Pub Date : 2022-07-17DOI: 10.1109/PESGM48719.2022.9916922
Guanyu Tian, Q. Sun
The heating, ventilation and air condition (HVAC) system consumes the most energy in commercial buildings, consisting over 60% of total energy usage in the U.S. Flexible HVAC system setpoint scheduling could potentially save building energy costs. This paper proposes a distributionally robust optimal (DRO) HVAC scheduling method that minimizes the daily operation cost with constraints of indoor air temperature comfort and mechanic operating requirement. Considering the uncertainties from ambient temperature, a Wasserstein metric-based ambiguity set is adopted to enhance the robustness against probabilistic prediction errors. The schedule is optimized under the worst-case distribution within the ambiguity set. The proposed DRO method is initially formulated as a two-stage problem and then reformulated into a tractable mixed-integer linear programming (MILP) form. The paper evaluates the feasibility and optimality of the optimized schedules for a real commercial building. The numerical results indicate that the costs of the proposed DRO method are up to 6.6% lower compared with conventional techniques of optimization under uncertainties. They also provide granular risk-benefit options for decision-makinz in demand response programs.
{"title":"Optimal HVAC Scheduling under Temperature Uncertainty using the Wasserstein Metric","authors":"Guanyu Tian, Q. Sun","doi":"10.1109/PESGM48719.2022.9916922","DOIUrl":"https://doi.org/10.1109/PESGM48719.2022.9916922","url":null,"abstract":"The heating, ventilation and air condition (HVAC) system consumes the most energy in commercial buildings, consisting over 60% of total energy usage in the U.S. Flexible HVAC system setpoint scheduling could potentially save building energy costs. This paper proposes a distributionally robust optimal (DRO) HVAC scheduling method that minimizes the daily operation cost with constraints of indoor air temperature comfort and mechanic operating requirement. Considering the uncertainties from ambient temperature, a Wasserstein metric-based ambiguity set is adopted to enhance the robustness against probabilistic prediction errors. The schedule is optimized under the worst-case distribution within the ambiguity set. The proposed DRO method is initially formulated as a two-stage problem and then reformulated into a tractable mixed-integer linear programming (MILP) form. The paper evaluates the feasibility and optimality of the optimized schedules for a real commercial building. The numerical results indicate that the costs of the proposed DRO method are up to 6.6% lower compared with conventional techniques of optimization under uncertainties. They also provide granular risk-benefit options for decision-makinz in demand response programs.","PeriodicalId":388672,"journal":{"name":"2022 IEEE Power & Energy Society General Meeting (PESGM)","volume":"58-60 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":"123127629","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}
With increasing penetration of renewable energy and active market participation, power system operation scenarios and patterns have increased exponentially. This has led to challenges in identifying a good subset of scenarios for routine planning, operation, and emerging machine learning applications. To address these challenges, we develop an approach integrating comprehensive exploratory data analyses and smart sampling techniques to identify and select a small subset of representative power system scenarios that maintain the coverage of system scenarios and operation envelope, therefore, leading to very efficient, yet representative studies and analysis. We propose a hierarchical Latin Hypercube Sampling (LHS) technique for smart sampling, which allows free-form distributions of system load and considers generator commitment status along with generation levels. A set of performance metrics are also defined for systematic evaluation of the adequacy and efficiency of the sampled cases. The developed approach and metrics are demonstrated using the Texas 2000 bus system in this paper and will be extended to the more complex real world systems such as Western Interconnect System.
