Professor Schell was interviewed by Stu Mills of CBC Ottawa to explain the need for battery energy storage systems in the Ontario power grid, and particularly in Ottawa. With electricity demand expected to increase 166% in the Ottawa area (compared to the 75% average across Ontario) Ottawa’s grid is in dire need of reinforcement, and batteries provide the cheapest and quickest solution to ensuring power quality and reliability in our area. You can listen to the full interview .

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In conjunction with the Marine Renewable Energy Technology group at Natural Resources Canada, and the Ocean, Coastal and River Engineering group at the National Research Council, a team of researchers in the APEX lab, including post-doctoral research fellow Carlos Prieto, undergraduate researcher Mohammed Abusalih and Professor Schell, developed a new method for hydropower prediction using only remotely-sensed data – an improvement over time-intensive and costly field campaigns to gather in-situ flow data. Their method was recently published in , entitled “Physics-Informed Neural Network for Open Channel Flow Velocity Prediction”.

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Voice your Opinions on Energy for the Future

We are looking for volunteers for a study on energy options for La Ronge and surrounding communities. This project aims to understand individual and community opinions about different types of energy, such as wind, solar and battery storage.  Participants who complete a survey will receive $50 cash. The survey takes place in person at Northlands College Canoe Campus, 1 – 4:30 pm on May 14.

The survey asks for your opinions on different types of energy, including wind, solar, battery storage and natural gas. It should take approximately 30 minutes to complete.

To be eligible, you must be 16 years or older and a current resident of La Ronge or surrounding communities.

This project was reviewed and cleared by the ĐÓ°ÉÔ­´´ University Research Ethics Board B. Clearance # 122735. If you have any ethical concerns with the study, please contact ĐÓ°ÉÔ­´´ University Research Ethics Board, preferably by email at ethics@carleton.ca or you can leave a message by phone at 613-520-2600 ext. 2517.

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Few credible experts see how we’ll reach net-zero emissions without actively removing carbon dioxide out of the air, perhaps through direct air capture technologies. This has become an active research area over the past two decades, but most existing work doesn’t consider how ambient environmental conditions—temperature, humidity, pressure, and carbon dioxide concentration—affect these engineered systems, which need to process massive amounts of atmospheric air to remove the carbon dioxide. Our group recently —the first to model how these plants would actually perform across the globe, considering different weather conditions, different emissions from electric power systems worldwide (some of which are dirtier than others), and different emissions from natural gas systems (some of which are leakier than others).

Our work is intended to stimulate serious discussion of the role of direct air capture technologies in nationally determined contributions submitted to UNFCCC. For nations that are making large bets on carbon dioxide removal despite having poor or fair conditions for the deployment of direct air capture, our work should stimulate discussion of cross-border carbon accounting mechanisms that would allow these nations to subsidize deployment in regions with more appropriate climatic conditions.

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Congratulations to 2nd year PhD student, Luis Lopez Diaz, for winning the Best Poster Award at the ! He presented his work on optimizing the flexibility of active distribution networks to support the larger transmission grid.

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assesses the cost and performance of integrated energy systems that employ offshore wind power and water electrolysis to supplant liquid and gaseous fuel consumption in the Atlantic Maritimes region of Canada.

Apart from hydropower, it is difficult to find an energy technology in Canada around which politics and policy are aligned. Hydrogen is one such technology: it presents an opportunity for such cross-provincial collaboration, because multiple disparate provinces see a role for hydrogen technologies in their net-zero transitions.

In this research, we employ a recursive simulation to estimate the size, cost, and performance of green hydrogen production pathways in the Maritimes, a region with excellent offshore wind resources, dormant gas pipeline infrastructure (including subsea pipelines), and a policy target to achieve net-zero emissions by 2050. We model a large integrated energy system comprising offshore wind farms and electrolyzers, the former deployed on the broad, relatively shallow, and wind-rich Scotian shelf. The system is scaled up in line with empirically grounded growth and learning rates. Multiple scenarios are considered, including natural gas replacement, freight diesel replacement, and direct integration of low-carbon electric power from the offshore wind farm (plus some back-up capacity).

We show that hydrogen production is at least four times more expensive than grid integration. Projects could only be implemented by 2050 and at <2 $/kgH₂ by assuming aggressive growth rates, learning rates, and electrolyzer capital costs of 500 $/kW. This analysis gives policymakers an appreciation of the effort, costs, and emission benefits of producing green hydrogen at scale.

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The Government of Canada’s Environmental Damages Fund, administered by Environment and Climate Change Canada, is allocating $1.489 million to ĐÓ°ÉÔ­´´ University for the research project entitled “Bending the Curve with NETS: Developing a New Modelling Framework for Negative Emissions Technologies”. The project is led by Dr. Kristen R. Schell, Assistant Professor in Mechanical and Aerospace Engineering, along with co-PIs Dr. Ahmed Abdulla (MAE), Dr. Amir Hakami (CEE) and Dr. Elisabeth Gilmore (CEE).

