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S. Sanchez Perez Moreno
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10 records found
1
Report
(2024)
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Samuel Kainz, Julian Quick, Mauricio Souza de Alencar, S. Sanchez Perez Moreno, Katherine Dykes, Christopher Bay, M B Zaaijer, Pietro Bortolotti
This report describes the first version of the regular and irregular IEA-Wind 740-10MW Reference Offshore Wind Plants (v0.1). The two plants have been developed within the second work package of IEA Wind Task 37 on Wind Energy Systems Engineering: Integrated RD&D. The plants aim at acting as reference for future research projects on wind energy, representing modern offshore wind plants. The designs are based on the Borssele III and IV offshore wind plant projects. The associated wind resource, allotted territory, and bathymetry measurements are used to define the site characteristics. 74 IEA 10-MW Reference Wind Turbines are arranged in two suggested layouts that are optimized for maximum annual energy production: one regular grid layout, one irregular layout. These reference wind plants have been described using the WindIO ontology and have been made available through an open-source repository on GitHub.
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This report describes the first version of the regular and irregular IEA-Wind 740-10MW Reference Offshore Wind Plants (v0.1). The two plants have been developed within the second work package of IEA Wind Task 37 on Wind Energy Systems Engineering: Integrated RD&D. The plants aim at acting as reference for future research projects on wind energy, representing modern offshore wind plants. The designs are based on the Borssele III and IV offshore wind plant projects. The associated wind resource, allotted territory, and bathymetry measurements are used to define the site characteristics. 74 IEA 10-MW Reference Wind Turbines are arranged in two suggested layouts that are optimized for maximum annual energy production: one regular grid layout, one irregular layout. These reference wind plants have been described using the WindIO ontology and have been made available through an open-source repository on GitHub.
The construction and management of a wind farm involve many disciplines. It is hard for a single designer or developer to keep an overview of all the relevant concepts, models, and tools. Nevertheless, this is needed when performing integrated modeling or analysis. To help researchers keep this overview, we have created WESgraph (the Wind Energy System graph), a knowledge base for the wind farm domain, implemented as a graph database. It currently contains 1222 concepts and 1725 relations between them. This paper presents the structure of this graph database – content stored in nodes and the relationships between them – as a foundational ontology, which classifies the domain's concepts. This foundational ontology partitions the domain in two: a part describing physical aspects and a part describing mathematical and computational aspects. This paper also discusses a number of generally difficult cases that exist when adding content to such a knowledge base. This paper furthermore discusses the potential applications of WESgraph and illustrates its use for computation pathway discovery – the application that triggered its creation. It also contains a description of our practical experience with its design and use as well as our thoughts about the community use and management of this tool.
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The construction and management of a wind farm involve many disciplines. It is hard for a single designer or developer to keep an overview of all the relevant concepts, models, and tools. Nevertheless, this is needed when performing integrated modeling or analysis. To help researchers keep this overview, we have created WESgraph (the Wind Energy System graph), a knowledge base for the wind farm domain, implemented as a graph database. It currently contains 1222 concepts and 1725 relations between them. This paper presents the structure of this graph database – content stored in nodes and the relationships between them – as a foundational ontology, which classifies the domain's concepts. This foundational ontology partitions the domain in two: a part describing physical aspects and a part describing mathematical and computational aspects. This paper also discusses a number of generally difficult cases that exist when adding content to such a knowledge base. This paper furthermore discusses the potential applications of WESgraph and illustrates its use for computation pathway discovery – the application that triggered its creation. It also contains a description of our practical experience with its design and use as well as our thoughts about the community use and management of this tool.
A system is a set of interconnected components whose individual behaviour and interactions determine the overall performance of the set. Wind farms are amongst the most complex systems deployed worldwide, based on their uncertainty, heterogeneity and complexity. Moreover, many technical and social disciplines may simultaneously describe the performance of a complex system such as wind farms.
