ScotWind

Understanding the Effects of Climate Change on Offshore Wind

 

Introduction

Digital Engineering has performed an initial analysis on the development of wind resource for the 17 ScotWind sites using an ensemble of the CMIP 6 Climate Change scenario data [1]. The findings could be alarming for UK policy makers and investors alike: on average the ScotWind sites lose between 3.0-3.5% annual expected production. [2]. This was observed across all climate scenarios that were analysed. The best sites see no material impact with respect to climate change, with a marginal upside in some scenarios. Poorly performing sites see annual production risks of 4%. This result was surprisingly consistent across various Climate Change scenarios.

The stakes are high for the ScotWind sites. The relevance for the UK’s Net Zero strategy, the financial exposure of participants and the current market environment with rising equipment prices and interest rates make accurate yield assessments for each site absolutely critical.

Traditional yield analyses rely on historical weather data. In the last two decades Climate Change has affected weather patterns so drastically that this methodology has to be questioned. Traditional retrospective modelling will no longer be enough to provide reliable yield assessments. The wind industry has a strong track record of adapting its methodologies to reflect the evolving understanding of the complexities of wind modelling (e.g. improved understanding of the blockage effect). In-depth analysis of the Climate Change effects on yield, O&M costs, asset lifetimes and power prices is urgently required. This will increase resilience of decisions to the uncertainty of Climate Change.

Strategic use of Climate Change scenarios can enable the selection of locations which benefit. It is crucial to incorporate the analysis at the point in the development process where it can create most value or help avoid unnecessary expenses. Forewarned is forearmed.

 

What is ScotWind?

The UK has set itself an ambitious decarbonisation goal: net zero greenhouse gas emissions by 2050. This strategy relies heavily on offshore wind farms, utilizing both the abundant wind resources of the north and the surrounding sea.

The ScotWind offshore auction of July 2021 saw 17 seabed plots earmarked for leasing across the Scottish coastline. These were awarded to a variety of bidders including oil companies, utility firms and investment funds from around the world. The area covers 7,000km² of the North Sea to the east of Angus, the outer Moray Firth, west of Orkney, east of Shetland and north-west of both Lewis and Islay.

Within the UK’s energy security strategy, offshore wind is targeted to expand to 50GW. 50GW would be enough to power every home in the UK by 2030. The project is estimated to prevent six million tonnes of carbon dioxide from entering our atmosphere each year. [3] The ScotWind projects are estimated to have a combined generation capacity of 25GW [4], therefore contributing up to 50% to the total UK offshore wind plan.

Investment to date has been near £1bn (around £700m for leases awarded plus tender preparation by over 70 participants). However, it is estimated that almost £50bn will have to be invested for the construction [5].

Factoring in Climate Change

Currently, global temperature rise stands at 1.1°C compared to preindustrial levels. [6] Analysis shows that the current global efforts to cut carbon emissions put the world on track for warming of 2.7°C increase by the end of the century.

This is far higher than the limit of 1.5°C agreed at the UN climate change conference in Paris in 2015. On our current trajectory the world has a less than 5% chance of keeping warming below 2°C [7]. Even if current net zero ambitions are achieved, a significant amount of change is already ‘locked in’. Our ‘new normal’ will be very different from the past: warmer winters with little snow, longer droughts, extreme heat periods, intense wind events and increased flooding risk.

Prudent long-term investment decisions should factor in Climate Change scenarios reflecting a +2°C trajectory at least. Including Climate Change scenarios will make critical infrastructure more resilient to future conditions.

 

Improving Yield Assessment Methodology

Traditionally, yield assessments rely on site measurements and 20 or 30 years of historical weather data to assess how much a wind farm will produce in the future. This methodology is well-established, has gone through several major improvement cycles and is widely accepted in the industry and for financing purposes. Recent improvements have included the switch to new reanalysis data sets and the introduction of the blockage effect.

