A Probability-Driven Multilayer Framework for Scheduling Intermittent Renewable Energy

A probability-driven, multilayer framework is proposed in this paper for ISOs to schedule intermittent wind power and other renewables. The fundamental idea is to view the intermittent renewable energy as a product with a lower quality (i.e., the probability of energy availability in real time) than dispatchable power plants, such as thermal or hydro plants, from the operators' viewpoint. Multiple layers which consider the probability of delivery are proposed such that various loads (critical or non-essential controllable loads) may participate in different layers in the energy market. A layer with a lower expected probability of energy availability is generally anticipated to have a lower price. This is similar to having different prices for commodities of varying qualities. A methodology is proposed to gradually merge the multilayers in the day-ahead market to a single deterministic layer in real time. The merge is necessary because the market must be deterministic in real time, whether sources are available or not. This is also aligned with the higher accuracy of forecasts when the time frame moves closer to real time. Further, the proposed scheduling framework is extended to consider the transmission constraints with a case study based on a modified PJM 5-bus system.

[1]  Tao Li,et al.  Cost of Reliability Analysis Based on Stochastic Unit Commitment , 2008, IEEE Transactions on Power Systems.

[2]  Daniel S. Kirschen,et al.  Estimating the Spinning Reserve Requirements in Systems With Significant Wind Power Generation Penetration , 2009, IEEE Transactions on Power Systems.

[3]  N.D. Hatziargyriou,et al.  An Advanced Statistical Method for Wind Power Forecasting , 2007, IEEE Transactions on Power Systems.

[4]  Ehab F. El-Saadany,et al.  Impact of wind generation control strategies, penetration level and installation location on electricity market prices , 2008 .

[5]  M. Shahidehpour,et al.  Stochastic Security-Constrained Unit Commitment , 2007, IEEE Transactions on Power Systems.

[6]  Ahlstrom,et al.  The future of wind forecasting and utility operations , 2005, IEEE Power and Energy Magazine.

[7]  M. Negnevitsky,et al.  Very short-term wind forecasting for Tasmanian power generation , 2006, 2006 IEEE Power Engineering Society General Meeting.

[8]  E. El-Saadany,et al.  Grey predictor for wind energy conversion systems output power prediction , 2006, IEEE Transactions on Power Systems.

[9]  P. Veers,et al.  To Capture the Wind , 2007, IEEE Power and Energy Magazine.

[10]  B. Zavadil,et al.  Grid impacts of wind power: a summary of recent studies in the United States , 2003 .

[11]  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.

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

[13]  Chun-Lung Chen,et al.  Optimal Wind–Thermal Generating Unit Commitment , 2008, IEEE Transactions on Energy Conversion.

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

[15]  V. Miranda,et al.  Economic dispatch model with fuzzy wind constraints and attitudes of dispatchers , 2005, IEEE Transactions on Power Systems.

[16]  C.-l. Chen,et al.  Simulated annealing-based optimal wind-thermal coordination scheduling , 2007 .

[17]  B. Venkatesh,et al.  Fuzzy MILP Unit Commitment Incorporating Wind Generators , 2008, IEEE Transactions on Power Systems.

[18]  David C. Yu,et al.  An Economic Dispatch Model Incorporating Wind Power , 2008, IEEE Transactions on Energy Conversion.

[19]  Jian Ma,et al.  Operational Impacts of Wind Generation on California Power Systems , 2009, IEEE Transactions on Power Systems.

[20]  T. Thiringer,et al.  Wind Farms as Reactive Power Ancillary Service Providers—Technical and Economic Issues , 2009, IEEE Transactions on Energy Conversion.

[21]  A. Llombart,et al.  Statistical Analysis of Wind Power Forecast Error , 2008, IEEE Transactions on Power Systems.

[22]  L. Soder,et al.  Minimization of imbalance cost trading wind power on the short term power market , 2005, 2005 IEEE Russia Power Tech.

[23]  J.B. Theocharis,et al.  Long-term wind speed and power forecasting using local recurrent neural network models , 2006, IEEE Transactions on Energy Conversion.

[24]  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.

[25]  W.L. Kling,et al.  Impacts of Wind Power on Thermal Generation Unit Commitment and Dispatch , 2007, IEEE Transactions on Energy Conversion.

[26]  S. Watson,et al.  Application of wind speed forecasting to the integration of wind energy into a large scale power system , 1994 .

[27]  Tsung-Ying Lee.  Optimal Spinning Reserve for a Wind-Thermal Power System Using EIPSO , 2007, IEEE Transactions on Power Systems.

[28]  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.

[29]  Fangxing Li,et al.  DCOPF-Based LMP simulation: algorithm, comparison with ACOPF, and sensitivity , 2007, 2008 IEEE/PES Transmission and Distribution Conference and Exposition.

[30]  Bikash C. Pal,et al.  Intermittent wind generation in optimal power flow dispatching , 2009 .

[31]  Wei-Jen Lee,et al.  Forecasting the Wind Generation Using a Two-Stage Network Based on Meteorological Information , 2009, IEEE Transactions on Energy Conversion.