Jean-François Toubeau

KU Leuven | ku leuven · Section of Applied Mechanics and Energy Conversion

In the current competition framework governing the electricity sector, complex dependencies exist between electrical and market data, which complicates the decision-making procedure of energy actors. These must indeed operate within a complex, uncertain environment, and consequently need to rely on accurate multivariate, multi-step ahead probabilis...

This paper presents a new spatio-temporal framework for the day-ahead probabilistic forecasting of Distribution Locational Marginal Prices (DLMPs). The approach relies on a recurrent neural network, whose architecture is enriched by introducing a deep bidirectional variant designed to capture the complex time dynamics in multi-step forecasts. In or...

High penetration of renewable energy such as wind power and photovoltaic (PV) requires large amounts of flexibility to balance their inherent variability. Making an accurate prediction of the future power system imbalance is an efficient approach to reduce these balancing costs. However, the imbalance is affected not only by renewables but also by...

The integration of distributed energy resources (DER) complicates the operation of the power distribution grids, and the nodal voltage may violate frequently. Making accurate predictions of the nodal voltage is fundamental for voltage regulation of the distribution grid. Even though energy forecasting has been widely studied, voltage is still a rar...

This paper presents a new privacy-preserving framework for the short-term (multi-horizon) probabilistic forecasting of nodal voltages in local energy communities. This task is indeed becoming increasingly important for cost-effectively managing network constraints in the context of the massive integration of distributed energy resources. However, t...

To enhance the quality of energy management tasks, accurately representing the thermal dynamics of buildings is crucial. Traditional methods aim to improve the building model in regards to an arbitrary statistical metric, before feeding the trained model to the optimization-based energy management process. In this paper, we advocate for a more inte...

Many decision-making applications in modern power systems involve solving two sequential problems: (i) predicting unknown parameters and (ii) optimizing decisions under prediction uncertainty. Conventionally, these two stages are applied independently: prediction tools are trained to minimize statistical errors (with the true observations), without...

Explainability is one of the keys to foster the acceptance of Machine Learning (ML) models in safety-critical fields such as power systems. Given an input instance x and a complex ML model f , the driving features of the corresponding output are commonly derived using model-agnostic approaches such as SHAP. Although being generic, such approaches o...

The Optimal Power Flow (OPF) problem is the cornerstone of power systems operations , providing generators' most economical dispatch for power demands by fulfilling technical and physical constraints across the power network. To pledge safe and reliable operation of power systems, grid operators must steadily solve the nonconvex nonlinear OPF probl...

Battery Energy Storage Systems (BESS) may exploit the increasing price volatility in imbalance settlement mechanisms via inter-temporal arbitrage. However, participating in these markets requires a careful trade-off between expected profits, accounting for the impact of BESS actions on prevailing imbalance prices, the financial risks and the incurr...

This paper presents a neural network-constrained optimization model for the optimal scheduling of pumped hydro energy storage. Neural networks are trained offline to capture the complex head-dependent performance curves in both pump and turbine modes using actual operation data. The trained models are then embedded into the optimization framework t...

Considering the increasing proportion of offshore wind generation in the energy mix, it becomes essential to properly account for aerodynamic effects that impact the power extracted from the wind. Indeed, due to computational con-straints, offshore wind energy is currently modelled in a very simple and approximate way in adequacy studies, neglectin...

Day-ahead wind power forecast (DWPF) is an indispensable component for accommodating wind energy in power systems. Traditional DWPF models focus on providing information, merely, around the average wind power for day-ahead periods. This paper goes beyond the traditional approach and formulates a DWPF problem that renders information on intra-period...

Uncertain distributed energy resources and uneven load allocation cause the three-phase unbalance in distribution networks (DNs), which may harm the health of power equipment and increase the operational cost. There are emerging opportunities to balance three-phase DNs with a number of power electronic devices installed in the system. In this paper...

This paper studies the risk management of a battery bidding in both day-ahead and intraday markets arising from the uncertain nature of electricity prices. To this end, a coherent risk measure, Second-order Stochastic Dominance (SSD), which is capable of expressing battery preferences in the form of a preset fixed benchmark (profit), is incorporate...

