A rolling-horizon unit commitment framework with flexible periodicity

Abstract In this work, we develop a mathematical model and framework to represent rolling-horizon unit commitment (UC) processes with multiple periodicities. In control center operations, UC is solved repeatedly to adjust device commands based on new information about load, generation availability, renewable energy production, and other aspects of system state as uncertain conditions are realized. We develop a three-level model including 24-h UC, rolling-horizon UC (RHUC), and economic dispatch (ED) and give formulations for the three problems including interdependencies. This framework allows for evaluation of, among other things, different periodicities of the rolling horizon problem and the benefits of more accurate forecasting information. Experimental results are shown for a 6-bus system and a 3012-bus system with wind generation in two wind scenarios. Although the generation costs are very similar, the deviation between RHUC schedules and actual deployment is noted to be superior for a 20-min periodicity compared to a 30-min periodicity. Additionally, less reserve is deployed in the 20-min RHUC case.

[1]  Marija D. Ilic,et al.  Model predictive economic/environmental dispatch of power systems with intermittent resources , 2009, 2009 IEEE Power & Energy Society General Meeting.

[2]  H. Daneshi,et al.  Real time load forecast in power system , 2008, 2008 Third International Conference on Electric Utility Deregulation and Restructuring and Power Technologies.

[3]  Nikos D. Hatziargyriou,et al.  Centralized Control for Optimizing Microgrids Operation , 2008 .

[4]  Marija D. Ilic,et al.  Model predictive dispatch in electric energy systems with intermittent resources , 2008, 2008 IEEE International Conference on Systems, Man and Cybernetics.

[5]  M. Anjos,et al.  Tight Mixed Integer Linear Programming Formulations for the Unit Commitment Problem , 2012, IEEE Transactions on Power Systems.

[6]  J. Latorre,et al.  Tight and Compact MILP Formulation for the Thermal Unit Commitment Problem , 2013, IEEE Transactions on Power Systems.

[7]  Claudia A. Sagastizábal,et al.  The value of rolling-horizon policies for risk-averse hydro-thermal planning , 2012, Eur. J. Oper. Res..

[8]  M. O'Malley,et al.  Rolling Unit Commitment for Systems with Significant Installed Wind Capacity , 2007, 2007 IEEE Lausanne Power Tech.

[9]  Daniel S. Kirschen,et al.  Cost recovery in a rolling horizon unit commitment with energy storage , 2014, 2014 Power Systems Computation Conference.

[10]  James W. Taylor.  An evaluation of methods for very short-term load forecasting using minute-by-minute British data , 2008 .

[11]  Rodrigo Palma-Behnke,et al.  A Microgrid Energy Management System Based on the Rolling Horizon Strategy , 2013, IEEE Transactions on Smart Grid.

[12]  A. T. Holen,et al.  Operation planning of hydrogen storage connected to wind power operating in a power market , 2006, IEEE Transactions on Energy Conversion.

[13]  Michela Robba,et al.  A Dynamic Decision Model for the Real-Time Control of Hybrid Renewable Energy Production Systems , 2010, IEEE Systems Journal.

[14]  Bri-Mathias Hodge,et al.  Wind power forecasting error distributions over multiple timescales , 2011, 2011 IEEE Power and Energy Society General Meeting.

[15]  Santiago Grijalva,et al.  Large-scale decentralized unit commitment , 2015 .

[16]  Yongpei Guan,et al.  Applying robust optimization to MISO Look-Ahead commitment , 2014, 2014 IEEE PES General Meeting | Conference & Exposition.

[17]  Sangmin Lee,et al.  A Computational Framework for Uncertainty Quantification and Stochastic Optimization in Unit Commitment With Wind Power Generation , 2011, IEEE Transactions on Power Systems.