Pub Date : 2019-07-04DOI: 10.1109/SYSOSE.2019.8753830
M. Packianather, Nury Leon Munizaga, S. Zouwail, M. Saunders
This paper presents a three phase methodology to automate quality control in healthcare clinical laboratory. The first phase consists in the automation of the performance assessment of the equipment in MS Excel. With the smart tools included in Excel, a macro was developed that not only saves the user time and makes the process more efficient, but also gives a clear idea of the quality of the test results. The second phase deals with the quality control management of the generated data through the application of manufacturing techniques; a code in Matlab was created that would allow the user to visualise the current performance of the equipment according to some specified limits in Statistical Process Control (SPC) charts. This enables the user to select the relevant information to visualise by analysing the control levels and dates. In the final phase a prediction algorithm applying data mining and machine learning techniques was developed, based on the historical data, which is used as a small sample of big data that could be potentially generated by the IoT enabled equipment interconnected via the internet enabling them to send and receive data. Using the K-Nearest Neighbour (KNN) classifier a performance accuracy of 94% was achieved which allows the user to predict future behaviour of the equipment.
{"title":"Development of soft computing tools and IoT for improving the performance assessment of analysers in a clinical laboratory","authors":"M. Packianather, Nury Leon Munizaga, S. Zouwail, M. Saunders","doi":"10.1109/SYSOSE.2019.8753830","DOIUrl":"https://doi.org/10.1109/SYSOSE.2019.8753830","url":null,"abstract":"This paper presents a three phase methodology to automate quality control in healthcare clinical laboratory. The first phase consists in the automation of the performance assessment of the equipment in MS Excel. With the smart tools included in Excel, a macro was developed that not only saves the user time and makes the process more efficient, but also gives a clear idea of the quality of the test results. The second phase deals with the quality control management of the generated data through the application of manufacturing techniques; a code in Matlab was created that would allow the user to visualise the current performance of the equipment according to some specified limits in Statistical Process Control (SPC) charts. This enables the user to select the relevant information to visualise by analysing the control levels and dates. In the final phase a prediction algorithm applying data mining and machine learning techniques was developed, based on the historical data, which is used as a small sample of big data that could be potentially generated by the IoT enabled equipment interconnected via the internet enabling them to send and receive data. Using the K-Nearest Neighbour (KNN) classifier a performance accuracy of 94% was achieved which allows the user to predict future behaviour of the equipment.","PeriodicalId":133413,"journal":{"name":"2019 14th Annual Conference System of Systems Engineering (SoSE)","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128912518","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-07-04DOI: 10.1109/SYSOSE.2019.8753828
Katja Schuitemaker, G. M. Bonnema
Risk assessment is mostly document-based at this moment. In this paper, we present a study to compare two ways of integral risk assessment of the Dutch railway departure process. First, a document-based risk assessment about the departure process executed by NS in 2011. Second, a risk assessment about this same departure process with modelling of integral risk assessment (MOIRA). The aim is to measure and compare four key performance indicators: (1) validity of the risk assessment elements, (2) consistency of the data processing, (3) clarity of the boundaries, and (4) comprehension of the overall safety architecture. For this purpose, we have designed experiments that included direct, detailed observations in which we controlled the execution of performing risk assessment tasks. In total, twelve test sessions were performed, with in total 42 participants. Data was collected through the month of January 2019. In the end, we compare the two, to see if there are any objective differences, and also, we validate their usability by surveying the findings of the participants, resulting in a fifth KPI.
