TECHNICAL PROGRAMME | Energy Technologies – Future Pathways
Smart Infrastructure for the Future Energy Industry: Digitalisation & Innovation
Forum 18 | Hall 10 - Technical Programme 4
12
October
14:00
15:30
UTC+3
As the energy industry evolves to meet the demands of a sustainable future, smart infrastructure is playing a crucial role in transforming the sector. This session will explore the cutting-edge technologies and strategies that are enabling smarter, more resilient, and adaptive energy systems. It will cover the latest developments in smart grids, intelligent energy management systems, IoT applications, AI-driven analytics, and the role of big data in optimising energy infrastructure. The session will bring together experts to discuss the challenges, opportunities, and future trends in smart energy infrastructure.
The global energy transition toward sustainable, efficient, and decentralized models is a vital necessity for achieving Sustainable Development Goals and tackling climate challenges. This transformation requires replacing the traditional, hardware-centric structures of the oil industry with innovative, data-driven, and technological approaches. However, oil-dependent economies still face deep structural challenges, including excessive consumption, an unfavorable energy intensity index, supply chain inefficiencies, and vast energy waste—all rooted in a vicious institutional cycle and resistance to creative destruction. This research focuses on Iran as a case study (due to the severity of its challenges and its generalizability to similar oil-rich nations) to propose a data-driven, Blockchain-based business model designed to overcome these structural barriers by integrating emerging technologies with institutional reforms. A data-driven analysis of Iran's energy balance sheet from 2001 to 2023, using Python, illustrates structural inefficiencies such as 25% energy resource waste across the production-to-consumption chain, consumption growth outpacing production, and extensive statistical contradictions.
The methodology is qualitative, based on Grounded Theory and case studies, utilizing MaxQDA for data analysis. This optimal model, designed to enhance energy efficiency in oil-dependent economies, was developed using the Osterwalder framework and analysis of the business models of 25 successful global energy startups.
The proposed business model is built around two key technologies:
This model shifts the prevailing paradigm from hardware-centric to data-driven approaches, offering values such as smart management, data democratization, an open data platform, and a peer-to-peer (P2P) energy network.
For practical validation, the model was piloted using AI-based Measurement and Verification (M&V) techniques on the data of 452,000 residential subscribers in one Iranian province. The results demonstrated a significant potential for the model in providing accurate consumption forecasts. Furthermore, through the smart implementation of incentive policies via an electronic energy market, an annual energy consumption reduction of 10% is expected. These findings confirm the operational viability of the model in smartening the entire energy chain: production, distribution, and consumption.
The key innovation of this model is the provision of a comprehensive, multi-faceted technical and strategic framework that uses incentive mechanisms to encourage active consumer participation in reduction, creating sustainable value. This scalable and generalizable solution for the global energy transition is applicable to oil-dependent economies with similar inefficiencies, transforming National Oil Companies (NOCs) from mere consumers into key players in smart energy management.
The methodology is qualitative, based on Grounded Theory and case studies, utilizing MaxQDA for data analysis. This optimal model, designed to enhance energy efficiency in oil-dependent economies, was developed using the Osterwalder framework and analysis of the business models of 25 successful global energy startups.
The proposed business model is built around two key technologies:
- Blockchain: Which establishes a decentralized, immutable platform that enhances data transparency, enables precise monitoring of consumption and production, strengthens systemic trust, and minimizes human intervention.
- Artificial Intelligence and Smart Data: By converting raw big data into structured, actionable information, it provides a layer for systematic data access via an Open Data Platform, facilitating the entry of new players to create value and shape an inclusive innovation ecosystem.
This model shifts the prevailing paradigm from hardware-centric to data-driven approaches, offering values such as smart management, data democratization, an open data platform, and a peer-to-peer (P2P) energy network.
For practical validation, the model was piloted using AI-based Measurement and Verification (M&V) techniques on the data of 452,000 residential subscribers in one Iranian province. The results demonstrated a significant potential for the model in providing accurate consumption forecasts. Furthermore, through the smart implementation of incentive policies via an electronic energy market, an annual energy consumption reduction of 10% is expected. These findings confirm the operational viability of the model in smartening the entire energy chain: production, distribution, and consumption.
The key innovation of this model is the provision of a comprehensive, multi-faceted technical and strategic framework that uses incentive mechanisms to encourage active consumer participation in reduction, creating sustainable value. This scalable and generalizable solution for the global energy transition is applicable to oil-dependent economies with similar inefficiencies, transforming National Oil Companies (NOCs) from mere consumers into key players in smart energy management.
