Renewable energy is central to the global effort to move to less carbon-intensive economies that support the United Nations Sustainable Development Goals (SDGs). The energy sector currently accounts for more than two thirds of global greenhouse gas emissions (Global Wind Energy Council 2016). Consequently, the global transition to a low-carbon future portends a fundamental and comprehensive change for the entire energy sector (Organisation for Economic Co-operation (OECD) and Development and International Energy Agency 2016).
Renewable energy comes from natural sources such as sunlight, wind, rain, tides, plants and geothermal heat. These sources all offer environmental, economic and energy-security benefits. They also bring big challenges. In particular, the generation of and operational planning for renewable energy are strongly affected by weather and climate, which cause wide variations in both energy supply and demand. For this reason, the Global Framework for Climate Services (GFCS) is leading international efforts to enhance the quality, quantity and application of climate information and predictions in support of decision-making by renewable energy producers (Hewitt et al. 2012).
The GFCS initiative, which is spearheaded by the WMO, coordinates efforts by the United Nations, governments and organizations to develop science-based climate information for various climate-sensitive sectors. It seeks to incorporate climate information and predictions into socio-economic planning, policy and practice. The initiative has five components: provider-user interface platforms; climate services information systems; observations and monitoring; research, modelling and prediction; and capacity development. The GFCS provides a roadmap for developing user-friendly climate services that can contribute to a greener and more efficient energy system (WMO 2017).
Wind energy has led the recent growth in renewables-based capacity, and it is expected to continue to be the largest source of renewable energy through to 2030 (GWEC 2016). However, the further expansion of wind-energy production will require, in particular, improved climate predictions that better estimate the changes in wind speed for upcoming seasons, years and decades. This is critical for anticipating wind-energy supplies, which in turn is needed to facilitate the large-scale integration of wind energy into the broader energy system.
To serve the needs of the renewable energy sector, the Earth System Services (ESS) group at the Barcelona Supercomputing Center (BSC) applies its scientific understanding of the Earth system and its expertise in climate prediction to providing climate services for decision-making. The aim is to inform climate-smart practices and decisions in the wind-energy sector that considerably improve its resilience to weather extremes and climate variability and change and that support the full chain of operations during the entire life-cycle of a wind farm.
Seasonal climate predictions
Forecasting the variability of wind-energy resources at different timescales is crucial for efficient energy management (Figure 1). Wind energy users have traditionally used weather forecasts from hours to a few days ahead because near-surface winds, and thus wind-energy production, strongly depend on short-term wind-speed fluctuations. However, to guide investments and the selection of wind farm sites over the longer term, as much as a few decades, the wind industry has become increasingly interested in long-term climate projections.
To cover the information gap between one month and up to a decade into the future, the wind energy sector currently assumes that future conditions will be like those of the past. This approach makes it impossible to anticipate events that, seemingly, have never happened before. Fortunately, it is becoming increasingly possible to use probabilistic seasonal forecasts to overcome this limitation by providing additional information for wind energy applications (Clark et al. 2017, Torralba et al. 2017).
Figure 1. Stages of wind farm development, the stakeholders involved at each stage and temporal horizons of the climate information used. While weather forecasts are limited to two weeks, climate predictions extend further into the future, from seasons up to decades, and climate projections are longer-term, from decades up to centuries.
Seasonal predictions range from one month to slightly more than a year into the future. These predictions are probabilistic. They provide information on the probability that certain outcomes will occur rather than a single ‘yes-no’ deterministic prediction. Seasonal wind predictions can be used to derive the probability that the seasonal wind speed will be above, near or below normal. These categories are obtained by dividing the distribution into terciles (equal thirds), but other categories (e.g. using quintiles, or fifths) could also be defined if they better support the required decisions (for example, if there is an interest in looking at extreme wind speeds). To evaluate whether these predictions fulfill the requirements of users, their quality has to be explored using various verification measures. The results provide wind-energy users with guidance on whether or not seasonal predictions can better inform the approaches they are currently using.
Figure 2. Structure of the Climate Services initiative based on the Global Framework for Climate Services
The main shortcoming of seasonal predictions is the systematic errors that result from the inability of global circulation models to reproduce all the relevant processes responsible for climate variability (Doblas-Reyes et al. 2013). Hence, seasonal predictions require that bias adjustments be made in order to minimize forecast errors and produce useful information. Several state-of-the-art statistical methods focus on the correction of these predictions. These include quantile mapping (Themeßl et al. 2012), the calibration method (Doblas-Reyes et al. 2005) and simple techniques that only adjust the mean bias (Leung et al. 1999). These methods will produce seasonal predictions whose statistical properties are similar to those in the underlying observational references, allowing the energy business to easily integrate bias-adjusted seasonal predictions in its models (Torralba et al. 2017).
