New Energy and Weather Services in the Context of the Energy Transition

Abstract

The potential of weather forecasting is increasingly being exploited by power generators and transmission‐system operators as renewable electricity generation has become a significant and increasing part of the energy mix in many countries. Showing how the forecasts are interpreted and actually used, we suggest avenues for policy makers on how best utilizing meteorology in the unfolding energy transition through new, integrated meteorology and energy services.

Digital Management of Solar Energy En Route to Energy Self‐Sufficiency

“…Remarkable recent progress in weather forecasting, particularly through neural network algorithms leveraging on historical meteorological data, enables predictions of renewable energy production from sun and wind of ever increasing accuracy …”

“…To maximize revenues and ensure better estimate of the return on their investment, many utilities and investor owners of large PV and wind energy parks today purchase similar energy forecasting services from companies specializing in weather‐energy forecasting …”

“…Remarkable recent progress in weather forecasting, particularly through neural network algorithms leveraging on historical meteorological data, enables predictions of renewable energy production from sun and wind of ever increasing accuracy. [18] This allows to maximize the hourly and daily input of renewable energy into the grid making renewable energy predictable, even though intermittent, and electricity supply ever more reliable, ultimately solving what Mauro, a scholar in artificial intelligence, has called the "new energy trilemma," meaning that we could not have "energy that is at the same time cheap, clean, and reliable." [17] To maximize revenues and ensure better estimate of the return on their investment, many utilities and investor owners of large PV and wind energy parks today purchase similar energy forecasting services from companies specializing in weather-energy forecasting.…”

“…[17] To maximize revenues and ensure better estimate of the return on their investment, many utilities and investor owners of large PV and wind energy parks today purchase similar energy forecasting services from companies specializing in weather-energy forecasting. [18] Similarly, "machine learning" algorithms, that is, algorithms through which a computer learns from past experience and tries to capture the best possible knowledge to make accurate decisions, currently help achieve longer service intervals for natural gas turbines of a large thermoelectric gas turbine manufacturer (algorithms automatically analyze operating data, environmental conditions, and component properties and suggest the most suited operating conditions to prolong the life of the steel turbines). [19] Carrying ever increasing amounts of renewable electricity, the grid will continue to fulfill the power needs of large manufacturing plants.…”

Southerly winds increase the electricity generated by solar photovoltaic systems

“…storage systems, demand-side management) to reduce power volatility and ensure electricity network resilience (Hanjalic et al, 2007;Schiermeier, 2016;Gaglia et al, 2017;Reindl et al, 2017;Saffari et al, 2018). Consequently, energy meteorology -where the grid utilises weather observations and machine learning techniques that predict renewable electricity outputs -has become increasingly pivotal in supporting industry decision-making and decarbonisation projects in both daily operations and long-term strategic planning (Traunmüller and Steinmaurer, 2010;Wan et al, 2015;Reindl et al, 2017;Agoua et al, 2018;Ciriminna et al, 2018). Moreover, improved understanding of meteorological-PV panel interactions could be used within climate models to assess long-term risks to electricity supply given various climate change scenarios, and thus inform future energy system needs (Jerez et al, 2015).…”