A new Danish-Indian project will enable the power grid to sense and compensate for loss of electricity and fluctuating input from renewable energy sources.
In Denmark, we have great expertise in integrating renewable energy into our power grid. We will need this expertise in the near future, when the power grid will store significantly larger amounts of energy from renewable energy sources than it does today. India is facing a conversion to solar cells and is also working to expand its power grid, which makes it a unique test zone for new, smart solutions. That is why a new Danish-Indian project aims to design the power grid of tomorrow.
The problem of basing a power system on energy sources such as wind and solar is that they depend on the wind to blow and the sun to shine, in order to produce electricity. An unstable supply of electricity to the power grid may, at worst, plunge streets and larger areas into darkness. The power grid must therefore be intelligent in order to anticipate any imbalances, explains Spyros Chatzivasileiadis, Project Manager at DTU Electrical Engineering.
“This will be achieved by enabling the power grid to sense and automatically prevent a power failure whenever the it becomes unstable. The aim is to train intelligent devices to quickly anticipate changes in the grid and reduce the loss of electricity on the way to the consumer,” he says.
Such devices could be solar PV inverters, which are normally used to convert the direct current produced by solar cells to alternating current, which can be used in homes, supermarkets, etc.
“Data from intelligent, local sensors will enable us to deduce the condition of the grid pretty much in real time. And we will be able to use the sensor data to train e.g. solar PV inverters to recognize grid disturbances and initiate emergency procedures to avoid power failures,” says Chatzivasileiadis.
Using machine learning methods, solar PV inverters and other devices could function both as telltale and repairer if the grid is getting unstable, which has not been possible until now.
“Machine learning methods will change the way the energy system is operated in the future. Groundbreaking new solutions that utilize digitization could lead to the 100 per cent green power grid of tomorrow,” says Professor Jacob Østergaard, Head of Centre and energy system specialist at DTU Electrical Engineering.
Lost electricity costs billions
8-15 per cent of electricity is lost on the way from the power station to the consumer. The electrical energy that does not reach the consumer will be converted into heat in the cables along the way.
“It costs society more than a billion Danish kroner in lost electricity per year in the Danish electricity system alone. And costs are much higher for larger systems such as that in India,” says Chatzivasileiadis.
To ensure that a larger share of the electricity reaches the consumer, you can, e.g., build larger cables or to use materials that conduct electricity even better than copper. This is because the heat in the cables develops when there is congestion and the electrons have difficulty moving forward.
The smartest solution is, however, to control the effect of electricity by maintaining a high voltage level, since the heat development typically occurs at a low voltage level, explains Chatzivasileiadis.
Therefore, the project will also develop methods for the grid to automatically maintain a high voltage level.
“The grid’s intelligent devices will be equipped to do this on their own, so the voltage level doesn’t become too low and result in loss of electricity as heat.”
The Project Manager predicts that if the project succeeds in developing methods to make the power grid intelligent and reduce the loss of electricity, it will save millions for the Danish consumers.
Furthermore, the solution can be used for the conversion of energy systems in other countries as well, as the project’s partners aim to develop advanced methods that apply to a variety of power grids. These will subsequently be tested in laboratory facilities and then on real power grids in India.