F.-Javier Heredia - Academic web site

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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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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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	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	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	Conference Paper
Year of Publication	2017
Authors	F.-Javier Heredia; Marlyn D. Cuadrado
Conference Name	WindFarms 2017
Conference Date	31/05-02/06/2017
Conference Location	Madrid, Spain
Type of Work	Invited presentation
Key Words	research; wind farms; Ion-Li battery; multistage stochastic programming; stochastic programming
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Publication Type	Conference Paper
Year of Publication	2016
Authors	F.-Javier Heredia; Cristina Corchero; Marlyn D. Cuadrado
Conference Name	28th European Conference on Operational Research
Series Title	Conference Handbook
Pagination	322
Conference Date	3-6/07/2016
Conference Location	Poznan, Poland
Type of Work	contributed presentation.
Key Words	research; VPP; wind generation; battery energy storage system; stochastic programming; electricity market; optimal bid
Abstract	The recent cost reduction and technologic advances in medium to large scale Battery Energy Storage Systems (BESS) makes these devices a real choice alternative for wind producers operating in electricity markets. The association of a wind power farm with a BESS (the so called Virtual Power Plant VPP) provides utilities with a tool to turn the uncertainty wind power production into a dispatchable technology enabled to operate not only in the spot and adjustment markets (day-ahead and intraday markets) but also in ancillary services markets that, up to now, was forbidden to non-dispatchable technologies. Even more, recent studies have shown that the capital cost investment in BESS can only be recovered through the participation of such a VPP in the ancillary services markets. We present in this study a stochastic programming model to find the optimal participation of a VPP to the day-ahead market and secondary reserve markets (the most relevant ancillary service market) where the uncertainty in wind power generation and markets prices (day-ahead ancillary services) has been considered. A case study with real data from the Iberian Electricity Market is presented.
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Publication Type	Conference Paper
Year of Publication	2015
Authors	F.-Javier Heredia; Antonio Rengifo
Conference Name	27th European Conference on Operational Research
Conference Date	12-15/07/2015
Conference Location	Glasgow, UK.
Type of Work	invited
Key Words	research; MTM2013-48462-C2-1; mixed-integer nonlinear programming; proximal bundle methods; multimarket electricity problems; parallelism
Abstract	The use of stochastic programming to solve real instances of optimal bid problems in electricity market usually implies the solution of large scale mixed integer nonlinear optimization problems that can't be tackled with the available general purpose commercial optimisation software. In this work we show the potential of proximal bundle methods to solve large scale stochastic programming problems arising in electricity markets. Proximal bundle methods was used in the past to solve deterministic unit commitment problems and are extended in this work to solve real instances of stochastic optimal bid problems to the day-ahead market (with embedded unit commitment) with thousands of scenarios. A parallel implementation of the proximal bundle method has been developed to take profit of the separability of the lagrangean problem in as many subproblems as generation bid units. The parallel proximal bundle method (PPBM) is compared against general purpose commercial optimization software as well as against the perspective cuts algorithm, a method specially conceived to deal with quadratic objective function over semi-continuous domains. The reported numerical results obtained with a workstation with 32 threads show that the commercial software can’t find a solution beyond 50 scenarios and that the execution times of the proposed PPBM are as low as a 15% of the execution time of the perspective cut approach for problems beyond 800 scenarios.
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