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Competitive and risk-adequate auction bids for onshore wind projects in Germany

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  • Stetter, Chris
  • Piel, Jan-Hendrik
  • Hamann, Julian F.H.
  • Breitner, Michael H.

Abstract

In recent years, auction mechanisms have gained in significance in the context of renewable energy deployment. An increasing number of countries have adopted auctions for the allocation of permits and financial support for renewable energy projects, thereby increasing competition among project developers. As a result, profit margins have decreased significantly while sensitivity to risks and uncertainty has increased. The adequate quantification of bid prices is a key challenge. We present a modeling approach to determine competitive and risk-adequate auction bids. The contribution of this paper is an improved method for quantifying marginal cost, which is the minimum sales price per unit of electricity through which the investment criteria of all project stakeholders are fulfilled. In our financial model, the risk-adequateness is determined through the investment criteria of equity investors by means of the adjusted present value, and those of debt investors by means of the debt service cover ratio, through Monte Carlo simulations. The resulting marginal cost serve as the starting point for strategic bidding optimization, regardless of the pricing rule in the contemplated auction design. To demonstrate the integrability of our mathematical model with strategic bidding optimization, we check its applicability in a case study, which shows how a German project developer should bid to realize an onshore wind farm project. We show that our model enables the quantification of bid prices that are both competitive and risk-adequate.

Suggested Citation

  • Stetter, Chris & Piel, Jan-Hendrik & Hamann, Julian F.H. & Breitner, Michael H., 2020. "Competitive and risk-adequate auction bids for onshore wind projects in Germany," Energy Economics, Elsevier, vol. 90(C).
    • Handle: RePEc:eee:eneeco:v:90:y:2020:i:c:s0140988320301894
    DOI: 10.1016/j.eneco.2020.104849
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    5. Min-Ji Park & Eul-Bum Lee & Seung-Yeab Lee & Jong-Hyun Kim, 2021. "A Digitalized Design Risk Analysis Tool with Machine-Learning Algorithm for EPC Contractor’s Technical Specifications Assessment on Bidding," Energies, MDPI, vol. 14(18), pages 1-31, September.
    6. Sheykhha, Siamak & Borggrefe, Frieder & Madlener, Reinhard, 2022. "Policy implications of spatially differentiated renewable energy promotion: A multi-level scenario analysis of onshore wind auctioning in Germany," Energy Policy, Elsevier, vol. 169(C).

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