Pub Date : 2020-12-16DOI: 10.1109/SGC52076.2020.9335746
M. R. Dehbozorgi, Mohammad Rastegar
Smart grid has been introduced to address power distribution system challenges. In conventional power distribution systems, when a power outage happens, the maintenance team tries to find the outage cause and mitigate it. After this, some information is documented in a dataset called outage dataset. If the team can estimate the outage cause before searching for it, the restoration time will be reduced. In line with smart grid concepts, an association rule-based method is presented in this paper to find the outage cause. To do this, we have first combined outage, load, and weather datasets and extracted features. Then, for every cause, the records are labelled main class or others. The association rules are extracted and evaluated. Through these rules, one can determine if the outage has happened because of a fault in a certain piece of equipment or not. Doing so alongside using smart devices may lead to reliability enhancement.
{"title":"Association rule mining application to diagnose smart distribution power system outage root cause","authors":"M. R. Dehbozorgi, Mohammad Rastegar","doi":"10.1109/SGC52076.2020.9335746","DOIUrl":"https://doi.org/10.1109/SGC52076.2020.9335746","url":null,"abstract":"Smart grid has been introduced to address power distribution system challenges. In conventional power distribution systems, when a power outage happens, the maintenance team tries to find the outage cause and mitigate it. After this, some information is documented in a dataset called outage dataset. If the team can estimate the outage cause before searching for it, the restoration time will be reduced. In line with smart grid concepts, an association rule-based method is presented in this paper to find the outage cause. To do this, we have first combined outage, load, and weather datasets and extracted features. Then, for every cause, the records are labelled main class or others. The association rules are extracted and evaluated. Through these rules, one can determine if the outage has happened because of a fault in a certain piece of equipment or not. Doing so alongside using smart devices may lead to reliability enhancement.","PeriodicalId":391511,"journal":{"name":"2020 10th Smart Grid Conference (SGC)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134183373","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 : 2020-12-16DOI: 10.1109/sgc52076.2020.9335730
Sara Ostovar, M. Moeini‐Aghtaie, M. Hadi
Providing sufficient flexibility in modern power systems has become an unavoidable feature of sustainable energy systems. This flexibility should be supplied by different sectors and players of power systems. The role of demand especially prosumers in providing flexibility have recently been highlighted. In this regard, this paper presents a new flexibility-based algorithm for a Home Energy Management System (HEMS). The HEMS focuses on Electric Vehicles (EVs) and Electrical Energy Storage (EES) as main sources of flexibility products. Two main products, namely, positive and negative flexibility are defined as deviations from optimal energy schedules attained by the HEMS. Each flexibility’ offer related to the device is offered at a specific price in the flexibility platform. Final flexibility plans are extracted after running a linear optimization model. It has been shown that these products can properly enhance the flexibility level of distribution systems and simultaneously impose no additional cost for prosumers.
{"title":"Developing a New Flexibility-Based Algorithm for Home Energy Management System (HEMS)","authors":"Sara Ostovar, M. Moeini‐Aghtaie, M. Hadi","doi":"10.1109/sgc52076.2020.9335730","DOIUrl":"https://doi.org/10.1109/sgc52076.2020.9335730","url":null,"abstract":"Providing sufficient flexibility in modern power systems has become an unavoidable feature of sustainable energy systems. This flexibility should be supplied by different sectors and players of power systems. The role of demand especially prosumers in providing flexibility have recently been highlighted. In this regard, this paper presents a new flexibility-based algorithm for a Home Energy Management System (HEMS). The HEMS focuses on Electric Vehicles (EVs) and Electrical Energy Storage (EES) as main sources of flexibility products. Two main products, namely, positive and negative flexibility are defined as deviations from optimal energy schedules attained by the HEMS. Each flexibility’ offer related to the device is offered at a specific price in the flexibility platform. Final flexibility plans are extracted after running a linear optimization model. It has been shown that these products can properly enhance the flexibility level of distribution systems and simultaneously impose no additional cost for prosumers.","PeriodicalId":391511,"journal":{"name":"2020 10th Smart Grid Conference (SGC)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134641330","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 : 2020-12-16DOI: 10.1109/SGC52076.2020.9335774
Hamed Delkhosh, M. P. Moghaddam, M. Ghaedi
Continuous load changes due to customers' new lifestyle and generation uncertainty arising from renewable energy resources will make load balancing a problem in the future grid. Energy Storage Systems (ESSs) can be a solution to this issue but using them in a distribution network needs considerable investment. On the other hand, capacitors can enhance the voltage profile, reduce the power loss of the distribution network, and due to cheapness, reduce the total system cost. This paper presents a model for the optimal sizing problem of ESSs and capacitors in a distribution network. The model is multi-objective and formulated as a mixed-integer non-linear programming problem. The optimization problem is solved to minimize the total system cost and enhance the voltage profile using the Genetic Algorithm (GA). The simulation results on the IEEE 33-bus network show the effectiveness of this method.
