Data-driven Infrastructure Planning for Offshore Wind Farms

数据驱动的海上风电场基础设施规划

• 批准号: 2744462
• 金额: --
• 依托单位: Durham University
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2744462
• 关键词: Data; driven; Infrastructure; Planning; Offshore

The UK's share of offshore wind energy has been steadily increasing in recent years; there is now 10.4 GW of installed capacity offshore (reference: UK Wind Energy Database online: (https://www.renewableuk.com/page/UKWEDhome/Wind-Energy-Statistics.htm). There are already plans for developing large-scale offshore wind farms with capacities exceeding 1 GW. There remains however a degree of uncertainty over how to best develop, maintain and operate the wind farms and their underlying connection system to achieve cost competitiveness compared to conventional generation technologies. For example, larger turbines located farther offshore are more difficult to access. Further, accessibility of offshore assets depends on weather conditions, which can have a significant impact on income and expenditure. Consequently, the operator does not always possess sufficient information to make the most cost-effective decisions relating to planning and maintaining their assets. Similar problems arise from uncertainty over long-term decisions for investment and integration of offshore wind assets. Existing modelling methods make simplifying assumptions which unfortunately lead to suboptimal solutions, especially in larger wind farms where many factors are at play. For instance, existing tools typically use two state Markov chains for failure and repair, meaning that failure and repair times are exponentially distributed. Whilst this can be a reasonable approximation for failure, repair is rarely exponentially distributed due to sometimes large and random lead times to reach the assets depending on weather conditions. Durham has experience with more accurately representing failure and repair processes and handling modelling assumptions, especially under severe uncertainty in the planning stage. The aim of this project is to develop new methods for modelling and optimising decisions involving planning and operation for larger offshore wind farms, especially when facing uncertainties in the available actionable information. We also aim to link failure and repair to the environmental conditions of the wind farm, and operational conditions of individual turbines. We expect this to improve the predictive capability of failures and repairs, including the consideration of turbine accessibility, thereby reducing costs. The outcome of this project will inform decision-making processes for years to come for efficient integration and operation of larger offshore wind farms paving the way for future developments in a more robust and cost-effective manner.

近年来，英国在海上风能的份额一直在稳步增长。现在有10.4 GW的近海装置容量（参考：英国风能数据库在线：（ https：//www.renewableuk.com/page/page/ukwedhome/wind-energy-statistics.htm.htm）。然而，已经有一个超过1 gw and dem and gw and dembor的近海风场的计划，这些计划又有多种多样的范围。与传统的发电技术相比，他们的基本连接系统要获得较大的涡轮机。不幸的是，现有的模型方法的整合使人们可以简化的假设，尤其是在许多因素正在发挥作用的较大的风电场中，现有工具使用两个州的马尔可夫链进行失败和维修，这意味着失败的时间很大。资产取决于天气条件，其经验更准确地代表失败，并处理建模假设，尤其是在计划阶段的严重不确定性下，该项目的目的是开发新的方法，以建模和优化计划，涉及较大的offerations conderion and contrion contristion the Actival and conviention the Actoriation the Origination the Aim the Aim to the Air the Air the Aim the Action the Action the Action the Action and opertiation。我们预计这将提高故障和维修的预测能力，包括考虑涡轮机的可及性，从而降低成本。