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The smart grid: How AI is powering today’s energy technologies

Like oxygen, the power grid is essential to modern life but is not always top of mind – until problems occur. Today, aging grid infrastructure is taking a beating from severe weather events around the world, resulting in power outages that threaten health, safety, and economic activity. At the same time, a number of other factors are also putting pressure on century-old grids. The way that energy is produced is rapidly changing – more wind and solar, less coal and fossil fuel. This shift requires new processes and ways of managing. The “who” is also shifting, with energy now produced not only by the major energy companies, but also by a plethora of new competitors and prosumers (consumers who produce energy).


And not only is the natural world changing fast, but the technological world is advancing at a gallop as well. Cloud-connected artificial intelligence (AI) technologies like machine learning, data analytics, and the Internet of Things (IoT) are driving the advancement of smart grids capable of managing far more complex power generation and distribution. These technologies herald significant opportunity for those in the complex energy ecosystem that are able to harness them.

What is a prosumer?

Prosumer is a portmanteau word combining “producer” and “consumer.” Energy prosumers typically remain connected to the central grid. However, they are also capable of producing and even storing energy – typically with photovoltaic solar panels and EV batteries.


Depending on the amount of power generated, this energy can either be used to offset monthly bills or be sold back as surplus to utilities companies or other energy distribution services. This model may be applied to both residential and commercial prosumers, with a growing number of businesses plugging their solar panels and EV fleets into the grid.

What is a smart grid?

A smart grid is a network that integrates energy distribution and digital communication technology in a two-way flow of electricity and data. This enables utility companies to optimise the generation, transmission, and distribution of electricity. And it also allows consumers to benefit from the stories all that data is telling – helping them to better understand the energy they use and even the energy they produce and store through things like solar panels and EV batteries.

What is the difference between the traditional grid and a smart grid?

The main difference between traditional systems and smart grids lies in the ability to exchange information in both directions across the network, from utility companies to consumers and vice versa. Some of the top features that differentiate smart grids include:

  • Technology: AIcloud, and digital technologies allow all the devices and assets within the grid to communicate, supporting better control and self-regulation.
  • Distribution: Energy generated by prosumers and other renewable energy sources – such as solar or wind – can be intermittent and uneven. Smart grid technologies help to coordinate, store, and distribute power from such sources into a steady and reliable stream.
  • Generation: Predictive analytics in smart systems means that high-demand strains can be forecasted and distributed to multiple plants and substations.
  • Sensors: IoT sensors across the network can help detect risk early on, redistributing power to decrease outages and help balance loads without direct intervention by operators.
  • Self-repair and predictive maintenance: Sensors can also be used to detect mechanical problems and do simple troubleshooting and repairs, notifying technicians only when necessary – before anything actually breaks down.
  • Customer choice: More energy suppliers, cooperatives, and micro-generators can join the grid, allowing consumers to have more choice in how they receive energy.

Applications of AI in smart energy solutions: The utilities sector perspective

Artificial Intelligence is the driving “intelligent agent” behind smart grids – evaluating the environment and taking actions to maximise a given goal. AI is fundamental to the integration of renewable energy, the stabilization of energy networks, and the reduction of financial risks associated with instability in the infrastructure.


For instance, the self-learning, adaptability, and calculation capabilities of AI have significant potential to address the intermittent nature of renewable energy. An imbalance in peaks of production and consumption are often represented through “the duck curve” and can make these sources of energy difficult to control. The use of AI in smart grids will help address this challenge by rebalancing inequity between production and consumption loads.


Smart grid technologies help to make utilities sector activities more transparent and competitive. Some of the applications of AI and machine learning in smart grids include: 

