Articles | Volume 9, issue 4
https://doi.org/10.5194/wes-9-759-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wes-9-759-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
HyDesign: a tool for sizing optimization of grid-connected hybrid power plants including wind, solar photovoltaic, and lithium-ion batteries
Department of Wind and Energy Systems, Technical University of Denmark, 4000 Roskilde, Denmark
Hajar Habbou
Department of Wind and Energy Systems, Technical University of Denmark, 4000 Roskilde, Denmark
Mikkel Friis-Møller
Department of Wind and Energy Systems, Technical University of Denmark, 4000 Roskilde, Denmark
Megha Gupta
Department of Wind and Energy Systems, Technical University of Denmark, 4000 Roskilde, Denmark
Rujie Zhu
Department of Wind and Energy Systems, Technical University of Denmark, 4000 Roskilde, Denmark
Kaushik Das
Department of Wind and Energy Systems, Technical University of Denmark, 4000 Roskilde, Denmark
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Simulating the turbulent flow inside large wind farms is inherently complex and computationally expensive. A new and fast model is developed based on data from high-fidelity simulations. The model captures the flow dynamics with correct statistics for a wide range of flow conditions. The model framework provides physical insights and presents a generalization of high-fidelity simulation results beyond the case-specific scenarios, which has significant potential for future turbulence modeling.
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Wind turbines extract kinetic energy from the flow to create electricity. This induces a wake of reduced wind speed downstream of a turbine and consequently downstream of a wind farm. Different types of numerical models have been developed to calculate this effect. In this study, we compared models of different complexity, together with measurements over two wind farms. We found that higher-fidelity models perform better and the considered rapid models cannot fully capture the wake effect.
Javier Andueza-Bueno, Leonardo Alcayaga, Mikkel Friis-Møller, and Pierre-Elouan Réthoré
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In a wind farm, each turbine slows down the wind behind it, so turbines downstream produce less energy. Therefore, their positions affect the farm's output, and algorithms are used to find the best layout. These algorithms estimate yearly energy output many times, which can make the process slow. This study compares faster ways of making this estimate and shows that the most useful methods are not necessarily the most accurate ones, but those that best capture how turbines affect each other.
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Wind turbine positions are often optimized to avoid wake losses. These losses depend on atmospheric conditions, such as the wind speed and direction. The typical optimization scheme involves discretizing the atmospheric inputs, then considering every possible set of these discretized inputs in every optimization iteration. This work presents stochastic gradient descent (SGD) as an alternative, which randomly samples the atmospheric conditions during every optimization iteration.
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Simulating the turbulent flow inside large wind farms is inherently complex and computationally expensive. A new and fast model is developed based on data from high-fidelity simulations. The model captures the flow dynamics with correct statistics for a wide range of flow conditions. The model framework provides physical insights and presents a generalization of high-fidelity simulation results beyond the case-specific scenarios, which has significant potential for future turbulence modeling.
Jana Fischereit, Kurt Schaldemose Hansen, Xiaoli Guo Larsén, Maarten Paul van der Laan, Pierre-Elouan Réthoré, and Juan Pablo Murcia Leon
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Short summary
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Wind turbines extract kinetic energy from the flow to create electricity. This induces a wake of reduced wind speed downstream of a turbine and consequently downstream of a wind farm. Different types of numerical models have been developed to calculate this effect. In this study, we compared models of different complexity, together with measurements over two wind farms. We found that higher-fidelity models perform better and the considered rapid models cannot fully capture the wake effect.
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Short summary
A methodology for an early design of hybrid power plants (wind, solar, PV, and Li-ion battery storage) consisting of a nested optimization that sizes the components and internal operation optimization. Traditional designs that minimize the levelized cost of energy give worse business cases and do not include storage. Optimal operation balances the increasing revenues and faster battery degradation. Battery degradation and replacement costs are needed to estimate the viability of hybrid projects.
A methodology for an early design of hybrid power plants (wind, solar, PV, and Li-ion battery...
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