It is the aim of this presentation to bring feedback from Interoperability testing of Secure Routable GOOSE communication at the UCA International Users Group Interoperability Tests and to explain how Routable GOOSEs operate, how they can be configured and finally what Power System Protection Use Cases become possible.
This presentation will describe the methodology and results from testing the authentication of an IED with KDC, the distribution of session keys, encryption of the session key, the message integrity check where the IED verifies the integrity of the message using the MAC and finally the secure, interoperable Routable GOOSE Communication between different vendor IEDs.
R-GOOSE communication can be the backbone of any peer-to-peer inter substation communication and is what will support many wide-area use cases such as Zonal Autonomous Controls, which can be used for better regulation of Voltages across buses with a high injection of Renewable Power as well as Congestion Management Schemes, Anti-Islanding Schemes, or System Integrity Protection Schemes.
Routable GOOSE has several similarities and differences to Ordinary GOOSE messages. The main difference is that an R-GOOSE can travel through a Router and be sent to devices outside the substation. Ordinary GOOSE messages can boast a very high-speed data exchange due to local network delivery whereas with Routable GOOSE typical delays are less than 20 milliseconds for a well-designed Wide Area Network. Ordinary Layer 2 GOOSE Messages are normally broadcast to all devices which then have to check the Destination MAC address to determine whether or not the IED has subscribed to the message. This is not replicable for messages which are routed over wide area networks, instead these Routable GOOSE messages require a UDP /IP header and are multicast to specific devices using the destination IP address in the R-GOOSE Control Block Configuration. Another important difference between the ordinary Layer 2 GOOSE messages and Routable GOOSE is that given that the messages travel outside the boundary of the Substation, they require a security mechanism to ensure confidentiality, integrity and authentication. Authentication is used to ensure that it is the original message that is received by the Subscribing device whilst Encryption ensures that such a message cannot be interpreted by unauthorized actors.
This presentation will describe the methodology and results from testing the authentication of an IED with KDC, the distribution of session keys, encryption of the session key, the message integrity check where the IED verifies the integrity of the message using the MAC and finally the secure, interoperable Routable GOOSE Communication between different vendor IEDs.
R-GOOSE communication can be the backbone of any peer-to-peer inter substation communication and is what will support many wide-area use cases such as Zonal Autonomous Controls, which can be used for better regulation of Voltages across buses with a high injection of Renewable Power as well as Congestion Management Schemes, Anti-Islanding Schemes, or System Integrity Protection Schemes.
Routable GOOSE has several similarities and differences to Ordinary GOOSE messages. The main difference is that an R-GOOSE can travel through a Router and be sent to devices outside the substation. Ordinary GOOSE messages can boast a very high-speed data exchange due to local network delivery whereas with Routable GOOSE typical delays are less than 20 milliseconds for a well-designed Wide Area Network. Ordinary Layer 2 GOOSE Messages are normally broadcast to all devices which then have to check the Destination MAC address to determine whether or not the IED has subscribed to the message. This is not replicable for messages which are routed over wide area networks, instead these Routable GOOSE messages require a UDP /IP header and are multicast to specific devices using the destination IP address in the R-GOOSE Control Block Configuration. Another important difference between the ordinary Layer 2 GOOSE messages and Routable GOOSE is that given that the messages travel outside the boundary of the Substation, they require a security mechanism to ensure confidentiality, integrity and authentication. Authentication is used to ensure that it is the original message that is received by the Subscribing device whilst Encryption ensures that such a message cannot be interpreted by unauthorized actors.
Renewable energy plants are key pillars in Saudi Arabia’s vision for a sustainable and clean energy future. However, maintaining these plants, especially solar and wind farms located in harsh and remote environments, presents significant operational challenges. This research proposes the development of Ruaa RoboEnergy, an intelligent electric robotic system designed to autonomously monitor, inspect, and maintain renewable energy installations, enhancing operational efficiency and reducing maintenance costs.
The name “Ruaa”, meaning “visions” in Arabic, reflects the project’s alignment with Saudi Arabia’s forward-thinking approach and commitment to innovative, sustainable energy solutions. The combination of “Ruaa” and “RoboEnergy” highlights the integration of robotics and electrical engineering in creating smart maintenance technologies for renewable energy infrastructures.
