What is the future of wind energy on the road to a carbon neutral world?

In response to the broad and dire impact of climate change, individuals, organizations, and governments are taking actions. The Paris Agreement aimed to keep the increase in the global average temperature below 2oC above pre-industrial level via greenhouse gas emission reduction1 and the UN targeted at a world of carbon neutrality by 20502. To achieve these goals, transitioning from fossil fuels to cleaner and more sustainable energy is imperative.

The global energy consumption has been trending upward. It increased by 40% in 2017 from 1990, largely driven by the rising consumption in Asia3. Despite that the global energy demand dropped about 5% in 2020 from 2019 due to the Covid-19 pandemic, the demand is expected to rebound to the pre-pandemic level in a few years4. Due to the cost reduction through technology advancement and government support, renewable energy has played a more important role in the total energy mix. The share of renewables for global electricity generation increased to about 28% in 2020 from about 25% in 20173. Despite the increasing share, renewables are still much less than the combined coal and gas, which supplies 60% of global electricity generation5. Of renewables, hydropower accounts for almost 60%, followed by the combination of solar and wind for 30%. In the U.S., renewable supplied 21% of electricity demand in 2020 and are projected to double the share in 20406, which will take over the share from retired coal and nuclear power plants.

Solar and wind energy has dominated the growth of renewable energy as hydropower is constrained by geographical requirements. In 2020, solar PV was the fastest growing renewable energy source globally, followed by wind. Wind energy will be the largest contributor to the renewable growth in the U.S. through 2024 before its tax credit phases out7. Considering its rising importance globally and in the U.S., the following content will focus on wind energy.

As of 2020, the global cumulative wind power capacity was 743 GW which included 35 GW from offshore wind. Although the expiration of incentive schemes will slow down installations, newly added wind capacity is projected to reach over 469 GW in the next five years under the present policies and pipelines, according to GWEC’s annual report8. The U.S. is one of the leading countries in onshore wind power, however, it lags behind the league in the offshore wind sector. There was about 122 GW of onshore wind capacity in 2020 in the U.S., which far exceeds 42 MW of offshore wind. The landscape of US wind industry may change under the current Biden administration, which commits to develop more offshore wind9. In the East Coast, offshore wind development will grow rapidly in the next five years as a pipeline of 34.8 GW has been planned and under construction. In comparison, the offshore wind development in the West Coast is much slower as no offshore wind farms are in operation so far. There are several proposals in the coast of California and Oregon, though they are currently at a nascent stage and could take years for siting and permitting before construction begins.

The cost of wind power generation can be quantified by a common metric, the levelized cost of energy (LCOE). In general, the LCOE is the lowest for land-based wind, followed by fixed-bottom offshore wind, and the highest for floating offshore wind in which wind turbines are installed in deep ocean. Such cost ranking is true for many countries including the U.S., although the LCOE of each project varies from location to location. For offshore wind, the LCOE increases with ocean depth and distance from grid connection. Due to different requirements and challenges facing each sector, the primary component of the LCOE differs. The dominant cost of land-based wind projects is wind turbine components, whereas that of offshore wind projects, regardless of fixed-bottom or floating, is the balance of the system including electrical infrastructure, substructure and foundation10.

The estimated LCOE of wind energy projects has declined substantially over the last decade and the downward trend will likely continue with its increasing share in the market. The primary driver of cost reduction differs between land-based and offshore wind10. For land-based wind, the future LCOE reduction is likely to be driven by the increasing power generation through larger turbines, improved control strategies, and decreasing losses. For offshore wind, it is operation and maintenance expenditure.

Compared to other energy sources, wind energy and other variable renewables such as solar energy are already cost effective and have cheaper price tags than traditional fossil fuels in many places, though the costs vary with national, regional, and local conditions. According to a report published by IEA11, the three energy sources with the lowest median LCOE from least to greatest are nuclear with long-term operation, onshore wind with installed capacity greater than 1 MW, and solar PV on utility scale. Offshore wind energy is still more expensive than its low-carbon technology counterparts, but its cost has fallen substantially from over $150 per MWh five years ago to below $100 per MWh now.

