7 reasons AI is the missing link in the energy transition

The global energy transition is commonly framed as a competition to build more renewable infrastructure such as solar farms and energy storage systems; however, with the shift to clean energy, the more significant challenge is managing the delivery of that energy in an intelligent manner. As renewable energy sources are inherently variable, decentralized, and data intensive by nature, a new type of system that can coordinate millions of assets in real-time is required to manage large quantities of renewable energy.

The future of clean energy will depend not only on how much renewable capacity is built, but also on how intelligently these distributed energy systems are managed and coordinated through advanced digital infrastructure. This orchestration challenge can be addressed through a four S Intelligence Framework: Sharper Analytics, Smarter AI, Scalable Systems, and Secured Governance. Ultimately, it provides the digital infrastructure needed to create a connected and intelligent energy ecosystem by transforming fragmented energy assets into an intelligent and resilient ecosystem.

From Reactive Grids to “Predictive Foresight” (Sharper Analytics)

Historically, traditional electric grids have been developed as centralised systems where energy flows only in one direction, from big generation source to users. Alternatively, renewable electricity generation has shown to fluctuate significantly based upon existing weather conditions, thus making both prediction and planning more complicated. However, utilities are now able to use big data analytics, through the use of artificial intelligence, to forecast changes in production based upon meteorological data and satellite imagery with incredible accuracy. As a result, utilities can now use predictive management to anticipate potential fluctuations in electricity generation and respond accordingly prior to actual fluctuations occurring. This major shift from reactive management to predictive foresight increases the level of sizeability and reliability of grid systems while also enhancing the operational efficiency of renewable generators.

Orchestrating the “Agentic Grid” (Smarter AI)

As energy systems have shifted from being centralised to being decentralised, with residential homes, businesses and microgrids now generating their own power, there are now millions of potential energy producers (often called “prosumers”) to manage in this new landscape. This makes managing such a disparate network nearly impossible with traditional methods; however, through the use of AI-driven multi-agent systems, it will be possible to autonomously coordinate these distributed resources through the negotiation of energy flows between residences, batteries and central grids. In practical terms, an AI agent would be able to dynamically determine when energy resources will be stored, traded or redirected throughout the distributed energy network. The result is what we refer to as the “agentic grid”, where a distributed energy system can operate as though it were a highly intelligent market, and supply and demand can be met in real time through the system, without needing constant guidance from people in order for them to do so.

Solving the Intermittency Gap

Sudden weather changes or unexpected demand increases can impact the grid. One way to overcome this challenge is using AI-based digital twins of the real-world energy structure. Digital twins are virtual simulations of entire power networks that allow business operators to evaluate numerous “what-if” scenarios. For instance, think about how utilities can use digital twins to evaluate the possibility of a severe storm, equipment failure, or an increase in energy demand at their location and be able to pinpoint weak areas before any actual occurrence of these types happens, assisting utilities in the design of more resilient systems and optimizing battery storage/distribution strategies.

Eradicating Maintenance Leakage

Traditional maintenance techniques usually involve either predetermined scheduled inspections or reactive repair activities that occur following equipment failure. AI provides the ability to perform predictive maintenance; by continuously processing sensor data using AI algorithms, it can detect minute issues with the equipment (e.g., small vibrations, fluctuations in temperature, changes in performance) and thus give you a warning of an impending breakdown weeks ahead of time.

Democratizing Energy through “Conversational BI”

Energy management has traditionally been an area reserved for people with expert level training, such as engineers and grid operators. With the addition of renewable energy in homes, factories and commercial buildings, now there’s an expanded group of people who need to have an understanding of how their energy choices impact their efficiency/costs. The introduction of AI-based conversational interfaces is changing the way users interact with complex energy information using natural language queries. Using easily interpreted and understandable forms, AI will provide all users with access to energy intelligence and allow for more intelligent decisions across the entire ecosystem.

Hardwiring “Sovereign Trust” (Secured Governance)

As power systems become increasingly connected and computerized, the threat to cyber security is growing greatly. The use of software to make decisions in a decentralized energy system creates a high level of vulnerability to cyber attacks or data manipulation. The use of AI to enhance these systems’ security is fundamental. In addition, through explainable Artificial Intelligence, there is an increased likelihood that automated decisions will have a level of transparency that can be easily audited, held accountable for their actions and considered valuable. As the simulation of energy infrastructure is highly integrated with digital technologies, there will be a critical requirement for AI-based security systems to secure the energy system.

Shifting Spend from “Maintenance to Innovation” (FinOps)

Many power generation companies struggle with an ever-increasing challenge of having most of their technology budgets tied up in the maintenance of older technologies. There are solutions using AI to improve business operations through more efficient use of resources/technology, automating repetitive tasks and integrating old systems into newer digital platforms. As it reduces the operational inefficiency, utility companies will also be able to allocate more personnel and financial resources toward developing other forms of renewable energy like advanced battery storage, Smart grid technologies or emerging new forms of energy. This change in how companies spend their money will give them the ability to continue to invest in creating a sustainable energy future while also providing a profitable company.

The AI revolution has given us the ability to create predictive analyses, autonomy, and resilience in our energy systems. This will allow for the transformation of decentralized and disconnected energy systems into a single integrated network providing flexible, adaptive solutions. As a global society seeks to create a low-carbon future, our energy ecosystems will have the ability to not only produce clean energy but also intelligently manage the clean energy being produced.

The views and opinions expressed in this article are the author’s own, and do not necessarily reflect those held by pv magazine.

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