{"title":"Smart Sampling for Reduced and Representative Power System Scenario Selection","authors":"Xueqing Sun, Xinya Li, Sohom Datta, Xinda Ke, Qiuhua Huang, Renke Huang, Z. Hou","doi":"10.1109/PESGM48719.2022.9916835","DOIUrl":"https://doi.org/10.1109/PESGM48719.2022.9916835","url":null,"abstract":"With increasing penetration of renewable energy and active market participation, power system operation scenarios and patterns have increased exponentially. This has led to challenges in identifying a good subset of scenarios for routine planning, operation, and emerging machine learning applications. To address these challenges, we develop an approach integrating comprehensive exploratory data analyses and smart sampling techniques to identify and select a small subset of representative power system scenarios that maintain the coverage of system scenarios and operation envelope, therefore, leading to very efficient, yet representative studies and analysis. We propose a hierarchical Latin Hypercube Sampling (LHS) technique for smart sampling, which allows free-form distributions of system load and considers generator commitment status along with generation levels. A set of performance metrics are also defined for systematic evaluation of the adequacy and efficiency of the sampled cases. The developed approach and metrics are demonstrated using the Texas 2000 bus system in this paper and will be extended to the more complex real world systems such as Western Interconnect System.","PeriodicalId":388672,"journal":{"name":"2022 IEEE Power & Energy Society General Meeting (PESGM)","volume":"55 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":"121825324","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/PESGM48719.2022.9916726
Rojan Bhattarai, H. Garcia
Distributed energy resources (DERs), like rooftop solar photovoltaic (PV) systems, contribute to the largest renew-able energy footprint in the US. With the proliferation of PV DERs, and its ever increasing adoption, stable distribution system operation is now highly dependent on the normal operation of PV DERs. The common notion, so far, for PV DERs was to “set it, and forget it”, with inadequate attention directed towards abnormality detection and operational health status monitoring over its deployment tenure. This paper presents an online operational health monitoring approach for PV DERs that integrates process variable estimators (PVEs), residual computation, statistical sequential analysis, and probabilistic health aggregation to monitor operational health of PV DERs. Implementation of these various components for health monitoring of PV DERs is discussed and their individual as well as integrated performance is illustrated through simulation. Various usage for health monitoring of PV DERs is discussed including large-scale PV DERs management, proactive maintenance of PV DER assets and situational awareness.
{"title":"Online Health Monitoring of Photovoltaic Distributed Energy Resources","authors":"Rojan Bhattarai, H. Garcia","doi":"10.1109/PESGM48719.2022.9916726","DOIUrl":"https://doi.org/10.1109/PESGM48719.2022.9916726","url":null,"abstract":"Distributed energy resources (DERs), like rooftop solar photovoltaic (PV) systems, contribute to the largest renew-able energy footprint in the US. With the proliferation of PV DERs, and its ever increasing adoption, stable distribution system operation is now highly dependent on the normal operation of PV DERs. The common notion, so far, for PV DERs was to “set it, and forget it”, with inadequate attention directed towards abnormality detection and operational health status monitoring over its deployment tenure. This paper presents an online operational health monitoring approach for PV DERs that integrates process variable estimators (PVEs), residual computation, statistical sequential analysis, and probabilistic health aggregation to monitor operational health of PV DERs. Implementation of these various components for health monitoring of PV DERs is discussed and their individual as well as integrated performance is illustrated through simulation. Various usage for health monitoring of PV DERs is discussed including large-scale PV DERs management, proactive maintenance of PV DER assets and situational awareness.","PeriodicalId":388672,"journal":{"name":"2022 IEEE Power & Energy Society General Meeting (PESGM)","volume":"15 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":"116715935","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/PESGM48719.2022.9917120
S. Maslennikov, Bin Wang
This paper describes in detail the design process and creation of 13 oscillatory test cases for the IEEE and NASPI cohosted Oscillation Source Locating Contest conducted in 2021. The challenges behind the 13 cases are fully explained. Based on the philosophy, implementation considerations, and techniques used for the case design presented in this paper, interested readers can create additional interesting cases for testing the efficiency of their oscillation source location methods.
{"title":"Creation of Simulated Test Cases for the Oscillation Source Location Contest","authors":"S. Maslennikov, Bin Wang","doi":"10.1109/PESGM48719.2022.9917120","DOIUrl":"https://doi.org/10.1109/PESGM48719.2022.9917120","url":null,"abstract":"This paper describes in detail the design process and creation of 13 oscillatory test cases for the IEEE and NASPI cohosted Oscillation Source Locating Contest conducted in 2021. The challenges behind the 13 cases are fully explained. Based on the philosophy, implementation considerations, and techniques used for the case design presented in this paper, interested readers can create additional interesting cases for testing the efficiency of their oscillation source location methods.","PeriodicalId":388672,"journal":{"name":"2022 IEEE Power & Energy Society General Meeting (PESGM)","volume":"50 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":"117132246","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/PESGM48719.2022.9916896
A. C. Melhorn, J. Taylor
A majority of probabilistic load flow studies assume independence or arbitrarily set linear correlation coefficients between the various loads and other inputs on the distribution system. These assumptions may not be applicable to the real world. A methodology is proposed that can model continuous and discrete random variables considering both linear and non-linear dependence through several transformations and inverse transform sampling. The methodology is validated looking at the summation of two random variables and a probabilistic load flow analysis of a residential distribution system with 50% penetration of electric vehicles. This paper hopes to continue the discussion and push for future research in understanding dependence between system inputs and its effects on distribution systems, and how to apply this knowledge in future studies.