The whole world is moving toward more sustainability and less carbon emissions. Our reliance on renewables in daily life can only be greater as their shares in the total energy mix increase and the electricity sector expands due to more demand and electrification of transport and heat. Wind has been a key renewable source in many countries particularly those with abundant resources. However, it is not easy to generate more wind energy and supply it whenever needed. There are many challenges and obstacles. Some can be resolved in the near future while others cannot. For example, the flexibility and reliability of wind power, which varies with weather conditions, can be improved by its connection to more energy storages when their technology becomes more mature and cost falls in coming years. However, it takes years to study the long-term impact of offshore wind farms on the environment and economy. It also takes time and effort to have transparent and continuous conversation among stakeholders and to build the trust and comprehensive compensation for the communities that will be impacted. Government support in subsidy and regulations are critical and government decision making should balance wind power development, biodiversity protection, and economic growth. Climate literacy and the involvement of citizens and community at early stages are equally critical to build wind development with maximum benefits and minimum impacts to avoid more dire damage by climate change.


  1. https://unfccc.int/sites/default/files/english_paris_agreement.pdf
  2. https://www.un.org/sg/en/content/sg/articles/2020-12-11/carbon-neutrality-2050-theworld%E2%80%99s-most-urgent-mission
  3. T. Ahmad, D. Zhang: A critical review of comparative global historical energy consumption and future demand: the story told so far Energy Rep., 6 (2020), pp. 1973-1991
  4. https://www.iea.org/reports/global-energy-review-2020/global-energy-and-co2-emissions-in-2020#energy-demand
  5. https://www.iea.org/reports/global-energy-review-2020/renewables#abstract
  6. https://www.eia.gov/todayinenergy/detail.php?id=46676
  7. https://www.eia.gov/outlooks/aeo/
  8. https://gwec.net/global-offshore-wind-report-2020/
  9. https://www.whitehouse.gov/briefing-room/statements-releases/2021/03/29/fact-sheet-biden-administration-jumpstarts-offshore-wind-energy-projects-to-create-jobs/
  10. T.J. Stehly, D.M. Heimiller, G.N. Scott: 2016 cost of wind energy review, Tech. rep. National Renewable Energy Lab.(NREL), Golden, CO (United States) (2017) (https://www.nrel.gov/docs/fy21osti/78471.pdf)
  11. https://www.iea.org/reports/projected-costs-of-generating-electricity-2020

Formosa – offshore wind farms in Taiwan

Taiwan government set a goal to obtain 20% of electricity from renewables by 2025. To reach this goal, offshore wind development plays a critical role. The government hopes to install more than 1000 wind turbines by 2030.

Formosa 1 is the first wind farm situated 6 km off Miaoli county. It consists of two phases – Phase 1 installed two 4-MW turbines for demonstration, which started operation in April of 2017, and Phase 2 is under construction to install 30 more turbines. Wind turbines are installed on monopile foundations in water depths between 15 m and 30 m. The project has a 20-year power purchase agreement with Taiwan Power Company under feed-in tariff. As the time of writing, Swancor Renewable Energy, the company which holds 15% of the Formosa 1’s stake, sold 95% of its share to a New-York-based company, Stonepeak.

Formosa 2 is adjacent to Formosa 1. It lies approximately 3.8 km off shore in water depth of 55 m. It expects to install 47 8 MW turbines using jacket foundations and start its construction in 2020.

Note that the proposed number of turbines may change slightly when construction.

update on 1/20/2020: The second phase of Formosa 1 went live in December of 2019. The project has the total capacity of 128 MW.

Things you need to know about operational weather forecasting

Renewable power generation and electricity demand forecast reply on weather forecast. The report ‘Forecasting wind and solar generation: improving system operations’ introduces what methods and weather forecasting data are used to generate wind and solar forecasts that inform system operators. Below are some characteristics of operational weather forecasting people should know.

