Issue link: https://fhpublishing.uberflip.com/i/721278
NETWORK / 23 / SEPTEMBER 2016 Modelling and analytics Technologies capable of moni- toring, healing and predicting faults on energy networks are changing the art of the possible as far as power system opera- tion is concerned. These are part of a smart energy technologies revolution which the Depart- ment of Energy and Climate Change (Decc), before its de- mise, estimated would achieve a global market worth £220 billion by 2020 and save network op- erators £12 billion by 2050. Key to active network man- agement and other intelligent network o- erings are modelling tools which can replicate, in the virtual world, a whole host of environmental conditions and fault scenarios for network assets. This allows engineers to understand how assets will respond before an event occurs. It is also rede‚ ning the ac- cepted safety tolerances which are built into equipment like transformers. Before the age of advanced thermal model- ling, engineering le… cautious room for error when calculating the likely operational limits of such†kit. "You used to build a network as tough as you could, and learn from what went wrong when it went down," Paul Barnfather, solutions architect at EA Tech- nology, tells Network. "Now you model complete networks under duress and see which parts of THERE IS HUGE SCOPE FOR ADVANCED SYSTEM MODELLING AND SIMULA- TION coupled with powerful analytics to assist with some of the evolution of a "whole system approach" to energy. Newcastle University's Professor Phil Taylor, who heads up the National Cen- tre for Energy Systems integration, has identi- fi ed additional matu- rity in this sphere as vital to progress on multi-vector energy. enhancing network mainte- nance activities is considerable. In emergency situations, drones are increasingly stepping into the breach to help restore power supplies and communi- cations. The USA is a leading developer of drones for disaster recovery applications, with Michigan Technological Univer- sity showing that drones can be tasked with the deployment of microgrids during environmen- tal disasters. Going underground, robots are also increasingly having an impact on gas network operation, maintenance and e" ciency. Project Graid, pro‚ led on page 27, shows National Grid embracing the bene‚ ts that robotics can bring. The project is funded by the Network Innovation Allowance- funded scheme and is testing the boundaries for using robots in high-pressure gas pipelines to analyse the condition of critical assets. them are going to need restoring before it happens." Crucially, however, as moni- toring and modelling technolo- gies mature, users need more and more advanced data analyt- ics to help them make sense of the unwieldy volumes of data produced. Market analyst GTM Research predicts global utility company expenditure on data analytics will grow from $700 million in 2012 to £3.8 billion in 2020 as ‚ rms look to support better planning and decision making. A weather monitoring programme run by the Euro- pean Centre for Medium-Range Weather Forecasts and known as Copernicus, has €4.3 billion of funding at its back to help advance analytics for weather systems. It is working with net- work companies to show how this intelligence can help them plan for extreme weather events and climate change, thereby im- proving resilience and reducing the risk of stranded investments in the future. The bene‚ ts of modelling technologies and data analytics are applicable across all energy networks and there is mount- ing interest in the cumulative shared bene‚ ts which could be realised between operators, and potentially across sectors, through sharing open data. It was recently estimated that sharing of open data on heat network performance in the UK could save £400 million in the coming decade. GET TO Find out more about the Copernicus programme at the Network As- set Performance Conference, 21 September, Birmingham: events.networks. online/asset WHAT'S NEXT? Kelvatek has used AI and machine learning techniques to analyse data collected at 1000's of LV substa- tions. This allowed them to classify individual feeders and phases into groups and select areas where LV ANM would provide large benefi ts.