Pub Date : 2022-07-17DOI: 10.1109/PESGM48719.2022.9916765
D. Hammerstrom
A novel method for harmonizing the advantages of dynamic retail electricity pricing with the protections of approved, regulated electricity tariffs is discussed and demonstrated. The method socializes and protects customers from long-term locational price variability that is unfair to those customers who are, by no fault of their own, served at congested locations on a distribution system. However, the method preserves short-term (e.g., diurnal) price variability that might induce helpful, mitigative responses from retail electricity customers. Because the method causes actual price recovery to track a customer class's approved, regulated price recovery, the method may remove regulators' objections to dynamic electricity pricing and thereby hasten adoption of market-based retail electricity pricing and transactive energy systems.
{"title":"On Harmonizing Today's Regulated Tariffs and Future Dynamic Electricity Pricing","authors":"D. Hammerstrom","doi":"10.1109/PESGM48719.2022.9916765","DOIUrl":"https://doi.org/10.1109/PESGM48719.2022.9916765","url":null,"abstract":"A novel method for harmonizing the advantages of dynamic retail electricity pricing with the protections of approved, regulated electricity tariffs is discussed and demonstrated. The method socializes and protects customers from long-term locational price variability that is unfair to those customers who are, by no fault of their own, served at congested locations on a distribution system. However, the method preserves short-term (e.g., diurnal) price variability that might induce helpful, mitigative responses from retail electricity customers. Because the method causes actual price recovery to track a customer class's approved, regulated price recovery, the method may remove regulators' objections to dynamic electricity pricing and thereby hasten adoption of market-based retail electricity pricing and transactive energy systems.","PeriodicalId":388672,"journal":{"name":"2022 IEEE Power & Energy Society General Meeting (PESGM)","volume":"133 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124583249","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 : 2022-07-17DOI: 10.1109/PESGM48719.2022.9917097
Lin Mei, Mengxue Qi, Zhiyi Li
As the penetration rate of renewables-based distributed generators soars in the power distribution network, the trustworthiness and timeliness of fault detection become a critical challenge. This paper embeds the concept of edge-cloud collaboration in the prevalent deep learning techniques so as to perform the data-driven fault detection in a fast and accurate way. First, an attention mechanism is proposed to associate the line fault probability with bus voltage profiles. On this basis, an attention-based deep neural network is designed for edge computing purposes, which manages to estimate the fault status of a certain line only by analyzing the voltage magnitude of adjacent buses. Besides, an efficient deep neural network is deployed in the cloud computing platform which work collaboratively with edge devices to detect any fault. Moreover, a batch training method is proposed to improve the training speed and the model accuracy while retaining the topology information. The validity of the proposed method is finally validated by numerical experiments based on several IEEE test feeder systems.
{"title":"Edge-Cloud Collaborative Fault Detection for Distribution Networks Using Attention Mechanism","authors":"Lin Mei, Mengxue Qi, Zhiyi Li","doi":"10.1109/PESGM48719.2022.9917097","DOIUrl":"https://doi.org/10.1109/PESGM48719.2022.9917097","url":null,"abstract":"As the penetration rate of renewables-based distributed generators soars in the power distribution network, the trustworthiness and timeliness of fault detection become a critical challenge. This paper embeds the concept of edge-cloud collaboration in the prevalent deep learning techniques so as to perform the data-driven fault detection in a fast and accurate way. First, an attention mechanism is proposed to associate the line fault probability with bus voltage profiles. On this basis, an attention-based deep neural network is designed for edge computing purposes, which manages to estimate the fault status of a certain line only by analyzing the voltage magnitude of adjacent buses. Besides, an efficient deep neural network is deployed in the cloud computing platform which work collaboratively with edge devices to detect any fault. Moreover, a batch training method is proposed to improve the training speed and the model accuracy while retaining the topology information. The validity of the proposed method is finally validated by numerical experiments based on several IEEE test feeder systems.","PeriodicalId":388672,"journal":{"name":"2022 IEEE Power & Energy Society General Meeting (PESGM)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129682536","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 : 2022-07-17DOI: 10.1109/PESGM48719.2022.9917226
Yong Hu, S. Bu
In this paper, a novel energy flow analysis (EFA) is proposed based on the signal reconstruction and decomposition to investigate the electromechanical oscillations. In contrast to the conventional EFA, the connection between the proposed EFA and modal analysis (MA) can be quantitatively revealed for arbitrary models of synchronous generators in multi-machine power systems. Firstly, the time-domain implementation (TDI) of the proposed EFA is designed. Specifically, the measurements at the terminal of a local generator are reconstructed through an exponential operator and then decomposed with respect to an angular frequency. Then, the mode-screened damping torque coefficient is defined to extract the damping feature with respect to an electromechanical oscillation mode. After that, the frequencydomain implementation (FDI) is derived. Specifically, the Parsevals Theorem is applied to transform the proposed EFA from the time domain to frequency domain. On this basis, the consistency between the proposed EFA and MA is strictly proved, which is applicable for arbitrary models of synchronous generators in multi-machine power systems. Additionally, the application procedure of the proposed EFA in investigating electromechanical oscillations is given. Finally, the proposed EFA is substantially demonstrated in multiple case studies.