Weather forecasting has a predictability limit around 10-14 days. The weather forecast people learn from radio, TV and online is short-range forecast. Mid-range forecast is limited to about 15 days. Although monthly and seasonal forecasting cannot reveal detailed weather conditions, they address some information on the general trend a few weeks and seasons ahead in terms of anomalies relative to climatology.

Ideally, weather forecast informs the state of future atmosphere in every location, both horizontally and vertically. Due to limited computing power, the atmosphere is divided into three-dimension grid boxes and the evolution of atmospheric variables is modeled within boxes. The atmospheric motion is govern by dynamical equations that describe the conservation of momentum, mass, energy, and water, and the physics pertains to their sources and sinks. With dynamical equations, pressure, temperature and water fields in the next time step can be derived from the previous time step.

Initial states are important to output accurate forecasting, known as initial value problems. Due to simplifications in physical parametrizations and numerical errors to solve equations, forecast can deviate from reality and therefore the forecast at this time step does not serve well as initial conditions for the forecast at the next time step. Data assimilation is the method used by NWP models to generate initial conditions. Data assimilation blends observations with the short-range forecast from the previous run of the model, and therefore it includes the laws of physics and real information about the atmosphere at specific times and locations. Assimilation process takes place in a simplified and lower resolution version of a NWP model in a window that is typically 3-6 hours. Through iterations of backward and forward runs, the model trajectory after correction gets closer to observations.

Monthly and seasonal predictability depends on boundary conditions of the system – that is the Earth’s surface (ocean and land) and top of atmosphere. To consider the impact of boundary conditions on atmosphere, one approach is to use empirical (statistical) methods to describe the relationship between the predictand and predictor variables. The other approach is to assimilate boundary conditions from observations in NWP models. Longer range predictability is largely dependent upon general atmospheric patterns over large areas, which are driven by internal variability of the atmosphere. Hence, NWP models for season predictions run at lower resolution and incorporate information on ocean variables that change with time, which are different from models for short-to-medium range equivalents which use fixed SSTs. To isolate the predictability that comes from boundary conditions, forecasters run an ensemble of forecasts and then average them to see if a signal is left.

Thanks to advancing computing power and more observations, weather forecast becomes more accurate these days but is not perfect yet. Therefore, human forecasters still play important roles in the NWP age. For example, they would assess NWP models by using radar and satellite imagery. Senior forecasters in forecast centers sometimes need to modify forecast based on their knowledge about systematic errors and local atmosphere.

Why did some offshore wind projects fail?

Although more and more offshore wind projects got up and running these days, a number of projects are dead in the water. As failure is the mother of success, what can we learn from failed projects? Why did they fail? Here are two cases in the US.

Cape Wind: It was proposed to install 130 wind turbines 4.8 miles off the south coast of Cape Cod, Massachusetts, to power 200,000 homes. However, the project encountered severe and well-funded oppositions and is abandoned eventually. One of main barriers is its close distance from shore that ruins scenic views. It’s difficult to blame the developer for choosing this site as technology at that time was not advanced enough to install turbines farther out to sea cost-effectively.

Coos Bay WindFloat Pacific: It was proposed by Principle Power in 2014 to be a 24 MW pilot-scale project in the Southern Oregon. However, utility companies were against it because wind power was too pricey for them to sign purchase power agreement at that time.

These projects are like trailblazers who failed but set a course for following projects.

Power transform for solar generation data

In California, solar generation has been increased substantially in recent years due to increased solar panel installation. As the top of the first figure has shown, solar generation was low in 2011 and began to take off since 2013. In general, its time series is characterized by a positive trend and a seasonal cycle. Furthermore, its mean and variance increase over time, leading to an asymmetric distribution with a long tail and a peak at low values. Because of this changeable characteristic, power transform is needed before modeling the data.

Top: Monthly mean solar generation (photovoltaic + thermal) in California from CAISO from 2011 January to 2018 December. Bottom: Distribution of the monthly mean solar generation.

