An ensemble of high‐resolution regional climate model simulation data is used to examine the impacts of climate change on offshore and onshore wind energy generation in Ireland. Two Representative ...Concentration Pathway (RCP) scenarios (RCP 4.5 and 8.5) are analysed for the mid‐term (2041–2060) and the long‐term (2081–2100) future. Wind energy is projected to decrease (≤2%) overall in future climate scenarios. Changes are evident by mid‐century and are more pronounced by late 21st century, particularly for RCP 8.5 offshore. Seasonally, wind energy is projected to decrease by less than 6% in summer and to increase slightly in winter (up to 1.1%). The distinct changes in different parts of the power curve, presented here for the first time, show a reversed pattern of duration at certain levels of the power curve. In summer, there is an increase of low‐power and a decrease of high‐power generation, whereas during winter, there is a projected increase in the time spent at high power. This could lead to diverse consequences for system operators depending on the season. The impacts of climate change on the duration and frequency of long periods (longer than 24 h) of low‐/high‐power wind energy events in Ireland are also presented. The frequency of low‐power events is projected to increase slightly, especially during summer. Onshore and offshore events are considered separately, demonstrating the complementarity of developing both onshore and offshore wind farms for future energy systems. Regional analysis highlights the benefit of developing a geographically dispersed wind farm network incorporating different local wind conditions.
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Methanogenic sludge granules are densely packed, small, spherical biofilms found in anaerobic digesters used to treat industrial wastewaters, where they underpin efficient organic waste conversion ...and biogas production. Each granule theoretically houses representative microorganisms from all of the trophic groups implicated in the successive and interdependent reactions of the anaerobic digestion (AD) process. Information on exactly how methanogenic granules develop, and their eventual fate will be important for precision management of environmental biotechnologies. Granules from a full-scale bioreactor were size-separated into small (0.6-1 mm), medium (1-1.4 mm), and large (1.4-1.8 mm) size fractions. Twelve laboratory-scale bioreactors were operated using either small, medium, or large granules, or unfractionated sludge. After >50 days of operation, the granule size distribution in each of the small, medium, and large bioreactor sets had diversified beyond-to both bigger and smaller than-the size fraction used for inoculation. Interestingly, extra-small (XS; <0.6 mm) granules were observed, and retained in all of the bioreactors, suggesting the continuous nature of granulation, and/or the breakage of larger granules into XS bits. Moreover, evidence suggested that even granules with small diameters could break. "New" granules from each emerging size were analyzed by studying community structure based on high-throughput 16S rRNA gene sequencing.
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represented the majority of the community in new granules. H2-using, and not acetoclastic, methanogens appeared more important, and were associated with abundant syntrophic bacteria. Multivariate integration (MINT) analyses identified distinct discriminant taxa responsible for shaping the microbial communities in different-sized granules.
Attention should always be given to which reanalysis dataset to use when preparing analysis for a project. The accuracies of three reanalysis datasets, two global (ERA5 and MERRA-2) and one ...high-resolution regional reanalysis (MÉRA), are assessed by comparison with observations at seven weather observing stations around Ireland. Skill scores are calculated for the weather variables at these stations that are most relevant to the renewable energy sector: 10 m wind for wind power; surface shortwave radiation (SW) and 2 m temperature for photovoltaic power generation. The choice of which reanalysis dataset to use is important when future planning depends on this data. The newer ERA5 generally outperforms the other two reanalyses. However, this is not always true, and the best performing reanalysis dataset often depends on the variable of interest and location. As errors are significant for these reanalysis datasets, consideration should also be given to datasets specifically tailored to renewable energy resource modelling.
"Theology after Heidegger must take into account history and language as elements in the pursuit of meaning. Quite often, this prompts a hurried flight from metaphysics to an embrace of an absence at ...the center of Christian narrativity. Conor Sweeney here explores the “postmodern" critique of presence in the context of sacramental theology, engaging the thought of Louis-Marie Chauvet and Lieven Boeve. Chauvet is an influential postmodern theologian whose critique of the perceived onto-theological constitution of presence in traditional sacramental theology has made big waves, while Boeve is part of a more recent generation of theologians who even more wholeheartedly embrace postmodern consequences for theology. Sweeney considers the extent to which postmodernism à la Heidegger upsets the hermeneutics of sacramentality, asking whether this requires us to renounce the search for a presence that by definition transcends us. Against both the fetishization of presence and absence, Sweeney argues that metaphysics has a properly sacramental basis, and that it is only through this reality that the dialectic of presence and absence can be transcended. The case is made for the full but restless signification of the mother’s smile as the paradigm for genuine sacramental presence."
