The aim of this project was to understand the opportunity flexibility and demand shifting presents for households. Flexibility and demand shifting means households changing when they use equipment and heating systems in their home to times during the day when electricity is cheaper.

The UK is decarbonising its heating and transport systems, placing greater demand on the electricity network. Increased demand will require an increase in generation and network reinforcements, but both can be alleviated if households are incentivised to change their usage habits and shift when demand occurs.

Households can already choose between various electricity tariffs that incentivise using electricity at different times of day by offering different unit rates. Some of these tariffs have been in operation for many years (e.g., Economy 7), but households have not typically been able to shift much of their demand around given the technologies they have.

However, households are increasingly adopting technologies such as air source heat pumps (ASHP) for space and hot water demand, solar photovoltaic (PV) panels for electricity generation, and batteries for electricity storage (BESS), with some households adopting all three in unison to maximise the benefits.

Adoption of these technologies makes it easier for households to move greater amounts of their electricity demand to different times of day, but the net effect for the householder is unknown. Will it reduce their annual energy bill by 5% or 50%, for example? Are there any trade-offs?

The innovation

This project uses a purpose built tool called Home Energy Dynamics (HED), created by the Catapult.

HED works like other building energy tools by analysing things like building materials, heating systems, size, shape, direction, and location. What makes HED different is that it uses a ‘first principles’ approach to create a dynamic simulation of every building component and how they interact with each other.

HED can also account for when people are in the building, how they use the heating, and the specific temperatures they want in different rooms at different times. It even factors in the heat generated by people and appliances.

The tool is built using Modelica programming language with a Dymola interface. When HED’s predictions are compared with actual measurements from buildings, they’re usually only 1-2% different. This accuracy makes HED perfect for simulating home energy use. It’s been used by:

• Government officials making policy decisions
• Innovators testing how their technologies work in different types of homes

The challenge

In this project the challenge was to understand the opportunity flexibility and demand shifting presents for householders.

To help understand the opportunity, comparisons are made between energy tariffs and ‘flexibility behaviour’ (optimised vs non-optimised – i.e., the amount of effort a household puts into shifting demand).

Three electricity tariffs (for all tariffs, a standing charge has not been used as it may obscure assessment of the potential benefits) have been selected for this project. They are:

• A ‘flat rate’ tariff, where the unit cost of electricity is a single value regardless of the time of day(29p/kWh). For both dwellings, this is considered the ‘base case’ and is presented first. Because the tariff rate is flat, there is not an optimised or non-optimised version of this simulation.
• A ‘dual rate’ tariff, where the unit cost of electricity is one of two values, a low rate overnight (7.5p/kWh: 23.30-05.30) and a flat rate for the rest of the day (31.5p/kWh).
• A ‘Cosy’ tariff, where the unit cost of electricity is one of three values; a ‘day’ rate similar to the flat rate (29.73p/kWh), two ‘low’ rate periods (17.84p/kWh: 04.00-07.00 and 13.00-16.00) and a ‘high’ rate (47.56p/kWh: 16.00-19.00).

In all scenarios, electricity generated by PV will first serve the household, then charge the battery, then be exported to the grid. This is set the same for both optimised and not-optimised households. The hot water tank is set to ‘recharge’ overnight in all simulations.

Two dwelling archetypes are used in the simulations:

• Dwelling 1: A pre-1919 two-bedroom mid-terrace dwelling, 81m², without floor or wall insulation, but with an insulated loft, old (pre-2002) double glazing.
• Two adults who are working from home, no children, and SAP heating profile.
• 5kW ASHP, 3.3kW PV array, 8kWh BESS, 180l hot water tank.
• Dwelling 2: A 1945-64 three-bedroom semi-detached dwelling, 93m², cavity wall and loft insulation, but without floor insulation, newer (post-2002) double glazing.
• Two adults, both at work during the daytime, one school aged child, SAP heating profile.
• 7kW ASHP, 3.57kW PV array, 8kWh BESS, 180l hot water tank.

Electricity bills

The simulations provided outputs that allowed assessments of electricity costs across an entire calendar year, but also on specific days in winter, spring, and summer. The opportunity of flexibility varied throughout the year, and making assessments on specific days at different times of year helped to understand these differences.

