Industrial Automation Magazine India

How AI Can Power The Future of Energy Systems?

Published on : Monday 03-06-2024

Understanding AI’s capabilities in enhancing efficiency, predicting maintenance needs, and integrating renewable sources for a smart energy future.

The increasing demand for power and energy has sparked the need for technologies that can rerevolutionizehe energy sector. Artificial Intelligence (AI) has emerged as an integral part of the modern technological landscape, influencing various industries, from healthcare to finance. One of the most promising sectors for AI is energy.

As the world shifts towards sustainable and renewable energy sources, AI plays a pivotal role in optimising energy systems, enhancing efficiency and supporting the integration of renewables into the power grid.

According to Infinium Global Research, the global market for AI in energy and power is predicted to value US$33.24 billion in 2030 from USD 4.80 billion in 2022, growing at a CAGR of 23.90% during the forecast period 2023-2030.

If you are the one looking to harness the potential of AI, this article will let you understand how AI can power the future of energy systems. It also highlights some top applications and leading companies leveraging AI technology in this domain.

Know all about the role of AI in energy systems

AI can revolutionise energy systems in several ways, including energy storage optimisation, demand forecasting, grid management, and the integration of renewable energy sources. Some of the key areas where this technology is making a significant impact are discussed here:

Grid Management and Optimisation: AI algorithms can help in assessing vast amounts of data from the power grid to envisage and manage electricity demand and supply more effectively, leading to improved grid stability and minimised downtime. AI can also identify patterns and anomalies in the grid, enabling predictive maintenance and lessening the risk of outages.

Energy Storage Optimisation: Energy storage systems, such as batteries, are crucial for balancing supply and demand, especially with intermittent renewable energy sources like solar and wind. This is where AI comes in optimising the charging and discharging cycles of these batteries. This, in turn, will enhance the batteries’ lifespan and efficiency. AI and machine learning models can also predict the best times to store and release energy, ensuring maximum utilisation of stored energy.

Renewable Energy Integration: AI facilitates the seamless integration of renewable energy sources into the power grid. It can help balance the grid and ensure a steady supply of electricity by predicting weather patterns and energy production from renewable sources. This is particularly important for solar and wind energy, which are inherently variable.

Demand Forecasting: For efficient energy management, accurate demand forecasting is essential. Using AI models can help in evaluating historical consumption data, weather conditions, and other variables to predict future energy demand with high accuracy. This helps energy providers to plan and allocate resources more efficiently, reducing waste and ensuring a reliable power supply.

Energy Efficiency and Conservation: AI-enabled systems can monitor and control energy consumption in real-time, identifying opportunities for energy savings. Smart meters and home automation systems use AI tools to optimise energy use in households and buildings, leading to significant cost savings and reduced environmental impact.

Many countries are now looking to harness the capabilities of AI in energy systems. 

The United States is investing trillions of dollars in energy infrastructure to achieve clean and resilient goals by 2050. AI is poised to significantly impact energy infrastructure design, deployment, operation, and maintenance.

Applications of AI in energy systems

AI is already being deployed in various renewable energy applications, showcasing its potential to transform the sector. Here are a few examples:

Wind Energy

AI is used in wind farms to predict wind patterns and optimise the positioning and operation of wind turbines. This maximises energy production and minimises wear and tear on the equipment. For instance, Google's DeepMind has developed a neural network for its wind energy projects to improve the accuracy of forecasts for its 700 MW renewable fleet.

Solar Energy

AI can analyse weather forecasts, satellite images, and historical data to predict solar power generation. This enables better grid management and energy storage decisions. Electric vehicle companies use AI to optimise solar panel positioning and maintenance schedules, enhancing efficiency and longevity.

Energy Trading

AI algorithms in energy trading platforms can predict price fluctuations and optimise trading strategies. This ensures better market stability and profitability. For example, the UK-based company GridBeyond uses AI software to optimise the participation of distributed and front-of-the-meter (FTM) energy assets in available market opportunities.

AI has also a crucial role in predictive maintenance, where it can continuously monitor the performance of energy assets and identify potential faults ahead of time. The research from E.ON – an international energy company focused on smart grids and customer solutions to drive the energy transition in Europe – shows that predictive maintenance could lessen outages in the grid by up to 30% compared with a conventional approach.

Potential uses of AI across energy systems are yet to come. Some of the applications could include:

• Grid controlling and management
• Facilitating demand response
• Improved consumer services
• Grid stability and security, and
• Microgrid management.

Risks associated with the use of AI in energy

The integration of AI into nuclear, renewable, and carbon management energy systems can present significant challenges. Cybersecurity and privacy, the influence of data, and miscorrelations due to insufficient training, data or coding mistakes can be other challenges that must be addressed.

The availability of talents with the right skills who can tap AI’s potential can help address these challenges. As there is a huge demand for AI and ML specialists across all industries, the energy industry will need to recruit the best professionals in data science and programming.

Leading companies in AI for energy systems

Several top companies around the world are at the forefront of integrating AI into energy systems, driving innovation and efficiency. Some of the leading companies in AI in energy are as follows:

• GE Renewable Energy
• Tata Power
• IBM
• Siemens
• ReNew Power
• Oracle Corporation
• Huawei Technology, and
• C3.ai.

The future AI in energy systems

The integration of AI into energy systems makes a significant impact towards a more sustainable and efficient energy future. It has the potential to transform the energy landscape by optimising grid management, improving renewable energy integration, enhancing energy storage, and enabling precise demand forecasting.

AI can accelerate technology development and deployment in the energy sector. As this technology continues to evolve, Industrial Automation believes that its applications within the industry will undoubtedly expand, paving the way for a smarter and greener future.

References

1. https://solarquarter.com/2024/04/30/transforming-energy-how-ai-is-reshaping-the-future-of-clean-energy-infrastructure-report/

2. https://www.iea.org/commentaries/why-ai-and-energy-are-the-new-power-couple

3. https://www.reuters.com/sustainability/climate-energy/how-ai-can-help-power-energy-systems-an-age-renewables

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