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There is a good chance that the electricity that powers your devices at home is generated at a powerplant miles away from you. Take for instance the Vindhyachal power plant in Madhya Pradesh, the largest in India, which supplies power to many states, including Madhya Pradesh, Chattisgarh, Gujarat, Maharashtra, and Goa. Such a design results in a substantial loss of electricity during transmission. Moreover, when a crisis like equipment failures or natural calamities strikes the power plant, it affects all the dependent areas.

An alternative to this design is microgrids—a smaller setup of power generation, consumption, and storage serving a small neighbourhood—which is now gaining popularity. In a recent study, researchers from the Indian Institute of Science (IISc), Bengaluru and IBM Research-India have developed a machine learning-based technique to manage the demand and supply of power in a network of microgrids while maximising profit. Since such local grids can run on renewable sources of energy instead of relying on fossil fuels, they also reduce carbon emissions and are sustainable in the future.

A city could have many such microgrids, which can be connected to the primary power grid or other microgrids to buy and sell power and meet the demand and the supply. The behaviour of the connected microgrids can impact the overall operation of such a network. Most electricity boards are modeling this interplay and studying its impact on the primary grid. However, the sheer number of microgrids on the system and their unpredictability to generate power makes this a challenging task.

One approach to satisfying the demand is to manage energy distribution on the supply side just among the microgrids. This method can significantly reduce the wastage of power and reliance on the primary grid. Alternatively, it is also possible to manage peak demands by load shifting—moving the consumption of load to different times within an hour, a day, or a week. This approach does not reduce the net quantity of energy consumed, but changes when it is consumed. With smart meters that communicate with electricity boards and smart appliances that can be run at a specific time when the peak load is less, it is possible to shift the load.

With microgrids powered with renewables, the amount of generated power can vary based on the availability of water or the intensity of wind to run the turbines. The ultimate goal, however, is to make a profit by selling excess power and ensuring constant supply when there is demand. Hence, managing power generation requires instantaneous decisions to be made, which cannot be done by humans alone.

I encourage you to Read more here:

https://researchmatters.in/news/researchers-show-how-machine-learning-could-solve-our-power-woes

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