Periodic Reporting for period 1 - InnoCyPES (Innovative Tools for Cyber-Physical Energy Systems)
Okres sprawozdawczy: 2021-06-01 do 2023-05-31
The increasing volume, velocity, and variety of data from a massive number of dispersed “Internet of things” sensors in the energy system offers opportunities for improved operational efficiency and reliability – but it also results in threats in the form of computational burden and cyber-attacks. The transformation towards a fully digitalized energy system requires substantial improvements in coordinated design of cyber and physical systems, end-to-end data processing tools, and enabling changes in policy, incentive and regulatory mechanisms. Their absence acts as a barrier for the energy industry in translating the fast accumulating data into actionable knowledge. This project addresses the key challenges from different sector’s perspective energy sector is facing at the moment regarding digitalization.
The project aims to address some of the key challenges related to digitalization of energy system, covering data acquisition and management, application of advanced data analytics, and sustainable design of cyber physical energy system in the midst of the digital transformation in energy sector and proliferation of artificial intelligence techniques through 15 early stage researcher projects.
The overarching goal of InnoCyPES is to deliver a system management platform applicable to the CPES development lifecycle.
The overall research objectives include:
1. Develop a robust data handling workflow for energy systems;
2. Architect a management platform tuned on field data encompassing the entire energy system development lifecycle;
3. Develop economic models and policy recommendations towards digitalization of the energy sector;
The project aims to address some of the key challenges related to digitalization of energy system, covering data acquisition and management, application of advanced data analytics, and sustainable design of cyber physical energy system in the midst of the digital transformation in energy sector and proliferation of artificial intelligence techniques through 15 early stage researcher projects.
The overarching goal of InnoCyPES is to deliver a system management platform applicable to the CPES development lifecycle.
The overall research objectives include:
1. Develop a robust data handling workflow for energy systems;
2. Architect a management platform tuned on field data encompassing the entire energy system development lifecycle;
3. Develop economic models and policy recommendations towards digitalization of the energy sector;
The reporting period has yielded significant achievements, which have been meticulously documented in the submitted deliverables.
The research activities conducted by the Early Stage Researchers (ESRs) have resulted in noteworthy highlights, which are summarized below:
• IoT/cloud enabled monitoring system for offshore wind power plants
• Software-defined network enabled communication framework for smart grids
• Information model specifications towards software-defined protection, automation, and control in power systems
• Real-time recognition of power system faults and cyber attacks
• Recommendations for smart grid architecture model
• Data requirements, fusion, and enrichment for reliability assessment of medium voltage cable faults in distribution grids
• Optimal design of hybrid offshore wind power plants to supply offshore oil & gas systems
• Transient stability analysis of grid-forming converters and benchmark simulation model
• Privacy-preserved federated learning model for distribution grid state estimation
• Machine learning based assessment driven by simulated and field measurements for grid compliance appraisal
• Data- and model-driven estimation of wind turbine behavior during faults
• Application of system dynamics to evaluate the costs and benefits of digital investments for technology developers and asset owners
• Identified digitalization barriers for distribution systems and preliminary policy recommendations
Apart from the technical advancements achieved through this research, notable progress has been made in the project's training plan. Three successful training schools have been conducted, imparting valuable knowledge and expertise to the ESRs. Additionally, several intersectoral secondments have taken place, fostering collaboration and knowledge sharing across sectors. The ESRs have also actively engaged with one another, fostering close interaction and exploring synergies among their respective projects. As a result, several joint publications have emerged during this reporting period.
The research activities conducted by the Early Stage Researchers (ESRs) have resulted in noteworthy highlights, which are summarized below:
• IoT/cloud enabled monitoring system for offshore wind power plants
• Software-defined network enabled communication framework for smart grids
• Information model specifications towards software-defined protection, automation, and control in power systems
• Real-time recognition of power system faults and cyber attacks
• Recommendations for smart grid architecture model
• Data requirements, fusion, and enrichment for reliability assessment of medium voltage cable faults in distribution grids
• Optimal design of hybrid offshore wind power plants to supply offshore oil & gas systems
• Transient stability analysis of grid-forming converters and benchmark simulation model
• Privacy-preserved federated learning model for distribution grid state estimation
• Machine learning based assessment driven by simulated and field measurements for grid compliance appraisal
• Data- and model-driven estimation of wind turbine behavior during faults
• Application of system dynamics to evaluate the costs and benefits of digital investments for technology developers and asset owners
• Identified digitalization barriers for distribution systems and preliminary policy recommendations
Apart from the technical advancements achieved through this research, notable progress has been made in the project's training plan. Three successful training schools have been conducted, imparting valuable knowledge and expertise to the ESRs. Additionally, several intersectoral secondments have taken place, fostering collaboration and knowledge sharing across sectors. The ESRs have also actively engaged with one another, fostering close interaction and exploring synergies among their respective projects. As a result, several joint publications have emerged during this reporting period.