I applied the Box-Cox transform, which automatically optimizes the transform, to the time series and obtained the lambda value ~ 0.41, implying a transform close to the square root transform. After the transform, the mean and variance stay relatively constant over time and its distribution is more symmetric.

Similar to the figure above, except for the monthly mean solar generation after the Box-Cox transform.

If I took a further step to remove the linear trend from the transformed time series, its residuals are around the zero value, although they are more negative in the last two years (2017 and 2018). The transformed time series after detrending is better used for downstream analysis.

Top: Time series of monthly mean solar generation after the Box-Cox transform with the linear fit. Middle: Detrended monthly mean solar generation using the linear fit. Bottom: Distribution of the detrended solar generation data.

How do machine learning algorithms help increase the profit of offshore wind energy industry?

As the cost of operations and maintenance (O&M) makes up about 30% of the total life-cycle cost, machine learning algorithms have been used to optimize O&M of wind turbines in order to reduce the cost.

With weather measurements and operating records of wind turbines, machine learning algorithms like neural networks can recognize patterns of electricity output in different weather and operating conditions, and then use the patterns to find the optimal settings in various weather scenarios. When similar weather conditions arise, the turbine can adjust its operation to the optimal setting.

Regarding maintenance, machine learning algorithms can help diagnose and predict failures of wind turbines, and further minimize downtime for maintenance and extend turbines’ lifespan. This development can shift maintenance from a calendar-based to condition-based schedule.

Earlier this year, Alphabet’s AI firm announced that it can now generate more accurate wind power forecast 36 hours ahead by applying machine learning techniques to weather forecast and turbine data. Electricity produced by wind at a set of time helps determine optimal time to deliver. Since uncertainty of wind power production decreases, wind power can be scheduled in the energy market and therefore its value in the grid system increases.

Notes of ‘The California Offshore Wind Project: A Vision for Industry Growth’ published by American Jobs Project

Here are a few things I learn.

The success of the first offshore wind project in California would be critical to pave the way for future projects.

Dialogue was kickstarted in 2016 by Trident Wind’s unsolicited federal lease request. After the public comment period ended in January 2019, BOEM will delineate Wind Energy Areas subject to environmental analysis, followed by a leasing process.

Stakeholders have different perspectives on the size of the first offshore wind farm in California. Some want a pilot-scale project to assess its environmental impact and verify its feasibility, while others push for a relatively large-scale project to be cost-effective in the energy market.

This publication shows the projection of California offshore wind development from 2019 to 2045 under two scenarios: status quo policy scenario and strategic growth policy. The main difference is policy intervention such as military use stance and state support.

This publication also lists the models from other offshore wind projects that can be learned and adjusted to California offshore wind projects. For instance, California governor could assign an offshore wind Czar who can appoint an California fisheries liaison, establish a public art fund for community education and cultural programs, develop an offshore wind tourism program, and even sponsor an offshore wind hackathon or pitch competition.

In addition, information about the energy market in California and new renewable technology is provided. For example, municipal utilities like Los Angeles Department of Water and Power procure land-based wind power, and large corporations like Google buy future land-based wind production from producers through power purchase agreement, direct ownership, and renewable energy certificate. Airborne wind is another option to harness wind energy by deploying flying blades or wings that are mounted with an onboard generator or tethered to a generator on the ground. One of leading developers of airborne wind developers is Alphabet subsidiary Makani.

Because offshore wind development is new to California, the state needs more informed perspectives on current research and a vision of future research. There are needs of comprehensive spatial analysis for environmental sensitivity, and studies to identify and weigh non-monetary variables like environmental impacts into the planning process.

Although this is not a peer-review publication, it provides some prospective of offshore wind development in California and gets me thinking my role in making Cal Poly a critical player in renewable development.

Floating solar power project in Taiwan’s fishing ponds

I didn’t know the existence of floating solar farms until I read the news of Google’s solar power project above fishing ponds in Taiwan.