{"title":"A Novel Energy Flow Analysis and its Connection with Modal Analysis for Investigating Electromechanical Oscillations in Multi-Machine Power Systems","authors":"Yong Hu, S. Bu","doi":"10.1109/PESGM48719.2022.9917226","DOIUrl":"https://doi.org/10.1109/PESGM48719.2022.9917226","url":null,"abstract":"In this paper, a novel energy flow analysis (EFA) is proposed based on the signal reconstruction and decomposition to investigate the electromechanical oscillations. In contrast to the conventional EFA, the connection between the proposed EFA and modal analysis (MA) can be quantitatively revealed for arbitrary models of synchronous generators in multi-machine power systems. Firstly, the time-domain implementation (TDI) of the proposed EFA is designed. Specifically, the measurements at the terminal of a local generator are reconstructed through an exponential operator and then decomposed with respect to an angular frequency. Then, the mode-screened damping torque coefficient is defined to extract the damping feature with respect to an electromechanical oscillation mode. After that, the frequencydomain implementation (FDI) is derived. Specifically, the Parsevals Theorem is applied to transform the proposed EFA from the time domain to frequency domain. On this basis, the consistency between the proposed EFA and MA is strictly proved, which is applicable for arbitrary models of synchronous generators in multi-machine power systems. Additionally, the application procedure of the proposed EFA in investigating electromechanical oscillations is given. Finally, the proposed EFA is substantially demonstrated in multiple case studies.","PeriodicalId":388672,"journal":{"name":"2022 IEEE Power & Energy Society General Meeting (PESGM)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129799486","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 : 2022-07-17DOI: 10.1109/PESGM48719.2022.9917109
A. Raab, Dominik Frauenknecht, M. Luther, Ananya Kuri, Anatoli Wellhoefer
The objective of this paper is the implementation and comparison of a hybrid phasor-based (RMS) and electromagnetic transient (EMT) modular multilevel converter high voltage direct current (MMC-HVDC) model for advanced and detailed studies of large power systems. The general modeling approach for hybrid simulation of modular multilevel converters for HVDC applications with the corresponding control concepts is described. The HVDC model can be divided into AC and DC components with different simulation time steps and representation using network partitioning. The connected AC grids and converter models are considered in the phasor-based time domain, while the DC connection is simulated in the electromagnetic-transient time domain. The coupling of the models is established by the total energy control of the MMC. The hybrid approach is evaluated in comparison to an average MMC HVDC model in an EMT simulation. The results show the advantages of the hybrid model. The model can be simulated with comparatively low computational effort, while the DC transients can be represented in detail during disturbances.
