Coordination of Hydro Units With Wind Power Generation Using Interval Optimization

The coordination of wind power and hydro power is an effective way for generating companies to enhance the ability of wind power dispatch and to avoid imbalance charges. However, specifying the probability distributions of random variables can be challenging for decision makers (DMs). This paper proposes a bidding strategy for wind farms and hydro stations in a generating company using interval optimization. Pessimistic preference ordering and DMs' degrees of pessimism are also adopted in the optimization to compare interval numbers. Variations in wind power, the day-ahead energy price, and the intrahour energy price are then considered and represented by interval numbers instead of probability distributions. Compared with stochastic optimization, interval optimization does not need an exact probability distribution for the random variables. Moreover, it can reduce computational complexity as well as determine and optimize profit intervals, as illustrated in the numerical example presented herein.

[1]  Yongpei Guan,et al.  Price-Based Unit Commitment With Wind Power Utilization Constraints , 2013, IEEE Transactions on Power Systems.

[2]  M. Shahidehpour,et al.  Price-based unit commitment: a case of Lagrangian relaxation versus mixed integer programming , 2005, IEEE Transactions on Power Systems.

[3]  M. Shahidehpour,et al.  Stochastic Price-Based Coordination of Intrahour Wind Energy and Storage in a Generation Company , 2013, IEEE Transactions on Sustainable Energy.

[4]  J. I. Muñoz,et al.  Optimal coordinated wind-hydro bidding strategies in day-ahead markets , 2013, IEEE Transactions on Power Systems.

[5]  Mohammad Shahidehpour,et al.  Transmission-constrained intrahour coordination of wind and pumped-storage hydro units , 2013 .

[6]  A. Kusiak,et al.  Short-Term Prediction of Wind Farm Power: A Data Mining Approach , 2009, IEEE Transactions on Energy Conversion.

[7]  Mohammad Shahidehpour,et al.  Enhancing the Dispatchability of Variable Wind Generation by Coordination With Pumped-Storage Hydro Units in Stochastic Power Systems , 2013, IEEE Transactions on Power Systems.

[8]  M. Trovato,et al.  Risk-Constrained Profit Maximization in Day-Ahead Electricity Market , 2009, IEEE Transactions on Power Systems.

[9]  Zuyi Li,et al.  Comparison of Scenario-Based and Interval Optimization Approaches to Stochastic SCUC , 2012, IEEE Transactions on Power Systems.

[10]  Tapan Kumar Pal,et al.  On comparing interval numbers , 2000, Eur. J. Oper. Res..

[11]  Debjani Chakraborty,et al.  Interpretation of inequality constraints involving interval coefficients and a solution to interval linear programming , 2001, Fuzzy Sets Syst..

[12]  M. Shahidehpour,et al.  Risk-Constrained Coordination of Cascaded Hydro Units With Variable Wind Power Generation , 2012, IEEE Transactions on Sustainable Energy.

[13]  A.M. Stankovic,et al.  An application of interval analysis and optimization to electric energy markets , 2006, IEEE Transactions on Power Systems.

[14]  R. Baker Kearfott,et al.  Introduction to Interval Analysis , 2009 .

[15]  B. Hartmann,et al.  Cooperation of a Grid-Connected Wind Farm and an Energy Storage Unit—Demonstration of a Simulation Tool , 2012, IEEE Transactions on Sustainable Energy.

[16]  A. Fabbri,et al.  Assessment of the cost associated with wind generation prediction errors in a liberalized electricity market , 2005, IEEE Transactions on Power Systems.

[17]  Wei-Jen Lee,et al.  An Integration of ANN Wind Power Estimation Into Unit Commitment Considering the Forecasting Uncertainty , 2007, IEEE Transactions on Industry Applications.

[18]  Yue Yuan,et al.  Optimal operation strategy of energy storage unit in wind power integration based on stochastic programming , 2011 .

[19]  Zhang Yan,et al.  A review on the forecasting of wind speed and generated power , 2009 .

[20]  R. Abrahart,et al.  Comparing neural network and autoregressive moving average techniques for the provision of continuous river flow forecasts in two contrasting catchments , 2000 .

[21]  N. Growe-Kuska,et al.  Scenario reduction and scenario tree construction for power management problems , 2003, 2003 IEEE Bologna Power Tech Conference Proceedings,.

[22]  A.M. Gonzalez,et al.  Stochastic Joint Optimization of Wind Generation and Pumped-Storage Units in an Electricity Market , 2008, IEEE Transactions on Power Systems.

[23]  Yang Wang,et al.  Unit Commitment With Volatile Node Injections by Using Interval Optimization , 2011, IEEE Transactions on Power Systems.

[24]  Le Xie,et al.  Risk Measure Based Robust Bidding Strategy for Arbitrage Using a Wind Farm and Energy Storage , 2013, IEEE Transactions on Smart Grid.

[25]  Andrew Boone,et al.  Simulation of Short-term Wind Speed Forecast Errors using a Multi-variate ARMA(1,1) Time-series Model , 2005 .

[26]  Kit Po Wong,et al.  Optimal Prediction Intervals of Wind Power Generation , 2014, IEEE Transactions on Power Systems.