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Optimal Participation of Energy Communities in Electricity Markets under Uncertainty. A Multi-Stage Stochastic Programming Approach

Thu, 10/24/2024 - 11:48 — admin
Publication TypeConference Paper
Year of Publication2024
AuthorsAlbert Solà Vilalta, Marlyn Cuadrado, Ignasi Mañé, F.-Javier Heredia
Conference NameISMP2024, 25th International Symposium on Mathematical Programming
Conference Date21-26/07/2024
Conference LocationMontréal, Canada.
Type of WorkInvited presentation
Key Wordsenergy communities; electricity markets; demand flexibility; prosumers; mathematical optimization; stochastic programming; research
AbstractAn 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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Multistage stochastic bid model for a wind-thermal power producer

Thu, 12/30/2021 - 13:20 — admin
Publication TypeTesis de Grau i Màster // BSc and MSc Thesis
Year of Publication2021
AuthorsIgnasi Mañé Bosch
DirectorF-Javier Heredia
Tipus de tesiMSc Thesis
TitulacióMaster in Statistics and Operations Reseafrch
CentreFacultat de matemàtiques i Estadística
Data defensa18/10/2021
Nota // mark9.5
Key Wordsteaching; 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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Master Thesis on electricity markets.

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

Multistage Scenario Trees Generation for Electricity Markets Optimization

Tue, 07/13/2021 - 10:51 — admin
Publication TypeConference Paper
Year of Publication2021
AuthorsMarlyn Dayana Cuadrado Guevara; F.-Javier Heredia
Conference Name31st European Conference on Operational Research.
Conference Date11-14/07/2021
Conference LocationAthens
Type of WorkInvited presentation
ISBN NumberISBN 978-618-85079-1-3
Key Wordsresearch; multistage stochastich programming; virtual power plants; electricity markets; scenarios tree generation
AbstractThe 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 di erent case studies corresponding to three di erent 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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Ph D. Thesis on multistage scenario tree generation for renewable energies.

Mon, 11/30/2020 - 19:40 — admin
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.

Multistage Scenario Trees Generation for Renewable Energy Systems Optimization

Mon, 11/30/2020 - 19:17 — admin
Publication TypeThesis
Year of Publication2020
AuthorsMarlyn Dayana Cuadrado Guevara
Academic DepartmentDept. of Statistics and Operations Research. Prof. F.-Javier Heredia, advisor.
Number of Pages194
UniversityUniversitat Politècnica de Catalunya
CityBarcelona
DegreePhD Thesis
Key Wordsresearch; Battery energy storage systems; Electricity markets; Ancillary services market; Wind power generation; Virtual power plants; Multistage Stochastic programming; phd thesis
AbstractThe 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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A multistage stochastic programming model for the optimal bid of a wind producer

Wed, 07/11/2018 - 11:20 — admin
Publication TypeConference Paper
Year of Publication2018
AuthorsF.-Javier Heredia; Marlyn D. Cuadrado; J.-Anton Sánchez
Conference Name23th International Symposium on Mathematical Programming
Conference Date01-06/07/2018
Conference LocationBordeaux
Type of Workcontributed presentation
Key Wordsresearch; Battery energy storage systems; Electricity markets; Ancillary services market; Wind power generation; Virtual power plants; Stochastic programming
AbstractAbstract: 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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On optimal participation in the electricity markets of wind power plants with battery energy storage systems

Tue, 05/15/2018 - 16:25 — admin
Publication TypeJournal Article
Year of Publication2018
AuthorsF.-Javier Heredia; Marlyn D. Cuadrado; Cristina Corchero
Journal TitleComputers and Operations Research
Volume96
Pages316-329
Journal Date08/2018
PublisherElsevier
ISSN Number0305-0548
Key Wordsresearch; Battery energy storage systems; Electricity markets; Ancillary services market; Wind power generation; Virtual power plants; Stochastic programming; paper
AbstractThe 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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DOI10.1016/j.cor.2018.03.004
Preprinthttp://hdl.handle.net/2117/118479
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A MIP formulation of a Hybrid AC-DC offshore wind power plant topology.

Thu, 02/01/2018 - 16:54 — admin
Publication TypeTesis de Grau i Màster // BSc and MSc Thesis
Year of Publication2017
AuthorsJosep Homs Moreno
DirectorF.-Javier Heredia Cervera; Cristina Corchero; Lucia Igualada
Tipus de tesiMSc Thesis
TitulacióInteruniversity Master in Statistics and Operations Research UPC-UB
CentreFaculty of Mathematics and Statistics
Data defensa23/10/2017
Nota // mark9.5 (A)
Key Wordsoffshore wind; Hybrid AC-DC grid; MIP; teaching; MSc Thesis
AbstractThis thesis analyses a design in which individual wind turbine converters are removed from wind turbines and are installed on intermediate o shore 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 / handlehttp://hdl.handle.net/2117/110482
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Contribution to the 4th International Conference on Optimization Methods and Software 2017, La Havana.

Sat, 12/16/2017 - 00:00 — admin

Last december I was invited to the 4th International Conference on Optimization Methods and Software 2017  that was held in La Havana, to present the study A Multistage Stochastic Programming Model for the Optimal Bid of Wind-BESS Virtual Power Plants to Electricity Markets. This study was developed in collaboration with Marlyn Cuadrado and Josep Anton Sánchez, from my same department in the UPC, and is a partial result of the research project FOWGEM. This study is a follow up of the previous work presented in  the WindFarms 2017 Conference extended with a new methodology to treat the uncertainty, based in forecasting models, and the study of the quality of the stochastic solution through the Value of the Stochastic Solution. In the animated graph you can observe how the the probability distribution of several recourse variables (optimal bid, imbalances, charge/discharge and SOC) evolves along five working days.

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