  • Agility and resilience: When renewable energy is generated by new partners like cooperatives and prosumers, it is often intermittent and variable. Sensors and automation can be used to identify parts of the grid that are vulnerable and respond with automated rerouting – storing surplus energy during peak generation times and rerouting it during gaps in the flow.
  • More precise forecasting: The utilities sector faces widespread price variability due to changes in consumption. Predictive analytics models can be used to more reliably predict power loads and renewable energy generation. By combining data from advanced metering infrastructure (AMI) with AI, predictions are more accurate than traditional approaches.
  • More sophisticated outage alerts: The network of sensors, meters, and actuators in a smart grid can give a “last gasp” short signal transmission, including time and date, to indicate a loss in power due to partial or complete outages. In addition, the predictive capabilities of AI and the real-time data of smart meters can notify operators of outages right before they happen. These systems can even differentiate between individual, street, and zonal outages.
  • Optimised power yield: The use of AI-powered sensor networks in generation stages can also be used to optimise power output. In a similar way, solar energy also benefits from AI tools to increase productivity by predicting solar radiation.
  • Improved automated switching: The ability of AI tools to predict grid imbalances and to differentiate between a brief power interruption and a full-on outage will soon allow switching protocols to be automated. This will allow utility companies to reroute energy or isolate affected areas before severe damages occur or the outage expands to other areas. These tools are a line of defence that ensures the safety of the essential equipment used to isolate and repair faults.
  • More flexible demand-side management (DSM): Peaks in energy demand put utility companies under great strain. Using AI and smart meters in homes and offices can help with scheduling, planning, executing, and monitoring changes in energy demand to ensure that providers can meet them. Doing this can have a major impact on power usage, as shown by the U.S. Federal Energy Regulatory Commission, which found that peak loads can be reduced by up to 150 GW through demand management. Similarly, the Electric Power Research Institute (EPRI) has estimated these smart tools could lead to a 175 GW reduction in summer energy peaks by 2030.
  • Improved security: Cybersecurity is a key concern for all business sectors. And the increasing number and complexity of cyberattack strategies presents a risk to both existing and new electrical grids. AI tools can help reduce this risk by detecting network attack features, malware, and intrusion and by providing network security protection for power systems. In addition, other technologies, like blockchain, can provide transparent, tamper-proof, and secure systems that enable novel business solutions, especially when combined with smart contracts.

Applications of AI in smart energy solutions: The consumer perspective

Recent surveys from the UK and U.S. show less-than-great customer attitudes toward utility companies. With the rise in energy suppliers and prosumers, utilities companies will need to leverage smart solutions to help nurture better customer engagement and satisfaction. Below are some of the ways smart grid technologies can help to improve customer satisfaction:

  • Lower costs: AI-powered smart grid management and smart metering allow customers to get hourly assessments of their power usage – helping them to see not only when and where they use the most energy but offering personalised tips and suggestions for optimising their typical daily routines to lower usage during peak times. It also helps prosumers manage energy production which can be sold back to the grid to reduce costs even further.
  • Improved sustainability and transparency: Smart grid data can help customers become more aware of where their energy is coming from, increasing their engagement and helping to democratize the grid. This can help to give them new perspectives on energy provision and the ability to choose more sustainable options.
  • Fewer outages: As mentioned, AI tools can help reduce the number of outages and mitigate their impact both for residential and commercial customers. This means an increase in security and confidence for consumers – especially as weather events and record temperatures bring fears of brownouts and other disruptions.


Recently, a UK distribution system operator announced a pilot project to use smart meter data to help consumers improve energy management, optimise network loads, and reduce carbon emissions. The trial could save customers millions of pounds and potentially reduce millions of tons of greenhouse gas emissions from the UK’s annual carbon footprint. This is just one example of the potential benefits of smart grids for customers and the environment.

Big Data in energy: Why it matters

From the point of view of both customers and utilities companies alike, it’s not simply the ability of these technologies to gather and manage large and disparate amounts of Big Data that matters – it’s the ability to leverage and understand all that data and use it to optimise power usage and inform operations. Big Data is key to helping:

  • Better integrate renewable and alternative power in utilities companies by learning to predict and manage intermittence, and balance a myriad of small inputs from prosumer players.
  • Protect consumers by anticipating outages and redirecting resources in a fraction of a second – rather than after everything has gone down.
  • Save money for companies and consumers alike by digitally learning from past activities and using that intel to better manage and automate day-to-day activities.
  • Provide fast, actionable insights that let utilities companies make confident and quick decisions in an increasingly competitive environment.

How today’s utilities industry is preparing for the smart grid of the future

There is no question that the future of energy is moving toward more decentralised, flexible, and sustainable power provision. But we are talking about a global industry that is over a century old – and often must rely upon infrastructures from nearly that long ago to serve billions of people and their rapidly changing demands.


Other challenges include complex regulatory changes, the rise of prosumers, and new startups emerging in deregulated regions. Like any journey of business and digital transformation, the move to smarter grid management starts with a few cautious steps before breaking into a run. Utilities sector technologies are undoubtedly powering and enabling the evolution of this sector. However, for meaningful change to occur, utilities companies will need establish strong communication, customer engagement, and change management plans including:

  • Communicating a vision of the smart grid and aligning teams and stakeholders around it
  • Strengthening consumer education about the changes and opportunities to come
  • Providing win/win motivation for consumers, prosumers, and potential distribution partners
  • Developing metrics to monitor progress in the implementation and effectiveness of smart grids
  • Keeping the customer experience and customer retention in mind, given the additional competition and decentralisation of a smart energy market


The first step on the journey is to communicate with team leaders and subject specialists across your business, to break down silo walls and find the wealth of information often hidden within. Then, look at how you can implement smart technologies to drive your business forward.


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