The current methodology does have one important weakness: By looking backwards, it does not allow for potential changes in the weather patterns. Climate Change is a known issue which the backward-looking methodology cannot capture. Most renewables investments (and other energy system assets) undertaken today will last into the 2050s and 60s. This period is known to be even more affected by Climate Change under realistic scenarios. [8] Under the current methodology, investment decisions are taken ignoring the physical effects of Climate Change.

Digital Engineering have enhanced the existing yield methodology by incorporating CMIP 6 Climate Change scenario data and assessing the impact Climate Change could have on the 17 ScotWind development sites, compared to the traditional yield assessment methodology.

In order to do this, Digital Engineering have taken Climate Change scenario data from multiple sources contributing to the IPCC report and applied a three-stage translation process to make this data useable for site specific yield assessments:

1. Bias correction: The CMIP 6 Climate Change scenario data has been compared to historical data. Any local biases between the Climate Change scenario data and actual recorded weather have been removed, whilst retaining the shape of the distribution over time and trends of the underlying Climate Change scenario data.
2. Model selection: Climate Change models are ranked by their performance against the historical data. Models with high scores are used. Those with low fit are scored down or disregarded entirely.
3. Ensemble creation: The selected Climate Change models are used to define a probability distribution of wind resource. The use of an ensemble is important and results of individual models on their own must be treated with caution.

Following this translation process the wind resource ensemble has been analysed and changes in average wind speed have been converted into changes to wind farm output. On average the sites see a 2.0-2.5% decline of wind resource which could translate into as much as 3.0-3.5% reduction in annual expected production [9]. The best sites see no material impact with respect to climate change, with a marginal upside in some scenarios. Poorly performing sites see annual production risks of 4%. This result was surprisingly consistent across various Climate Change scenarios. Individual climate change models even show more than an 8% reduction in yield.

 

The Bigger Picture

The ScotWind site developments are both integral to and reliant on, an entire network of partners, investors, suppliers and employees. Climate Change will also affect the other stakeholders of renewables assets, the wider electricity system and security of supply.

• Higher peak wind speeds could put assets under more stress increasing O&M costs or even reducing lifetimes (without the benefit of higher production). Critical infrastructure should be made more resilient for future conditions
• The buildout of green power production may fall short of the necessary transformation. If the production downside risk is material enough, assets may be overleveraged also introducing risk into the financial system
• System load will vary with changing temperatures, irradiation and wind speeds. At the same time higher temperatures will also affect the transmission capacity of overhead lines in potentially critical times
• Changes could affect network design and thus security of supply. Inevitably this will affect power price levels which will be felt all the way down to the British tax payer

The lessons learned from the ScotWind case study should be applied to all parts of the energy system and all long-term assets. Climate Change should be factored into all long-term projection methodologies. Digital Engineering’s data services make this possible.

 

Taking Action

The ScotWind projects are a vital part of the UK’s Net Zero strategy, and must deliver on the objectives. The results of the ScotWind case study could also have material financial implications for the companies involved.

In a wider context, our analyses can help understand uncertainty, mitigate risks and more importantly identify opportunities. In particular, the scenario analysis should be incorporated early in the development process to avoid incurring unnecessary development expense. Introducing this analysis early enough can even help businesses to identify sites and technical solutions which may benefit from Climate Change.

Adjustments to the long-term yield methodology should be made now to improve resilience of decision making to the expected changes in our climate. With our support you can be more confident in making proactive decisions which benefit the future of your business.

 

[1] Using data from multiple sources of the Coupled Model Intercomparison Project 6 (CMIP 6).

[2] Comparing the periods 1991-2020 and 2021-2050.

[3] https://www.carbonbrief.org/qa-what-does-the-uks-new-energy-security-strategy-mean-for-climate-change/#offshore

[4] https://www.bbc.co.uk/news/uk-scotland-scotland-business-60002110

[5] https://www.theguardian.com/environment/2020/oct/06/powering-all-uk-homes-via-offshore-wind-by-2030-would-cost-50bn

[6] See Working Group 1 of 6th Assessment Report of the IPCC

[7] https://www.telegraph.co.uk/global-health/climate-and-people/climate-changes-locked-unless-governments-take-drastic-action/

[8] See Working Group 1 of 6th Assessment Report of the IPCC

[9] Comparing the periods 1991-2020 and 2021-2050

The electrical grid is constantly undergoing transformation. As rural regions become more populated, transmission lines must be uprated or upgraded in order to have the capacity to transport more energy. With an influx of renewable sources of energy such as solar and wind, utilities need to adapt and redirect energy from solar and wind farms. Upgrading an entire line can prove to be extremely costly and time consuming while building a new right of way would entail lengthy struggles for permissions and public acceptance.

