Weather-forecast based optimal unit scheduling with solar power integration

In order to reduce the CO2 emission of electric power grid, renewable energy resources such as solar power has become an important source of energy in our daily life. When integrated into the power grid, both the randomness of solar power and the ramping capacity of the thermal generation units should be considered in order to provide a feasible and economic schedule. We consider this important problem in this paper, and make the following major contributions. First, we formulate the scheduling problem in the day-ahead market as a stochastic programming, which uses the weather forecasting as well as the ramping capability of the units. Second, we generate different scenarios for the weather and apply the scenario-tree method to approximately solve the problem. Third, we compare our method with existing methods. Numerical results show that our method can improve the integration of solar power while maintain the constraint on ramping capacity of thermal units.

[1]  Dick Duffey,et al.  Power Generation , 1932, Transactions of the American Institute of Electrical Engineers.

[2]  Henrik Madsen,et al.  Online short-term solar power forecasting , 2009 .

[3]  A. Conejo,et al.  Optimal response of a thermal unit to an electricity spot market , 2000 .

[4]  P. Cooper.  The absorption of radiation in solar stills , 1969 .

[5]  Viorel Badescu,et al.  A new kind of cloudy sky model to compute instantaneous values of diffuse and global solar irradiance , 2002 .

[6]  M. Carrion,et al.  A computationally efficient mixed-integer linear formulation for the thermal unit commitment problem , 2006, IEEE Transactions on Power Systems.

[7]  P. Sauer,et al.  Uncertainty Management in the Unit Commitment Problem , 2009, IEEE Transactions on Power Systems.

[8]  K. Conradsen,et al.  Net incoming radiation estimated from hourly global radiation and/or cloud observations , 1981 .

[9]  A. L. Diniz.  Test cases for unit commitment and hydrothermal scheduling problems , 2010, IEEE PES General Meeting.

[10]  G. G. Stokes.  "J." , 1890, The New Yale Book of Quotations.

[11]  W. Gu,et al.  Economic dispatch considering integration of wind power generation and mixed-mode electric vehicles , 2012, 2012 IEEE Power and Energy Society General Meeting.

[12]  John M. Wilson,et al.  Introduction to Stochastic Programming , 1998, J. Oper. Res. Soc..

[13]  Yongpei Guan,et al.  A Chance-Constrained Two-Stage Stochastic Program for Unit Commitment With Uncertain Wind Power Output , 2012 .

[14]  M. Shahidehpour,et al.  Security-Constrained Unit Commitment With Volatile Wind Power Generation , 2008, IEEE Transactions on Power Systems.

[15]  Júlio Tóta da Silva,et al.  Cloud modulation of surface solar irradiance at a pasture site in southern Brazil , 2001 .

[16]  Francisco J. Batlles,et al.  Cloud detection, classification and motion estimation using geostationary satellite imagery for cloud cover forecast , 2013 .

[17]  Athanasios Sfetsos,et al.  Univariate and multivariate forecasting of hourly solar radiation with artificial intelligence techniques , 2000 .

[18]  Gordon Reikard.  Predicting solar radiation at high resolutions: A comparison of time series forecasts , 2009 .

[19]  P. Sauer,et al.  Wind power day-ahead uncertainty management through stochastic unit commitment policies , 2009, 2009 IEEE/PES Power Systems Conference and Exposition.

[20]  B. J. Kirby,et al.  Spinning Reserve From Responsive Loads , 2003 .

[21]  Yongpei Guan,et al.  A Chance-Constrained Two-Stage Stochastic Program for Unit Commitment With Uncertain Wind Power Output , 2012, IEEE Transactions on Power Systems.

[22]  Janet E. Shields,et al.  Day/night whole sky imagers for 24-h cloud and sky assessment: history and overview. , 2013, Applied optics.

[23]  Collin G. Homer,et al.  Automated cloud and shadow detection and filling using two-date Landsat imagery in the USA , 2013 .

[24]  Kara Clark,et al.  Western Wind and Solar Integration Study , 2011 .

[25]  Marcelo Gradella Villalva,et al.  Comprehensive Approach to Modeling and Simulation of Photovoltaic Arrays , 2009, IEEE Transactions on Power Electronics.

[26]  M. Bollen,et al.  Simulation of global solar radiation based on cloud observations , 2005 .

[27]  Jinye Zhao,et al.  Unit commitment with intermittent wind generation via Markovian analysis with transmission capacity constraints , 2012, 2012 IEEE Power and Energy Society General Meeting.

[28]  Thomas Ackermann,et al.  Wind Power in Power Systems , 2005 .

[29]  Xu Andy Sun,et al.  Adaptive Robust Optimization for the Security Constrained Unit Commitment Problem , 2013, IEEE Transactions on Power Systems.

[30]  M. Lange.  On the Uncertainty of Wind Power Predictions—Analysis of the Forecast Accuracy and Statistical Distribution of Errors , 2005 .

[31]  J. Kleissl,et al.  Evaluation of numerical weather prediction for intra-day solar forecasting in the continental United States , 2011 .