New software could help utilities analyse demand, set better rates
When applied to customer data, Artificial Intelligence (AI) can deliver insights into how and when customers use energy, the type of appliances they use, and inform utilities if they have an EV.
Armed with this knowledge, utilities can encourage grid-stabilising customer behaviours and better understand the load impact on grid-facing assets, such as distribution transformers, feeders and substations.
AI-powered software provider Bidgely announced a new behind-the-meter solution “8760 Energy Insights” that it says will provide utilities with this type of information about customer energy use – all 8,760 hours of the year.
With granular insights into how the load curve of each customer contributes to the grid, utilities have the ability to identify trends and predict future grid patterns, says the company.
“Energy consumption is constantly changing. As a result, utilities must develop agile responses to evolving pressures on the grid,” said Abhay Gupta, CEO of Bidgely in a press release.
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Gupta believes utilities will be able to use the information to develop new rates, products, services and infrastructure investments that deliver greater value on both sides of the meter, he added.
Bidgely’s 8760 granularity provides detailed information about energy consumption patterns, broken down by hour and by 12 different appliance types (including EV and solar), across different geographic areas and rate plans.
This level of analysis enables utilities to identify emerging trends and plan more strategically for grid management, especially in the face of challenges such as Distributed Energy Resources (DERs), EVs and extreme weather events that can affect the predictability of the grid, according to the company.
The software will then help utilities explore non-wire alternatives for managing the grid and could assist them in targeting the right customers with load shifting programmes.
Further, says Bidgely, the insights can be used to better understand the relationship between temperature and grid capacity, which can help utilities better prepare for future heat waves and other extreme weather events that could strain the grid.
Originally published on Power-Grid International.
