I would like to highlight a nice improvement to Emoncms and the Emoncms android app that has been contributed by community memebers @jumpmaster and @andy_taylor. See development forum thread on our shiny new forums.
Emoncms MyAccount page now displays a QR code and a couple of handy ‘Copy to Clipboard’ buttons for the API keys:
We have been busy for the past couple of months building some new websites to improve OpenEnergyMonitor.
Introducing a shiny new:
Later this week on Thursday 5th May 2016 the old forums will be frozen and we will be moving to the new community forums
Read on →One of the main problems with looking at demand at the single household level is that demand at this level is very spiky, there are often long periods of low use around 100W and then suddenly a kettle or electric car charger turns on and demand increases by 20-30x in an instant. Solar generation can be very spiky too and so there is room for matching but it can be difficult to get the timing right.
One solution to increasing supply & demand matching at the household level is to have in home battery storage and diversion to hot water stores – but it may not be the best solution for all cases.
Aggregating supply and demand across households provides an example of how the degree of matching can be increased significantly by what could be described as sequential demand. Not everyone boils a kettle at the same time or a cloud passing over one households solar pv system may not be passing over another’s.
In order to explore the extent of increased matching possible through aggregation of supply and demand I have been working with Dominic McCann from Carbon Coop on what could be called a ‘virtual microgrid’ concept, we have a provisional front page for this here - please note that its still in early development:
This post describes an approach we have been exploring that uses the MyEnergy emoncms app and work on virtual smart grids with Dominic McCann from Carbon Coop. There’s a good blog and video on what we’ve been working on over on the Carbon Coop blog.
The last post described two approaches to grid carbon intensity that consider the UK grid as a whole. This approach explores what the overall household carbon intensity might be when on-site renewable energy is considered such as home solar but also when renewable energy is bought over the grid, this could be from a green electricity tariff.
If imported electricity is supplied from a 100% renewable energy supplier in the UK: a large portion of the supply will likely be wind energy (54% of good energy’s ‘fuel’ mix comes from wind) and the UK has the best wind resource in Europe. Incorporating grid wind data and exploring matching with wind and solar, means we can really start to test scenarios such as ZeroCarbonBritain in the present.
There is real-time data available on UK wind supply and its possible to scale the total UK wind output in order to estimate a household ‘share of UK wind’ in real-time.
Read on →Zero carbon energy scenarios such as ZeroCarbonBritain feature significant electrification of energy demands previously provided by fossil fuels such as heating and transport. With electrification it becomes possible to supply these energy demands with renewably generated electricity from primarily wind, solar and backed up by storage technologies from short term stores such as batteries to long term stores such as power to gas.
We do not of course yet have a zero carbon grid. However there has been significant growth of renewable energy and a decline in coal generation in recent years.
The CO2 emissions of demand side solutions such as heat pumps and electric vehicles are significantly affected by the co2 intensity of the supply but in many cases installing and using these technologies make sense at the UK’s present supply mix and can make more or less sense depending on how you calculate co2 intensity and where you assume the additional electricity required comes from.
Read on →Update: PlatformIO does library management much better then git-submodules, we have switched to using PatformIO to manage library dependencies, see blog posts
Library management for Arduino has always been a bit of a pain; having to go and download and install all the required libraries for various locations on the internet and hope that they are the correct version. Library management becomes even more important when debugging and supporting a project as libraries get changed and updated.
Things have improved in recent years with the increased use of GitHub for Arduino libs allowing changes to be tracked easier and the later version of Arduino IDE including a library manager which can auto-update.
However I have always wanted to have all required libs and files for a particular project all in once place. I feel that I have now found the best solution (for now!).
Read on →Home Assistant (HA) is a fully open-source home automation platform.
In terms of functionality it’s quite similar to openHAB however it’s architecture is very different it runs on Python 3 as opposed to java.
I found home assistant very easy to setup and configure. It’s got some nice auto detect features e.g. It auto-detected by Chromecast devices. Home Assistant has got pre-built component integrations with many home automation and monitoring devices and platforms. I was also impressed with the presence detection support, I tested using nmap to scan my home router for presence of my phone connected to my home WiFi (indication that I’m home…or I forget my phone!) and OwnTracks MQTT based GPS tracking.
Read on →