The Future of Energy: Navigating the Transition with AI and Digital Transformation
/In a recent webinar, “Energy Transition Acceleration with Digital Twins,” hosted by our industry partner, the Digital Twin Consortium, Achalesh Pandey, VP of Artificial Intelligence and Digital Transformation at GE Digital, dug into the critical challenges and opportunities presented by the integration of renewable energy resources into the electric power grid. His insights provided a comprehensive overview of the current landscape and future direction of energy transformation.
The Impact of Renewables and Distributed Energy Resources
From Siemens
Pandey emphasized the significance of the ongoing energy transition, driven predominantly by decarbonization efforts. He highlighted the rapidly growing demand for electric vehicles (EVs) and the corresponding increase in electricity consumption. According to the VNF Electric Vehicle Outlook, electricity demand from EVs is projected to reach 5,500 terawatt-hours by 2050, with renewable energy capacity expected to increase by around 5,100 terawatt-hours within the same period. This surge necessitates substantial energy storage to manage the intermittent nature of renewable resources like wind and solar energy.
GE's equipment currently produces one-third of the world's power, with their grid software facilitating the movement of 40% of the world's electricity. Pandey discussed the vital role of conventional power sources, such as gas turbines, in reducing emissions by transitioning from coal to gas. He also underscored the importance of carbon capture, utilization, and storage (CCUS) and the potential of hydrogen to further decarbonize the energy sector.
Challenges in the Grid Integration of Renewables
The integration of renewable energy into the grid presents numerous challenges, primarily due to its intermittent and probabilistic nature. Pandey explained how the grid is transitioning from a grid-following to a grid-forming mode, reducing inertia and necessitating more sophisticated management strategies. Electrification is increasing the load on the grid, requiring innovative demand response solutions.
Utilities must navigate the complexities of two-way energy and information flow, ensuring grid stability and resiliency. Pandey pointed out that achieving corporate zero-carbon goals by 2050 adds another layer of uncertainty and complexity for organizations. Regulatory and legislative frameworks, evolving energy markets, and localized approaches further complicate the landscape.
Orchestrating the Future Grid with Digital Twins and AI
Digital twins and AI are pivotal in addressing the challenges of integrating distributed energy resources (DERs) and managing the future grid. Digital twins provide a virtual representation of the physical grid, enabling utilities to plan, monitor, optimize, and control the grid efficiently. They help determine optimal locations for new infrastructure, assess the impact of DERs, and ensure power quality.
Visibility is crucial in this interconnected landscape. Real-time situational awareness, supported by virtual sensors and AI, allows utilities to understand and respond to grid conditions promptly. Optimization and control mechanisms—leveraging AI and machine learning—facilitate efficient energy distribution, demand response, and market interactions.
The Future Tech Stack: A Comprehensive Ecosystem
The tech stack supporting this transformation includes several layers:
Physical Layer: Comprising transmission utilities, distribution networks, and consumption sources—now also generation sources due to DERs
Market Layer: Encompassing wholesale and retail markets, increasingly interconnected and bidirectional
Application Layer: Featuring transmission energy management, distribution management, and DER management systems, each with planning, monitoring, optimization, control, and transaction applications
Data and Digital Twin Foundation: Integrating diverse data sources, geospatial information, network models, physics-based digital twins, state estimators, forecasting engines, and AI-driven optimization tools
This comprehensive ecosystem enables the reliable, resilient, and affordable operation of the future grid.
Overcoming Future Challenges
Looking ahead, Pandey identified key challenges:
Accurate Digital Twin Development: Ensuring accurate representation of the grid with extensive DER integration
Probabilistic Grid Operation: Transitioning from deterministic to probabilistic models to manage renewable penetration
Low Inertia Management: Maintaining stability with reduced inertia through fast, flexible control systems
Leveraging EVs for Reliability: Tracking and utilizing millions of EVs for grid stability
Modernizing Energy Markets: Evolving business models and markets to accommodate DER participation
Cybersecurity: Protecting the interconnected grid from cyber threats
AI and ML Utilization: Harnessing AI and machine learning to optimize grid operations and handle complex, probabilistic scenarios
The insights shared by Pandey during the Digital Twin Consortium webinar highlight the transformative potential of digital twins in the energy sector. As the industry navigates the complexities of decarbonization and renewable integration, digital twins offer a promising solution to ensure a stable, resilient, and sustainable energy future.
