Achieving Artificial Intelligence and Machine Learning to accelerate the energy transition

A successful and timely energy transition needs Artificial Intelligence and Machine Learning (AI\ML ) to accelerate change.

Reducing costs, enabling more performant (new) energy businesses and the complex coordination of multiple energy players are crucial in this transformation. However we’re still in the early stages of AI\ML, how can we achieve AI\ML rapid adoption at scale?

Why there is no energy transition without Intelligence Intensity

For the green deal to succeed, we need to start moving towards a whole system approach, interconnecting sectors from diverse energy carriers to industries, transport, and buildings, driving Power-to-X, industrial clusters, industrial smart steering, 24 by 7 green energy matching, hybrid energy parks, and new low-carbon energy value chains leading to billions of networked “things”. Flexible yet complex coordination is required that is close to real-time and optimised for multiple, varying stakeholder interests – impossible to be done by humans.

The key role AI/ML plays in reducing the gigantic investments required for the energy transition can lower the levelised costs of energy, accelerate the issuing of permits and grid connections, and optimise yield, thus speeding up the deployment of the massive renewable generation required. Grid capacity can be expanded digitally, avoiding traditional grid reinforcements that are expensive and time-consuming to build. AI\ML also enables flexibility services coordination for maximum DERs value and infrastructure usage.

Microsoft is fully committed to a rapid AI\ML adoption at scale which is already evolving into a technical reality with higher use than anticipated. Partnerships and co-innovation with clients and partners and the wider ecosystem accelerate the creation of missing digital solutions and the development of digital accelerators for wider, faster, and simpler adoption of digital.

Accelerating AI\ML innovation through open data platforms, open ecosystems, open-source

AI\ML needs a lot of data! Strengthened open energy data platforms give innovators in the ecosystem access in a safe, scalable and performant way to vast volumes of quality data essential to train AI models. Microsoft joined OSDU (Open Subsurface Data Universe) to create an open-source, cloud-agnostic platform to collect subsurface data from O&G operations valuable to O&G but also to renewable offshore players.

Energy Datahubs in Europe also play a vital role in driving innovation. This is why Microsoft and Energinet partnered to co-create the open-source Green Energy Hub blueprints on GitHub for experts to contribute and for others to develop their own data hubs, creating an accelerator for the future smart green solutions.

With AI still in its early stages, it is key to inspire energy players of its successful, tangible impact and to facilitate access to solutions. Microsoft launched the Open AI Energy Initiative (OAI), an open ecosystem for operators, independent software vendors, and equipment providers to offer additional solutions, and the global AI Centre of Excellence for Energy called Microsoft Energy Core features over 40 partner solutions.

The driving co-innovation force of strategic partnerships with energy leaders

Strategic partnerships with market makers enables the acceleration of transformation but also to co-invest deeper and wider in the creation of leading-edge digital solutions for current operations and for the complex chain orchestration needed for a successful energy transition. Foundational research for AI in energy and energy-specific platform-based capabilities are not only developed faster.

These intelligence-intense, leading-edge lighthouse use cases inform the industry for fast followers and create digital optimism for speed. Together we become a driving force for the formation of new value chains, ecosystems, and business models that accelerate meeting the goals of the green agenda.

Utilities specific digital accelerators for wider, faster, and simpler adoption

Energy players want more pre-built capabilities specific to utilities for faster time to market AI\ML models. The 15 years of enhanced utilities-specific industry data models acquired from ADRM exemplify the current enrichment with automation of data ingestion from multiple sources, addressing a major hurdle on data.

Another example is the common domain-specific ontologies that are fundamental to accelerating the development of digital twin solutions. Microsoft, together with Agder Energi, launched the open-source Energy Grid Ontology to be added by others for smart cities and smart buildings.

More broadly, the road ahead is for industry clouds. Energy players can focus much higher in the technology stack at the business applications layer, thus shortening innovation cycles, getting faster into the predictive era, and simplifying adoption.

Through co-investment, Microsoft is accelerating the development of energy-specific platform-based capabilities allowing energy players to focus their AI efforts at the business applications level such as for portfolio optimisation, risk management, and also trading.

WATCH: Why AI is key in solving complex energy transition challenges

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