The system integrates advanced electrical engineering, robotics, and artificial intelligence to create a robust, energy-efficient robot capable of operating in extreme environmental conditions. Equipped with precise electric actuators, multi-joint robotic arms, and AI-powered vision systems, Ruaa RoboEnergy detects faults, predicts failures, and performs timely maintenance tasks without human intervention.
By harvesting energy from renewable sources and incorporating smart energy management, the robot ensures continuous operation even in remote locations with limited human access. This project focuses on designing the electrical control systems, robotic mechanisms, AI algorithms for predictive maintenance, and simulating real-world conditions to validate performance.
The expected outcomes include improved reliability and lifespan of renewable energy plants, cost reduction in maintenance, and contribution towards Saudi Arabia’s energy leadership and sustainability goals. This research aligns closely with the Energy Technology domain, aiming to advance smart, autonomous solutions for the future of energy infrastructure.
The name “Ruaa”, meaning “visions” in Arabic, reflects the project’s alignment with Saudi Arabia’s forward-thinking approach and commitment to innovative, sustainable energy solutions. The combination of “Ruaa” and “RoboEnergy” highlights the integration of robotics and electrical engineering in creating smart maintenance technologies for renewable energy infrastructures.
The system integrates advanced electrical engineering, robotics, and artificial intelligence to create a robust, energy-efficient robot capable of operating in extreme environmental conditions. Equipped with precise electric actuators, multi-joint robotic arms, and AI-powered vision systems, Ruaa RoboEnergy detects faults, predicts failures, and performs timely maintenance tasks without human intervention.
By harvesting energy from renewable sources and incorporating smart energy management, the robot ensures continuous operation even in remote locations with limited human access. This project focuses on designing the electrical control systems, robotic mechanisms, AI algorithms for predictive maintenance, and simulating real-world conditions to validate performance.
The expected outcomes include improved reliability and lifespan of renewable energy plants, cost reduction in maintenance, and contribution towards Saudi Arabia’s energy leadership and sustainability goals. This research aligns closely with the Energy Technology domain, aiming to advance smart, autonomous solutions for the future of energy infrastructure.
Energy-intensive industries have many problems in simultaneously optimize operational efficiency, economic performance, and environmental sustainability while maintaining product quality and safety standards. This research presents a novel real-time optimization method that integrates four critical performance dimensions include: Energy, Exergy, Economic, and Environmental (4E) through advanced digital twin technology coupled with machine learning algorithms and soft sensor networks.
The proposed system addresses a fundamental limitation in current industrial practice: the temporal disconnect between operational decisions and their comprehensive impact assessment. Traditional Life Cycle Assessment (LCA) methodologies, while scientifically robust, are typically conducted periodically, so limiting their utility for real-time process optimization. Now this paradigm changed by implementing continuous LCA calculations based on the IMPACT 2002+ methodology, enabling real-time monitoring and prediction of 15 environmental impact categories including human health effects, ecosystem quality degradation, climate change potential, and resource depletion.
The digital twin creates multiple modeling methods: physics-based process simulation, data-driven machine learning models, and hybrid form of these. This multi-fidelity approach ensures both theoretical rigor and practical applicability across diverse industrial contexts including petroleum refining, cement manufacturing, and thermal power generation. Soft sensors compensate for measurement limitations in industrial environments, utilizing advanced inference techniques for state estimation and uncertainty quantification.
The optimization engine employs a multi-objective logical algorithm that simultaneously minimizes energy consumption, exergy destruction, environmental impacts, and operational costs while satisfying quality constraints and safety limitations. The mathematical formulation integrates thermodynamic principles (first and second laws), economic modeling (net present value optimization), and environmental impact assessment through a weighted objective function with adaptive parameter tuning based on real-time conditions and regulatory requirements.
Implementation validation in pilot-scale applications demonstrates significant performance improvements: 8-15% reduction in specific energy consumption, 12-25% decrease in exergy destruction, 5-20% improvement in economic performance metrics, and substantial reductions across multiple environmental impact categories. The system's predictive capabilities enable proactive decision-making, with machine learning models achieving 92-95% accuracy in forecasting key performance indicators over operational time horizons.