In competitive electricity markets the optimal trading problem of an electricity market agent is commonly formulated as a bi-level program, and solved as mathematical program with equilibrium constraints (MPEC). In this paper, an alternative paradigm, labeled as mathematical program with neural network constraint (MPNNC), is developed to incorporat...

In competitive electricity markets, the optimal bid or offer problem of a strategic agent is commonly formulated as a bi-level program and solved as a mathematical program with equilibrium constraints (MPEC). If the lower-level part of the problem can be well approximated as a convex problem, this approach leads to a global optimum. However, electr...

Bayesian Optimization (BO) with Gaussian process regression is a popular framework for the optimization of time-consuming cost functions. However, the joint exploitation of BO and parallel processing capabilities remains challenging, despite intense research efforts over the last decade. In particular, the choice of a suitable batch-acquisition pro...

The increasing share of renewables in the electricity generation mix comes along with an increasing uncertainty in power supply. In the recent years, distributionally robust optimization has gained significant interest due to its ability to make informed decisions under uncertainty, which are robust to misrepresentations of the distributional infor...

This paper formulates an energy community’s centralized optimal bidding and scheduling problem as a time-series scenario-driven stochastic optimization model, building on real-life measurement data. In the presented model, a surrogate battery storage system with uncertain state-of-charge (SoC) bounds approximates the portfolio’s aggregated flexibil...

This paper addresses the problem of forecasting, over a daily horizon, quarter hourly profiles of residential photovoltaic (PV) power production for sites with no historical data available. Typically, such forecasts are required for improving the local operation of low-voltage systems, where observability is still a practical challenge. In this con...

Offshore wind generation has developed rapidly in the past few years, leading to an increasing importance in power systems. Therefore, it becomes essential to properly account for aerodynamic effects that affect the power extracted from the wind, and to assess their impact on the power system adequacy. In adequacy studies, due to computational cons...

The current energy transition promotes the convergence of operation between the power and natural gas systems. In that direction, it becomes paramount to improve the modeling of non-convex natural gas flow dynamics within the coordinated power and gas dispatch. In this work, we propose a neural-network-constrained optimization method which includes...

By sharing common assets such as the power grid, prosumers are closely interrelated by their actions and interests. Game theory provides powerful tools for increased coordination among the prosumers to optimize the energy resources. However, depending on the prosumer profiles and the market rules, the individual bills may notably differ and prove t...

To represent the cross-border exchange capacities defined by the flow-based approach in the European resource adequacy assessments, transmission system operators currently employ a data-driven methodology that consists of sequential clustering and correlation steps. This methodology entails assumptions and simplifications within both clustering and...

Background Despite the therapeutic efficacy of Ustekinumab (UST) in Crohn's disease (CD), loss of response (LOR) is observed over time. This study aims to evaluate the impact of the UST pharmacokinetics (PK) at induction on clinical and endoscopic outcomes, as well as to find predictive markers of UST response. Methods This retrospective study inc...

Real-time electricity prices are economic signals incentivizing market players to support real-time system balancing. These price signals typically switch between low- and high-price regimes depending on whether the power system is in surplus or shortage of generation, which is hard to capture. In this context, we propose a new Transformer-based mo...

Electricity markets around the world are opening up to a greater contribution from wind power producers (WPPs). In this regard, WPPs are incentivised to participate in both energy and reserve market floors while being responsible for real-time deviations from their submitted bids. Therefore, despite uncertainties in wind speed and system frequency,...

The increased contribution of uncertain and fluctuating renewable generation, originating mainly from wind and photovoltaic sources, is substantially impacting the operation of power systems. In order to efficiently hedge against these uncertainties, there is a growing need for flexibility that can be provided by Pumped Hydro Energy Storage (PHES)...

Offshore wind generation has developed rapidly in the past few years, leading to an increasing importance in power systems. Therefore, it becomes essential to properly account for aerodynamic effects that affect the power extracted from the wind, and to assess their impact on the power system adequacy. In adequacy studies, due to computational cons...

This paper addresses the problem of forecasting, over a daily horizon, quarter hourly profiles of residential pho-tovoltaic (PV) power production for sites with no historical data available. Typically, such forecasts are required for improving the local operation of low-voltage systems, where observability is still a practical challenge. In this co...

