Modelling Bidding Behaviour on German Photovoltaic Auctions

Enikő Kácsor
Additional contact information
Enikő Kácsor: Regional Centre for Energy Policy Research, REKK, Corvinus University of Budapest, 1093 Budapest, Hungary

Energies, 2021, vol. 14, issue 2, 1-32

Abstract: In this article renewable energy support allocation through different types of auctions are assessed. The applied methodological framework is auction theory, based on the rules governing the German photovoltaic (PV) Feed-in Premium (FIP) auctions. The work focuses on bidding strategies based on an extended levelised cost of electricity (LCOE) methodology, comparing two different set of rules: uniform price and pay-as-bid. When calculating the optimal bids an iteration is developed to find the Nash-equilibrium optimal bidding strategy. When searching for the bid function, not only strictly monotone functions, but also monotone functions are considered, extending the framework typically applied in auction theory modelling. The results suggest that the PV support allocation in the German auction system would be more cost efficient using the uniform pricing rule, since many participants bid above their true valuation in the pay-as-bid auction Nash-equilibrium. Thus from a cost minimising perspective, the application of uniform pricing rule would be a better policy decision.

Keywords: pay-as-bid; uniform price; renewable; auction; optimal bidding (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/14/2/516/pdf (application/pdf)
https://www.mdpi.com/1996-1073/14/2/516/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:14:y:2021:i:2:p:516-:d:483242

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().