Pub Date : 2023-04-10DOI: 10.1109/GridEdge54130.2023.10102717
Jinshun Su, Chengzhi Xie, P. Dehghanian, S. Mehrani
The wide deployment of advanced computer technologies and evolving digitalization in power systems monitoring and control will inevitably make the power grid more vulnerable to cyber adversaries. Regarded as a viable cyber attack mechanism against power grids, load redistribution (LR) attack may mislead the power re-dispatch and cause unnecessary load outages. In this research, we develop a strategy for optimal allocation of limited defensive resources to safeguard power systems against LR attacks. The proposed defense scheme against LR attack is formulated as a trilevel optimization problem. To capture the uncertainty of attacking resources, we present a chance-constrained programming formulation where chance constraint is used to capture the possible variations in the attacker’s actions constrained by the uncertain available resources. The Karush- Kuhn-Tucker (KKT) condition and Benders decomposition algorithm are applied to solve the trilevel optimization problem. Case studies on the IEEE 57-bus test system demonstrate the efficiency of the resulting defense decisions against LR attacks.
{"title":"Optimal Defense Strategy Against Load Redistribution Attacks under Attacker’s Resource Uncertainty: A Trilevel Optimization Approach","authors":"Jinshun Su, Chengzhi Xie, P. Dehghanian, S. Mehrani","doi":"10.1109/GridEdge54130.2023.10102717","DOIUrl":"https://doi.org/10.1109/GridEdge54130.2023.10102717","url":null,"abstract":"The wide deployment of advanced computer technologies and evolving digitalization in power systems monitoring and control will inevitably make the power grid more vulnerable to cyber adversaries. Regarded as a viable cyber attack mechanism against power grids, load redistribution (LR) attack may mislead the power re-dispatch and cause unnecessary load outages. In this research, we develop a strategy for optimal allocation of limited defensive resources to safeguard power systems against LR attacks. The proposed defense scheme against LR attack is formulated as a trilevel optimization problem. To capture the uncertainty of attacking resources, we present a chance-constrained programming formulation where chance constraint is used to capture the possible variations in the attacker’s actions constrained by the uncertain available resources. The Karush- Kuhn-Tucker (KKT) condition and Benders decomposition algorithm are applied to solve the trilevel optimization problem. Case studies on the IEEE 57-bus test system demonstrate the efficiency of the resulting defense decisions against LR attacks.","PeriodicalId":377998,"journal":{"name":"2023 IEEE PES Grid Edge Technologies Conference & Exposition (Grid Edge)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124734371","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 : 2023-04-10DOI: 10.1109/GridEdge54130.2023.10102732
Traci Hopkins
As decreased dependency on fossil fuels grows and demand for an all-electric world increases, so does the impact to the traditional grid design, especially with regards to the reliability and sustainability of in-service transformers. These demands exacerbate the current challenges placed upon transformer owners such as higher electrical stress, from increased transients and harmonics to higher temperatures resulting from increased loads. All of which lead to accelerated aging of the transformers’ insulation systems and reduces the reliable life of the transformer and challenges the industry’s ability to provide consistent reliable power to end users. This paper identifies current challenges transformer owners face, including new challenges that are exacerbating current obstacles and how a social demand for uninterrupted power is affecting the management of transformer reliability and sustainability. We will also identify solutions to assist the transformer custodian with increasing transformer asset reliability.
{"title":"Effectively Managing Today’s Transformer Challenges for Increased Asset Reliability & Sustainability","authors":"Traci Hopkins","doi":"10.1109/GridEdge54130.2023.10102732","DOIUrl":"https://doi.org/10.1109/GridEdge54130.2023.10102732","url":null,"abstract":"As decreased dependency on fossil fuels grows and demand for an all-electric world increases, so does the impact to the traditional grid design, especially with regards to the reliability and sustainability of in-service transformers. These demands exacerbate the current challenges placed upon transformer owners such as higher electrical stress, from increased transients and harmonics to higher temperatures resulting from increased loads. All of which lead to accelerated aging of the transformers’ insulation systems and reduces the reliable life of the transformer and challenges the industry’s ability to provide consistent reliable power to end users. This paper identifies current challenges transformer owners face, including new challenges that are exacerbating current obstacles and how a social demand for uninterrupted power is affecting the management of transformer reliability and sustainability. We will also identify solutions to assist the transformer custodian with increasing transformer asset reliability.","PeriodicalId":377998,"journal":{"name":"2023 IEEE PES Grid Edge Technologies Conference & Exposition (Grid Edge)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129207648","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 : 2023-04-10DOI: 10.1109/GridEdge54130.2023.10102715
Yiming Chen, S. Grijalva, L. Graber
Recent advances in DC hybrid circuit breakers (HCBs) and wide bandgap (WBG) devices have created an opportunity to deploy new DC and hybrid AC/DC grid options. With the prevailing distributed energy resources (DERs) and DC loads, Medium Voltage Direct Current (MVDC) grids are considered a promising solution for highly reliable, efficient, and safe power distribution. This paper proposes a techno-economical assessment framework and conducts a comparative analysis between MVAC and MVDC micro-grids with high PV penetration. The performance is evaluated through a two-step probabilistic optimal power flow (OPF) and reflected in a set of economic value streams. Numerical results show that the MVDC study case leads its AC counterpart in the key economic metrics.
