Earlier this week I came across an article about how New England Solar was planning to sell off its solar farms in Australia due to the impact of network congestion:
New Energy Solar’s CEO Liam Thomas said a flood of new projects had resulted in curtailment challenges spreading to other parts of the grid and cut the output of the Manildra solar farm by 40 per cent just as the company was looking for a buyer.
I thought that it would be interesting to dig into some of AEMO’s constraints data in order to investigate exactly how Manildra Solar Farm has been impacted by network congestion in FY-21.
Interestingly, it appears that the vast majority of the time a constraint impacted this solar farm, it could be attributed to a single factor: the 132 KV Molong to Orange North transmission line which you can see in the map below. This transmission line has a thermal limit that determines how much power can flow through it from generators in the area.
So how does this actually impact Manildra Solar Farm in practice? Well I thought I could answer that question by visualising the generation of the solar farm on a day when the constraint was binding a lot.
Zero generation between 8am-4pm – no wonder New England Solar are unhappy! This also got me wondering how many other solar farms in the region were impacted by the same constraint that day.
Ten solar farms with an extremely correlated pattern of generation are all impacted in some form by this transmission constraint. It’s just fascinating to follow the profile of each one in the figure above – almost everyone takes some kind of a haircut from the constraint.
I thought this was a neat case study demonstrating just how significant a role constraints play in the NEM. It really highlights how vital it is to take constraints into account when analysing market events and the behaviour of generators. I’m really looking forward to diving deeper into more of this constraint analysis in future articles!
