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Merit Order Effect In The Transitional Tender Process In Germany

How to compare your project with peers for the coming transitional tenders

The recent decision of the cabinet on the new EEG 2017 (German Renewables Energy Act) has been digested and the new legislation is available in its final version. As time will be critical, tender preparations are already ongoing in many project owners’ offices.  We at DNV GL have done our homework and considered our contribution to help our customers to best “qualify” for the upcoming tenders.

The first set of tenders to come, according to the EEG 2017, are the so called transitional tenders which means they have already consented offshore wind farm projects (sites) will compete. According to our assessment there will be more than 20 projects with 7 GW potential capacity in the North and Baltic Sea that are qualified to participate. These projects are competing on a tendered and available grid capacity of 1,55 GW in 2017 & 1,55 GW in 2018.

Thus only the most cost effective projects will win the bid. The qualified offshore wind farm projects compare as displayed in the graph:

blog-graph-1

That of course will raise several questions such as: Is there a strategic component? What will be the environment in which you will bid? Is it worth bidding? When to bid?

There are a lot of questions that should be answered in a technical meaningful way to best prepare the bid

We have pulled together our most talented advisors & engineers and brainstormed how we as a technical advisor can best answer these questions and provide qualified and meaningful input. However one thing was clear from the start: we need to come up with a quantitative solution as we like numbers…

Our approach: As simple as powerful!

We decided to assess the list of the qualified projects in a detailed quantitative assessment considering all available and relevant offshore wind farm data of the individual offshore wind farms. Thus the wording “Benchmarking”  was born and our current market intelligence collected in DNV GL Offshore Wind Farm Database [2] and DNV GL Offshore Wind Farm Spatial Cost Model [1] were considered as the systematic approach to this task. We decided instead of benchmarking or comparing only the primary parameters (such as distance to shore, water depth, etc), the solution should include the secondary parameters as well. These parameters include, but are not limited to:

The overall cost of energy from an offshore wind farm is influenced by a long list of factors. The technical benchmarking will involve the use of DNV GL’s proven offshore wind cost model to assess the impact of each factor on the cost of energy from each wind farm, resulting in an estimated Levelised Cost of Energy for each project. The combined results for all of the benchmarked projects have then been ranked for comparison.

Some primary parameters have not been publically available (turbine type, O&M plan, etc.). In the event that this information is not available, we made reasonable assumptions for the analysis. Based on the information at hand by public sources or evaluated via engineering guesses, we came up with a set of input parameters for a detailed Cost of Energy calculation. This then leads to a ranking between the individual projects based on the Levelised Cost of Energy calculation. Thus the “first level of competition” is the comparison of all Offshore Wind Farms against each other and a ranking of all projects. The following picture/graph gives a good idea of the results schema:

blog-pic-1

This graph was also seen as the “merit order” graph – the right hand projects will drop off due to non-competitive prices (driven by the environmental parameters at the site).

In interpreting this, there is also a second level on competition according to the recent interpretation of the EEG 2016 the competition on the individual and specific planned converters. And that is an interesting one – again the following picture/graph gives a good idea of the results schema:

blog-pic-2

We see a strategic advantage for later installed projects

Again when looking into these results, we had to learn that there is a timely order of the projects (year commissioned/installed) and this really is a crucial input parameter. The later installed offshore wind farms (driven by the fact that the location they sited will receive grid in a later point in time) have a strategic advantage. The later the project can procure technology, the more modern technology can be utilised. In terms of turbine technology, the later projects can make usage of more modern turbine technology (bigger rated power and rotor diameter) and  according to our results this is a benefit, as internal wake is reduced and other effects lead to a more competitive Levelised Cost Of Energy.

Unfortunately, we were confronted then to a new challenging question: what kind of turbine technology will be available in the course of this transitional tender period? More precise: what will the turbine technology look like in 2025? Are we installing 15 MW turbines which come with a competitive 850€/MW price tag? Or will we see 8 MW capacity saturation and a seller market when it comes to prices? Luckily we have a turbine technology unit that could also answer these questions in a very quantitative and specific manner. They have provided a turbine technology outlook reporting to us that was a good guidance for the right assessment of the individual offshore wind farms and a likely turbine choice.

However that is a different story that I or one of my colleagues will tell in a following blog in order to give you an impression on our opinion on the technology development…


[1] The DNV GL Offshore Wind Farm Spatial Cost Model (“the Model”) has been developed to enable estimation of the values of CapEx, OpEx and energy production for a variety of offshore wind farm configurations; the results can then be compared across different projects. The model was originally developed in 2011 and builds on previous work such as the Windspeed EU R&D project and modelling for the UK Crown Estate. The model is regularly updated with data from across the offshore wind industry to maintain accuracy and relevance. The cost centres of the model are described in the table below.

[2] The DNV GL Offshore Wind Farm Database was developed over years and filled with all available project specific information. The setup allows extracting and further specifying details of the individual offshore wind farms. Thus e.g. the underlying conceptual design formulas give an indication of the most beneficial foundation type with respect to the preferred turbine type and water depth.

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