{"title":"Techno-Economical Comparison of MVAC and MVDC Micro-Grids with High PV Penetration","authors":"Yiming Chen, S. Grijalva, L. Graber","doi":"10.1109/GridEdge54130.2023.10102715","DOIUrl":"https://doi.org/10.1109/GridEdge54130.2023.10102715","url":null,"abstract":"Recent advances in DC hybrid circuit breakers (HCBs) and wide bandgap (WBG) devices have created an opportunity to deploy new DC and hybrid AC/DC grid options. With the prevailing distributed energy resources (DERs) and DC loads, Medium Voltage Direct Current (MVDC) grids are considered a promising solution for highly reliable, efficient, and safe power distribution. This paper proposes a techno-economical assessment framework and conducts a comparative analysis between MVAC and MVDC micro-grids with high PV penetration. The performance is evaluated through a two-step probabilistic optimal power flow (OPF) and reflected in a set of economic value streams. Numerical results show that the MVDC study case leads its AC counterpart in the key economic metrics.","PeriodicalId":377998,"journal":{"name":"2023 IEEE PES Grid Edge Technologies Conference & Exposition (Grid Edge)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125992329","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 : 2023-04-10DOI: 10.1109/GridEdge54130.2023.10102739
Surya Pandiyan, Jayaprakash Rajasekharan
Hot water demand forecasting is crucial for estimating aggregated flexibility from distributed electric water heaters (EWHs). High-performance deep learning models for forecasting require large amounts of training data that may not be available to aggregators. Privacy preserving edge learning methods for individual EWHs cannot learn from other EWHs. To address these challenges, this paper proposes a federated learning (FL) framework for hot water demand class forecasting across multiple distributed EWHs with limited demand data. Feed-forward neural network (FNN) based global model is collaboratively trained by multiple edge devices without sharing data to predict hot water demand. The global model is specifically further fine-tuned to individual EWHs using their own data. The proposed approach is tested with synthetic hot water demand data from 40 households and the results indicate that the proposed FL framework can perform better than edge learning and has numerous other advantages.
{"title":"Federated Learning vs Edge Learning for Hot Water Demand Forecasting in Distributed Electric Water Heaters for Demand Side Flexibility Aggregation","authors":"Surya Pandiyan, Jayaprakash Rajasekharan","doi":"10.1109/GridEdge54130.2023.10102739","DOIUrl":"https://doi.org/10.1109/GridEdge54130.2023.10102739","url":null,"abstract":"Hot water demand forecasting is crucial for estimating aggregated flexibility from distributed electric water heaters (EWHs). High-performance deep learning models for forecasting require large amounts of training data that may not be available to aggregators. Privacy preserving edge learning methods for individual EWHs cannot learn from other EWHs. To address these challenges, this paper proposes a federated learning (FL) framework for hot water demand class forecasting across multiple distributed EWHs with limited demand data. Feed-forward neural network (FNN) based global model is collaboratively trained by multiple edge devices without sharing data to predict hot water demand. The global model is specifically further fine-tuned to individual EWHs using their own data. The proposed approach is tested with synthetic hot water demand data from 40 households and the results indicate that the proposed FL framework can perform better than edge learning and has numerous other advantages.","PeriodicalId":377998,"journal":{"name":"2023 IEEE PES Grid Edge Technologies Conference & Exposition (Grid Edge)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131998656","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 : 2023-04-10DOI: 10.1109/GridEdge54130.2023.10102738
Rostan Rodrigues, Mohammad Razeghi-Jahromi, Amanuel Melese, J. Stoupis
Digital computing hardware and software technologies have seen a massive improvement in recent years. More specifically, lower cost for computation and storage, compact size, compatibility with a large selection of operating systems and communication protocols have increased the penetration of single board computers in many consumer and industrial applications. This paper presents the application of state-of-the-art edge computing infrastructure to the electrical power distribution grid. Electrical power distribution is becoming increasingly complex with the large integration of distributed renewable resources. The distribution grid also experiences many different undesired events, such as different types of temporary and permanent faults, loss of measurement data, cybersecurity, etc. This paper shows the small-scale experimental validation of edge computing in power distribution automation that can be used for classifying different faults, detect anomalies in the grid, measurement data recovery, advanced analytics, etc.
