• Energy Research

In the last decades renewable energy production has been growing exponentially. In this sector, electricity generation through wind turbines has become relevant. The aim of research is to enhance efficiency and energy production. In this article, in fact, the optimization analysis of a 3 MW horizontal axis wind turbine (HAWT) blade is presented. The aerodynamic performances are evaluated through an in house BEM code, developed by Airworks s.r.l., which is coupled with modeFrontier® in order to manage incoming data and post process results obtained in an automatic way. Airfoil type, section length, chord and twist distributions have been taken as input parameters. Power coefficient and annual energy production are the output variables used to judge the aerodynamic performance of the resulting blade. Several runs have been done in order to collect information and find the best design for the blade.

Upwind 20 MW Wind Turbine Pre-Design

Upwind 20 MW Wind Turbine Pre-Design

Traditionally, wind turbine and wind farm designs have been optimized to minimize the cost of energy. Such a design would make sense when bidding in price-based auctions. However, in a future with a high share of renewables and zero subsidies, the wind farm developer could be completely exposed to the volatility of market prices, where the price paid per kWh of energy would not be a constant anymore. The developer might then have to maximize the revenue earned by participating in di_erent energy, capacity, or ancillary services markets. In such a scenario, a turbine designed for maximizing its market value could be more pro_table for the developer compared to a turbine designed for minimizing the Levelized Cost of Electricity (LCoE). This study is in line with this paradigm shift in the _eld of turbine and farm design. The goal is to optimize the de- sign for a new set of objective functions and constraints, and analyze the impact of these new designs on the system as a whole. The power density of the turbine is optimized to maximize the Internal Rate of Return (IRR) and is compared to the turbine design optimized for LCoE. A multivariate model is developed to derive the spot price from the existing nationwide wind power and demand forecast. For the future years, the forecasts are scaled up w.r.t the increase in installed wind turbine capacity and demand derived from trends/government targets. Various scenarios are simulated wherein the installed wind turbine capacity and demand are varied. A gradient-free optimization is performed by using the rotor diameter as a design variable while keeping the machine rating constant. Using IRR as an objective function results in larger rotor sizes enabling the turbine to produce a higher power at lower wind speeds, corresponding to times with higher spot prices. The result of a scenario (Target) where the installed wind turbine capacity follows government targets and demand is extrapolated linearly, is shown in Figure 1a. Here, the power density of a 5 MW baseline turbine is optimized for IRR, where the revenue from the Dutch day-ahead market is considered along with the turbine costs. Results for a single (onshore) turbine will be compared with a similar IRR optimization of power density of a turbine in a sample o_shore wind farm. At a wind farm level, the e_ects of power density variations on the farm layout, wake losses, ca- bling costs, etc. are also included. Moreover, insights into the consequences of optimizing the turbines on 'system-friendliness' are provided. Figure 1b illustrates a comparison between the farm capacity factor and farm power ramps. It is observable that while the capacity factor of the farm with a revenue-driven turbine is higher, the power ramps are steeper as well. A system-level trade-o_ is apparent as higher capacity factors ensure a better supply of demand at lower wind speeds while higher ramps need further compensation. This shows how moving beyond LCoE, by only considering energy markets, might not necessarily produce the most system-friendly turbines. To avoid negative implications, this study emphasizes the need to examine the consequences of selecting a revenue-based objective function on the system as a whole.