This research is combination of process systems engineering, environmental science, and artificial intelligence by providing the new method for continuous multi-criteria optimization in industries and also addresses critical challenges in sustainable process intensification and Industry 4.0 implementation, offering a scalable solution for the digital transformation of energy-intensive industries. The modular structure ensures adaptability across different industrial sectors while maintaining scientific rigor and practical feasibility, representing a significant advancement toward achieving simultaneously profitable and environmentally responsible industrial operations in the context of global sustainability imperatives.
The proposed system addresses a fundamental limitation in current industrial practice: the temporal disconnect between operational decisions and their comprehensive impact assessment. Traditional Life Cycle Assessment (LCA) methodologies, while scientifically robust, are typically conducted periodically, so limiting their utility for real-time process optimization. Now this paradigm changed by implementing continuous LCA calculations based on the IMPACT 2002+ methodology, enabling real-time monitoring and prediction of 15 environmental impact categories including human health effects, ecosystem quality degradation, climate change potential, and resource depletion.
The digital twin creates multiple modeling methods: physics-based process simulation, data-driven machine learning models, and hybrid form of these. This multi-fidelity approach ensures both theoretical rigor and practical applicability across diverse industrial contexts including petroleum refining, cement manufacturing, and thermal power generation. Soft sensors compensate for measurement limitations in industrial environments, utilizing advanced inference techniques for state estimation and uncertainty quantification.
The optimization engine employs a multi-objective logical algorithm that simultaneously minimizes energy consumption, exergy destruction, environmental impacts, and operational costs while satisfying quality constraints and safety limitations. The mathematical formulation integrates thermodynamic principles (first and second laws), economic modeling (net present value optimization), and environmental impact assessment through a weighted objective function with adaptive parameter tuning based on real-time conditions and regulatory requirements.
Implementation validation in pilot-scale applications demonstrates significant performance improvements: 8-15% reduction in specific energy consumption, 12-25% decrease in exergy destruction, 5-20% improvement in economic performance metrics, and substantial reductions across multiple environmental impact categories. The system's predictive capabilities enable proactive decision-making, with machine learning models achieving 92-95% accuracy in forecasting key performance indicators over operational time horizons.
This research is combination of process systems engineering, environmental science, and artificial intelligence by providing the new method for continuous multi-criteria optimization in industries and also addresses critical challenges in sustainable process intensification and Industry 4.0 implementation, offering a scalable solution for the digital transformation of energy-intensive industries. The modular structure ensures adaptability across different industrial sectors while maintaining scientific rigor and practical feasibility, representing a significant advancement toward achieving simultaneously profitable and environmentally responsible industrial operations in the context of global sustainability imperatives.
Amir Abedpour
Speaker
Innovation Strategist | Advisor to CEO, NIOC
National Iranian Oil Company
Iran
The global energy transition toward sustainable, efficient, and decentralized models is a vital necessity for achieving Sustainable Development Goals and tackling climate challenges. This transformation requires replacing the traditional, hardware-centric structures of the oil industry with innovative, data-driven, and technological approaches. However, oil-dependent economies still face deep structural challenges, including excessive consumption, an unfavorable energy intensity index, supply chain inefficiencies, and vast energy waste—all rooted in a vicious institutional cycle and resistance to creative destruction. This research focuses on Iran as a case study (due to the severity of its challenges and its generalizability to similar oil-rich nations) to propose a data-driven, Blockchain-based business model designed to overcome these structural barriers by integrating emerging technologies with institutional reforms. A data-driven analysis of Iran's energy balance sheet from 2001 to 2023, using Python, illustrates structural inefficiencies such as 25% energy resource waste across the production-to-consumption chain, consumption growth outpacing production, and extensive statistical contradictions.
The methodology is qualitative, based on Grounded Theory and case studies, utilizing MaxQDA for data analysis. This optimal model, designed to enhance energy efficiency in oil-dependent economies, was developed using the Osterwalder framework and analysis of the business models of 25 successful global energy startups.
The proposed business model is built around two key technologies:
This model shifts the prevailing paradigm from hardware-centric to data-driven approaches, offering values such as smart management, data democratization, an open data platform, and a peer-to-peer (P2P) energy network.
For practical validation, the model was piloted using AI-based Measurement and Verification (M&V) techniques on the data of 452,000 residential subscribers in one Iranian province. The results demonstrated a significant potential for the model in providing accurate consumption forecasts. Furthermore, through the smart implementation of incentive policies via an electronic energy market, an annual energy consumption reduction of 10% is expected. These findings confirm the operational viability of the model in smartening the entire energy chain: production, distribution, and consumption.
