Pub Date : 2022-09-26DOI: 10.1109/ISC255366.2022.9921934
L. Hadjidemetriou, A. Akrytov, K. Kyriakou, C. D. Charalambous, M. Asprou, I. Ciornei, G. Ellinas, C. Panayiotou
Towards the green transition of modern power systems, a massive deployment of distributed energy resources (DERs) based on renewable sources is required. These DERs are grid integrated through power electronics converters which reduce the commitment of synchronous generators and as a result, the overall inertia of the system. Maintaining the frequency stability in such low-inertia power systems is a crucial aspect for the system operators; therefore, local control schemes are integrated with conventional generators and DERs to ensure fast reaction in case of frequency disturbances so as to preserve system stability. Existing practices for maintaining the frequency stability rely on the response of a local governor controller integrated with the synchronous generators. Some new approaches introduce frequency support services by DERs, by considering the droop control and virtual inertia concept within the inverter local controller. In another approach, wide area control schemes can also be utilized to further enhance the system stability. Such a wide area control scheme is introduced in this work to coordinate the operation of DERs through 5G communication for enabling fast frequency support services. Due to the fast dynamics of a power system, a reliable and high-speed communication is particularly important for the deployment of the proposed method. Therefore, an experimental hardware in the loop setup has been developed to investigate how the communication performance can affect the stability of the power system. Moreover, a benchmarking is implemented to evaluate the frequency stability when local controllers are providing support and when the proposed wide area controller is applied under a different communication infrastructure. Experimental results demonstrate that when a reliable 5G network is used, then a significant stability improvement can be achieved by the proposed wide area controller.
{"title":"Wide Area Control of Distributed Resources through 5G Communication to Provide Frequency Support","authors":"L. Hadjidemetriou, A. Akrytov, K. Kyriakou, C. D. Charalambous, M. Asprou, I. Ciornei, G. Ellinas, C. Panayiotou","doi":"10.1109/ISC255366.2022.9921934","DOIUrl":"https://doi.org/10.1109/ISC255366.2022.9921934","url":null,"abstract":"Towards the green transition of modern power systems, a massive deployment of distributed energy resources (DERs) based on renewable sources is required. These DERs are grid integrated through power electronics converters which reduce the commitment of synchronous generators and as a result, the overall inertia of the system. Maintaining the frequency stability in such low-inertia power systems is a crucial aspect for the system operators; therefore, local control schemes are integrated with conventional generators and DERs to ensure fast reaction in case of frequency disturbances so as to preserve system stability. Existing practices for maintaining the frequency stability rely on the response of a local governor controller integrated with the synchronous generators. Some new approaches introduce frequency support services by DERs, by considering the droop control and virtual inertia concept within the inverter local controller. In another approach, wide area control schemes can also be utilized to further enhance the system stability. Such a wide area control scheme is introduced in this work to coordinate the operation of DERs through 5G communication for enabling fast frequency support services. Due to the fast dynamics of a power system, a reliable and high-speed communication is particularly important for the deployment of the proposed method. Therefore, an experimental hardware in the loop setup has been developed to investigate how the communication performance can affect the stability of the power system. Moreover, a benchmarking is implemented to evaluate the frequency stability when local controllers are providing support and when the proposed wide area controller is applied under a different communication infrastructure. Experimental results demonstrate that when a reliable 5G network is used, then a significant stability improvement can be achieved by the proposed wide area controller.","PeriodicalId":277015,"journal":{"name":"2022 IEEE International Smart Cities Conference (ISC2)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122166894","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 : 2022-09-26DOI: 10.1109/ISC255366.2022.9921991
Muhammad Naveed, Syed Muhammad Usman, Muhammad Islam Satti, Sama Aleshaiker, Aamir Anwar
An intrusion Detection System (IDS) is a system that resides inside the network and monitors all incoming and outgoing traffic. It prevents unethical activities from happening over the network. With the use of IoT devices, network traffic is also increased. Intruders and hackers are attracted to this network because of its low processing power and openness. IoT has transformed diagnostic and monitoring systems for patients in the healthcare industry. However, a secure network is needed for these health care devices. This research proposes a hybrid model to secure the IoT network from external intrusions. The proposed method consists of preprocessing data with the help of normalization and feature selection by removing high correlated features with the help of the Pearson correlation coefficient and Support Vector Machine (SVM) for classification. The proposed approach has achieved an accuracy of 99.3%, precision of 99.1% and an F-1 score of 99.25% on the standard dataset. Results have been compared with state-of-the-art, and the proposed method outperforms all performance measures.