One way to increase the system’s ability to transmit electricity, whilst avoiding upgrading costs, is to the raise the static line rating (the amount of electricity that can be transmitted down the line without the conductors exceeding their designated operating temperature) of certain parts of the network.

Currently most utilities only account for weather effects at a regional level, it is therefore very likely that the static line rating for many transmission routes are overly conservative and can be increased by considering higher resolution weather data

Problem:

To keep up with increasing low-carbon generation and changes in consumer demand patterns, the capacity of existing electricity transmission networks has to be increased. Upgrading transmission lines to a higher capacity is expensive and developing new right of ways, particularly in urban areas is very difficult. Utility companies are therefore looking for ways to avoid upgrading the lines, while still increasing the system’s capacity.

The temperature of the conductors is affected by various factors. For instance, it is raised by electric current passing through them, high ambient temperatures and solar radiation, and lowered by the cooling effect of wind and low ambient temperatures. Of these factors, ambient temperature, solar radiation, wind speed and wind direction are weather related, although many electrical utilities use the same line ratings in areas that experience very different weather patterns.

Some companies are looking into dynamic line rating, which requires an accurate, real-time monitoring system to ensure no thermal damage occurs. With the installation of sensors and management of the monitoring system, this option can also prove to be quite expensive and problematic.

Solution:

Another way to increase the system’s ability to transmit electricity, whilst avoiding upgrading costs or installing expensive sensors and intricate monitoring systems, is to the raise the static line rating (the amount of electricity that can be transmitted down the line without the conductors exceeding their designated operating temperature) of certain parts of the network, using modelled weather data.

DE developed a technique to assess the effects of weather on the static line rating, in order for their clients to understand if it is possible to safely increase the capacity on certain lines.

DE was contracted to assess each span in every route of a transmission system operator’s network. In order to gather detailed weather variables at each span, modelled weather data had to be used, while data from weather stations was simply used to train the models further. A wide range of statistical tests were performed to assess the impact of using numerical weather prediction data to calculate operating temperatures, and to quantify the capabilities and limits of this approach. Figure 1 demonstrates the similarity between modelled and measured average operating temperature, confirming the accuracy of modelled weather data for this particular application.

Routes that can safely have their static line ratings increased were identified and probabilistic operating temperatures were calculated for different probabilities of exceedance (PoE), as demonstrated in Figure 2. The client was able to use all of this information to assess the possibility of increasing the capacity of the indicated sections of line without the unnecessary costs of upgrading the entire route.

Results:

DE provided a final report listing each specific span, as well as detailed information about how weather affects the static line rating in terms of wind speed, wind direction, ambient temperature and solar radiation.

The results were provided as a report but also summarized within a KML overlay. A randomized example of several spans has been provided in Figure 3. Seasonal line ratings based on different operating temperature exceedance values (e.g. pre- and post-fault) were provided for the routes, which was very useful when trying to decide whether a line should be upgraded or whether it can be simply uprated. Probabilities of exceedance were used to assess risk or to make sure to limit the level of risk.

In order to ensure resiliency, the client also used the results of the study to create a plan for rerouting energy in the case of an emergency situation

Customer: RTE France

Electrical utilities around the world are dealing with aging infrastructure. Time based inspection and maintenance cycles are scheduled in order to maximize the usable lifetimes of each asset. Overhead lines, particularly when located in remote areas, are costly to inspect, making asset management difficult and time consuming. For older routes or newly acquired lines, data on the condition of assets is critical but often missing.

