A Wind Power Forecasting System to Optimize Grid Integration

Mahoney, William P.; Parks, Keith; Wiener, Gerry; Liu, Yubao; Myers, William; Sun, Juanzhen; Monache, Luca Delle; Hopson, T. M.; Johnson, Diane; Haupt, Sue Ellen

doi:10.1109/tste.2012.2201758

Cited by 140 publications

(71 citation statements)

References 22 publications

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“…Mahoney et al (2012) describe the Variational Doppler Radar Analysis System (VDRAS) that assimilates radar data into a cloud-resolving model to better predict winds. Because that model does not include the full physics, it can be updated at frequencies as high as every 15 minutes.…”

Section: Nowcastingmentioning

confidence: 99%

“…To improve forecasts at specific points, such as at a wind farm, it is advantageous to also assimilate specialized data (such as wind speed measurements) at that farm. Mahoney et al (2012) and Wilczak et al (2015) provide evidence that assimilating local wind farm data can improve the NWP forecasts. In a case study, Cheng et al (2017) show that real-time four-dimensional data assimilation can reduce the mean absolute error in the forecast by 30-40% in the first three hours.…”

Section: Numerical Weather Predictionmentioning

confidence: 99%

“…Ensemble MOS (Wilks and Hamill 2007) not only corrects the individual models but also optimizes weights for blending forecasts from multiple models. It is typical to blend multiple models together using some statistical learning technique to produce a better forecast than any single model could produce consistently, often with a 10-15% improvement over the best model forecast (Mahoney et al 2012;. Figure 7.2 displays an example of wind speed forecasts using National Center for Atmospheric Research (NCAR's) Dynamic Integrated foreCast (DICast®) system to blend multiple models, with the blended forecast showing a lower average root mean square error than any input model.…”

Section: Blending the Forecasts And Predicting Powermentioning

confidence: 99%

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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: Nowcastingmentioning

confidence: 99%

Section: Numerical Weather Predictionmentioning

confidence: 99%

Section: Blending the Forecasts And Predicting Powermentioning

confidence: 99%

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Short-Range Forecasting for Energy

Haupt

2018

Weather &Amp; Climate Services for the Energy Industry

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“…Hourly wind scenarios were obtained from a commercial vendor [49] according to an analogue method [50]. These scenarios were designed to represent a future representing 20% penetration of wind energy in the eastern U.S. in 2024 [51].…”

Section: Scenario Generationmentioning

confidence: 99%

Solution sensitivity-based scenario reduction for stochastic unit commitment

Feng

Ryan

2014

Comput Manag Sci

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A two-stage stochastic program is formulated for day-ahead commitment of thermal generating units to minimize total expected cost considering uncertainties in the day-ahead load and the availability of variable generation resources. Commitments of thermal units in the stochastic reliability unit commitment are viewed as first-stage decisions, and dispatch is relegated to the second stage. It is challenging to solve such a stochastic program if many scenarios are incorporated. A heuristic scenario reduction method termed forward selection in recourse clusters (FSRC), which selects scenarios based on their cost and reliability impacts, is presented to alleviate the computational burden. In instances down-sampled from data for an Independent System Operator in the US, FSRC results in more reliable commitment schedules having similar costs, compared to those from a scenario reduction method based on probability metrics. Moreover, in a rolling horizon study, FSRC preserves solution quality even if the reduction is substantial. Abstract A two-stage stochastic program is formulated for day-ahead commitment of thermal generating units to minimize total expected cost considering uncertainties in the day-ahead load and the availability of variable generation resources. Commitments of thermal units in the stochastic reliability unit commitment (SRUC) are viewed as first-stage decisions, and dispatch is relegated to the second stage. It is challenging to solve such a stochastic program if a lot of scenarios are incorporated. A heuristic scenario reduction method termed forward selection in recourse clusters (FSRC), which selects scenarios based on their cost and reliability impacts, is presented to alleviate the computational burden. In instances down-sampled from data for an Independent System Operator in the U.S., FSRC results in more reliable commitment schedules having similar costs, compared to those from a scenario reduction method based on probability metrics. Moreover, in a rolling horizon study, FSRC preserves solution quality even if the reduction is substantial.