{"title":"Intrusion Detection in Smart IoT Devices for People with Disabilities","authors":"Muhammad Naveed, Syed Muhammad Usman, Muhammad Islam Satti, Sama Aleshaiker, Aamir Anwar","doi":"10.1109/ISC255366.2022.9921991","DOIUrl":"https://doi.org/10.1109/ISC255366.2022.9921991","url":null,"abstract":"An intrusion Detection System (IDS) is a system that resides inside the network and monitors all incoming and outgoing traffic. It prevents unethical activities from happening over the network. With the use of IoT devices, network traffic is also increased. Intruders and hackers are attracted to this network because of its low processing power and openness. IoT has transformed diagnostic and monitoring systems for patients in the healthcare industry. However, a secure network is needed for these health care devices. This research proposes a hybrid model to secure the IoT network from external intrusions. The proposed method consists of preprocessing data with the help of normalization and feature selection by removing high correlated features with the help of the Pearson correlation coefficient and Support Vector Machine (SVM) for classification. The proposed approach has achieved an accuracy of 99.3%, precision of 99.1% and an F-1 score of 99.25% on the standard dataset. Results have been compared with state-of-the-art, and the proposed method outperforms all performance measures.","PeriodicalId":277015,"journal":{"name":"2022 IEEE International Smart Cities Conference (ISC2)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126680334","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 : 2022-09-26DOI: 10.1109/ISC255366.2022.9921948
B. I. Nasution, Irfan Dwiki Bhaswara, Y. Nugraha, J. Kanggrawan
Two years have passed since COVID-19 broke out in Indonesia. In Indonesia, the central and regional governments have used vast amounts of data on COVID-19 patients for policymaking. However, it is clear that privacy problems can arise when people use their data. Thus, it is crucial to keep COVID-19 data private, using synthetic data publishing (SDP). One of the well-known SDP methods is by using deep generative models. This study explores the usage of deep generative models to synthesise COVID-19 individual data. The deep generative models used in this paper are Generative Adversarial Networks (GAN), Adversarial Autoencoders (AAE), and Adversarial Variational Bayes (AVB). This study found that AAE and AVB outperform GAN in loss, distribution, and privacy preservation, mainly when using the Wasserstein approach. Furthermore, the synthetic data produced predictions in the real dataset with sensitivity and an F1 score of more than 0.8. Unfortunately, the synthetic data produced still has drawbacks and biases, especially in conducting statistical models. Therefore, it is essential to improve the deep generative models, especially in maintaining the statistical guarantee of the dataset.
