sustainable Energy, 5 (2014), no. 3, 896–906. [9] C. Grigg, P. Wong, P. Albrecht, R. Allan, M. Bhavaraju, R. Billinton, Q. Chen, C. Fong, S. Haddad, and S. Kuruganty, The IEEE reliability test system-1996. A report prepared by the Reliability Test System Task Force of the Application of Probability Methods Subcommittee, IEEE Trans. Power Syst. 14 (1999), no. 3, 1010–1020. [10] Z. Parvini, A. Abbaspour, M. Fotuhi-Firuzabad, and M. Moeini-Aghtaie, An analytical framework for operational reliability studies of highly wind integrated power systems, IEEE Int. Conf. Probab. Meth. Appl. Power Syst., 2016, pp. 1–6. [11] Z. Parvini, A. Abbaspour, M. Fotuhi-Firuzabad, and M. Moeini-Aghtaie, Operational reliability studies of power systems in the presence of energy storage systems, IEEE Transactions on Power Systems, 33 (2018), no. 4, 3691–3700. [12] I.S. Qamber, Power Systems Control and Reliability: Electric Power Design and Enhancement, Apple Academic Press, 2020. [13] A. Roxana and M. Gibescu, Round the year security analysis with large scale wind power integration, IEEE Power Energy Soc. Gen. Meet. 3 (2012), no. 1. [14] C. Shao, X. Wang, M. Shahidehpour, X. Wan, and B. Wang, Security-constrained unit commitment with flexible uncertainty set for variable wind power, IEEE Trans. Sustain. Energy 8 (2017), no. 3, 1237–1246. [15] R. Sioshansi and W. Short, Evaluating the impacts of real time pricing on the usage of wind generation, Trans. Power Syst. 24 (2009), no. 2, 516–524. [16] S. Thaps, R. Karki, and R. Billinton, Utilization of area risk concept for operational reliability evaluation of a wind-integrated power system, IEEE Trans. Power Syst. 28 (2013), no. 4, 4771–4779. [17] P. Wang, Z. Gao, and L. Bertling-Tjerberg, Operational adequacy studies of power systems with wind farms and energy storages, IEEE Trans. Power Syst. 27 (2012), no. 4, 2377–2384. [18] W. Wangdee and R. Billinton, Bulk electric system well-being analysis using sequential Monte Carlo simulation, Trans. Power Syst. 21 (2006), no. 1, 188–193. [19] N. Xu and C. Chung, Uncertainties of EV charging and effects on well-being analysis of generating systems, Trans. Power Syst. 30 (2015), no. 5, 2547–2557. | ||
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