ThA09 - Innovation and Modern Challenges In Wind Farm Control

Description

Organizer: Bart Doekemeijer, Christopher Bay, Andrew Scholbrock, Paul Fleming (National Renewable Energy Laboratory), Jan-Willem van Wingerden (Delft University of Technology)
Time: May 27, 2021, Thursday, 10:15-12:15 AM CDT

The tutorial session will focus towards lower technology readiness level ideas and novel contributions on the topic of wind farm control. First, the tutorial talk will detail modern innovations in wind farm control that are currently being investigated, such as helical induction control and wind farm control in floating offshore wind turbines. Additionally, in continuation of the speakers’ recent publication an expert elicitation on wind farm control, main findings of a recent survey among industry and academia experts inquiring the main challenges in the adoption of wind farm control are presented.

Presenters

Title: Modern Innovations And Current Bottlenecks In The Adoption Of Wind Farm Control
Authors: J. W. van Wingerden, P. A. Fleming, B. M. Doekemeijer
Time: 10:15-11:15 AM EST

This tutorial talk will detail modern innovations in wind farm control that are currently being investigated, such as dynamic induction control, helical induction control, wind farm control in floating offshore wind turbines, and turbine reposition to minimize wake interaction in floating wind farms. Furthermore, in continuation of the authors’ recent publication an expert elicitation on wind farm control, the authors will outline the main findings of their survey among industry and academia experts inquiring for the main challenges in the adoption of wind farm control.

Title: On The Effectiveness Of One-Sided Wake Steering – A Wind Tunnel Study With Dynamic Direction Changes
Authors: F. Campagnolo, C. L. Bottasso
Time: 11:15-11:35 AM EST

In recent years, the interest of the scientific community has been directed to the development of cooperative control strategies for wind farms, with the goal of increasing power capture, of extending their life and providing additional services to improve their operation. Wake steering appears to be one of the most promising strategies. Using this technique, upstream wind turbines are intentionally misaligned with respect to the ambient wind direction, which results in a lateral displacement of the wake path. Results indicate that the wind farm power gains (dPn,WF) achieved by one-sided wake-steering are significantly lower that the ones obtained by misaligning the turbines both for positive and negative angles. In addition, one-sided wake steering also seems to have little beneficial effects on fatigue loads on the rotating shaft. In fact, almost no reduction is observed in the extra loading for the upstream machine, and a marked reduction in the beneficial effects on the DELs at the downstream turbines is achieved.

Title: Design Of Wind Farms For Maximizing Value To The Electricity System
Author: K. Dykes
Time: 11:35-11:55 AM EST

Design and operational objectives for future wind farms must are shifting from levelized cost of energy (LCOE) to account for system value and overall project profitability for farms participating actively in electricity markets. ”Beyond LCOE” objectives that account for system value are applied here in multi-objective optimization case studies in wind farm layout design – including operational strategy (e.g. wind farm control strategies for derating). The case studies result in a Pareto front of designs that maximize value but with a trade-off to LCOE and vice versa.

Title: Power maximization in floating offshore wind farms via real-time platform repositioning
Authors: A. C. Kheirabadi, R. Nagamune
Time: 11:55-12:15 AM EST

This tutorial paper will outline methods and sample results from our research on power maximization in floating offshore wind farms. The control approach involves using the aerodynamic force acting on each wind turbine rotor to reposition the floating platforms in real-time. The objective is to minimize the overlap areas between adjacent rotors, while distributed economic model predictive control (DEMPC) is used to automate this process. Our paper uses of our recently developed low-fidelity dynamic simulation tool for floating offshore wind farms, along with new DEMPC theory that we developed specifically for the non-convex floating wind farm control problem. Finally, neural networks are used to estimate the dynamic behaviour of floating wind turbines. These neural networks are tuned using simulation data and are used to speed up the optimization procedure in DEMPC. Results demonstrate that the real-time relocation of the floating platforms along with the effects of this motion on dynamic power production.