modellization
Wed, 12/01/2021 - 13:35 — admin
On November 2021 Mr. Ignasi Mañé presented the MsC thesis dissertation Multistage stochastic bid model for a wind-thermal power producer to opt for the master's degree in Statistics and Operations Research (UPC-UB), advised by prof. F.-Javier Heredia. This master thesis explores different multi-stage stochastic programming models for generation companies to find optimal bid functions in electric spot markets capturing the uncertainty of electric prices of different markets and financial products, and coupling together wind and thermal generation unit
Mon, 11/30/2020 - 19:17 — admin
| 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. |
| URL | Click Here |
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Tue, 07/21/2020 - 16:46 — admin
Tue, 05/15/2018 - 16:25 — admin
| 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. |
| URL | Click Here |
| DOI | 10.1016/j.cor.2018.03.004 |
| Preprint | http://hdl.handle.net/2117/118479 |
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Tue, 01/30/2018 - 21:12 — admin
| Publication Type | Journal Article |
| Year of Publication | 2018 |
| Authors | F.-Javier Heredia; Julián Cifuentes-Rubiano; Cristina Corchero |
| Journal Title | Journal of Environmental Management |
| Volume | 207 |
| Issue | 1 |
| Pages | 12 |
| Start Page | 432 |
| Journal Date | February 2018 |
| Publisher | Elsevier |
| ISSN Number | 0301-4797 |
| Key Words | research; OR in Energy; Stochastic Programming; Risk Management; Electricity market; Emissions reduction; paper |
| Abstract | There are many factors that influence the day-ahead market bidding strategies of a generation company (GenCo) within the framework of the current energy market. Environmental policy issues are giving rise to emission limitation that are becoming more and more important for fossil-fueled power plants, and these must be considered in their management. This work investigates the influence of the emissions reduction plan and the incorporation of the medium-term derivative commitments in the optimal generation bidding strategy for the day-ahead electricity market. Two different technologies have been considered: the high-emission technology of thermal coal units and the low-emission technology of combined cycle gas turbine units. The Iberian Electricity Market (MIBEL) and the Spanish National Emissions Reduction Plan (NERP) defines the environmental framework for dealing with the day-ahead market bidding strategies. To address emission limitations, we have extended some of the standard risk management methodologies developed for financial markets, such as Value-at-Risk (VaR) and Conditional Value-at-Risk (CVaR), thus leading to the new concept of Conditional Emission at Risk (CEaR). This study offers electricity generation utilities a mathematical model for determining the unit’s optimal generation bid to the wholesale electricity market such that it maximizes the long-term profits of the utility while allowing it to abide by the Iberian Electricity Market rules as well as the environmental restrictions set by the Spanish National Emissions Reduction Plan. We analyze the economic implications for a GenCo that includes the environmental restrictions of this National Plan as well as the NERP’s effects on the expected profits and the optimal generation bid. |
| URL | Click Here |
| DOI | 10.1016/j.jenvman.2017.11.010 |
| Preprint | http://hdl.handle.net/2117/114024 |
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Fri, 07/07/2017 - 10:16 — admin
Tue, 09/27/2016 - 09:19 — admin
| 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 diff erent 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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Mon, 07/18/2016 - 19:04 — admin
| Publication Type | Conference Paper |
| Year of Publication | 2016 |
| Authors | Daniel Ramon Lumbierres; Asier Muguruza; Robert Gimeno Feu; Ping Guo; Mary Hamilton; Kiron Shastry; Sunny Webb; Joaquim Minguella; F.-Javier Heredia |
| Conference Name | 28th European Conference on Operational Research |
| Series Title | Conference Handbook |
| Pagination | 330 |
| Conference Date | 3-6/07/2016 |
| Conference Location | Poznan, Poland |
| Type of Work | contributed presentation. |
| Key Words | research; supply chain; 3D printing; stochastic programming; postponment; modeling; additive manufacturing |
| Abstract | In this contribution we would like to present the results of a research project developed by Accenture and BarcelonaTech aiming at studying the advantages of ultra-postponement with 3D printing using the analytical tools of operational research. In this project a new two-stage stochastic programming decision model has been developed to assess (a) the convenience of the introduction of 3D printing in any generic supply chain and (b) the optimal degree of postponement, the so called Customer Order Decoupling Point (CODP), assuming uncertainty in demand for multiple markets. To this end we propose the formulation
of a generic supply chain through an oriented graph that represents all the alternative technologies that can be deployed, defined through a set of operations for manufacturing, assembly and distribution, each one characterized by a lead time and cost parameters. Based on this graph we develop a mixed integer two-stage stochastic program that finds the optimal manufacturing technology to meet the demand of each market, the optimal production quantity for each operation and the optimal CODP for each technology. The results obtained with several case studies from real manufacturing companies are presented and analyzed. |
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Mon, 07/18/2016 - 18:43 — admin
| 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. |
| URL | Click Here |
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Wed, 06/24/2015 - 13:06 — admin
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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.