As part of Carbon Coop’s Green Shift project members and other participants will be encouraged to reduce their electricity use at certain times of day in order to help reduce their carbon footprints. In the electricity industry this kind of scheme is called ‘demand side response’ and is believed to be crucial to helping to de-carbonise our electricity supply and better utilise renewable energy. In this blog post I will look at what this demand side response might look like.
In previous posts (link) and (link) Dom looked at many of the possibilities Green Shift could offer to members for reducing their carbon emissions – from battery storage added to existing solar PV installations to much more detailed information about their electricity use. One thing that was highlighted is the importance of considering how much renewable capacity is available on the grid when deciding whether or not to use electricity. A useful proxy for this is the ‘carbon intensity’ of grid electricity, which is the amount of CO2 emissions produced for each kWh of electricity used at the plug. This amount changes throughout the day, month, and year as you can see in the plot for 2014 below.
It is calculated by considering the mix of different generators (such as gas power plants or wind farms) which are online at any given time and estimating how much CO2 they produce to satisfy the demand. For example, average carbon intensity on the UK grid fell sharply across 2015 as many dirty coal fired power stations were shut down.
It follows that the potential for CO2 emission reduction depends greatly on the state of the UK grid at the time electricity is used. This can be roughly predicted ahead of time based on weather forecasts and whether or not there will be any national events causing significant coincident demand. In Green Shift this information can be used to inform members about when to reduce their electricity use to achieve the greatest CO2 emission reductions.
To try and get an idea for what this might look like I simulated a possible Green Shift demand side response programme over the course of a year. In order to determine when to tell members to reduce their emissions I used a threshold based on a moving average calculated from previous values. This is the thick black line in the above plot. This is needed as the carbon intensity changes over the course of the year so a fixed threshold would not be effective. When grid carbon emissions go above some multiple of this line (indicated in green) members could be asked to reduce their electricity use in order to reduce their carbon emissions.
In order to estimate how much energy and carbon emissions can be saved I made some assumptions about how much electricity people use and how they would respond; using the average load profile from the UK Household Electricity Study and a nominal figure of 10% reduction in electricity use during the times when members are told to reduce their electricity use (this is a realistic figure based on existing studies e.g. link). How these values change based on the position of the threshold is captured in the plot below based on the UK grid carbon intensity in 2014:
What this plot shows is that as the threshold is increased the potential for CO2 emission reductions decreases because there are less times in the year when grid carbon emissions are above this line and less CO2 emissions (in total) in those times that are. But the times during the day when members are told to reduce their electricity use also goes down. The value that should be used for the threshold depends on what is considered possible; if members are asked to reduce their electricity use at all times every day they may not participate therefore undermining the programme, so there is probably a trade off between how long and how much in order to achieve high enough participation to see significant reductions.
In reality we would probably want to limit the periods during the day to 8 hours or less as we cannot recommend participants decrease their electricity use all the time or they might stop listening (and many will not be able to as some electricity use cannot be put off for that long such as cooking or washing)! I have tried to visualise what such a programme would look like in the following plot which shows the times in a year (in red) when people might be asked to reduce their electricity use:
We could also consider using ‘critical peak periods’, which are smaller periods of time within the existing times where the highest carbon reductions are possible and where we even more strongly encourage people to reduce their carbon emissions! These have the advantage of being shorter lengths of time giving people more flexibility in how they make their energy reductions. This programme might look something like the following:
We can think of this programme as being like ‘traffic lights’ – when it’s green you are good to use energy, when it’s amber you should think about reducing your energy use, and when it’s red you should really think about reducing your energy use! These ‘traffic lights’ can be sent to Green Shift participants to inform them about the times when it is best to reduce energy use. In Green Shift this will be done using an app on a smart phone offered to participants in the project.
This is a first attempt at trying to visualise what demand side response in Green Shift might look like. In future blog posts and newsletters we will provide more details on how the programme will work in practice. If you want to get involved in Green Shift get in touch: email@example.com.