{"title":"Multi-Objective Sizing of Energy Storage Systems (ESSs) and Capacitors in a Distribution System","authors":"Hamed Delkhosh, M. P. Moghaddam, M. Ghaedi","doi":"10.1109/SGC52076.2020.9335774","DOIUrl":"https://doi.org/10.1109/SGC52076.2020.9335774","url":null,"abstract":"Continuous load changes due to customers' new lifestyle and generation uncertainty arising from renewable energy resources will make load balancing a problem in the future grid. Energy Storage Systems (ESSs) can be a solution to this issue but using them in a distribution network needs considerable investment. On the other hand, capacitors can enhance the voltage profile, reduce the power loss of the distribution network, and due to cheapness, reduce the total system cost. This paper presents a model for the optimal sizing problem of ESSs and capacitors in a distribution network. The model is multi-objective and formulated as a mixed-integer non-linear programming problem. The optimization problem is solved to minimize the total system cost and enhance the voltage profile using the Genetic Algorithm (GA). The simulation results on the IEEE 33-bus network show the effectiveness of this method.","PeriodicalId":391511,"journal":{"name":"2020 10th Smart Grid Conference (SGC)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131101608","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 : 2020-12-16DOI: 10.1109/SGC52076.2020.9335762
Javid Majidi Chaharmahali, Morteza Shabanzadeh
Due to the growing integration of renewable generations into power networks and the operational challenges made by their volatility and variability, one major concern of power system and market operators to provide the security of the network and to efficiently run the day-ahead and the balancing markets is an accurate load prediction in a short-term horizon. So as to reach this goal, in this research, different data-clustering methods based on machine-learning algorithms are tested whereby a hybrid forecasting engine composed of a search technique called Symbiotic Organisms Search (SOS) and Levenberg-Marquardt learning algorithm is proposed, aiming to increase the precision of short-term forecasting. In this approach, the adjustable weighting coefficients of the Artificial Neural Network (ANN) can be automatically fine-tuned using SOS. The efficiency and applicability of the proposed engine are analyzed and illustrated by its implementation on the EUNITE competition test case and thereby relevant conclusions are drawn to suitably demonstrate the merits of the proposed method.
{"title":"A Machine Learning-Assisted Clustering Engine to Enhance the Accuracy of Hourly Load Forecasting","authors":"Javid Majidi Chaharmahali, Morteza Shabanzadeh","doi":"10.1109/SGC52076.2020.9335762","DOIUrl":"https://doi.org/10.1109/SGC52076.2020.9335762","url":null,"abstract":"Due to the growing integration of renewable generations into power networks and the operational challenges made by their volatility and variability, one major concern of power system and market operators to provide the security of the network and to efficiently run the day-ahead and the balancing markets is an accurate load prediction in a short-term horizon. So as to reach this goal, in this research, different data-clustering methods based on machine-learning algorithms are tested whereby a hybrid forecasting engine composed of a search technique called Symbiotic Organisms Search (SOS) and Levenberg-Marquardt learning algorithm is proposed, aiming to increase the precision of short-term forecasting. In this approach, the adjustable weighting coefficients of the Artificial Neural Network (ANN) can be automatically fine-tuned using SOS. The efficiency and applicability of the proposed engine are analyzed and illustrated by its implementation on the EUNITE competition test case and thereby relevant conclusions are drawn to suitably demonstrate the merits of the proposed method.","PeriodicalId":391511,"journal":{"name":"2020 10th Smart Grid Conference (SGC)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133602224","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 : 2020-12-16DOI: 10.1109/SGC52076.2020.9335738
Ali Bannae Razavi, Mohammad Mahdi Borhan Elmi
Fluctuations in the output power of Photovoltaic (PV) systems at high penetration levels in distribution system lead to problems such as voltage imbalance, overheating, and reverse power dissipation. In this paper, the voltage profile level for a part of the Mashhad's residential distribution system has been simulated. First, real energy consumption data in the residential sector with a time step of one hour is calculated. In order to investigate the impact of different penetrations of different solar systems, generating power of single-phase PV panels with capacities of 6 and 10 kVA was extracted. To improve the voltage profile, adjusting the power factor of solar inverters and automatic control of transformer tap changers have been used. According to the obtained results, by applying the mentioned control methods, not only can the voltage profile be reduced by more than 7%, but also its stability can be better by reducing the network imbalance.
