Project Overview
The UK energy sector is undergoing a significant transformation, driven by the government’s ambitious target to achieve net-zero carbon emissions by 2050. Central to this transition are the decarbonisation of heat and transport, which are expected to have profound impacts on the country’s energy networks. The electrification of heat and transport, in particular, will place significant pressure on both electricity and gas networks. The shift to electric heating systems and electric vehicles will increase electricity demand, necessitating substantial infrastructure upgrades and deployment of smart grid technologies. Conversely, gas networks are likely to see a decline in demand, leading to challenges in planning and operation. Gas network operators, such as SGN, are exploring new business models, including the repurposing of gas pipelines for hydrogen and biomethane.
In this evolving landscape, the integration of gas distribution networks (GDNs) and electricity distribution network operators (DNOs) is becoming increasingly important. Historically, these networks have operated in silos, with limited data sharing and coordination. However, the increasing interdependence of gas and electricity networks, particularly in the context of decarbonisation, highlights the need for greater cross-vector integration.
Objectives
The cross-vector data sharing project was initiated in 2023 to explore and identify potential use cases for data sharing between SGN and DNOs. By fostering a deeper understanding of the operational dynamics and challenges faced by SGN and leveraging the expertise of PNDC, the project seeks to develop practical recommendations for enhancing data sharing and collaboration. Cross-vector data sharing can lead to several significant benefits:
The project’s specific objectives were:
Improved operational efficiency: enhanced data sharing can help optimise the operations of both gas and electricity networks, reducing costs, and planning/avoiding outages.
Improved demand forecasting: Accurate data on energy usage patterns can help better predict future demand, leading to more efficient resource planning.
Enhanced safety: sharing data on gas and electricity usage can help identify potential hazards more quickly, improving overall network safety.
Improved customer service: better data sharing can enable faster responses to outages and service disruptions, enhancing customer satisfaction.
PNDC’s Role
PNDC played a crucial role in facilitating the project by:
Conducting Workshops: Organising and leading a series of workshops with SGN staff to gain insights into the network’s operations and challenges.
Expertise and Analysis: Utilising its extensive knowledge of DNO network control and associated challenges to provide a comparative analysis.
Use Case Identification: Identifying high-level, cross-vector data sharing opportunities and grouping them into thematic areas.
The workshops provided a comprehensive overview of SGN’s network control and monitoring strategies. These sessions also served as a platform for discussing specific challenges that could be mitigated through cross-vector data sharing. PNDC’s deep understanding of DNO operations and the broader energy sector was instrumental in identifying and evaluating potential use cases.
Delivery and Outcomes
The project identified several high-level use cases for cross-vector data sharing, grouped into three main themes:
Theme 1: Gas-Fired Generation
1. Peaking Plant Advanced Notification: Share data to optimize the operation of gas-fired peaking plants during periods of high electricity demand.
2. Gas-Fired Generation During Periods of High Gas Demand: Coordinate gas and electricity generation to ensure reliable supply during peak demand.
Theme 2: Coordinated Cold Start
1. Outage Response Coordination: Collaborate on outage response to minimize downtime and restore services more quickly.
2. Cold Start Procedure: Develop coordinated procedures for restarting systems after outages to ensure safety and efficiency.
Theme 3: Combined Network Mapping
1. Turboexpander Site Identification: Identify sites for turboexpanders to optimize gas and electricity network operations.
2. Vector Constraint Identification: Address constraints in both gas and electricity networks through coordinated planning.
3. Coordinated Infrastructure Planning and Maintenance: Plan and execute infrastructure upgrades and maintenance activities in a coordinated manner.
While the identified use cases offer significant potential, the project also highlighted the need for careful consideration of the following:
Data Communication Protocols: Develop secure and timely communication protocols to ensure the effective sharing of data.
Detailed Data Analysis: Conduct a more detailed analysis of the specific data required, including availability, sourcing, formatting, and frequency.
Strategic Partnerships and Funding: Identify suitable funding sources and strategic partnerships to support the implementation of these use cases.
The project sets a foundation for future collaborations and innovations in cross-vector data sharing, aligning with Ofgem’s strategic aims and the broader goals of the UK’s energy transition. By addressing the challenges and leveraging the opportunities identified, SGN and DNOs can work together to create a more resilient, efficient, and sustainable energy system.
The collaboration between SGN and PNDC allowed us to collectively identify key opportunities to enhance operational efficiency, safety, and network resilience. PNDC’s expertise in DNO operations, combined with SGN’s forward-thinking approach, has laid strong foundations for future cross-sector initiatives, aligning with the broader goals of decarbonising the UK energy system.
Dr Priya Bhagavathy
Lead R&D Engineer
Get in touch
This project was delivered by Dr Priya Bhagavathy, Dr Callum Rae and Dr Kyle Jennett.
Established in 2013, PNDC is one of the University of Strathclyde’s industry-facing innovation centres and focuses on accelerating the development and deployment of novel energy and transport solutions through multiple collaboration models and open access facility provision.
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