To provide further tailored information for decision-making, seasonal forecasts of the capacity factor can be generated. The capacity factor is a widely used indicator that allows fair comparisons between wind farms of different sizes. Over a period of time, it gives the percentage of generated energy with respect to the maximum achievable if the farm were operating at full capacity all the time. In this sense, the capacity factor of a wind farm measures how good the atmospheric conditions have been for producing energy during a specific time period. The capacity factor is computed using manufacturer-provided power curves that relate wind speed to power output for a specific turbine.
The ESS Climate Services initiative
The ESS Climate Services initiative for renewable energy focuses on the provision of useful and user-friendly climate information for the wind energy sector at sub-seasonal, seasonal and decadal time-scales. The main aim of this initiative is to provide climate services for the renewable energy sector that help users to understand and manage climate-related risks and opportunities. This initiative is led by BSC and has been developed by climate and energy researchers together with industrial partners, taking advantage of the key lessons learned from previous climate services projects (CLIM-RUN, SPECS, EUPORIAS and RESILIENCE).
Providers of climate services must understand and characterize the value chain of potential users and the impacts that climate services can have on their decisions in each section of the chain. To achieve this, the ESS initiative encompasses most of the GFCS components (Figure 2):
- a user interface platform, with the ESS webpage serving as an online space where stakeholders can define their needs and provide feedback to ensure that final products satisfy their requirements;
- a climate service information system hosted on the ESS webpage (www.bsc.es/ess/wind-energy), which functions as an online platform for producing and distributing climate products for renewable energy and targeted communication materials to assist decision-making;
- research, modelling and prediction, as the initiative is underpinned by cutting-edge research on climate science and climate prediction at different spatial and temporal scales, allowing BSC to push the climate services and the renewable energy applications forward; and
- capacity development that covers all five GFCS components and is fostered by the ESS multidisciplinary team of climate scientists, renewable energy experts, social scientists and communicators, and draws on the big data infrastructure and supercomputing facilities provided by BSC to enable this initiative to develop effective and timely climate services.
The ESS webpage invites user feedback to ensure that results are tailored to their needs. Below are examples of the targeted materials featured there:
- factsheets that explain climate concepts that may be difficult for non-specialists to understand, such as the concept of probabilistic prediction, the temporal scales associated with climate science, the quality assessment (skill and accuracy) and the reliability of climate predictions;
- seasonal climate prediction bulletins from previous winter seasons that compare predictions with observations in order to engage stakeholders in the use of seasonal climate forecasts as an additional tool to guide their decision-making;
- case studies of specific past events relevant to industrial partners for which a comparison between climate predictions and what actually happened is shown in order to assess the added value of using seasonal predictions against current prediction approaches based on historical observations; and
- research material targeted at specialized audiences such as technical notes, scientific publications and a repository of figures.
The best-known product delivered by the ESS Climate Services initiative is the RESILIENCE prototype for wind. This is an interactive climate-service interface that the wind industry can use to explore probabilistic wind-speed predictions for the coming season (Figure 3). It was designed and developed under the EUPORIAS and CLIM4ENERGY projects to support wind-farm owners, operators and energy traders who need to understand how wind will vary over the coming months in order to anticipate revenues, plan maintenance operations or foresee energy prices.
The web application at http://www.bsc.es/ess/resilience makes it possible to spot global patterns of anomalies in future wind conditions and to drill into detailed predictions at the regional level. The user interface presents a thematic map with wind-prediction data visualized in line symbols for around 100 000 grid points covering the globe. It encodes predicted wind-speed values and prediction-quality (skill) estimates together. When a grid point is clicked on, a panel displays site-specific information on past observations, individual predictions and probabilities for above-normal, normal or below-normal wind conditions. The current version of the RESILIENCE prototype only includes seasonal predictions of wind speed, but capacity-factor seasonal predictions will be also available in the near future.
The Weather Roulette application has been developed to illustrate the added value of using these probabilistic predictions. It translates the performance of seasonal predictions for wind speed into commonplace concepts such as interest ratio and return on investment, which are more informative for the wind industry.
Towards a sustainable, low-carbon future
Figure 3. RESILIENCE prototype data visualization tool and results for the predicted season (March to May 2017). 1-selected geographical region; 2-predicted change in wind speed; 3-seasonal average wind speed in the selected geographical region over the last 36 years based on the ERA-Interim reanalysis; 4-median wind speed over the last 36 years based on ERA-Interim; 5-wind prediction for the next season (the percentage of simulations in each of the terciles gives the probability to lower, equal or higher than normal wind speed conditions); 6-skill or measure of how well the prediction system has performed over the last 36 years in the selected region; 7-currently installed wind power in the selected region.
