Last July 4 2022 I was invited at the EURO 2022 conference , Aalto University, Espoo, near Helsinki, to present the work Multistage stochastic programming for the optimal bid of a wind-thermal power production pool with battery storage, which is a continuation of the MSc  and PhD thesis of Mr. Ignasi Manyé and Ms. Marlyn D. Cuadrado respectively. This work tackles with an extensive study along a complete timespan of on year analyzing the benefits of a joint operation of a wind and thermal generation system in the elecrticity markets and bilateral contracts. Numerical results show that the total profit increases by  13% in average, but that it can be as high as 77%, with a reduction of the thermal operation costs of 61%.

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	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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Last July 21th, the work "Multistage Scenario Trees Generation for Electricity Markets Optimization" was presentated at the 31st European Conference on Operational Research  in an invited session of the stream "Stochastic and Robust Optimization". This study is a result of the PH.D. Thesis of Ms. Marlyn D. Cuadrado on scenarios trees generation for multistage stochastic programming in optimal electricity bid problems.

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-BarcelonaTech
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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