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 ( 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.

Offshore wind farm visit in Scotland

I am very fortunate to visit the first demonstration scale (<100 MW) floating wind farm (Hywind Scotland Pilot Park) in the world in August of 2018. There are five 6 MW wind turbines about 25 km from shore off Peterhead in Scotland. On the day of our site visit, the weather was so calm that the wind turbines did not operate. Despite this, it is amazing to see these 178-m tall turbines in short distance. I could barely feel their movement on water and could not see cables and anchoring of their foundations underwater.

Its launch makes a landmark which can demonstrate the viability of a commercial scale wind farm in near future. Although air flows from different directions in this region, its fast speed generates more power than initial assessments. The capacity factor can reach up to more than 60% in winter months, which is much higher than that of land-based wind turbines. The surveys taken between pre-construction and post-construction would shed more light on the impact of deep-water wind farms on the environment and commercial activities like fishing and the benefits for local communities. It paves the way for more efficient and comprehensive marine planning and development. Moreover, its cutting-edge technology and industry experiences can be leveraged and adjusted to other regions.

Following the Hywind project, the second demonstration-scale floating wind project, Kincardine, is under development off Aberdeen in Scotland. It is located only 15 km from shore and can be seen on land with binoculars under a clear day. There is an increasing trend of deep water wind farms as shallow near-shore sites are exhausted and floating foundation technology becomes advanced.


Our boat

Wind turbines from the bridge

Wind turbines from the deck

A closer look