However, this is by no means the only play in town. Investors should also be paying attention to other potential players that improve the yield of these assets, which is a necessity for wind power to truly dominate as so many governments are planning.
The race to roll out wind farms has begun
The push to make our electrical grids more renewable continues to pick up steam, with governments worldwide setting in motion ambitious plans to increase wind farm installations. At the end of 2020, Boris Johnson famously stated that he wanted the UK to become the “Saudi Arabia of wind power.” His government has committed to quadrupling offshore wind generation by 2030 and has already taken action to speed things up by cutting the approval time needed for new offshore wind farms from four years to one.
Over in Germany, the newly elected government plans to further scale up wind power development by building between 1,000 and 1,500 new wind towers a year. If achieved, it is estimated that new wind projects will cover up to 2% of the country. Across the pond, the Biden administration recently announced a bold plan to scale up leasing for offshore wind energy projects along the Atlantic, Gulf, and Pacific coasts with a goal of deploying 30,000 MW of offshore wind energy in US waters by 2030.
The UK, Germany, and the US are not alone. Similar plans and commitments are being rolled out in France, Spain, China, India, and many other countries. All told, we’re about to see a dramatic increase in wind farm installations in the coming years. This is great news for environmentalists and investors alike, but there’s a catch. For wind farms to dominate power generation and help meet the climate change targets set by governments, they’ll need to operate more like traditional non-renewable facilities in terms of efficiency and reliability. That’s where weather forecasting technologies come in.
Hyperlocal weather forecasting is now possible
Weather data and forecasts affect nearly every aspect of the wind sector, from where wind farms are located and how they are designed to yield optimisation, management of supply and demand, and daily operations and maintenance.
Fortunately, forecasting has come a long way and is set to become even more advanced in the coming years thanks to advances in data collection and increased computational firepower.
Over the next decade, the number of satellites (earth observation, meteorological, and other satellites) will ramp up from around 3,000 today to 65,000+, which will lead to an explosion in direct coverage and the availability of highly-accurate and frequently-refreshed data available for weather forecasting. AI/ML models will be able to make predictions based on these exploding datasets. Advances in computational firepower mean that GPUs optimised for vector math used in fluid mechanics calculations and AI/ML will also get significantly more effective. Modelling environmental fluid mechanics and forecasting will be possible at ever more localised levels.
With high resolution, hyperlocal weather forecasting, commercial entities will be able to produce and action unique and granular insights relevant to their operations and locations rather than just relying on governmental organisations such as the Met Office.
Weather forecasting can be used to improve power generation. For example, with enhanced forecasting of local wind directions, it’s possible to dynamically turn individual wind turbines to the oncoming wind direction to minimise wake drag/loss to downstream turbines to maximise power output across an entire wind farm.
Recently, Siemens Gamesa Renewable Energy started working with NVIDIA to create digital twins of its wind farms using its Omniverse solution and Modulus AI framework, which comprise its digital twin platform. Using NVIDIA’s Digital twin platform, Siemens Gamesa can achieve quicker calculations to optimise wind farm layouts, increasing overall production by maximising the power produced by each turbine while reducing loads and operating costs.
More accurate, hyperlocal weather forecasting also helps wind farms better predict energy needs and plan ahead for extreme weather conditions. For example, in hot countries or during heat waves, electricity usage soars due to increased use of air conditioning units and other cooling devices. This requires the grid to deliver additional GWatts of power. The water-cooling process also takes much longer during hot weather, making power generation far less efficient. With more accurate forecasting capabilities, energy operators can prepare load forecasts and spread energy needs to avoid power outages and ensure that conventional power plants and the grid operate more safely and efficiently during hot weather conditions.
Today, there are approximately 100 weather intelligence companies globally, with many now offering various valuable weather forecasting solutions for the wind energy sector. Investors should pay close attention to plays in the space that enable direct data collection by multiple technologies with high refresh rates, such as radar, satellite, UAVs, air quality sensors, other sensors, and training models focused on different aspects such as air quality, temperature, wind, precipitation, and lightning.
Spire, for example, is a leading provider of space-based weather data and insights. The company collects global weather data through its fully deployed private constellation of over 100 satellites, which can reach remote regions and oceans where wind farms are often located. By running radio occultation readings and weather data from various sources through machine learning algorithms, the company is able to offer hyper-local forecasts that accurately predict a spectrum of weather possibilities.
Earth Networks is another leader in the space. The company operates weather and climate sensor networks worldwide. In conjunction with proprietary forecasting algorithms and technology, the company’s extensive weather network provides the most current conditions from more than 2.6 million locations worldwide. They offer the wind energy sector several critical capabilities, including automated weather alerts to ensure safety, real-time lightning tracking, and the ability to visualise historical lightning data to discover if lightning struck any turbines.
As dependence on wind energy grows, so will demand for weather data and analytics
We see vast potential in weathertech and its applications for the wind energy sector. While predicting and controlling energy production is easy for traditional power producers such as coal, the same cannot be said for wind farms, which are inherently volatile.
As more wind farms are rolled out and wind makes up a more significant percentage of the grid supply, it will become increasingly important that wind farms be able to control this volatility and operate more like traditional non-renewable facilities. More accurate, hyperlocal weather forecasts can help achieve this.
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