{"title":"Data Analysis and Synthesis of COVID-19 Patients using Deep Generative Models: A Case Study of Jakarta, Indonesia","authors":"B. I. Nasution, Irfan Dwiki Bhaswara, Y. Nugraha, J. Kanggrawan","doi":"10.1109/ISC255366.2022.9921948","DOIUrl":"https://doi.org/10.1109/ISC255366.2022.9921948","url":null,"abstract":"Two years have passed since COVID-19 broke out in Indonesia. In Indonesia, the central and regional governments have used vast amounts of data on COVID-19 patients for policymaking. However, it is clear that privacy problems can arise when people use their data. Thus, it is crucial to keep COVID-19 data private, using synthetic data publishing (SDP). One of the well-known SDP methods is by using deep generative models. This study explores the usage of deep generative models to synthesise COVID-19 individual data. The deep generative models used in this paper are Generative Adversarial Networks (GAN), Adversarial Autoencoders (AAE), and Adversarial Variational Bayes (AVB). This study found that AAE and AVB outperform GAN in loss, distribution, and privacy preservation, mainly when using the Wasserstein approach. Furthermore, the synthetic data produced predictions in the real dataset with sensitivity and an F1 score of more than 0.8. Unfortunately, the synthetic data produced still has drawbacks and biases, especially in conducting statistical models. Therefore, it is essential to improve the deep generative models, especially in maintaining the statistical guarantee of the dataset.","PeriodicalId":277015,"journal":{"name":"2022 IEEE International Smart Cities Conference (ISC2)","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132604074","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 : 2022-09-26DOI: 10.1109/ISC255366.2022.9922177
Adnane Founoun, L. E. Ghazouani, A. Haqiq, A. Hayar, H. Radoine
To provide different services to smart cities, many new approaches were used, including the strategic approach and the techno-human-centric approach. When it combines both the human aspect through its sensitivities and the technical aspect of data management to enable the city's stakeholders to act better. Indeed, a new governance framework is possible through the introduction of human intelligence to answer specific questions. It's about putting citizens at the center of the smart city issue while putting a coalition with new technologies. In this paper, we suggest validation of weather data, temperature, and wind, according to citizens' perceptions through a gaming scenario. The use of this mobile platform will assist stakeholders towards a roadmap for future urban design. This will be done by building up a database of citizens' perceptions and appreciations. Also, the wide range of sites of interest will allow an urban promotion and accompany the cultural life of the city. This will be possible by inviting subscribers to go and visit these places subject to promotions.
{"title":"Bringing human perception to validate weather measurements in Smart City: Human-Techno Centric Approach","authors":"Adnane Founoun, L. E. Ghazouani, A. Haqiq, A. Hayar, H. Radoine","doi":"10.1109/ISC255366.2022.9922177","DOIUrl":"https://doi.org/10.1109/ISC255366.2022.9922177","url":null,"abstract":"To provide different services to smart cities, many new approaches were used, including the strategic approach and the techno-human-centric approach. When it combines both the human aspect through its sensitivities and the technical aspect of data management to enable the city's stakeholders to act better. Indeed, a new governance framework is possible through the introduction of human intelligence to answer specific questions. It's about putting citizens at the center of the smart city issue while putting a coalition with new technologies. In this paper, we suggest validation of weather data, temperature, and wind, according to citizens' perceptions through a gaming scenario. The use of this mobile platform will assist stakeholders towards a roadmap for future urban design. This will be done by building up a database of citizens' perceptions and appreciations. Also, the wide range of sites of interest will allow an urban promotion and accompany the cultural life of the city. This will be possible by inviting subscribers to go and visit these places subject to promotions.","PeriodicalId":277015,"journal":{"name":"2022 IEEE International Smart Cities Conference (ISC2)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129444049","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 : 2022-09-26DOI: 10.1109/ISC255366.2022.9922346
Sheila Maulida Intani, B. I. Nasution, M. E. Aminanto, Y. Nugraha, Nurhaya Muchtar, J. Kanggrawan
The DKI Jakarta provincial government is ready to support the digital transformation program with a series of digitally integrated policies. Residents of DKI Jakarta can now easily submit complaints about problems in their surrounding environment through the JakLapor service feature on the JAKI application. However, incoming reports are still manually classified. As a result, many citizens still report unsuitable complaints based on their category. This research aims to compare and find the best complaint classification model by applying multiple machine learning models to classify texts automatically. We also use feature extraction to see which model performs the best. This study employed Support Vector Machine (SVM), Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Adaptive Boosting (AdaBoost) algorithms as the machine learning model. Meanwhile, we use Count Vectorizer, Terms Frequency-Inverse Document Frequency (TF-IDF), N-Gram, and Latent Semantic Analysis (LSA) as the feature extraction algorithms. The classification results show that the Random Forest method model with TFIDF feature extraction is the most accurate and optimal model among the others, with a 90% accuracy rate.
