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485 KiB

Open AccessCommunication

Wind Resource Quality Affected by High Levels of Renewables

by Victor Diakov

Resources 2015, 4(2), 378-383; https://doi.org/10.3390/resources4020378 - 17 Jun 2015

Cited by 2 | Viewed by 3984

For solar photovoltaic (PV) and wind resources, the capacity factor is an important parameter describing the quality of the resource. As the share of variable renewable resources (such as PV and wind) on the electric system is increasing, so does curtailment (and the [...] Read more.

For solar photovoltaic (PV) and wind resources, the capacity factor is an important parameter describing the quality of the resource. As the share of variable renewable resources (such as PV and wind) on the electric system is increasing, so does curtailment (and the fraction of time when it cannot be avoided). At high levels of renewable generation, curtailments effectively change the practical measure of resource quality from capacity factor to the incremental capacity factor. The latter accounts only for generation during hours of no curtailment and is directly connected with the marginal capital cost of renewable generators for a given level of renewable generation during the year. The Western U.S. wind generation is analyzed hourly for a system with 75% of annual generation from wind, and it is found that the value for the system of resources with equal capacity factors can vary by a factor of 2, which highlights the importance of using the incremental capacity factor instead. The effect is expected to be more pronounced in smaller geographic areas (or when transmission limitations imposed) and less pronounced at lower levels of renewable energy in the system with less curtailment. Full article

(This article belongs to the Special Issue Spatial and Temporal Variation of the Wind Resource)

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1975 KiB

Open AccessArticle

Climate Change Impacts on Oklahoma Wind Resources: Potential Energy Output Changes

by Stephen Stadler, James Mack Dryden, Jr. and J. Scott Greene

Resources 2015, 4(2), 203-226; https://doi.org/10.3390/resources4020203 - 21 Apr 2015

Cited by 7 | Viewed by 5816

Abstract

An extensive literature on climate change modeling points to future changes in wind climates. Some areas are projected to gain wind resources, while others are projected to lose wind resources. Oklahoma is presently wind rich with this resource extensively exploited for power generation. [...] Read more.

An extensive literature on climate change modeling points to future changes in wind climates. Some areas are projected to gain wind resources, while others are projected to lose wind resources. Oklahoma is presently wind rich with this resource extensively exploited for power generation. Our work examined the wind power implications under the IPCC’s A2 scenario for the decades 2040–2049, 2050–2059 and 2060–2069 as compared to model reanalysis and Oklahoma Mesonetwork observations for the base decade of 1990–1999. Using two western Oklahoma wind farms as examples, we used North American Regional Climate Change Assessment Program (NARCCAP) modeling outputs to calculate changes in wind power generation. The results show both wind farms to gain in output for all decades as compared to 1990–1999. Yet, the results are uneven by seasons and with some decades exhibiting decreases in the fall. These results are of interest in that it is clear that investors cannot count on wind studies of the present to adequately characterize future productivity. If our results are validated over time, Oklahoma stands to gain wind resources through the next several decades. Full article

(This article belongs to the Special Issue Spatial and Temporal Variation of the Wind Resource)

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3291 KiB

Open AccessArticle

The Impact of Future Offshore Wind Farms on Wind Power Generation in Great Britain

by Daniel R. Drew, Dirk J. Cannon, David J. Brayshaw, Janet F. Barlow and Phil J. Coker

Resources 2015, 4(1), 155-171; https://doi.org/10.3390/resources4010155 - 17 Mar 2015

Cited by 40 | Viewed by 10989

Abstract

In the coming years the geographical distribution of wind farms in Great Britain is expected to change significantly. Following the development of the “round 3” wind zones (circa 2025), most of the installed capacity will be located in large offshore wind farms. However, [...] Read more.

In the coming years the geographical distribution of wind farms in Great Britain is expected to change significantly. Following the development of the “round 3” wind zones (circa 2025), most of the installed capacity will be located in large offshore wind farms. However, the impact of this change in wind-farm distribution on the characteristics of national wind generation is largely unknown. This study uses a 34-year reanalysis dataset (Modern-Era Retrospective Analysis for Research and Applications (MERRA) from National Aeronautics and Space Administration, Global Modeling and Assimilation Office (NASA-GMAO)) to produce a synthetic hourly time series of GB-aggregated wind generation based on: (1) the “current” wind farm distribution; and (2) a “future” wind farm distribution scenario. The derived data are used to estimate a climatology of extreme wind power events in Great Britain for each wind farm distribution. The impact of the changing wind farm distribution on the wind-power statistics is significant. The annual mean capacity factor increased from 32.7% for the current wind farm distribution to 39.7% for the future distribution. In addition, there are fewer periods of prolonged low generation and more periods of prolonged high generation. Finally, the frequency and magnitude of ramping in the nationally aggregated capacity factor remains largely unchanged. However, due to the increased capacity of the future distribution, in terms of power output, the magnitude of the ramping increases by a factor of 5. Full article

