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BIG DATA – ANALYTICS 3 Ways Analytics Sheds Lights on “Black Box” Distribution Networks By Krishan Gupta, Director of Product Management, eMeter The power distribution network is essential infrastructure for any utility – yet most utilities have had remarkably few tools that provide insight into what is actually happening on their network at any given time. This has made it challenging for utilities to respond to underlying problems, adapt to consumer engagement and other systemic changes, and plan for the future. they were lucky, they may have been able to differentiate between houses and apartments. It was on this scant micro basis that utilities made major decisions about their current and future distribution network. With enough time and money, it is possible to add sensors to distribution network assets such as transformers and substations. However, many utilities have already rolled out smart meters or will start to do so soon. Forward-thinking utilities are using that Smart Meter investment to provide real-time and detailed feedback on a distribution network’s performance, in addition to analysis on customers’ energy consumption. Anything a utility might do to replace or expand physical grid infrastructure will typically cost significantly more than pragmatically implementing analytics. By comparison, AMI analytics is cheap and if such a small investment helps optimize hundreds of millions in investment, it makes sense to install smart meters and implement analytics up front to maximize business awareness and enable high value decisions based on relevant data. Here are three ways utilities can apply analytics to smart meter data to get more value out of their distribution network investments and preserve the reliability and quality of grid operations: 1. Understand and manage the impact of renewable energy on the grid. Many countries are aggressively promoting the distributed generation of renewable energy on customer premises through economic incentives. This can not only serve to dramatically shift load profiles; it also means excess energy is being returned to the grid. Reverse load is a potentially destabilizing force, which one European utility executive recently compared to “water flowing Fortunately, applying analytics to smart meter data can help utilities operate more smoothly and avoid considerable expenses associated with distribution, in the short and long term. For decades, all distribution network performance information came from SCADA (supervisory control and data acquisition) systems that monitor delivery of power out to substations. This allowed utilities to make very rough estimates and form perpetually outdated models of load curves for any particular class of users – if METERING INTERNATIONAL ISSUE - 4 | 2013 57