A mighty wind

Smith, James C.; Thresher, R. W.; Zavadil, R.; DeMeo, E.; Piwko, R.J.; Ernst, Bernhard; Ackermann, Thomas

doi:10.1109/mpe.2008.931492

Cited by 51 publications

(17 citation statements)

References 2 publications

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“…Smith et al [331] describe the current status of integrating wind energy into the electric power system. The paper reviews the history of wind technology development, today's commercial wind energy, and grid connection issues with wind.…”

Section: Wind Integration Studiesmentioning

confidence: 99%

Wind power forecasting : state-of-the-art 2009.

Monteiro¹,

Bessa²,

Miranda³

et al. 2009

300

264

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Section: Wind Integration Studiesmentioning

confidence: 99%

Wind power forecasting : state-of-the-art 2009.

Monteiro¹,

Bessa²,

Miranda³

et al. 2009

300

264

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“…A primary objective of the present work is to look at this problem, with the aim of evaluating the quality of the ensemble forecasts of wind speed issued over Europe by the European Centre for Medium‐range Weather Forecasts (ECMWF) against analysis and actual observations while accounting for observational uncertainty. The choice for this domain and for the wind speed variable arises from the growing interest in wind energy and its short‐term forecasting (Giebel et al , ; Lange and Focken, ; Costa et al , ; Smith et al , , among others). A subsequent objective is to illustrate the disparities that appear if performing forecast verification against analysis or against observations.…”

Section: Introductionmentioning

confidence: 99%

Verification of the ECMWF ensemble forecasts of wind speed against analyses and observations

Pinson

Hagedorn

2011

Meteorological Applications

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ABSTRACT:A framework for the verification of ensemble forecasts of near-surface wind speed is described. It is based on existing scores and diagnostic tools, though considering observations from synoptic stations as reference instead of the analysis. This approach is motivated by the idea of having a user-oriented view of verification, for instance with the wind power applications in mind. The verification framework is specifically applied to the case of ECMWF ensemble forecasts and over Europe. Dynamic climatologies are derived at the various stations, serving as a benchmark. The impact of observational uncertainty on scores and diagnostic tools is also considered. The interest of this framework is demonstrated from its application to the routine evaluation of ensemble forecasts and to the assessment of the quality improvements brought in by the recent change in horizontal resolution of the ECMWF ensemble prediction system. The most important conclusions cover (1) the generally high skill of these ensemble forecasts of near-surface wind speed when evaluated at synoptic stations, (2) the noteworthy improvement of scores brought by the change of horizontal resolution, and, (3) the scope for further improvements of reliability and skill of wind speed ensemble forecasts by appropriate post-processing.

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“…For instance, the variability of wind and solar power seldom correlates with the load variability. There is evidence of difficulty in maintaining system balance due to wind variability [28]. Accurate and fast frequency tracking is a prerequisite to the system responding quickly to such problems, and signal processing is certain to play a large role in dealing with the complexity and uncertainty associated with frequency estimation in smart grid.…”

Section: From the Hierarchical Grid To Smart Grid Mi-crogrids And Ementioning

confidence: 99%

Adaptive Frequency Estimation in Smart Grid Applications: Exploiting Noncircularity and Widely Linear Adaptive Estimators

Xia

Douglas

Mandic

2012

IEEE Signal Process. Mag.

159

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Signal processing is a crucial technology for the efficient use of limited and intermittent power resources in the smart grid of the future, and a number of challenges remain to be met. One major issue, as we move towards distributed energy production and use (microgrid) is real time estimation of power quality parameters (frequency, voltages, power factor). The accurate knowledge of frequency is a key parameter of a power system, and its optimal estimation becomes critical in the future smart grid, where the generation, loading and topology are all dynamically updated. In this work, we first consolidate the existing approaches to real-time frequency estimation in a three phase system, and then provide a unified framework for the estimation of the instantaneous frequency in both balanced and unbalanced conditions of a three phase power system. This is achieved by using recent developments in the statistics of complex variables (augmented statistics), by employing the associated widely linear models, and by rigorously accounting for the different degrees of noncircularity associated with various natures of frequency variations in real-world conditions. The usefulness of the proposed framework for frequency tracking in smart grids is illustrated in the context of two major issues in power quality control, namely the tracking of false frequency perturbations in the presence of unbalanced voltage sags (here both synthetic and real-world) and in adaptive frequency tracking in microgrids and islands where there is mismatch between production and consumption. THE NEED FOR FREQUENCY ESTIMATION IN SMART GRIDGovernments, utilities and consumers are all interested in making the ways we produce and use energy more efficient and sustainable. For the electrical power grid this involves fundamental paradigm shifts as we build a smart grid, adopt more renewable energy sources, and promote more energy efficient practices. A smart grid delivers electricity from suppliers to users using digital technology and has a number of properties, including incorporating all forms of energy generation and storage, using sensor information, enabling active participation by end users, being secure and reliable, and using optimization and control to make decisions [1]. This will require the interplay between sensor networks, generation systems, and the power grid, with key technologies from signal processing.It is estimated that the financial loss due to outages in the US economy approaches USD $45.7 billion annually, with power quality issues costing USD $6.7 billion annually [2,3]. Among them, voltage sags, that is, an increase in load current over up to few hundred cycles, are the most frequent problem [4] that severely affects medical centres, semiconductor plants, and broadcasting stations, among others [5]. Voltage sags are typically followed by frequency variations and occur due to switching between the main grid and microgrids, short circuits, motor starting, transformer inrush, fast reclosing of circuit breakers, unexpectedly large or ...

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A mighty wind

Cited by 51 publications

References 2 publications

Wind power forecasting : state-of-the-art 2009.

Wind power forecasting : state-of-the-art 2009.

Verification of the ECMWF ensemble forecasts of wind speed against analyses and observations

Adaptive Frequency Estimation in Smart Grid Applications: Exploiting Noncircularity and Widely Linear Adaptive Estimators

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