Exploring the interactions between ramping wind events (fronts and thunderstorms) and wind farms

Project description

A critical issue for the large-scale penetration of wind power into modern electricity grids is the successful management of so-called ramp events. During these events, rapid changes in wind speed or direction across a wind farm lead to large variability in power generation over short periods time. This variability can destabilize power grid and the forecasting of such events is a key area of wind energy research at this time.

Meteorological phenomena, such as cold front, low-level jet and thunderstorm outflows are primary triggers of ramp events. Ramping can occur either when these wind events begin (ramp-up) or when they end (ramp-down), with management of both time periods critical for the operation of a wind farm. Despite the current research focus on developing tools to use machine learning or AI to help forecast the occurrence of these ramp events, power forecast errors persist because relatively little is understood about how these transient and often localised wind events interact with wind farms and in turn, how wind farms may modify the behaviour of these wind storms.

To improve our understanding of how ramp-event generating wind storms interact with wind farms, numerical simulations of frontal and thunderstorm outflow passage over wind farms will be undertaken. Through collaborations with industry partners in both Australia and India a number of historic ramp events will be reconstructed and examined to ascertain the efficacy of both meso- and micro-scale simulation tools (e.g. WRF, CFD) in replicating observed wind speeds at wind farm sites and also power outputs from their wind turbines. Simulation results will provide valuable information on how interactions between wind farms and ramping wind events differ from what exists during neutral wind conditions and will lead to an improved ability to forecast the power ramps associated with them.

Outcomes

• Improved understanding of how well mesoscale models (e.g. WRF) can replicate ramping wind events of importance to wind farm operation.
• Improved understanding of the wind science associated with interactions between meteorological phenomena such as thunderstorm outflows and cold fronts and wind farms.
• Insight into whether short-time wind forecasts may be improved with enhanced knowledge of event-specific flow physics.
• Publication of high-quality research papers in leading wind energy journals.

Essential capabilities

A strong interest in atmospheric or wind science, experience developing and/or running mathematical codes, interest in renewable energy.

Desireable capabilities

Experience with meso- or micro-scale meteorological models, experience with computational fluid dynamics models.

Expected qualifications (Course/Degrees etc.)

Bachelors (with honours) or Masters degree in atmospheric science or a related (e.g. engineering, mathematics) field.