The increasing share of gas-fired power plants in the electricity generation mix has tightened the existing link between power and gas systems. This may bring technical and economical risks, e.g., congestions and peak prices, which mainly stem from the non-coordinated operation of both systems and the failure to properly anticipate the multivariate...

div>This paper formulates an energy community's centralized optimal bidding and scheduling problem as a time-series scenario-driven stochastic optimization model, building on real-life measurement data. In the presented model, a surrogate battery storage system with uncertain state-of-charge (SoC) bounds approximates the portfolio's aggregated flex...

In competitive electricity markets the optimal trading problem of an electricity market agent is commonly formulated as a bi-level program, and solved as mathematical program with equilibrium constraints (MPEC). In this paper, an alternative paradigm, labeled as mathematical program with neural network constraint (MPNNC), is developed to incorporat...

The current wind farm control schemes qualify wind power producers (WPPs) to provide balancing services in complement to energy in modern electricity markets. Accordingly, WPPs are responsible for real-time deviations in both energy and reserve market floors, which are settled at different time scales. WPPs should adjust their output to cope with f...

To represent the cross-border exchange capacities defined by the flow-based approach in the European resource adequacy assessments, transmission system operators currently employ a data-driven methodology that consists of sequential clustering and correlation steps. This methodology entails assumptions and simplifications within both clustering and...

We present a new privacy-preserving probabilistic forecasting of nodal voltage levels in renewable energy communities.

Background The loss of response to biologics remains a clinical challenge for which the discovery of predictive factors is helpful. In this direction, exploiting tree-based models on top of logistic regression (LR) allows to uncover unexpected valuable predictors. The aim of the study is to develop performant predictions of ustekinumab (USK) respon...

In the context of transition towards sustainable, cost-efficient and reliable energy systems, the improvement of current energy and reserve dispatch models is crucial to properly cope with the uncertainty of weather-dependent renewable power generation. In contrast to traditional approaches, distributionally robust optimization offers a risk-aware...

The role of biomass resources to diminish the dependency on fossil fuels is steadily increasing worldwide. More importantly, governments set goals to boost the share of renewable energy resources in the power sector to face up to global warming issues. In this paper, a coalitional game model for the trading of a Biomass Power Plant (BPP) paired wit...

In competitive electricity markets the optimal trading problem of an electricity market agent is commonly formulated as a bi-level program, and solved as mathematical program with equilibrium constraints (MPEC). In this paper, an alternative paradigm, labeled as mathematical program with neural network constraint (MPNNC), is developed to incorporat...

State-of-the-art trading strategies in short-term electricity markets employ risk awareness for reducing, inter alia, their exposure to the volatility of electricity prices. To ensure an optimal balance between risk and profit, risk-aversion parameters are traditionally fine-tuned via an offline out-of-sample analysis. Such a computationally-intens...

This paper presents a new privacy-preserving framework for the short-term (multi-horizon) probabilistic forecasting of nodal voltages in local energy communities. This task is indeed becoming increasingly important for cost-effectively managing network constraints in the context of the massive integration of distributed energy resources. However, t...

div>This paper formulates an energy community's centralized optimal bidding and scheduling problem as a time-series scenario-driven stochastic optimization model, building on real-life measurement data. In the presented model, a surrogate battery storage system with uncertain state-of-charge (SoC) bounds approximates the portfolio's aggregated flex...

This paper presents an energy arbitrage strategy of a lithium-ion Battery Storage System (BSS) in sequential Day-ahead and Intraday (DA+INT) markets, considering its Cycle Aging Cost (CAC). One of the critical queries of the BSS in such problems is how to tackle the risk of uncertain prices in both market floors. Towards this end, a financial risk...

This paper presents a stochastic framework for offering and bidding strategies of a hybrid power generation system (HPGS) with a wind farm and two types of energy storage facilities, i.e., compressed air energy storage (CAES) and battery energy storage (BES) systems. The model considers the participation of the HPGS in consecutive electricity marke...

