The perfect storm
[Published article in LA TRIBUNA DE ENERGÍA – EL ECONOMISTA. José Antonio Luque]
As we prepare to bid farewell to 2022, the energy situation is becoming increasingly precarious. The global economy is still recovering from the impact of the pandemic, and there is also rising inflation due to the post-pandemic recovery process. To make things worse, the conflict in Ukraine has caused a significant increase in energy costs and threatens energy security. In the short term, this has led to increased dependence on fossil fuels and reduced resources for the energy transition. It has also hindered regional and global coordination towards achieving “net zero emissions.” Society must find a way to overcome these challenges and resume the environmental agenda set by the United Nations while committing to fulfilling long-term global goals.
Decentralized renewable energy is one of the key solutions for addressing the current energy situation, along with more advanced distribution networks and rationalized demand vs. supply strategies. At the same time, these dynamic energy markets bring new challenges that need to be addressed. One such challenge is the management of flexible energy markets, which are becoming increasingly prevalent and must deal with highly volatile factors such as renewable energy sources. This new approach is expected to significantly reduce the number of start-up hours of combined-cycle power plants, leading to reduced emissions and dependence on fossil fuels. However, increased reliance on wind and solar energy sources means that there will be more instances of start-ups to address production shortfalls.
Effective management will require accurate forecasting of user demand and production capacity, enabling the power plants to be operated under optimal safety and performance conditions. In the case of wind and solar energy, the predictive component is particularly delicate, and efforts are being made to improve the accuracy of forecasts and mitigate the volatility of these energy sources. A production and demand forecast based on data analytics is essential for addressing short- and medium-term strategies that take into account a wide range of factors, including climatic conditions, consumption and distribution infrastructure, and transportation.
In addition to traditional energy generation, there is an increasing number of small-scale energy producers that supply their own energy needs and feed excess energy back into the grid. This new paradigm of demand forecasting must take into account not only the energy consumption needs of users but also the production capacity of small and medium-sized energy installations. Climate conditions and other factors will continue to play a significant role in dictating energy consumption needs.
"Data analytics is taking a leading role in almost all sectors related to the energy market"
José Antonio Luque – Innova-tsn’s CEO
The distribution network is subject to a large and growing demand, which has been modernized in record time, a positive outcome. However, high consumption levels have put significant strain on the infrastructure, especially with the rise and promotion of electric vehicles, which is one of the most prominent use cases in the analysis of distribution grids or Smart Grids. The increase in the number of electric car users and their high power demand raises questions about how to manage peak power demand on the grid. If all users charge their vehicles at the same time, their requirements will exceed the grid’s capacity, making it difficult to ensure a balanced distribution. Can we balance the needs of each driver and encourage them to charge at a time that still supports their use, but does not compromise supply? This can be achieved through analytics, which predicts consumption based on usage patterns and optimizes the network as a whole by suggesting and even rewarding charging at the most convenient time.
The energy sector faces many challenges, such as building a global operations management that includes new players, energy communities, resource aggregators, and active customers (distribution grid managers), as well as developing renewable energy and improving energy transport and storage tools. To address these challenges, data, proper data management, and the widespread use of artificial intelligence in decision-making processes have become key strategic assets.
The current consumption and supply scenario is unprecedented, and in the electricity market, where investments are in the millions and high-risk, every step is carefully calculated. The adoption of data-based strategies has been a turning point in the industry. Data analytics in the energy sector has initially developed timidly in areas of innovation and was somewhat marginalized in an environment dominated by engineering and regulation. However, it has now taken a leading role in virtually all sectors of the energy market, from generation to retail. The challenge for the industry is to continue promoting data-driven change management, where a new qualitative leap is needed: to evolve from a reality where data analytics and prediction support decision-making to one where data and Artificial Intelligence drive the sector towards achieving agreed emissions and efficiency targets, while meeting society’s growing demand for energy.