Setting the Operating Reserve Using Probabilistic Wind Power Forecasts

By Manuel A. Matos, Ricardo J. Bessa. IEEE Transactions on Power Systems, 2011

306 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/cc54e56d7a4424c884923d2fffef19b19413e3a5

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Citations in the corpus

A Review of Power System Flexibility With High Penetration of Renewables (2019, background): “In [8], a comprehensive reserve management tool is presented.”
Probabilistic Forecasting of Wind Power Generation Using Extreme Learning Machine (2014, background): “With high penetration of wind power, the knowledge of uncertainties ahead can be extremely valuable to a number of power system operation and management procedures, including but not limited to, optimal operation reserve determination [14], [15], system steady-state security assessment [15], economi”
Scenario Generation of Wind Power Based on Statistical Uncertainty and Variability (2013, background): “Meanwhile, the uncertainty caused by the ineluctable forecast error of wind power [4], [5] requires an increase in the amount of reserves that are needed to balance generation and load as described in [6] and [7].”
Wind Energy: Forecasting Challenges for Its Operational Management (2013, background): “After such a description of system-wide uncertainties, the system operator can plug this density into a cost-loss analysis (Matos and Bessa, 2010), similar in essence to the market participation problem presented in the above.”
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].”
Probabilistic weather forecasting with machine learning (2024, unknown): “In the electricity sector, power grid operators use regional wind power forecasts for tasks such as unit commitment and reserve quantification 40 , in which leveraging forecast uncertainty can improve decision-making 41,42 .”
Global Energy Forecasting Competition 2012 (2014, background): “…densities or scenarios as inputs, such as annual peak demand forecasting for system planning (Hyndman & Fan, 2009), systems reserve quantification (Matos & Bessa, 2011), unit commitment (Tuohy, Meibom, Denny, & O’Malley, 2009), and trading of wind power generation (Pinson, Chevallier, &…”