Digital Engineering (DE), in partnership with a number of major utilities, has developed an asset health assessment methodology that provides asset condition data in less time and at a reduced expense when compared to traditional asset health assessments.

Problem:

With nearly 105,000 km of lines in France, RTE’s Transmission Grid is the biggest in Europe. With such a complex transmission system, it is a challenge to understand which assets need to be inspected and/or replaced immediately and which can remain functional for longer.

RTE was interested to see if Digital Engineering’s asset condition assessment could provide accurate condition data routes in its overhead line network.

If RTE could gain a better understanding of the expected condition on a span-by-span basis, it would be able to better optimize its asset management decisions and expenditure.

Solution:

RTE engaged DE to assess two overhead line routes, as a part of a pilot project, to validate the accuracy and quality of its asset health assessments.

DE started by running its wind-induced wear model, taking into consideration wind speed and direction, turbulence intensity, conductor icing as well as span and conductor configuration. RTE simply provided the positional and mechanical data for the spans to be included in the study. All other data required for the model, for example concerning weather and topography, was collated and processed by DE.

Wind induced wear

In order to provide crucial, span-specific weather information that is not provided by its numerical weather prediction model, DE considered local terrain features such as ground elevation and land-use (e.g. trees and buildings). 

DE used these data sets as input for its aeolian vibration model. One of the routes in this study exhibited a much higher risk of aeolian vibration due to the relatively exposed terrain in the area surrounding the route.

Corrosion

DE also ran its proprietary corrosion model which is based on machine learning techniques and is trained using records of relevant weather variables and pollutants throughout Europe. It takes into consideration weather effects which transport pollutants onto assets. It also looks for the warm and humid ambient weather conditions which accelerate metal corrosion.

Some pollutants stay relatively stable over time, such as atmospheric salinity. For other pollutants, DE considered the most recent and accurate records available. Several spans were identified with a high likelihood for deterioration due to corrosion, particularly on one of the selected lines

Results:

After presenting the results of the study, RTE provided DE with defect data from the past couple of years for the two lines. They were categorized as either wind-related, corrosion-related or both.

The model outputs were compared to historical defect records at each line and the recorded defects were aligned with DE’s asset health model predictions for both wind and corrosion.

Traditional electricity systems are designed to provide unidirectional power flow from generators to consumers via the transmission and distribution networks. However, with the increased use of weather-sensitive renewables, energy storage, and demand side response, electricity networks are becoming increasingly complex. Understanding the changing behavior of electricity consumers requires new analytical solutions that account for this complexity, including the increased sensitivity of transmission and distribution systems to weather events.

Established long-term demand forecasts rely on a combination of historical records of demand and socio-economic models broadly describing changes in population, consumer behavior, and the adoption of new technologies (e.g. rooftop PV installations, or electric cars). Underpinning these future scenarios is a baseline extrapolation of current weather-corrected demand trends.

Traditionally, this analysis is conducted on a network wide level, which does not allow for geographical variations in the weather, nor the varied behavior of consumers at different network connection points. This makes it difficult to determine the long-term baseline demand at a local level, and thus the level of investment required in different parts of the network.

Problem:

The capacity of each piece of infrastructure in an electric utility’s transmission or distribution network depends on peak level of consumer demand. It is therefore very important to have the most accurate consumer demand data when planning both the size of the future network and the upgrade and improvements program.

One factor that skews the “raw” consumer demand data is the influence of the weather. For example, consumer demand is higher when the weather is cold than it is in milder conditions.

At present, many utilities correct for these weather effects at a regional level (i.e. national or state), assuming that weather patterns are similar over large distances. This is not true, but lack of credible weather data means it is the only option. Typically, only the effects of temperature are included, but other weather variables such as wind chill and cloud cover can have a big impact. Both of these factors can lead to incorrect assumptions being made about the peak level of consumer demand at a given substation. This, in turn, can mislead decision makers in terms of future planning and upgrade schedules. Figure 1 demonstrates the diverse projections of observed versus weather-corrected annual peak demand at a single substation, which could potentially lead to very different decisions.

