F.-Javier Heredia - Academic web site

Publication Type	Conference Paper
Year of Publication	2024
Authors	Albert Solà Vilalta, F.-Javier Heredia
Conference Name	EURO24, 33rd European Conference on Operational Research
Conference Date	30/06-3/07/2024
Conference Location	Technical University of Denmark (DTU), Copenhagen, Denmark.
Type of Work	Contributed presentation
Key Words	electricity markets; energy communities, mathematical optimization; stochastic programming; research
Abstract	An energy community is a legal figure, recently coined by the European Union, that creates a framework to encourage active participation of citizens and local entities in the energy transition to net-zero. In this work, we study the optimal participation of energy communities in day-ahead, reserve, and intraday electricity markets. The motivation to do so is that there are time periods where energy communities cannot meet their internal demand, and periods where they generate excess electricity. This is because most of the electricity they generate comes from variable renewable resources like solar and wind. Electricity market participation is a natural way to ensure they meet their internal demand at all times, and, simultaneously, make the most of the excess electricity. We propose a multi-stage stochastic programming model that captures variable renewable and electricity price uncertainty. The multi-stage aspect models the dierent times at which variable renewable generation is considered to be known and electricity prices from dierent markets are revealed. This results in a very large scenario tree with 34 stages, and hence a very large optimization problem. Scenario reduction techniques are applied to make the problem tractable. Case studies with real data are discussed, considering dierent energy community configurations, to analyse proposed regulatory frameworks in Europe. The added value of considering stochasticity in this problem is also analysed.
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Publication Type	Conference Paper
Year of Publication	2022
Authors	F.-Javier Heredia; Ignasi Mañé; Marlyn Dayana Cuadrado Guevara
Conference Name	EURO 2022
Conference Date	03-06/07/2022
Conference Location	Espoo, Finland.
Type of Work	Invited presentation
ISBN Number	978-951-95254-1-9
Key Words	research; multistage stochastic programming; virtual power plants; unit commitment
Abstract	In this study we present a multistage stochastic programming model to find the joint optimal bid to electricity markets of a pool of dispatchable (thermal) and non-dispatchable (wind) production units with battery storage facilities. The assumption is that these programming units are operated by the same utility that, previous to the market clearing, has to dispatch some bilateral contracts with the joint production of the production pool. The multistage model mimics the multimarket bidding process in the Iberian Electricity Market (MIBEL). First, the utility has to decide how to cover the energy of the bilateral contracts with the available units. Second, the production capacity of each unit, not allocated to the bilateral contracts, must be offered to the seven consecutives spot markets (day-ahead and six intraday markets) plus the secondary reserve market (the most relevant ancillary services market). The stochasticity of the electricity clearing prices and the hourly generation of the wind-power units is considered. The stochastic process associated to this multistage decision-making process is modelled through multistage scenario trees with thirty-four stages that are built from forecasting models based on real data of the Iberian Electricity Market. The numerical results show the advantage of the joint operation of the pool of production units with an increase of the overall expected profits, mainly due to a strong reduction of the operational costs.
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Publication Type	Conference Paper
Year of Publication	2021
Authors	Marlyn Dayana Cuadrado Guevara; F.-Javier Heredia
Conference Name	31st European Conference on Operational Research.
Conference Date	11-14/07/2021
Conference Location	Athens
Type of Work	Invited presentation
ISBN Number	ISBN 978-618-85079-1-3
Key Words	research; multistage stochastich programming; virtual power plants; electricity markets; scenarios tree generation
Abstract	The presence of renewables in electricity markets optimization have generated a high level of uncertainty in the data, which has led to a need for applying stochastic optimization to model this kind of problems. In this work, we apply Multistage Stochastic Programming (MSP) using scenario trees to represent energy prices and wind power generation. We developed a methodology of two phases where, in the first phase, a procedure to predict the next day for each random parameter of the MSP models is used, and, in the second phase, a set of scenario trees are built through Forward Tree Construction Algorithm (FTCA) and a modified Dynamic Tree Generation with a Flexible Bushiness Algorithm (DTGFBA). This methodology was used to generate scenario trees for the Multistage Stochastic Wind Battery Virtual Power Plant model (MSWBVPP model), which were based on MIBEL prices and wind power generation of a real wind farm in Spain. In addition, we solved three dierent case studies corresponding to three dierent hypotheses on the virtual power plant’s participation in electricity markets. Finally, we study the relative performance of the FTCA and DTGFBA scenario trees, analysing the value of the stochastic solution through the Forecasted Value of the Stochastic Solution (FVSS) and the classical VSS for the 366 daily instances of the MSWBVPP problem spanning a complete year.
