How can Machine Learning Improve Grid Stability?

The rapid growth of solar, wind, and other renewable energy sources has brought significant benefits for reducing greenhouse gas emissions and transitioning towards a more sustainable energy future. However, unlike traditional fossil fuel-based power plants, renewable energies fluctuate due to seasonal patterns and weather conditions.[1]This intermittency can cause imbalances between energy supply and demand, potentially destabilising the power grid.[2]^,[3]Ensuring a reliable and efficient electricity supply from renewable energy is vital for their widespread application in the energy system.

Wind generation seasonal patterns vary across the United States (Image source: USEIA)

Machine learning, a subset of artificial intelligence, may provide an answer to the challenge. When applied to the renewable energy sector, machine learning algorithms can help optimise the integration of these intermittent sources by forecasting energy generation.[4]These algorithms can learn from historical data, weather patterns, and real-time measurements to make accurate predictions and optimise the dispatch of energy resources.[5] For example, in the realm of solar farms, machine learning can analyse factors such as sunlight intensity, cloud cover, and shading patterns to optimise the placement and tilt of solar panels, ensuring maximum energy generation throughout the day.[6]

Overview of power grid with integrated renewable sources and its usage of machine learning in different steps (Image source: Perera et al.)

Accurate renewable energy forecasting also facilitates optimal scheduling of conventional power plants, reducing the need for backup fossil fuel-based generation. By continuously analysing energy flows, voltages, and other grid parameters, machine learning can also be used to detect anomalies or signs of instability.[7],[8] This allows for timely maintenance interventions, avoiding costly breakdowns and maximising the lifespan of renewable energy assets.

As machine learning algorithms continue to evolve and improve over time, their predictive capabilities and accuracy will further enhance.[9]This will strengthen the integration of renewables, improving grid stability and efficiency. However, challenges remain, such as data privacy and security concerns, the need for standardised data formats, and regulatory frameworks.[10]^,[11]Addressing these challenges will require collaboration among stakeholders, including energy companies, policymakers, and technology providers.

Frequency chart of the number of publications related to Machine Learning in the field of energy systems (Image source: Forootan et al.)

By leveraging machine learning algorithms for renewable energy forecasting, real-time grid monitoring and control, and predictive maintenance, the energy sector can effectively manage the intermittent nature of renewables and ensure a reliable and sustainable electricity supply.