Solution:

Digital Engineering (DE) has developed a demand model which considers the effect of different weather parameters on consumer demand at a substation level. This significantly improves the quality of data and improves planning and maintenance operations, ensuring budgets are spent in the areas that need them most, leading to a better network with fewer faults and less customer downtime.

The project removed the effect of weather using long histories of weather data, a process known as ‘normalizing’ demand, exposing the underlaying changes in customer demand behavior.

The first step of the project was to cluster the substations based on consumer behavior, because different types of customers respond differently to the same weather conditions. Residential areas are quite sensitive to the weather, while commercial areas show little seasonal changes.

State-of-the-art weather models were applied to recreate ten years of historical weather data at high spatial and temporal resolution across Scotland. Weather varies significantly across an electricity network, because of ground elevation, weather patterns, coastal effects or urban heat-islands. Figure 2 demonstrates how annual average temperature can vary across a 150 mile (240 km) section of land.

A suite of consumer demand response models was trained using weather variables such as temperature, wind speed and solar irradiance, as well as non-weather variables such as day of the week. These models used advanced machine learning algorithms to determine the response of consumers to these different environmental and non-environmental conditions. The demand response of the consumer types was identified using a clustering algorithm, including rural villages to inner-city commercial districts and everything in between.

The substation models can be used on their own for granular data about the networks, or they can be combined to provide accurate regional or network level data. These models are data driven and adaptable to any geographical location worldwide.

Results:

The project processed and filtered the demand data from primary substations, it calculated ‘weather correction factors’ by comparing the peak demand in any one year to peak demand values predicted using ten years of weather data. It helped analyze historical trends in annual peak demand at each substation and predicted the impacts of extreme weather on different substations to allow the asset owners to see how their network will respond to future weather events.

This analysis is helping the client improve prioritization of investments, assess the potential impact of extreme weather scenarios on different parts of their network and improve baseline projections for future demand scenarios. It is also being used to enable early warning of substation-level shifts in demand, to cleanse SCADA data, provide more accurate load forecasting and transition to the role of distribution system operator.

Client:

Bloomberg

 

Client Problem:

With weather the most frequent driver of short-term movements in the power and gas markets, Bloomberg wanted access to reliable energy forecasts.

 

Solution:

We created solar and wind models that relied on detailed generator location databases to ensure accuracy, and were designed to process and display forecasts (and changes from previous ones) as quickly as possible – ensuring Bloomberg users stayed ahead of the market. After sending out a tender, and judging the proponents on the basis of speed, reliability and accuracy, Bloomberg awarded Digital Engineering a 5-year contract to provide these forecasts.

 

Benefit:

Our model now provides Bloomberg, and all their customers, with direct access to the most accurate, most reliable weather data available.

 

Client:

National Grid

 

Client Problem

The National Grid wanted to find a way to accurately predict the degradation of their assets due to environmental exposure.

 

Solution:

The project looked at characterizing weather exposure and degradation rates for more than 3,500 OHL assets across the UK. We generated tens of terabytes of information, processing it using our in-house, high-performance computing system, before analyzing it using a combination of high-resolution meteorology and big data statistical techniques. The results were then compared against 10 years of maintenance schedules and on-site observations.

 

Benefit:

Our approach accurately predicted more than 75% of all damage observed by National Grid engineers on OHL assets across the country. It’s now being used to optimize asset management strategies in order to increase system reliability and protect investment, saving them tens of millions of pounds a year.

Client:

SP Energy Networks

 

Client Problem:

SP Energy Networks wanted to find a way to separate the effects of weather and consumer behavior on peak demand at a more localized substation level, instead of just license level.

 

Solution:

By analyzing historical demand data from SP Energy Networks, we were able to create normalization models that disaggregated electrical demand into load driven by weather conditions and load driven by consumer behavior. The underlying trends were analyzed, providing us with the necessary data to make suggestions in regard to network planning and the completion of regulatory reports.

 

Benefit:

The project delivered detailed data, which enabled SP Energy Networks to make more effective, informed decisions about their systems and network. It also meant that they could prove to the UK regulatory body that any and all actions taken were completely necessary.