Artificial Intelligence (AI) is all the rage now, especially applications for image & speech recognition, natural language processing (NLP) for language translation & chatbots and for things like fraud detection, helping develop autonomous vehicle and financial trading / portfolio management. But there is an area of AI application that may have a huge and positive impact on the future of man – the global transition to green energy.
The following AI applications are currently being used to help facilitate the transition to green energy / carbon footprint reduction worldwide:
Energy Production Optimization / Use:
AI helps by providing energy demand forecasting – using data from smart meters and other sources to predict energy demand. This helps energy producers optimize their production and distribution efforts. This also reduces the need for energy storage and improves grid stability. A leader in next-gen industrial AI for the energy sector is Siemens AG. Their efforts are helping electricity grids better integrate renewable sources using data-driven decisions.
Oil and gas companies are using machine learning algorithms to improve well placement to help increase production. By analyzing data collected from seismic surveys and other sources, the companies can make smarter decisions about where to drill for oil and gas or activate idle well heads. For example Exxon Mobil is using AI identify and efficiently drill new wells, and using high-performance computing analytics (HPCA) to increase the efficiency of its production facilities and reduce carbon emissions.
AI-driven energy management systems can monitor and control energy use in real-time, helping industrial customers reduce energy waste, thus lowering their carbon footprint.
Smart Grid Management:
Grids can now be integrated with sensors, data analytics tools, energy storage systems, energy management platforms, and other types of energy technology to become ‘smart’. By using smart grids, energy companies can collect energy usage data from every single device on the grid, and then use this information to develop energy efficiency projects for their customers.
It also allows energy companies to monitor energy flow and energy use in near real-time. Energy companies can then reduce energy consumption through automated demand response systems which can turn off energy during peak hours, resulting in energy savings for homeowners as well as energy companies.
One example of a company using smart grid management is Pacific Gas & Electric (PG&E). They have implemented a comprehensive smart grid system to modernize its electric grid, improve reliability and enhance customer service. The system includes advanced metering infrastructure (AMI) that give real-time information on electricity use to both the utility and its customers. PG&E also offers demand response programs that incent customers to reduce their energy use at peak demand times. This helps balance the grid and prevent blackouts. The utility has also deployed an advanced distribution management system (ADMS) and distribution automation system (DAS) to improve the reliability and efficiency of their grid.
A number of start-ups and technology firms are making significant contributions to the development of smart grid technology as well – including Itron (innovative gas & electricity forecasting, water management and smart city power management) and Landis + Gyr (smart metering, grid edge intelligence and smart infrastructure technology).
Improved Energy Efficiency:
AI is being used to help optimize energy grids by managing energy flows between homes, businesses, storage batteries, renewable energy sources, micro-grids, and the power grid itself. This is especially important when managing peak energy demand periods sparked by cold snaps or heat waves.
Renewable energy sources like wind and solar are becoming more popular, but they provide intermittent production, meaning if it is calm and/ or cloudy, energy from these sources may not be available when needed. As the energy grid needs to be managed in real-time, AI can help energy companies predict in advance when renewable energy will be available and manage energy grids accordingly.
A leader in energy management technology is Generac Grid Services. Their software, hardware and end-to-end services make it easy to optimize energy performance and build more powerful, balanced, and sustainable grids. Their AI algorithms dynamically balance real-time supply and demand on electrical grids by monitoring energy generation from renewable sources like wind / solar and then adjusting flow to ensure the grid stays in balance when outputs from renewable sources fluctuate.
Predictive Maintenance:
Using predictive analytics, energy companies can predict when a machine or piece of equipment will need maintenance or worse yet be likely to fail, based on real-time data collection / analytics from sensors. Predictive modeling is created based analyzed data that points to the likelihood of equipment failure. These models can account for the age of the equipment, frequency of use and operating environment. Notifications can then be sent to maintenance teams, allowing them to perform work before failures occur.
All these efforts help increase overall equipment reliability and prevent unexpected outages / avoid unplanned emergency maintenance work that can be costly.
Leaders in this part of the energy sector include GE Predix specializing in industrial internet of things (IIOT) solutions, Predictive Maintenance Technologies (PMT) a software company that specializes in predictive maintenance solutions for the energy sector, and Predictive Maintenance Solutions (PdMS) a tech company that designs machine learning algorithms to help energy companies optimize operations.
Carbon Capture:
Carbon capture technology helps reduce greenhouse gas emissions from industrial processes like power generation and manufacturing by capturing carbon dioxide (CO2) before it is released into the atmosphere. The CO2 is then stored (typically underground) in a process known as carbon capture and storage (CCS). AI can help this process by optimizing the performance of CCS efforts.
Emission monitoring algorithms can be used to monitor and analyze carbon emissions from industrial processes – helping target the most promising venues for CCS efforts. Predictive maintenance also comes into play here – where AI can be uses to help predict when CCS equipment will need maintenance to help reduce down-time and thus improve CCS efficiencies. AI is also great for CCS process control – to help regulate the carbon capture process – reducing the risk of failure and help achieve optimal operations.
Leaders in the area include NET Power – a Houston based company that is developing advanced natural gas power plants that use carbon capture technology and London-based Carbon Clean Solutions – a company that provides carbon capture solutions for power generation and cement production.
Improved Wind Power:
Wind turbines are a common site these days from the cornfields of Illinois to the North Sea. Using AI, wind farm turbines are now able to communicate with each other to generate more power, more efficiently. AI allows these wind farms to integrate into the grid and feed forward load patterns. In essence these wind farms now “act as one” rather than individual units which minimizes the flow of turbulent air through the farm and adjusts the position of the blades in real-time based on wind speed / direction which helps maximize power generation.
AI is also used to improve wind forecasting – which is critical for the efficient operation of wind turbines. These smart weather models provide more accurate predictions of wind directions and speeds which allow the operators to optimize energy production and better manage costs.
Leaders in this area include GE Renewable Energy – they have developed an AI-powered platform called “Digital Wind Farm” that uses data from sensors on wind turbines to optimize their performance and improve energy production. Pattern Energy is using AI to predict wind energy outputs and optimize their wind farm operations. Portland, OR based Vestas has created an AI-based system called “Integrated Smart Planet Management” that uses data from turbines and weather forecasts to improve wind farm operations and maintenance.
Conclusion
It’s clear that employing AI applications is helping improve the efficiency, reliability of and stability of energy grids worldwide. AI is helping reduce costs and power outages with predictive maintenance. Energy security is helped as well – as AI allows for increased use of renewable energy, lessening the dependance on imported fossil fuels. It is also facilitating the transition from fossil fuels to green energy – which will reduce the carbon footprint of the energy sector and deliver a more sustainable future for generations to come.
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