{"title":"Modelling Integral Risk Assessment (MOIRA): Experiments on the Dutch Railway Departure Process","authors":"Katja Schuitemaker, G. M. Bonnema","doi":"10.1109/SYSOSE.2019.8753828","DOIUrl":"https://doi.org/10.1109/SYSOSE.2019.8753828","url":null,"abstract":"Risk assessment is mostly document-based at this moment. In this paper, we present a study to compare two ways of integral risk assessment of the Dutch railway departure process. First, a document-based risk assessment about the departure process executed by NS in 2011. Second, a risk assessment about this same departure process with modelling of integral risk assessment (MOIRA). The aim is to measure and compare four key performance indicators: (1) validity of the risk assessment elements, (2) consistency of the data processing, (3) clarity of the boundaries, and (4) comprehension of the overall safety architecture. For this purpose, we have designed experiments that included direct, detailed observations in which we controlled the execution of performing risk assessment tasks. In total, twelve test sessions were performed, with in total 42 participants. Data was collected through the month of January 2019. In the end, we compare the two, to see if there are any objective differences, and also, we validate their usability by surveying the findings of the participants, resulting in a fifth KPI.","PeriodicalId":133413,"journal":{"name":"2019 14th Annual Conference System of Systems Engineering (SoSE)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126678223","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-05-19DOI: 10.1109/SYSOSE.2019.8753841
B. Tekinerdogan
An increasing number of application domains have to deal with system-of-systems (SoS) with the purpose to design or integrate a number of systems to create value that cannot be obtained from single systems alone. Although there seems to be a general understanding of what an SoS is, different types of SoS appear to be needed for different contexts. A number of studies have distinguished these different types of SoS and proposed various classifications. In general, though, each study proposes a classification from a particular, fixed perspective, which as such leads to a limited view of the SoS. This paper proposes a novel multi-dimensional classification approach that builds on and enhances the reported existing classifications. In contrast to previous studies, a multidimensional classification of SoS is presented that has been derived after a thorough domain analysis process. The multidimensional classification is represented as a feature diagram that defines seven perspectives, with a total of 26 different types of SoSs, from which multiple different configuration types can be derived.
{"title":"Multi-Dimensional Classification of System-of-Systems","authors":"B. Tekinerdogan","doi":"10.1109/SYSOSE.2019.8753841","DOIUrl":"https://doi.org/10.1109/SYSOSE.2019.8753841","url":null,"abstract":"An increasing number of application domains have to deal with system-of-systems (SoS) with the purpose to design or integrate a number of systems to create value that cannot be obtained from single systems alone. Although there seems to be a general understanding of what an SoS is, different types of SoS appear to be needed for different contexts. A number of studies have distinguished these different types of SoS and proposed various classifications. In general, though, each study proposes a classification from a particular, fixed perspective, which as such leads to a limited view of the SoS. This paper proposes a novel multi-dimensional classification approach that builds on and enhances the reported existing classifications. In contrast to previous studies, a multidimensional classification of SoS is presented that has been derived after a thorough domain analysis process. The multidimensional classification is represented as a feature diagram that defines seven perspectives, with a total of 26 different types of SoSs, from which multiple different configuration types can be derived.","PeriodicalId":133413,"journal":{"name":"2019 14th Annual Conference System of Systems Engineering (SoSE)","volume":"55 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121002752","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The internet of things (IoT) system integrates heterogeneous systems using centric services to provide a single open solution to process sensor data. During the whole life cycle of IoT systems, developers face the problems of interface management and data interoperability led by increasing complexity. Such problems decrease the efficiency of IoT system development and implementation, such as interface configurations for domain specific systems are difficult if there is not a unified specification. This paper proposed an Open Services for Lifecycle Collaboration (OSLC) approach supporting IoT system development and implementation. The approach integrates domain specific data across the whole lifecycle including development models and sensor data. Moreover, it enables interface management for IoT system development and real-time monitoring for implementations. From the case study, we find an OSLC-based tool, Datalinks, supports data integration and interface management which improves the development efficiency and data interoperability of IoT systems. The integrated data based on OSLC acts the mid-wares of data exchange between physical space and virtual space of IoT system. Moreover, the OSLC-based interfaces are developed based on unified specifications whose reusability is promoted for the future development.
