تعداد نشریات | 11 |
تعداد شمارهها | 208 |
تعداد مقالات | 2,090 |
تعداد مشاهده مقاله | 2,829,683 |
تعداد دریافت فایل اصل مقاله | 2,049,148 |
Transmission Congestion Management Considering Uncertainty of Demand Response Resources’ Participation | ||
Journal of Electrical and Computer Engineering Innovations (JECEI) | ||
مقاله 2، دوره 3، شماره 2 - شماره پیاپی 6، مهر 2015، صفحه 77-88 اصل مقاله (963.23 K) | ||
نوع مقاله: Original Research Paper | ||
شناسه دیجیتال (DOI): 10.22061/jecei.2016.400 | ||
نویسندگان | ||
A. Tabandeh* ؛ A. Abdollahi؛ M. Rashidinejad | ||
Department of Electrical Engineering, Shahid Bahonar University, Kerman, Iran | ||
تاریخ دریافت: 11 مهر 1394، تاریخ بازنگری: 02 دی 1394، تاریخ پذیرش: 13 دی 1394 | ||
چکیده | ||
Under the smart grid environment, demand response resources (DRRs) are introduced as a virtual power plant to enhance power system adequacy. DRRs often fail to reduce their load due to some external factors. In this paper, a reliability model of a DRR is constructed as multi-state conventional generation units, where the probability, frequency of occurrence, and departure rate of each state can be acquired. DRRs as consequence of demand response program implementation can be applied to transmission congestion management. Therefore, this paper presents an optimal model of congestion management (CM) by means of multi-state DRRs, namely CM_DRR. In the proposed approach, in addition to DRRs, independent system operator relieves the existing transmission line congestions using the combination of generating unit rescheduling and involuntary load shedding. The hourly historical data associated with the Connecticut region in New England is employed to achieve the DRRs’ participation regime. Moreover, the impact of different capacities of DRRs on the congestion management cost and load shedding cost is evaluated. Results of applying the aforementioned model to the 24-bus Reliability Test System (RTS) indicate the efficiency of CM_DRR framework. | ||
کلیدواژهها | ||
Congestion management؛ Demand response resource؛ Customer’s uncertainty؛ Multi-state model؛ Smart grid | ||
مراجع | ||
[1] F. D. Galiana and M. Ilic, "A mathematical framework for the analysis and management of power transactions under open access," Power Systems, IEEE Transactions on, vol. 13, pp. 681- 687, 1998.
[2] A. Kumar, S. C. Srivastava, and S. N. Singh, "Congestion management in competitive power market: A bibliographical survey," Electric Power Systems Research, vol. 76, pp. 153-164, sept. 2005.
[3] Y. R. Sood and R. Singh, "Optimal model of congestion management in deregulated environment of power sector with promotion of renewable energy sources," Renewable Energy, vol. 35, pp. 1828-1836, Aug. 2010.
[4] M. Esmaili, N. Amjady, and H. A. Shayanfar, "Multi-objective congestion management by modified augmented ε-constraint method," Applied Energy, vol. 88, pp. 755-766, March. 2011.
[5] M. Khanabadi, H. Ghasemi, and M. Doostizadeh, "Optimal transmission switching considering voltage security and N-1 contingency analysis," Power Systems, IEEE Transactions on, vol. 28, pp. 542-550, 2013.
[6] L. S. Vargas, G. Bustos-Turu, and F. Larrain, "Wind power curtailment and energy storage in transmission congestion management considering power plants ramp rates," Power Systems, IEEE Transactions on, vol. 30, pp. 2498-2506, 2015. [7] J. Hu, A. Saleem, S. You, L. Nordström, M. Lind, and J. Østergaard, "A multi-agent system for distribution grid congestion management with electric vehicles," Engineering Applications of Artificial Intelligence, vol. 38, pp. 45-58, Feb. 2015.
[8] M. A. López, S. Martín, J. A. Aguado, and S. de la Torre, "V2G strategies for congestion management in microgrids with high penetration of electric vehicles," Electric Power Systems Research, vol. 104, pp. 28-34, Nov. 2013.
