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Efficient temperature estimation for thermally stratified storage tanks with buoyancy and mixing effects

Soares, Ana (author), Camargo, Juliano (author), Al-Koussa, Jad (author), Diriken, Jan (author), Van Bael, Johan (author), Lago, Jesus (author)

Estimating the state thermal storage devices is key to use them efficiently to reduce the uncertainty of renewable sources. Although stratified storage tanks are one of the most efficient and cost-effective storage systems, they lack accurate state estimation methods. In this paper, we propose a general methodology for estimating the state of...

journal article 2022

document

Making the most of short-term flexibility in the balancing market: Opportunities and challenges of voluntary bids in the new balancing market design

Poplavskaya, K. (author), Lago, Jesus (author), Strömer, Stefan (author), De Vries, Laurens (author)

Electricity balancing is one of the main demanders of short-term flexibility. To improve its integration, the recent regulation of the European Union introduces a common standalone balancing energy market. It allows actors that have not participated or not been awarded in the preceding balancing capacity market to participate as voluntary...

journal article 2021

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Electricity price forecasting in European Day Ahead Markets: a greedy consideration of market integration

van der Heijden, T.J.T. (author), Lago, Jesus (author), Palensky, P. (author), Abraham, E. (author)

In this manuscript we explore European feature importance in Day Ahead Market (DAM) price forecasting models, and show that model performance can deteriorate when too many features are included due to over-fitting. We propose a greedy algorithm to search over candidate countries for European features to be used in a DAM price forecasting model,...

journal article 2021

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Optimal Control Strategies for Seasonal Thermal Energy Storage Systems with Market Interaction

Lago, Jesus (author), Suryanarayana, Gowri (author), Sogancioglu, Ecem (author), De Schutter, B.H.K. (author)

Seasonal thermal energy storage systems (STESSs) can shift the delivery of renewable energy sources and mitigate their uncertainty problems. However, to maximize the operational profit of STESSs and ensure their long-term profitability, control strategies that allow them to trade on wholesale electricity markets are required. While control...

journal article 2021

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A probabilistic building characterization method for district energy simulations

De Jaeger, Ina (author), Lago, Jesus (author), Saelens, Dirk (author)

To assess the impact of implementing energy efficiency and renewable energy measures, urban building energy models are emerging. In these models, due to the lack of data, the natural variability of the existing building stock is often highly underestimated and uncertainty on the simulated energy use arises. Therefore, this work proposes a...

journal article 2021

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A data driven method for optimal sensor placement in multi-zone buildings

Suryanarayana, Gowri (author), Arroyo, Javier (author), Helsen, Lieve (author), Lago, Jesus (author)

In this paper, we propose a data-driven methodology to identify the optimal placement of sensors in a multi-zone building. The proposed methodology is based on statistical tests that study the (in) dependence of measurements from various available sensors. The tests advice on a set of most dissimilar sensors to be retained, as they would...

journal article 2021

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Forecasting day-ahead electricity prices: A review of state-of-the-art algorithms, best practices and an open-access benchmark

Lago, Jesus (author), Marcjasz, Grzegorz (author), De Schutter, B.H.K. (author), Weron, Rafał (author)

While the field of electricity price forecasting has benefited from plenty of contributions in the last two decades, it arguably lacks a rigorous approach to evaluating new predictive algorithms. The latter are often compared using unique, not publicly available datasets and across too short and limited to one market test samples. The...

review 2021

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Scenario-based nonlinear model predictive control for building heating systems

Pippia, T.M. (author), Lago, Jesus (author), De Coninck, Roel (author), De Schutter, B.H.K. (author)

State-of-the-art Model Predictive Control (MPC) applications for building heating adopt either a deterministic controller together with a nonlinear model or a linearized model with a stochastic MPC controller. However, deterministic MPC only considers one single realization of the disturbances and its performance strongly depends on the...

journal article 2021

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A market framework for grid balancing support through imbalances trading

Lago, Jesus (author), Poplavskaya, K. (author), Suryanarayana, Gowri (author), De Schutter, B.H.K. (author)

To correct grid imbalances and avoid grid failures, the transmission system operator (TSO) deploys balancing reserves and settles these imbalances by penalizing the market actors that caused them. In several countries, it is forbidden to influence the grid imbalances in order to let the TSO retain full control of grid regulation. In this...

journal article 2021

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Incentivizing renewables and reducing grid imbalances through market interaction: A forecasting and control approach

Lago, Jesus (author)

As the penetration of renewable energy sources (RESs) increases, so does the dependence of electricity production on weather and, in turn, the uncertainty in electricity generation, the volatility in electricity prices, and the imbalances between production and consumption. In this context, while RES integration does complicate grid balance and...

doctoral thesis 2020

document

Fault diagnosis in low voltage smart distribution grids using gradient boosting trees