Most net-zero pathways rely to some extent on negative emissions technologies (NETs), especially when it comes to mitigating warming emissions from hard-to-abate sectors. However, existing assessments of NETs either assume that they can scale up to meet a large, arbitrary target by mid-century (e.g., 10 Gigatons of CO₂ removals per year, globally), or analyze the performance of facilities of arbitrary size (e.g., plants that remove a million tons of CO₂ per year). The point of departure for this effort is that any realistic assessment of the role that NETs might play in the net-zero transition must consider the wider system in which they will be deployed: NETs require substantial resources like energy, water, land, and materials. Thus, the cost, net emissions, and risks of deploying NETs differ radically depending on location and growth rates, not to mention how the wider energy system evolves to achieve emission targets. This project will develop a large-scale modeling framework for Canada that will estimate the scale of potential NET deployment, considering scenarios of how our energy system is likely to evolve between now and 2050. The modeling framework will allow for the evaluation of optimal deployment pathways for NETs that account for risks and costs to businesses and social benefits to Canadians.

Nationally, the funding was part of a $58 million announcement by Minister Guilbeault for research projects that will advance climate change science and technology.  This five-year project was announced by Environment and Climate Change Canada on November 23. It has a budget of $1.763 million, with $1.489 million in financial support from the Government of Canada.

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Accurate probabilistic forecasting of wind power output is critical to maximizing network integration of this clean energy source. New research by Professors Arrieta-Prieto and Schell, published in the, developed a spatio-temporal framework that outperformed competitive models in both the univariate and multivariate contexts. It can provide a simple and reliable forecasting mechanism that efficiently handles the challenging aspects of wind power data, by (1) introducing nonlinear transformations of the data and sinusoidal basis functions to handle the eventual nonlinear and nonstationary patterns exhibited, (2) considering imputation mechanisms and temporal models that can handle discrete-continuous outcomes to overcome potential boundedness/censoring issues, (3) establishing parsimonious models that can fairly capture the features derived from high-observation frequency data and can be estimated with reasonable computational resources, and (4) using DVINE copula models to allow asymmetric spatial dependence structures among the different variables.

There is a large literature on temporal modeling of wind power forecasting, but considerably less work combining spatial dependence into the forecasting framework. Through the careful consideration of the temporal modeling component, complemented by support vector regression of the temporal model residuals, this work demonstrates that a DVINE copula model most accurately represents the residual spatial dependence. Additionally, this work proposes a complete set of validation mechanisms for multi-h-step forecasts that, when considered together, comprehensively evaluate accuracy. The model and validation mechanisms are demonstrated in two case studies, totaling ten wind farms in the Texas electric grid. The proposed method outperforms baseline and competitive models, with an average Continuous Ranked Probability Score of less than 0.15 for individual farms, and an average Energy Score of less than 0.35 for multiple farms, over the 24-hour-ahead horizon. Results show the model’s ability to replicate the power output dynamics through calibrated and sharp predictive densities.

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Public policy has grown increasingly reliant on complex analytical models that, while valuable, rarely integrate behavioural constraints in theoretically appropriate or practically relevant ways. By offering a simplistic representation of the real world, these models risk producing unrealistic results and leading to potentially myopic decision making.

For example, many complex socio-technical systems are modeled with the objective of minimizing costs or maximizing societal welfare. Although these models prescribe an optimal course of action for a decision maker, they ignore a large number of societal preferences that can slow technological transitions, including public opposition and inertia in the values and beliefs that determine societal norms and market preferences. Moreover, for models limited to a single economic objective, the optimal solution given to the decision maker is usually dependent on the developer’s structuring and parameterization of the model, and these decisions are subject to human bias—often excluding processes and parameter values that the developer ex-ante deems unlikely or infeasible.

These limitations are especially acute and consequential in models of the global climate and energy systems, which explore pathways to achieve deep decarbonization while minimizing cost or greenhouse gas (GHG) emissions. In elaborating these pathways, models choose among a range of energy technologies, some of which contend with low levels of public acceptance or heightened risk perceptions. Recognizing these limitations, some modellers have recently sought to explicitly incorporate challenges to technology deployment in the real world using exogenous choices—for instance, by constructing narrative scenarios that justify their choices, or by excluding certain technological options entirely (e.g. nuclear power) on the grounds that they are undesirable and contrasting those results with full portfolio, least-cost scenarios.

The goal of this project is to endogenously integrate societal preferences for different energy technologies into large-scale energy system optimization models. This is important because societal preferences have a direct bearing on the extent to which capital mobilizes in support of a technology’s adoption and diffusion, not to mention the range of political and policy measures that can be feasibly deployed to support it. By ignoring these real-world constraints, least-cost or least-emission optimization models generate unrealistic results.

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Since the conclusion of World War II, policymakers in the U.S. have maintained a broad and firm consensus regarding the critical role that government-supported innovation plays in that nation’s welfare. For a number of reasons, however, the pillars of American innovation are under threat at a time when urgent challenges require government to make large, near-term investments in innovative technologies. The size of these investments and the uncertainty inherent in the process deter private funding and render government support essential.

There are large uncertainties about which innovation management strategies perform best when it comes to government-supported technology development. In the case of energy, Richard Lester of MIT and David Hart of George Mason , “today there is no agreed framework for federal involvement in these activities. This is one of the most serious gaps in the current U.S. energy innovation system–especially for large-scale technologies where public risk and cost-sharing at the demonstration stage is unavoidable”. We are extending the methods of applied machine learning and quantitative decision science to investigate how best to structure technology development programs supported by the U.S. government. Program outcomes are driven not only by technical risk and cost, but also by organizational behavior. Therefore, we adopt a pandisciplinary approach to assessing the impact on program outcomes of different innovation management strategies, as embodied within different organizational structures.

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