This leads to a development where solar PV is regarded as uncompetitive by the model and all capacity additions are policy driven. Figure one from IEA PVPSiv shows that this was not an entirely unreasonable assumption, as the majority of the solar PV market has been driven by subsidies. What these high cost assumptions did nevertheless, was that the added capacity in the model was lower than what we saw in reality, which in return lead to low learning and continuously high costs (despite using a high learning rate) and concluding in a conservative projection for capacity additions.
As there are no public cost assessments after WEO 2014 (more on this later), it is difficult to give a qualified comment on cost assumptions used in WEO 2015 and 2016. But it is reasonable to assume that the cost assessments have been updated according to the price decline the last years, and that this means that solar PV experiences larger learning effects due to both more capacity built per subsidy and also because solar PV becomes cost competitive in several markets (especially in the 450 scenario).
2. Issues with the World Energy Model and how it models renewables in the power market
Apart from high cost assumptions, the main critique in my master thesis was that the WEM used an approach with one merit-order and an annual aggregated load curve for each region. This means that the load profile even 25 years into the future is set by exogenous data based on the current demand profile. This is a general problem with techno-economic models. How do you describe change in cultural preferences? Is it probable that when the Indian population gets richer, they will adopt the same car-dependency of the Western middle class, or will urbanization and other cultural trends lead to radically different approaches for personal transport and ways of living?
This way of describing the power market, also ignores important characteristics for PV, characteristics that gives this technology market segments (both in time of day and in types of consumers) where it outcompetes other power producers. This again makes the model underestimate the effect increased PV capacity will have on the economics of baseload power plants.
The introduction of the WEM Hourly Model is a huge improvement to the WEM and WEO 2016, and there is a thorough discussion on integration and variability of VREs. In a world where the majority of capacity additions comes from renewables, understanding the inherent traits of different VREs and their effect on the power system is key. Thus the analysis done by the WEM hourly model is very interesting, but after reading the report and also the Model documentation it is still unclear how the results from the WEM hourly model analysis is used in the general WEM framework. For example:
- How do the results impact the capacity factor (CF) of new, and existing, power plants? In other words: Does the CF of new/old power plants (both VRE and dispatchable) change based on the results, and is this then taken into account when deciding new generation capacity/closure the following year?
- How do the results impact the relative financial attractiveness of VRE compared with dispatchable power plants?
- How do the results impact dispatch/ramping costs for baseload plants?
And does the WEM hourly model make it possible to look at the competitiveness of distributed generation (DG) in different sectors of the economy? Different sectors of the economy have different demand profiles and these may diverge from the aggregated demand profile of the region.
These are examples of where the WEM might have a "baseload"-bias. That is, an overvaluation of production from baseload power plants compared to production from VREs. That said, there are also model choices that gives a "VRE-bias". A concrete example is that the hourly dispatch model does not represent the transmission and distribution system, and the costs this might add to VRE expansion.
3. The "Black box"-approach
For readers not so interested in energy models, IEAs "Black box"-approach should be the most problematic. Even though the WEO 2016 gets reviewed by almost two hundred reviewers from different organizations and experts, neither their comments or IEA's assessment of these are public. The same goes for the models used and the data IEA relies on. Even today – the latest public available cost assessment is from the WEO 2014, meaning that the data probably are from 2012 or earlier. In short, the general public must take the results and underlying data and analyses from IEA at face value.
This is in contrast to other large contributors to the energy debate, like the US EIA and IPCC. EIA publishes all data and a thorough model documentation for their NEMS-model on their web page, and IPCC had an open review process. This resulted in 140 000 comments, all of whom was assessed and commented by specialists.
Conclusion
First of all, if we look at the historical capacity additions, it is the 450 scenario that best fits the historical trends. How large will the slowdown be in 2017 and what progress will we see in 2018? Is it realistic that the capacity additions will fall to a 2014 level and stay there (with a slight increase due to replacements of retired capacity)?
It looks like IEA have revised their cost assumptions, and that we start to see PV competing on a commercial basis as we see in the market today, especially in the 450 scenario.
The introduction of the WEM Hourly Model is a huge improvement to the WEM and WEO 2016, as is the thorough discussion on integration and variability of VREs for stakeholders. Next year, the focus should be on how large amounts of VRE affects the economics of existing and new baseload power plants. This would be an important tool for policy makers and stakeholders.
More problematic is the "Black box"-approach. A more open review-process and publication of data, model documentation and an overview of the research used, would both give IEA and the World Energy Outlook more credibility and be a huge addition to the scientific community. Thus IEA should be inspired by the openness of EIA and IPCC.
Literature