Sapountzoglou, Nikolaos (author), Lago, Jesus (author), Raison, Bertrand (author)

In this paper, a gradient boosting tree model is proposed to detect, identify and localize single-phase-to-ground and three-phase faults in low voltage (LV) smart distribution grids. The proposed method is based on gradient boosting trees and considers branch-independent input features to be generalizable and applicable to different grid...

journal article 2020

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Effect of market design on strategic bidding behavior: Model-based analysis of European electricity balancing markets

Poplavskaya, K. (author), Lago, Jesus (author), De Vries, Laurens (author)

Market-based procurement of balancing services in Europe is prone to strategic bidding due to the relatively small market size and a limited number of providers. In the European Union, balancing markets are undergoing substantial regulatory changes driven the efforts to harmonize the market design and better align it with the goals of the...

journal article 2020

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A generalizable and sensor-independent deep learning method for fault detection and location in low-voltage distribution grids

Sapountzoglou, Nikolaos (author), Lago, Jesus (author), De Schutter, B.H.K. (author), Raison, Bertrand (author)

Power outages in electrical grids can have very negative economic and societal impacts rendering fault diagnosis paramount to their secure and reliable operation. In this paper, deep neural networks are proposed for fault detection and location in low-voltage smart distribution grids. Due to its key properties, the proposed method solves some...

journal article 2020

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Building day-ahead bidding functions for seasonal storage systems: A reinforcement learning approach

Lago, Jesus (author), Sogancioglu, Ecem (author), Suryanarayana, Gowri (author), Ridder, Fjo De (author), De Schutter, B.H.K. (author)

Due to the increasing integration of renewable sources in the electrical grid, electricity generation is expected to become more uncertain. In this context, seasonal thermal energy storage systems (STESSs) are key to shift the delivery of renewable energy sources and tackle their uncertainty problems. In this paper, we propose an optimal...

journal article 2019

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A 1-dimensional continuous and smooth model for thermally stratified storage tanks including mixing and buoyancy

Lago, Jesus (author), De Ridder, Fjo (author), Mazairac, Wiet (author), De Schutter, B.H.K. (author)

To mitigate the effects of the intermittent generation of renewable energy sources, reliable and efficient energy storage is critical. Since nearly 80% of households energy consumption is destined to water and space heating, thermal energy storage is particularly important. In this context, we propose and validate a new model for one of the...

journal article 2019

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Scenario-based model predictive control approach for heating systems in an office building

Pippia, T.M. (author), Lago, Jesus (author), Coninck, Roel De (author), Sijs, J. (author), De Schutter, B.H.K. (author)

In the context of building heating systems control in office buildings, the current state-of-the-art applies either a deterministic Model Predictive Control (MPC) controller together with a nonlinear model, or a linearized model with a stochastic MPC controller. Deterministic MPC considers only one realization of the external disturbances,...

conference paper 2019

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Forecasting spot electricity prices: Deep learning approaches and empirical comparison of traditional algorithms

Lago, Jesus (author), De Ridder, Fjo (author), De Schutter, B.H.K. (author)

In this paper, a novel modeling framework for forecasting electricity prices is proposed. While many predictive models have been already proposed to perform this task, the area of deep learning algorithms remains yet unexplored. To fill this scientific gap, we propose four different deep learning models for predicting electricity prices and...

journal article 2018

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Forecasting day-ahead electricity prices in Europe: The importance of considering market integration

Lago, Jesus (author), De Ridder, Fjo (author), Vrancx, Peter (author), De Schutter, B.H.K. (author)

Motivated by the increasing integration among electricity markets, in this paper we propose two different methods to incorporate market integration in electricity price forecasting and to improve the predictive performance. First, we propose a deep neural network that considers features from connected markets to improve the predictive...

journal article 2018

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Thermal load forecasting in district heating networks using deep learning and advanced feature selection methods

Suryanarayana, Gowri (author), Lago, Jesus (author), Geysen, Davy (author), Aleksiejuk, Piotr (author), Johansson, Christian (author)

Recent research has seen several forecasting methods being applied for heat load forecasting of district heating networks. This paper presents two methods that gain significant improvements compared to the previous works. First, an automated way of handling non-linear dependencies in linear models is presented. In this context, the paper...

journal article 2018

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Short-term forecasting of solar irradiance without local telemetry: A generalized model using satellite data

Lago, Jesus (author), De Brabandere, Karel (author), De Ridder, Fjo (author), De Schutter, B.H.K. (author)

Due to the increasing integration of solar power into the electrical grid, forecasting short-term solar irradiance has become key for many applications, e.g. operational planning, power purchases, reserve activation, etc. In this context, as solar generators are geographically dispersed and ground measurements are not always easy to obtain,...

journal article 2018