Page 11 - North American Clean Energy January/February 2019 Issue

A better question would be, is the turbine producing enough energy (performance)? Many wind turbines su er chronic or acute technical performance issues that do not cause shutdowns. SCADA and data collection systems are not built to capture, communicate, and manage these issues e ectively.
Turbine suppliers and wind farm service providers (operators) have con icting interests, which combine to harm wind farm owners.
e wind industry is plagued by poorly structured service agreements that do not align incentives to maximize farm output. Even the incentives for internal self- perform teams are awry; they don’t create a culture and contractual environment
that aligns the project team (owner, asset manager and operator) to maximize energy output over the life of the wind farm.
At the same time, not enough suppliers engage with wind farm owners to address potential performance issues in a lean, dynamic, and transparent way. Many prefer to maintain their expert position relative to their customer base.
erefore, the only people with large amounts of cross-sectional turbine operating data (the suppliers) do not share or learn from their customers. While those at the front line (the operators) are working with smaller datasets, and poorly structured data collection systems.
e result is systemic underperformance across the wind industry.
Overly-optimistic, pre- construction energy yield estimates have left wind farm owners with limited resources to investigate and address performance issues. Wind farms are not massively lucrative operations. On the revenue side,
projects often do not achieve their pre- construction energy yield forecasts.
On the cost side, as developers seek to minimize capex to get through nancing, owners end up paying for it on the opex.
Current projects are nanced on merchant markets with sub two cents per kwh hedges.
Any big data strategy looking to have an industrywide impact must come
in with a clear and de nable value proposition that can increase annual energy production quickly.
Knowledge sharing between turbine suppliers, industry consultants, and asset operators is very limited.
In the next ve years, we are on target
to pass one terawatt of wind energy installed globally. Who will hold the right ‘big labelled data’ to give future arti cial intelligence tools the best chance of
bringing meaningful performance increases? Turbine suppliers have the scale, but their closed box business model prevents them from e ectively
implementing an agile improvement model that would see that big data labeled e ectively.
e largest operators might be active on over 20 gigawatts of assets – a mere 2 percent of the market, given the inherently local, and hence fragmented industry structure. Will they have the tools to e ectively capture, label, and analyze the big data from those assets?
Gareth has over a decade of experience leading identi cation, development, construction, nancing, and operation of renewable energy assets for a renewable energy technical consultancy. He is an entrepreneur, chartered engineer with the IMechE, and has degrees in
mathematics and mechanical engineering. Currently, he serves as the CEO for the Renewable Energy Software Startup, Clir Renewables.
Clir Renewables /// clir.eco
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