{"title":"Modeling Discrete Random Variables with Linear and Nonlinear Dependence for Probabilistic Load Flow","authors":"A. C. Melhorn, J. Taylor","doi":"10.1109/PESGM48719.2022.9916896","DOIUrl":"https://doi.org/10.1109/PESGM48719.2022.9916896","url":null,"abstract":"A majority of probabilistic load flow studies assume independence or arbitrarily set linear correlation coefficients between the various loads and other inputs on the distribution system. These assumptions may not be applicable to the real world. A methodology is proposed that can model continuous and discrete random variables considering both linear and non-linear dependence through several transformations and inverse transform sampling. The methodology is validated looking at the summation of two random variables and a probabilistic load flow analysis of a residential distribution system with 50% penetration of electric vehicles. This paper hopes to continue the discussion and push for future research in understanding dependence between system inputs and its effects on distribution systems, and how to apply this knowledge in future studies.","PeriodicalId":388672,"journal":{"name":"2022 IEEE Power & Energy Society General Meeting (PESGM)","volume":"23 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":"127184084","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/PESGM48719.2022.9917236
Tamer Ibrahim, A. del Rosso, Swaroop S. Guggilam, Kevin Dowling, Mahendra Patel
An adequate dynamic reactive reserve is essential for the operational reliability of a power system. Transmission system operators have recognized the need for improved voltage control and reactive power management approaches to handle the more complex coordination and interactions among controllers under the stringent operating conditions imposed by the emerging generation landscape and other system changes. The Electric Power Research Institute (EPRI) has developed a methodology and software tool to help transmission operators schedule and control static and dynamic reactive power (var) resources in a systematic manner. The software tool, called VCA Studio, identifies voltage control areas and schedules var resources solving a voltage secure multi-period optimal reactive power dispatch optimization (MP-ORPD) problem. The MP-ORPD problem considers the critical buses and critical contingencies and finds the set of preventive control set-points to ensure the system is secure under critical contingencies. This paper describes the methodology and main characteristics of the VCA Studio tool and a sample set of results obtained from case studies on various utility systems in Eastern Interconnection.
{"title":"EPRI-VCA: Optimal Reactive Power Dispatch Tool","authors":"Tamer Ibrahim, A. del Rosso, Swaroop S. Guggilam, Kevin Dowling, Mahendra Patel","doi":"10.1109/PESGM48719.2022.9917236","DOIUrl":"https://doi.org/10.1109/PESGM48719.2022.9917236","url":null,"abstract":"An adequate dynamic reactive reserve is essential for the operational reliability of a power system. Transmission system operators have recognized the need for improved voltage control and reactive power management approaches to handle the more complex coordination and interactions among controllers under the stringent operating conditions imposed by the emerging generation landscape and other system changes. The Electric Power Research Institute (EPRI) has developed a methodology and software tool to help transmission operators schedule and control static and dynamic reactive power (var) resources in a systematic manner. The software tool, called VCA Studio, identifies voltage control areas and schedules var resources solving a voltage secure multi-period optimal reactive power dispatch optimization (MP-ORPD) problem. The MP-ORPD problem considers the critical buses and critical contingencies and finds the set of preventive control set-points to ensure the system is secure under critical contingencies. This paper describes the methodology and main characteristics of the VCA Studio tool and a sample set of results obtained from case studies on various utility systems in Eastern Interconnection.","PeriodicalId":388672,"journal":{"name":"2022 IEEE Power & Energy Society General Meeting (PESGM)","volume":"50 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":"127264776","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/PESGM48719.2022.9917189
Mengmeng Cai, Xin Fang, A. Florita
Traditionally, distribution system state estimations (DSSE) are challenged by the lack of measurements at both primary and secondary sides of the system. The widely available cable television (CATV) voltage sensors installed in low-voltage (LV) networks bring opportunities to achieve higher quality DSSE covering a broader area of the distribution network. This study proposes a medium-/low-voltage (MV/LV) joint distribution system state estimation approach using the untapped CATV measurements. It aims at addressing the need for system situational awareness at the grid edge while improving the estimation accuracy at both the primary and secondary sides compared to its disjointed counterpart. Linearized measurement functions and boundary condition uncertainty propagation rules are derived to ensure the computational efficiency and accuracy of the joint state estimator. Numerical experiments are conducted on an IEEE test feeder to demonstrate the efficacy of the proposed method and the value of CATV measurements.
