How AI will play a significant role in disrupting the Utility Industry and driving the Utility of the Future
Electricity generation is expected to triple from 2020 to 2050, reaching a total of 83,000 TWh[i]. During this period, the grid size will double by 2040, with the addition or refurbishment of 80,000 km[ii]. Furthermore, the share of renewables in the power mix is projected to grow significantly, accounting for 65-85% of the total energy production by 2050[iii].? In addition to all of this, utility companies have an ambitious goal of achieving zero emissions by 2050 necessitating a massive overhaul of the global power grid. The estimated cost for this transformation stands at a staggering $21 trillion[iv].? However, the energy transition faces intricate challenges, including regulatory hurdles and financial complexities.
In response to the rapid transformation of the electric grid, utilities are increasingly turning to artificial intelligence (AI). The North American Electric Reliability Corporation (NERC) recognizes the need for advanced solutions to ensure reliability and security[v]. AI holds great promise in addressing the complexities introduced by this transformation.
By integrating AI, utilities can significantly enhance data analysis, planning, regulatory functions, and cybersecurity within the Bulk Power System. Rather than requiring a complete infrastructure overhaul, AI enables pragmatic problem-solving by analyzing vast amounts of data. This approach provides a viable alternative to the monumental task of revamping the entire electricity grid.
The critical role of AI lies in precise power forecasting and agile responses to challenges such as equipment malfunction and fluctuating weather patterns.
In March 2024, the DOE The Office of Critical and Emerging Technologies released a RFI for a public report on the potential for AI to improve planning, permitting, investment, and operations for electric grid infrastructure and to enable the provision of clean, affordable, reliable, resilient, and secure electric power to all Americans. ?The request focused on three main areas:
Currently there is a great deal of research being done on AI for the industry.? Here are some examples of how AI may be used in the industry:
Generation / Microgrids
Managing Distributed Energy Resources: With the increasing adoption of distributed energy resources like solar panels and battery storage, AI can play a crucial role in managing the bi-directional flow of energy. As more homes and businesses generate their own energy through solar panels, wind turbines, and other renewable sources, utilities face the challenge of integrating these decentralized energy inputs into the grid. AI algorithms can analyze real-time data from these distributed resources, predict energy production, and optimize their contribution to the grid. By balancing supply and demand, AI helps ensure grid stability and reliability.
Renewable Energy Source Management: In the context of renewable energy, short-term forecasting is essential. AI models can analyze historical weather data, sensor readings, and other relevant factors to predict energy production from wind, solar, and other renewable sources. These forecasts enable utilities to plan for fluctuations in energy supply and demand. Additionally, AI can optimize equipment maintenance schedules, ensuring that renewable energy systems operate efficiently and have a longer lifespan. For instance, by analyzing sensor data from wind turbines and solar panels, AI can assess the intensity of sunlight or wind, helping energy units calculate the lifetime value of these resources.
The transition to renewables presents challenges due to their intermittent nature. Solar and wind energy production depends on weather conditions, which can be unpredictable. To address this, AI-based projections combined with energy storage infrastructure can reduce the need for fossil fuel backup systems. By accurately forecasting energy availability and optimizing storage solutions, AI contributes to a more sustainable and reliable energy grid.
Transmission / Distribution
Defect Detection:?Energy and utilities companies face challenges in detecting defects in fault-susceptible processes, such as suspect pipes, wiring, or machinery. These defects can lead to significant losses for energy companies, turbine owners, manufacturers, and budget resources. AI-powered Defect Detection solutions offer cost-effective alternatives to traditional methods. By utilizing deep learning for pattern recognition, video streams from cameras can alert operators if employees are not adequately addressed for specific operations. Additionally, predictive analytics can warn about equipment health, enabling proactive actions to prevent safety and environmental consequences.
Fault Prediction:?Fault prediction, combined with real-time maintenance and optimal maintenance planning, is a crucial application of artificial intelligence in the energy sector. Given the widespread equipment failures and their serious implications, AI, when paired with proper sensors, can monitor equipment, and detect faults before they lead to catastrophic outcomes. The use of IoT and AI further enhances the quality, composition, and timing of these preventive procedures.
Power distribution and transmission benefit from load balancing, congestion management, and asset utilization strategies.? AI can enhance data analysis and manipulation, improving the operational efficiency of grid operators.
Consumer / Prosumer???????
Energy Efficiency Decision Making:?Customers can connect their thermostats and other control systems to monitor energy usage using smart devices like Amazon Alexa, Google Home, and Google Nest. With the digital revolution in household energy management, autonomous meters can leverage AI to enhance energy usage and storage. For instance, appliances can be automatically shut off during expensive power periods. Electricity can be stored using vehicle batteries or other storage solutions when power is cheap or solar energy is abundant.
Demand Management for Energy:?AI-powered demand management systems focus on managing the demand response of multiple devices operating concurrently. These systems improve user behavior by providing feedback on energy performance in buildings. By measuring and predicting future consumption trends and integrating them with industrial equipment (e.g., air conditioning units, furnaces), utilities can automatically shut them down during low-power periods, effectively managing their demand.
Additionally, self-service automation can enhance customer engagement by addressing outage inquiries, emergency summoning, record updates, billing issues, and more.
Asset management
Asset management, including monitoring, maintenance, project planning, and lifecycle management, is a critical area where AI plays a crucial role.? For Grid Resiliency and Asset Inspection, utilities use AI to enhance the inspection of transmission and distribution assets.? For example, drones equipped with cameras capture images of conductors, transformers, and other equipment.? AI analyzes these images quickly and thoroughly, identifying equipment at risk of failure faster and more safely than manual inspections.
