Better seasonal forecasts for the renewable energy industry

Abstract

Anomalous seasons such as extremely cold winters or low wind summers can seriously disrupt renewable energy productivity and reliability. Better seasonal forecasts providing more accurate information tailored to stakeholder needs can help the renewable energy industry prepare for such extremes. The climate mitigation benefits of clean energy come with great challenges. Compared to conventional fossil-fuel based energy sources, renewable sources, such as wind, solar, and hydropower, are highly weather-dependent. Ambitious mitigation policies aiming to reach a 1.5°C target require a radical increase in supplies of clean energy. This implies that total power supply will be more vulnerable to climate-induced weather variability. Balancing the demand and supply of intermittent renewable energy sources thus imposes a big challenge. Consequently, renewable energy technologies must become more resilient to climate variability and high impact events. Weather and climate variability affect the demand as well as supplies of renewable energy. High and low temperatures imply a high energy demand for cooling and heating, respectively 1. The relationship between electricity consumption and temperature is non-linear 2 : at a certain threshold level, an increase in temperatures results in a step increase in energy demand for cooling in summer. For

Climate Services for Renewable Energy in the Nordic Electricity Market

“…For planning purposes, information on potential shifts towards earlier flow peaks from snowmelt and lower peak volumes in the future are important for different seasonal regulation, e.g., for reducing the need to store large volumes of meltwater for the winter months [14]. In general, producers only use dynamic CSs such as seasonal forecasts as indicators (e.g., more/less precipitation) and not as quantitative information to feed into operational models, though various studies [22,46] suggest that they would benefit from using accurate quantitative information for longerterm planning. Thus, a main barrier is that the available seasonal forecasts are currently quite coarse or lack sufficient reliability or skill [22,46].…”

“…In general, producers only use dynamic CSs such as seasonal forecasts as indicators (e.g., more/less precipitation) and not as quantitative information to feed into operational models, though various studies [22,46] suggest that they would benefit from using accurate quantitative information for longerterm planning. Thus, a main barrier is that the available seasonal forecasts are currently quite coarse or lack sufficient reliability or skill [22,46]. Conversely, both large stateowned and private energy companies make intensive use of historical data, observations and climate statistics (averages and tendencies of particular months) in many aspects of their planning and operations [22,46].…”

“…Thus, a main barrier is that the available seasonal forecasts are currently quite coarse or lack sufficient reliability or skill [22,46]. Conversely, both large stateowned and private energy companies make intensive use of historical data, observations and climate statistics (averages and tendencies of particular months) in many aspects of their planning and operations [22,46]. Such data are used for forecasting future demand, understanding the potential value of generation mix, improving optimization, developing new facilities, and understanding potential risks to assets from extreme events [22,46].…”

“…Conversely, both large stateowned and private energy companies make intensive use of historical data, observations and climate statistics (averages and tendencies of particular months) in many aspects of their planning and operations [22,46]. Such data are used for forecasting future demand, understanding the potential value of generation mix, improving optimization, developing new facilities, and understanding potential risks to assets from extreme events [22,46]. In addition to historical data, companies use weather forecasts (up to two weeks) to feed their operational models, e.g., to understand electricity demand.…”

“…This is due in particular to increases in water originating from increased spring melting and increased precipitation, causing an increase in the hydropower production and modifications to existing management practices. On this background, it is not surprising that current CSs focus on the hydropower sector, including both seasonal forecasting and decadal-to-multi-decadal climate projections, though it is equally evident that improvements are urgently needed [46,52]. Like hydropower, wind power production is also very sensitive to climate change effects.…”