The project is at the forefront of pioneering advancements in both cyber system and physical system design and assessment. In addition to publications, the project aims to leverage its results through contributions to standardization (eg. IEC), commercial exploitation (patents), and development of open-source software packages.
In WP1, the project introduces a new information model that expands beyond the conventional models that often appliable to components of similar category. Instead it encompasses components with different properties and systems. Furthermore, the integration of IoT/Cloud into the existing SCADA system is explored. Intrusion detection mechanisms will be developed at the end to ensure the safe operation of information technology (IT) and operating technology (OT). The results achieved have shown promising potential in contributing to the design of the next generation SCADA system for offshore wind and standardization for smart grids.
WP2 focuses on the development of methods to enhance dataset quality and efficient and secure management. Through data enrichment strategies applied to different types of data. This facilitates further extraction of information and knowledge for the operation and reliability assessment of distribution grids. A new data management system will be designed to facilitate distributed data collection, curation, and extraction. Technologies enabling efficient management of data from diverse locations and types over time will be developed for both relational and non-relational data.
In WP3, the project develops novel applications based on data within and outside the standard SCADA system in the energy sector. Data collected from the industry undergoes processing and enrichment using advanced data analytics methods to optimize system performance, enhance reliability, and ensure stability. New methods utilizing diverse and heterogeneous data sources are developed to improve system operation and management beyond traditional SCADA systems. For the first time, a significant amount of historical fault data collected from different distribution grids, along with other data sources such as weather and digging activities, is curated to establish a more accurate correlation between grid reliability and external factors.
WP4 contributes to the digital twin development of renewable generation and the system through the creation of novel models driven by data and model-driven techniques. Data incorporates historical data, measurements, simulation, and testing results. The use of digital twin representations during the design and testing phase of components can save time and effort by reducing the reliance on full-scale field tests before market delivery. The project also explores digital representations for power plants and power grids. The achieved results in WP4 have already and will make valuable contributions to the latest standardization efforts concerning Software/Hardware-in-the-loop (S/HiL) test benches for renewable based power systems.
WP5 focuses on developing innovative methods to evaluate the cost-benefit analysis of digital investments. Current methods often struggle to quantify the impacts on physical system operation resulting from new digital investments. Additionally, WP5 examines the environmental sustainability impact of increased digitalization and develops an enabling framework encompassing governance, regulations, and policies necessary to facilitate the energy sector's transformation towards digitalization. Thus far, WP5 has proposed models built upon system dynamics to evaluate different sectors such as distribution grids and offshore wind. It has also identified the key barriers on digitalization for distribution grids through expert interviews.
In WP1, the project introduces a new information model that expands beyond the conventional models that often appliable to components of similar category. Instead it encompasses components with different properties and systems. Furthermore, the integration of IoT/Cloud into the existing SCADA system is explored. Intrusion detection mechanisms will be developed at the end to ensure the safe operation of information technology (IT) and operating technology (OT). The results achieved have shown promising potential in contributing to the design of the next generation SCADA system for offshore wind and standardization for smart grids.
WP2 focuses on the development of methods to enhance dataset quality and efficient and secure management. Through data enrichment strategies applied to different types of data. This facilitates further extraction of information and knowledge for the operation and reliability assessment of distribution grids. A new data management system will be designed to facilitate distributed data collection, curation, and extraction. Technologies enabling efficient management of data from diverse locations and types over time will be developed for both relational and non-relational data.
In WP3, the project develops novel applications based on data within and outside the standard SCADA system in the energy sector. Data collected from the industry undergoes processing and enrichment using advanced data analytics methods to optimize system performance, enhance reliability, and ensure stability. New methods utilizing diverse and heterogeneous data sources are developed to improve system operation and management beyond traditional SCADA systems. For the first time, a significant amount of historical fault data collected from different distribution grids, along with other data sources such as weather and digging activities, is curated to establish a more accurate correlation between grid reliability and external factors.
WP4 contributes to the digital twin development of renewable generation and the system through the creation of novel models driven by data and model-driven techniques. Data incorporates historical data, measurements, simulation, and testing results. The use of digital twin representations during the design and testing phase of components can save time and effort by reducing the reliance on full-scale field tests before market delivery. The project also explores digital representations for power plants and power grids. The achieved results in WP4 have already and will make valuable contributions to the latest standardization efforts concerning Software/Hardware-in-the-loop (S/HiL) test benches for renewable based power systems.
WP5 focuses on developing innovative methods to evaluate the cost-benefit analysis of digital investments. Current methods often struggle to quantify the impacts on physical system operation resulting from new digital investments. Additionally, WP5 examines the environmental sustainability impact of increased digitalization and develops an enabling framework encompassing governance, regulations, and policies necessary to facilitate the energy sector's transformation towards digitalization. Thus far, WP5 has proposed models built upon system dynamics to evaluate different sectors such as distribution grids and offshore wind. It has also identified the key barriers on digitalization for distribution grids through expert interviews.