![]() Time and flexibility are also key aspects of data performance for grid management. Moving data from data sources to a data lake, where artificial intelligence (AI) and ML can extract actionable insights for grid management, has an inherent cost that grows with the volume of data collected and exchanged. Companies must manage this explosion of data in terms of transport, storage and accessibility. However, IoT environments such as smart grids utilize vast amounts of data from different sources, including high-resolution data from sensors. The adoption of new digital technologies in adjacent sectors like smart cities is adding an extra dimension to the potential of smart grids By pairing these technologies with a distributed business architecture, energy companies can capture real-time insights for optimal product development and delivery. Internet of things (IoT) sensors and smart meters, paired with machine learning (ML), enable utilities to distribute intelligence across the grid to manage supply and demand more efficiently. Emerging innovations in power storage and grid forecasting will help, but will require agile digital infrastructure to make the transition.ĭigitization can modernize the entire energy value chain, from generation and transport, distribution, supply and consumption, as well as orchestration of the grid. However, this is not efficient and does not meet the recommendations of the Paris Climate Accord to fully decarbonize over the next three decades, which several utility companies have already committed to. To maintain grid stability and avoid the risk of cascading failures and blackouts, TSOs may run existing (fossil-based) grid infrastructure on high capacity as a buffer. Managing this increase of local (renewable) power generation with fluctuations in energy demand is a real challenge for Transmission System Operators (TSOs). For example, local homes and businesses may contribute power back to the grid from solar roof panels. At the same time, energy generation is rapidly evolving from the traditional one-way production from large suppliers to power cogeneration with energy consumers also acting as micro suppliers. Supply is less predictable with renewable energy – you don’t always know when the sun will shine or the wind will blow. Addressing these requirements will require utilities to shift to modern, distributed digital infrastructure. And while EV and other environmental initiatives will decrease fossil fuel-based, noncircular power generation, they will increase demand for supporting IT, such as equipment, applications and services. Global electricity demand from (connected) EV’s is expected to reach 550 terawatts per hour (TWh) by 2030, a six-fold increase from 2019 levels, requiring more grid capacity to meet the demand. These developments are also driving a massive shift in the electrification of sectors traditionally based on fossil fuels such as mobility. This combined with the fact that the cost of renewables has fallen dramatically in the past ten years – solar and onshore wind now cost less than traditional energy sources – is accelerating the push to clean energy sources. And nearly 300 companies have pledged to achieve 100% renewable energy by 2050 or sooner. Over 110 countries have pledged climate neutrality by 2050 with over 5,500 supporting policies currently in place. ![]() Recent increased government support and corporate demand for climate neutrality is pushing the energy sector to move to clean energy even faster. Smart meters capable of collecting real-time data on power consumption were introduced over two decades ago, and renewable energy generation, like solar and wind, has an even longer history. ![]() Transformation is not a new phenomenon in the industry. Trends driving change in the energy industry
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