Advanced AI for Integrated Financial Optimization of Wind Energy Assets

REOptimize Systems(REOS)是为了利用爱丁堡大学开展的研究而成立的，该研究实施了一种独特的方法来提高风力涡轮机的效率。通过先进的建模和新的机器学习技术的应用，算法将系统中的端到端损失降至最低。这项技术正在申请专利，是爱丁堡大学7年来的研究成果。算法的成功已经在小规模风力涡轮机上得到了实验证明，并发现能量捕获能力提高了6%。根据具体的涡轮机和位置的不同，能源捕获增加6%可以为运营商带来约50%-100%的净利润增长。如果只有一半的英国涡轮机实现6%的增长，这将导致额外的3000 GWr的发电量，并在一年内节省130万吨碳。这相当于从街道上移走了大约29万辆汽油乘用车。然而，这6%的收益仅在额定功率约为100千瓦的中等规模风力中得到了证实。预计更大的涡轮机将从更好的控制位置开始，这将使我们实现约3%的收益。REOS目前正在准备一个试点项目，在一台兆瓦级的西门子2.3-92涡轮机上验证这项技术，这是英国陆上舰队的主力机型。现在，通过这个新项目，REO将开发新的机器学习技术，并将其集成到一个单一平台中，该平台将提供端到端的风电资产财务优化，并真正全面地了解整个风电系统。该项目将开发和集成：*持续的每个涡轮机设置优化*高级错误警报检测，以增加服务时间*基于高级人工智能的风电场控制这将在风能资产的控制和性能方面产生阶段性变化，目标是在大型现代风电场中保持6%的能量输出增长。这将有助于创造可持续的创新，并有助于实现向净零的过渡。

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.