{"title":"Improvement of Voltage Profiles in Mashhad Distribution Systemwith Presence of Rooftop PV","authors":"Ali Bannae Razavi, Mohammad Mahdi Borhan Elmi","doi":"10.1109/SGC52076.2020.9335738","DOIUrl":"https://doi.org/10.1109/SGC52076.2020.9335738","url":null,"abstract":"Fluctuations in the output power of Photovoltaic (PV) systems at high penetration levels in distribution system lead to problems such as voltage imbalance, overheating, and reverse power dissipation. In this paper, the voltage profile level for a part of the Mashhad's residential distribution system has been simulated. First, real energy consumption data in the residential sector with a time step of one hour is calculated. In order to investigate the impact of different penetrations of different solar systems, generating power of single-phase PV panels with capacities of 6 and 10 kVA was extracted. To improve the voltage profile, adjusting the power factor of solar inverters and automatic control of transformer tap changers have been used. According to the obtained results, by applying the mentioned control methods, not only can the voltage profile be reduced by more than 7%, but also its stability can be better by reducing the network imbalance.","PeriodicalId":391511,"journal":{"name":"2020 10th Smart Grid Conference (SGC)","volume":"170 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123193179","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 : 2020-12-16DOI: 10.1109/SGC52076.2020.9335747
Jamal Faraji, A. Ketabi, H. Hashemi‐Dezaki
Renewable energy sources (RESs) are playing a significant role in the optimal operation of residential prosumers. Uncertainty of weather parameters such as solar irradiance and wind speed can affect the output power of RESs and consequently day-ahead operation of prosumers. Therefore, in this study, a new energy management system (EMS) has been proposed to mitigate fluctuations of RESs by proposing a 2-Level corrective weather forecasting method based on multilayer perceptron artificial neural networks (MLP-ANN). Forecasted data of the proposed method is applied to a peer-to-peer (P2P) prosumer environment. And the effects of weather uncertainty on operation cost and operational decisions of the residential prosumer are investigated. Simulation results indicate the effectiveness of the suggested data-driven method for weather prediction in the optimization of the prosumer.
{"title":"Developing an Energy Management System for Optimal Operation of Prosumers Based on a Modified Data-Driven Weather Forecasting Method","authors":"Jamal Faraji, A. Ketabi, H. Hashemi‐Dezaki","doi":"10.1109/SGC52076.2020.9335747","DOIUrl":"https://doi.org/10.1109/SGC52076.2020.9335747","url":null,"abstract":"Renewable energy sources (RESs) are playing a significant role in the optimal operation of residential prosumers. Uncertainty of weather parameters such as solar irradiance and wind speed can affect the output power of RESs and consequently day-ahead operation of prosumers. Therefore, in this study, a new energy management system (EMS) has been proposed to mitigate fluctuations of RESs by proposing a 2-Level corrective weather forecasting method based on multilayer perceptron artificial neural networks (MLP-ANN). Forecasted data of the proposed method is applied to a peer-to-peer (P2P) prosumer environment. And the effects of weather uncertainty on operation cost and operational decisions of the residential prosumer are investigated. Simulation results indicate the effectiveness of the suggested data-driven method for weather prediction in the optimization of the prosumer.","PeriodicalId":391511,"journal":{"name":"2020 10th Smart Grid Conference (SGC)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114867468","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 : 2020-12-16DOI: 10.1109/SGC52076.2020.9335739
Seyyed Mohammad Sadegh Ghiasi, M. Nazari, S. Hosseinian, M. B. Sanjareh, M. Moradi
Among the various structures presented for inverters in photovoltaic systems, multilevel inverters are the key structure due to their numerous advantages over other inverters, are now widely used in photovoltaic systems. However, a variety of inverters makes different choices for users of photovoltaic systems, that each has its own advantages and disadvantages. Therefore, it seems necessary to study multilevel inverters and their performance. In this paper, by studying different structures of multilevel inverters, the optimal structure in terms of structural complexity, electrical stresses on various components, cost, weight, losses and THD harmonic index for photovoltaic systems is presented. In order to identify the optimal structure, the most common multilevel inverters are compared and two main categories of inverter related indices and indices related to their performance have been studied. Matlab software is used to perform the simulations. The structures investigated are neutral-point-clamped multilevel inverters (NPC-MLI), flying capacitor multilevel inverter (FC-MLI) and cascade H bridge multilevel inverter (CHB-MLI).