{"title":"Automating Public Complaint Classification Through JakLapor Channel: A Case Study of Jakarta, Indonesia","authors":"Sheila Maulida Intani, B. I. Nasution, M. E. Aminanto, Y. Nugraha, Nurhaya Muchtar, J. Kanggrawan","doi":"10.1109/ISC255366.2022.9922346","DOIUrl":"https://doi.org/10.1109/ISC255366.2022.9922346","url":null,"abstract":"The DKI Jakarta provincial government is ready to support the digital transformation program with a series of digitally integrated policies. Residents of DKI Jakarta can now easily submit complaints about problems in their surrounding environment through the JakLapor service feature on the JAKI application. However, incoming reports are still manually classified. As a result, many citizens still report unsuitable complaints based on their category. This research aims to compare and find the best complaint classification model by applying multiple machine learning models to classify texts automatically. We also use feature extraction to see which model performs the best. This study employed Support Vector Machine (SVM), Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Adaptive Boosting (AdaBoost) algorithms as the machine learning model. Meanwhile, we use Count Vectorizer, Terms Frequency-Inverse Document Frequency (TF-IDF), N-Gram, and Latent Semantic Analysis (LSA) as the feature extraction algorithms. The classification results show that the Random Forest method model with TFIDF feature extraction is the most accurate and optimal model among the others, with a 90% accuracy rate.","PeriodicalId":277015,"journal":{"name":"2022 IEEE International Smart Cities Conference (ISC2)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133541346","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 : 2022-09-26DOI: 10.1109/ISC255366.2022.9922067
Daniel Mejia, N. Villanueva-Rosales
Data is a crucial factor for monitoring and understanding events related to Smart Cities. Data can be discovered and integrated from different sources and has the potential to be interpreted in multiple ways. Traffic crashes, for example, are common events that occur in cities. A significant amount of historical data related to traffic crashes is publicly available for analysis and can be used by a wide range of stakeholders. Measuring the impact of Smart Cities solutions usually relies on data collection, analysis, and metrics before and after such solutions are implemented. This paper presents an observable data-driven bottom-up methodology to create the Critical Composite Index (CCI), a Key Performance Indicator developed to measure traffic crash severity as a singular value. The CCI can be used by both domain experts and non-domain experts to be informed about traffic crashes on the roadways. This paper the development of the CCI using historical, government agency reported, and publicly accessible traffic crash data. The CCI can be modified or extended to align with specific reporting traffic crash criteria by modifying the weights of traffic crash features. The observable data-driven bottom-up methodology development enables the transformation of raw data into a metric that can contribute to the observability of Smart Cities.
{"title":"Data-Driven Metrics Applied to Traffic Crashes to Improve Observability in Smart Cities","authors":"Daniel Mejia, N. Villanueva-Rosales","doi":"10.1109/ISC255366.2022.9922067","DOIUrl":"https://doi.org/10.1109/ISC255366.2022.9922067","url":null,"abstract":"Data is a crucial factor for monitoring and understanding events related to Smart Cities. Data can be discovered and integrated from different sources and has the potential to be interpreted in multiple ways. Traffic crashes, for example, are common events that occur in cities. A significant amount of historical data related to traffic crashes is publicly available for analysis and can be used by a wide range of stakeholders. Measuring the impact of Smart Cities solutions usually relies on data collection, analysis, and metrics before and after such solutions are implemented. This paper presents an observable data-driven bottom-up methodology to create the Critical Composite Index (CCI), a Key Performance Indicator developed to measure traffic crash severity as a singular value. The CCI can be used by both domain experts and non-domain experts to be informed about traffic crashes on the roadways. This paper the development of the CCI using historical, government agency reported, and publicly accessible traffic crash data. The CCI can be modified or extended to align with specific reporting traffic crash criteria by modifying the weights of traffic crash features. The observable data-driven bottom-up methodology development enables the transformation of raw data into a metric that can contribute to the observability of Smart Cities.","PeriodicalId":277015,"journal":{"name":"2022 IEEE International Smart Cities Conference (ISC2)","volume":"27 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131805771","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 : 2022-09-26DOI: 10.1109/ISC255366.2022.9922102
