AI for Smart Grid Energy Balancing

Utilities Business Review | Tuesday, December 10, 2024

Monitoring the reliability of renewable supplies can be achieved using artificial intelligence in on-grid and off-grid energy management.

FREMONT, CA: Several countries are switchover from the old model of a net energy supplier to one that encompasses clean and green power and a bi-directional flow from the user to the grid. Offering reliable power and controlling a consistent baseload is a complex process that involves SCADA, HMI, and IoT devices. In addition, smart meters, the potential of a prosumer to transmit energy back to the grid from his electric vehicle or solar rooftop, and intermittent energy sources such as solar and wind make the equation too complex for human monitoring.

Energy storage disrupts new energy paradigms. There are numerous types of energy storage, from pumped storage and molten salt for massive on-grid solutions to lithium-ion batteries for mobile devices and municipal utility backups. AI and machine learning will refine energy efficiency and scale, but it's not perfect. For superior forecasting and decision-making, connecting more data must be essential. There is less faith in AI forecasts until there is an important base of non-skewed data and machine learning, so judgments will still need human scrutiny. As software develops and data becomes more reliable, AI applications may be confided on more to make optimal energy decisions.

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AI can increase energy storage in many ways.

Grid safety - When a fault is detected, both lines are live. AI can discern defects and deliver proactive warning messages to technical support and consumers. AI can observe battery life and malfunctions and transmit the information to support. Industrial and residential peak demand was time-bound, like when homeowners aroused their cookers to make dinner. Peak demand, which customarily occurs during the day, reduces to night when solar is unavailable. AI can communicate with a resident's smart meter to decrease domestic geysers and balance the load.

Improving energy storage and release - AI can discover when extra energy may be stored or necessary to meet peak demand. Efficient supply and demand control using battery storage minimizes carbon-positive demand management therapies such as diesel. AI can manage supply and demand by testing smart meter data to foresee real-time demand peaks and troughs. It uses AI to become a leader in energy forecasting.

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