Traditionally, wind turbine and wind farm designs have been optimized to minimize the cost of energy. Such a design would make sense when bidding in price-based auctions. However, in a future with a high share of renewables and zero subsidies, the wind farm developer could be completely exposed to the volatility of market prices, where the price paid per kWh of energy would not be a constant anymore. The developer might then have to maximize the revenue earned by participating in di_erent energy, capacity, or ancillary services markets. In such a scenario, a turbine designed for maximizing its market value could be more pro_table for the developer compared to a turbine designed for minimizing the Levelized Cost of Electricity (LCoE). This study is in line with this paradigm shift in the _eld of turbine and farm design. The goal is to optimize the de- sign for a new set of objective functions and constraints, and analyze the impact of these new designs on the system as a whole. The power density of the turbine is optimized to maximize the Internal Rate of Return (IRR) and is compared to the turbine design optimized for LCoE. A multivariate model is developed to derive the spot price from the existing nationwide wind power and demand forecast. For the future years, the forecasts are scaled up w.r.t the increase in installed wind turbine capacity and demand derived from trends/government targets. Various scenarios are simulated wherein the installed wind turbine capacity and demand are varied. A gradient-free optimization is performed by using the rotor diameter as a design variable while keeping the machine rating constant. Using IRR as an objective function results in larger rotor sizes enabling the turbine to produce a higher power at lower wind speeds, corresponding to times with higher spot prices. The result of a scenario (Target) where the installed wind turbine capacity follows government targets and demand is extrapolated linearly, is shown in Figure 1a. Here, the power density of a 5 MW baseline turbine is optimized for IRR, where the revenue from the Dutch day-ahead market is considered along with the turbine costs. Results for a single (onshore) turbine will be compared with a similar IRR optimization of power density of a turbine in a sample o_shore wind farm. At a wind farm level, the e_ects of power density variations on the farm layout, wake losses, ca- bling costs, etc. are also included. Moreover, insights into the consequences of optimizing the turbines on 'system-friendliness' are provided. Figure 1b illustrates a comparison between the farm capacity factor and farm power ramps. It is observable that while the capacity factor of the farm with a revenue-driven turbine is higher, the power ramps are steeper as well. A system-level trade-o_ is apparent as higher capacity factors ensure a better supply of demand at lower wind speeds while higher ramps need further compensation. This shows how moving beyond LCoE, by only considering energy markets, might not necessarily produce the most system-friendly turbines. To avoid negative implications, this study emphasizes the need to examine the consequences of selecting a revenue-based objective function on the system as a whole. Wind Energy

The focus of my works for the year were floating wind turbines. Floating wind turbines are energy generating devices that are placed offshore on platforms that float on top of water, primarily the ocean. My two works seek to explore two aspects of these turbines. My technical work focuses on designing an active stabilization system for the turbines in order to keep them upright in varying conditions. My research focuses on the impacts of a wind turbine project completed by the Japanese Government in the 2010s and how its removal has failed the people of Fukushima. Together, these works provide a unique perspective and design possibility for bringing these turbines into society. Considering technical and ethical issues are crucial when creating a new product and my papers seek to address both issues. The goal of my technical project was to design a scale-model of a floating wind turbine base which used active methods for maintaining its stability. Introducing active stabilization to a floating wind turbine design provides the unique ability to reliably counteract forces acting upon the structure from wind, waves, and currents. The active stabilization method had to be designed considering constraints of codes, constructability, cost, functionality, maintainability, sustainability, standards, and more. Following a meticulous design process, I was able to assemble a physical prototype of an active stabilization method and test its effectiveness in water. My research paper focuses on the care ethics associated with the Fukushima Offshore Floating Wind Turbine Farm in Fukushima, Japan. This wind farm was created in 2013 and removal was started in 2018. The project was designed as a symbol of hope for the citizens of Japan and to provide necessary power after the loss of the Fukushima Nuclear Power Plant, however, the project failed to exercise care to the people of Japan. Based on Carol Gilligan’s Care Ethics with a revision by Elisa Warford, it can be found that the turbine project failed to meet the ethical goals of attentiveness, responsiveness, and competence associated with care for the people of Fukushima. Working on these projects together dramatically changed how I approached my view of engineering and changed my analysis of my work. Engineering involves not only designing products but also considering their impact on society once released. By performing an analysis of a project that employed the technological components I was researching, I was able to gain a deeper understanding of the potential short- and long-term implications of a project on people. On the other hand, seeing how one project failed a group of people influences design when proposing and implementing new ideas. Overall, this process had added to the experience of intense technical design as well as understanding the implication of a design on society.