{"title":"Hybrid EMT and Phasor based MMC-HVDC Model for Advanced Power System Simulation","authors":"A. Raab, Dominik Frauenknecht, M. Luther, Ananya Kuri, Anatoli Wellhoefer","doi":"10.1109/PESGM48719.2022.9917109","DOIUrl":"https://doi.org/10.1109/PESGM48719.2022.9917109","url":null,"abstract":"The objective of this paper is the implementation and comparison of a hybrid phasor-based (RMS) and electromagnetic transient (EMT) modular multilevel converter high voltage direct current (MMC-HVDC) model for advanced and detailed studies of large power systems. The general modeling approach for hybrid simulation of modular multilevel converters for HVDC applications with the corresponding control concepts is described. The HVDC model can be divided into AC and DC components with different simulation time steps and representation using network partitioning. The connected AC grids and converter models are considered in the phasor-based time domain, while the DC connection is simulated in the electromagnetic-transient time domain. The coupling of the models is established by the total energy control of the MMC. The hybrid approach is evaluated in comparison to an average MMC HVDC model in an EMT simulation. The results show the advantages of the hybrid model. The model can be simulated with comparatively low computational effort, while the DC transients can be represented in detail during disturbances.","PeriodicalId":388672,"journal":{"name":"2022 IEEE Power & Energy Society General Meeting (PESGM)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130412414","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 : 2022-07-17DOI: 10.1109/PESGM48719.2022.9917188
Maximiliano F. Ferrari, L. Tolbert
The resiliency offered by a microgrid may be lost if the microgrid is not properly protected during short-circuit faults inside its boundaries. Many studies conclude that protecting microgrids in islanded mode is very challenging due to the limited short-circuit capability of distributed energy resources (DERs). The limited short-circuit capability of DERs typically inhibits the use of reliable and affordable overcurrent protective devices in microgrids. Although extensive research on microgrid protection is available in the literature, to date this research has not led to a cost-effective, commercially available relay that effectively tackles the challenges of microgrid protection. This work proposes hardware modifications to enhance the current contribution of an energy storage inverter with the objective of enabling the use of legacy overcurrent protection for islanded microgrids. This paper demonstrates through experimental results that few modifications are required in the inverter to significantly enhance its current contribution. In this study, a three-phase energy storage inverter was modified to provide three times its rated current during three-phase faults, which proved sufficient current for enough time to enable fuse-relay, and relay-to-relay coordination. The proposed modifications effectively increase the current contribution of the inverter, which is a promising advancement to allow the adoption of overcurrent protective devices for protecting microgrids.
{"title":"Inverter Design with High Short-Circuit Fault Current Contribution to Enable Legacy Overcurrent Protection for Islanded Microgrids","authors":"Maximiliano F. Ferrari, L. Tolbert","doi":"10.1109/PESGM48719.2022.9917188","DOIUrl":"https://doi.org/10.1109/PESGM48719.2022.9917188","url":null,"abstract":"The resiliency offered by a microgrid may be lost if the microgrid is not properly protected during short-circuit faults inside its boundaries. Many studies conclude that protecting microgrids in islanded mode is very challenging due to the limited short-circuit capability of distributed energy resources (DERs). The limited short-circuit capability of DERs typically inhibits the use of reliable and affordable overcurrent protective devices in microgrids. Although extensive research on microgrid protection is available in the literature, to date this research has not led to a cost-effective, commercially available relay that effectively tackles the challenges of microgrid protection. This work proposes hardware modifications to enhance the current contribution of an energy storage inverter with the objective of enabling the use of legacy overcurrent protection for islanded microgrids. This paper demonstrates through experimental results that few modifications are required in the inverter to significantly enhance its current contribution. In this study, a three-phase energy storage inverter was modified to provide three times its rated current during three-phase faults, which proved sufficient current for enough time to enable fuse-relay, and relay-to-relay coordination. The proposed modifications effectively increase the current contribution of the inverter, which is a promising advancement to allow the adoption of overcurrent protective devices for protecting microgrids.","PeriodicalId":388672,"journal":{"name":"2022 IEEE Power & Energy Society General Meeting (PESGM)","volume":"98 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126754195","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 : 2022-07-17DOI: 10.1109/PESGM48719.2022.9917038
M. Kelker, Lars Quakernack, J. Haubrock, D. Westermann
Even today, electric vehicles (EVs) can endanger grid stability by overloading equipment at the low-voltage (LV) level due to high charging power at private charging points and simultaneity. With a high share ofEVs in the future, it is therefore necessary to control their charging power in such a way that congestion of equipment in the LV grid is avoided. In order to increase the user acceptance of EVs, a fast charging time of the EVs has to be guaranteed despite the control of the charging power. For achieving this, an autonomously acting control algorithm using a multi agent double deep Q-network (MADDQN) has been defined and validated in simulation in the following paper. For this purpose, multiple reward functions have been defined. In the validation on the modified CIGRE LV grid with a share of 100% EVs, it has been shown that the MADDQN can reduce the transformer utilization by up to 50 % relative to the uncontrolled case. At the same time, the charging time can be increased by 51 % relative to the minimum EV charging power of 1.4 kW.
