Beyond LCOE – Economic performance of renewable auctions

This study analyses how different types of remuneration schemes in renewable auctions can lead to economically efficient or inefficient outcomes in technology-specific and multi-technology settings. The main metric in this assessment is the Extended Support Need (ESN) indicator – defined as the balance between social costs and benefits of variable renewable energy source (vRES) technologies – incorporating not only the costs of vRES development through their LCOE values but also their market benefits for society measured by the market value of these technologies. Depending on the level of the ESN and the technology set-up, the analysed three remuneration schemes (one-sided, two-sided or fixed premium scheme) perform very differently from an economic efficiency point of view. In the negative ESN range (meaning technologies require support) fixed premium schemes are the most advantageous, but in case of positive ESNs two-sided schemes may outperform the others. However, sliding premium systems may present technology selection bias in a multi-technology setup applied in many EU countries, meaning that suboptimal technology is selected in the auction. The findings show that fixed premium schemes should be the preferred option if adjusted to accommodate positive ESN projects by introducing the option of negative bids.

Authors

Institutions

Publication Details

Journal
Energy Reports
Published
2026-10-09
DOI
https://doi.org/10.1016/j.egyr.2026.109729
Primary Topic
Renewable Energy and Sustainability
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Beyond LCOE – Economic performance of renewable auctions

Sandor Szabo, András Mezősi, László Szabó, Alfa Diallo et al.
Energy Reports
Renewable Energy and Sustainability
article

Beyond LCOE – Economic performance of renewable auctions

Sandor Szabo, András Mezősi, László Szabó, Alfa Diallo, László Paizs
article en

Abstract

This study analyses how different types of remuneration schemes in renewable auctions can lead to economically efficient or inefficient outcomes in technology-specific and multi-technology settings. The main metric in this assessment is the Extended Support Need (ESN) indicator – defined as the balance between social costs and benefits of variable renewable energy source (vRES) technologies – incorporating not only the costs of vRES development through their LCOE values but also their market benefits for society measured by the market value of these technologies. Depending on the level of the ESN and the technology set-up, the analysed three remuneration schemes (one-sided, two-sided or fixed premium scheme) perform very differently from an economic efficiency point of view. In the negative ESN range (meaning technologies require support) fixed premium schemes are the most advantageous, but in case of positive ESNs two-sided schemes may outperform the others. However, sliding premium systems may present technology selection bias in a multi-technology setup applied in many EU countries, meaning that suboptimal technology is selected in the auction. The findings show that fixed premium schemes should be the preferred option if adjusted to accommodate positive ESN projects by introducing the option of negative bids.

Energy ReportsVol. 16
Corvinus University of Budapest (HU), Joint Research Centre (IT), Regionális Energiagazdasági Kutatóközpont (Hungary) (HU)
Nemzeti Kutatási és Technológiai Hivatal, Joint Research Centre
Affordable and clean energy, Climate action
Openalex Percentile: Top 34%
Renewable Energy and Sustainability
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.