Community Smart Grid - a prosumer perspective (part 2)
Delving Deeper into (the marginal rate of) Carbon Intensity
The aim in this blog post is to set out the main environmental and electricity consumption parameters that will need to be collated, measured and acted upon as part of the community smart grid project.
As with part 1 the analysis will focus on householder level data (as this is what I have to hand), but it is anticipated that the aggregate approach for several houses can also be made to work in a similar way.
Hence, this section of the blog will try to look at:
- Metrics that could be used to test whether it is a good time of day (or week) to reduce energy use or whether storage of electricity is a good option “right now”
- The metrics will probably be used to derive some form of logic which outputs instructions over the smart grid in the form of: “traffic light” displays or smartphone apps available to each household, on/off parameters to smart appliances or devices and battery can store electricity now commands
- Finally the metrics we adopt here will allow the performance of the Community Smart Grid to be quantified in one or more units; be that CO2 emissions (saved), smoothness / spikiness of the electricity consumption, the degree of matching with the available renewable energy sources.
Presenting Information to Consumers / Prosumers
As stated in part one information will need to be made available to consumers, prosumers and other members of the community smart grid team in an appropriate and understandable manner. Consumers / prosumers are likely to ask:
- How will I know what to do and when?
- How can I tell whether my actions are having any effect?
- Have I (or would I have) saved any money?
- Are my actions helping to reduce carbon emissions?
What might that mean in terms of presentation of information:
- Different levels of information for different types of consumer / prosumer.
- A simple traffic light display showing RED to indicate definitely try and save electricity, AMBER to save electricity where possible and GREEN a perfect time to put on appliances delayed from earlier (RED or AMBER)?
- An additional bar graph display similar to My Electric EmomCMS app; possibly with an additional line to show “average” usage level.
- Some sort of information to describe “how well we’ve done” (with we being household and/or aggregated community) over the last 7 days or last 30 days.
From an aggregator / Carbon-Coop point of view (and a question for other Nobel Grid partners) the related questions might include (but not limited to):
- Was a message sent out to consumers RED / AMBER / GREEN?
- How many users (or devices) received the demand side message?
- How likely is it that demand side messages were acted on by users?
- How many kWh of demand were shifted by demand side measures?
Remaining unanswered questions:
- Should Carbon Coop smart grid include some measure of equitable use of energy? Is household occupancy the best measure for this (number of people)?
- Should the smart grid differentiate between different types of electricity use? e.g. heating, hot water, cooking, washing, other appliances?
- The question of space heating and hot water might re-open the question of whether gas CO2 emissions need to be consider in the equation and also interacts with questions of equitable use of energy;
- i.e. a household with electric heating (or heat pump) will inevitably have higher electricity consumption than one with gas central heating.
- How can householders be incentivised to participate in demand shifting? Is ‘doing the right thing’ enough of are other social and/or financial incentives required?
What Metrics are Required
Whilst the kWh being consumed and the degree of carbon intensity may be two of the parameters that I have most focussed on there are likely to be a fairly large number of parameters or variables that will need to be quantified in order to fully assess the performance of the community smart grid. The following table lists some of the possible parameters that this might entail:
Based on what we have seen to date it would appear to me that the best time to be using electricity from the grid is when there are plenty of renewables being generated and when the overall grid CO2 intensity is low. In general terms this would be characterised by night-time periods when the wind is blowing at a reliable rate so that there are plenty of MWatts being generated from clean energy sources.
Having looked at the output from EmonCMS grid intensity and wind power over various time frames the following graphic attempts to describe when these conditions might be met.
The following graphic presents the available wind power and grid carbon intensity data over a 2 week period in December. The proposed (or possible metric) that might determine when the RED / AMBER / GREEN messages might be sent out to consumers is shown on the graphic.
The following graphic presents a possible schematic for the flow of data needed to assess / act on carbon intensity, on-site solar and wind power parameters at an individual household level. The schematic assumes that the RED / AMBER / GREEN decisions happen locally in the house, however, this could equally take place at a centralised Carbon Coop location.
The most recent development using the OpenEnergyMonitor is that it is now possible to calculate the CO2 generated by a household electricity consumption taking account of on-site solar PV and a share of national wind generation.
One important observation from this graphic is that the benefits of demand side response on a single household level may appear to be limited, ie the peaks in demand from certain appliances (washing machine, kettle etc) can be moved from one time of day to another but the peaks themselves will usually be far higher than the available pool of renewable energy.
However, this is where the “community smart grid” really comes in, ie the aggregated demand should be more easily made to match the larger pool of aggregated renewable energy shared by all the households across the smart gird. The potential benefits of consumers acting together as a “community” will clearly be an important issue to assess for the Carbon Coop project.
Conclusion to Part 2 - where are we heading
The main conclusions to be drawn from part 2 of this blog are:
- Carbon intensity and size of the pool of available renewable energy are likely to be the main factors which determine the best time to use electricity from an environmental point of view.
- The spot price (if that can be established) will be the main parameter which determines the best time to use electricity from a financial point of view.
- Some sort of RED / AMBER / GREEN messaging system (with either centralised and/or distributed control) will be required in order to inform consumers when best to reduce electricity demand.
- Data will need to flow back to the aggregating system at CarbonCoop in order to assess whether demand side measures have taken place across the cohort of houses.
- Acting as a Community on a Smart Grid clearly has a lot of potential; let’s do it.