{"title":"Techno-Economic Comparison of Multilevel Inverters and Determining the Optimal Structure for using in Solar Plants","authors":"Seyyed Mohammad Sadegh Ghiasi, M. Nazari, S. Hosseinian, M. B. Sanjareh, M. Moradi","doi":"10.1109/SGC52076.2020.9335739","DOIUrl":"https://doi.org/10.1109/SGC52076.2020.9335739","url":null,"abstract":"Among the various structures presented for inverters in photovoltaic systems, multilevel inverters are the key structure due to their numerous advantages over other inverters, are now widely used in photovoltaic systems. However, a variety of inverters makes different choices for users of photovoltaic systems, that each has its own advantages and disadvantages. Therefore, it seems necessary to study multilevel inverters and their performance. In this paper, by studying different structures of multilevel inverters, the optimal structure in terms of structural complexity, electrical stresses on various components, cost, weight, losses and THD harmonic index for photovoltaic systems is presented. In order to identify the optimal structure, the most common multilevel inverters are compared and two main categories of inverter related indices and indices related to their performance have been studied. Matlab software is used to perform the simulations. The structures investigated are neutral-point-clamped multilevel inverters (NPC-MLI), flying capacitor multilevel inverter (FC-MLI) and cascade H bridge multilevel inverter (CHB-MLI).","PeriodicalId":391511,"journal":{"name":"2020 10th Smart Grid Conference (SGC)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128926299","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 : 2020-12-16DOI: 10.1109/SGC52076.2020.9335767
M. Shahraeini, P. Kotzanikolaou
Wide Area Measurement Systems (WAMS) enable the real time monitoring and control of smart grids by combining digital measurement devices, communication and control systems. As WAMS consist of various interdependent infrastructures, they imply complex cyber, physical and geographical dependencies among their underlying components. Although several existing works have studied these types of dependencies independently, there is a lack of a holistic approach that can simultaneously capture all different dependency types. The main goal of this paper is to examine the overall dependencies of WAMS infrastructures. We present preliminary results based on simple and well defined set of rules, that unifies cyber, physical and geographical dependencies of WAMS. Such a unified approach may support the design of WAMS infrastructures that are inherently more resilient to disruptions caused by different kinds of unwanted events such as cyber attacks (affecting cyber dependencies), faults (affecting physical dependencies) or natural disasters that may affect geographically dependent WAMS components.
{"title":"Towards an Unified Dependency Analysis Methodology for Wide Area Measurement Systems in Smart Grids","authors":"M. Shahraeini, P. Kotzanikolaou","doi":"10.1109/SGC52076.2020.9335767","DOIUrl":"https://doi.org/10.1109/SGC52076.2020.9335767","url":null,"abstract":"Wide Area Measurement Systems (WAMS) enable the real time monitoring and control of smart grids by combining digital measurement devices, communication and control systems. As WAMS consist of various interdependent infrastructures, they imply complex cyber, physical and geographical dependencies among their underlying components. Although several existing works have studied these types of dependencies independently, there is a lack of a holistic approach that can simultaneously capture all different dependency types. The main goal of this paper is to examine the overall dependencies of WAMS infrastructures. We present preliminary results based on simple and well defined set of rules, that unifies cyber, physical and geographical dependencies of WAMS. Such a unified approach may support the design of WAMS infrastructures that are inherently more resilient to disruptions caused by different kinds of unwanted events such as cyber attacks (affecting cyber dependencies), faults (affecting physical dependencies) or natural disasters that may affect geographically dependent WAMS components.","PeriodicalId":391511,"journal":{"name":"2020 10th Smart Grid Conference (SGC)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124191257","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 : 2020-12-16DOI: 10.1109/SGC52076.2020.9335761
Abolfazl Najar, H. Karegar, Saman Esmaeilbeigi
Microgrids operation mode automatically changes to islanded-mode in case of major disturbances in the main grid. Detection of unintentional islanding events is a significant challenge for microgrid operators. This paper proposes an efficient scheme for islanding detection of the microgrid. The proposed scheme is based on distributed generations (DGs) parameters. DGs parameters have been used as input of the proposed scheme. Discrete wavelet transform (DWT) is used to extract the hidden features of DGs parameters. Hidden features are the input of a deep neural network (DNN). The proposed scheme has significant accuracy in islanding detection, and it could be applied to different microgrids. We used three software to detect islanding events. First, DIg SILENT Power Factory is used to simulate operation cases of the microgrid. Then we used MATLAB for applying DWT to the measured parameters of DGs. Finally, we used a DNN created by TensorFlow as a machine learning technique.