The key innovation of this model is the provision of a comprehensive, multi-faceted technical and strategic framework that uses incentive mechanisms to encourage active consumer participation in reduction, creating sustainable value. This scalable and generalizable solution for the global energy transition is applicable to oil-dependent economies with similar inefficiencies, transforming National Oil Companies (NOCs) from mere consumers into key players in smart energy management.
The methodology is qualitative, based on Grounded Theory and case studies, utilizing MaxQDA for data analysis. This optimal model, designed to enhance energy efficiency in oil-dependent economies, was developed using the Osterwalder framework and analysis of the business models of 25 successful global energy startups.
The proposed business model is built around two key technologies:
- Blockchain: Which establishes a decentralized, immutable platform that enhances data transparency, enables precise monitoring of consumption and production, strengthens systemic trust, and minimizes human intervention.
- Artificial Intelligence and Smart Data: By converting raw big data into structured, actionable information, it provides a layer for systematic data access via an Open Data Platform, facilitating the entry of new players to create value and shape an inclusive innovation ecosystem.
This model shifts the prevailing paradigm from hardware-centric to data-driven approaches, offering values such as smart management, data democratization, an open data platform, and a peer-to-peer (P2P) energy network.
For practical validation, the model was piloted using AI-based Measurement and Verification (M&V) techniques on the data of 452,000 residential subscribers in one Iranian province. The results demonstrated a significant potential for the model in providing accurate consumption forecasts. Furthermore, through the smart implementation of incentive policies via an electronic energy market, an annual energy consumption reduction of 10% is expected. These findings confirm the operational viability of the model in smartening the entire energy chain: production, distribution, and consumption.
The key innovation of this model is the provision of a comprehensive, multi-faceted technical and strategic framework that uses incentive mechanisms to encourage active consumer participation in reduction, creating sustainable value. This scalable and generalizable solution for the global energy transition is applicable to oil-dependent economies with similar inefficiencies, transforming National Oil Companies (NOCs) from mere consumers into key players in smart energy management.
Renewable energy plants are key pillars in Saudi Arabia’s vision for a sustainable and clean energy future. However, maintaining these plants, especially solar and wind farms located in harsh and remote environments, presents significant operational challenges. This research proposes the development of Ruaa RoboEnergy, an intelligent electric robotic system designed to autonomously monitor, inspect, and maintain renewable energy installations, enhancing operational efficiency and reducing maintenance costs.
The name “Ruaa”, meaning “visions” in Arabic, reflects the project’s alignment with Saudi Arabia’s forward-thinking approach and commitment to innovative, sustainable energy solutions. The combination of “Ruaa” and “RoboEnergy” highlights the integration of robotics and electrical engineering in creating smart maintenance technologies for renewable energy infrastructures.
The system integrates advanced electrical engineering, robotics, and artificial intelligence to create a robust, energy-efficient robot capable of operating in extreme environmental conditions. Equipped with precise electric actuators, multi-joint robotic arms, and AI-powered vision systems, Ruaa RoboEnergy detects faults, predicts failures, and performs timely maintenance tasks without human intervention.
By harvesting energy from renewable sources and incorporating smart energy management, the robot ensures continuous operation even in remote locations with limited human access. This project focuses on designing the electrical control systems, robotic mechanisms, AI algorithms for predictive maintenance, and simulating real-world conditions to validate performance.
The expected outcomes include improved reliability and lifespan of renewable energy plants, cost reduction in maintenance, and contribution towards Saudi Arabia’s energy leadership and sustainability goals. This research aligns closely with the Energy Technology domain, aiming to advance smart, autonomous solutions for the future of energy infrastructure.
The name “Ruaa”, meaning “visions” in Arabic, reflects the project’s alignment with Saudi Arabia’s forward-thinking approach and commitment to innovative, sustainable energy solutions. The combination of “Ruaa” and “RoboEnergy” highlights the integration of robotics and electrical engineering in creating smart maintenance technologies for renewable energy infrastructures.
The system integrates advanced electrical engineering, robotics, and artificial intelligence to create a robust, energy-efficient robot capable of operating in extreme environmental conditions. Equipped with precise electric actuators, multi-joint robotic arms, and AI-powered vision systems, Ruaa RoboEnergy detects faults, predicts failures, and performs timely maintenance tasks without human intervention.
