Publications associées à Forecasting price spikes in European day-ahead electricity markets using decision trees

Dataset for the article "Frequency regulation with storage: On losses and profits"

Daniel Kuhn, François Richard Vuille, Dirk Lauinger

This dataset complements the article "Frequency regulation with storage: On losses and profits" by Dirk Lauinger, François Vuille, and Daniel Kuhn, available at https://arxiv.org/abs/2306.02987. The dataset contains the following files: 1.

Zenodo2023

Enabling inter-area reserve exchange through stable benefit allocation mechanisms *

Maryam Kamgarpour, Gabriela Hug, Orcun Karaca

The establishment of a single European day-ahead market has accomplished the integration of the regional day-ahead markets. However, reserve provision and activation remain an exclusive responsibility of regional operators. This limited spatial coordinatio ...

PERGAMON-ELSEVIER SCIENCE LTD2022

Advanced Frameworks to Aggregate and Unlock the Power Flexibility of Distributed Energy Resources Located in Active Distribution Networks

Mohsen Kalantar Neyestanaki

Environmental concerns are leading to a fast transition in electric power systems towards replacing fossil fuel and nuclear generation with renewable generation. This transition has two significant implications for electric power systems: 1-The capacity o ...

EPFL2022

Predicting involuntary hospitalization in psychiatry: A machine learning investigation

Mehdi Gholam

Background Coercion in psychiatry is a controversial issue. Identifying its predictors and their interaction using traditional statistical methods is difficult, given the large number of variables involved. The purpose of this study was to use machine-lear ...

CAMBRIDGE UNIV PRESS2021

Optimal path to a high share of distributed PV in low-voltage distribution grids

Lionel Benjamin Bloch

To limit global warming, the energy transition from fossil fuels to renewables is one of the main challenges of this century. This transition will help decrease our CO2 emissions, create jobs, contribute to a broader revenues distribution, improve energy s ...

EPFL2020

Wasserstein Distributionally Robust Learning

Soroosh Shafieezadeh Abadeh

Many decision problems in science, engineering, and economics are affected by uncertainty, which is typically modeled by a random variable governed by an unknown probability distribution. For many practical applications, the probability distribution is onl ...

EPFL2020

Industrial flexibility as demand-side response for electrical grid stability

François Maréchal, Ivan Daniel Kantor, Alessio Santecchia

Electricity markets are currently experiencing a period of rapid change. The intermittent nature of renewable energy is disrupting the conventional methods used in operational planning of the electrical grid, causing a shift from a day-ahead forecast polic ...

ECOS 2020 Local Organizing Committee2020

Aspects of the energy transition and their effects on power systems

Laurent Vincent Pagnier

Most European countries are committed to an energy transition which consists in the substitution of conventional CO2 emitting energy sources by new renewable energy sources (RES), in particular wind and solar power. As opposed to conventional energy source ...

EPFL2019

Economic Advantages of Office Buildings Providing Ancillary Services with Intraday Participation

Colin Neil Jones, Ioannis Lymperopoulos, Faran Ahmed Qureshi, Ali Ahmadi Khatir

Controlling the consumption profile of office buildings can be used to provide balancing services to the power grid at a financial benefit without violating its thermal comfort constraints. The economic advantage of such a service is a reduction in the tot ...

2018

On the potential of ruled-based machine learning for disruption prediction on JET

Olivier Sauter, Ambrogio Fasoli, Basil Duval, Stefano Coda, Martinus Gijs, Jonathan Graves, Yves Martin, Duccio Testa, Patrick Blanchard, Bruno Emanuel Ferreira De Sousa Correia

In the last years, it has become apparent that detecting disruptions with sufficient anticipation time is an essential but not exclusive task of predictors. It is also important that the prediction is accompanied by appropriate qualifications of its reliab ...

2018