Seasonal Climate Prediction: A New Source of Information for the Management of Wind Energy Resources

Verónica Torralba,Melanie Davis,Dave Macleod,Isadora Christel,Francisco J Doblas-Reyes

doi:10.1175/jamc-d-16-0204.1

Abstract

AbstractClimate predictions tailored to the wind energy sector represent an innovation in the use of climate information to better manage the future variability of wind energy resources. Wind energy users have traditionally employed a simple approach that is based on an estimate of retrospective climatological information. Instead, climate predictions can better support the balance between energy demand and supply, as well as decisions relative to the scheduling of maintenance work. One limitation for the use of the climate predictions is the bias, which has until now prevented their incorporation in wind energy models because they require variables with statistical properties that are similar to those observed. To overcome this problem, two techniques of probabilistic climate forecast bias adjustment are considered here: a simple bias correction and a calibration method. Both approaches assume that the seasonal distributions are Gaussian. These methods are linear and robust and neither requires parameter estimation—essential features for the small sample sizes of current climate forecast systems. This paper is the first to explore the impact of the necessary bias adjustment on the forecast quality of an operational seasonal forecast system, using the European Centre for Medium-Range Weather Forecasts seasonal predictions of near-surface wind speed to produce useful information for wind energy users. The results reveal to what extent the bias adjustment techniques, in particular the calibration method, are indispensable to produce statistically consistent and reliable predictions. The forecast-quality assessment shows that calibration is a fundamental requirement for high-quality climate service.

Highlights

The demand for renewable energy sources as an alternative to fossil-fuel sources has increased due to reasons such as the need to mitigate the climate change resulting from anthropogenic greenhouse gas emissions, the interest in the creation of new economic opportunities and the provision of energy access to people living in areas without access to other sources of energy (Renewable Energy Policy Network for the 21st Century 2015; Solomon 2007)
This country is an important player in terms of energy resources (Vaillancourt et al 2014) and a global leader in the sustainable development of wind energy
In this paper we illustrate a strategy for the use of wind speed seasonal predictions by the wind energy sector

Summary

Introduction

The demand for renewable energy sources as an alternative to fossil-fuel sources has increased due to reasons such as the need to mitigate the climate change resulting from anthropogenic greenhouse gas emissions, the interest in the creation of new economic opportunities and the provision of energy access to people living in areas without access to other sources of energy (Renewable Energy Policy Network for the 21st Century 2015; Solomon 2007). Wind energy is the cheapest option for the new sources of power generating capacity and the second leading renewable energy source worldwide, only exceeded by hydropower in terms of installed capacity (Pryor and Barthelmie 2010; Santos et al 2015). Operational and economic issues related to wind energy, such as the need to match supply with demand at all times under the intermittent nature of wind, require the modeling and forecasting of wind power generation processes at a range of temporal and spatial scales (Pinson 2013). Wind energy forecasting options have been traditionally limited to short (from hours to a few days) time scales because near-surface winds and wind energy production, strongly depend on the meso- and synoptic-scale variability (Graff et al 2014; Pryor and Barthelmie 2010). The assessment of the economic feasibility of future wind farms is a function of, among other things, the expected energy yield and the maintenance requirements over their life span of periods from a month to several decades

Methods

Results

Conclusion