By harvesting energy from renewable sources and incorporating smart energy management, the robot ensures continuous operation even in remote locations with limited human access. This project focuses on designing the electrical control systems, robotic mechanisms, AI algorithms for predictive maintenance, and simulating real-world conditions to validate performance.
The expected outcomes include improved reliability and lifespan of renewable energy plants, cost reduction in maintenance, and contribution towards Saudi Arabia’s energy leadership and sustainability goals. This research aligns closely with the Energy Technology domain, aiming to advance smart, autonomous solutions for the future of energy infrastructure.
Energy-intensive industries have many problems in simultaneously optimize operational efficiency, economic performance, and environmental sustainability while maintaining product quality and safety standards. This research presents a novel real-time optimization method that integrates four critical performance dimensions include: Energy, Exergy, Economic, and Environmental (4E) through advanced digital twin technology coupled with machine learning algorithms and soft sensor networks.
The proposed system addresses a fundamental limitation in current industrial practice: the temporal disconnect between operational decisions and their comprehensive impact assessment. Traditional Life Cycle Assessment (LCA) methodologies, while scientifically robust, are typically conducted periodically, so limiting their utility for real-time process optimization. Now this paradigm changed by implementing continuous LCA calculations based on the IMPACT 2002+ methodology, enabling real-time monitoring and prediction of 15 environmental impact categories including human health effects, ecosystem quality degradation, climate change potential, and resource depletion.
The digital twin creates multiple modeling methods: physics-based process simulation, data-driven machine learning models, and hybrid form of these. This multi-fidelity approach ensures both theoretical rigor and practical applicability across diverse industrial contexts including petroleum refining, cement manufacturing, and thermal power generation. Soft sensors compensate for measurement limitations in industrial environments, utilizing advanced inference techniques for state estimation and uncertainty quantification.
The optimization engine employs a multi-objective logical algorithm that simultaneously minimizes energy consumption, exergy destruction, environmental impacts, and operational costs while satisfying quality constraints and safety limitations. The mathematical formulation integrates thermodynamic principles (first and second laws), economic modeling (net present value optimization), and environmental impact assessment through a weighted objective function with adaptive parameter tuning based on real-time conditions and regulatory requirements.
Implementation validation in pilot-scale applications demonstrates significant performance improvements: 8-15% reduction in specific energy consumption, 12-25% decrease in exergy destruction, 5-20% improvement in economic performance metrics, and substantial reductions across multiple environmental impact categories. The system's predictive capabilities enable proactive decision-making, with machine learning models achieving 92-95% accuracy in forecasting key performance indicators over operational time horizons.
This research is combination of process systems engineering, environmental science, and artificial intelligence by providing the new method for continuous multi-criteria optimization in industries and also addresses critical challenges in sustainable process intensification and Industry 4.0 implementation, offering a scalable solution for the digital transformation of energy-intensive industries. The modular structure ensures adaptability across different industrial sectors while maintaining scientific rigor and practical feasibility, representing a significant advancement toward achieving simultaneously profitable and environmentally responsible industrial operations in the context of global sustainability imperatives.
The proposed system addresses a fundamental limitation in current industrial practice: the temporal disconnect between operational decisions and their comprehensive impact assessment. Traditional Life Cycle Assessment (LCA) methodologies, while scientifically robust, are typically conducted periodically, so limiting their utility for real-time process optimization. Now this paradigm changed by implementing continuous LCA calculations based on the IMPACT 2002+ methodology, enabling real-time monitoring and prediction of 15 environmental impact categories including human health effects, ecosystem quality degradation, climate change potential, and resource depletion.
The digital twin creates multiple modeling methods: physics-based process simulation, data-driven machine learning models, and hybrid form of these. This multi-fidelity approach ensures both theoretical rigor and practical applicability across diverse industrial contexts including petroleum refining, cement manufacturing, and thermal power generation. Soft sensors compensate for measurement limitations in industrial environments, utilizing advanced inference techniques for state estimation and uncertainty quantification.
The optimization engine employs a multi-objective logical algorithm that simultaneously minimizes energy consumption, exergy destruction, environmental impacts, and operational costs while satisfying quality constraints and safety limitations. The mathematical formulation integrates thermodynamic principles (first and second laws), economic modeling (net present value optimization), and environmental impact assessment through a weighted objective function with adaptive parameter tuning based on real-time conditions and regulatory requirements.
