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，英国海上风能的份额一直在稳步增加;目前海上装机容量为10.4吉瓦（参考：英国风能数据库在线：（https：//www.renewableuk.com/page/UKWEDhome/Wind-Energy-Statistics.htm）。已经有计划开发容量超过1吉瓦的大型海上风电场。然而，与传统发电技术相比，如何最好地开发，维护和运营风电场及其基础连接系统以实现成本竞争力仍然存在一定程度的不确定性。例如，位于离岸较远的较大涡轮机更难以接近。此外，能否获得离岸资产取决于天气状况，这可能对收入和支出产生重大影响。因此，经营者并不总是掌握足够的信息，就规划和维护其资产作出最具成本效益的决定。类似的问题也来自于海上风电资产投资和整合的长期决策的不确定性。现有的建模方法使简化的假设，不幸的是，导致次优的解决方案，特别是在大型风电场，其中许多因素在发挥作用。例如，现有的工具通常使用两个状态的马尔可夫链的故障和修复，这意味着故障和修复时间是指数分布的。虽然这可能是故障的合理近似值，但由于有时根据天气条件到达资产的时间很长且随机，因此维修很少呈指数分布。达勒姆在更准确地表示故障和维修过程以及处理建模假设方面拥有丰富的经验，尤其是在规划阶段存在严重不确定性的情况下。该项目的目的是开发新的方法，用于建模和优化涉及大型海上风电场规划和运营的决策，特别是在面临可用可操作信息的不确定性时。我们还旨在将故障和维修与风电场的环境条件以及单个涡轮机的运行条件联系起来。我们期望这将提高故障和维修的预测能力，包括考虑涡轮机的可达性，从而降低成本。该项目的成果将为未来几年的决策过程提供信息，以便更有效地整合和运营大型海上风电场，为未来以更稳健和更具成本效益的方式进行开发铺平道路。