{"title":"An Open Source Lifecycle Collaboration Approach Supporting Internet of Things System Development","authors":"Jinwei Chen, Zhenchao Hu, Jinzhi Lu, Hui-sheng Zhang, Sihan Huang, Martin Törngren","doi":"10.1109/SYSOSE.2019.8753883","DOIUrl":"https://doi.org/10.1109/SYSOSE.2019.8753883","url":null,"abstract":"The internet of things (IoT) system integrates heterogeneous systems using centric services to provide a single open solution to process sensor data. During the whole life cycle of IoT systems, developers face the problems of interface management and data interoperability led by increasing complexity. Such problems decrease the efficiency of IoT system development and implementation, such as interface configurations for domain specific systems are difficult if there is not a unified specification. This paper proposed an Open Services for Lifecycle Collaboration (OSLC) approach supporting IoT system development and implementation. The approach integrates domain specific data across the whole lifecycle including development models and sensor data. Moreover, it enables interface management for IoT system development and real-time monitoring for implementations. From the case study, we find an OSLC-based tool, Datalinks, supports data integration and interface management which improves the development efficiency and data interoperability of IoT systems. The integrated data based on OSLC acts the mid-wares of data exchange between physical space and virtual space of IoT system. Moreover, the OSLC-based interfaces are developed based on unified specifications whose reusability is promoted for the future development.","PeriodicalId":133413,"journal":{"name":"2019 14th Annual Conference System of Systems Engineering (SoSE)","volume":"189 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128994741","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-05-19DOI: 10.1109/SYSOSE.2019.8753845
J. Sleuters, Yonghui Li, J. Verriet, M. Velikova, Richard Doornbos
The behavior of a large-scale distributed IoT system is often hard to verify and validate. The reasons include: 1) the specification is often unclear, ambiguous and incomplete resulting in misunderstandings and undesired behavior. 2) It is almost impossible for a human to reason about the correctness of a system consisting of thousands of components. 3) It is very hard to observe all related components when trying to solve a problem because the system is geographically distributed over large areas. A digital twin capturing the system operational behavior will be of great help to assist a human in detecting behavioral anomalies and reasoning about root-causes. This paper proposes a method to develop digital twins for automated behavior analysis of large-scale distributed IoT systems. We present a real-life use-case of a smart office lighting system for which the method was successfully applied. The developed digital twin was used for anomaly detection and reasoning in a semi-automated root-cause analysis (RCA) approach.
{"title":"A Digital Twin Method for Automated Behavior Analysis of Large-Scale Distributed IoT Systems","authors":"J. Sleuters, Yonghui Li, J. Verriet, M. Velikova, Richard Doornbos","doi":"10.1109/SYSOSE.2019.8753845","DOIUrl":"https://doi.org/10.1109/SYSOSE.2019.8753845","url":null,"abstract":"The behavior of a large-scale distributed IoT system is often hard to verify and validate. The reasons include: 1) the specification is often unclear, ambiguous and incomplete resulting in misunderstandings and undesired behavior. 2) It is almost impossible for a human to reason about the correctness of a system consisting of thousands of components. 3) It is very hard to observe all related components when trying to solve a problem because the system is geographically distributed over large areas. A digital twin capturing the system operational behavior will be of great help to assist a human in detecting behavioral anomalies and reasoning about root-causes. This paper proposes a method to develop digital twins for automated behavior analysis of large-scale distributed IoT systems. We present a real-life use-case of a smart office lighting system for which the method was successfully applied. The developed digital twin was used for anomaly detection and reasoning in a semi-automated root-cause analysis (RCA) approach.","PeriodicalId":133413,"journal":{"name":"2019 14th Annual Conference System of Systems Engineering (SoSE)","volume":"89 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115329063","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-05-19DOI: 10.1109/SYSOSE.2019.8753842
F. Oquendo
A challenging issue in the architectural design of a System-of-Systems (SoS) is how to cope with the uncertainty raised by the limited knowledge of the operational environment where the SoS will actually be deployed as well as the constituent systems which will concretely participate in the SoS at run-time. It is especially the case of SoSs being architected on the Internet-of-Things (IoT). Indeed, due to the open and dynamic nature of the IoT, on the one hand, at design-time, most often the SoS architects do not know which will be the concrete IoT systems that will become constituents of an SoS, these being predominantly identified at run-time; on the other hand, the correct architecture depends not only on the constituent IoT systems but also, largely, on the operational environment where the SoS will be positioned on the IoT. The consequent research question is thereby how to design and describe the SoS architecture in a way that is flexible enough to cope with these different uncertainties. To address this challenge, this paper investigates the notion of uncertainty in SoS and presents how SosADL, a formal SoS Architecture Description Language (ADL), enables to cope with uncertainty in the architecture modeling of SoSs. It presents the concepts and constructs that makes possible to describe SoS architectures which will operate in unpredictable environments on the IoT based on the SosADL support for dealing with partial knowledge, grounded on concurrent constraints.
