by Gregory E. Bautista

SMART energy grid engineering is advancing rapidly, central to the global shift towards sustainable energy. Integrating technologies like artificial intelligence, machine learning, and big data analytics, smart grids modernize traditional power systems to improve efficiency, reliability, and renewable energy integration.

Last week we outlined some examples of the utilization of AI is smart grid engineering. This week we continue AI and the smart grid.

German energy company E.ON uses AI to forecast the generation of its renewable energy assets. Their AI models predict solar and wind generation, helping grid operators balance electricity supply and demand and integrate renewable sources seamlessly.

AI not only predicts energy generation but also optimizes the performance of renewable energy assets. Machine learning algorithms monitor the condition of solar panels and wind turbines, scheduling maintenance and optimizing performance to maximize energy output and asset longevity.

Iberdrola, a major Spanish utility, employs AI to manage its renewable energy portfolio. The company uses AI to monitor and optimize the performance of wind turbines, enhancing operational efficiency and reducing downtime, contributing to a more resilient and efficient renewable energy grid.

Accurate demand forecasting is crucial for managing energy generation, distribution, and storage. AI enhances the ability of utilities to forecast energy demand by analyzing various factors such as historical consumption data, weather conditions, and even socio-economic trends. This capability allows energy providers to adjust their operations to meet demand without overproducing or underdelivering.

Machine learning models use a variety of datasets to forecast energy demand more accurately than traditional methods. By incorporating weather data, historical consumption patterns, and grid data, AI predicts demand surges and enables utilities to optimize power generation and distribution accordingly.

Japan’s TEPCO uses AI for advanced demand forecasting. AI models integrate real-time weather data and customer behavior to predict energy demand, allowing the utility to optimize its energy production and reduce waste, particularly in peak demand periods.

Governments and utilities are using AI to model future energy scenarios, assessing the impact of various renewable energy sources on the grid and planning infrastructure upgrades. AI-driven simulations enable stakeholders to make informed decisions about energy policy and investment.

The State Grid Corporation of China uses AI to plan the integration of renewable energy into the grid. AI models help the utility assess the best locations for new renewable energy projects and optimize grid infrastructure to accommodate the growing share of renewables.

As the world moves towards decarbonization, AI plays a pivotal role in monitoring emissions, optimizing energy use, and reducing the carbon footprint of energy production. AI systems help identify inefficiencies in energy use and suggest solutions for reducing emissions.

CarbonTracker uses AI to monitor global CO2 emissions in real-time, allowing utilities and governments to track their decarbonization progress. AI tools analyze satellite data to estimate emissions from power plants, providing insights that help reduce carbon footprints.

For offshore wind energy, AI plays a vital role in turbine performance optimization by adjusting settings based on real-time weather conditions. It also aids in predictive maintenance, preventing costly breakdowns by detecting faults or wear early on.

Ørsted, a leader in offshore wind energy, uses AI to predict wind flows and optimize turbine performance. This increases the overall efficiency of their offshore wind farms. This optimizes the performance of wind turbines by adjusting their settings based on real-time weather conditions. Machine learning models can predict wind patterns, allowing turbines to adjust blade angles and rotation speeds for maximum energy output.

Siemens Gamesa employs AI to monitor offshore wind turbines, analyzing vibration and temperature data to predict potential failures before they happen. In current offshore wind farms, AI is instrumental in managing the integration of offshore wind energy into power grids, balancing energy production and demand.

With increasing adoption across the globe, AI is set to play an indispensable role in helping the energy sector meet the challenges of the 21st century, ensuring reliable energy delivery while supporting global decarbonization goals.