For a long time I’ve wanted to put a tool on the internet that anyone can use to build their own electricity system. But I wanted it to go beyond a simple annual energy balance where you can quickly assemble your favourite technologies into a system which could never work! For me it needed to be one that balanced supply and demand at a more granular level whilst avoiding a full scale 8760 hour model. I wanted one that you could easily choose the capacity of various low carbon technologies, but which would do the hard maths of checking the system would work in difficult times as well as easy ones, and was based on real data. I thought it important to report clearly three key metrics
(1) What does this system cost (after all this is what the consumers – or taxpayers perhaps – are going to have to pay for),
(2) CO2 emissions – the assumption is that a user will be trying to reduce, or at least understand carbon emissions.
(3) Have the lights stayed on? l imagine most of us will want to build a system that supplies power 24/7, even if not then at least understand how often consumers will be out of power
So what do I mean by difficult times? Well a good way to get started in decarbonising a system is to add renewables, in particular wind, and, in a sunny country, PV as well. These will, on most systems, inexpensively displace fossil consumption at legacy plant and hence reduce emissions. But wind is very variable in time, weather patterns regularly go through a cycle of low pressure systems bringing wind, and calmer high pressure systems. It might be thought that geographical diversity would help here – it will always be windy somewhere – but actually the high pressure systems with little wind can stretch more than 1000km and persist for some time. The variation is huge, the wind duration chart from the GB system in 2020 shows that on the windiest day it covered 48% of demand, but there were a couple of days at 1%, and worst of all these were consecutive days, part of a 5 day wind drought averaging 3% of demand. (thanks to Gridwatch for the data). So even with 5x as much wind capacity, there would still be a 5 day period needing 85% of production from elsewhere.
However, trying to model complex weather systems, or a windless week, is beyond what I wanted to achieve (or could achieve within a website model), but the simpler task demonstrating that a system could get through a low renewables day was possible. I was keen that it was built on real data so this was achieved by sorting 3 years of demand and renewable output data into 8 different day types. For each of two seasons I took the worst 1% of days (those with the lowest renewable proportion), then divided the remainder into 3 according to renewable production. For each day renewable availability profiles were made by averaging all days in that category at 3 hourly timesteps.
Storage is one way to overcome the variation in renewables. The website model considers two types, batteries which are able to move energy within the day (I don’t know of many batteries greater than 12 hours in duration), and pumped storage which allows energy to be moved from high renewable days to drought days. The modelling is of course simplistic and probably over estimates the ability of pumped storage to move energy within a season as it assumes that you don’t get a run of low renewables days together and there’s sufficient days of abundance in between to recharge.
Having built the GB model, my colleagues in Australia undertook the same analysis of the National Electricity Market and provided data for a NEM version. Additionally I was encouraged to provide a less techy interface (but with the same background calculations) to be more attractive as a game. This is linked to social media to facilitate discussion about solutions discovered.
My hope is that this will provide a first level check on whether a solution is viable, or help point the way towards a decarbonised system. It’s not a detailed model, and it would be worrying if policy makers or system planners were using it as their main tool, but I hope it will further the discussion and provide insights that can be checked out with more granular analysis.
Huge thanks to Dab-Online for doing the clever website stuff, Gamma Energy Technology for crunching the Australian numbers and keeping me on track, and ANLEC R&D for sponsoring the inclusion of the NEM and fun front-end.