Two methods of post-processing the uncalibrated wind speed forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF) ensemble prediction system (EPS) are presented here. Both ...methods involve statistically post-processing the EPS or a downscaled version of it with Bayesian model averaging (BMA). The first method applies BMA directly to the EPS data. The second method involves clustering the EPS to eight representative members (RMs) and downscaling the data through two limited area models at two resolutions. Four weighted ensemble mean forecasts are produced and used as input to the BMA method. Both methods are tested against 13 meteorological stations around Ireland with 1 yr of forecast/observation data. Results show calibration and accuracy improvements using both methods, with the best results stemming from Method 2, which has comparatively low mean absolute error and continuous ranked probability scores.
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The 3‐hourly gridded ECMWF ERA‐Interim climate reanalysis dataset, spanning 1979–2013, was used to investigate the spatial stationarity of the previously documented relationships between wind speeds ...and the North Atlantic Oscillation (NAO) state in Europe. Over much of western Europe, wind speeds were found to be affected strongly by the concomitant states of the secondary and tertiary atmospheric teleconnections, namely the East Atlantic (EA) and the Scandinavian (SCA) patterns. These modify the geographic positions of the NAO dipole and modulate the influence of the NAO on wind statistics on regional scales, producing non‐stationarities in the NAO–wind speed relationships. The interactions of these teleconnections play an important role in modifying wind speeds within Europe. Finally, systematic north–south changes in the Weibull distribution scale and shape parameters are documented along the western margin of Europe, as a function of different states of the NAO, the EA and the SCA. These effects influence both monthly averaged wind speeds and the statistical distributions of 3‐hourly wind data, implying strong impacts on wind energy resources and expected wind power production. The results have implications for regional to continent‐scale long‐term planning of wind‐farm siting to minimise the impact of resource intermittency.
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9.
The future of forecasting for renewable energy Sweeney, Conor; Bessa, Ricardo J.; Browell, Jethro ...
Wiley interdisciplinary reviews. Energy and environment,
March/April 2020, 2020-03-00, 20200301, Volume:
9, Issue:
2
Journal Article
Peer reviewed
Open access
Forecasting for wind and solar renewable energy is becoming more important as the amount of energy generated from these sources increases. Forecast skill is improving, but so too is the way forecasts ...are being used. In this paper, we present a brief overview of the state‐of‐the‐art of forecasting wind and solar energy. We describe approaches in statistical and physical modeling for time scales from minutes to days ahead, for both deterministic and probabilistic forecasting. Our focus changes then to consider the future of forecasting for renewable energy. We discuss recent advances which show potential for great improvement in forecast skill. Beyond the forecast itself, we consider new products which will be required to aid decision making subject to risk constraints. Future forecast products will need to include probabilistic information, but deliver it in a way tailored to the end user and their specific decision making problems. Businesses operating in this sector may see a change in business models as more people compete in this space, with different combinations of skills, data and modeling being required for different products. The transaction of data itself may change with the adoption of blockchain technology, which could allow providers and end users to interact in a trusted, yet decentralized way. Finally, we discuss new industry requirements and challenges for scenarios with high amounts of renewable energy. New forecasting products have the potential to model the impact of renewables on the power system, and aid dispatch tools in guaranteeing system security.
This article is categorized under:
Energy Infrastructure > Systems and Infrastructure
Wind Power > Systems and Infrastructure
Photovoltaics > Systems and Infrastructure
This article highlights interesting areas of high potential in the future of forecasting for wind and solar energy, including different business models in renewable energy forecasting.
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Summary
In this study, we begin a comprehensive characterization of temperature extremes in Ireland for the period 1981–2010. We produce return levels of anomalies of daily maximum temperature ...extremes for an area over Ireland, for the 30‐year period 1981–2010. We employ extreme value theory (EVT) to model the data using the generalized Pareto distribution (GPD) as part of a three‐level Bayesian hierarchical model. We use predictive processes in order to solve the computationally difficult problem of modeling data over a very dense spatial field. To our knowledge, this is the first study to combine predictive processes and EVT in this manner. The model is fit using Markov chain Monte Carlo algorithms. Posterior parameter estimates and return level surfaces are produced, in addition to specific site analysis at synoptic stations, including Casement Aerodrome and Dublin Airport. Observational data from the period 2011–2018 are included in this site analysis to determine if there is evidence of a change in the observed extremes. An increase in the frequency of extreme anomalies, but not the severity, is observed for this period. We found that the frequency of observed extreme anomalies from 2011 to 2018 at the Casement Aerodrome and Phoenix Park synoptic stations exceed the upper bounds of the credible intervals from the model by 20% and 7%, respectively. Using predictive processes made possible a fourfold increase in the domain considered, while still allowing all data across the grid to be used to inform the posterior distributions.
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