Nauman Nazar, Iman Darvishi, Abel Yeboah-Ofori
The impact of COVID-19 pandemic affected the whole world leading to threats to the healthcare, economies, governments, and education sectors. During this challenging period, online learning and educational tools such as Zoom, Google Meet, Microsoft Teams, and Cisco Webex gained immense popularity in academic institutions. However, these tools provided vulnerabilities for malicious attackers to exploit these online platforms. That posed a huge cyber threat to the online educational system to continue and survive under such circumstances. The paper aims to explore and analyze the cyber threats to these online learning platforms to understand the security posture and mitigation techniques. The contribution of this paper is threefold: First, we explore the various attacks on online tools such as Zoom, Google Meet, Microsoft Teams, and Cisco Webex and determine how much security and privacy they offer. Secondly, we analyze the encryption's capabilities to assess the level of confidentiality, integrity, and availability they provide to the users and present the results as a table. Finally, we discussed a common vulnerability framework comprising common threats faced by users and the service provider for the mitigation techniques to improve security.
{"title":"Cyber Threat Analysis on Online Learning and Its Mitigation Techniques Amid Covid-19","authors":"Nauman Nazar, Iman Darvishi, Abel Yeboah-Ofori","doi":"10.1109/ISC255366.2022.9922102","DOIUrl":"https://doi.org/10.1109/ISC255366.2022.9922102","url":null,"abstract":"The impact of COVID-19 pandemic affected the whole world leading to threats to the healthcare, economies, governments, and education sectors. During this challenging period, online learning and educational tools such as Zoom, Google Meet, Microsoft Teams, and Cisco Webex gained immense popularity in academic institutions. However, these tools provided vulnerabilities for malicious attackers to exploit these online platforms. That posed a huge cyber threat to the online educational system to continue and survive under such circumstances. The paper aims to explore and analyze the cyber threats to these online learning platforms to understand the security posture and mitigation techniques. The contribution of this paper is threefold: First, we explore the various attacks on online tools such as Zoom, Google Meet, Microsoft Teams, and Cisco Webex and determine how much security and privacy they offer. Secondly, we analyze the encryption's capabilities to assess the level of confidentiality, integrity, and availability they provide to the users and present the results as a table. Finally, we discussed a common vulnerability framework comprising common threats faced by users and the service provider for the mitigation techniques to improve security.","PeriodicalId":277015,"journal":{"name":"2022 IEEE International Smart Cities Conference (ISC2)","volume":"186 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122425865","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 : 2022-09-26DOI: 10.1109/ISC255366.2022.9921923
F. Villanueva, Cristina Bolaños Peño, A. Rubio, Rubén Cantarero, Jesús Fernández-Bermejo Ruiz, Javier Dorado
One challenge of any smart city is the management of crowded events (concerts, protests, marathons, etc.). For civil servants in charge of management, in-advance attendance prevision, real-time situational awareness and its evolution forecasting are crucial to resource assignment. These massive events put under stress public resources, organization and safety of smart cities. In this paper, we describe an ongoing effort to model urban layout, sensors deployed, and citizen information (from social networks and smartphone application) to deal with these situations. We use the concept of a digital twin applied to a city by modelling different flows of information which are integrated with a 3D virtual representation with forecasting possibilities. The main contribution of this paper is the architecture proposed and GUI using the augmented virtuality concept. The main purpose of our proposal is to facilitate the knowledge of the situation and the management of this type of event.
