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吉瓦的近海装置容量（参考：英国风能数据库在线：（ https：//www.renewableuk.com/page/page/ukwedhome/wind-energy-statistics.htm）。比例的距离距离越野车超过1 GW。越来越多的近海很难进一步访问，离岸资产的可访问性取决于天气条件，这可能会对收入和支出产生重大影响。规划和维持其资产。对海上风能资产的投资和整合的不确定性产生了类似的问题。现有的建模方法使简化的假设不幸地导致了次优的解决方案，尤其是在许多因素正在发挥作用的大型风电场中。例如，现有工具通常使用两个州马尔可夫链进行故障和维修，这意味着故障和维修时间成倍分布。尽管这可能是失败的合理近似值，但由于有时较大的随机交货时间，根据天气条件，维修很少被指数分布。达勒姆（Durham）具有更准确地代表故障和维修过程以及处理建模假设的经验，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用于建模和优化决策的新方法，涉及较大海上风电场的计划和操作，尤其是在面临可用可行信息的不确定性时。我们还旨在将故障和修复与风电场的环境条件以及单个涡轮机的操作条件联系起来。我们希望这可以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。该项目的结果将为多年来的决策过程提供依据，以便以更强大，更具成本效益的方式为较大的离岸风电场的有效整合和运营，为未来的发展铺平道路。