Maximising the Carbon Impact of Wind Power

最大限度地提高风电的碳影响

• 批准号: EP/N005996/1
• 负责人: Richard Green
• 金额: $ 30.04万
• 依托单位: Imperial College London
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2015
• 资助国家: 英国
• 起止时间: 2015 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FN005996%2F1
• 关键词: Maximising; Carbon; Impact; Wind; Power

The UK has invested heavily in wind power in recent years, and is widely expected to build much more capacity in future. One of the driving reasons is to reduce carbon emissions, but there has been no in-depth study of how effective wind power has been, or will be, at achieving this. The simple question of 'how much carbon dioxide does a wind farm save?' has a surprisingly complex answer as it depends not just on how much power the farm produces, but on how the rest of the electricity system responds to its production.Past work by academics and government bodies has concentrated on calculating the average emissions (in grams of carbon dioxide per unit of electricity) from the entire UK power sector in various future scenarios. This project will be the first to understand the marginal emissions from wind power: the change in national emissions from adding one more or one less wind farm to the power system, the driving factors behind this, and how those factors can be used to maximise the savings. The more carbon dioxide that each turbine saves, the fewer turbines will have to be built, and the lower the cost to consumers and the UK economy. This detailed study is necessary because not all power stations respond equally to the output from wind farms. We must identify which specific power stations reduce their output when wind generation increases: high-carbon coal or lower-carbon gas? Secondly, more power stations will have to run part-loaded to cope with the weather-driven variability in wind output. We must understand how large this effect is, how great an impact it has on station efficiency and thus on national emissions. Third, large-scale investment in wind power will change the mix of other power stations that the rest of the industry chooses to build, and those stations will have different emissions at times when the wind is not blowing. Finally, to provide a holistic view of emissions we must consider the carbon emitted when power stations are built or fossil fuels are extracted from the ground using Life Cycle Assessment methodology. We will investigate these issues using a range of techniques intelligently integrated across several academic disciplines to give a complete whole-systems picture of the emissions displaced by wind, and: 1) Address fundamental problems in the emerging field of using reanalysis weather data to simulate historic wind farm outputs, allowing the output from the UK's future mix of wind farms to be quantified.2) Produce the most detailed estimation of British power sector emissions, combining the output from every power station with their likely efficiency, derived from hourly emissions data from similar stations in the US (as these are not reported in Britain).3) Develop statistical regression techniques to discover how these emissions vary with the level of wind output, with fuel and carbon prices, and the accuracy of the wind forecast.4) Employ both engineering and economic models of the future electricity system to investigate how investment and operating decisions change with more wind power, and what this will mean for emissions. 5) Develop a reduced-order model of the global electricity system to replicate this analysis for other countries to ask whether the UK is well- or badly-placed to reduce emissions with wind power.Our aim is to understand the factors that affect the emissions savings from investing in wind power, so that these savings can be maximised. Energy storage, international interconnections, accurate output forecasts and a high carbon price will all help to increase the emissions savings from wind power, and we will quantify the effects of each.

近年来，英国在风电领域投入巨资，普遍预计未来将建设更多产能。驱动原因之一是减少碳排放，但目前还没有深入研究风力发电在实现这一目标方面已经或将如何有效。 “风电场能节省多少二氧化碳？”这个简单的问题有一个令人惊讶的复杂答案，因为它不仅取决于农场生产多少电力，还取决于电力系统的其他部分如何对其生产做出反应。学术界和政府机构过去的工作集中在计算平均排放量（以克为单位）在未来的各种情况下，整个英国电力部门的二氧化碳（每单位电力的二氧化碳）。该项目将是第一个了解风电边际排放的项目：在电力系统中增加或减少风电场导致的全国排放量变化、其背后的驱动因素，以及如何利用这些因素来最大化发电量。节省。每台涡轮机节省的二氧化碳越多，需要建造的涡轮机就越少，消费者和英国经济的成本就越低。这项详细的研究是必要的，因为并非所有发电站都对风电场的输出做出同样的反应。我们必须确定当风力发电增加时哪些特定发电站减少了发电量：高碳煤炭还是低碳天然气？其次，更多的发电站将不得不部分负荷运行，以应对天气导致的风力输出的变化。我们必须了解这种影响有多大，它对电站效率以及国家排放量有多大影响。第三，风电的大规模投资将改变其他行业选择建设的其他发电站的结构，这些发电站在无风时会产生不同的排放。最后，为了提供排放的整体视图，我们必须考虑使用生命周期评估方法建造发电站或从地下提取化石燃料时排放的碳。我们将使用一系列跨多个学科智能集成的技术来研究这些问题，以提供风排放排放的完整全系统图景，并且： 1) 解决使用再分析天气数据来模拟历史数据这一新兴领域的基本问题风电场的输出，从而可以量化英国未来风电场组合的输出。2) 对英国电力部门的排放进行最详细的估算，将每个发电站的输出与其可能的效率相结合，这些效率来自于每小时的排放数据美国的类似电台（因为英国没有报告这些）。3) 开发统计回归技术，以发现这些排放如何随风力输出水平、燃料和碳价格以及风力预测的准确性而变化。4) 同时采用工程和经济手段未来电力系统的模型，以研究投资和运营决策如何随着风力发电的增加而变化，以及这对排放意味着什么。 5) 开发全球电力系统的降阶模型，为其他国家复制这一分析，以了解英国在利用风电减少排放方面处于有利还是不利地位。我们的目标是了解影响排放的因素通过投资风电节省资金，从而最大限度地实现这些节省。储能、国际互联、准确的产量预测和高碳价都将有助于增加风电的减排量，我们将量化每一项的效果。

项目成果

An iterative algorithm for regret minimization in flexible demand scheduling problems

灵活需求调度问题中遗憾最小化的迭代算法

• DOI: 10.1002/adc2.92
• 发表时间: 2021
• 期刊: Advanced Control for Applications
• 作者: Dong Z

Global levelised cost of electricity from offshore wind

• DOI: 10.1016/j.energy.2019.116357
• 发表时间: 2019-12-15
• 期刊: ENERGY
• 影响因子: 9
• 作者: Bosch, Jonathan;Staffell, Lain;Hawkes, Adam D.
• 通讯作者: Hawkes, Adam D.

Calculating system integration costs of low-carbon generation technologies in future GB electricity system

计算未来英国电力系统中低碳发电技术的系统集成成本

• DOI: 10.1049/cp.2016.0529
• 发表时间: 2016
• 作者: Aunedi M

Transmission Network Investment With Distributed Energy Resources and Distributionally Robust Security

• DOI: 10.1109/tpwrs.2018.2867226
• 发表时间: 2019-11
• 期刊: IEEE Transactions on Power Systems
• 影响因子: 6.6
• 作者: Diego Alvarado;Alexandre Moreira;R. Moreno;G. Strbac

A stochastic dual dynamic programming approach for optimal operation of DER aggregators

• DOI: 10.1109/ptc.2017.7981213
• 发表时间: 2017-06
• 期刊: 2017 IEEE Manchester PowerTech
• 作者: Panagiotis Fatouros;I. Konstantelos;D. Papadaskalopoulos;G. Strbac