{"title":"Applying Edge Computing to Distribution Automation","authors":"Rostan Rodrigues, Mohammad Razeghi-Jahromi, Amanuel Melese, J. Stoupis","doi":"10.1109/GridEdge54130.2023.10102738","DOIUrl":"https://doi.org/10.1109/GridEdge54130.2023.10102738","url":null,"abstract":"Digital computing hardware and software technologies have seen a massive improvement in recent years. More specifically, lower cost for computation and storage, compact size, compatibility with a large selection of operating systems and communication protocols have increased the penetration of single board computers in many consumer and industrial applications. This paper presents the application of state-of-the-art edge computing infrastructure to the electrical power distribution grid. Electrical power distribution is becoming increasingly complex with the large integration of distributed renewable resources. The distribution grid also experiences many different undesired events, such as different types of temporary and permanent faults, loss of measurement data, cybersecurity, etc. This paper shows the small-scale experimental validation of edge computing in power distribution automation that can be used for classifying different faults, detect anomalies in the grid, measurement data recovery, advanced analytics, etc.","PeriodicalId":377998,"journal":{"name":"2023 IEEE PES Grid Edge Technologies Conference & Exposition (Grid Edge)","volume":"101 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134541056","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 : 2023-04-10DOI: 10.1109/GridEdge54130.2023.10102746
Syed Qaseem Ali, Shadi Chuangpishit, F. Katiraei, Chad S. Abbey, Samrat Datta
Distributed Energy Resources Management Systems (DERMS) are increasingly becoming a need for many utilities to manage DERs on their networks. DERMS deployment includes a variety of actors arranged in varying architectures, communicating with each other employing various protocols. With the complexity around DERMS deployment rigorous testing is required at various stages of the deployment to ensure correct functionality. This paper presents the various architectures for DERMS deployment, presents its testing requirements, and discusses the available methods and tools to meet the testing requirements. Finally, two case studies are presented where two different kinds of testbeds were used to meet the clients DERMS testing requirements.
{"title":"Testing Challenges and Approaches for DERMS Deployment","authors":"Syed Qaseem Ali, Shadi Chuangpishit, F. Katiraei, Chad S. Abbey, Samrat Datta","doi":"10.1109/GridEdge54130.2023.10102746","DOIUrl":"https://doi.org/10.1109/GridEdge54130.2023.10102746","url":null,"abstract":"Distributed Energy Resources Management Systems (DERMS) are increasingly becoming a need for many utilities to manage DERs on their networks. DERMS deployment includes a variety of actors arranged in varying architectures, communicating with each other employing various protocols. With the complexity around DERMS deployment rigorous testing is required at various stages of the deployment to ensure correct functionality. This paper presents the various architectures for DERMS deployment, presents its testing requirements, and discusses the available methods and tools to meet the testing requirements. Finally, two case studies are presented where two different kinds of testbeds were used to meet the clients DERMS testing requirements.","PeriodicalId":377998,"journal":{"name":"2023 IEEE PES Grid Edge Technologies Conference & Exposition (Grid Edge)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117321092","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 : 2023-04-10DOI: 10.1109/GridEdge54130.2023.10102725
Wenqi Zhang, C. Feng, B. Hodge
The inherent variability and uncertainty in distributed energy resources can presents myriad challenges to the planning and operations of power systems. These risks are poised to become larger as the penetration of renewable energy sources rises in the power generation mix. Hybrid solar-wind energy systems are able to mitigate some of these risks by their complementary resource availability. Surface solar and wind fields are coupled and correlated in both space and time. Appropriately estimating the hybrid solar wind energy system requires simulating the spatio-temporal structure of these fields that can be produced for each time horizon. We introduce a novel joint spatio-temporal stochastic differential equation (SPDE) approach that captures the spatio-temporal dynamics of solar and wind fields and their joint dependency over a domain for each time step. In the case study on Colorado, we consider nonstationary three-level hierarchical spatio temporal models for both hourly solar irradiance data and wind speed data in Colorado. Dependence between the solar irradiance data and wind speed data is captured by a shared spatio-temporal random effect. Our approach performs well in terms of the prediction score criterion.
