Recent developments in AI have sent ripples of excitement across the globe. Companies in virtually every industry are either looking to implement or already implementing AI into their operations—and so are utility companies in the energy sector.
As we transition from a traditional to a modern grid, operations are becoming more sophisticated. Between balancing supply and demand, defending against cyber and physical threats, managing intermittent power generation and a two-way flow of electricity, and reacting to anomalous weather events—supplying electricity to millions of users consistently is an increasingly multi-faceted and dynamic task.
To keep up with the tsunami of information, utilities use sensors, smart meters, and software systems to gather, analyze, and organize data. This has led to faster and more accurate responses to disturbances.
Yet, despite these developments, grid operations are only getting more complicated. While automation is implemented where possible, decision-making often falls into the hands–or rather, the mind–of a human operator. Running the grid in a reliable and efficient way moving forward will require faster and smarter decision-making.
What does AI bring to the table?
There are innumerable variables involved in operating a grid. While utilities do their best to anticipate and react as quickly as humanly possible to changes in the grid, circumstances are often time-sensitive. In an industry where fast decision-making is vital, automation is the ticket to superior grid efficiency and reliability.
AI’s processing power is in another realm compared to human capabilities. It handles a lot more data and can analyze and act on it in real time. However, what really makes AI a game-changer is machine learning, i.e., how a computer system develops its intelligence. Using software-coded heuristics, developers can mimic human behaviors like learning, reasoning, and problem-solving. It’s like human thinkingbut faster. Machine learning ultimately means that AI can make decisions or predictions based on patterns in historical data. AI-powered monitoring systems can continuously analyze vast amounts of data from sensors, smart meters, SCADA Systems and other devices deployed across the grid in real time.
How AI can change utility grid operations
Today’s grid operations employ an impressive amount of automation. These functions exist without AI today and AI is unlikely to completely replace them any time soon. The question we should be asking isn’t “Will AI replace that?” but “How can AI make that better?”
By continuously analyzing real-time data from sensors and smart devices across the grid, AI can optimize grid performance by dynamically adjusting power flows, voltage levels, and grid configuration. This means rerouting power flows to minimize transmission losses, detecting anomalies, identifying faults, and predicting potential issues. It can also optimize allocating available resources, like DERs or energy storage systems, to ensure efficient and reliable power delivery.
There are direct benefits from any AI enhancements to forecast accuracy. For example, using AI, utilities can leverage that forecasting to develop plans for pricing signals (timing and incentive) to be offered, to increase customer satisfaction and overall DR effectiveness.
Through machine learning techniques, AI can monitor the condition of grid infrastructure components and predict potential failures or malfunctions. By analyzing historical data, sensor readings, and maintenance records, AI algorithms can also identify patterns and indicators of equipment degradation or faults. This will enable utilities to proactively schedule maintenance activities to replace components before they fail and even prevent unplanned outages.
Renewable energy integration
Grid operations are becoming increasingly complex as renewable energy production and DERs increase. With real-time monitoring, AI can optimize the grid by dispatching renewable resources, rerouting energy flow, and managing energy storage systems effectively.
AI can strengthen critical infrastructure security and protect the grid from cyber threats, implementing algorithms that detect patterns indicating abnormal behavior in the network or servers or cyber-attacks like unauthorized access attempts. AI can identify potential anomalies or threats and autonomously trigger appropriate actions like isolating compromised systems or blocking suspicious traffic.
AI chatbots can convert mountains of technical documentation into an on-demand resource, an automated assistant that can serve up consolidated documentation and step-by-step guidance to operations staff at any hour. AI’s ability to learn means that more advanced interactions can be added to its knowledge base, increasing its support range over time.
AI has the potential to address the shortage of industry experts. In its role as a repository, AI can preserve corporate knowledge and experience. Acting as a trained assistant, that training is available to directly support operations staff in daily activities.
The grid of the future
AI is poised to revolutionize utility grid operations with enhanced processing capabilities, real-time surveillance, sophisticated algorithms that enable human-like cognition, and, ultimately, swift responses to various circumstances. As we increasingly leverage AI over an array of grid operations, consumers will enjoy an energy network that offers greater reliability and efficiency.
However, every silver lining has a cloud. While AI offers grid operations numerous advantages, it also comes with challenges. We cover those details in Part 2. Check it out!