{"title":"Coping with Uncertainty in Systems-of-Systems Architecture Modeling on the IoT with SosADL","authors":"F. Oquendo","doi":"10.1109/SYSOSE.2019.8753842","DOIUrl":"https://doi.org/10.1109/SYSOSE.2019.8753842","url":null,"abstract":"A challenging issue in the architectural design of a System-of-Systems (SoS) is how to cope with the uncertainty raised by the limited knowledge of the operational environment where the SoS will actually be deployed as well as the constituent systems which will concretely participate in the SoS at run-time. It is especially the case of SoSs being architected on the Internet-of-Things (IoT). Indeed, due to the open and dynamic nature of the IoT, on the one hand, at design-time, most often the SoS architects do not know which will be the concrete IoT systems that will become constituents of an SoS, these being predominantly identified at run-time; on the other hand, the correct architecture depends not only on the constituent IoT systems but also, largely, on the operational environment where the SoS will be positioned on the IoT. The consequent research question is thereby how to design and describe the SoS architecture in a way that is flexible enough to cope with these different uncertainties. To address this challenge, this paper investigates the notion of uncertainty in SoS and presents how SosADL, a formal SoS Architecture Description Language (ADL), enables to cope with uncertainty in the architecture modeling of SoSs. It presents the concepts and constructs that makes possible to describe SoS architectures which will operate in unpredictable environments on the IoT based on the SosADL support for dealing with partial knowledge, grounded on concurrent constraints.","PeriodicalId":133413,"journal":{"name":"2019 14th Annual Conference System of Systems Engineering (SoSE)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122274231","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-05-19DOI: 10.1109/SYSOSE.2019.8753859
Siv Engen, M. Mansouri, G. Muller
The oil and gas industry are continuously performing development projects to improve their offerings. In this industry the typical input to the development project is high-level business requirement mixed with specific solutions. The stakeholders setting the requirements and dictating the solutions are often more concerned with the business needs and are lacking a holistic view of how this affect the technical systems solution through its life cycle. In this paper, we apply systems thinking to understand the impact of the high-level business requirements and gain a deeper insight in how to tackle them to meet the target of the project. We apply systematic analysis to an actual development project in a supplier company to understand the problem and the forces acting on it. Using a systemigram we model the problem and then apply the system principle of openness to understand what parts of the problem we can affect. Based on the analysis we propose three focus areas for the project development to move forward and meet the project target
{"title":"Application of system thinking to frame the problem in a subsea development projects with high-level business requirements","authors":"Siv Engen, M. Mansouri, G. Muller","doi":"10.1109/SYSOSE.2019.8753859","DOIUrl":"https://doi.org/10.1109/SYSOSE.2019.8753859","url":null,"abstract":"The oil and gas industry are continuously performing development projects to improve their offerings. In this industry the typical input to the development project is high-level business requirement mixed with specific solutions. The stakeholders setting the requirements and dictating the solutions are often more concerned with the business needs and are lacking a holistic view of how this affect the technical systems solution through its life cycle. In this paper, we apply systems thinking to understand the impact of the high-level business requirements and gain a deeper insight in how to tackle them to meet the target of the project. We apply systematic analysis to an actual development project in a supplier company to understand the problem and the forces acting on it. Using a systemigram we model the problem and then apply the system principle of openness to understand what parts of the problem we can affect. Based on the analysis we propose three focus areas for the project development to move forward and meet the project target","PeriodicalId":133413,"journal":{"name":"2019 14th Annual Conference System of Systems Engineering (SoSE)","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125489066","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-05-19DOI: 10.1109/SYSOSE.2019.8753868
