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Incorporating Wind Power Generation And Demand Response into Security-Constrained Unit Commitment | ||
| AUT Journal of Electrical Engineering | ||
| مقاله 5، دوره 50، شماره 2، اسفند 2018، صفحه 141-148 اصل مقاله (817.5 K) | ||
| نوع مقاله: Research Article | ||
| شناسه دیجیتال (DOI): 10.22060/eej.2018.14001.5200 | ||
| نویسندگان | ||
| M.H. Hemmatpour* 1؛ E. Zarei2؛ M. Mohammadian2 | ||
| 1Department of Electrical Engineering, Faculty of Engineering, Jahrom University, Jahrom, Fars, Iran | ||
| 2Department of Electrical Engineering, Faculty of Engineering, Shahid Bahonar University of Kerman, Kerman, Iran | ||
| چکیده | ||
| Wind generation with an uncertain nature poses many challenges in grid integration and secure operation of power system. One of these operation problems is the unit commitment. Demand Response (DR) can be defined as the changes in electric usage by end-use customers from their normal consumption patterns in response to the changes in the price of electricity over time. Further, DR can be also defined as the incentive payments designed to induce lower electricity use at the times of high wholesale market prices or when system reliability is jeopardized. This paper presents a novel approach for incorporating stochastic wind power generation and DR with Security-Constrained Unit Commitment (SCUC) for improving the security and economic operation in power systems. DR is one of the methods of managing the economic filed in unit commitment. Demand includes the fixed and responsive loads, and the volatile nature of wind power is modeled. Responsive loads can be curtailed or shifted to another off peak hours. The combination of wind power generation and DR to SCUC problem makes a large scale optimization problem which needs a heavy mathematical computation and time consuming process. So, benders decomposition technique is applied to reduce the volume of the computation and problem complexity. To reach a fast approach, a proposed statistical and probabilistic method for omitting infeasible terms is used. Numerical simulation and final results on a modified IEEE 6- and 118-bus systems show the performance and effectiveness of the proposed approach. | ||
| کلیدواژه‌ها | ||
| Demand Response؛ Security-Constraint Unit Commitment؛ Restructured Power Market؛ Wind Farms؛ Stochastic Power Generation | ||
| مراجع | ||
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[1] M. Shahidehpour, H. Yamin, Z. Li, Market overview in electric power systems, Market Operations in Electric Power Systems: Forecasting, Scheduling, and Risk Management, (2002) 1-20. [2] L. Goel, Q. Wu, P. Wang, Reliability enhancement and nodal price volatility reduction of restructured power systems with stochastic demand side load shift, in: Power Engineering Society General Meeting, 2007. IEEE, IEEE, 2007, pp. 1-8. [3] H.Y. Yamin, S.M. Shahidehpour, Risk and profit in self-scheduling for GenCos, IEEE Transactions on Power Systems, 19(4) (2004) 2104-2106. [4] J. Wang, S. Kennedy, J. Kirtley, A new wholesale bidding mechanism for enhanced demand response in smart grids, in: Innovative Smart Grid Technologies (ISGT), 2010, IEEE, 2010, pp. 1-8. [5] S.J. Rassenti, V.L. Smith, B.J. Wilson, Controlling market power and price spikes in electricity networks: Demand-side bidding, Proceedings of the National Academy of Sciences, 100(5) (2003) 2998-3003. [6] T. Li, M. Shahidehpour, Strategic bidding of transmission-constrained GENCOs with incomplete information, IEEE Transactions on power Systems, 20(1) (2005) 437-447. [7] C. Su, D. Kirschen, Direct participation of demand-side in a pool-based electricity market, POWER SYSTEM TECHNOLOGY-BEIJING-, 31(20) (2007) 7. [8] R.H. Patrick, F.A. Wolak, Real-time pricing and demand side participation in restructured electricity markets, Electricity Pricing in Transition, (2002) 345-356. [9] R.L. Earle, Demand elasticity in the California power exchange day-ahead market, The Electricity Journal, 13(8) (2000) 59-65. [10] C.