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Humanity has sought artificial intelligence (AI) for more than two millennia. The Greek writer Hesiod first wrote about Talos – a giant bronze man built by Hephaestus, the Greek god of invention and blacksmithing, in 700 BC. Today, AI is no longer a distant dream or a work of fiction. It’s reality, and it promises transformation.
Generative artificial intelligence is arguably our most powerful tool in grid decarbonization and combatting climate change. It systematically collects and learns from vast datasets on emissions, weather patterns, population movements, and efficiency in low-voltage power networks. Its algorithms optimize energy generation, distribution, and consumption based on demand, enhancing efficiency and cutting carbon emissions. It predicts equipment failures, forecasts loads, detects faults, and integrates renewable energy sources. This empowers stakeholders by providing comprehensive, high-quality data analysis.
AI’s ascendancy in LV network management
Integrating AI with LV network management is a seismic shift in our approach to energy systems. Machine learning has evolved from basic rule-based systems to sophisticated dynamic modeling systems, learning and adapting based on the data they process.
This evolution isn’t a random occurrence. It follows a clear trajectory. First, establishing rules through machine learning, then model creation using regression analysis and mathematical grid modeling, and finally, a generative AI system that continually learns and adapts.
For example, managing load flow becomes more complex due to increased distributed energy resources (DERs). AI improves low-voltage electrical network management by creating digital twins. These twins allow operators to test scenarios without physical intervention, make informed decisions, optimize grid performance, and anticipate issues. AI is crucial for demand forecasting and infrastructure planning. Generative AI’s advanced analytics offer real-time visibility into the network, aiding efficient grid management, automating the supply chain, and ensuring a reliable power supply.
Prosumer behavior will continue to change
Tomorrow’s prosumer behavior will be very different from today. The acceleration of renewable penetration (e.g., solar rooftops), electrification of heating systems, the increase of EV usage, and wider spread of storage will radically change consumer participation in the energy market. Due to this evolution, digitized low-voltage networks are starting to generate huge data streams that will reshape prosumer decisions. Generative AI can manage these growing datasets to provide more effective, dynamic insights on real-time energy usage and better capacity to modelize and forecast prosumer behaviors and their impact on electrical grids. This will turn prosumers into active participants in grid efficiency, stability, and decarbonization, directly or through an ecosystem of stakeholders (energy trading platforms).
Who is accountable?
Generative AI holds significant potential for automating decision-making on crucial infrastructure. It also raises important questions about regulation and accountability. Who takes responsibility for the automatic decisions made by an AI system? Does it lie with the manufacturer, the operator of the system, or even the AI system itself? If decisions don’t go as planned, who is held accountable?
This topic has been widely discussed in the context of autonomous transportation. But it is equally critical in the context of grid management. Imagine an AI making the autonomous decision to switch off a part of a national electrical grid, putting in the dark hospitals and network systems. Jeopardizing lives and economic systems.
Various initiatives are addressing AI’s legal and ethical framework. For example, governments now propose an AI bill of rights for ethical, safe use. The EU has also studied AI’s legal issues, including liability for tortious, criminal, and contractual misconduct. These critical factors need addressing as we go forward.
Contributing to a sustainable future
We know AI models can analyze vast quantities of data, which helps us with our net zero objectives. Data that includes population density, transportation patterns, energy consumption, and environmental factors helps predict energy demand, optimize energy distribution, and reduce waste. It also enhances the reliability and resilience of low-voltage electrical networks, making them more efficient and less carbon-intensive by integrating renewable energy sources and better forecasting prosumer energy behavior.
In the next decade, we will see an increasing focus on environmental sustainability. And as our understanding and awareness of climate change improves, it will drive new prosumer behaviors that we don’t yet see today.
A practical example of AI
In our fast-evolving energy landscape, the capacity to share accurate representation of networks and assets across all energy stakeholders will become critical. At Schneider Electric, we leverage AI to validate data quality and topology model accuracy, helping significantly reduce labor-intensive human activities. AI is becoming one of the major tools to enable high data quality and, therefore, address one of the critical challenges of grid digitization.
The road ahead
Automation and efficiency aside, AI integration will reimagine and reshape how we produce, distribute, and consume energy. It’s more than an add-on or a nice-to-have feature – it’s a game changer. It’s the critical component of the future energy landscape. With increasing clarity, we see that the future of grid decarbonization lies in our ability to leverage this technology. By creating dynamic, self-evolving models that adapt to our changing behaviors, we can achieve greater efficiency, sustainability, and resilience and reduce harmful CO2 emissions.
The journey ahead promises to be enlightening, disruptive, and a milestone in our pursuit of sustainable energy ecosystems. Those who fail to recognize its significance or delay its adoption risk being left behind.
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