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“…Of course, that impact will be higher as wind penetration grows. Independent System Operators (ISO) can take different general approaches to accommodate the additional uncertainty at the unit commitment level, such as improving forecasting models for the wind (Mahoney et al, 2012), adjusting reserve requirements for the system (NERC, 2010;Ortega-Vazquez & Kirschen, 2009;Wang & Hedman, 2015;Luna-Ramírez, Torres-Sánchez, & Pavas-Martínez, 2015), implementing stochastic unit commitment (Ruiz, Philbrick, Zak, Cheung, & Sauer, 2009;Papavasiliou, Oren, & O'Neill, 2011;Holttinen et al, 2012), and/or using a rolling-planning scheme (Tuohy, Meibom, Denny, & O'Malley, 2009). Thus, among the ancillary services needing revision as wind prominence grows are operational reserves, that is, the extra generation capacity put online in the day-ahead unit commitment.…”

Section: Introductionmentioning

confidence: 99%

Power system reliability impacts of wind generation and operational reserve requirements

Gil

2015

ing.inv.

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Due to its variability, wind generation integration presents a significant challenge to power system operators in order to maintain adequate reliability levels while ensuring least cost operation. This paper explores the trade-off between the benefits associated to a higher wind penetration and the additional operational reserve requirements that they impose. Such exploration is valued in terms of its effect on power system reliability, measured as an amount of unserved energy. The paper also focuses on how changing the Value of Lost Load (VoLL) can be used to attain different reliability targets, and how wind power penetration and the diversity of the wind energy resource will impact quality of supply (in terms of instances of unserved energy). The evaluation of different penetrations of wind power generation, different wind speed profiles, wind resource diversity, and different operational reserve requirements, is conducted on the Chilean Northern Interconnected System (SING) using statistical modeling of wind speed time series and computer simulation through a 24-hour ahead unit commitment algorithm and a Monte Carlo simulation scheme. Results for the SING suggest that while wind generation can significantly reduce generation costs, it can also imply higher security costs to reach acceptable reliability levels.Keywords: Wind power, uncertainty modeling, variable generation, reserve requirements, value of lost load. RESUMENDebido a su variabilidad, la integración de generación eólica presenta desafíos significativos para los operadores de los sistemas eléctricos a la hora de mantener niveles de confiabilidad adecuados asegurando, de forma simultánea, una operación a mínimo costo. Este artículo explora los beneficios asociados a una mayor penetración de generación eólica y los requerimientos adicionales de reservas operacionales que esta impone. Dicha exploración se realiza en términos de los efectos de la energía eólica en la confiabilidad del sistema, medida como cantidad de energía no servida. El artículo, además, se enfoca en cómo es que al cambiar el costo de falla se puede motivar el alcance de diferentes metas de confiabilidad, y cómo la penetración y diversidad del recurso eólico impactan la calidad de servicio (en términos de instancias de energía no servida). La evaluación de diferentes niveles de penetración, diferentes perfiles de velocidad de viento, diversidad del recurso eólico y distintos requerimientos de reservas operacionales, se lleva a cabo en el Sistema Interconectado del Norte Grande (SING) chileno usando modelos estadísticos de velocidad de viento y simulación computacional mediante un algoritmo de pre-despacho y un esquema de Monte Carlo. Los resultados para el SING sugieren que mientras la generación eólica puede reducir significativamente los costos de generación del sistema, también implica mayores costos de seguridad para alcanzar niveles de confiabilidad adecuados.

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A Wind Power Forecasting System to Optimize Grid Integration

Cited by 140 publications

References 22 publications

Short-Range Forecasting for Energy

Short-Range Forecasting for Energy

Solution sensitivity-based scenario reduction for stochastic unit commitment

Power system reliability impacts of wind generation and operational reserve requirements

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