Making flexibility happen effectively and equitably

The aim of this project is to generate new evidence and to develop methods and tools that can be used to release demand side flexibility with the most appropriate interventions, in a manner that is effective as well as equitable. The methods and tools will be designed to consider the flexibility needs of the energy system and the flexibility availability within various sectors, including buildings and transport. This is being achieved by developing energy demand models from the consumer perspective; and undertaking surveys to better understand the differences across population segments in the degree of demand flexibility and how it might affect their lives. Policy contexts such as adoption of new residential heating technologies and demand side response (DSR) are analysed from the consumer perspective. 

There are three key tasks planned:

1. to collate existing data and evidence from trials and surveys and to develop statistical models of energy demand at the level of the individual consumer;
2. to generate new evidence, using stated choice experiments, that can be used to better understand energy demand flexibility across different population segments;
3. to develop a modular agent-based modelling platform for simulating energy demand and flexibility, that can interact with other model systems such as energy network models. 

The project relies on stated choice experiments for generating new data from the consumer perspective and uses statistical and econometric methods for analysing the energy demand. For instance, during October-December 2023, a stated choice survey was undertaken across the UK to understand the decisions of home-owners with regards to their choice of sustainable heating technologies. The data was then used to develop discrete choice models of heating technology adoption, which provides insights into consumer willingness to pay and demand elasticities and how these vary across population segments. Alongside this, we are developing an agent-based microsimulation model of energy demand that is based on the detailed spatio-temporal modelling of consumer activities and needs.

A sample of the findings from a survey conducted in the UK, and results from analysis of the survey data, are outlined below.  

• Participants are more sensitive to electricity costs than to potential savings from demand-side response (DSR), suggesting DSR promotion should focus on bill comparisons. 

• Lower-income households and those with children experience greater disruption under DSR, highlighting equity concerns in flexibility interventions. 

While some of these findings are intuitive, the underlying data (which is available for further research) enables in-depth research into the flexibility question. 

Early results based on the analyses of data from a UK-wide survey of residential consumers’ adoption of heating technologies include findings such as the following: 

• In the context of domestic heating, customers inherently prefer fixed prices across times and days. To accept price variation in heating costs, with 1 hour notice, they would need to see on average a reduction in heating costs of ~£26 per month.  

• For DSR ‘heating switch off’ events (up to 1 hour a month) with compensation of £14 per event, we observe that customers would be willing to accept such DSR events if they further led to reduction in heating costs of £20-£65 for a scenario with 1h advance notice, and £23- £45 with 7h advance notice. 

More details of the above analyses are available through journal papers and other outputs listed on this page. 

Ongoing and next steps within this project are as follows: 

• Explore the use of large language models to simulate consumer preferences on demand-side flexibility, aiming to enhance efficiency, reduce costs, and improve scalability. 

• Explore the extent of use and trust in energy advice provided by emerging artificial intelligence tools, especially large language models. 

• Analysis of time use and lifestyle changes (fragmentation, flexibility) and their implications for opportunities to enhance availability of demand-side flexibility 

• Expand agent-based modelling platform (MEDUSA) to incorporate models of demand flexibility (via adaptation of activity schedules) and heating-related choices, including participation in DSR  

• Research into potential use of urban bus fleets to deliver localised flexibility services by means of vehicle-to-grid (V2G) technologies, integrated with route planning and scheduling.