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The world needs to move to a cleaner energy system if it is to meet growing energy demand while tackling climate change. In April 2020, Shell shared its ambition to become a net-zero emissions energy business by 2050, or sooner.

Renewable electricity is central to this ambition. Electricity is the fastest-growing part of the energy system and, when generated from renewable sources such as wind, has a big role to play in reducing greenhouse gas emissions. We see digitalisation and Artificial Intelligence as key enablers to the energy transition.

About the Shell.ai Hackathon for Sustainable and Affordable Energy 

The Shell.ai Hackathon for Sustainable and Affordable Energy kicked-off on September 14th, focusing on a Windfarm Layout Optimisation coding challenge. The participants were invited to optimise the placement of 50 wind turbines of ‘100 m rotor diameter to help maximise the AEP (Annual Energy Production), each on a hypothetical offshore wind farm area. One of the key problems of an unoptimized layout is the combined effect wind turbines can have on the wind speed distribution in a windfarm. As a wind turbine extracts energy from incoming wind, it creates a region behind it downstream where the wind speed is decreased- this is called a wake region. Note that wind turbines automatically orient their rotors, to face incoming wind from any direction. Due to the induced speed deficit, a turbine placed inside the wake region of an upstream turbine will naturally generate reduced electrical power. This inter-turbine interference is known as a wake effect. An optimal windfarm layout is important to ensure a minimum loss of power during this combined wake effect.

The contestants faced challenges such as a high dimensionality, complex multimodality and the discontinuous nature of the search space. This made optimising the layout analytics difficult. But, armed with optimisation strategies and computer algorithms, around 5000 teams signed up to compete in this challenge.
 

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