Smart Grid Analytics for Sustainability and Urbanization in Big Data - Aslam, Sheraz; Herodotou, Herodotos; Ashraf, Nouman (My University, 2023)
Recently, microgrids have become a fundamental element within the framework of a smart grid. They bring together distributed renewable energy sources (RESs), prediction of RESs, energy storage units, and load control to enhance the reliability of the power system, promote sustainable growth, and decrease carbon emissions. Simultaneously, the swift progress in sensor and metering technologies, wireless and network communication, IoT-based technologies, as well as cloud and fog computing, is resulting in the gathering and storage of substantial volumes of data, such as device status information, energy generation statistics, and consumption data. Furthermore, IoT devices are found in various parts of the smart grid, such as smart appliances, smart meters, and substations. These IoT devices generate petabytes of data, which are known to be one of the most scalable properties of a smart grid. Without smart grid analytics, it is difficult to make efficient use of data and to make sustainable decisions related to smart grid operations. With the energy system of the developing world heading towards smart grids, there needs to be a forum for analytics that can collect and interpret data from multiple endpoints. Data analytics platforms can analyze data to produce invaluable results that lead to many advantages, such as operational efficiency and cost savings. In addition, proper forecasting of energy generation from RESs and energy theft detection help a lot while maintaining smart and sustainable energy systems. This reprint comprises a variety of noteworthy and original research contributions that pertain to smart grid analytics for sustainability and urbanization in big data. It also plays a fundamental part in sharing and promoting novel ideas within this field.
Keywords: Smart Grid Analytics