(This article belongs to the Special Issue Spatial and Temporal Variation of the Wind Resource)

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2679 KiB

Open AccessArticle

Wind Energy Integration through District Heating. A Wind Resource Based Approach

by George Xydis

Resources 2015, 4(1), 110-127; https://doi.org/10.3390/resources4010110 - 5 Mar 2015

Cited by 14 | Viewed by 5733

Abstract

The aim of this paper is to examine if the surplus of wind energy could be added to electricity-to-heat conversion systems when there is increased congestion in the grid or when there is wind power curtailment. In this way, the produced power can [...] Read more.

The aim of this paper is to examine if the surplus of wind energy could be added to electricity-to-heat conversion systems when there is increased congestion in the grid or when there is wind power curtailment. In this way, the produced power can be utilized for contributing to the local district heating (DH) system needs. After examining scenarios, optimized energy distribution is recommended. A case study near Kozani, Greece with an onshore wind farm (WF) to be installed was thoroughly investigated exploring the options for increased wind energy integration analyzing thermal utilization possibilities based on the local DH needs. A wind resource assessment for the area was done, which optimizes the WF planning and links the DH system with the operation of the WF. The utilization rate between the electric and the DH grid was examined in order to describe the optimal way of the energy to be distributed reassuring profitability for the power producer and robust energy management for the system. It was found that the curtailed wind energy can be locally utilized in a DH system, by covering part of the demand that the diesel-based peak load boiler system does currently. Full article

(This article belongs to the Special Issue Spatial and Temporal Variation of the Wind Resource)

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983 KiB

Open AccessArticle

A Markov-Switching Vector Autoregressive Stochastic Wind Generator for Multiple Spatial and Temporal Scales

by Amanda S. Hering, Karen Kazor and William Kleiber

Resources 2015, 4(1), 70-92; https://doi.org/10.3390/resources4010070 - 12 Feb 2015

Cited by 17 | Viewed by 6672

Abstract

Despite recent efforts to record wind at finer spatial and temporal scales, stochastic realizations of wind are still important for many purposes and particularly for wind energy grid integration and reliability studies. Most instances of wind generation in the literature focus on simulating [...] Read more.

Despite recent efforts to record wind at finer spatial and temporal scales, stochastic realizations of wind are still important for many purposes and particularly for wind energy grid integration and reliability studies. Most instances of wind generation in the literature focus on simulating only wind speed, or power, or only the wind vector at a particular location and sampling frequency. In this work, we introduce a Markov-switching vector autoregressive (MSVAR) model, and we demonstrate its flexibility in simulating wind vectors for 10-min, hourly and daily time series and for individual, locally-averaged and regionally-averaged time series. In addition, we demonstrate how the model can be used to simulate wind vectors at multiple locations simultaneously for an hourly time step. The parameter estimation and simulation algorithm are presented along with a validation of the important statistical properties of each simulation scenario. We find the MSVAR to be very flexible in characterizing a wide range of properties in the wind vector, and we conclude with a discussion of extensions of this model and modeling choices that may be investigated for further improvements. Full article

(This article belongs to the Special Issue Spatial and Temporal Variation of the Wind Resource)

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758 KiB

Open AccessArticle

Analysis of North Sea Offshore Wind Power Variability

by Aymeric Buatois, Madeleine Gibescu, Barry G. Rawn and Mart A.M.M. Van der Meijden

Resources 2014, 3(2), 454-470; https://doi.org/10.3390/resources3020454 - 26 May 2014

Cited by 4 | Viewed by 10140

Abstract

This paper evaluates, for a 2030 scenario, the impact on onshore power systems in terms of the variability of the power generated by 81 GW of offshore wind farms installed in the North Sea. Meso-scale reanalysis data are used as input for computing [...] Read more.

This paper evaluates, for a 2030 scenario, the impact on onshore power systems in terms of the variability of the power generated by 81 GW of offshore wind farms installed in the North Sea. Meso-scale reanalysis data are used as input for computing the hourly power production for offshore wind farms, and this total production is analyzed to identify the largest aggregated hourly power variations. Based on publicly available information, a simplified representation of the coastal power grid is built for the countries bordering the North Sea. Wind farms less than 60 km from shore are connected radially to the mainland, while the rest are connected to a hypothetical offshore HVDC (High-Voltage Direct Current) power grid, designed such that wind curtailment does not exceed 1% of production. Loads and conventional power plants by technology and associated cost curves are computed for the various national power systems, based on 2030 projections. Using the MATLAB-based MATPOWER toolbox, the hourly optimal power flow for this regional hybrid AC/DC grid is computed for high, low and medium years from the meso-scale database. The largest net load variations are evaluated per market area and related to the extra load-following reserves that may be needed from conventional generators. Full article

