8 research outputs found
Expert Elicitation on Wind Farm Control
Wind farm control is an active and growing field of research in which the
control actions of individual turbines in a farm are coordinated, accounting
for inter-turbine aerodynamic interaction, to improve the overall performance
of the wind farm and to reduce costs. The primary objectives of wind farm
control include increasing power production, reducing turbine loads, and
providing electricity grid support services. Additional objectives include
improving reliability or reducing external impacts to the environment and
communities. In 2019, a European research project (FarmConners) was started
with the main goal of providing an overview of the state-of-the-art in wind
farm control, identifying consensus of research findings, data sets, and best
practices, providing a summary of the main research challenges, and
establishing a roadmap on how to address these challenges. Complementary to the
FarmConners project, an IEA Wind Topical Expert Meeting (TEM) and two rounds of
surveys among experts were performed. From these events we can clearly identify
an interest in more public validation campaigns. Additionally, a deeper
understanding of the mechanical loads and the uncertainties concerning the
effectiveness of wind farm control are considered two major research gaps
A control-oriented dynamic wind farm model: WFSim
Wind turbines are often sited together in wind farms as it is
economically advantageous. Controlling the flow within wind farms to
reduce the fatigue loads, maximize energy production and provide
ancillary services is a challenging control problem due to the
underlying time-varying non-linear wake dynamics. In this paper, we
present a control-oriented dynamical wind farm model called the
WindFarmSimulator (WFSim) that can be used in closed-loop wind farm
control algorithms. The three-dimensional Navier–Stokes equations were
the starting point for deriving the control-oriented dynamic wind farm
model. Then, in order to reduce computational complexity, terms
involving the vertical dimension were either neglected or estimated in
order to partially compensate for neglecting the vertical
dimension. Sparsity of and structure in the system matrices make this
model relatively computationally inexpensive. We showed that by taking the
vertical dimension partially into account, the estimation of flow data
generated with a high-fidelity wind farm model is improved relative to
when the vertical dimension is completely neglected in WFSim. Moreover,
we showed that, for the study cases considered in this work, WFSim is
potentially fast enough to be used in an online closed-loop control
framework including model parameter updates. Finally we showed that the
proposed wind farm model is able to estimate flow and power signals
generated by two different 3-D high-fidelity wind farm models
In COVID-19 health messaging, loss framing increases anxiety with little-to-no concomitant benefits: Experimental evidence from 84 countries
The COVID-19 pandemic (and its aftermath) highlights a critical need to communicate health information effectively to the global public. Given that subtle differences in information framing can have meaningful effects on behavior, behavioral science research highlights a pressing question: Is it more effective to frame COVID-19 health messages in terms of potential losses (e.g., “If you do not practice these steps, you can endanger yourself and others”) or potential gains (e.g., “If you practice these steps, you can protect yourself and others”)? Collecting data in 48 languages from 15,929 participants in 84 countries, we experimentally tested the effects of message framing on COVID-19-related judgments, intentions, and feelings. Loss- (vs. gain-) framed messages increased self-reported anxiety among participants cross-nationally with little-to-no impact on policy attitudes, behavioral intentions, or information seeking relevant to pandemic risks. These results were consistent across 84 countries, three variations of the message framing wording, and 560 data processing and analytic choices. Thus, results provide an empirical answer to a global communication question and highlight the emotional toll of loss-framed messages. Critically, this work demonstrates the importance of considering unintended affective consequences when evaluating nudge-style interventions