“India has one of the most advanced energy transmission systems”

NEW DELHI : The electrical grid infrastructure is important for all countries today. However, with climate change, the advent of renewable energies such as wind and solar, and higher demand, the pressure on the 100-year-old grid infrastructure has increased. Companies like GE Electric are turning to advanced and emerging technologies like artificial intelligence (AI), machine learning (ML) and blockchain to build smarter power grids. In an interview, Vera Silva, Chief Technology Officer, Grid Solutions, GE Renewable Energy, explains how technical innovations are making the grid smarter. Edited excerpts:

How smart are the power grids in India?

The networks in India are not resilient enough. There are interruptions, flickers, etc. Much still needs to be done to improve the power quality of the grids, also because it has not kept pace with the growing demand for electricity. We also don’t have much redundancy in the network due to the infrastructure. Distribution stations and even some transmission stations are outdated and do not have the latest technology. There is a strong pressure on costs that brings shorter benefits but can increase costs later.

What needs improvement to address these issues?

The digitization of the network is a journey. It started with analog relays in the 60’s and 70’s. Transmission networks were more advanced by default. India has one of the most sophisticated transmission-level energy management systems with the largest number of phasor measurement units – sensors that help keep the grid stable. The area where we need maximum advancement is the distribution grid as it is not designed to accommodate, control or behave and modify the way you (energy) regulate electricity generation. What we are doing today is better observability of the distribution via sensors, monitoring, communication and management systems based on software. In some areas of Europe there is a lot of development in so-called self-healing networks that automatically reconfigure themselves when they detect a failure. Therefore, digital orchestration and automation of the network are key areas. Last, but not least, is the protection of the network, since we need to protect devices and users.

Can you give us examples of technologies used in smart grids?

The backbone of the smart grid is the software used to orchestrate the grid – to manage the distribution, control, monitoring and data from the grid. All factors help you to work (more efficiently). More data, information and transparency open up new possibilities. This is why AI and ML are crucial. For example, to run the grid, the better we can predict generation and demand to pinpoint the storms, the better we can prepare. We collect a lot of data to predict what photovoltaics will produce, how demand will behave etc. This is where the combination of imaging, deep learning and other AI techniques is beneficial to make predictions about (things like) distributed energy resource management wind demand forecast. The second is asset performance management. We insert sensors into all devices that go online. Accessing data from these sensors allows us to be more effective in how we conduct predictive maintenance rather than time-based maintenance.

Finally, in a decentralized grid, you could create islands and microgrids that could survive and deliver power more quickly locally (in the event of a grid failure) rather than waiting for the entire grid to come back up. This is another area where many of the machine learning techniques can be used. And I’m talking about the self-healing network.

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