Leonie Hallo, B. Payne, A. Gorod
The complexity of modern engineered systems is growing quickly due to the rapidly evolving nature of today's environment. Current management practices are limited in their structural capacity for coping with such swift transformation, pointing to an urgent need for new approaches in management of complex engineered systems. The model-based approach has emerged as a new promising method to complexity management. However, it is still unclear what role simulation plays in this approach and whether the role differs based on the type of a system under consideration and/or the degree of complexity involved. In this paper, the authors conduct a comparative analysis between the disciplines of System Engineering (SE) and System of Systems Engineering (SoSE), and the significance of simulation in these areas. The paper concludes that simulation represents a major part of modeling of System of Systems (SoS), leading the authors to introduce a new term, Modeling and Simulation Based System of Systems Engineering (M&SBSoSE), to underline the importance of incorporating and integrating simulation into modeling applications in SoSE. It is further also proposed that simulation of SoS should be multi-paradigm to increase the scope of simulation effectiveness in a complex dynamic environment.
{"title":"Model-Based Approach to System of Systems Engineering: Reevaluating the Role of Simulation","authors":"Leonie Hallo, B. Payne, A. Gorod","doi":"10.1109/SYSOSE.2019.8753868","DOIUrl":"https://doi.org/10.1109/SYSOSE.2019.8753868","url":null,"abstract":"The complexity of modern engineered systems is growing quickly due to the rapidly evolving nature of today's environment. Current management practices are limited in their structural capacity for coping with such swift transformation, pointing to an urgent need for new approaches in management of complex engineered systems. The model-based approach has emerged as a new promising method to complexity management. However, it is still unclear what role simulation plays in this approach and whether the role differs based on the type of a system under consideration and/or the degree of complexity involved. In this paper, the authors conduct a comparative analysis between the disciplines of System Engineering (SE) and System of Systems Engineering (SoSE), and the significance of simulation in these areas. The paper concludes that simulation represents a major part of modeling of System of Systems (SoS), leading the authors to introduce a new term, Modeling and Simulation Based System of Systems Engineering (M&SBSoSE), to underline the importance of incorporating and integrating simulation into modeling applications in SoSE. It is further also proposed that simulation of SoS should be multi-paradigm to increase the scope of simulation effectiveness in a complex dynamic environment.","PeriodicalId":133413,"journal":{"name":"2019 14th Annual Conference System of Systems Engineering (SoSE)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114605550","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-05-19DOI: 10.1109/SYSOSE.2019.8753846
J. Axelsson
In the field of systems-of-systems (SoS) engineering, there is broad agreement on a few characterizations, and these are currently being standardized. However, many aspects in the field still lack an established terminology. In particular, there are unclarities related to the internal structure of an SoS, and on the internal states of constituent systems. In this paper, a refined terminology is therefore proposed, which covers the internal substructure of an SoS; the states of constituent systems in relation to those substructures; and how it relates to hierarchical levels. This terminology can also be used to classify the characteristics of an SoS through different metrics. The terminology is illustrated through three examples of SoS applications in various domains.
