The Silent Bottleneck of the Clean Energy Transition

Electrical transmission cables connecting to Quebec at the Churchill Falls hydroelectric project in Churchill Falls, Newfoundland, Canada July 2007. REUTERS/Greg Locke/File Photo Purchase Licensing Rights

Written by: Avery Seeley

Edited by: Liad Wolch

Junior Division

The global energy sector is undergoing an unprecedented transition to clean energy, driven primarily by wind and solar energy. In 2024, more than 40% of global electricity generation came from clean energy (Ember, 2025). From 2000 to 2023, wind and solar rose from 0.2% to 13.4% of global electricity generation (Carbon Brief, 2024), while other clean energy sources, although still prominent, have steadily decreased. (Ember, 2025). The boom of wind and solar is driven not only by environmental imperatives but also by lower electricity generation costs. In 2024, average global costs of solar and wind energy were 41% and 53% cheaper, respectively, than the lowest-cost fossil fuel alternatives (International Renewable Energy Agency [IRENA], 2024). For the first time in history, renewables, not fossil fuels, are driving economic development. However, there is a critical infrastructure challenge that we must overcome to safely transition to this new energy economy.

The electrical grid, which connects energy generators to consumers, was developed mainly for dispatchable energy sources, meaning that their power output can be turned on or off - or increased or decreased - at will. Examples of dispatchable energy sources include fossil fuels, nuclear, and hydro power. Wind and solar, however, are non-dispatchable. By contrast, the power output of non-dispatchable sources cannot be controlled at will. Wind turbines and solar panels generate electricity from the intensity of the wind and sun, respectively, which is inherently random and uncontrollable. The key challenge to growing wind and solar energy is integrating these variable energy sources into a grid originally built for controllable power. Now more than ever, this challenge is amplified: energy demands from data centers supporting artificial intelligence require unprecedented levels of energy generation. Global electricity consumption for these centers is projected to double to around 945 terawatt-hours by 2030 (International Energy Agency [IEA], 2025). Integrating non-dispatchable energy sources into the electrical grid while meeting rising energy demands presents a bottleneck in the energy transition.

There are several solutions to help the electrical grid integrate more non-dispatchable energy sources. Large-scale energy storage, such as batteries or pumped hydro, can store excess energy and release it when demand is high. Smart grid technologies enable real-time monitoring and automatic balancing of supply and demand. High-voltage direct current (HVDC) lines can efficiently transmit power over long distances, allowing regions with excess generation to supply areas with deficits.

Power resilience modeling addresses one of the most immediate challenges associated with integrating non-dispatchable energy sources: increased outages. It provides a quantitative framework for evaluating a power system’s ability to prevent, withstand, and recover from extreme events. In the United States, annual outage duration is projected to increase from approximately 100 hours to more than 800 hours per year (U.S. Department of Energy [DOE], 2024). Power resilience modeling could forecast outage behavior to prevent or reduce the impact of outages. By analyzing historical and real-time data and predicting outage parameters, operators could shorten restoration times and maintain a reliable and stable power supply despite the growing randomness on the grid.

Over the summer, I had the opportunity to research power resilience under Professor Xiaozhe Wang at McGill University. My project used over a decade’s worth of North American outage data from NERC (North American Electric Reliability Corporation) to create models that predicted outage restoration time. Using ETH Zurich’s open-source framework UQLAB in MATLAB, I trained models on parameters such as the number of outages, line voltage, and line length to predict average outage restoration time. With an average percent error under 7%, these results could theoretically provide insights into reducing outage restoration times and, by varying power system parameters, help to understand other outage behaviors.

Of course, while this work will not solve the ever-growing outages in our grid, it demonstrates how modeling techniques can provide actionable insights for improving grid resilience. With the increasing availability of historical and real-time grid data, these approaches can be applied more effectively to anticipate stress points and optimize responses. In the coming years, solutions such as large-scale energy storage, smart grid technologies, and advanced modeling will be critical for modernizing the electrical grid. These approaches can help the system adapt to increasing shares of non-dispatchable energy sources, improve resilience against outages, and ensure a reliable and stable power supply for consumers.

We are amid a historic economic transformation as reliance on fossil fuels gives way to the proliferation of clean energy. However, this transition could be catastrophic without proper grid solutions to handle the explosion of the wind and solar industry and rising energy demands. Increasing reliance on non-dispatchable energy sources places unprecedented stress on a grid originally designed for controllable power. Modernizing the grid through large-scale storage, smart technologies, HVDC lines, and power resilience modelling is essential to maintain reliability, balance variable generation, and mitigate blackout risks. The growing availability of power system data provides a great opportunity to anticipate and manage grid stress effectively. Addressing this technological bottleneck is critical to a more productive, affordable, and environmentally friendly economy.

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References

Carbon Brief. (2024). Wind and solar are the fastest-growing electricity sources in history. https://www.carbonbrief.org/wind-and-solar-are-fastest-growing-electricity-sources-in-history/

Ember. (2025). Global Electricity Review 2025. https://ember-energy.org/latest-insights/global-electricity-review-2025/

International Renewable Energy Agency. (2024). Renewable power generation costs in 2024.   https://www.irena.org/Publications/2024/Apr/Renewable-Power-Generation-Costs-in-2024

International Energy Agency. (2025). Energy and AI – Analysis.   https://www.iea.org/reports/energy-and-ai

U.S. Department of Energy. (2024, July 7). Department of Energy releases report on evaluating U.S. grid reliability and security. https://www.energy.gov/articles/department-energy-releases-report-evaluating-us-grid-reliability-and-security

UQLab. (n.d.). UQLab: Uncertainty quantification laboratory. ETH Zurich. https://www.uqlab.com