AI and Predictive Grid Modeling: Tools for the Dynamic Management of Complex Grids

Grid modeling

A new white paper from Veritone describes how utilities can leverage artificial intelligence (AI) and predictive grid modeling to dynamically manage complex grids, while helping them prepare for the growing challenges that will be involved in maintaining day-to-day reliability.

Grid Modeling

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Electric power systems are becoming more complex as distribution networks rely more on renewable sources, energy storage and microgrids. According to the paper, “to manage constant change and rising complexity, utilities must significantly improve their ability to see accurately into the very near future of the power grid.” This means they need reliable tools to proactively and automatically control the grid in a targeted manner, both across the entire grid and at the local level. Artificial intelligence is a powerful solution to these challenges.

Artificial intelligence allows utilities to be both reactive and proactive when it comes to managing complexity, adapting to change, coping with changing energy supply and demand, and improving resilience. Veritone says that “even the most advanced distribution management systems (ADMS) exercise grid control that is primarily reactive, based on a complex set of if/then rules, and requiring at least some human intervention.” Adding an artificial intelligence layer allows a utility to be proactive because the technology can monitor all the dynamic conditions on the grid.

“AI can prevent more outages and help accelerate outage recovery. Also, because AI is self-learning, grid predictions and control will improve with experience.”  — Veritone, “Predictive Grid Modeling and Control: How Utilities Can Leverage AI to Dynamically Manage Complex Grids

The author explains that artificial intelligence provides real-time grid resilience by efficiently managing the vast amounts of data generated by the grid and connected devices. “Insights from AI can inform and target storm mobilization, vegetation management, spinning reserve use, energy storage charging and discharging, demand response programs, bulk power transfers, decisions about when microgrids should disconnect and reconnect to the utility grid and more.” The paper also discusses the role of AI with microgrids and distributed energy resources.

Artificial intelligence can also be used for dynamic grid modeling, according to the author. This can unlock new grid opportunities and revenue streams. “AI can help clarify the value that new resources, from microgrids to EVs and more, can bring to a utility.”

Download the full report, “Predictive Grid Modeling and Control: How Utilities Can Leverage AI to Dynamically Manage Complex Grids” to learn more about how artificial intelligence can provide grid resilience, suggest new business strategies and improve grid efficiencies.

via Microgrid Knowledge

Categories: Energy