[9] K. S. Verma, S. N. Singh, and H. O. Gupta, "Location of unified power flow controller for congestion management," Electric Power Systems Research, vol. 58, pp. 89-96, June. 2001.
[10] M. Esmaili, H. A. Shayanfar, and N. Amjady, "Congestion management considering voltage security of power systems," Energy Conversion and Management, vol. 50, pp. 2562-2569, Oct. 2009.
[11] N. Amjady and M. Hakimi, "Dynamic voltage stability constrained congestion management framework for deregulated electricity markets," Energy Conversion and Management, vol. 58, pp. 66-75, June. 2012.
[12] F. C. Schweppe, R. D. Tabors, R. E. Bohn, and M.C. Caramanis, "Spot pricing of electricity", 1988 edition. Boston: Springer, 1988.
[13] N. Mahmoudi-Kohan, M. P. Moghaddam, and M. K. Sheikh-ElEslami, "An annual framework for clustering-based pricing for an electricity retailer," Electric Power Systems Research, vol. 80, pp. 1042-1048, Sept. 2010. [14] S. Yousefi, M. P. Moghaddam, and V. J. Majd, "Optimal real time pricing in an agent-based retail market using a comprehensive demand response model," Energy, vol. 36, pp. 5716-5727, Sept. 2011.
[15] Z. Jun Hua, D. Zhao Yang, P. Lindsay, and W. Kit Po, "Flexible transmission expansion planning with uncertainties in an electricity market," Power Systems, IEEE Transactions on, vol. 24, pp. 479-488, 2009.
[16] N. Venkatesan, J. Solanki, and S. K. Solanki, "Residential demand response model and impact on voltage profile and losses of an electric distribution network," Applied Energy, vol. 96, pp. 84-91, Aug. 2012.
[17] A. Abdollahi, M. P. Moghaddam, M. Rashidinejad, and M. K. Sheikh-el-Eslami, "Investigation of economic and environmental-driven demand response measures incorporating UC," Smart Grid, IEEE Transactions on, vol. 3, pp. 12-25, 2012.
[18] M. Mollahassani-pour, A. Abdollahi, and M. Rashidinejad, "Investigation of market-based demand response impacts on security-constrained preventive maintenance scheduling," Systems Journal, IEEE, vol. PP, pp. 1-11, 2015.
[19] M. P. Moghaddam, A. Abdollahi, and M. Rashidinejad, "Flexible demand response programs modeling in competitiveelectricity markets," Applied Energy, vol. 88, pp. 3257-3269, Sept. 2011.
[20] GAMS (General Algebraic Modeling System) software package. www.gams.com.
[21] “The historical data of DRR in DRPs.” [Online]. Available: www.iso-ne.com.
[22] R. Billinton, Power system reliability evaluation: Taylor & Francis, 1970.
[23] M. Čepin, Assessment of Power System Reliability: Methods and Applications: Springer Science & Business Media, 2011.
[24] C. C. Aggarwal and C. K. Reddy, Data clustering: algorithms and applications: CRC Press, 2013.
[25] F. Castro Sayas and R. N. Allan, "Generation availability assessment of wind farms," Generation, Transmission and Distribution, IEE Proceedings, vol. 143, pp. 507-518, 1996.
[26] W. Lei, M. Shahidehpour, and L. Tao, "Cost of reliability analysis based on stochastic unit commitment," Power Systems, IEEE Transactions on, vol. 23, pp. 1364-1374, 2008.
[27] P. Wong, P. Albrecht, R. Allan, R. Billinton, Q. Chen, C. Fong, et al., "The IEEE reliability test system-1996. A report prepared by the reliability test system task force of the application of probability methods subcommittee," Power Systems, IEEE Transactions on, vol. 14, pp. 1010-1020, 1999.
[28] M. Esmaili, N. Amjady, and H. A. Shayanfar, "Stochastic congestion management in power markets using efficient scenario approaches," Energy Conversion and Management, vol. 51, pp. 2285-2293, Nov. 2010. | ||
آمار تعداد مشاهده مقاله: 2,119 تعداد دریافت فایل اصل مقاله: 2,022 |