{"title":"Multi agent double deep Q-network with multiple reward functions for electric vehicle charge control","authors":"M. Kelker, Lars Quakernack, J. Haubrock, D. Westermann","doi":"10.1109/PESGM48719.2022.9917038","DOIUrl":"https://doi.org/10.1109/PESGM48719.2022.9917038","url":null,"abstract":"Even today, electric vehicles (EVs) can endanger grid stability by overloading equipment at the low-voltage (LV) level due to high charging power at private charging points and simultaneity. With a high share ofEVs in the future, it is therefore necessary to control their charging power in such a way that congestion of equipment in the LV grid is avoided. In order to increase the user acceptance of EVs, a fast charging time of the EVs has to be guaranteed despite the control of the charging power. For achieving this, an autonomously acting control algorithm using a multi agent double deep Q-network (MADDQN) has been defined and validated in simulation in the following paper. For this purpose, multiple reward functions have been defined. In the validation on the modified CIGRE LV grid with a share of 100% EVs, it has been shown that the MADDQN can reduce the transformer utilization by up to 50 % relative to the uncontrolled case. At the same time, the charging time can be increased by 51 % relative to the minimum EV charging power of 1.4 kW.","PeriodicalId":388672,"journal":{"name":"2022 IEEE Power & Energy Society General Meeting (PESGM)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124232792","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 : 2022-07-17DOI: 10.1109/PESGM48719.2022.9916969
Huayi Wu, Youwei Jia, Zhao Xu
The management and control of the power systems rely on reliable and timely distribution system state estimation, which is present to be challenging due to significant voltage variations caused by high renewables. To tackle this problem, a graph convolutional network (AGCN) is proposed for the distribution system state estimation (DSSE) by considering highly volatile renewable generation. In particular, the AGCN can enable prompt state estimation for viable system states. In the proposed model, the graph convolutional layer can capture the correlations of the nodal power injections so that enhanced estimation accuracy can be achieved. Moreover, the node-embedding technique is employed in the graph convolutional layer to represent the nonlinear correlation nature, through which the proposed model is allowed to cover general scenarios in the application. The simulation results have been provided to verify the accuracy and effectiveness of the proposed model through IEEE 33-node and the 118-node distribution systems.
{"title":"Adaptive Graph Convolutional Network-Based Distribution System State Estimation","authors":"Huayi Wu, Youwei Jia, Zhao Xu","doi":"10.1109/PESGM48719.2022.9916969","DOIUrl":"https://doi.org/10.1109/PESGM48719.2022.9916969","url":null,"abstract":"The management and control of the power systems rely on reliable and timely distribution system state estimation, which is present to be challenging due to significant voltage variations caused by high renewables. To tackle this problem, a graph convolutional network (AGCN) is proposed for the distribution system state estimation (DSSE) by considering highly volatile renewable generation. In particular, the AGCN can enable prompt state estimation for viable system states. In the proposed model, the graph convolutional layer can capture the correlations of the nodal power injections so that enhanced estimation accuracy can be achieved. Moreover, the node-embedding technique is employed in the graph convolutional layer to represent the nonlinear correlation nature, through which the proposed model is allowed to cover general scenarios in the application. The simulation results have been provided to verify the accuracy and effectiveness of the proposed model through IEEE 33-node and the 118-node distribution systems.","PeriodicalId":388672,"journal":{"name":"2022 IEEE Power & Energy Society General Meeting (PESGM)","volume":"145 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123147694","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 : 2022-07-17DOI: 10.1109/PESGM48719.2022.9917004
Alexander Anderson, Jonathan Barr, S. Vadari, A. Dubey
To help bridge the gap between traditional power system engineering instruction and emerging industry needs, new classroom and research tools are needed. Real-time simulation tools emulating power system control room software present an opportunity to introduce students to the array of operational considerations, technical challenges, and decision-making associated with power system operations. The GridAPPS-D platform is proposed to support coursework and academic research as it provides an open-source platform for simulation, application development, and software integration. The GridAPPS-D platform, simulation capabilities, development environment, and interactive training are discussed in the context of lessons-learned from implementation for undergraduate and graduate students in the US and India. A series of potential GridAPPS-D supported academic capabilities are introduced.
