Loughborough University
Leicestershire, UK
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Loughborough University

Centre for Renewable Energy Systems Technology (CREST)

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Case Study for the UK Data Service
20 June 2016

A model to estimate the diversity of domestic energy demand at high-resolution

About the research

The Climate Change Act 2008 established a target to reduce the UK’s greenhouse gas emissions by at least 80% by 2050 from 1990 levels and meeting the target requires major changes in how we use and generate energy. Heating accounts for 45% of all energy use in the UK and, according to the Department of Energy and Climate Change, one of the greatest challenges to meeting the long-term emissions target is decarbonising heating demand.

Part of the challenge involves decarbonising electricity supplied from the national grid, and then electrifying heating, by replacing most of the oil and gas boilers used to heat buildings today with technologies such as heat pumps which run on electricity. However, the electrification of heat in the domestic sector is expected to be a difficult task. It could cause a significant increase of electricity peak demand, which could have adverse consequences on the electricity system, in particular on the ‘last mile’ low-voltage distribution networks that deliver power from the substations along cables down residential streets. The cost of having to reinforce existing electricity networks to accommodate these heat pumps and other low-carbon technologies could be very considerable and therefore it is important to make best use of existing network assets, and to ensure that any reinforcement is based on an accurate assessment of need.

Eoghan McKenna and Murray Thomson from Loughborough University have developed a high-resolution model of thermal-electrical domestic demand which allows to appropriately represent electricity demand and the timing of that demand, and can therefore provide a suitable basis for future low-carbon network strategies.

The project received funding from the Engineering and Physical Sciences Research Council (EPSRC) as part Transformation of the Top and Tail of Energy Networks, a project focusing on the physical infrastructure change in energy networks required to move the UK to a low carbon economy.

The model has been developed as free open-source software to promote transparency and further research. The model can be accessed here.

Methodology

The model is an integrated thermal-electrical demand model based on a bottom-up activity-based structure to provide high temporal resolution and uses stochastic programming techniques to appropriately represent the diversity of demand. 

The integrated thermal–electrical demand model is constructed from several sub-models as shown in Figure 1. The occupancy model generates stochastic sequences of occupancy for each dwelling, which form a basis for the calculation of appliance, lighting and water-fixture switch-on events. These are aggregated to determine the dwelling’s electricity and hot-water demands. The thermal demand model simulates the dwelling’s thermal dynamics and gas demands given the climate data, internal heat gains, and dwelling-specific building fabric data.

Figure 1: Overall architecture of the integrated thermal-electrical demand model (Source:McKenna and Thomson, 2016)

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To ensure accuracy, the model has been validated against three independent datasets; simulated gas demands are compared with data from the Energy Demand Research Project: Early Smart Meter Trials, 2007-2010 and the gas-boiler control group of theCarbon Trust Micro-CHP Accelerator, whereas hot water demands are compared with the data from the Energy Saving Trust Measurement of Domestic Hot Water Consumption in Dwellings. Data from the Energy Demand Research Project: Early Smart Meter Trials, 2007-2010 was used in the study because it can be considered reasonably representative of the UK average and because of the high time resolution. It consists of half-hourly electricity and gas smart meter data for around 18,000 households.

For example, to validate the model's gas demand, the researchers have compared daily gas demand data for winter and summer, as shown in Figure 2. The Energy Demand Research Project (EDRP) data used for the validation consists of half-hourly gas smart meter data for 8062 dwellings for the months of January and July over three years, corresponding to 65 days for January and 93 days for July. The winter Carbon Trust data is at 5-minute resolution and for 18 dwellings for the months of December, January and February for the years 2006 and 2007 (90 days in total), while the summer data is for 20 dwellings for the months June, July, and August for the same years (68 days in total). The model data consists of 104 dwellings for seven days in January and July for a total of 728 days each. In general, Fig. 10 illustrates that the model captures much of the timing of heating demand, but results in lower overall demand than the validation datasets. Average daily gas demand for winter is 101.9 kW h, 75.5 kW h and 54.0 kW h for the ERDP, Carbon Trust and model respectively. This suggests that the buildings included for the model are more thermally efficient than the UK national average.

Figure 2: Validation of the model's gas demand (Source: McKenna and Thomson, 2016)

Findings for policy

'The Future of Heating: Meeting the challenge' report by the Department of Energy and Climate Change outlines that "as we move towards a greater electrification of heating supply, this will naturally cause a greater strain on the UK’s capability not only to generate the extra electricity required, but also to establish the network to deliver it to homes". Thethermal-electrical demand model can estimate electricity demand peaks and has the potential to inform future plans concerning the electrification of building heating without an extremely expensive wholesale asset replacement of the current distribution network. It also has the potential to stimulate improvements in operation management and efficient use of distribution network assets.

The model has been developed within the Transformation of the Top and Tail of Energy Networks project, which involved network and system operators such as National Grid, Scottish Power and Central Networks to tackle the real barriers to the development of low carbon networks (Research Councils UK).

The model is open source, allowing users to inspect every internal detail and to modify or extend its operation for their own specific application.

To read the report in full:

McKenna, E., Thomson, M. (2016) 'High-resolution stochastic integrated thermal–electrical domestic demand model', Applied Energy, 165, pp.445-461.http://dx.doi.org/10.1016/j.apenergy.2015.12.089