Multiarea Stochastic Unit Commitment for High Wind Penetration in a Transmission Constrained Network

References

• Ackermann T. Wind Power in Power Systems (2005) (John Wiley & Sons, Hoboken, NJ) Crossref, Google Scholar
• Arroyo JM, Galiana FD. Energy and reserve pricing in security and network-constrained electricity markets. IEEE Trans. Power Systems (2005) 20(2):634–643Crossref, Google Scholar
• Bertsimas D, Sim M. The price of robustness. Oper. Res. (2004) 52(1):35–53Link, Google Scholar
• Bertsimas D, Litvinov E, Sun XA, Zhao J, Zheng T. Adaptive robust optimization for the security constrained unit commitment problem. IEEE Trans. Power Systems (2013) 28(1):52–63Crossref, Google Scholar
• Bouffard F, Galiana FD. Stochastic security for operations planning with significant wind power generation. IEEE Trans. Power Systems (2008) 23(2):306–316Crossref, Google Scholar
• Bouffard F, Galiana FD, Conejo AJ. Market-clearing with stochastic security—Part II: Case studies. IEEE Trans. Power Systems (2005) 20(4):1827–1835Crossref, Google Scholar
• Box GEP, Jenkins GM. Time Series Analysis: Forecasting and Control (1976) (Holden-Day, San Francisco) Google Scholar
• Brown BG, Katz RW, Murhpy AH. Time series models to simulate and forecast wind speed and wind power. J. Climate Appl. Meteorology (1984) 23:1184–1195Crossref, Google Scholar
• CAISO Oasis California Independent System Operator website. (2011) . Accessed May 2, 2013, http://oasis.caiso.com/mrtu-oasis/home.jspGoogle Scholar
• Callaway D. Sequential reliability forecasting for wind energy: Temperature dependence and probability distributions. IEEE Trans. Energy Conversion (2010) 25:577–585Crossref, Google Scholar
• Carpentier P, Cohen G, Culioli J-C, Renaud A. Stochastic optimization of unit commitment: A new decomposition framework. IEEE Trans. Power Systems (1996) 11(2):1067–1073Crossref, Google Scholar
• CEA Forced outage rates. (2006) . Accessed May 2, 2013, http://www.science.smith.edu/~jcardell/Courses/EGR325/Readings/FOR%202006Update.pdf. Canadian Energy Assocation, Ottawa, Ontario, CanadaGoogle Scholar
• Constantinescu EM, Zavala VM, Rocklin M, Lee S, Anitescu M. A computational framework for uncertainty quantification and stochastic optimization in unit commitment with wind power generation. IEEE Trans. Power Systems (2011) 26(1):431–441Crossref, Google Scholar
• Dupacova J, Gröwe-Kuska N, Römisch W. Scenario reduction in stochastic programming: An approach using probability metrics. Math. Programming (2003) 95(3):493–511Crossref, Google Scholar
• Fisher ML. An applications oriented guide to Lagrangian relaxation. Interfaces (1985) 15(2):10–21Link, Google Scholar
• Galiana FD, Bouffard F, Arroyo JM, Restrepo JF. Scheduling and pricing of coupled energy and primary, secondary, and tertiary reserves. Proc. IEEE (2005) 93(11):1970–1983Crossref, Google Scholar
• Grigg C, Wong P, Albrecht P, Allan R, Bhavaraju M, Billinton R, Chen O, et al. The IEEE reliability test system—1996. IEEE Trans. Power Systems (1999) 14(3):1010–1020Crossref, Google Scholar
• Gröwe-Kuska N, Kiwiel KC, Nowak MP, Romisch W, Wegner I, Greengard C, Ruszczyński A. Power management in a hydro-thermal system under uncertainty by Lagrangian relaxation. Decision Making Under Uncertainty: Energy and Power (2002) 128(Springer-Verlag, New York) 39–70IMA Volumes in Mathematics and Its ApplicationsCrossref, Google Scholar
• Heitsch H, Römisch W. Scenario reduction algorithms in stochastic programming. Comput. Optim. Appl. (2003) 24(2–3):187–206Crossref, Google Scholar
• Held M, Wolfe P, Crowder HP. Validation of subgradient optimization. Math. Programming (1974) 6(1):62–68Crossref, Google Scholar
• Hoyland K, Wallace SW. Generating scenario trees for multistage decision problems. Management Sci. (2001) 47(2):295–307Link, Google Scholar
• Hoyland K, Kaut M, Wallace SW. A heuristic for moment-matching scenario generation. Comput. Optim. Appl. (2003) 24:169–185Crossref, Google Scholar
• Infanger G. Monte Carlo (importance) sampling within a Benders decomposition algorithm for stochastic linear programs. Ann. Oper. Res. (1992) 39(1):69–95Crossref, Google Scholar
