REOptimize Systems (REOS), was formed to exploit research developed at University of Edinburgh, which has implemented a unique approach to increasing the efficiency of wind turbines. Through advanced modelling and the application of novel machine learning techniques the algorithms minimise the end-to-end losses in the system. This technique has patents pending and is the result of 7 years of research at The University of Edinburgh. The success of the algorithms has been proven experimentally in small-scale wind turbines, and found to yield increases in energy capture of 6%. A 6% increase in energy capture can drive net profit increases for the operator on the order of 50%-100% depending on the specific turbine and location. If only half of UK turbines achieved a 6% gain, it would result in an additional 3000 GWhr of generation and a saving of 1.3 million tonnes of carbon in a single year. This is equivalent to removing around 290,000 petrol passenger cars from the streets. However, this 6% gain has been proven only in medium-scale wind at approximately 100 kW ratings. It is expected that larger turbines will start from a position of better control which will allow us to achieve gains on the order of 3%. REOS is currently preparing a pilot project to validate the technique on a MW scale Siemens 2.3-92 turbine, which is a workhorse of the UK onshore fleet. Now, through this new project, REOS will develop and integrate novel machine learning technologies into a single platform which will provide end-to-end financial optimization of wind power assets, with a truly holistic view of the entire wind system. The project will develop and integrate: * Continuous per-turbine settings optimization * Advanced detection of false alarms to increase in-service time * Advanced AI-based wind farm control This will create a step-change in the control and performance of wind energy assets with the aim of maintaining the gain of 6% increase in energy output in large modern wind farms. This will contribute to creating sustainable innovation and help deliver the transition to net-zero.

"Current WT control systems do not optimise the end to end system and are set up with factory default settings to optimise individual elements of the drive train such as the generator and inverter. Factory settings are not optimal due to site specific wind turbulence and machine to machine variability caused by wear and tear. Non-optimised control leads not only to reduced electrical output, but increasing stresses on the WT structure and components, which can reduce useful asset life and increase maintenance expenditure. The PES company vision is to be the leading global player in the application of next generation control algorithms to optimise the end to end performance of Wind Turbines (WTs), increasing electrical output), reduce maintenance cost, and extend asset life. PES is developing AWTOS (Autonomous Wind Turbine Optimisation Software) which has the potential to increase WT electrical output by 6% which is significant for the wind industry as on a typical wind farm, a 6% improvement in AEO can double the net profit to the owner. To date PES has received public funding which has developed the initial AWTOS prototype which has full patent protection in a number of countries and has full freedom to operate. The initial AWTOS prototype has been shown to work in a controlled environment and the next stage is to test it on real WTs. The aim of this project is to develop AWTOS by testing AWTOS on a WT in the field. PES has partnered with a leading European WT manufacturer and operator to gain access to a number of operational WTs. This project will be an important stage in developing AWTOS in to a proven control system that can be sold to WT operators globally. PES was part of ICURe cohort 12 which provided invaluable market intelligence. The insights have helped refocus the business model and the initial market target. The initial offering will improve WT output by 6% which provides a 12 month payback to the operator. Initial revenues will be generated from a one off fee but this will develop into a service fee as the business develops."