Floating solar farms are more popular in Asia as Asian countries are more densely populated. Currently, China has the world’s largest floating solar farm, which includes 166,000 solar panels to power about 15,000 homes. It is installed on a lake that was once to be a coal mine.

The economic impact of a floating solar power project is certainly different from place to place. A group of Taiwanese researchers investigated the impact of solar panel installation above fish ponds on the production of clams and several fish species. They found that solar panels can keep water temperature more stable, resulting in increasing production of the fish species that prefer to live in colder water.

A floating solar farm can bring other benefits including the reduction of algae growth and evaporation. Hence, it is attractive to build floating solar farms especially on top of bodies of water that are not ecologically sensitive such as reservoirs and wastewater treatment ponds. As declining solar prices are expected to continue, there may be more floating solar farms around the world in near future.

Offshore wind development in California

When I wrote my previous post, the status of offshore commercial developments in California seemed to be stagnant. Last October, Bureau of Ocean Energy Management (BOEM), part of Interior Department which coordinates federal and state planning and permitting, announced three sites for lease along the long coastline of California, two in the central coast, near Hearst Castle and Diablo Canyon nuclear power plant, and one off Redwood coast in Northern California. BOEM called for interested developers to submit nominations within these sites, and seeks public opinions over the next 100 days. Details can be found from BOEM’s website (https://www.boem.gov/California/). Potential contenders for bidding include Castle Wind and the Redwood Coast Energy Authority (RCEA). The information of Castle Wind’s proposal has been introduced in the post of Public information session’. RCEA partners with Principle Power, EDPR Offshore North America LLC, and Aker Solutions. It plans to develop a 100-150 MW floating offshore wind farm about 20 miles off the coast of Eureka.

If the industry leaps the hurdles like technological challenges and conflicts with military, wind farms are expected to generate electricity around 2025. It’s exciting to see offshore wind developments in California come closer to reality. However, as the process starts to move quickly, I wish that the government can reveal more information about how they chose the called areas and what data layers they used. Documents with details would be helpful not just for these leases but for future projects.

Update on May in 2019: 14 companies expresses their interest in commercial development. 11 of them applied for the lease in Morro Bay and Diablo Canyon call areas.

Public information session

I went to a public information session of the offshore wind project off shore of Morro Bay, hosted by the developer, Seattle-based company Trident Winds Inc. and its joint venture partner, EnBW from Germany.

The speakers presented the progress of this proposed project, which intends to generate up to 1000 MW using 100 12-MW turbines with floating structure technology. Following presentations, they answered questions from the public, which focused on the economic and environmental impact on the local community. According to the developer, this project can not only create local jobs but also invigorate other business activities. Based on pictures of the coastline taken from Hearst Castle, people may see the structure 30 miles off the shore under clear sky with naked eyes but its visual impact (view shed) is pretty minor. Since there isn’t any construction on site, the environmental impact to this region is still unclear. But experiences and studies from other existing offshore wind projects showed that sea birds and marine mammals like whales are smart enough to avoid huge turbines and thick anchors. Even if they don’t collide or become entangled, it is uncertain how animals behave when they don’t use the area the same way as they do. To better understand this topic, more studies are needed.

Since this event was organized by the developer, it is not surprising to hear the positive tone for this project despite of hurdles and unknowns ahead. For example, the conflict of the proposed site and the training routes managed by Department of Defense has not been resolved. Personally, I think that generating 1000 MW production in this proposed site is overly optimistic, considering the latest floating wind technology development on deep ocean and the power loss in operation. But this goal may be realistic years later by the time when the construction begins. Although details were not given in this two-hour meeting, the speakers addressed the positive impact this project can make to the central coast of California. They helped the public to visualize the transformation by using the pictures of the town before and after an offshore wind farm installation in Germany. They also proposed creative ideas of repurposing the stacks of Morro Bay Power Plant to train workers for turbine mainetanence.

Although it’s informal, this meeting provided a chance for the developer and the local community to make a communication. As a researcher on this topic, I learn a lot from the perspectives of the local community and the developer that I should keep in mind in my study.