{"title":"Crowded event management in smart cities using a digital twin approach","authors":"F. Villanueva, Cristina Bolaños Peño, A. Rubio, Rubén Cantarero, Jesús Fernández-Bermejo Ruiz, Javier Dorado","doi":"10.1109/ISC255366.2022.9921923","DOIUrl":"https://doi.org/10.1109/ISC255366.2022.9921923","url":null,"abstract":"One challenge of any smart city is the management of crowded events (concerts, protests, marathons, etc.). For civil servants in charge of management, in-advance attendance prevision, real-time situational awareness and its evolution forecasting are crucial to resource assignment. These massive events put under stress public resources, organization and safety of smart cities. In this paper, we describe an ongoing effort to model urban layout, sensors deployed, and citizen information (from social networks and smartphone application) to deal with these situations. We use the concept of a digital twin applied to a city by modelling different flows of information which are integrated with a 3D virtual representation with forecasting possibilities. The main contribution of this paper is the architecture proposed and GUI using the augmented virtuality concept. The main purpose of our proposal is to facilitate the knowledge of the situation and the management of this type of event.","PeriodicalId":277015,"journal":{"name":"2022 IEEE International Smart Cities Conference (ISC2)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115380945","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 : 2022-09-26DOI: 10.1109/ISC255366.2022.9922504
Nafiseh Ghorbani-Renani, Philip Odonkor
In this study, we proposed a mixed-integer linear programming model to determine the optimal trading and operational strategies necessary to enable efficient peer-to-peer (P2P) energy trading and resource utilization within fully cooperative community microgrids. The proposed model considers tiered utility tariffs accounting for (i) the time-of-use (TOU) rate and (ii) the level of cumulative consumption. Given the heterogenous mix of prosumers and consumers common in community microgrids, the proposed model seeks to provide decision support for the optimal utilization of generated electricity by determining if it should be self-consumed, stored for future use, curtailed, or traded with peers. Likewise, the proposed approach determines operational strategies for non-prosumer peers with regards to sourcing electricity to satisfy their respective energy deficits. The model presents a scalable approach for energy cost savings for both prosumers and energy consumers regardless of their role in the peer market. To demonstrate this functionality, we leverage the proposed model to solve for the optimal trading strategy within a 5-building community microgrid. Real-world energy demand and generation data pertinent to 5 households in the New York region was sampled using the Pecan Street Inc. Dataport database. Results were compared to that of a traditional centralized grid model. The results highlight the benefits of P2P market design in comparison with the traditional unidirectional grid model. In addition, the outcomes underline that energy consumers satisfy most of their demand from the P2P market during peak hours to obtain greater cost savings.
在这项研究中，我们提出了一个混合整数线性规划模型，以确定在完全合作的社区微电网中实现高效点对点(P2P)能源交易和资源利用所必需的最佳交易和运营策略。拟议的模型考虑了公用事业分层电价，考虑了(i)分时电价(TOU)费率和(ii)累计消费水平。考虑到社区微电网中常见的产消者和消费者的异质组合，所提出的模型试图通过确定是否应该自行消耗、储存以备将来使用、削减或与同行交易来为发电的最佳利用提供决策支持。同样，所提出的方法确定了非生产消费者同行在采购电力以满足各自能源短缺方面的运营策略。该模型为生产消费者和能源消费者提供了一种可扩展的能源成本节约方法，无论他们在对等市场中的角色如何。为了证明这一功能，我们利用所提出的模型来解决5栋建筑社区微电网内的最佳交易策略。使用Pecan Street Inc.对纽约地区5个家庭的实际能源需求和发电数据进行了抽样。Dataport数据库。结果与传统的集中式网格模型进行了比较。与传统的单向网格模型相比，研究结果突出了P2P市场设计的优势。此外，研究结果强调，能源消费者在高峰时段满足P2P市场的大部分需求，以获得更大的成本节约。
{"title":"An Energy Cost Optimization Model for Electricity Trading in Community Microgrids","authors":"Nafiseh Ghorbani-Renani, Philip Odonkor","doi":"10.1109/ISC255366.2022.9922504","DOIUrl":"https://doi.org/10.1109/ISC255366.2022.9922504","url":null,"abstract":"In this study, we proposed a mixed-integer linear programming model to determine the optimal trading and operational strategies necessary to enable efficient peer-to-peer (P2P) energy trading and resource utilization within fully cooperative community microgrids. The proposed model considers tiered utility tariffs accounting for (i) the time-of-use (TOU) rate and (ii) the level of cumulative consumption. Given the heterogenous mix of prosumers and consumers common in community microgrids, the proposed model seeks to provide decision support for the optimal utilization of generated