A recent collaboration between the Renewable Energy System Laboratory and the Energy ICT Research Department at the Korea Institute of Energy Research (KIER) has resulted in the development of advanced technologies that could transform urban electrification using artificial intelligence (AI).
Urban electrification is an important initiative aimed at reducing dependence on fossil fuels and embracing renewable energy sources like building-integrated solar technology to revamp urban energy systems. While this approach is relatively new in South Korea, it has already proven to be a key strategy in the U.S. and Europe for achieving carbon neutrality and promoting sustainable urban environments.
In traditional urban settings, energy supply can be easily adjusted with fossil fuels to meet electricity demand. However, in electrified cities, the reliance on renewable energy leads to greater variability in energy supply due to weather changes, causing mismatches in electricity demand across buildings and making the power grid operation more challenging.
To tackle these issues, a revolutionary energy management algorithm based on AI analysis has been developed and successfully integrated into the system. The results are impressive, showing an 18% reduction in electricity costs compared to traditional methods. This innovative solution not only enhances power grid stability but also sets the stage for more efficient and sustainable urban energy management.
The research team used AI to analyze energy consumption patterns by building type and renewable energy production patterns. They identified factors affecting the power grid, from weather to human behavior and the status of renewable energy facilities. Their research uncovered that Low-Probability, High-Impact Events, which occur around 1.7 days per year, significantly impact power grid stability and operational costs.
This knowledge was translated into an algorithm and system designed to optimize energy sharing between buildings, manage peak demand and energy production, and respond to unexpected events. The result is a robust system that ensures power grid stability even in extreme circumstances.
When tested in a real urban setting, the system showed impressive results, with a 38% energy self-sufficiency rate and a 58% self-consumption rate. This represents a significant improvement from buildings without the system. Additionally, there was an 18% reduction in electricity costs and increased power grid stability.
The annual energy consumption during the demonstration was 107 megawatt-hours (MWh), seven times larger than simulated by top international institutions. This breakthrough opens the door for widespread adoption in real urban environments, promising a more sustainable future.
Dr. Gwangwoo Han, the lead author of the study, emphasized the importance of AI in enhancing urban electrification efficiency and addressing power grid stability issues. By implementing this system in various urban environments, energy efficiency can be improved, grid stability enhanced, and significant progress made towards achieving carbon neutrality.