{"title":"Real-time Distribution Simulation and Application Development for Power Systems Education","authors":"Alexander Anderson, Jonathan Barr, S. Vadari, A. Dubey","doi":"10.1109/PESGM48719.2022.9917004","DOIUrl":"https://doi.org/10.1109/PESGM48719.2022.9917004","url":null,"abstract":"To help bridge the gap between traditional power system engineering instruction and emerging industry needs, new classroom and research tools are needed. Real-time simulation tools emulating power system control room software present an opportunity to introduce students to the array of operational considerations, technical challenges, and decision-making associated with power system operations. The GridAPPS-D platform is proposed to support coursework and academic research as it provides an open-source platform for simulation, application development, and software integration. The GridAPPS-D platform, simulation capabilities, development environment, and interactive training are discussed in the context of lessons-learned from implementation for undergraduate and graduate students in the US and India. A series of potential GridAPPS-D supported academic capabilities are introduced.","PeriodicalId":388672,"journal":{"name":"2022 IEEE Power & Energy Society General Meeting (PESGM)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123341066","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 : 2022-07-17DOI: 10.1109/PESGM48719.2022.9917133
Kuldip Nayak, Soumitri Jena, A. Pradhan
In this letter, an alternative solution for travelling wave based directional relaying scheme is presented by utilizing the prefault voltage sample acquired at a much lower sampling rate along with current travelling wave. There is no requirement of voltage travelling waves for the method. The performance of the proposed method is evaluated for faults simulated on a 400 kV transmission network which reveals its advantage for faults when change in voltage signal is low. The proposed method is independent of the transient response of the capacitive voltage transformer.
{"title":"Travelling Wave Based Directional Relaying Without Using Voltage Transients","authors":"Kuldip Nayak, Soumitri Jena, A. Pradhan","doi":"10.1109/PESGM48719.2022.9917133","DOIUrl":"https://doi.org/10.1109/PESGM48719.2022.9917133","url":null,"abstract":"In this letter, an alternative solution for travelling wave based directional relaying scheme is presented by utilizing the prefault voltage sample acquired at a much lower sampling rate along with current travelling wave. There is no requirement of voltage travelling waves for the method. The performance of the proposed method is evaluated for faults simulated on a 400 kV transmission network which reveals its advantage for faults when change in voltage signal is low. The proposed method is independent of the transient response of the capacitive voltage transformer.","PeriodicalId":388672,"journal":{"name":"2022 IEEE Power & Energy Society General Meeting (PESGM)","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123365981","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 : 2022-07-17DOI: 10.1109/PESGM48719.2022.9917011
Muhammad Nadeem, A. Taha, Sebastian A. Nugroho
The widespread installation of renewable energy resources in electrical power systems poses a new set of challenges primarily because of their variability. Their uncertain nature can highly affect electrical grid parameters which are essential for dynamic state estimation (DSE). In the current literature of power systems DSE usually a linearized or nonlinear ordinary differential equation model of power systems is used which cannot capture the uncertainties associated with the loads and renewable energy resources. These uncertainties can often only be taken into account via the nonlinear differential-algebraic (NL-DAE) model of power systems. This model explicitly captures renewables and the topological changes in the power system model. In this paper, we present an estimator for the complete NL-DAE representation of the power system which can provide robust state estimation in the presence of uncertainties from renewables, and investigate the impact of renewables on state estimation performance.
{"title":"Impact of Uncertainty from Renewables on Dynamic State Estimation of Power Networks","authors":"Muhammad Nadeem, A. Taha, Sebastian A. Nugroho","doi":"10.1109/PESGM48719.2022.9917011","DOIUrl":"https://doi.org/10.1109/PESGM48719.2022.9917011","url":null,"abstract":"The widespread installation of renewable energy resources in electrical power systems poses a new set of challenges primarily because of their variability. Their uncertain nature can highly affect electrical grid parameters which are essential for dynamic state estimation (DSE). In the current literature of power systems DSE usually a linearized or nonlinear ordinary differential equation model of power systems is used which cannot capture the uncertainties associated with the loads and renewable energy resources. These uncertainties can often only be taken into account via the nonlinear differential-algebraic (NL-DAE) model of power systems. This model explicitly captures renewables and the topological changes in the power system model. In this paper, we present an estimator for the complete NL-DAE representation of the power system which can provide robust state estimation in the presence of uncertainties from renewables, and investigate the impact of renewables on state estimation performance.","PeriodicalId":388672,"journal":{"name":"2022 IEEE Power & Energy Society General Meeting (PESGM)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123463833","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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