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Using AI Load Management for Microgrids

image credit: © Andrew Balcombe |
Julian Jackson's picture
Staff Writer, Energy Central BrightGreen PR

Julian Jackson is a writer whose interests encompass business and technology, cryptocurrencies, energy and the environment, as well as photography and film. His portfolio is here:...

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  • Oct 29, 2021

With more DERs coming into action, how to manage them is an issue for the owners of the system. Microgrids are valuable assets, but optimizing their operations is not easy, especially where they interface with the existing, conventional grid network.

Microgrids can supplement the grid when it is carrying too much load, contributing energy and services and receiving revenue in return. For example, when the network is in near to demand exceeding supply, such as when wind and solar resources are insufficient, or when there is a heavy load on a cold evening in winter.

Planning and managing microgrids can be complex because they operate within a constantly changing ecosystem. Microgrids act dynamically, using the optimal mix of resources, which changes based on fuel availability, pricing, emissions and other factors. This is made more complex because they have to navigate dynamic energy markets, which pose price opportunities and challenges. They also have to balance sometimes unpredictable energy demand and intermittent forms of renewable supply, both within their own systems and on the grid.

It appears that the most effective management of microgrids will come from AI systems. AI can be utilized in the planning, installation and operational stages of a microgrid array which will benefit everyone involved, including grid operators. For microgrid developers, AI offers swift capacity planning via real-time modeling of a massive amount of data, helping them make decisions about grid equipment, solar and wind configurations, and use of electric vehicle charging infrastructure.

AI is the likely to be the only system able to process sufficient data to ensure that the microgrids can optimize their load management, so that the microgrid and its is not brought to a halt from lack of power, which is a real possibility in unstable times.



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