energy and power systems

Unified multi-market participation of energy communities in energy markets (OptiREC)

Publication TypeFunded research projects
Year of Publication2022
AuthorsF.-Javier Heredia; Albert Solà Vilalta; Marlyn Dayana Cuadrado Guevara
Type of participationLeader
Duration01/23-12/24
CallPROYECTOS DE TRANSICIÓN ECOLÓGICA Y TRANSICIÓN DIGITAL 2021
Funding organizationMINISTERIO DE CIENCIA E INNOVACIÓN
PartnersIREC, Universitat de Girona, Universidad Comillas.
Full time researchers2,5
Budget149.500€
Project codeTED2021-131365B-C44
Key Wordsresearch; TED2021-131365B-C44; energy communities; electricity markets; bilevel stochastic programming
AbstractThe general purpose of this subproject is to study the participation of energy communities in the multimarket structure of the wholesale electricity market in order to develop mathematical models and computational tools for the optimal bid to wholesale markets. From the point of view of the wholesale electricity market, all the complexity of the inner structure of energy communities (dispatchable and nondispatchable generation, storage, demand,) can be conceptually understood as a single virtual programming unit ( a Community Virtual Programming Unit, CVPU) that participate in the wholesale electricity multimarket structure (spot and ancillary-services markets). The final goal of this project is to develop a multi-stage stochastic-programming model for the Multimarket Optimal Bid of Energy Communities (MOBEC) problem that will be validated with real data from the Iberian Electricity Market (MIBEL). Conceptually speaking, energy communities are a complex energy system comprising dispatchable and non-dispatchable generation, energy storage systems and an own demand, that participate in the wholesale electricity market. Based on the experience of previous studies the extension of stochastic programming models to energy communities is a fairly natural and a sounded research methodology. First, the participation of energy communities in spot markets will be analysed. Spot markets (day and intraday) are the most important one in terms of the amount of energy traded. A CVPU will be defined allowing the energy community to submit bids to the single day ahead and to the multiple intraday markets, according to the MIBEL rules. Second, the participation of energy communities in ancillary services markets will be studied. Dispatchable generation units (thermal) together with storage devices and demand flexibility provide the energy community with reserve capabilities that can be bid to the secondary reserve market. The reserve of the CVPU will be defined, formulated and integrated in the (MOBEC) optimization model. The resulting model will be implemented and validated with real data from the real energy communities and the MIBEL. Multi-stage stochastic programming optimal bid models have been successfully developed so far for generation+storage units and will be extended in this project and adapted to the specific features of an energy community through a CVPU that participates in the wholesale electricity market as a single programming unit.
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Presentation at the EURO 2022 conference in Finland.

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%.

Ph D. Thesis on multistage scenario tree generation for renewable energies.

 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

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-BarcelonaTech
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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New paper published in Applied Energy

 The paper entittled Critical evaluation of European balancing markets to enable the participation of Demand Aggregators has been published in Applied Energy This paper has been done in collaboration with the Institute for Energy Research of Catalonia (IREC). This study analyzes barriers and enablers of four European electricity markets and proposes a new market framework that would enhance Demand Aggregators' participation. Main barriers for Demand Aggregators have been identified and analyzed and a regulation scheme is proposed to enable Demand Aggregators participation. According to our results, small tertiary building aggregation is still not economic viable but existing municipal retailers could consider to extend their operations to Demand Aggregation.

 

Critical evaluation of European balancing markets to enable the participation of Demand Aggregators

Publication TypeJournal Article
Year of Publication2020
AuthorsMattia Barbero; Cristina Corchero; Lluc Canals; Lucia Igualada; F.-Javier Heredia
Journal TitleApplied Energy
Volume264
Journal Date04/2020
PublisherElsevier
ISSN Number0306-2619/
Key WordsDemand Aggregator; Regulatory framework; Ancillary Services; Demand Response; Tertiary building management; research; paper
AbstractEuropean Directives are incentivizing consumers to play an active role in the electricity system and to collaborate to maintain its stability, which has been historically provided by large generation power plants. However, it is not easy for the System Operator to handle the coexistence of consumers and generators in the same markets. Under these circumstances, a new actor allows small residential and commercial consumers to participate in flexibility markets: The Demand Aggregator. However, balancing markets opened to Demand Aggregators still present several barriers that do not allow their practical participation. This study analyzes barriers and enablers of four European electricity markets and proposes a new market framework that would enhance Demand Aggregators’ participation. To validate the proposed market and to understand the economic potentials of aggregated small tertiary buildings, a Demand Aggregator is simulated using real building’s consumption data. Results show that technical requirements to participate in balancing markets such as the minimum bid size, the symmetricity of the offer and the product resolution strongly affect incomes for Demand Aggregators. However, neither in the proposed market, the creation of a Demand Aggregator whose business model is focused on small tertiary buildings does not seem realistic due to low incomes in comparison to the fixed costs necessary to enable Demand Response, especially if only the air conditioning system is considered.
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DOI10.1016/j.apenergy.2020.114707
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A MIP formulation of a Hybrid AC-DC offshore wind power plant topology

Publication TypeConference Paper
Year of Publication2018
AuthorsCristina Corchero; Josep Homs-Moreno; F.-Javier Heredia; Lucia Igualada; Mikel de Prada
Conference Name23th International Symposium on Mathematical Programming
Conference Date01-06/07/2018
Conference LocationBordeaux
Type of Workcontributed presentation
Key Wordsresearch; hybrid AC-DC; offshore wind power plant; local branching
AbstractThe current study analyses a hybrid o↵shore wind farm design in which individual wind turbine power converters are removed from wind turbines and are installed on intermediate o↵shore collector platforms. In this study a compact and small-sized mixedinteger linear optimisation model makes four decisions with the goal of minimising installation and operation costs: the location and the number of o↵shore platforms and power converters to be installed, the optimal wind farm cable layout and the cluster optimal operating frequency of each wind turbine. The solutions found either for small and large o↵shore wind farms improve notoriously real-world designs, reducing up to 8 percent installation and maintenance costs. On the optimization technical results, a variant of Local Branching has been developed, reducing in some cases more than half of computing time with respect to default Local Branching. The model developed serves as a mathematical tool to provide rigorous evidences of the suitability of the hybrid design versus traditional o↵shore wind farm designs.
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A multistage stochastic programming model for the optimal bid of a wind producer

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

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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Stochastic optimal generation bid to electricity markets with emissions risk constraints.

Publication TypeJournal Article
Year of Publication2018
AuthorsF.-Javier Heredia; Julián Cifuentes-Rubiano; Cristina Corchero
Journal TitleJournal of Environmental Management
Volume207
Issue1
Pages12
Start Page432
Journal DateFebruary 2018
PublisherElsevier
ISSN Number0301-4797
Key Wordsresearch; OR in Energy; Stochastic Programming; Risk Management; Electricity market; Emissions reduction; paper
AbstractThere 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.
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DOI10.1016/j.jenvman.2017.11.010
Preprinthttp://hdl.handle.net/2117/114024
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