-L. Su, D. Kirschen, Quantifying the effect of demand response on electricity markets, IEEE Transactions on Power Systems, 24(3) (2009) 1199-1207. [11] A. David, Y. Li, Effect of inter-temporal factors on the real time pricing of electricity, IEEE transactions on power systems, 8(1) (1993) 44-52. [12] E.M. Larsen, P. Pinson, F. Leimgruber, F. Judex, Demand response evaluation and forecasting — Methods and results from the EcoGrid EU experiment, Sustainable Energy, Grids and Networks, 10 (2017) 75-83. [13] L. Goel, Q. Wu, P. Wang, Nodal price volatility reduction and reliability enhancement of restructured power systems considering demand–price elasticity, Electric Power Systems Research, 78(10) (2008) 1655-1663. [14] A. David, Y.-C. Lee, Dynamic tariffs: theory of utility-consumer interaction, IEEE Transactions on Power Systems, 4(3) (1989) 904-911. [15] R. Fernández-Blanco, Y. Dvorkin, M.A. Ortega-Vazquez, Probabilistic security-constrained unit commitment with generation and transmission contingencies, IEEE Transactions on Power Systems, 32(1) (2017) 228-239. [16] M.H. Hemmatpour, M. Mohammadian, A.-A. Gharaveisi, Simple and efficient method for steady-state voltage stability analysis of islanded microgrids with considering wind turbine generation and frequency deviation, IET Generation, Transmission & Distribution, 10(7) (2016) 1691-1702. [17] G. Kariniotakis, I.H. Waldl, I. Marti, G. Giebel, T.S. Nielsen, J. Tambke, J. Usaola, F. Dierich, A. Bocquet, S. Virlot, Next generation forecasting tools for the optimal management of wind generation, in: Probabilistic Methods Applied to Power Systems, 2006. PMAPS 2006. International Conference on, IEEE, 2006, pp. 1-6. [18] M. Gibescu, B. Ummels, W. Kling, Statistical wind speed interpolation for simulating aggregated wind energy production under system studies, in: Probabilistic Methods Applied to Power Systems, 2006. PMAPS 2006. International Conference on, IEEE, 2006, pp. 1-7. [19] J. Li, F. Liu, Z. Li, S. Mei, G. He, Impacts and benefits of UPFC to wind power integration in unit commitment, Renewable Energy, 116 (2018) 570-583. [20] N. Gong, X. Luo, D. Chen, Bi-level two-stage stochastic SCUC for ISO day-ahead scheduling considering uncertain wind power and demand response, The Journal of Engineering, 2017(13) (2017) 2549-2554. [21] B.D.H. Kiran, M.S. Kumari, Demand response and pumped hydro storage scheduling for balancing wind power uncertainties: A probabilistic unit commitment approach, International Journal of Electrical Power & Energy Systems, 81 (2016) 114-122. [22] S. Abedi, M. He, D. Obadina, Congestion Risk-Aware Unit Commitment with Significant Wind Power Generation, IEEE Transactions on Power Systems, (2018). [23] P. Xiong, C. Singh, A Distributional Interpretation of Uncertainty Sets in Unit Commitment under Uncertain Wind Power, IEEE Transactions on Sustainable Energy, (2018). [24] P. Glasserman, Monte Carlo methods in financial engineering, Springer Science & Business Media, 2013. [25] J. Dupačová, N. Gröwe-Kuska, W. Römisch, Scenario reduction in stochastic programming, Mathematical programming, 95(3) (2003) 493-511. [26] N. Growe-Kuska, H. Heitsch, W. Romisch, Scenario reduction and scenario tree construction for power management problems, in: Power tech conference proceedings, 2003 IEEE Bologna, IEEE, 2003, pp. 7 pp. Vol. 3. [27] E. Zarei, M.H. Hemmatpour, M. Mohammadian, The Effects of Demand Response on Security-Constrained Unit Commitment, Scientia Iranica, (2017) -. [28] A. Khodaei, M. Shahidehpour, S. Bahramirad, SCUC with hourly demand response considering intertemporal load characteristics, IEEE Transactions on Smart Grid, 2(3) (2011) 564-571. [29] J. Wang, M. Shahidehpour, Z. Li, Security-constrained unit commitment with volatile wind power generation, IEEE Transactions on Power Systems, 23(3) (2008) 1319-1327. | ||
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