Behavior of the aggregate wind resource in the ISO regions in the United States

Gunturu, Udaya Bhaskar; Schlosser, C. Adam

doi:10.1016/j.apenergy.2015.02.013

Cited by 13 publications

(8 citation statements)

References 18 publications

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“…Consistent with previous research [9,10], we find that geographic aggregation of wind energy does reduce output variability, but only to a point. While other studies [7,9,10,12,13,15,17] have focused on finding the efficient number of wind sites or the geographic scope to minimize the wind output volatility or increase firm power output by considering available wind site locations, we have proposed a systematic method to find wind site locations that achieve the same goals. Unlike previous work, we do not consider the location of wind sites as already given; our analysis, rather, is intended to describe the location of wind build-out over space in order to achieve best-case variability reduction over several temporal scales.…”

Section: Introductionsupporting

confidence: 91%

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Scaling of wind energy variability over space and time

Shahriari

Blumsack

2017

Applied Energy

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We use a large data set of simulated wind energy production in the United States to quantify the geographic and temporal scaling of energy output variability reduction when multiple sites are aggregated. We add to the existing literature on-geographic smoothing‖ by (i) quantifying the scaling of geographic smoothing over multiple spatial and temporal scales; (ii) bounding such smoothing through the use of an algorithm that produces minimum-variance sets of wind energy production sites; and (iii) quantifying inherent tradeoffs in optimizing wind energy site selection to minimize output variability along a specific frequency. The number of wind farms required to minimize output variability increases linearly with spatial scale of aggregation, but the scaling factor is small, on the order of relative to geographic distances. These scaling factors increase by a factor of two as the frequency considered increases by three orders of magnitude (minutes to months). Our analysis indicates that optimizing wind deployment over one particular frequency increases output variability over other frequencies by nearly 30% in some cases.

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Section: Introductionsupporting

confidence: 91%

“…Gunturu et al [10] studied the variability of wind power across Independent System Operator (ISO) regions in the US. They incorporated generation duration curves to show that even with aggregation, a large number of hours without wind (or less than 5% capacity) can be observed.…”

Section: Introductionmentioning

confidence: 99%

Scaling of wind energy variability over space and time

Shahriari

Blumsack

2017

Applied Energy

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“…Cong Wu, Xiao-Ping Zhang, Fellow, CSEE, IEEE, and Michael Sterling, Fellow, CSEE I (ISO) of North American were interconnected [24,26] and it also became smoother when wind farms are aggregated respectively within seven individual ISO of the US [28]. A novel quantitative index describing the wind power smoothing effect based on set pair analysis theory was proposed in [30].…”

Section: Wind Power Generation Variations and Aggregationmentioning

confidence: 99%

“…The geographic smoothing effects of wind power variability using frequency-dependent analysis was first presented together with a review of earlier studies using time-domain analysis [9]. In recent years, the complementary/smoothing effects were characterized in terms of standard deviation [10][11][12][13][14][15][16], coefficient of variation [17][18][19], hourly variation/ramp rate [20][21][22]24], power deviation to scheduled value [25], power fluctuation spectrum [26,27], duration curve [24,26,28], as well as some new proposed metrics [29,30,32]. Most recently, an 'Energy Quality' framework was defined to measure and characterize the variations of renewable power generation [7].…”

Section: Introductionmentioning

confidence: 99%

“…Most recently, an 'Energy Quality' framework was defined to measure and characterize the variations of renewable power generation [7]. Most existing studies were conducted based on two categories of data, i.e., converted wind power from meteorological reanalysis data (e.g., ERA5 [21,29], MERRA [14][15][16][17]28], HIRLAM [18], NARR [19], GEOS-5 [20], COSMO-REA6 [31]) and historical generation data [22][23][24][25][26]. Moreover, the aggregation of wind power along with the complementarity analysis was conducted within different geographical scale, e.g., Nova Scotia [23], single region of US [9,28], Eastern US [12], Midwestern US [19], Nordic region [18], the US [24,26], Spain [25], China [15][16][17]20], Iberian Peninsula [13], western Europe [29], Europe [10,14,21,31,32].…”

Section: Introductionmentioning

confidence: 99%

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Wind power generation variations and aggregation

Zhang

Sterling

2021

CSEE JPES

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Wind power driven by climate and weather is characterized by significant spatial and temporal variability. It is evidenced that the variability of wind power will not only cause instability of power grids but also increase the balancing costs of electricity markets. Existing studies have shown that such variability can be reduced through aggregation of a geographic spread of production sites but there is a lack of comprehensive analysis for different levels/scales of aggregation, especially from a global perspective. The aim of this paper is to provide a fundamental understanding of the relationship between wind power variations and aggregations from a systematic viewpoint based on highly detailed wind power data, enabling the aggregation benefits to be quantified from a state scale up to a global scale. Firstly wind power variations are analyzed comprehensively at 6 different levels by converting global seven year hourly meteorological re-analysis data with a high spatial resolution of 𝟎. 𝟐𝟓 °× 𝟎. 𝟐𝟓 ° (~ 𝟐𝟖 𝐤𝐦 × 𝟐𝟖 𝐤𝐦) into wind power series. Then in the proposed assessment framework, a coefficient of variation of wind power, and a standard deviation of wind power ramping rate are used to quantify wind power variations and wind power ramping rate variations to show the characteristics and benefits of wind power aggregation at the 6 different levels. A system planning example is used to show the correlation between CV reduction of wind power and investment reduction, and hence the benefits in terms of significant investment reduction via aggregation. Additionally, a wind power duration curve is further used to characterize the availability of wind power aggregated at different levels. Finally, the results would provide insights to shape an international approach to the deployment of wind power, especially under the framework of Net-Zero.

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Adding wind power to a wind‐rich grid: Evaluating secondary suitability metrics

Pearre

Swan

2023

Wind Energy

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As the quantity of renewable electricity generation from wind farms increases in a region, the costs associated with integrating it into the broader electricity system also grow. This is primarily due to the need for dispatchable generators that vary power output to compensate for wind farm power variations. Such “balancing services” are an economic cost to the system that is typically not passed on to wind farms. We propose including the use of technical merits other than capacity factor and cost of energy for evaluating new wind farm sites and present a new graphical geospatial method, with the intention of identifying sites that minimize the need for additional electricity balancing service and transmission congestion. Specifically, locations with low correlation to existing wind farms, locations with high correlation to load, locations with high characteristic power time‐shift from existing wind farms, and locations that relieve or do not negatively impact electricity transmission congestion are identified. A geospatial Venn diagram‐based method of visualization is presented. These methods will equip regional planners with new tools to encourage wind farm development in areas that benefit the electricity grid beyond the lowest bid price.

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Behavior of the aggregate wind resource in the ISO regions in the United States

Cited by 13 publications

References 18 publications

Scaling of wind energy variability over space and time

Scaling of wind energy variability over space and time

Wind power generation variations and aggregation

Adding wind power to a wind‐rich grid: Evaluating secondary suitability metrics

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