Wind power forecasting uncertainty and unit commitment

By J. Wang, Audun Botterud, Ricardo J. Bessa, H. Keko, L. Carvalho, D. Issicaba, J. Sumaili, Vladimiro Miranda. 2011

290 citations recorded by the source corpus.

Subfields: Electrical and Electronic Engineering, Safety, Risk, Reliability and Quality

View Paper: https://www.semanticscholar.org/paper/c7ee54a2369015da827bd08dc516269ca059c37c

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In Electrical and Electronic Engineering

In Safety, Risk, Reliability and Quality

Citations in the corpus

Modeling of financial incentives for investments in energy storage systems that promote the large-scale integration of wind energy (2013, background): “This in turn requires that support is provided to wind energy production through introduction of back-up power [6] that may compensate for calm spells or extremely high wind speed.”
Comprehensive review of renewable energy curtailment and avoidance: A specific example in China (2015, background): “The impact of WPF on unit commitment (UC) and economic dispatch is investigated, which is possible for operators to determine spinning reserves accurately [55].”
On-line quantile regression in the RKHS (Reproducing Kernel Hilbert Space) for operational probabilistic forecasting of wind power (2016, unknown): “For Transmission 6 System Operators (TSO), this information is vital for setting the operat7 ing reserve requirements [2, 3], stochastic unit commitment [4] and technical 8 constraints evaluation [5].”
Chance-Constrained Two-Stage Unit Commitment Under Uncertain Load and Wind Power Output Using Bilinear Benders Decomposition (2016, methodology): “Among those methods, two most popular approaches are stochastic programming [3], [6]–[14] and robust optimization [4], [15]–[19].”
An analog ensemble for short-term probabilistic solar power forecast (2015, background): “Even though there are several contributions on the topic of probabilistic WPF [20–23]for probabilistic PV forecasts only a few methods have been proposed in the literature.”
Current status and future advances for wind speed and power forecasting (2014, background): “Accurate forecasting tools reduce operating costs and improve reliability associated with the integration of wind power into the existing electricity supply system [14-29].”