electricity by determining if it should be self-consumed, stored for future use, curtailed, or traded with peers. Likewise, the proposed approach determines operational strategies for non-prosumer peers with regards to sourcing electricity to satisfy their respective energy deficits. The model presents a scalable approach for energy cost savings for both prosumers and energy consumers regardless of their role in the peer market. To demonstrate this functionality, we leverage the proposed model to solve for the optimal trading strategy within a 5-building community microgrid. Real-world energy demand and generation data pertinent to 5 households in the New York region was sampled using the Pecan Street Inc. Dataport database. Results were compared to that of a traditional centralized grid model. The results highlight the benefits of P2P market design in comparison with the traditional unidirectional grid model. In addition, the outcomes underline that energy consumers satisfy most of their demand from the P2P market during peak hours to obtain greater cost savings.","PeriodicalId":277015,"journal":{"name":"2022 IEEE International Smart Cities Conference (ISC2)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116811267","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 : 2022-09-26DOI: 10.1109/ISC255366.2022.9922293
Chao Su, Xiaomei Wu, Yanming Guo, Chun Sing Lai, Liang Xu, Xuan Zhao
With the increasing scale and complexity of powerline construction, the challenges of powerline system operation and maintenance are gradually increasing. The research and application of unmanned aerial vehicle (UAV) Lidar technology for powerline inspections is developing rapidly. The Lidar point cloud and visible light measurement are processed intelligently by the powerline multi-source and heterogeneous data automatic fusion technology. Then the three-dimensional model of the powerline system and electrical equipment is obtained. Consequently, the efficient resolving of point cloud data for powerlines, identification of equipment locations and types are realized. The fast measurement and elaborating modeling of the three-dimensional system for powerlines is obtained, which may effectively and comprehensively show the operation status of powerlines. The point cloud classification algorithm is adopted in this paper. Experimental results demonstrated that the proposed method performed well in the detection accuracy of identification and classification of lines and pylons in a complex environment. The classification accuracies for transmission lines and distribution lines are 97.26% and 95.29% respectively. The average classification accuracies of both lines and pylons are 80.88% and 82.25%, respectively.
{"title":"Automatic Multi-source Data Fusion Technique of Powerline Corridor using UAV Lidar","authors":"Chao Su, Xiaomei Wu, Yanming Guo, Chun Sing Lai, Liang Xu, Xuan Zhao","doi":"10.1109/ISC255366.2022.9922293","DOIUrl":"https://doi.org/10.1109/ISC255366.2022.9922293","url":null,"abstract":"With the increasing scale and complexity of powerline construction, the challenges of powerline system operation and maintenance are gradually increasing. The research and application of unmanned aerial vehicle (UAV) Lidar technology for powerline inspections is developing rapidly. The Lidar point cloud and visible light measurement are processed intelligently by the powerline multi-source and heterogeneous data automatic fusion technology. Then the three-dimensional model of the powerline system and electrical equipment is obtained. Consequently, the efficient resolving of point cloud data for powerlines, identification of equipment locations and types are realized. The fast measurement and elaborating modeling of the three-dimensional system for powerlines is obtained, which may effectively and comprehensively show the operation status of powerlines. The point cloud classification algorithm is adopted in this paper. Experimental results demonstrated that the proposed method performed well in the detection accuracy of identification and classification of lines and pylons in a complex environment. The classification accuracies for transmission lines and distribution lines are 97.26% and 95.29% respectively. The average classification accuracies of both lines and pylons are 80.88% and 82.25%, respectively.","PeriodicalId":277015,"journal":{"name":"2022 IEEE International Smart Cities Conference (ISC2)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116467021","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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