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A system is a set of interconnected components whose individual behaviour and interactions determine the overall performance of the set. Wind farms are amongst the most complex systems deployed worldwide, based on their uncertainty, heterogeneity and complexity. Moreover, many technical and social disciplines may simultaneously describe the performance of a complex system such as wind farms.
Motivated by the need to develop reference wind energy systems for optimisation and technology assessment studies, the International Energy Agency Wind Task 37 on Wind Energy Systems Engineering is developing a reference offshore wind power plant at the Dutch offshore wind energy areas Borssele III and IV. This paper presents a comparison between two approaches for developing the preliminary design of an offshore wind plant turbine layout, electrical collection system, and support structures. The first is a sequential approach, where components of the wind farm are optimised sequentially, each with its own objective function, thus neglecting potential interactions between them. The second approach uses Multidisciplinary Design Analysis and Optimisation (MDAO), where all components are jointly optimised with the overall system levelised cost of energy (LCOE) as a global objective function. Studying the cases of regular and irregular layouts, the integrated approach always shows a greater improvement in the LCOE of the final design compared to the design resulting from the traditional sequential approach. The most significant trade-off exploited by the MDAO approach used in this study is between losses in energy production due to turbine wake effects and the costs of electrical cable infrastructure.
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Motivated by the need to develop reference wind energy systems for optimisation and technology assessment studies, the International Energy Agency Wind Task 37 on Wind Energy Systems Engineering is developing a reference offshore wind power plant at the Dutch offshore wind energy areas Borssele III and IV. This paper presents a comparison between two approaches for developing the preliminary design of an offshore wind plant turbine layout, electrical collection system, and support structures. The first is a sequential approach, where components of the wind farm are optimised sequentially, each with its own objective function, thus neglecting potential interactions between them. The second approach uses Multidisciplinary Design Analysis and Optimisation (MDAO), where all components are jointly optimised with the overall system levelised cost of energy (LCOE) as a global objective function. Studying the cases of regular and irregular layouts, the integrated approach always shows a greater improvement in the LCOE of the final design compared to the design resulting from the traditional sequential approach. The most significant trade-off exploited by the MDAO approach used in this study is between losses in energy production due to turbine wake effects and the costs of electrical cable infrastructure.
Multidisciplinary Design Analysis and Optimisation (MDAO) workflows consist of coupled tools driven by an algorithm for a specific purpose, e.g. optimisation. MDAO users may have at their disposal a set of tools of varying levels of fidelity. As a result, many permutations or MDAO workflows may arise, for which no clear methodology exists to evaluate, compare and rank them based on their performance. Our research question is then how to find the most useful MDAO workflows for a given purpose. This paper provides a guideline for solving this multiple criteria decision analysis problem. Our guideline includes a method to define the criteria and metrics for evaluating the performance of MDAO workflows, a strategy to aggregate the scores, and an optimisation algorithm for
categorical variables used to find the best alternatives. We apply this guideline to the offshore wind farm layout optimisation problem to demonstrate its use. This case study evidenced how critical t
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Multidisciplinary Design Analysis and Optimisation (MDAO) workflows consist of coupled tools driven by an algorithm for a specific purpose, e.g. optimisation. MDAO users may have at their disposal a set of tools of varying levels of fidelity. As a result, many permutations or MDAO workflows may arise, for which no clear methodology exists to evaluate, compare and rank them based on their performance. Our research question is then how to find the most useful MDAO workflows for a given purpose. This paper provides a guideline for solving this multiple criteria decision analysis problem. Our guideline includes a method to define the criteria and metrics for evaluating the performance of MDAO workflows, a strategy to aggregate the scores, and an optimisation algorithm for
categorical variables used to find the best alternatives. We apply this guideline to the offshore wind farm layout optimisation problem to demonstrate its use. This case study evidenced how critical t
With the increase of installed wind power capacity, the contribution of wind power curtailment to power balancing becomes more relevant. Determining the available power during curtailment at the wind farm level is not trivial, as curtailment changes the wake effects in a wind farm. Current best practice to estimate the available power is to sum the available power calculated by every wind turbine. However, during curtailment the changed local wind conditions at the wind turbines lead to inaccurate results at the wind farm level. This paper presents an algorithm to determine the available power of a wind farm during curtailment. Moreover, results of curtailment experiments are discussed that were performed on nearshore wind farm Westermeerwind to validate the algorithm. For the case where a single turbine is being curtailed, it is shown that the algorithm reduces the estimation error for the first downstream turbine significantly. Further development of the algorithm is required for accurate estimation of the second turbine. All further downstream turbines did not experience a change in wake conditions.