In winter, with less electricity generated via the PV array, the optimised household benefits from charging the BESS overnight on the dual rate tariff compared to the non-optimised household that does not, and their daily electricity bill is reduced by 25-30%.

In spring, PV generation increases and is sufficient to meet household demand and charge the BESS, the difference between optimised and non-optimised households with a dual rate tariff is only overnight for dwelling 1, when the optimised household takes advantage of cheaper rates to power appliances, whereas the non-optimised household uses the battery and then has to use more expensive electricity later in the day. By summer, this difference between optimised and non-optimised households on the dual rate tariff no longer exists, and the daily electricity bill is identical.

In winter when the simulations use the Cosy tariff, results are similar to the dual rate tariff. The optimised household reduces its daily electricity bill by 20-30% when compared to the non-optimised household. As with the dual rate tariff, the BESS charges during the two price ‘troughs’ and therefore avoids buying electricity during the evening peak period.

As with the dual rate tariff, the difference between the optimised and non-optimised households during a spring and a winter day are minimal, the PV array providing sufficient electricity to charge the BESS and therefore minimise opportunities for using the tariff to make savings.

Future weather profiles were also used to understand how the opportunity of flexibility may change with a warmer climate in 2050. Both ‘low’ and ‘high’ emissions future scenarios were simulated and the same comparisons between optimised and non-optimised households were made, for dwelling 2 only.

In winter, the savings for the optimised household increased to 30-35% when compared to the non-optimised household, but again differences in both spring and summer were minimal.

The total annual electricity bill was also compared for optimised vs non-optimised households. For dwelling 1, the simulation of the dual rate tariff showed that being optimised can reduce the electricity bill by around 25%, with a saving of just under 20% for the cosy tariff. For dwelling 2, for both dual rate and cosy tariffs, savings for the optimised household were around 30%. These savings were around 25% when the future weather profiles were used.

Comfort

In addition to assessments of electricity bills, assessments were also made of the ‘comfort’ that householders experience. Whilst being optimised may reduce electricity bills, if it does so at the cost of the comfort of the householders – the heating isn’t on because the electricity price is high – then this consideration should be presented as context for making a fuller assessment of electricity costs.

The time to warm-up the dwellings varied according to whether the dwelling was optimised or not, with the non-optimised dwelling having a fixed warm-up period, whereas the optimised dwelling used both weather compensation and tariff information to determine the warm-up period.

Whilst a single ‘value’ for comfort was not calculated, graphs that show room temperatures were assessed at different times of the year and times of day and used alongside assessment of useful heat provided to households. Results showed that there was little difference between optimised and non-optimised households. In some instances, the optimised household met temperature set-points for longer periods when compared to the non-optimised household due to the more complex calculation of warm-up times, suggesting that being optimised can improve comfort and deliver electricity bill reductions.

Conclusions

The project made several conclusions:

• The simulations show that optimising a household’s approach to flexibility can result in savings of up to 30% on an annual electricity bill.
• Households optimising their approach to flexibility and reducing their costs did not make sacrifices with regards to comfort; in fact, the opposite was the case, with optimised households using up to 10% more useful heat than non-optimised households.
• The difference in approach to optimisation is more pronounced in winter, when less generated electricity from PV is available and more electricity for space heating is required, meaning a much greater reliance on the grid and exposure to tariff rates.
• This may suggest that households need only ‘be concerned’ with their flexibility approach for a few months of the year and use their house ‘without concern’ for the remainder of the year.

Next steps

Several observations were made whilst the project was being delivered that may inform any future study:

• Although it wasn’t an objective to identify the ‘best’ tariff for a household, the dual rate tariff resulted in a lower annual bill. ‘Optimised’ households required the same space heating demand across the tariffs, therefore the dual rate tariff not only offers the lowest bill but also maintains occupant comfort.
• Although it wasn’t an objective to identify the ‘best’ tariff for the electricity grid, the Cosy tariff resulted in more demand being shifted than the dual rate tariff.
• Broader concerns about the effects on the electricity grid from the scenarios modelled require further exploration. As households adopt ASHP, PV, and BESS, some issues will be amplified (i.e., winter peak demand), others will alleviate (i.e., spring and summer demand), and variable rate tariffs may need to adapt further during the transition.
• The electrification of personal transport, which was not modelled in this study, is a further demand that will exacerbate grid concerns but can potentially be shifted if incentivised with tariff rates.