ABSTRACT
Over the last decade the concept of an advanced grid has been in fact identical with smart grid technology, which has a central role to integrate the increasing renewable generation, electric vehicle and the internet of things devices. According to a study sponsored by the US Department of Energy (NETL 2008), Smart Grid (SG) can be considered as an ensemble of several applications such as demand response, demand forecast, emergency management, anomaly detection and adaptive pricing, built upon an Advanced Metering Infrastructure (AMI)—a system that measures, collects and analyzes data about energy usage. As it is remarked in Stimmel 2014, smart grid technologies provide universal and clean electrification, alleviate climate change by enabling a variety of efficiencies and renewable generation, and get us closer to a guarantee of affordable, safe and reliable electricity. To fully realize this mandate, utilities have no other course but to transform themselves into datadriven businesses. The data sets collected from the power systems are intrinsically large, heterogeneous and distributed, and therefore pose efficiency difficulties to the traditional approach wherein the data sets
are first moved to a centralized location and afterwards are analyzed. In order to efficiently analyze such large data sets, there is a need of new distributed algorithms which are moved closer to the location where the data is collected. The utilities of Big Data analytics technologies refer to the set of technologies within the digital energy ecosystem which can help capture, organize and analyze massive quantity of information as it flows and provides meaningful insight that helps people explain, predict, and expose hidden opportunities in order to assess specific real situations and improve operational and business efficiency.
