Case study (UK): Digitalising energy systems for net zero

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Industry sector

  • Distribution Network
  • Generation and Wholesale Market
  • Supply and Retail Market
  • Transmission Network

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Watch on-demand: Delivering the flexible energy system of the future event – 29 July 2021

Electricity distribution network operator UK Power Networks is developing and trialling a software-based, machine learning tool to enhance visibility and unlock fresh insights into network demands so they can plan targeted investment in infrastructure and enable flexible response to distribution-level conditions and market signals.



This case study is supplied by Simone Torino, Head of Product and Business Development – Utilities, CKDelta, for the software platform.

  1. The challenge

    As the UK moves to achieve the government’s legally binding targets for net zero by 2050 and transition to a renewables-based energy future, we face major changes in power generation and how the energy system needs to be able to respond so we maintain reliable, safe and cost-effective operation of the networks.

    The increase in low carbon technologies and a world where anyone can be both a producer and a consumer of energy means network operators need more information than ever before on what is happening on their low voltage (‘distribution’) network. 

    Until recently, demand on the distribution network has been stable and highly predictable. But this is changing with increasing demand for low carbon electricity to power transport and heat, and the rise of intermittent renewable energy sources to generate this power.

    A lack of historical data for electrical loads at distribution level means distribution network operators have limited network load visibility. 

    In simple terms, ‘visibility’ means collected data about what’s happening on the network at any given time. The data can include anything from how power is flowing through the network to where demand for electricity is high and at what times of the day.

    This is a challenge for network operators who must factor a high degree of uncertainty when planning network interventions, such as building new infrastructure and when designing new connections.

  2. The approach

    Through its Envision Programme, UK Power Networks is exploring ways in which their own data can be used to enhance visibility of the network and model demand for use in forecasting. Doing so will allow them to plan ahead and invest strategically to deliver on the green agenda, and mean improved services could be delivered more quickly for customers.



    Data views could enable the safe release of more capacity on the network at the right time in the right places without needing to spend customers’ money on new infrastructure, lowering system costs. It could also enable faster and cheaper network connections for low carbon technologies, like electric vehicles and heat pumps.



    The Envision programme is split into two work packages:

    1. Developing and trialing a software-based, machine learning tool to enhance visibility and unlock fresh insights into demand on the network so they can continue to facilitate net zero.

       
    2. Exploring what third-party data is available by speaking to those who hold the data.  This could include data from distributed energy resources, those who install electric vehicle charging points, local heat networks and community energy schemes. This information will help understanding on if and how new datasets could improve network visibility.

    Partnering with data specialists CKDelta, UK Power Networks aims to meet two key objectives:

    1. Create a predictive low voltage demand model, more accurate than existing estimation methods
    2. Respond to stakeholders requesting expanded public information and data

    The Envision programme also aims to explore how the software can be put to best use, how it can integrate with other devices and additional data streams and how it can deliver value to stakeholders.

  3. Impact and outcomes

    The partnership is a groundbreaking data-driven approach to network ‘visibility’ monitoring.



    We’ve used advanced analytics and AI to create a ‘virtual sensing network’ of sufficiently accurate estimated low voltage network demand measures. We’ve built on innovations from the telecoms industry, trialing co-development of a data-driven model with machine-learning experts and data scientists.



    In June 2020, CKDelta partially developed and validated the core foundations of the model in a Proof of Concept (PoC). The PoC project demonstrated the value of using high-quality datasets such as anonymised, GDPR-compliant mobility data, demographic data, and energy performance data to provide a characterisation of the nature of demand at each low voltage transformer.

  4. Next steps

    The datasets and approaches proven in the Envision programme are being reapplied to other network demand estimation and simulation challenges in diverse fields including water and gas utilities, transport networks, and mobile telecommunications.



    With the PoC having proven the value of demand estimation modelling, UK Power Networks is now working with CKDelta to extend the results further. The model is being rolled out to a variety of asset categories, including a wide range of low voltage substations and transformers. Such modelling capabilities will be trialed across the whole UK Power Networks network to drive down customer costs and furthering the road to a greener energy system.

    UK Power Networks

    UK Power Networks own and maintain the electricity cables in South East England, the East of England and London, distributing approximately 27% of the UK’s electricity, and serving eight million homes and businesses.



    For more information about the Envision Programme, contact UK Power Networks at innovation@ukpowernetworks.co.uk.

Consumer benefits

UK Power Networks’ new capability to estimate low voltage transformer demand provides the distribution network operator with several benefits:

  • ‘Blanket visibility’ of network load across all low voltage distribution networks. This enables a comprehensive view of network dynamics and implementation of innovative technologies, while opening up better services and lower energy bills to consumers.
  • Financial savings on network resilience and capacity programmes through improved use of current assets. With the new model providing more accurate and reliable visibility of electricity demand vs safe capacity at each transformer, UK Power Networks can optimise network performance and release additional capacity from existing assets, while prioritising capital programmes where most needed.
  • Improved customer service times for new network connection requests. The availability of low voltage demand profile estimates together with the benefits, generates a reduction of the times required to produce a quote for customers, and could in future potentially contribute to the reduction of customer costs.

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