The purpose of this project is to generate a holistic turbine health score in an effort to better understand long-term wind turbine performance and health. In the preliminary stages of the project, research was done to benchmark existing solutions and discover which signals should be included in the analysis. Signals were then discussed with Invenergy, the sponsor of this project, to determine which were of the highest priority. These signals include active power, reactive power, blade pitch angles, digital states, and tip speed ratio, which is calculated from wind speed and rotor speed measurements. The specific requirements for the health score were also fleshed out, with the score needing to be at both the site- and turbine-level and span multiple different time periods (monthly, annual). The score should also be dynamic, with the ability for subsystem performance metrics to be added or removed in future iterations. In analyzing data signals, various performance metrics were calculated using Python and compared to one another to discern trends. These metrics include the ratio of measured active power to rated power (active power ratio), the ratio of measured reactive power to the average reactive power across the site (reactive power ratio), the standard deviation in blade pitch angles, the tip speed ratio, and the time spent in each digital state. To compare these metrics, they were each plotted as a function of time, turbine number, and wind speed. It was determined that there was a correlation between active power ratio and tip speed ratio, which makes intuitive sense as they are both measures of turbine efficiency. However, the data did not indicate a correlation between the other metrics. With this lack of correlation, too much understanding and information would be lost in synthesizing these metrics into a single health score. Therefore, the proposed solution to this project is a turbine health dashboard that displays the percent downtime, active power ratio, and standard deviation in blade pitch angles. This dashboard satisfies all of the project requirements, with each metric being displayed at the turbine-level and averaged across the site. Each metric is displayed at the annual-level for direct comparison and the monthly-level to view trends over time. Metrics can also be easily added or removed from the dashboard in future iterations.

A comprehensive computational tool for the aeroelastic analysis of horizontal axis wind turbines (HAWT) is presented. The proposed aeroelastic tool couples a nonlinear beam model for blades structural dynamics with an unsteady state-space sectional aerodynamic model taking into account dynamic stall. Three-dimensional wake inflow effects are described by a Boundary Element Method for the solution of incompressible, potential, attached flows. To this aim, different coupling approaches are compared. The resulting aeroelastic differential system is integrated through the Galerkin method, with the introduction of a novel technique for the spatial integration of the additional aerodynamic states related to wake vorticity and dynamic stall. Periodic blade responses are determined by a harmonic balance approach. The effectiveness of the proposed unsteady aerodynamic modelling is discussed, with numerical and experimental comparisons.

In the case of floating wind turbines (FOWTs), international design standards currently leave a larger degree of freedom to engineers in the specification of the boundary conditions for turbine simulation and certification. This is due to fact that FOWTs are still a young technology, and standards are still evolving. To analyze offshore wind turbines and estimate parameters such as AEP, fatigue and extreme loads, site-specific reference environmental conditions need to be defined. Then, a probabilistic model of the installation site must be built in order to compute lifetime quantities. Finding a trade-off between simulation number and length and good long-term estimation of fatigue and ultimate loads, as well as the selection of relevant loading metrics requires a significant amount of research or experience in the field. The current work aims at exploiting a procedure that was developed within the H2020 FLOATECH project and made available open source with this study to the scientific community, with the objective of addressing what is required to perform a load and performance evaluation of a FOWT in a real environment. A procedure to obtain environmental conditions if the ones available in the literature do not meet the designer’s needs is first illustrated. Then, the most important parameters that need to be considered when performing an analysis of a FOWT are detailed; taking these into account and their corresponding metrics, a detailed guideline on how to define a suitable list of Design Load Cases (DLCs) is presented, as well as different methods to reduce the number of model evaluations deriving from the DLCs’ list, and thus reduce computational time.

With the increasing focus on renewable energy, there is a need to improve the efficiency of vertical-axis wind turbines (VAWTs). The Ugrinsky wind turbine is a type of VAWT, but there are few studies on this turbine. Previous studies have shown that the maximum power coefficient of the Ugrinsky wind turbine reaches 0.170, which is 54.5% higher than that of the Savonius type (0.110), and this turbine maintains a high power coefficient over a wide range of tip speed ratios (TSR). In this study, the dimensions of the two semicircles of the Ugrinsky wind turbine were further optimized to obtain a higher power coefficient. An analysis of the effect of the blade dimensions on the performance was conducted. The flow around the turbine was simulated using the regularized lattice Boltzmann method. The geometry of the turbine was simulated using the virtual flux method for the Cartesian grid. The optimization was conducted in terms of the output power coefficient and the average value of the power coefficient for neighboring TSR to consider the fluctuation of the TSR. This study demonstrates that a closer vortex distance favored the growth of the vortex and improved the power coefficient.