Evidence on Wind Farm Performance Decline in the UK

Onshore wind farms in the UK have aged at about the same rate as other kinds of power station. The average wind farm has an annual load factor of about 28% when first commissioned, which declines by about 0.4 percentage points per year. After 15 years, the load factor would have fallen to 23%. This ageing does not appear to have made developers replace their farms early. Forty out of the first forty-five wind farms commissioned in the UK were still operating at this age; four had been repowered. Taking this deterioration into account raises the levelised cost of electricity by around 9% over a 24-year lifespan, discounting at 10 per cent a year. This is a summary of the peer-reviewed paper “How does wind farm performance decline with age?” published in Renewable Energy, vol. 65, pp 775-786, which is available to download from http://tinyurl.com/wind-decline

Daily marginal CO2Emissions eeductions from wind and solar generation

This paper estimates the half-hourly and daily CO 2 emissions from electricity generation in Britain, and the influence that wind and solar output has on these. Emissions are inferred from the output of individual plants and their expected efficiency, accounting for the penalty of part-loading thermal generators. Empirical Willans lines are created for typical coal, oil and combined-cycle gas generators from the US CEMS database, giving the first fully-empirical treatment of the British power system. We compare regressions of half-hourly and daily emissions to estimate the impact of plant start-ups, which may not occur in the specific hours when wind and solar output drops, and thus may be mis-identified in half-hourly regressions. Our preliminary findings show that dynamic plant efficiency may reduce the carbon savings from wind by 5-12% and for solar by 0-6%. The effect is strengthening with increasing penetration

Increasing price granuality in electricity system models

Electricity system models are widely used to study future designs for power markets. They are commonly used to represent electricity dispatch decisions but struggle to reproduce realistic variation in prices. We show that current assumptions of generators bidding short-run marginal cost underestimates the spread and volatility of hourly wholesale prices. Imperfect competition makes market prices differ from the theoretical optimum. Therefore, a simple modification to the short-run marginal cost approach is considered in a way that allows generators to make a spread of bids. Additionally, we add volatility into the model by making a post-optimizer transformation in the cost function. The objective is to propose a model to simulate prices on day-ahead markets that accounts for generators’ ability to bid below marginal costs for their first megawatts of capacity and above for their last, as well as to consider other variables that have an impact on power prices and that cannot be captured by the typical approaches. Using this method, we show the impacts of price volatility and price spreads in the power market

Time-averaged wind power data hides variability critical to renewables integration

Most publicly available wind data are aggregated to a temporal resolution of 30 or 60 min. This is adequate for some purposes, such as large-scale wind integration studies. However, the consequent loss of high-frequency power fluctuations from the data can significantly impact analysis on local and regional scales. The importance of this missing variability to the accurate assessment of renewables integration is increasingly being recognised as wind power is considered for energy autarky and local energy systems. Here, we investigate the statistics of the lost variability using two high-temporal-resolution datasets from France and the US. In particular, we focus on the likelihood that minimum and maximum thresholds are exceeded and illustrate the importance of sub-half-hourly variability for assessing sector-coupling applications, such as wind farm – electrolyser systems. We find that using half-hourly averaged turbine data can underestimate the occurrence of zero power by a factor of two. This matters if a wind turbine is coupled to an electrolyser, either directly or with only short-duration storage, because of dynamic operating constraints. The lower output limit means that half-hourly wind output could overestimate the hydrogen production and energy storage capabilities of alkaline electrolysers by up to 70 %

Hydrogen and fuel cell technologies for heating: A review

The debate on low-carbon heat in Europe has become focused on a narrow range of technological options and has largely neglected hydrogen and fuel cell technologies, despite these receiving strong support towards commercialisation in Asia. This review examines the potential benefits of these technologies across different markets, particularly the current state of development and performance of fuel cell micro-CHP. Fuel cells offer some important benefits over other low-carbon heating technologies, and steady cost reductions through innovation are bringing fuel cells close to commercialisation in several countries. Moreover, fuel cells offer wider energy system benefits for high-latitude countries with peak electricity demands in winter. Hydrogen is a zero-carbon alternative to natural gas, which could be particularly valuable for those countries with extensive natural gas distribution networks, but many national energy system models examine neither hydrogen nor fuel cells for heating. There is a need to include hydrogen and fuel cell heating technologies in future scenario analyses, and for policymakers to take into account the full value of the potential contribution of hydrogen and fuel cells to low-carbon energy systems

Projecting the future levelized cost of electricity storage technologies

The future role of stationary electricity storage is perceived as highly uncertain. One reason is that most studies into the future cost of storage technologies focus on investment cost. An appropriate cost assessment must be based on the application-specific lifetime cost of storing electricity. We determine the levelized cost of storage (LCOS) for 9 technologies in 12 power system applications from 2015 to 2050 based on projected investment cost reductions and current performance parameters. We find that LCOS will reduce by one-third to one-half by 2030 and 2050, respectively, across the modeled applications, with lithium ion likely to become most cost efficient for nearly all stationary applications from 2030. Investments in alternative technologies may prove futile unless significant performance improvements can retain competitiveness with lithium ion. These insights increase transparency around the future competitiveness of electricity storage technologies and can help guide research, policy, and investment activities to ensure cost-efficient deployment