Balancing Needs Assessment Using Advanced Probabilistic Forecasts

This paper presents a comprehensive approach to predict Balancing Authority (BA) regulation and load following requirements in order to improve BA control performance. In this paper the Pacific Northwest National Laboratory's (PNNL) “ramp and uncertainty prediction tool (RUT) and day-ahead regulation prediction (DARP) tool” were upgraded to incorporate advanced probabilistic forecast information provided by AWS Truepower. The proposed methodology has been tested and validated using actual California Independent System Operator (CAISO) data. Simulation confirmed that integration probabilistic forecast information can reduce the predicted regulation range by about 12-31%. This means that BAs can procure fewer balancing resources without compromising their reliability and control performance requirements.

[1]  Daniel Kirschen,et al.  A hybrid stochastic/interval approach to transmission-constrained unit commitment , 2015, 2015 IEEE Power & Energy Society General Meeting.

[2]  Y. Mishra,et al.  Impact of Wind Power Development on Transmission Planning at Midwest ISO , 2012, IEEE Transactions on Sustainable Energy.

[3]  Marc Keyser,et al.  Knowledge Is Power: Efficiently Integrating Wind Energy and Wind Forecasts , 2013, IEEE Power and Energy Magazine.

[4]  Naomi S. Altman,et al.  Quantile regression , 2019, Nature Methods.

[5]  T. Dominguez,et al.  The CECRE: Supervision and control of wind and solar photovoltaic generation in Spain , 2012, 2012 IEEE Power and Energy Society General Meeting.

[6]  Jian Ma,et al.  Incorporating Uncertainty of Wind Power Generation Forecast Into Power System Operation, Dispatch, and Unit Commitment Procedures , 2011, IEEE Transactions on Sustainable Energy.

[7]  Sue Ellen Haupt,et al.  Solar Forecasting: Methods, Challenges, and Performance , 2015, IEEE Power and Energy Magazine.

[8]  S. Sharma,et al.  ERCOT tools used to handle wind generation , 2012, 2012 IEEE Power and Energy Society General Meeting.

[9]  Yuri V. Makarov,et al.  Online Analysis of Wind and Solar Part I: Ramping Tool , 2012 .

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

[11]  Georges Kariniotakis,et al.  The state-of-the-art in short term prediction of wind power from a danish perspective , 2018 .

[12]  Farrokh Habibi-Ashrafi,et al.  Comprehensive Solutions for Integration of Solar Resources into Grid Operations , 2016 .

[13]  Nader Samaan,et al.  Prediction of regulation reserve requirements in california ISO balancing authority area based on BAAL , 2013, 2013 IEEE Power & Energy Society General Meeting.

[14]  Jianhui Wang,et al.  Demand Dispatch and Probabilistic Wind Power Forecasting in Unit Commitment and Economic Dispatch: A Case Study of Illinois , 2013, IEEE Transactions on Sustainable Energy.

[15]  Kenneth Bruninx,et al.  A Statistical Description of the Error on Wind Power Forecasts for Probabilistic Reserve Sizing , 2014, IEEE Transactions on Sustainable Energy.