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On November 30th 2020 took place the defense of the Ph.D. Thesis entittled "Multistage Scenario Trees Generation for Renewable Energy Systems Optimization", authored by Ms. Marlyn D. Cuadrado Guevara and advised by prof. F.-Javier Heredia. In this thesis a new methodology to generate and validate probability scenario trees for multistage stochastic programming problems arising in two different energy systems with renewables are proposed. The first problem corresponds to the optimal bid to electricity markets of a virtual power plant that consists on a wind-power plant plus a battery storage energy systems. The second one is the optimal operation of a distribution grid with some photovoltaic production.

Publication Type	Thesis
Year of Publication	2020
Authors	Marlyn Dayana Cuadrado Guevara
Academic Department	Dept. of Statistics and Operations Research. Prof. F.-Javier Heredia, advisor.
Number of Pages	194
University	Universitat Politècnica de Catalunya
City	Barcelona
Degree	PhD Thesis
Key Words	research; Battery energy storage systems; Electricity markets; Ancillary services market; Wind power generation; Virtual power plants; Multistage Stochastic programming; phd thesis
Abstract	The presence of renewables in energy systems optimization have generated a high level of uncertainty in the data, which has led to a need for applying stochastic optimization to modelling problems with this characteristic. The method followed in this thesis is Multistage Stochastic Programming (MSP). Central to MSP is the idea of representing uncertainty (which, in this case, is modelled with a stochastic process) using scenario trees. In this thesis, we developed a methodology that starts with available historical data; generates a set of scenarios for each random variable of the MSP model; defines individual scenarios that are used to build the initial stochastic process (as a fan or an initial scenario tree); and builds the final scenario trees that are the approximation of the stochastic process.
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Publication Type	Tesis de Grau i Màster // BSc and MSc Thesis
Year of Publication	2019
Authors	Roger Serra Castilla
Director	F.- Javier Heredia; Marlyn D. Cuadrado
Tipus de tesi	BSc Thesis
Titulació	Grau en Estadística
Centre	Facultat de matemàtiques i Estadística
Data defensa	01/2019
Nota // mark	9.0
Key Words	teaching; scenario generation; scenario trees; electricity markets; BSc Thesis
Abstract	The electricity markets (EM) is a regulated system which allows producers and consumers to sell and buy energy at a given price that is fixed through an auction mechanism. Thanks to the tasks done by this system, the safe generation, transportation and distribution of electricity needed to satisfy the demand of the national population is assured. The electricity markets is made up of several markets, that can be considered either spot markets (day-ahead and intraday markets, whose trading commodity is energy) or ancillary services markets (the commodity negotiated is energy used to assure the stability of the energy delivering). On the other hand, interacting with the EM there is a wind power plant (WPP) which is in charge of the wind power energy production. A battery energy storage system (BESS) is usually associated to the WPP. The union of the WPP and the BESS is called virtual power plant (VPP). In this context, an optimization model can be presented: the WBVPP model (WPP+BESS Virtual Power Plant). The aim of this optimization model is the maximization of the expected value of the total profit of the VPP. To calculate this value, it is necessary to quantify the amount of wind power energy that fluctuates between the VPP and the EM, as well as the clearing prices of the auctions. The main purpose of this project is to obtain scenario trees using a dynamic generation algorithm in order to satisfy the need to solve, in a reasonable period of time, complex optimization problems of optimal supply of wind power plants in electricity markets. The scenarios trees that are going to be obtained include information regarding the production of wind power energy and the clearing prices of the auctions of the different studied electricity markets. In this study a scenario tree generation algorithm is presented, together with all the theoretical background needed to understand and assure a correct implementation of it, as well as the definition of the different agents that perform in the context of electricity markets. In addition to that, some data will be applied to this algorithm in order to obtain a representation of the scenario trees and to understand the interpretation of them.
