Learnings

Outcomes

The project is progressing, and the outcomes of the research phase is available to read here.

Key outcomes to date:

Constraints are not expected at primary substation level; however some secondary substations are expected to incur network constraints.

Isolated tourist sites and popular routes are predicted to be the worst affected. This highlights the need for a just and fair transition to ensure those in rural communities are not left behind.

The season when highest network demand is predicted depends on the balance between existing network demand and predicted charging demand. This needs to be explored further however this could be a significant challenge for the electricity as transport and heat converge. For full details on the findings of each use case see the final report available here https://www.ssen.co.uk/WorkArea/DownloadAsset.aspx?id=19789

Lessons Learnt

Understanding Covid-19 impacts on Tourism
Very little is understood about the long-term impacts of Covid-19 on tourism. Particularly the increase in ‘staycations’ and potential higher volumes of EV Tourism. The methodology which was developed as part of stage 1 is being exploited further to analyse the Isle of Wight. There will be consideration made for uncertainty around Covid-19 long term impacts.

Whole System Planning
Whole system planning combines electricity, gas, transport, tourism, a local and national government, regulated networks and community stakeholders to provide a better understanding of infrastructure requirements. Alongside this, Local Energy Plans are being developed by the local authorities. The data and methodology around seasonality obtained in the first part associated with EV Tourism are being incorporated into the Regional Energy System Optimisation Planning (RESOP) tool which is part of the NIA_SSEN_0043 Whole System Growth Scenario Modelling Phase 2 project.

This will allow DNOs to assess specific geographical challenges and risks to the demand capacity and identify specific solutions for flexibility around EVs. For example, Dundee is close to St Andrews and when large golfing events take place in St Andrews, large numbers of visitors choose accommodation in Dundee and travel to the event. This has the potential to increase the number of EVs in the surrounding area which will produce a temporary increase in electrical demand for a few days only. Studying this influx of visitors on Dundee and the resultant energy demands will help to plan for temporary infrastructure or travel hubs for instance.