AI-Utilities Perspective

From the standpoint of both customers and utility companies alike, it’s not just the ability of these technologies to collect and manage large and disparate amounts of Big Data that matters – it’s the power to leverage and understand all that data and use it to optimize power usage and notify operations. Big Data is essential to helping:

  • Better integrate renewable and alternative power into utility companies by learning to predict and manage intermittency and balancing an awfully sizable amount of small inputs from prosumer players.
  • Protect consumers by expecting outages and redirecting resources in a split second rather than after everything has gone down.
  • - Save money for companies and consumers equally by digitally learning from previous activities and using that intel to better manage and automate day-to-day operations.

We provide rapid, actionable insights that let utility companies make confident and quick decisions in a progressively competitive environment.

There is no question that the future of energy is moving toward a more decentralized, flexible, and sustainable power supply. But we are discussing a worldwide industry that is over a century old – and often depends on infrastructure from nearly that long ago to serve billions of people and their rapidly changing requirements.

Other difficulties include complex regulatory changes, the rise of prosumers, and new startups emerging in deregulated areas. Like any journey of business and digital transformation, the move to smarter grid management begins with a few careful measures before breaking into a run. Utility sector technologies are undeniably powering and allowing the evolution of this sector.

The term "smart grid" comprises much more than just power delivery, though that is an important factor. At its core, the main pillar of a smart grid is a two-way connection of energy and information, but it goes much further than that. For optimum effectiveness and efficiency, a smart grid infrastructure should also comprise two more pillars: distributed generation and AI.

Artificial Intelligence is the driving "intelligent agent" behind smart grids—studying the environment and taking actions to optimize a given goal. AI is important to the integration of renewable energy, the stabilization of energy networks, and the mitigation of financial risks associated with instability in the infrastructure are essential.

The use of AI in smart grids will help tackle this challenge by rebalancing inequity between production and consumption loads.

Smart grid technologies help to create utility division activities that are more transparent and competitive. Some of the applications of AI and machine learning in smart grids comprise:

Agility and resilience: When renewable energy is generated by new partners like cooperatives and prosumers, it is often sporadic and variable. Sensors and automation can be utilized to recognize parts of the grid that are vulnerable and respond with automated rerouting—storing excess energy during peak generation times and rerouting it during gaps in the flow.

More precise forecasting: The utility industry faces widespread price instability due to changes in consumption patterns. Predictive analytics models can be used to more accurately predict power loads and renewable energy generation. By integrating data from advanced metering infrastructure (AMI) with AI, predictions are more precise than conventional approaches.

More advanced outage alerts: The network of sensors, meters, and actuators in a smart grid can give a "last gasp" short signal transmission, comprising time and date, which will indicate power loss due to partial or complete outages. Furthermore, the predictive capabilities of AI and the real-time data of smart meters can alert operators of outages right before they happen. These smart systems can indeed distinguish between individual, street, and zonal outages.

Optimized power yield: The use of AI-powered sensor networks in generation stages can also be utilized to maximize power output. In a similar fashion, solar energy also benefits from AI tools to improve productivity by predicting solar radiation.

Improved automated switching: The ability of AI tools to predict grid imbalances and to distinguish between a brief power interruption and a full-on outage will soon permit switching protocols to be automated. This will enable utility companies to reroute energy or isolate affected areas before serious damage occurs or the outage expands to other regions. These tools are a line of defense that ascertains the safety of the crucial equipment used to isolate and repair faults.

More flexible demand-side management (DSM): Peaks in energy demand put utility companies under great pressure. Using AI and smart meters in homes and offices can help with scheduling, planning, executing, and monitoring changes in energy demand to make sure that providers can meet them. Doing this can have a great impact on power usage. It has been estimated that smart tools could result in a reduction in summer energy peaks.

Improved security: Cybersecurity is a major concern for all business sectors. And the increasing complexity and number of cyberattack policies present a risk to both existing and new electrical grids. By spotting network attack features, malware, and intrusions, and by providing network security protection for power systems, AI tools can help decrease this risk. Furthermore, other technologies, like blockchain, can provide transparent, tamper-proof, and secure systems that allow novel business solutions, particularly when combined with smart contracts.

Like oxygen, the power grid is vital to modern life but is not always top of mind – until difficulties occur. Today, ageing grid infrastructure is taking a beating from harsh weather incidents around the world, resulting in power outages that jeopardize health, safety, and economic activity. At the same time, there are a number of other factors which are putting pressure on century-old grids. The way that energy is generated is quickly changing — more wind and solar, less coal and fossil fuel. This shift demands new processes and ways of managing. The "who" is also shifting, with energy now being produced not only by the major energy companies but also by many new competitors and "prosumers" (consumers who produce energy).

In a recent report, Navigant predicts a huge disruption across the entire energy value chain in the next 5-15 years that will affect a broad set of stakeholders. This change is primarily being fueled by multilateral efforts focused on decarbonizing the global economy to tackle climate change and a shift toward “an increasingly clean, intelligent, mobile and distributed energy ecosystem. ”

The report also debates how linear value chains supporting one-way power flow from centralized generation to end customers will give way to a more sustainable, highly digitized, and dynamic energy system.

Moving toward a multidirectional network of networks and away from a linear hub-and-spoke model, this system will help two-way energy flows in which customers’ choice (optionality), clean energy, innovation, and agility command a premium."

With the sheer volume of data needed for the successful operation of smart grid infrastructure, AI will play the role of taking into account the millions of variables and data points, including weather, demand, location, generation assets, etc, and proactively determine for every home where the power will come from and how much it will cost. We don’t just want switches flipped millions of times a second; we need decisions to be made. This is where the strength of AI comes in.