The ground breaking Demand Flexiblity Service (DFS) allowed over 1.6 million households and businesses the opportunity to participate in a national flexibility service and be rewarded for the first time. Given the speed that the sevice was set up and the implications for future flexibility services this project will explore how consumers participated in DFS and crucially the barriers to participation, through 2 phases of work.
- A social research element (diaries, opinion poll, survey and interviews).
- Using consenting consumer data, smart meter data will be analysed and linked to the social research.
The findings of this work should inform future iterations of DFS, DSO flexibility services and flexibility service provider offers (suppliers and aggregators).
Benefits
The potential revenue streams of domestic flexibility through existing energy markets and flexibility services are as follows:
- Redispatch avoidance - £105/kW/yr (Element Energy analysis based on FES 2021),
- Wholesale arbitrage - £85/kW/yr (daily 4h price spread based on 2021 data),
- DNO network reinforcement - £64/kW/yr (Element Energy analysis),
- Balancing Mechanism - £47/kW/yr (Element Energy analysis),
- TSO reinforcement avoidance - £37/kW/yr (Element Energy analysis based on FES 2020/21),
- Capacity Market - £12/kW/yr (2021 T-4 clearing price; a conservative value lower than Cost Of New Entrant),
- Operating Reserve - £1.4/kW/yr (Element Energy analysis).
CrowdFlex Aplpha found that the value of flexibility could be worth £1.25Bn/yr to the end consumer across GB when the cost of providing flexibility services is accounted for. This includes £3.8Bn of avoided DN reinforcement and £2.2Bn of avoided transmission network reinforcement investments between 2024-2050.
Learnings
Outcomes
As of now, the DFS Evaluation project has made substantial progress despite the delays. The social research phase, which captured the perspectives of over 23,500 participants, has provided valuable insights into consumer motivations and engagement with flexibility services. The findings have already influenced key design modifications for the DFS, such as the removal of the in-day adjustment mechanism. This early outcome underscores the project’s ability to generate actionable insights that can directly impact the development and refinement of flexibility services.
In parallel, the smart meter data analysis phase has begun, with data collection and the establishment of secure analysis environments underway. Although this phase has faced delays due to necessary enhancements in data security protocols, it is expected to yield comprehensive insights into the effectiveness of DFS in real-world settings. The integration of data from both social research and smart meters will soon provide a holistic view of how consumers interact with demand flexibility services. These combined insights are anticipated to drive future recommendations and innovations, shaping the next generation of flexibility services and contributing to more effective energy management strategies.
Lessons Learnt
The project has highlighted several critical lessons regarding the handling of data and the management of extensive data volumes. One of the key challenges encountered was navigating the complexities of GDPR compliance while ensuring that data handling and analysis met the highest security standards. The necessity for secure data environments and rigorous consent processes significantly extended the project timeline, underscoring the need for early and thorough planning in these areas. Future projects should incorporate robust data protection strategies from the outset, including clear protocols for managing consent, data anonymisation, and secure storage to mitigate potential delays and ensure compliance.
Additionally, the scale of data generated by the DFS project emphasised the importance of having a well-defined framework for data management and analysis. The sheer volume of data, combined with the need for detailed and accurate evaluation, revealed potential challenges for future projects in this area. To address this, future projects should invest in scalable data processing solutions and consider the integration of advanced data management tools early in the project lifecycle.
