How the renewable energy transition is accelerating the digital transformation of the energy industry
Whilst digitalization and the development of new renewable energy assets can be challenging, it is also apparent that one of the key concerns for a sustainable energy transition is to be able to forecast renewable energy accurately.
Traditionally, classic fossil fuel-based energy is very easy to forecast, as it is a tangible fuel stock (coal, uranium, gas or oil). In comparison, renewable energy is very difficult to forecast correctly. How much wind, sun or water will be available tomorrow? What about in 1 month or 6 months’ time? Without such insights, huge uncertainty is created in future energy availability, which translates into volatility problems in energy markets, leading to futures/long term contract price spikes, and, in worst case scenario, resulting in energy shortages and even outages.
Given this situation, having a clear understanding of future energy production is a must if we want to complete the transition to renewable energy, whilst maintaining affordable energy prices.
Over time, different models, sensors and forecasts have been implemented to mitigate the intermittent nature of renewable energy production. Some of them based in meteorological models, some others in AI, with geospatial data or with IoT sensor networks. However, there is a key component that is preventing forward-thinking energy companies from fully embracing renewable energy datasets and being able to completely integrate these new forecasting capabilities into their infrastructure. Digitalization.
The energy industry is, still, a legacy industry, and in many countries, strategic. Energy producing companies have always been focused on hardware solutions, their whole business orbits towards heavy CAPEX investments with which to develop, commission, operate and decommission gigantic energy generation assets. Some of these assets have been transferred in several of these countries from the public sector, or have been directly privatized by the companies, making natural competition difficult. Additionally, these assets have been historically managed from an engineering perspective, with in-house software that has maintained and operated these energy assets. This situation, combined with low competition, has made the need for digital solutions non-essential from an operational perspective.
Because of all these factors, energy companies have still not fully embraced digitalization, making it more difficult for new emerging datasets, AI models, or forecasting systems to be accepted.