Reducing Renewable Energy Uncertainty

Two of the most important technologies for mitigating global warming, according to the Intergovernmental Panel on Climate Change, are wind and solar power.Both are crucial if we are to wean ourselves off fossil fuels and significantly reduce the amount of carbon dioxide being released into the atmosphere.

Thankfully, technological advances over the past couple decades have made wind and solar installations much more effective. During some peak hours, for example, Portugal generates almost all of its electricity from renewables; in Canada, wind and solar power have been the cheapest forms of energy to produce (in dollars per kilowatt-hour) since 2014.

Moreover, in the United States, the Biden administration’s multi-billion-dollar Inflation Reduction Act includes “tax incentives aimed at increasing the manufacturing of wind turbines [and] solar panels,” reports the CBC — a financial “carrot,” rather than the “stick”-like carbon tax, that is expected to put pressure on Canada and other countries around the world to “offer similar subsidies toward green energy or risk losing out on valuable investment dollars.”

Yet the inconsistency and unpredictability of renewable energy supply, because of natural variances in sun and wind dynamics, limits investment in the sector and makes electrical grid managers reluctant to commit. If a municipality is responsible for ensuring that thousands of homes have power, they must be sure the energy is there when it’s needed.

Which is why Carleton University Mechanical and Aerospace Engineering professor Kristen Schell is using geospatial data, mathematical models and deep learning to develop more accurate forecasts about the availability of renewables such as wind and solar.

A woman poses for a photo at the bottom of a staircase.
Mechanical and Aerospace Engineering professor Kristen Schell

Her work, as co-director of Carleton’s Alternative Pathways for the Energy Transition (APEX) research group, aims to make the essential shift to renewables happen faster, allowing wind and solar energy to play a bigger role in the fight against climate change.

“We’re trying to make their power output more predictable, so they can be relied upon,” says Schell.

“Eventually, one day, if we get the models good enough, they could replace coal and nuclear as our baseload source of power.”

Making More Accurate Projections

Forecasting the behaviour of natural forces and phenomena is always a challenge. Just ask the people who are responsible for telling you what the weather will be like next week.

But because Schell knows that the “intermittency” of wind and solar is unavoidable, more accurate projections are key to their widespread adoption.

To make her wind models, she incorporates a long list of inputs: topographical maps showing the location of wind farms as well as the arrangement of turbines within those farms; the historical output from these farms; and weather information such as wind speed, temperature and air pressure. She combines large datasets of statistics with physics principles, such as the fluid flow of air, and then feeds everything into deep learning algorithms — which can calculate the power that individual wind installations will generate — to come up with the best possible predictions.

An over the shoulder view of a woman using an elaborate computer setup.

Solar energy forecasting relies upon a similar process and similar equations, since “the main uncertainty with solar is related to the movement of clouds,” explains Schell.

“When you have a lot of cloud cover, or other precipitation factors, like snow covered solar arrays, that impacts the electricity being produced.”

By approaching renewable energy models from multiple angles, “we’re hoping to develop better forecasting,” she says, “that reduce the error to almost zero.”

Fine-Tuning the Models with Market Data

Most of Schell’s research is based in Ontario and Alberta — the provinces with most wind farms, and the two jurisdictions within Canada in which the electricity systems operate as markets. This setup allows Schell to use market data from the two provinces and compare the actual production of wind farms with what they were forecast to produce, helping her further fine-tune the models.

Late last year, she also started a national, five-year, $1.763-million project to explore how emerging carbon dioxide emission reduction and capture technologies can be powered by renewable energy, regardless of its variable supply.

“We want to find out what operational effects wind and solar power will have on these technologies,” she says, “and then where we should put these solutions within Canada.”

Beyond the predictability and generating capacity of renewables, the need for transmission lines is also an important consideration. Economic and policy experts know that wind and solar electricity can only be scaled up if there’s a way to move it from sources to the market, another area in which financial incentives are important.

A chart use to explain process used to reduce renewable energy uncertainty

“I think there’s a lot of opportunity for leadership from Canada,” says Schell, noting how much public money was marshalled — and how quickly — to protect people from COVID-19.

“It will be hard to change the fundamentals of our energy system, but we could be one of countries that shows what’s possible, and the up-front capital investment will follow.”

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