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Driving Returns And Reducing Risk With Data

Why understanding information is the secret answer to unlocking an investable green energy future

Despite the disruption from Covid-19 in the previous 18 months, projections for the solar and wind energy industries are looking strong. Based on the International Energy Agency, 270 GW of renewable capacity are required to become added in 2022 and 280 GW in 2022. Although this is a hugely exciting growth trajectory, there are still some impediments towards the increased deployment of both solar and wind power power. As alternative energy technologies have matured, governments have stepped back from underwriting its deployment through tariffs and incentives. It has created what's termed a 'post subsidy' marketplace.

What this means used is the fact that where governments have stepped back, private capital must now come forward. Capital will only take investment risk if it believes it will see a return and believes that it is investment will be protected throughout the lifecycle of alternative energy facilities.

Protecting capital and driving investor returns in solar and wind power is becoming an industry in itself. Firms like Clir Renewables have invested significantly in creating ip that allows use of massive amounts of industry-wide data and combs data using the latest artificial intelligence and machine learning technologies. By making use of AI and ML technology created by the industry to massive levels of alternative energy operational data, all project stakeholders, from owners and investors to insurers and lenders, can know where they have to make operational changes, and manage the performance and perils of assets. As our datasets rise in size and our modelling techniques advance, we discover that we can better help guide the operational running of wind and solar projects for improved project returns.

For example, to manage the longevity of apparatus, which will help keep renewable power prices low, manufacturers will 'derate' their wind generators – that's, intentionally impede their power output. The unintended consequences of derating can reduce project power generation for extended intervals, rather than the specific time period intended. It is because derating strategies are often employed simplistically.

Avoiding pitfalls such as this – and comprehending the interplay from the different dynamics that affect project performance – is only possible with deep and granular data. Data must be leveraged holistically, using not just the information from individual wind turbines, but in the entire project site.

If wind power generation will be intelligently managed for maximum returns, then it's important too to know what environmental factors are also at play – the effect of nearby vegetation, for example. To supply this understanding, we not only may need to look at equipment, but we need to measure and monitor multiple streams of information.

Similarly, reducing the financial risk of solar and wind power projects is possible through in-depth analysis of their components. All too often there is a reticence among asset managers to embrace data in a deep level, invest further time and resources into its understanding, and then implement changes to asset management programmes.

This hesitation may come from the perspective that 'current performance is nice enough'. Yet, with the shift to post-subsidy markets, investors are recognizing the better margins, and reduced costs of insurance, financing, and operations, available with an rise in power generation – and subsequently energy sales – of a single to 2 percent.

Similarly, across the solar industry, a belief that greatly impedes optimal results may be the idea that solar power is straightforward. Although solar photovoltaic plants may not have as many moving parts like a wind generator, it doesn't mean that data collection – which is essential to understanding asset health – is not challenging.

Gathering solar information is made complicated through the sheer number of original equipment manufacturers existing within the space. While wind energy has a smaller quantity of OEMs – owing to the larger costs of wind turbine development and production – solar has countless different manufacturers. This creates data choas, as data from their individual components is labelled differently.

Additionally, the unpredictability of solar resources, the possible lack of standardization across components, plant topography and also the quality of project data can all allow it to be hard to obtain a clear understanding of the project's health insurance and performance.

Attempting to translate all this information into an understanding of asset performance, and then contextualising it with the additional factors needed, eludes most owners and asset managers. However, by having an AI-driven platform, time required to translate and analyze data can be reduced to hours instead of weeks, because the many streams of information in various OEM 'languages' can be translated right into a coherent thread that identifies specific actions necessary to improve performance.

As the transition to renewable energy progresses, and we see more projects going live, it is important that the knowledge around data continues to progress. This will allow projects, and by extension, their go back to investors, reach their full potential. At Clir, we have use of over 100 GW of operational alternative energy projects worldwide – helping asset owners, investors and companies enhance their returns, ensure the smooth and efficient running of projects, and fulfil their obligations to low carbon power supplies.