DOI / handle	http://hdl.handle.net/2445/128065
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Publication Type	Conference Paper
Year of Publication	2017
Authors	F.-Javier Heredia; Marlyn D. Cuadrado; J.-Anton Sánchez
Conference Name	4th International Conference on Optimization Methods and Software 2017
Conference Date	16-21/12/2017
Conference Location	La Havana
Type of Work	Invited presentation
Key Words	multistage; VSS; wind-BESS VPP; wind power; energy storage; battery; research
Abstract	One of the objectives of the FOWGEN project (https://fowgem.upc.edu) was to study the economic feasibility and optimal operation of a wind-BESS Virtual Power Plant (VPP): In [1] an ex-post economic analysis shows the economic viability of a wind-BESS VPP thanks to the optimal operation in day-ahead and ancillary electricity markets; In [2] a new multi-stage stochastic programming model (WBVPP)for the optimal bid of a wind producer both in spot and ancillary services electricity markets is developed. The work presented here extends the study in [2] with a new methodology to treat the uncertainty, based in forecasting models, and the study of the quality of the stochastic solution. [1] F-Javier Heredia et al. Economic analysis of battery electric storage systems operating in electricity markets 12th International Conference on the European Energy Market (EEM15), 2015 DOI: 10.1109/EEM.2015.7216739. [2] F-Javier Heredia et al. On optimal participation in the electricity markets of wind power plants with battery energy storage system. Submitted, under second revision. 2017.
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Publication Type	Proceedings Article
Year of Publication	2015
Authors	Leire Citores; Cristina Corchero; F.-Javier Heredia
Conference Name	12th International Conference on the European Energy Market (EEM15)
Pagination	1-6
Conference Start Date	19-22/05/2015
Publisher	IEEE
Conference Location	Lisbon, Portugal.
ISBN Number	978-1-4673-6691-5
Key Words	Microgrids; Optimization; Production; Stochastic processes; Uncertainty; Wind power generation; Wind speed; energy system optimization; microgrid; scenario generation; stochastic programming; paper; research
Abstract	In this work a scenario-based two-stage stochastic programming model is proposed to solve a microgrid's tertiary control optimization problem taking into account some renewable energy resource's uncertainty as well as uncertain energy deviation prices in the electricity market. Scenario generation methods for wind speed realizations are also studied. Results show that the introduction of stochastic programming represents a significant improvement over a deterministic model.
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DOI	10.1109/EEM.2015.7216761
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Publication Type	Conference Paper
Year of Publication	2015
Authors	Leire Citores; Cristina Corchero; F.-Javier Heredia
Conference Name	12th International Conference on the European Energy Market
Conference Date	19-22/05/2015
Conference Location	Lisbon, Portugal
Type of Work	contributed presentation
Key Words	research; MTM2013-48462-C2-1; microgrid; stochastic programming; scenario generation; wind power
Abstract	In this work a scenario-based two-stage stochastic programming model is proposed to solve a microgrid’s tertiary control optimization problem taking into account some renewable energy resource’s uncertainty as well as uncertain energy deviation prices in the electricity market. Scenario generation methods for wind speed realizations are also studied. Results show that the introduction of stochastic programming represents a significant improvement over a deterministic model.
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Publication Type	Tesis de Grau i Màster // BSc and MSc Thesis
Year of Publication	2014
Authors	Leire Citores
Director	F.-Javier Heredia, Cristina Corchero
Tipus de tesi	MSc Thesis
Titulació	Master in Statistics and Operations research
Centre	Faculty of Mathematics and Statistics
Data defensa	27/06/2014
Nota // mark	10 MH (A with Honours)
Key Words	research; teaching; microgrids, stochastic programming; scenario generation; wind generation; day-ahead electricity market; imbalances; MSc Thesis
Abstract	In this thesis a scenario-based two-stage stochastic programming model is proposed to solve a microgrid's tertiary control optimization problem taking into account some renewable energy resource s uncertainty as well uncertain energy deviation prices in the electricity market. Scenario generation methods for wind speed realizations are also studied. Results show that the introduction of stochastic programming represents an improvement over a deterministic model.
DOI / handle	http://hdl.handle.net/2099.1/23235
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