Session: 09-16-02: Energy-Related Multidisciplinary II

Paper Number: 168098

Advancing Transactive Energy in Industry 5.0: A Cyber-Physical Decision-Making Framework 

The transition from Industry 4.0 to Industry 5.0 represents a fundamental shift in industrial and energy systems, moving beyond full automation and data-driven decision-making to a human-centric, collaborative approach. While Industry 4.0 has improved industrial efficiency through automation, artificial intelligence, and IoT-enabled smart systems, its reliance on rigid, predefined algorithms limits adaptability in complex, real-world energy markets. Energy demand is influenced by dynamic factors such as consumer behavior, industrial activity, weather conditions, and supply chain disruptions—many of which cannot be fully predicted using rule-based automation alone. Industry 5.0 addresses this challenge by integrating cyber-physical systems (CPS) with human-in-the-loop decision-making, creating a more flexible, resilient, and efficient energy market. This paper proposes a cyber-physical transactive energy framework that leverages real-time data acquisition from IoT sensors, smart meters, and industrial energy management systems to enhance predictive analytics for energy demand forecasting. By employing machine learning algorithms and historical consumption data, the framework can provide adaptive energy forecasting models that adjust dynamically to changing market conditions. This data-driven approach enables improved energy planning, allowing grid operators, energy producers, and consumers to make more informed decisions. Dynamic pricing mechanisms are incorporated to ensure that electricity prices reflect real-time supply and demand fluctuations, optimizing consumer energy costs while enhancing grid stability and resilience. A key feature of this framework is its integration with transactive energy systems, which shift from a traditional centralized grid model to a decentralized, market-driven system. In this approach, prosumers—consumers who generate their own energy through solar panels, battery storage, or other distributed energy resources—can actively trade excess electricity in a peer-to-peer marketplace. This decentralized model reduces reliance on large-scale power plants and improves grid reliability by balancing supply and demand more efficiently. To encourage industries to participate in demand-side management and improve energy efficiency, this framework incorporates nudge theory, a behavioral economics approach that subtly influences decision-making without imposing strict regulations. By providing real-time energy consumption feedback, personalized price signals, and incentive-based mechanisms, industrial consumers can optimize their energy usage without compromising productivity. For instance, businesses can receive automated recommendations to shift energy-intensive operations to off-peak hours, reducing electricity costs and lowering demand pressure on the grid. Additionally, this research explores human-in-the-loop decision-making, where human expertise is integrated with AI-driven analytics to fine-tune energy optimization strategies. By balancing automation with human judgment, this approach ensures that market inefficiencies, consumer behavior, and ethical considerations are accounted for in energy transactions. Unlike fully automated systems that rely solely on algorithms, a human-in-the-loop model allows for adaptive responses to unforeseen challenges, such as market disruptions, policy changes, and grid instabilities. This study presents a comprehensive cyber-physical framework that integrates nudging strategies, market-driven optimization, and human-in-the-loop decision support to advance grid resilience, economic sustainability, and adaptive energy transactions in the Industry 5.0 paradigm. As global energy systems undergo rapid transformation toward decarbonization, decentralization, and digitalization, this framework offers a scalable and adaptable approach to balance efficiency, flexibility, and human oversight in the modern energy landscape.

Presenting Author: John Hall University of North Carolina at Charlotte

Presenting Author Biography: Dr. John Hall has held senior level positions in design, manufacturing, maintenance, and reliability engineering and is a licensed professional engineer. He has provided engineering consultation for GEC-Alstom Electric, TECO-Westinghouse, Lockheed-Martin, Advanced Micro Devices, Hewlett Packard, Accretech USA, and IBM. Following a career in industry, Dr. Hall, completed his graduate work at The University of Texas at Austin. He subsquently joined the faculty at the University at Buffalo. His research concentrates on design methodologies and novel control techniques that promote sustainable systems. Key aspects of the work are system adaptability, maintenance, reliability, and lifecycle analysis. Dr. Hall is interested in the applied area of renewable energy systems. He has developed innovative design methods that promote the productivity and longevity of wind turbines. He has acquired a patent for his innovation, which has resulted in a startup company that is developing adaptive blade software and technology.

Authors:

Maryam Yaghoubirad University of North Carolina at Charlotte
John Hall University of North Carolina at Charlotte