{"title":"Joint Resource Modeling and Assessment for Hybrid Distributed Solar and Wind Systems","authors":"Wenqi Zhang, C. Feng, B. Hodge","doi":"10.1109/GridEdge54130.2023.10102725","DOIUrl":"https://doi.org/10.1109/GridEdge54130.2023.10102725","url":null,"abstract":"The inherent variability and uncertainty in distributed energy resources can presents myriad challenges to the planning and operations of power systems. These risks are poised to become larger as the penetration of renewable energy sources rises in the power generation mix. Hybrid solar-wind energy systems are able to mitigate some of these risks by their complementary resource availability. Surface solar and wind fields are coupled and correlated in both space and time. Appropriately estimating the hybrid solar wind energy system requires simulating the spatio-temporal structure of these fields that can be produced for each time horizon. We introduce a novel joint spatio-temporal stochastic differential equation (SPDE) approach that captures the spatio-temporal dynamics of solar and wind fields and their joint dependency over a domain for each time step. In the case study on Colorado, we consider nonstationary three-level hierarchical spatio temporal models for both hourly solar irradiance data and wind speed data in Colorado. Dependence between the solar irradiance data and wind speed data is captured by a shared spatio-temporal random effect. Our approach performs well in terms of the prediction score criterion.","PeriodicalId":377998,"journal":{"name":"2023 IEEE PES Grid Edge Technologies Conference & Exposition (Grid Edge)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116866142","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 : 2023-04-10DOI: 10.1109/GridEdge54130.2023.10102747
Ida Fuchs, S. Balderrama, P. C. del Granado, S. Quoilin, Jayaprakash Rajasekharan
In this paper, surplus energy (SE) from solar home systems (SHS) with energy storage is studied from the perspective of bottom-up grids. The paper addresses two central research questions: 1) How much SE do SHS generate depending on system size parameters and load metrics? and 2) Can SE provide the basis for bottom-up grids in rural swarm electrification? We first analyze different SE profiles for different stochastic demands from three households with various PV and battery system sizes. Next, we analyze how these SE profiles can be used to supply additional households with lower or equal demand. We find that the SE is highly dependent on PV size and cannot be significantly reduced by increasing battery size. Demand profiles with high Peak to Average Power Ratio (PAPR) and low Load at Day Time Ratio (LDTR) are shown to have the highest levels of SE. Simulations show that households with PV and battery system can supply several other households with its SE only. This study shows excellent potential for developing a framework model for bottom-up grids through SHS sizing and bottom-up planning of interconnections in rural communities.
{"title":"Surplus energy in solar home systems as driver for bottom-up grids: When grids emerge from the edge","authors":"Ida Fuchs, S. Balderrama, P. C. del Granado, S. Quoilin, Jayaprakash Rajasekharan","doi":"10.1109/GridEdge54130.2023.10102747","DOIUrl":"https://doi.org/10.1109/GridEdge54130.2023.10102747","url":null,"abstract":"In this paper, surplus energy (SE) from solar home systems (SHS) with energy storage is studied from the perspective of bottom-up grids. The paper addresses two central research questions: 1) How much SE do SHS generate depending on system size parameters and load metrics? and 2) Can SE provide the basis for bottom-up grids in rural swarm electrification? We first analyze different SE profiles for different stochastic demands from three households with various PV and battery system sizes. Next, we analyze how these SE profiles can be used to supply additional households with lower or equal demand. We find that the SE is highly dependent on PV size and cannot be significantly reduced by increasing battery size. Demand profiles with high Peak to Average Power Ratio (PAPR) and low Load at Day Time Ratio (LDTR) are shown to have the highest levels of SE. Simulations show that households with PV and battery system can supply several other households with its SE only. This study shows excellent potential for developing a framework model for bottom-up grids through SHS sizing and bottom-up planning of interconnections in rural communities.","PeriodicalId":377998,"journal":{"name":"2023 IEEE PES Grid Edge Technologies Conference & Exposition (Grid Edge)","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123877239","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 : 2023-04-10DOI: 10.1109/GridEdge54130.2023.10102728
Taha Khan, H. Nazaripouya
This paper proposes an adaptive Extremum Seeking Control (ESC) based method for extracting the impedance of a distribution grid. An adaptive version of ESC is developed for speeding up the impedance identification process by dynamically increasing the convergence rate of ESC. An impedance change can occur at any time and may produce a disturbance in the grid voltage. Disturbance caused by a change in impedance value can cause the Extremum Seeking Controller to re-adjust its operating point. At first, this may cause the controller to take huge steps in the direction of the new impedance value. Once the value comes closer to the new impedance value, the controller then steadily converges. This steady convergence (especially observed in the distribution grid) is often too slow and may cause an unnecessary delay in estimating the correct impedance value. This work proposes a method of observing the external disturbance and dynamically adjusting the gain of demodulating signal to make the convergence process faster while keeping the ESC-based impedance identification process stable. The increased convergence rate is achieved while keeping the magnitude of applied perturbation constant throughout the process and only changing the magnitude of the demodulating signal.