Reducing Equipment Downtime in Energy & Utilities with AI holds great promise for the future of the Energy & Utilities sector. Sensor-rich utility businesses are already leveraging data analytics from big-data engines. Additionally, field personnel are equipped with mobile devices, creating a connected ecosystem. When combined, these technologies can revolutionize connectivity, especially for water companies detecting leaks.
One specific application of AI is in maintenance facilitated by image processing. The United Kingdom’s National Grid employs drones equipped with high-resolution still and infrared cameras to monitor energy infrastructure such as cables and pylons. These drones cover vast geographical regions and rugged terrain, making them effective for fault detection. By analyzing the collected data, AI can identify when electricity assets need replacement or repair, ultimately reducing downtime and improving overall efficiency.
Optimizing Maintenance Schedules by considering operational factors, we can recommend efficient and cost-effective maintenance schedules.?? Analyzing equipment use and performance data helps minimize downtime and maximize equipment availability.
Generative AI advancements automate tasks, predict equipment failures, and improve network reliability, ensuring seamless operations.? A generative AI voice assistant can guide field employees, investigate maintenance history, and resolve technical issues, freeing up their hands for other tasks.
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Planning/Analytics /Optimization
Optimizing Energy Production and Scheduling for Offshore Wind Farms:? Offshore wind farm projects face significant challenges related to cost and schedule overruns. These issues can be attributed to various factors, including weather delays, resource limitations, product constraints, and scheduling risks. The complexity of the problem is further compounded by considerations such as platform installation, fishing restrictions, environmental regulations, and local authority requirements.
To tackle these challenges effectively, robust project planning and scheduling models are essential. These models should consider the interacting components and associated risks specific to offshore wind farm projects. For instance, AI-based applications can predict turbine blade, generator, and gearbox failures on wind turbines. Additionally, cloud-based platforms equipped with advanced analytics software and AI algorithms analyze incoming data for anomalies, providing early warnings for potential equipment issues.
Disaster Recovery: In 2017, Hurricane Irma struck South Florida, causing widespread damage. The restoration process took 10 days, significantly faster than the 18 days it took to recover from Hurricane Wilma.?? Advances in disaster recovery processes, including AI-driven solutions, contributed to this reduced recovery time.
AI-Enhanced Planning:? AI can significantly enhance the planning phase by simulating scenarios, predicting faults, and accounting for extreme weather impacts. Adaptive modeling powered by AI could revolutionize generation expansion, reliability assessment, transmission expansion, and reactive power planning. This could result in a more resilient and adaptable grid infrastructure, capable of meeting future demands and challenges.
Resource Management and Optimization:? Generative AI is well-suited for generating accurate electricity demand forecasts and optimizing resource allocation. It can also analyze local factors such as weather patterns, solar radiation, and wind speed to improve solar panel and wind turbine designs.
Operational Preparedness:? By forecasting the potential impact of upcoming weather events based on historical data and geographic distribution, AI can enhance operational preparedness and minimize disruptions.
Regulatory
Regulatory Functions and AI: The integration of artificial intelligence (AI) into regulatory functions represents a groundbreaking shift, streamlining processes and enhancing compliance monitoring efficacy. NERC recognizes AI’s potential to revolutionize how regulatory bodies and the electric sector approach compliance challenges.
Emission Tracking: Energy companies are setting net-zero emissions targets despite economic challenges. Many are actively decarbonizing operations and value chains. According to BCG studies, applying AI to corporate sustainability could generate $1.3 trillion to $2.6 trillion in value through additional revenues and cost savings by 2030. Energy and utility producers also deploy AI software to track fugitive emissions of greenhouse gases, improving control measures.
Cybersecurity
Utility companies are seeing twice as many cyber-attacks in 2022 as they did in 2020.? The 2016 hack of Ukraine’s power grid serves as a stark reminder of the potential consequences for European power markets when cybersecurity measures are lacking.
To address this, organizations are increasingly turning to artificial intelligence (AI) tools. These tools help encrypt critical systems and enhance overall security. Video cameras, functioning as sensors, play a crucial role in monitoring security threats around the clock. When combined with software solutions, utilities can secure every endpoint effectively.
Other areas
AI-Driven Inventory Management:? When inventory falls short of demand, companies face losses. AI can enhance network planning and predictive demand, enabling merchandisers to be proactive.? Energy and utilities companies, with increased visibility into demand patterns, can adapt to changes. For instance, adjusting the number of vehicles charging points and directing customers to underutilized locations leads to happier customers and lower operational costs.
Optimized Procurement:? AI-powered procurement solutions create interconnected digital supply networks (DSNs), enhancing planning and execution efficiency.? Procurement experts benefit from AI insights by analyzing complex data. Challenges in energy procurement, such as understanding spend categories, automating purchase-to-pay, and identifying bottlenecks, can be addressed using AI-based solutions.
Prevention of Losses Due to Informal Connections:?Unauthorized electricity connections pose a significant challenge for the power sector. Artificial intelligence (AI) can play a crucial role in addressing this issue by detecting anomalies in consumption patterns, payment histories, and other customer data. When combined with automated meters, AI can enhance monitoring and optimize costly physical inspections.
In summary, AI is becoming mainstream in the utility industry, transforming operations, customer relationships, and business models. Researchers and practitioners are actively exploring its potential to improve efficiency, reliability, and sustainability.
SEEKING NEW OPPORTUNITIES | Technical Leadership | Artificial Intelligence (AI) | Enterprise Architecture
4 个月So true, can you imagine the impact on the physical trading floors? Whoa.
Chief Information Officer, retired
4 个月Great article, Benjamin!