{"title":"A Refined Terminology on System-of-Systems Substructure and Constituent System States","authors":"J. Axelsson","doi":"10.1109/SYSOSE.2019.8753846","DOIUrl":"https://doi.org/10.1109/SYSOSE.2019.8753846","url":null,"abstract":"In the field of systems-of-systems (SoS) engineering, there is broad agreement on a few characterizations, and these are currently being standardized. However, many aspects in the field still lack an established terminology. In particular, there are unclarities related to the internal structure of an SoS, and on the internal states of constituent systems. In this paper, a refined terminology is therefore proposed, which covers the internal substructure of an SoS; the states of constituent systems in relation to those substructures; and how it relates to hierarchical levels. This terminology can also be used to classify the characteristics of an SoS through different metrics. The terminology is illustrated through three examples of SoS applications in various domains.","PeriodicalId":133413,"journal":{"name":"2019 14th Annual Conference System of Systems Engineering (SoSE)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133182375","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-05-19DOI: 10.1109/SYSOSE.2019.8753861
C. Binder, Jounes-Alexander Gross, C. Neureiter, G. Lastro
Ahstract- Today's electricity system faces major challenges by the ongoing integration of decentralized, renewable energy resources and individual participants benefitting from the Internet of Things (IoT), like Electric Vehicles (EVs) or Smart Meters. The interplay of these autonomous components forms the popular term of the so-called Smart Grid. Since social mannerism may result in simultaneous charging cycles of EVs in such a System of Systems (SoS), ominous peak loads are expected to emerge. Thus, to deal with this often unpredictable behavior before implementing the system, usually a simulation is applied. Therefore, this paper proposes a co-simulation approach using Mosaik, a framework tailored to the Smart Grid domain. By doing so, the power system including several EVs and their charging strategy is modeled according to the Smart Grid Architecture Model (SGAM) in the first step. Next, in order to simulate and validate the system's emergent behavior, an excerpt of a real-world case study is utilized. Based on the outcome of this co-simulation, the practical investigation of Smart Grids can be improved by applying protruded demand side response approaches.
摘要:当今的电力系统面临着分散的、可再生能源的持续整合以及从物联网(IoT)(如电动汽车(ev)或智能电表)中受益的个人参与者的重大挑战。这些自主组件的相互作用形成了所谓的智能电网的流行术语。由于社会行为可能导致电动汽车在这种“系统的系统”(System of Systems, SoS)中同时充电，因此预计会出现不祥的峰值负荷。因此，为了在实现系统之前处理这种经常不可预测的行为，通常会应用模拟。因此，本文提出了一种使用Mosaik(一种为智能电网领域量身定制的框架)的联合仿真方法。在此基础上，根据智能电网体系结构模型(Smart Grid Architecture Model, SGAM)对包含多辆电动汽车的电力系统及其充电策略进行建模。接下来，为了模拟和验证系统的紧急行为，使用了现实世界案例研究的摘录。基于该联合仿真的结果，可以通过应用需求侧突出响应方法来改进智能电网的实际研究。
{"title":"Investigating Emergent Behavior caused by Electric Vehicles in the Smart Grid using Co-Simulation","authors":"C. Binder, Jounes-Alexander Gross, C. Neureiter, G. Lastro","doi":"10.1109/SYSOSE.2019.8753861","DOIUrl":"https://doi.org/10.1109/SYSOSE.2019.8753861","url":null,"abstract":"Ahstract- Today's electricity system faces major challenges by the ongoing integration of decentralized, renewable energy resources and individual participants benefitting from the Internet of Things (IoT), like Electric Vehicles (EVs) or Smart Meters. The interplay of these autonomous components forms the popular term of the so-called Smart Grid. Since social mannerism may result in simultaneous charging cycles of EVs in such a System of Systems (SoS), ominous peak loads are expected to emerge. Thus, to deal with this often unpredictable behavior before implementing the system, usually a simulation is applied. Therefore, this paper proposes a co-simulation approach using Mosaik, a framework tailored to the Smart Grid domain. By doing so, the power system including several EVs and their charging strategy is modeled according to the Smart Grid Architecture Model (SGAM) in the first step. Next, in order to simulate and validate the system's emergent behavior, an excerpt of a real-world case study is utilized. Based on the outcome of this co-simulation, the practical investigation of Smart Grids can be improved by applying protruded demand side response approaches.","PeriodicalId":133413,"journal":{"name":"2019 14th Annual Conference System of Systems Engineering (SoSE)","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134478008","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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