• Jiang R, Zhang M, Li G, Guan Y. Robust unit commitment with wind power and pumped storage hydro. IEEE Trans. Power Systems (2012) 27(2):800–810Crossref, Google Scholar
• Latorre JM, Cerisola S, Ramos A. Clustering algorithms for scenario tree generation: Application to natural hydro inflows. Eur. J. Oper. Res. (2007) 181:1339–1353Crossref, Google Scholar
• Mazumdar M. Importance Sampling in Reliability Estimation, Reliability and Fault Tree Analysis (1975) (SIAM, Philadelphia) 153–163Google Scholar
• Morales JM, Conejo AJ, Perez-Ruiz J. Economic valuation of reserves in power systems with high penetration of wind power. IEEE Trans. Power Systems (2009b) 24(2):900–910Crossref, Google Scholar
• Morales JM, Minguez R, Conejo AJ. A methodology to generate statistically dependent wind speed scenarios. Appl. Energy (2010) 87:843–855Crossref, Google Scholar
• Morales JM, Pineda S, Conejo AJ, Carrion M. Scenario reduction for futures trading in electricity markets. IEEE Trans. Power Engrg. (2009a) 24(2):878–888Crossref, Google Scholar
• Nowak MP, Römisch W. Stochastic Lagrangian relaxation applied to power scheduling in a hydro-thermal system under uncertainty. Ann. Oper. Res. (2000) 100(1–4):251–272Crossref, Google Scholar
• O'Neill RP, Hedman KW, Krall EA, Papavasiliou A, Oren SS. Economic analysis of the N - 1 reliable unit commitment and transmission switching problem using duality concepts. Energy Systems (2010) 1:165–195Crossref, Google Scholar
• Ozturk UA, Mazumdar M, Norman BA. A solution to the stochastic unit commitment problem using chance constrained programming. IEEE Trans. Power Systems (2004) 19(3):1589–1598Crossref, Google Scholar
• Papavasiliou A, Oren SS, O'Neill RP. Reserve requirements for wind power integration: A scenario-based stochastic programming framework. IEEE Trans. Power Systems (2011) 26(4):2197–2206Crossref, Google Scholar
• Pereira MVF, Balu NJ. Composite generation/transmission reliability evaluation. Proc. IEEE (1992) 80(4):470–491Crossref, Google Scholar
• Pflug GCh. Scenario tree generation for multiperiod financial optimization by optimal discretization. Math Programming B (2001) 89:251–271Crossref, Google Scholar
• Piwko R, Clark K, Freeman L, Jordan G, Miller N. Western wind and solar integration study. (2010) . Technical report, National Renewable Energy Laboratory. http://www.nrel.gov/docs/fy10osti/47781.pdfGoogle Scholar
• Rockafellar RT, Wets RJ-B. Scenarios and policy aggregation in optimization under uncertainty. Math. Oper. Res. (1991) 16(1):119–147Link, Google Scholar
• Ruiz PA, Philbrick RC, Sauer PW. Wind power day-ahead uncertainty management through stochastic UC policies. Power Systems Conf. Exposition (2009a) (IEEE Power and Energy Society, Piscataway, NJ) 1–9Google Scholar
• Ruiz PA, Philbrick RC, Zack E, Cheung KW, Sauer PW. Uncertainty management in the unit commitment problem. IEEE Trans. Power Systems (2009b) 24(2):642–1651Crossref, Google Scholar
• Shiina T, Birge JR. Stochastic unit commitment problem. Internat. Trans. Oper. Res. (2004) 11(95):19–32Crossref, Google Scholar
• Sioshansi R, Short W. Evaluating the impacts of real time pricing on the usage of wind power generation. IEEE Trans. Power Systems (2009) 24(2):516–524Crossref, Google Scholar
• Takriti S, Birge JR, Long E. A stochastic model for the unit commitment problem. IEEE Trans. Power Systems (1996) 11(3):1497–1508Crossref, Google Scholar
• Torres JL, Garcia A, De Blas M, De Francisco A. Forecast of hourly wind speed with ARMA models in Navarre (Spain). Solar Energy (2005) 79(1):65–77Crossref, Google Scholar
• Tuohy A, Meibom P, Denny E, O'Malley M. Unit commitment for systems with high wind penetration. IEEE Trans. Power Systems (2009) 24(2):592–601Crossref, Google Scholar
• van Hulle F. Large scale integration of wind energy in the European power supply: Analysis, recommendations and issues. (2005) . Technical report, European Wind Energy Association, BrusselsGoogle Scholar
• Wang J, Shahidehpour M, Li Z. Security-constrained unit commitment with volatile wind power generation. IEEE Trans. Power Systems (2008) 23(3):1319–1327Crossref, Google Scholar
• Yu N-P, Liu C-C, Price J. Evaluation of market rules using a multi-agent system method. IEEE Trans. Power Systems (2010) 25:470–479Crossref, Google Scholar