High-Resolution Meteorological Data to Plan the Placement of an Offshore Wind Grid
Using a sophisticated weather model, environmental engineers at Stanford University (USA) have defined the optimal placement of a grid of four wind farms off the US East Coast. The model successfully balances production at times of peak demand and significantly reduces costly spikes and zero-power events.
By Andrew Myers, Associate Director of Communications, Stanford University School of Engineering, USA
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Using a sophisticated weather model, environmental engineers at Stanford University (USA) have defined the optimal placement of a grid of four wind farms off the US East Coast. The model successfully balances production at times of peak demand and significantly reduces costly spikes and zero-power events.By Andrew Myers, Associate Director of Communications, Stanford University School of Engineering, USA
As a result of the rapid growth in the offshore wind sector, the demand for spatial environmental data is constantly increasing. Traditional visual aerial survey methods suffer from reliability, safety and data quality drawbacks. These can be overcome by new digital methods, and in this article we discuss the application of these new technologies to offshore aerial bird and mammal surveys.
Layout tools attempt to identify the best layout of wind turbines on a land or offshore area according to energy capture. They model free stream wind flowing through an area with sited turbines and calculate the energy output of successive turbines while taking wake effects and turbulence intensities into account. A key component of such tools is the ‘optimiser’ algorithm used to efficiently search through a modest proportion of candidate layouts to identify the best one. This article discusses earlier bio-inspired algorithms, and then the CMA-ES algorithm, an evolutionary algorithm that performs stochastic sampling optimisation by mimicking fundamental aspects of the neo-Darwinian evolutionary process.
Consolidation within the wind sector has been much discussed over the past 12–18 months due to weakening demand and ongoing price pressure. However, with global demand down and a manufacturing overcapacity already existing, the consolidation of Tier 2/3 wind OEMs into Tier 1 global competitors is not likely unless there are other factors at play. These other factors include leveraging an intellectual property portfolio, gaining strategic access to a market, gaining a manufacturing footprint in an underserved market, or providing some level of vertical integration. In this article, Philip Totaro considers the wind turbine market and OEM outlook for the next 12–18 months, both globally and for different regions of the world.
