Operations Research 2018 Abstract Submission

Enhancing Strategic Bidding Optimization for Renewable Energy Auctions: A Risk-Adequate Marginal Cost Model

Invited abstract in session WC-17: Financial Modeling II, stream Finance.

Wednesday, 14:00-15:40
Room: 1q. Rome

Authors (first author is the speaker)

1. Chris Stetter
Information Systems Institute, Leibniz University Hannover
2. Jan-Hendrik Piel
Leibniz Universität Hannover, Institut für Wirtschaftsinformatik
3. André Koukal
Leibniz Universität Hannover, Institut für Wirtschaftsinformatik
4. Michael H. Breitner
Leibniz Universität Hannover, Institut für Wirtschaftsinformatik

Abstract

In recent years, there has been a rapidly increasing number of countries adopting auctions for the allocation of permissions and financial support to renewable energy projects. The shift toward auction mechanisms has introduced competitive price discovery of financial support levels for new projects. In common auction mechanisms, project developers compete by specifying their required sales price per unit of electricity (in €/MWh) as well as a capacity to be installed (in MW) and only the most cost-competitive projects with the lowest required financial support are granted until the auction volume (in MW) is reached. An optimal bidding strategy for these mechanisms always depends on the country-specific auction design. Such strategies commonly propose to obscure the true cost of a project by adding certain premiums on top of the marginal cost in order to maximize the expected profit.

Consequently, the starting point of finding an optimal bidding strategy must always be a reliable determination of the marginal cost, which is the minimum sales price per unit of electricity required to permit an economically viable project construction and operation at an acceptable level of risk. In this study, we thus focus on enhancing the strategic bidding by integrating a holistic financial modelling approach for a risk-adequate quantification of the marginal cost into a strategic bidding optimization model. The latter typically consider traditional discounted cash-flow models without incorporating project-specific risks and uncertainties and, thus, result in a biased and unprecise bidding strategy. We enhance current estimation approaches for the marginal cost by providing a derivative of the adjusted present value with respect to the sales price per unit of electricity. The adjusted present value is based on a state-of-the-art cash-flow calculation combined with a Monte Carlo simulation accounting for project risks.

In order to permit a proof-of-concept and in-depth understanding of our model enhancement, we conducted a simulation study of a wind farm in Lower Saxony, Germany with a prototypical implementation in Python. In particular, the simulation study focuses on the comparison of current estimation approaches and our enhanced approach incorporating the manifold risks and uncertainties into the estimation of cash-flows that determine the optimal bidding strategy to a large extend. The results show significant differences, with quantifiable advantages of our risk-considering adjusted present value method. As our modelling approach permits the direct quantitative incorporation of risks and uncertainties within strategic auction bids, we contribute to an enhanced strategic bidding optimization and comprehensive methodological support for project developers in competitive renewable energy auctions.

Keywords

Status: accepted


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