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With the increase of installed wind power capacity, the contribution of wind power curtailment to power balancing becomes more relevant. Determining the available power during curtailment at the wind farm level is not trivial, as curtailment changes the wake effects in a wind farm. Current best practice to estimate the available power is to sum the available power calculated by every wind turbine. However, during curtailment the changed local wind conditions at the wind turbines lead to inaccurate results at the wind farm level. This paper presents an algorithm to determine the available power of a wind farm during curtailment. Moreover, results of curtailment experiments are discussed that were performed on nearshore wind farm Westermeerwind to validate the algorithm. For the case where a single turbine is being curtailed, it is shown that the algorithm reduces the estimation error for the first downstream turbine significantly. Further development of the algorithm is required for accurate estimation of the second turbine. All further downstream turbines did not experience a change in wake conditions.
OWFgraph
A graph database for the offshore wind farm domain
The construction and management of an offshore wind farm involves many disciplines, e.g. meteorology and economics. It is hard for a single researcher to keep an overview of all the relevant concepts, models, and tools from these disciplines. Nevertheless, this is needed when performing integrated modeling or analysis in the offshore wind farm domain. To help researchers keep this overview, we present OWFgraph, a knowledge base for the offshore wind farm domain, implemented as a graph database that is meant to be open and ever-improving.
A graph database stores content in nodes and relationships. A relationship is a directed edge between two nodes. In our implementation, each of the nodes and relationships can have multiple properties, i.e., key-value pairs; moreover, nodes can have multiple labels, whereas relationships have only a single type. ...
A graph database stores content in nodes and relationships. A relationship is a directed edge between two nodes. In our implementation, each of the nodes and relationships can have multiple properties, i.e., key-value pairs; moreover, nodes can have multiple labels, whereas relationships have only a single type. ...
The construction and management of an offshore wind farm involves many disciplines, e.g. meteorology and economics. It is hard for a single researcher to keep an overview of all the relevant concepts, models, and tools from these disciplines. Nevertheless, this is needed when performing integrated modeling or analysis in the offshore wind farm domain. To help researchers keep this overview, we present OWFgraph, a knowledge base for the offshore wind farm domain, implemented as a graph database that is meant to be open and ever-improving.
A graph database stores content in nodes and relationships. A relationship is a directed edge between two nodes. In our implementation, each of the nodes and relationships can have multiple properties, i.e., key-value pairs; moreover, nodes can have multiple labels, whereas relationships have only a single type.
A graph database stores content in nodes and relationships. A relationship is a directed edge between two nodes. In our implementation, each of the nodes and relationships can have multiple properties, i.e., key-value pairs; moreover, nodes can have multiple labels, whereas relationships have only a single type.
Abstract
(2017)
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K Dykes, Sebastian Sanchez Perez Moreno, Frederik Zahle, A Ning, M. McWilliam, Michiel Zaayer
This presentation will provide an overview of progress to date in the development of a system modeling framework and ontology for wind turbines and plants as part of the larger IEA Wind Task 37 on wind energy systems engineering. The goals of the effort are to create a set of guidelines for a common conceptual architecture for wind turbines and plants so that practitioners can more easily:
• Share descriptions of wind turbines and plants across multiple parties and reduce the Effort for translating
descriptions between models,
• Integrate different models together and collaborate on model development, and
• Translate models among different levels of fidelity in the system. ...
• Share descriptions of wind turbines and plants across multiple parties and reduce the Effort for translating
descriptions between models,
• Integrate different models together and collaborate on model development, and
• Translate models among different levels of fidelity in the system. ...