Advancing the implementation of climate services will contribute to having a much larger share of clean energy sources in the energy sector. The ESS Climate Services initiative can provide a suitable framework for supporting this transformation. It will achieve this by identifying key elements that match the needs of the energy industry and by using a common language and classification scheme that reflects the various ways the industry applies climate information. This will bring the research community and the private sector closer together and ensure collaboration on the development of climate services that support energy stakeholders. ESS should allow energy-sector stakeholders to acquire wider access to relevant climate expertise, information, tools and energy policies, thus enabling them to improve planning, policy and operational activities.
The next steps for the ESS Climate Services initiative will be to foster the transition from a global pre-operational prediction system (the current RESILIENCE prototype) to a fully operational one. This will be developed under the framework of the European project S2S4E. The predictions of wind speed provided by the operational system will be updated every month, integrating a combination of sub-seasonal and seasonal probabilistic predictions. This constitutes a challenge not only for research but also for communication and visualization, since complexity of interpretation increases at the expense of having a more complete picture of wind-speed variation. In addition, an assessment of the quality of the prediction will lead to regular improvements in the tools that support decision-making for the wind industry.
S2S4E will also develop similar climate services for other renewable energy sectors, such as solar energy and hydropower. The integration of additional renewable sources has the potential to further increase the share of clean energy in the total energy mix, thereby advancing the worldwide transition to a low-carbon future and the implementation of the Sustainable Development Goals. Having reliable climate services for various renewable energy sectors will improve efficiency and reduce the risk associated with climate hazards that affect and will continue to affect the energy sector under future climate change.
The research leading to the results described here has received funding from the Clim4Energy for the Copernicus Climate Change Service contract implemented by the European Centre for Medium-range Weather Forecasting (ECMWF) on behalf of the European Commission and the European H2020 collaborative project S2S4E.
Clark, R. T., Bett, P. E., Thornton, H. E., & Scaife, A. A. (2017). Skilful seasonal predictions for the European energy industry. Environmental Research Letters, 12(2), 024002. doi:10.1088/1748-9326/aa57ab
Doblas-Reyes, F. J., Hagedorn, R., & Palmer, T. N. (2005). The rationale behind the success of multi-model ensembles in seasonal forecasting–II. Calibration and combination. Tellus A, 57(3), 234-252. doi:10.1111/j.1600-0870.2005.00104.x
Doblas-Reyes, F. J., García-Serrano, J., Lienert, F., Biescas, A. P., & Rodrigues, L. R. (2013). Seasonal climate predictability and forecasting: status and prospects. Wiley Interdisciplinary Reviews: Climate Change, 4(4), 245-268. doi:10.1002/wcc.217
Hewitt, C., Mason, S., & Walland, D. (2012). The global framework for climate services. Nature Climate Change, 2(12), 831. doi:10.1038/nclimate1745
Griggs, D., Stafford-Smith, M., Gaffney, O., Rockström, J., Öhman, M. C., Shyamsundar, P., Steffen, W., Glaser, G., Kanie, N., Noble, I. (2013). Policy: Sustainable development goals for people and planet. Nature, 495(7441), 305-307. doi:10.1038/495305a
Global Wind Energy Council (GWEC, 2016) Global Wind Energy Outlook, accessed 22 July 2017, http://www.gwec.net/publications/global-wind-energy-outlook/.
Leung, L. R., Hamlet, A. F., Lettenmaier, D. P., & Kumar, A. (1999). Simulations of the ENSO hydroclimate signals in the Pacific Northwest Columbia River basin. Bulletin of the American Meteorological Society, 80(11), 2313-2329. doi: 10.1175/1520-0477(1999)080<2313:SOTEHS>2.0.CO;2
Organisation for Economic Co-operation and Development and International Energy Agency (OECD/IEA, 2016) World Energy Outlook. Part B: special focus on renewable energy. International Energy Agency, accessed 22 July 2017, https://www.iea.org/media/publications/weo/WEO2016SpecialFocusonRenewabl...
Themeßl, M. J., Gobiet, A., & Heinrich, G. (2012). Empirical-statistical downscaling and error correction of regional climate models and its impact on the climate change signal. Climatic Change, 112(2), 449-468. doi: 10.1002/joc.2168
Torralba, V., Doblas-Reyes, F. J., MacLeod, D., Christel, I., & Davis, M. (2017). Seasonal Climate Prediction: A New Source of Information for the Management of Wind Energy Resources. Journal of Applied Meteorology and Climatology, 56(5), 1231-1247. doi: 10.1175/JAMC-D-16-0204.1
World Meteorological Organization (WMO; 2017) Energy Exemplar to the User Interface Platform of the Global Framework for Climate Services, http://www.wmo.int/gfcs/sites/default/files/Priority-Areas/Energy/GFCS_E...
 Strengthening the European Energy Network using Climate Services (RESILIENCE)
 Sub-seasonal to seasonal climate forecasting for energy