当主电网发生重大扰动时，微电网自动转为孤岛运行模式。对于微电网运营商来说，检测无意的孤岛事件是一个重大挑战。本文提出了一种有效的微电网孤岛检测方案。该方案基于分布式代(dg)参数。DGs参数被用作该方案的输入。采用离散小波变换(DWT)提取dg参数的隐藏特征。隐藏特征是深度神经网络(DNN)的输入。该方法具有较高的孤岛检测精度，可应用于不同的微电网。我们使用了三种软件来检测孤岛事件。首先，利用DIg SILENT Power Factory对微网运行案例进行仿真。然后利用MATLAB对dg的实测参数进行DWT处理。最后，我们使用TensorFlow创建的DNN作为机器学习技术。
{"title":"Intelligent Islanding Detection Scheme for Microgrid Based on Deep Learning and Wavelet Transform","authors":"Abolfazl Najar, H. Karegar, Saman Esmaeilbeigi","doi":"10.1109/SGC52076.2020.9335761","DOIUrl":"https://doi.org/10.1109/SGC52076.2020.9335761","url":null,"abstract":"Microgrids operation mode automatically changes to islanded-mode in case of major disturbances in the main grid. Detection of unintentional islanding events is a significant challenge for microgrid operators. This paper proposes an efficient scheme for islanding detection of the microgrid. The proposed scheme is based on distributed generations (DGs) parameters. DGs parameters have been used as input of the proposed scheme. Discrete wavelet transform (DWT) is used to extract the hidden features of DGs parameters. Hidden features are the input of a deep neural network (DNN). The proposed scheme has significant accuracy in islanding detection, and it could be applied to different microgrids. We used three software to detect islanding events. First, DIg SILENT Power Factory is used to simulate operation cases of the microgrid. Then we used MATLAB for applying DWT to the measured parameters of DGs. Finally, we used a DNN created by TensorFlow as a machine learning technique.","PeriodicalId":391511,"journal":{"name":"2020 10th Smart Grid Conference (SGC)","volume":"94 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127909798","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 : 2020-12-16DOI: 10.1109/SGC52076.2020.9335758
M. Rajabinezhad, Arman Ghaderi Baayeh, J. Guerrero
In traditional low voltage networks, energy storage systems are mainly used by consumers to protect against short-term power outages. With the advent of microgrids and the expansion of distributed generation resources and also, the development of energy storage system technology, the role of energy storages are increasing. By the proposed control system, the battery energy storage system (BESS) participates in the microgrid energy management and also plays a role in voltage regulation and compensating for current harmonic, unbalance, and reactive power due to the presence of nonlinear loads. In this paper, fuzzy logic-based energy management is proposed, which considers the battery state of charge (SOC), matching between load and wind turbine unit power curves, and the cost of purchased or injected electricity into the grid. In the proposed control system, the limitation of inverter in reactive power injection and the battery's protection from over-charge and over-discharge is also considered. To validate the application of the proposed control system, simulations are performed in MATLAB/Simulink software. The simulation results confirm and prove the efficiency of the proposed control system.
{"title":"Fuzzy-Based Power Management and Power Quality Improvement in Microgrid using Battery Energy Storage System","authors":"M. Rajabinezhad, Arman Ghaderi Baayeh, J. Guerrero","doi":"10.1109/SGC52076.2020.9335758","DOIUrl":"https://doi.org/10.1109/SGC52076.2020.9335758","url":null,"abstract":"In traditional low voltage networks, energy storage systems are mainly used by consumers to protect against short-term power outages. With the advent of microgrids and the expansion of distributed generation resources and also, the development of energy storage system technology, the role of energy storages are increasing. By the proposed control system, the battery energy storage system (BESS) participates in the microgrid energy management and also plays a role in voltage regulation and compensating for current harmonic, unbalance, and reactive power due to the presence of nonlinear loads. In this paper, fuzzy logic-based energy management is proposed, which considers the battery state of charge (SOC), matching between load and wind turbine unit power curves, and the cost of purchased or injected electricity into the grid. In the proposed control system, the limitation of inverter in reactive power injection and the battery's protection from over-charge and over-discharge is also considered. To validate the application of the proposed control system, simulations are performed in MATLAB/Simulink software. The simulation results confirm and prove the efficiency of the proposed control system.","PeriodicalId":391511,"journal":{"name":"2020 10th Smart Grid Conference (SGC)","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124384421","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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