The next meeting will take place at Utrecht University on Wednesday 25 March 2026.
Scientific focus of this edition: Mathematical and algorithmic methods for trading, forecasting, and risk management in electricity and energy markets.
Location: Cosmos room, Victor J. Koningsberger building, Budapestlaan 4a-b, 3584 CD Utrecht, The Netherlands
Online attendance: link
Registration: here
Schedule:
08:30 - 09:00 : Welcome and Coffee
09:00 - 09:50 : Huyên Pham
09:50 - 10:45 : David Wozabal
10:45 - 11:15: Coffee Break
11:15 - 12:05 : Fang Fang
12:10 - 13:00 : Ronald Huisman
13:10 : Workshop closing
Speakers, Titles and Abstracts:
Huyên Pham (Ecole Polytechnique): Trading with propagators and constraints: applications to optimal execution and optimal storage
Abstract: Motivated by optimal execution with stochastic signals, market impact and constraints in financial markets, and optimal storage management in commodity markets, we formulate and solve an optimal trading problem with a general propagator model under linear functional inequality constraints. The optimal control is given explicitly in terms of the corresponding Lagrange multipliers and their conditional expectations, as a solution to a linear stochastic Fredholm equation. We propose a stochastic version of the Uzawa algorithm on the dual problem to construct the stochastic Lagrange multipliers numerically via a stochastic projected gradient ascent, combined with a least-squares Monte Carlo regression step to approximate their conditional expectations. We illustrate our findings on two different practical applications with stochastic signals: (i) an optimal execution problem with an exponential or a power law decaying transient impact, with either a ‘no-shorting’ constraint in the presence of a ‘sell’ signal, a ‘no-buying’ constraint in the presence of a ‘buy’ signal or a stochastic ‘stop-trading’ constraint whenever the exogenous price drops below a specified reference level; (ii) a battery storage problem with instantaneous operating costs, seasonal signals and fixed constraints on both the charging power and the load capacity of the battery.
David Wozabal (Vrije Universiteit Amsterdam): Opportunities in Algorithmic Trading for Battery Storage in Short-Term Electricity Markets
Abstract: This talk explores the potential, strategies, and challenges of high-frequency algorithmic trading in continuous intraday power markets. We examine key market characteristics and explore the design of profitable high-frequency trading strategies. Additionally, we discuss the opportunities for multi-market trading, integrating the intraday market with the day-ahead market and the market for primary frequency control.
Fang Fang (Return Energy, TU Delft): A Conditional Fourier-Based Tensor Method for Probabilistic Electricity Price Forecasting
Abstract: This study investigates probabilistic forecasting of electricity prices in the Dutch Intraday Continuous (IDC) market, where rising renewable penetration has increased short-term volatility. We contribute to the literature in two ways. First, we replicate and extend recent probabilistic electricity price forecasting (PEPF) models, developing a modified Python implementation of the Generalized Additive Model for Location, Scale, and Shape (GAMLSS) as a benchmark. The main contribution is the Conditional COS-TT framework, a novel method that reformulates probabilistic forecasting as a conditionalexpectation problem and solves it efficiently using the COS-TT approach— a dimension-reduced Fourier-cosine expansion with tensor-train (TT) decomposition. It has two variants: the non-parametric ECF-COS-TT and the parametric COS-TT. Using Dutch IDC data (2021–2022) in arolling-window backtest, the parametric COS-TT model outperforms the GAMLSS-based benchmark across multiple metrics. Parametric COS-TT yields the best point and distributional accuracy is tested to be a promising method for probabilistic electricity price forecasting.
Ronald Huisman (School of Economics (Utrecht), Erasmus School of Economics (Rotterdam)): How does the growth of renewable energy affect the prices of power futures contracts?
Abstract: The presentation will focus on a model of the power market from which power forward and futures prices are derived. The presentation is based on a paper that presents a mathematical representation of power markets, wherein renewable energy sources, fossil fuelled power plants and retailers operate. We introduce mean-variance-skewness preferences as we argue that the growth of renewable energy coincides with more skewed price distributions. The model that we propose has four factors that explain the forward premium. Besides factors related to spot price variance and skewness as found in the existing literature, our model suggests new factors related to the covariance and co-skewness between RES supply uncertainty and spot prices. We provide empirical support for the factors and their predicted signs explaining the forward premiums observed in the German power market.