All Publications
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Unlocking Transmission Flexibility Under Uncertainty: Getting Dynamic Line Ratings Into Electricity Markets
IEEE TRANSACTIONS ON ENERGY MARKETS POLICY AND REGULATION
2026; 4 (2): 274-291
View details for DOI 10.1109/TEMPR.2025.3636820
View details for Web of Science ID 001795874800002
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Simplified short-term electricity market designs: Evidence from Europe
ELECTRICITY JOURNAL
2026; 39 (1)
View details for DOI 10.1016/j.tej.2025.107486
View details for Web of Science ID 001754485400001
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Cost-Effective Capacity Markets
ENERGY JOURNAL
2025
View details for DOI 10.1177/01956574251379783
View details for Web of Science ID 001594640800001
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Multi-Objective Transmission Expansion: An Offshore Wind Power Integration Case Study
IEEE TRANSACTIONS ON ENERGY MARKETS POLICY AND REGULATION
2024; 2 (4): 519-535
View details for DOI 10.1109/TEMPR.2024.3390760
View details for Web of Science ID 001485009900004
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Computational Performance of Deep Reinforcement Learning to Find Nash Equilibria.
Computational economics
2024; 63 (2): 529-576
Abstract
We test the performance of deep deterministic policy gradient-a deep reinforcement learning algorithm, able to handle continuous state and action spaces-to find Nash equilibria in a setting where firms compete in offer prices through a uniform price auction. These algorithms are typically considered "model-free" although a large set of parameters is utilized by the algorithm. These parameters may include learning rates, memory buffers, state space dimensioning, normalizations, or noise decay rates, and the purpose of this work is to systematically test the effect of these parameter configurations on convergence to the analytically derived Bertrand equilibrium. We find parameter choices that can reach convergence rates of up to 99%. We show that the algorithm also converges in more complex settings with multiple players and different cost structures. Its reliable convergence may make the method a useful tool to studying strategic behavior of firms even in more complex settings.
View details for DOI 10.1007/s10614-022-10351-6
View details for PubMedID 38304891
View details for PubMedCentralID PMC10827988
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Frequent Auctions for Intraday Electricity Markets
ENERGY JOURNAL
2024; 45 (1): 231-256
View details for DOI 10.5547/01956574.45.1.cgra
View details for Web of Science ID 001196096900007
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Computational Performance of Deep Reinforcement Learning to Find Nash Equilibria (Jan 2023, 10.1007/s10614-022-10351-6)
COMPUTATIONAL ECONOMICS
2023
View details for DOI 10.1007/s10614-023-10360-z
View details for Web of Science ID 001011236900001
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Computational Performance of Deep Reinforcement Learning to Find Nash Equilibria
COMPUTATIONAL ECONOMICS
2023
View details for DOI 10.1007/s10614-022-10351-6
View details for Web of Science ID 000906829600001
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Is Daylight Saving Time worth it in tourist regions?*
TOURISM MANAGEMENT PERSPECTIVES
2023; 45
View details for DOI 10.1016/j.tmp.2022.101068
View details for Web of Science ID 000922700900001
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(Machine) learning from the COVID-19 lockdown about electricity market performance with a large share of renewables
JOURNAL OF ENVIRONMENTAL ECONOMICS AND MANAGEMENT
2021; 105
View details for DOI 10.1016/j.jeem.2020.102398
View details for Web of Science ID 000607089900003
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Installation entries and exits in the EU ETS: patterns and the delay effect of closure provisions
ENERGY ECONOMICS
2019; 78: 508-524
View details for DOI 10.1016/j.eneco.2018.11.032
View details for Web of Science ID 000462105100038
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Renewable energy and its impact on thermal generation
ENERGY ECONOMICS
2017; 66: 421-430
View details for DOI 10.1016/j.eneco.2017.07.009
View details for Web of Science ID 000412033900040
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The effect of intermittent renewables on the electricity price variance
OR SPECTRUM
2016; 38 (3): 687-709
View details for DOI 10.1007/s00291-015-0395-x
View details for Web of Science ID 000378820400007
https://orcid.org/0000-0003-2477-9082