Comparing the results of renewable energy auctions: A model-based analysis of recent trends from around the world
Over the past decade, renewable energy auctions (REA) have emerged as a key policy instrument to promote the transition to a renewable energy future. The history of prices achieved in REA shows two main features: (1) a remarkable decline in prices, and (2) significant price differences between different countries. While there is consensus in the literature that several factors influence the auction result, there is a lack of research that quantifies these effects in order to explain the observed differences in auction results. Hence, the main objective of this research is to close this research gap and attain a better understanding of the price drivers in REA.
To quantitatively account for the various factors, a discounted cash flow (DCF) model was developed which uses a comprehensive set of factors as input variables. These factors range from parameters pertaining to the country context (e.g. capacity factor, costs of finance, labour and land) and the auction design (e.g. contract length, indexation of contract prices, inclusion/exclusion of connection costs). The DCF model was then used to standardise the achieved contract prices. This standardisation was conducted for solar photovoltaics and for onshore and offshore wind auction results worldwide.
The standardised auction prices yield several interesting findings. First, and most importantly, it is shown that the standardised prices for most countries follow a similar price curve over time, which suggests that the observed price differences in REA are a result of country specific conditions rather than differences in technology costs. Second, it is found that the price decline over time is likely caused by genuine technological progress, learning effects with each auction, and the increasing confidence of investors. Third, the main price drivers in REA are identified to be the capacity factor and the cost of capital, and in the case of offshore wind, also grid connection costs and the water depth.