In competitive electricity markets the optimal trading problem of an electricity market agent is commonly formulated as a bi-level program, and solved as mathematical program with equilibrium constraints (MPEC). In this paper, an alternative paradigm, labeled as mathematical program with neural network constraint (MPNNC), is developed to incorporat...

In competitive electricity markets the optimal trading problem of an electricity market agent is commonly formulated as a bi-level program, and solved as mathematical program with equilibrium constraints (MPEC). In this paper, an alternative paradigm, labeled as mathematical program with neural network constraint (MPNNC), is developed to incorporat...

Virtual Power plants (VPPs) offer a cost-effective solution to incentivize coordination between different resources participating in joint energy and reserve markets. However, emerging technologies such as storage and demand response cannot deliver flexibility over long periods due to inherent energy limitations. In this paper, we therefore inform...

The optimal coordination of maintenances is becoming increasingly important to guarantee the security of supply in renewable-dominated power systems. However, current planning tools are plagued with tractability issues arising from the need to comply with operational security standards. The grid must indeed safely accommodate any unexpected conting...

In this paper, a chance-constrained (CC) framework is developed to manage the voltage control problem of medium-voltage (MV) distribution systems subject to model uncertainty. Such epistemic uncertainties are inherent in distribution system analyses given that an exact model of the network components is not available. In this context, relying on th...

The resource adequacy of the interconnected Central Western Europe (CWE) electricity system is assessed considering the cross-border exchange capacities defined through the Flow-Based (FB) domains. Integration of FB domains into adequacy assessments poses several challenges since the FB domains depend on factors which are not known over the horizon...

Solar energy and bioenergy are two leading renewable forms of energy in the move toward a near-zero-emission electric power industry. Concentrated solar power units coupled with thermal storage and biomass power plant offer dispatchable electricity, raising their ever-growing role in future renewable-dominated networks. This paper proposes a day-ah...

Considering the increasing proportion of offshore wind generation in the energy mix, it becomes essential to properly account for aerodynamic effects that impact the power extracted from the wind. Indeed, due to computational matters, offshore wind energy is currently modelled in a very simple and approximate way in adequacy studies, neglecting imp...

Solar energy and bioenergy are two leading renewable forms of energy in the move toward a near-zero-emission electric power industry. Concentrated solar power units coupled with thermal storage and biomass power plant offer dispatchable electricity, raising their ever-growing role in future renewable-dominated networks. This paper proposes a day-ah...

Passive balancing refers to the intentional deviations of market actors from their position in energy markets to support the real-time system balancing. Such a service is typically incentivized in a single imbalance pricing mechanism, where all imbalances are settled at a unique price. In this context, this paper presents a novel distributionally r...

The growing share of distributed energy resources is leading to deep changes in the electricity landscape. In this context, this work investigates how, in the context of renewable energy communities, a collaborative framework can positively impact global electricity costs. In that way, a tool has been developed with the aim of optimally taking into...

div>This paper presents new risk-based constraints for the participation of an energy community in day-ahead and real-time energy markets. Forming communities offers indeed an effective way to manage the risk of the overall portfolio by pooling individual resources and associated uncertainties. However, the diversity of flexible resources and the r...

div>This paper presents new risk-based constraints for the participation of an energy community in day-ahead and real-time energy markets. Forming communities offers indeed an effective way to manage the risk of the overall portfolio by pooling individual resources and associated uncertainties. However, the diversity of flexible resources and the r...

This paper goes beyond the current state of the art related to Wasserstein distributionally robust optimal power flow problems, by adding dependence structure (correlation) and support information. In view of the space-time dependencies pertaining to the stochastic renewable power generation uncertainty , we apply a moment-metric-based distribution...

Low voltage distribution networks have not been traditionally designed to accommodate the large-scale integration of decentralized photovoltaic (PV) generations. The bidirectional power flows in existing networks resulting from the load demand and PV generation changes as well as the influence of ambient temperature led to voltage variations and in...

Immune-mediated inflammatory diseases are characterized by variability in disease presentation and severity but studying it is a challenging task. Defining the limits of a healthy immune system is therefore a prior step to capture variability in disease conditions. The goal of this study is to characterize the global immune cell composition along w...