Utility Week

A I A N D T H E S M A R T G R I D "Solar forecasting has been machine learning driven since last year and we are in the process of standing up enhanced forecasting capability for all other aspects of demand to enable a more efficient integration of renewables on the system to minimise consumer cost and carbon dioxide emissions from the generation fleet." E V s a n d g r i d m a n a g e m e n t e other major factor in the shift to a smarter grid is the transition to EVs. Although opinions are divided about when the shift from petrol to EVs will occur, there is no doubt that it will have a huge effect on grid management. T he art of balancing supply and demand on the grid used to be a relatively straightforward affair. In days of old, the grid was awash with fixed assets, such as coal-fired power stations, which could be relied upon to generate a constant stream of power, night and day. But now the UK has a more complicated energy ecosystem, with more assets to manage and a greater share of renewable energy. And the advent of electric vehicles (EVs), localised grids such as Moixa's recently announced virtual power plant in Sussex, and home energy trading platforms such as the one developed by Social Energy, all point to a world in which the manual balancing of the grid will become increasingly difficult, if not impossible. For many in the energy sector, the answer lies in artificial intelligence (AI) and other machine learning systems to predict what energy will be needed and how best to supply it. Speaking to Flex, the National Grid Electricity System Operator's (ESO) energy intelligence manager, James Kelloway, says machine learning will be integrated into "many aspects" of the power grid and its control systems "in the not-so- distant future". "Machine learning is exceptional at spotting patterns that are not always obvious to even the most talented people," explains Kelloway. "It can predict ahead quickly, accurately and adjust automatically to changes in the grid and the external conditions that influence its operation. "Decentralisation results in a great increase in data and consequently there needs to be a much larger capacity to deal with that data and associated actions and patterns. For context, Germany went from 1,000 generators in 2000 to an estimated 1.5 million not long ago." Kelloway adds that the core machine learning work at the ESO is currently all based around forecasting. 32 www.utilityweek.co.uk/fLeX S t r i k i n g t h e r i g h t b a l a n c e o f p o w e r Machine learning is at the heart of a smart grid and future localised energy system By Jamie Hailstone // Machine learning is exceptional at spotting patterns that are not always obvious to even the most talented people. It can predict ahead quickly, accurately and adjust automatically to changes in the grid // James Kelloway, energy intelligence manager, National Grid Electricity System Operator

• Cover