SP Energy Networks is investing significantly in pioneering software that applies data science and machine learning algorithms to predict both electricity network demand and generation output. This new approach will allow the network operator to maximise capacity and reliability across the electricity network, benefiting customers and generators alike.
The forecasting software uses historical network data, detailed weather data and artificial intelligence to predict the energy flowing to the electricity distribution network. It will be used in the real-time management of the network as well as forward planning when assessing the impact of new connections across the system.
The GB energy system is experiencing rapid transition from fossil fuel generation to renewables, low carbon options and energy efficiency programmes. The transition is significantly increasing the number of generators connected to the system, especially directly on to the distribution electricity network. It is therefore crucial for all operating in the industry that spare capacity on the network can be identified and for developers to be encouraged to connect in areas where additional infrastructure costs can be avoided.
Sia’s software, which will go live in March 2020, can be adapted to include the future influence of electric vehicles, heat pumps and other low carbon technologies which will significantly increase demand on the system. Its use of artificial intelligence, data analytics and software modelling will allow better understanding of the impact this will have on the electricity network and where investment should be targeted years in advance.
The project is a successful collaboration between experienced network operators and data scientists to solve the real life problems we are likely to experience as we transition to a zero carbon economy.
Grant McBeath, control room manager at SP Energy Networks, said: “Demand on the network is forecast to increase considering all future energy scenarios as we transition towards a zero-carbon economy. We, therefore, have to change the way we manage the network – transitioning from passive approach to much more active and agile management, which requires a more dynamic approach to ensure capacity is maximised and customers’ supplies remain uninterrupted.
“Working with Sia on forecasting software will allow us a better understanding of the future flows of energy on the network right down to a half hourly basis. This will ultimately allow my team to ensure the network is optimised to deliver the supply resilience and customer service our customers expect and deserve.”
