An Improved Stochastic Unit Commitment Formulation to Accommodate Wind Uncertainty

The United States targets to supply 20% of its electricity generation using wind energy by 2030. The expansion of renewable resources, especially weather-based resources such as wind, creates more uncertainty and variability in the operation of the power grid. New methods and approaches in electricity market operations are needed to efficiently manage the continuing increase in variability and uncertainty caused by expanding intermittent wind. This paper proposes an improved stochastic programming approach for incorporating wind uncertainty into energy markets. The proposed formulation improves the two-stage stochastic unit commitment problem by introducing a dynamic decision making approach similar to a multi-stage formulation in the presence of wind power scenarios which are not well represented by a scenario tree. The numerical results present up to 1%-2% decrease in operational costs compared to the two-stage stochastic unit commitment formulation.

[1]  H. Madsen,et al.  From probabilistic forecasts to statistical scenarios of short-term wind power production , 2009 .

[2]  Aoife Foley,et al.  Current methods and advances in forecasting of wind power generation , 2012 .

[3]  Dissertação De Mestrado Apresentada,et al.  WIND POWER FORECASTING UNCERTAINTY AND UNIT COMMITMENT , 2014 .

[4]  David L. Woodruff,et al.  PySP: modeling and solving stochastic programs in Python , 2012, Mathematical Programming Computation.

[5]  Heike Brand,et al.  WILMAR: A Stochastic Programming Tool to Analyze the Large-Scale Integration of Wind Energy , 2009 .

[6]  Panos M. Pardalos,et al.  A decomposition approach to the two-stage stochastic unit commitment problem , 2012, Annals of Operations Research.

[7]  Henrik Madsen,et al.  Using quantile regression to extend an existing wind power forecasting system with probabilistic forecasts , 2006 .

[8]  R. Tyrrell Rockafellar,et al.  Scenarios and Policy Aggregation in Optimization Under Uncertainty , 1991, Math. Oper. Res..

[9]  F. Bouffard,et al.  Stochastic security for operations planning with significant wind power generation , 2008, 2008 IEEE Power and Energy Society General Meeting - Conversion and Delivery of Electrical Energy in the 21st Century.

[10]  Andreas Grothey,et al.  Stochastic Unit Commitment Problem , 2008 .

[11]  Michael Ferris,et al.  Co-optimization of generation unit commitment and transmission switching with N-1 reliability , 2010, IEEE PES General Meeting.

[12]  Antonio J. Conejo,et al.  Economic Valuation of Reserves in Power Systems With High Penetration of Wind Power , 2009, IEEE Transactions on Power Systems.

[13]  M. O'Malley,et al.  Unit Commitment for Systems With Significant Wind Penetration , 2009, IEEE Transactions on Power Systems.

[14]  John R. Birge,et al.  A stochastic model for the unit commitment problem , 1996 .

[15]  A. Conejo,et al.  Economic Valuation of Reserves in Power Systems With High Penetration of Wind Power , 2009 .

[16]  V. Miranda,et al.  Unit commitment and operating reserves with probabilistic wind power forecasts , 2011, 2011 IEEE Trondheim PowerTech.

[17]  Anthony Papavasiliou,et al.  Multiarea Stochastic Unit Commitment for High Wind Penetration in a Transmission Constrained Network , 2013, Oper. Res..

[18]  David L. Woodruff,et al.  Pyomo — Optimization Modeling in Python , 2012, Springer Optimization and Its Applications.

[19]  David L. Woodruff,et al.  Toward scalable, parallel progressive hedging for stochastic unit commitment , 2013, 2013 IEEE Power & Energy Society General Meeting.

[20]  Mohammad Shahidehpour,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 , 1999 .

[21]  B. F. Hobbs,et al.  Commitment and Dispatch With Uncertain Wind Generation by Dynamic Programming , 2012, IEEE Transactions on Sustainable Energy.

[22]  Victor M. Zavala,et al.  A Stochastic Electricity Market Clearing Formulation with Consistent Pricing Properties , 2015, Oper. Res..

[23]  John R. Birge,et al.  Stochastic Unit Commitment Problem (あいまいさと不確実性を含む状況の数理的意思決定 研究集会報告集) , 2002 .

[24]  Gregor Giebel,et al.  The State-Of-The-Art in Short-Term Prediction of Wind Power. A Literature Overview , 2003 .