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	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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The paper entitled Optimal Postponement in Supply Chain Network Design Under Uncertainty: An Application for Additive Manufacturing (preprint ) has been published in the International Journal of Production Research. This paper is the result of projects Strategical Models in Supply Chain Design, and Digitalizing Supply Chain Strategy with 3D Printing a successful collaboration between GNOM with Accenture Technology Labs (Silicon Valley), Accenture Analytics Innovation Center (Barcelona) and the Fundació CIM-UPC. This study This study presents a new two-stage stochastic programming decision model for assessing how to introduce some new manufacturing technology into any generic supply and distribution chain. It additionally determines the optimal degree of postponement, as represented by the so-called customer order decoupling point (CODP), while assuming uncertainty in demand for multiple products. Finally, it presents and analyses a case study for introducing additive manufacturing technologies.

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	2016
Authors	Maksims Sisovs
Director	F.-Javier Heredia
Tipus de tesi	MSc Thesis
Titulació	"KIC InnoEnergy" Master of Science in Smart Electrical Networks and Systems
Centre	Escola Tècnica Superior d'Enginyeria Industrial de Barcelona (ETSEIB)
Data defensa	16/09/2016
Nota // mark	10 MH (A+ with honours)
Key Words	teaching; BEES; battery energy storage systems; electrical vehicle; smart meters; retail energy market; MSc Thesis
Abstract	The main focus of this master thesis project is to evaluate the economic, technical and regulatory feasibility of distributed battery energy storage systems (BESS) and the potential opportunity of electricity companies to increase their prots through advanced operation in energy services, such as electric energy time-shift, ancillary or electric vehicle incentives in Spanish electricity market. To assess the feasibility, an optimization tool has been developed. This tool simulates energy trading between different market participants with particular features extracted from data analysis and literature. Load consumption proles had been developed from smart meter real data by applying several data mining techniques. This part had been guided by external collaborating entity Minsait. Electricity market analysis includes the overview of its functionality principles and regulatory side regarding storage adaptation and specific service applicability. Market historical prices were used for further electricity trading simulation. A brief technical insight explains current storage situation and tells about high-potential technologies in emerging markets. Benchmark analysis covers several products of battery manufacturers with relevant technical and price information. Spanish electricity market showed low adaptability to distributed BESS solutions: energy arbitrage incomes have resulted being insuficient. Ancillary services, despite promising economic gures, are to a large extent prohibited to be provided by distributed storage. Electric vehicle incentives, though, resulted being of a high interest due to absence of direct investment.
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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	Cristina Corchero; Andina Brown; Oriol Serch; Miguel Cruz; F.-Javier Heredia
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; multi objective optimization; electrical vehicle, charging point location
Abstract	The aim of this study is to address the problem of locating fast charging stations for electric vehicles in the early stages of infrastructure implementation. Despite existence of successful trials and pilot projects, there are barriers preventing the successful development of a private EV market in its present state; investors are reluctant to invest in infrastructure due to the relatively small number of EV users, and conversely consumers are hesitant about purchasing EVs due high prices and a lack of charging infrastructure. It has been identified that introducing fast charging stations can aid this process, in particular by easing users’ concerns about running out of charge before reaching their destination. This study approaches the problem from the perspective of a central planner wishing to install fast charging stations. A multi-objective approach is used to simultaneously consider two conflicting objectives in the optimisation problem: (1) to minimise the distance that potential consumers would need to deviate from their normal journeys in order to reach their nearest fast charging station and (2) to minimise the set up costs associated with the installation of the stations. A mathematical model is formulated and implemented to obtain results for the case study of Barcelona. The optimal solutions are found and used to depict the Pareto front, offering insight into the nature of the trade-offs between the objectives and aiding the decision making process.
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Publication Type	Journal Article
Year of Publication	2014
Authors	Lucia Igualada; Cristina Corchero; Miguel Cruz; F-.Javier Heredia
Journal Title	IEEE Transactions on Smart Grid
Volume	5
Issue	4
Pages	2163-2172
Journal Date	07/2014
Publisher	IEEE
ISSN Number	1949-3053
Key Words	research; paper; smart grids; vehicle- to-grid (V2G); renewable generation; microgrids; smartgrids; modeling
Abstract	An optimization model is proposed to manage a residential microgrid including a charging spot with a vehicle-to-grid system and renewable energy sources. In order to achieve a realistic and convenient management, we take into account: (1) the household load split into three different profiles depending on the characteristics of the elements considered; (2) a realistic approach to owner behavior by introducing the novel concept of range anxiety; (3) the vehicle battery management considering the mobility profile of the owner and (4) different domestic renewable energy sources. We consider the microgrid operated in grid-connected mode. The model is executed one-day-ahead and generates a schedule for all components of the microgrid. The results obtained show daily costs in the range of 2.82 to 3.33 ; the proximity of these values to the actual energy costs for Spanish households validate the modeling. The experimental results of applying the designed managing strategies show daily costs savings of nearly 10%.
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DOI	10.1109/TSG.2014.2318836
Preprint	http://hdl.handle.net/2117/20642
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