Implementation validation in pilot-scale applications demonstrates significant performance improvements: 8-15% reduction in specific energy consumption, 12-25% decrease in exergy destruction, 5-20% improvement in economic performance metrics, and substantial reductions across multiple environmental impact categories. The system's predictive capabilities enable proactive decision-making, with machine learning models achieving 92-95% accuracy in forecasting key performance indicators over operational time horizons.
This research is combination of process systems engineering, environmental science, and artificial intelligence by providing the new method for continuous multi-criteria optimization in industries and also addresses critical challenges in sustainable process intensification and Industry 4.0 implementation, offering a scalable solution for the digital transformation of energy-intensive industries. The modular structure ensures adaptability across different industrial sectors while maintaining scientific rigor and practical feasibility, representing a significant advancement toward achieving simultaneously profitable and environmentally responsible industrial operations in the context of global sustainability imperatives.
It is the aim of this presentation to bring feedback from Interoperability testing of Secure Routable GOOSE communication at the UCA International Users Group Interoperability Tests and to explain how Routable GOOSEs operate, how they can be configured and finally what Power System Protection Use Cases become possible.
This presentation will describe the methodology and results from testing the authentication of an IED with KDC, the distribution of session keys, encryption of the session key, the message integrity check where the IED verifies the integrity of the message using the MAC and finally the secure, interoperable Routable GOOSE Communication between different vendor IEDs.
R-GOOSE communication can be the backbone of any peer-to-peer inter substation communication and is what will support many wide-area use cases such as Zonal Autonomous Controls, which can be used for better regulation of Voltages across buses with a high injection of Renewable Power as well as Congestion Management Schemes, Anti-Islanding Schemes, or System Integrity Protection Schemes.
Routable GOOSE has several similarities and differences to Ordinary GOOSE messages. The main difference is that an R-GOOSE can travel through a Router and be sent to devices outside the substation. Ordinary GOOSE messages can boast a very high-speed data exchange due to local network delivery whereas with Routable GOOSE typical delays are less than 20 milliseconds for a well-designed Wide Area Network. Ordinary Layer 2 GOOSE Messages are normally broadcast to all devices which then have to check the Destination MAC address to determine whether or not the IED has subscribed to the message. This is not replicable for messages which are routed over wide area networks, instead these Routable GOOSE messages require a UDP /IP header and are multicast to specific devices using the destination IP address in the R-GOOSE Control Block Configuration. Another important difference between the ordinary Layer 2 GOOSE messages and Routable GOOSE is that given that the messages travel outside the boundary of the Substation, they require a security mechanism to ensure confidentiality, integrity and authentication. Authentication is used to ensure that it is the original message that is received by the Subscribing device whilst Encryption ensures that such a message cannot be interpreted by unauthorized actors.
This presentation will describe the methodology and results from testing the authentication of an IED with KDC, the distribution of session keys, encryption of the session key, the message integrity check where the IED verifies the integrity of the message using the MAC and finally the secure, interoperable Routable GOOSE Communication between different vendor IEDs.
R-GOOSE communication can be the backbone of any peer-to-peer inter substation communication and is what will support many wide-area use cases such as Zonal Autonomous Controls, which can be used for better regulation of Voltages across buses with a high injection of Renewable Power as well as Congestion Management Schemes, Anti-Islanding Schemes, or System Integrity Protection Schemes.
Routable GOOSE has several similarities and differences to Ordinary GOOSE messages. The main difference is that an R-GOOSE can travel through a Router and be sent to devices outside the substation. Ordinary GOOSE messages can boast a very high-speed data exchange due to local network delivery whereas with Routable GOOSE typical delays are less than 20 milliseconds for a well-designed Wide Area Network. Ordinary Layer 2 GOOSE Messages are normally broadcast to all devices which then have to check the Destination MAC address to determine whether or not the IED has subscribed to the message. This is not replicable for messages which are routed over wide area networks, instead these Routable GOOSE messages require a UDP /IP header and are multicast to specific devices using the destination IP address in the R-GOOSE Control Block Configuration. Another important difference between the ordinary Layer 2 GOOSE messages and Routable GOOSE is that given that the messages travel outside the boundary of the Substation, they require a security mechanism to ensure confidentiality, integrity and authentication. Authentication is used to ensure that it is the original message that is received by the Subscribing device whilst Encryption ensures that such a message cannot be interpreted by unauthorized actors.