(This article belongs to the Special Issue Spatial and Temporal Variation of the Wind Resource)

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3327 KiB

Open AccessArticle

Consideration of Wind Speed Variability in Creating a Regional Aggregate Wind Power Time Series

by Lucy C. Cradden, Francesco Restuccia, Samuel L. Hawkins and Gareth P. Harrison

Resources 2014, 3(1), 215-234; https://doi.org/10.3390/resources3010215 - 27 Feb 2014

Cited by 9 | Viewed by 7181

Abstract

For the purposes of understanding the impacts on the electricity network, estimates of hourly aggregate wind power generation for a region are required. However, the availability of wind production data for the UK is limited, and studies often rely on measured wind speeds [...] Read more.

For the purposes of understanding the impacts on the electricity network, estimates of hourly aggregate wind power generation for a region are required. However, the availability of wind production data for the UK is limited, and studies often rely on measured wind speeds from a network of meteorological (met) stations. Another option is to use historical wind speeds from a reanalysis dataset, with a resolution of around 40–50 km. Mesoscale models offer a potentially more desirable solution, with a homogeneous set of wind speeds covering a wide area at resolutions of 1–50 km, but they are computationally expensive to run at high resolution. An understanding of the most appropriate choice of data requires knowledge of the variability in time and space and how well that is represented by the choice of model. Here it is demonstrated that in regions offshore, or in relatively smooth terrain where variability in wind speeds is smaller, lower resolution models or single point records may suffice to represent aggregate power generation in a sub-region. The need for high resolution modelling in areas of complex terrain where spatial and temporal variability is higher is emphasised, particularly when the distribution of wind generation capacity is uneven over the region. Full article

(This article belongs to the Special Issue Spatial and Temporal Variation of the Wind Resource)

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4158 KiB

Open AccessArticle

The Impacts of Atmospheric Stability on the Accuracy of Wind Speed Extrapolation Methods

by Jennifer F. Newman and Petra M. Klein

Resources 2014, 3(1), 81-105; https://doi.org/10.3390/resources3010081 - 23 Jan 2014

Cited by 59 | Viewed by 9776

Abstract

The building of utility-scale wind farms requires knowledge of the wind speed climatology at hub height (typically 80–100 m). As most wind speed measurements are taken at 10 m above ground level, efforts are being made to relate 10-m measurements to approximate hub-height [...] Read more.

The building of utility-scale wind farms requires knowledge of the wind speed climatology at hub height (typically 80–100 m). As most wind speed measurements are taken at 10 m above ground level, efforts are being made to relate 10-m measurements to approximate hub-height wind speeds. One common extrapolation method is the power law, which uses a shear parameter to estimate the wind shear between a reference height and hub height. The shear parameter is dependent on atmospheric stability and should ideally be determined independently for different atmospheric stability regimes. In this paper, data from the Oklahoma Mesonet are used to classify atmospheric stability and to develop stability-dependent power law fits for a nearby tall tower. Shear exponents developed from one month of data are applied to data from different seasons to determine the robustness of the power law method. In addition, similarity theory-based methods are investigated as possible alternatives to the power law. Results indicate that the power law method performs better than similarity theory methods, particularly under stable conditions, and can easily be applied to wind speed data from different seasons. In addition, the importance of using co-located near-surface and hub-height wind speed measurements to develop extrapolation fits is highlighted. Full article

(This article belongs to the Special Issue Spatial and Temporal Variation of the Wind Resource)

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1283 KiB

Open AccessArticle

Multivariate Weibull Distribution for Wind Speed and Wind Power Behavior Assessment

by Daniel Villanueva, Andrés Feijóo and José L. Pazos

Resources 2013, 2(3), 370-384; https://doi.org/10.3390/resources2030370 - 3 Sep 2013

Cited by 25 | Viewed by 9423

Abstract

The goal of this paper is to show how to derive the multivariate Weibull probability density function from the multivariate Standard Normal one and to show its applications. Having Weibull distribution parameters and a correlation matrix as input data, the proposal is to [...] Read more.

The goal of this paper is to show how to derive the multivariate Weibull probability density function from the multivariate Standard Normal one and to show its applications. Having Weibull distribution parameters and a correlation matrix as input data, the proposal is to obtain a precise multivariate Weibull distribution that can be applied in the analysis and simulation of wind speeds and wind powers at different locations. The main advantage of the distribution obtained, over those generally used, is that it is defined by the classical parameters of the univariate Weibull distributions and the correlation coefficients and all of them can be easily estimated. As a special case, attention has been paid to the bivariate Weibull distribution, where the hypothesis test of the correlation coefficient is defined. Full article

(This article belongs to the Special Issue Spatial and Temporal Variation of the Wind Resource)

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