Project Summary
Scenario Analysis for Non-domestic Network Decarbonisation (SANND) will be a software tool to visually display forecast scenarios of additional demand on electricity distribution networks at different time points based on bottom-up modelling. It will model the propensity for individual large energy users to take different decarbonisation routes and build into a whole network model.
This tool would facilitate network planners understanding of probable future network needs (how much demand, where and when). Allowing network planners to pre-emptively plan network infrastructure upgrades/ flexibility to ensure the network is ready to cost effectively support each customers decarbonisation journey.
Innovation Justification
We are proposing to address DNO's long term strategic network planning challenges through modelling of HV connected site-based decarbonisation pathways. Using existing network data, novel planning methods and forecasting site-specific energy transition opportunities, the tool will allow better strategic planning and investment especially at HV, emphasising l&C customers and their impact.
Presently, network reinforcement planning is based on aggregated DFES scenario load forecasts. They are statistically robust due to the large number of customer, however it is unlikely loading changes can be uniformly applied to all networks and assets. Our proposal models loading variation within the distribution network, to determine where network capacity will be constrained, and importantly, when reinforcement will be required.
Building upon NPG's Inform Alpha project developed by EA Technology with work undertaken the Energy Systems Catapult in projects such as Modern Energy
Partners, Local Area Energy Planning and the modelling of decarbonisation of industry in ESME Industry, SANN□hypothesises that granular decarbonisation
pathways can be created for HV users, providing insight of the types of initiatives, timelines and changing in demand.
Combining national scenario with local pathway data sets described is innovative, with no guarantee what will be achievable.
Insight from these projects is that adoption of technologies vary depending on activity, can include electrification of heat, sometimes with onsite generation, or the adoption of other technologies with different energy sources. Understanding these better can additional granularity be added to future forecasting. DFES cannot offer that level of knowledge.
This Discovery phase will specify the proposed tool (TRL3) rather than develop it. It is envisaged to build upon existing tools and therefore development time accelerated during an Alpha phase.
This phase will scope stakeholders priority outputs. It is envisaged that the tool will topographically provide measure of network demand from 2030, 2035, 2050
according to specified decarbonisation scenario, outputting asset sizes needed and when. It will also explore data update processes.
Software development is complex and inherently expensive. The tool proposed, if proven successful, could be utilised by other network operators but carries development and data quality risks that need to be better understood. SIF funding is initially required to scope this project and understand available data sources, including best utilisation of existing data sources used by strategic planners and incorporate new data streams. This level of risk combined with potential for scalability to other DNOs makes it applicable for a multi-stage SIF funding approach.
Impacts and Benefits
Decarbonising HV connected customers will provide significant carbon savings, but changing load will have the greatest impact and create most risk to ONOs. Anticipating load change among a changing technology landscape that influences load growth creates uncertainty, however doing nothing until a connection alteration proposal is received risks slowing realisation of national decarbonisation targets. SANNO aims to create more detailed future scenario analysis for ONOs reducing the risk of vital decarbonisation projects being impacted and ensuring the full value of those projects is realised.
Financial: Future Reductions in the Cost of Operating the Network
Enhanced understanding of probable demand growth, leads to more efficient and timely network investments, cutting long-term operational costs, allowing pre emptive network investment based on 'least-regrets' capacity assessments, mitigating the risk of over or under-investing in infrastructure.
Improved forecasting of network demand facilitates the identification of potential flexibility options, reducing the need for expensive network reinforcements and emergency responses to capacity issues. The proposed SANNO project seeks to refine the starting position of aggregated demand feeding into the OFES forecasts, improving network loading forecasts on a geographic and temporal basis.
Financial: Cost Savings Per Annum on Energy Bills for Consumers
Building operators and industries experience cost savings as projects are de risked and delivered faster due to networks being prepared for new demands. For consumers, this reduces energy bills, as efficient network upgrades create a
stable and cost-effective energy supply. Enabling network reinforcement works at the point of first requirement, designed to provide capacity for the longer term modelled requirements, will enable customers to implement cost-saving measures (e.g. generation) at the earliest opportunity.
Environmental: Carbon Reduction Indirect CO2 Savings Per Annum
Our tool supports carbon reduction by accelerating low carbon technology deployment, facilitating indirect CO2 savings. Mapping major energy users within DFES scenarios and aligning them with domestic energy use and other factors, enhances the effectiveness of the network to handle renewable generation and demand-side flexibility. Early identification of new barriers allows for quicker policy interventions, proactively steering the market towards low-carbon solutions, reducing reinforcement need, providing further indirect carbon savings.
Revenues: Improved access to revenues for users of network services
Timely increase in network capacity facilitates decarbonisation of HV sites and opens potential for further network services as low carbon technologies become more widespread. Early sign-posting of potential constraint issues enables sites to implement generation, electrified heat and transport opportunities for local balancing services through the tool.
