Maximising revenue from your wind asset by utilising data and technology

Posted 19th May 2021 | 407 words | 2 minutes

Zeigo’s Reuters presentation highlighted some of the problems with the PPA market and how these can be fixed through Zeigo, so you can maximise revenue from your asset.

We are all striving to maximise our top line, and when it comes to renewable assets data and technology are a driving force. Our Senior Price Analyst, Frederic Lyons, talked through the four ways Zeigo maximises revenue for wind assets at the Wind Operations Europe 2021 Reuters event. If you missed the conference here are the key takeaways and how Zeigo can help you get the most out of your asset:

The need for pricing transparency

PPA markets in Europe remain opaque, they are often highly bespoke agreements conducted behind the scenes with little or no feedback for participating developers. Increased data can improve price visibility in the long-term Corporate PPA markets. This gives the ability to analyse offers and help align developer & corporate expectations through constructive feedback. For shorter-term Utility PPA’s, technology can help developers navigate complex energy markets and fully understand the multitude of different supplier off-takers.

Zeigo offers speed & simplification in a complex PPA market

The existing PPA market is often complex, and deals can be frustratingly slow and time-consuming. Price volatility is a key risk that must be managed to preserve and increase revenue. By standardising terms and conditions, creating a streamlined two-stage tendering process & a digital contract signature, Zeigo is shortening the tendering process, saving developers’ time, and reducing their exposure to volatile energy markets.

Harnessing data to match participants

Zeigo is at the forefront of harnessing data to match developers with experienced corporate and utility off-takers. Technology can be used to direct sellers towards a PPA based on specific criteria such as term, credit, commissioning operating date, etc. Data and technology can aggregate corporate buyers based on their consumption profiles, as well as provide useful insight into supplier “soft benefits,” helping developers differentiate between some 40 different utilities in the market for PPA’s.

Predicting future energy prices through Machine Learning

Most energy forward curves are based on economic modeling over a range of different policy and market scenarios. Machine Learning prefers to take a backward-looking view, based on previous energy market data. By looking at historical pricing, wind speeds, and a whole host of other important metrics, developers can accurately predict revenue from an asset for up to 5 years.

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