Data-driven chance-constrained stochastic unit commitment under wind power uncertainty

Rapid integration of cheap, clean but highly intermittent wind energy into power systems brings challenges to ISOs to maintain the system reliability. Stochastic Programs (SP) may result in biased and unreliable unit commitment (UC) and economic dispatch (ED) decisions by fixing the probability dist...

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Bibliographic Details
Main Authors: Bagheri, Ali, Chaoyue Zhao, Yuanxiong Guo
Format: Conference Proceeding
Language:English
Subjects:
Chance Constraint
Data-driven
Load modeling
Power system reliability
Robustness
Stochastic processes
Stochastic Unit Commitment
Uncertainty
Wind power generation
Wind Uncertainty
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Summary:Rapid integration of cheap, clean but highly intermittent wind energy into power systems brings challenges to ISOs to maintain the system reliability. Stochastic Programs (SP) may result in biased and unreliable unit commitment (UC) and economic dispatch (ED) decisions by fixing the probability distribution of wind output. Robust Optimization (RO) approaches sacrifice system's cost-effectiveness in exchange of reliable UC and ED schedules. In this paper, we develop a data-driven chance-constrained two-stage stochastic UC model to bridge the gap between SP and RO. Without any particular assumption of wind output distribution, the data-driven chance constraint limits the worst-case chance of load imbalance to be no more than a specified tolerance, by taking advantage of historical data. We apply Column-and-Constraints Generation to solve our model. By experiments, we show the effectiveness of our model and the value of data.
ISSN:1944-9933
DOI:10.1109/PESGM.2017.8273948