{"title":"Adaptive-Extremum Seeking Method for Impedance Identification in Distribution Grids","authors":"Taha Khan, H. Nazaripouya","doi":"10.1109/GridEdge54130.2023.10102728","DOIUrl":"https://doi.org/10.1109/GridEdge54130.2023.10102728","url":null,"abstract":"This paper proposes an adaptive Extremum Seeking Control (ESC) based method for extracting the impedance of a distribution grid. An adaptive version of ESC is developed for speeding up the impedance identification process by dynamically increasing the convergence rate of ESC. An impedance change can occur at any time and may produce a disturbance in the grid voltage. Disturbance caused by a change in impedance value can cause the Extremum Seeking Controller to re-adjust its operating point. At first, this may cause the controller to take huge steps in the direction of the new impedance value. Once the value comes closer to the new impedance value, the controller then steadily converges. This steady convergence (especially observed in the distribution grid) is often too slow and may cause an unnecessary delay in estimating the correct impedance value. This work proposes a method of observing the external disturbance and dynamically adjusting the gain of demodulating signal to make the convergence process faster while keeping the ESC-based impedance identification process stable. The increased convergence rate is achieved while keeping the magnitude of applied perturbation constant throughout the process and only changing the magnitude of the demodulating signal.","PeriodicalId":377998,"journal":{"name":"2023 IEEE PES Grid Edge Technologies Conference & Exposition (Grid Edge)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134237134","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 : 2023-04-10DOI: 10.1109/GridEdge54130.2023.10102743
Roozbeh Karandeh, Hong Chun, D. Tholomier
Electric vehicle (EV) sales are booming everywhere. Extensive EV charging infrastructure integration into the legacy distribution networks may cause several system vulnerabilities, including system voltage drop. In this paper, Dynamic VAr Controllers (DVC) are proposed as a cost-effective, non-wires alternative (NWA) and distributed control solution to mitigate the impacts on system voltage. In this approach, the single-phase DVCs are deployed at the secondary of the service transformers at locations with lowest voltages to provide voltage support through dynamic VAr injection. To verify the mitigating effect of DVCs, several scenario-based time-series simulations are performed in OpenDSS on actual distribution networks, using historical load and EV profiles, while the DVC controller is modeled in Python. Simulation results confirm the significant performance of DVCs in resolving the under-voltage issue, as well as system voltage unbalance. The proposed solution is a promising alternative to costly distribution infrastructure expansion and electro-mechanical voltage control devices.
{"title":"Grid-Edge Dynamic Volt-VAr Control Solution to Mitigate System Impacts Caused by Vast EV Charging Infrastructure Integration","authors":"Roozbeh Karandeh, Hong Chun, D. Tholomier","doi":"10.1109/GridEdge54130.2023.10102743","DOIUrl":"https://doi.org/10.1109/GridEdge54130.2023.10102743","url":null,"abstract":"Electric vehicle (EV) sales are booming everywhere. Extensive EV charging infrastructure integration into the legacy distribution networks may cause several system vulnerabilities, including system voltage drop. In this paper, Dynamic VAr Controllers (DVC) are proposed as a cost-effective, non-wires alternative (NWA) and distributed control solution to mitigate the impacts on system voltage. In this approach, the single-phase DVCs are deployed at the secondary of the service transformers at locations with lowest voltages to provide voltage support through dynamic VAr injection. To verify the mitigating effect of DVCs, several scenario-based time-series simulations are performed in OpenDSS on actual distribution networks, using historical load and EV profiles, while the DVC controller is modeled in Python. Simulation results confirm the significant performance of DVCs in resolving the under-voltage issue, as well as system voltage unbalance. The proposed solution is a promising alternative to costly distribution infrastructure expansion and electro-mechanical voltage control devices.","PeriodicalId":377998,"journal":{"name":"2023 IEEE PES Grid Edge Technologies Conference & Exposition (Grid Edge)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117106294","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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