The rapid uptake of LCTs is likely to cause secondary peaks, herding behaviour and congestion in certain parts of the network. Shift 2.0 will look at the potential for dynamic and locational pricing to address these issues, and how any options can complement flexibility procurement.
Shift 2.0 will:
· Understand the scale and timing of secondary peaks and herding behaviour.
· Investigate the potential for locational and dynamic price signals (both time of use and capacity-based price signals).
· Understand the regulatory, commercial, and technical barriers that would need to be addressed in the design of mechanisms and/or price signals and the enablers, roles, business models and data flows to make dynamic/locational pricing a viable mechanism.
· Further stimulate the development/evolution of market-led customer propositions and business.
Benefits
The benefits come from CO2 emission reductions and air quality improvements from an additional uptake of EVs.
We assume that without Shift 2.0, secondary peaks and herding behaviour in response to wholesale price signals reduces the diversity of EVs on our network. This would result in fewer EVs being able to be connected to our network, and so lower uptake of EVs.
We assume that this would mean that the number of EVs would fall to our low scenario (Steady Progression DFES).
Shift 2.0 will develop a project to avoid this situation and therefore allow the number of EVs reach our target scenario (Consumer Transformation DFES).
Learnings
Outcomes
The final report including the analysis and learnings have been published on our website and can be found here: Shift 2.0 - Final Report. The key outcomes of the project are summarised below.
The network impact of changing load profiles and their interaction with changing wholesale price profiles. The trials showcased that dynamic price signals have the potential of managing price herding behaviours and could achieve up to a 35% reduction in EV charging during peak periods, effectively shifting demand to alleviate network stress benefits
The project identified risks of secondary peaks from price herding during low-price periods, emphasising the need for proactive demand management. This risk is particularly significant for networks, as EVs responding to low wholesale prices may charge during periods of high non-smart load demand, potentially straining grid capacity.
Understanding of customer views on different products that could be implemented was gathered through engagement. We considered three mechanisms: a LV flexibility product; a time-varying price signal; and a capacity-based incentive. Following workshops and iterations throughout the trial it was agreed the mechanism to implement was a dynamic price signal, expressed in £/kWh, that could differ across the 48 half-hourly settlement periods in a day.
It should be noted that no benefits are expected from the project without a follow up phase/project that would develop the price signal further and scale to an automated, commercial business as usual (BAU) product.
Lessons Learnt
One significant lesson learned from the initial stage was the importance of early engagement with potential trial partners and other DNOs. Through substantial engagement with flexibility providers and other DNOs, we discovered that the market desires more market-led flexibility mechanisms, including implicit price signals. This early engagement allowed us to focus on developing truly innovative options and ensured buy-in from potential participants from the outset.
As planned, the primary focus of the analysis was on the impact of changing LCT load profiles and their interaction with wholesale prices. We have learnt through engagement with energy suppliers and aggregators that it is not solely wholesale prices that influence their flexibility dispatch decisions. Other system services, imbalance positions, and carbon intensity of electricity were cited as other factors and are being considered as part of the trial options being explored.
When scoping and structuring the project, we realised the importance of allowing sufficient time for project trials to accommodate any delays. The trial took place over a period of 2-3 months over which we had to update the price signal a few times, firstly to correct technical issues and then subsequently improve its effectiveness. Making sure there was enough time to account for any issues was important as it prevented any unexpected delays and ensured the outcomes produced were sufficient to learn from. Similarly, time should be allowed for any delays for projects that require technical setup and exchange of sensitive data with multiple commercial parties.
The trial results show that the method could be deployed on a large scale if improvements are made. The price signal mechanism demonstrated its potential to manage EV herding behaviour and reduce network stress. However, the project identified several key areas for improvement some which are discussed below:
Price signal efficiency: The project showed that dynamic price signals could effectively manage EV charging and reduce network risks caused by herding behaviour. Since energy suppliers and aggregators are exposed to different costs and objectives (and potentially have different levels of sophistication in optimisation) the price signal was not equally effective across all project partners. The price signal was primarily designed to influence a supplier with direct exposure to wholesale prices and network utilisation costs. Future projects should consider the efficiency of price signals across various aggregators and suppliers to ensure broader scalability
Supplier and Aggregator engagement: The process of enrolling suppliers and aggregators into the price signal mechanism was not explored extensively. Clear guidelines and criteria need to be developed to ensure smooth future implementations
Automation and commercialisation: Further work is needed to automate the pricing process and determine how it should be deployed alongside flexibility markets. This could involve developing a standardised framework for calculating the cost-effectiveness of price signals compared to traditional flexibility procurement
Price signal extension to other assets: While the focus was on EV charging, dynamic price signals could also be applied to other flexible assets, such as heat pumps, opening opportunities for future projects