This presentation will provide an overview of progress to date in the development of a system modeling framework and ontology for wind turbines and plants as part of the larger IEA Wind Task 37 on wind energy systems engineering. The goals of the effort are to create a set of guidelines for a common conceptual architecture for wind turbines and plants so that practitioners can more easily:
• Share descriptions of wind turbines and plants across multiple parties and reduce the Effort for translating
descriptions between models,
• Integrate different models together and collaborate on model development, and
• Translate models among different levels of fidelity in the system.
• Share descriptions of wind turbines and plants across multiple parties and reduce the Effort for translating
descriptions between models,
• Integrate different models together and collaborate on model development, and
• Translate models among different levels of fidelity in the system.
Poster
(2016)
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Sebastian Sanchez Perez Moreno, Michiel Zaayer, C.L. Bottasso, K Dykes, K.O. Merz, P.E. Rethore
A research agenda is described to further encourage the application of Multidisciplinary Design Analysis and Optimisation (MDAO) methodologies to wind energy systems. As a group of researchers closely collaborating within the International Energy Agency (IEA) Wind Task 37 for Wind Energy Systems Engineering: Integrated Research, Design and Development, we have identi_ed challenges that will be encountered by users building an MDAO framework. This roadmap comprises 17 research questions and activities recognised to belong to three research directions: model _delity, system scope and workow architecture. It is foreseen that sensible answers to all these questions will enable to more easily apply MDAO in the wind energy domain. Beyond the agenda, this work also promotes the use of systems engineering to design, analyse and optimise wind turbines and wind farms, to complement existing compartmentalised research and design paradigms.
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A research agenda is described to further encourage the application of Multidisciplinary Design Analysis and Optimisation (MDAO) methodologies to wind energy systems. As a group of researchers closely collaborating within the International Energy Agency (IEA) Wind Task 37 for Wind Energy Systems Engineering: Integrated Research, Design and Development, we have identi_ed challenges that will be encountered by users building an MDAO framework. This roadmap comprises 17 research questions and activities recognised to belong to three research directions: model _delity, system scope and workow architecture. It is foreseen that sensible answers to all these questions will enable to more easily apply MDAO in the wind energy domain. Beyond the agenda, this work also promotes the use of systems engineering to design, analyse and optimise wind turbines and wind farms, to complement existing compartmentalised research and design paradigms.
Journal article
(2016)
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Sebastian Sanchez Perez Moreno, Michiel Zaayer, C.L. Bottasso, K Dykes, K.O. Merz, P.E. Rethore
A research agenda is described to further encourage the application of Multidisciplinary Design Analysis and Optimisation (MDAO) methodologies to wind energy systems. As a group of researchers closely collaborating within the International Energy Agency (IEA) Wind Task 37 for Wind Energy Systems Engineering: Integrated Research, Design and Development, we have identi_ed challenges that will be encountered by users building an MDAO framework. This roadmap comprises 17 research questions and activities recognised to belong to three research directions: model _delity, system scope and workow architecture. It is foreseen that sensible answers to all these questions will enable to more easily apply MDAO in the wind energy domain. Beyond the agenda, this work also promotes the use of systems engineering to design, analyse and optimise wind turbines and wind farms, to complement existing compartmentalised research and design paradigms.
...
A research agenda is described to further encourage the application of Multidisciplinary Design Analysis and Optimisation (MDAO) methodologies to wind energy systems. As a group of researchers closely collaborating within the International Energy Agency (IEA) Wind Task 37 for Wind Energy Systems Engineering: Integrated Research, Design and Development, we have identi_ed challenges that will be encountered by users building an MDAO framework. This roadmap comprises 17 research questions and activities recognised to belong to three research directions: model _delity, system scope and workow architecture. It is foreseen that sensible answers to all these questions will enable to more easily apply MDAO in the wind energy domain. Beyond the agenda, this work also promotes the use of systems engineering to design, analyse and optimise wind turbines and wind farms, to complement existing compartmentalised research and design paradigms.