Knowledge Is Power: Efficiently Integrating Wind Energy and Wind Forecasts

Ahlstrom, Mark; Bartlett, Drake; Collier, Craig; Duchesne, Jacques; Edelson, D.; Gesino, Alejandro; Keyser, Marc; Maggio, David; Milligan, Michael; Möhrlen, Corinna; O’Sullivan, Jonathan; Sharp, John T.; Storck, Pascal; Rodriguez, Miguel de la Torre

doi:10.1109/mpe.2013.2277999

Cited by 48 publications

(18 citation statements)

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“…Because NWP run at high resolution over a sizable domain requires on the order of hours to run on supercomputers and often requires spin-up time, it is not available for real-time use in the shortest ranges. Modern methods of forecasting renewable energy output employing postprocessing methods to blend disparate models or ensem-bles are shown to greatly improve the forecast skill (Giebel and Kariniotakis 2007;Monteiro et al 2009;Mahoney et al 2012;Ahlstrom et al 2013;Orwig et al 2014;Tuohy et al 2015). Figure 7.1 indicates that this blending can provide value beyond that of the input models.…”

Section: Overview Of Scalesmentioning

confidence: 99%

Short-Range Forecasting for Energy

Haupt

2018

Weather &Amp; Climate Services for the Energy Industry

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Short-range forecasts for periods on the order of hours to days and up to two weeks ahead are necessary to smoothly run transmission and distribution systems, plan maintenance, protect infrastructure and allocate units. In particular, forecasting the renewable energy resources on a day-to-day basis enables integration of increasing capacities of these variable resources. This chapter describes the basics of this short-range forecasting, beginning with the observation-based "nowcasting" of the first 15 minutes and ranging up to two weeks using numerical weather prediction. We discuss how blending multiple forecasts can increase accuracy and how probabilistic forecasts are being used to quantify the forecast uncertainty.

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Section: Overview Of Scalesmentioning

confidence: 99%

Short-Range Forecasting for Energy

Haupt

2018

Weather &Amp; Climate Services for the Energy Industry

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“…For the off-line analysis, different operational conditions and contingencies are considered while power transfer through an interface is changed incrementally. Operating conditions including generation dispatch, topology and load level are obtained from SCADA every 5 minutes [11]. Contingencies are pre-defined from a relatively worst contingency list including most of the 3-phase faults.…”

Section: Proposed Approachmentioning

confidence: 99%

Prediction of critical generator buses in transient stability using synchrophasor data

Musavi

Lerley³

et al. 2014

2014 International Conference on Renewable Energy Research and Application (ICRERA)

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Many interconnected power systems are constructed to meet the ever increasing electric power transfer demand and the integration of renewable energy. A disturbance anywhere in such a system will directly influence the power transfer capability of major transfer interfaces, as well as the transient stability margin of the system. An index is proposed to locate the critical buses, which will be used to monitor the transient stability of a power system. Furthermore, a real-time Artificial Neural Network-based prediction model is proposed to allow the operators to predict the post-fault transient oscillation of critical buses by using several samples after fault clearing. The Eastern North American Power System, which has a large penetration of renewable energy, is used as a platform to illustrate the proposed method.

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“…Contingency reserves are traditional resources, such as natural gas and coal, that are ready to dispatch energy to the grid on short notice . However, contingency reserves are costly when not used and require unit commitment ahead of time …”

Section: Introductionmentioning

confidence: 99%

Benefits of a multimodel ensemble for hub‐height wind prediction in mountainous terrain

Siuta

Stull

2018

Wind Energy

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While numerical weather prediction models can be used to estimate future wind power, no single model is perfect. A better approach is to run many models (an ensemble) and use the average to estimate future wind speeds. The goal of this manuscript is to demonstrate the benefits of using a multimodel ensemble to predict wind speeds at wind‐turbine hub heights. We do this for a 1‐year period at 4 wind farms in mountainous terrain. The ensemble‐mean forecast has higher accuracy than the climatology forecast until a forecast horizon of 6.5 days. The ensemble‐mean forecast has higher correlation to the observations than the climatology forecast has to the observations through the 7‐day forecast horizon tested. Use of the ensemble‐mean forecast results in at least a 1‐ to 2‐day skill advantage (increase in time that a forecast remains more skilled than climatology) over use of a single, deterministic ensemble member for both forecast accuracy and correlation. For probabilistic forecasts, use of the multimodel ensemble mean is most beneficial to improvements in probabilistic sharpness (narrowing of uncertainty). A comparison of Weather Research and Forecasting model forecasts initialized by the National Centers for Environmental Prediction Global Forecast System and North American Mesoscale models, the Canadian Meteorological Centre Global Deterministic Prediction System, and Fleet Numerical Meteorology and Oceanography Center Navy Global Environmental Model showed that the Canadian Meteorological Centre Global Deterministic Prediction System provided the best initial conditions for the locations tested.

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Knowledge Is Power: Efficiently Integrating Wind Energy and Wind Forecasts

Cited by 48 publications

References 0 publications

Short-Range Forecasting for Energy

Short-Range Forecasting for Energy

Prediction of critical generator buses in transient stability using synchrophasor data

Benefits of a multimodel ensemble for hub‐height wind prediction in mountainous terrain

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