Publication Type	Conference Paper
Year of Publication	2024
Authors	Albert Solà Vilalta, Marlyn Cuadrado, Ignasi Mañé, F.-Javier Heredia
Conference Name	ISMP2024, 25th International Symposium on Mathematical Programming
Conference Date	21-26/07/2024
Conference Location	Montréal, Canada.
Type of Work	Invited presentation
Key Words	energy communities; electricity markets; demand flexibility; prosumers; 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. where energy communities cannot meet their internal demand, and periods where they generate excess electricity. This is because the electricity they generate often 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 di¿erent times at which variable renewable generation is considered to be known and electricity prices from di¿erent 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 di¿erent energy community configurations, to analyse proposed regulatory frameworks in Europe. The added value of considering stochasticity in this problem is also analysed. The motivation to do so is that there are time periods
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Publication Type	Tesis de Grau i Màster // BSc and MSc Thesis
Year of Publication	2021
Authors	Ignasi Mañé Bosch
Director	F-Javier Heredia
Tipus de tesi	MSc Thesis
Titulació	Master in Statistics and Operations Reseafrch
Centre	Facultat de matemàtiques i Estadística
Data defensa	18/10/2021
Nota // mark	9.5
Key Words	teaching; electricity markets; multistage stochastic programming
Abstract	For many political and economic reasons, over the last decades, electricity markets in developed countries have been liberalised. Markets regulated by governments in which prices were set by the competent authority are now the exception. In this new setting, electricity agents, both consumers and producers, compete to maximise their protability in a series of auctions designed to efficiently match supply and demand. Many energy producers manage together wind and thermal generation units to meet their contractual obligations such as bilateral contracts, as well as bid on the electric market to sell their production capacity. This master thesis explore different multi-stage stochastic programming models for generation companies to nd optimal bid functions in electric spot markets. The explored models not only capture the uncertainty of electric prices of different markets and financial products, but also couples together wind and thermal generation units, offering producers that combine both technologies a more suitable approach to nd their best possible bidding strategy among the space of possible actions.
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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	Conference Paper
Year of Publication	2018
Authors	F.-Javier Heredia; Marlyn D. Cuadrado; J.-Anton Sánchez
Conference Name	23th International Symposium on Mathematical Programming
Conference Date	01-06/07/2018
Conference Location	Bordeaux
Type of Work	contributed presentation
Key Words	research; Battery energy storage systems; Electricity markets; Ancillary services market; Wind power generation; Virtual power plants; Stochastic programming
Abstract	Abstract: Battery Energy Storage Systems (BESS) can be used by wind producers to improve the operation of wind power plants (WPP) in electricity markets. Associating a wind power plant with a BESS (the so-called Virtual Power Plant (VPP)) provides utilities with a tool that converts uncertain wind power production into a dispatchable technology that can operate not only in spot and adjustment markets (day-ahead and intraday markets) but also in ancillary services markets that, up to now, were forbidden to non-dispatchable technologies. We present in this study a multi-stage stochastic programming model to find the optimal operation of a VPP in the day-ahead, intraday and secondary reserve markets while taking into account uncertainty in wind power generation and clearing prices (day-ahead, secondary reserve, intraday markets and system imbalances). A new forecasting procedure for the random variables involved in stochastic programming model has been developed. The forecasting model is based on Time Factor Series Analysis (TFSA) and gives suitable results while reducing the dimensionality of the forecasting mode. The quality of the scenario trees generated using the TFSA forecasting models with real electricity markets and wind production data has been analysed through multistage VSS.
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Publication Type	Journal Article
Year of Publication	2018
Authors	F.-Javier Heredia; Marlyn D. Cuadrado; Cristina Corchero
Journal Title	Computers and Operations Research
Volume	96
Pages	316-329
Journal Date	08/2018
Publisher	Elsevier
ISSN Number	0305-0548
Key Words	research; Battery energy storage systems; Electricity markets; Ancillary services market; Wind power generation; Virtual power plants; Stochastic programming; paper
Abstract	The recent cost reduction and technological advances in medium- to large-scale battery energy storage systems (BESS) makes these devices a true alternative for wind producers operating in electricity markets. Associating a wind power farm with a BESS (the so-called virtual power plant (VPP)) provides utilities with a tool that converts uncertain wind power production into a dispatchable technology that can operate not only in spot and adjustment markets (day-ahead and intraday markets) but also in ancillary services markets that, up to now, were forbidden to non-dispatchable technologies. What is more, recent studies have shown capital cost investment in BESS can be recovered only by means of such a VPP participating in the ancillary services markets. We present in this study a multi-stage stochastic programming model to find the optimal operation of a VPP in the day-ahead, intraday and secondary reserve markets while taking into account uncertainty in wind power generation and clearing prices (day-ahead, secondary reserve, intraday markets and system imbalances). A case study with real data from the Iberian electricity market is presented.
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DOI	10.1016/j.cor.2018.03.004
Preprint	http://hdl.handle.net/2117/118479
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Publication Type	Tesis de Grau i Màster // BSc and MSc Thesis
Year of Publication	2017
Authors	Josep Homs Moreno
Director	F.-Javier Heredia Cervera; Cristina Corchero; Lucia Igualada
Tipus de tesi	MSc Thesis
Titulació	Interuniversity Master in Statistics and Operations Research UPC-UB
Centre	Faculty of Mathematics and Statistics
Data defensa	23/10/2017
Nota // mark	9.5 (A)
Key Words	offshore wind; Hybrid AC-DC grid; MIP; teaching; MSc Thesis
Abstract	This thesis analyses a design in which individual wind turbine converters are removed from wind turbines and are installed on intermediate oshore platforms. Thus, many turbines may be controlled by only few power converters, dening clusters. All wind turbines in a cluster operate at the same cluster optimal frequency. This approach is cheaper in terms of capital cost savings than having individual power converters integrated in wind turbines because less components must be installed and maintained. At the same time, it gives enough exibility to turbines to still operate with high power eciency. The generated power in turbines is transmitted with alternating current to AC/DC power converters. In addition, power from AC/DC converters is transmitted in direct current to a single large high voltage direct current converter, which transmits power to the onshore collection grid.
DOI / handle	http://hdl.handle.net/2117/110482
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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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