{"title":"A Medium-/Low-Voltage Joint State Estimator Through Linear Uncertainty Propagation","authors":"Mengmeng Cai, Xin Fang, A. Florita","doi":"10.1109/PESGM48719.2022.9917189","DOIUrl":"https://doi.org/10.1109/PESGM48719.2022.9917189","url":null,"abstract":"Traditionally, distribution system state estimations (DSSE) are challenged by the lack of measurements at both primary and secondary sides of the system. The widely available cable television (CATV) voltage sensors installed in low-voltage (LV) networks bring opportunities to achieve higher quality DSSE covering a broader area of the distribution network. This study proposes a medium-/low-voltage (MV/LV) joint distribution system state estimation approach using the untapped CATV measurements. It aims at addressing the need for system situational awareness at the grid edge while improving the estimation accuracy at both the primary and secondary sides compared to its disjointed counterpart. Linearized measurement functions and boundary condition uncertainty propagation rules are derived to ensure the computational efficiency and accuracy of the joint state estimator. Numerical experiments are conducted on an IEEE test feeder to demonstrate the efficacy of the proposed method and the value of CATV measurements.","PeriodicalId":388672,"journal":{"name":"2022 IEEE Power & Energy Society General Meeting (PESGM)","volume":"96 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":"127281914","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/PESGM48719.2022.9917010
Abinet Tesfaye Eseye, Bernard Knueven, Deepthi Vaidhynathan, J. King
Integrating a large number of distributed energy resources (DERs) into the power grid needs a scalable power balancing method. We formulate the power balancing problem as a look-ahead optimization problem to be solved sequentially by a power distribution system aggregator based on a model predictive control (MPC) framework. Solving large-scale look-ahead control problems requires proper configuration of the control steps. In this paper, to solve large-scale control problems, we propose a variable time granularity where control time steps nearby the current control step have finer resolutions. The aggregator objective includes maximization of power production revenue and minimization of power purchasing expense, renewable power curtailment, and mileage costs for energy storage and electric vehicle (EV) charging stations while satisfying system capacity and operational constraints. The control problem is formulated as a mixed-integer linear program (MILP) and solved using the XpressMP solver. We perform simulations considering a copper plate representation of a large distribution network consisting of 2507 devices (control-lable DERs), including curtailable photovoltaics (PVs), energy storage batteries, EV charging stations, and buildings with heating, ventilation, and air conditioning units (HVACs). We show the effectiveness of the proposed approach in managing DERs interactively for maximum energy trading profit and local supply-demand power balancing. Finally, we demonstrate that the proposed method outperforms other benchmark controllers regarding computation time without compromising operational performance.
{"title":"Scalable Predictive Control and Optimization for Grid Integration of Large-scale Distributed Energy Resources","authors":"Abinet Tesfaye Eseye, Bernard Knueven, Deepthi Vaidhynathan, J. King","doi":"10.1109/PESGM48719.2022.9917010","DOIUrl":"https://doi.org/10.1109/PESGM48719.2022.9917010","url":null,"abstract":"Integrating a large number of distributed energy resources (DERs) into the power grid needs a scalable power balancing method. We formulate the power balancing problem as a look-ahead optimization problem to be solved sequentially by a power distribution system aggregator based on a model predictive control (MPC) framework. Solving large-scale look-ahead control problems requires proper configuration of the control steps. In this paper, to solve large-scale control problems, we propose a variable time granularity where control time steps nearby the current control step have finer resolutions. The aggregator objective includes maximization of power production revenue and minimization of power purchasing expense, renewable power curtailment, and mileage costs for energy storage and electric vehicle (EV) charging stations while satisfying system capacity and operational constraints. The control problem is formulated as a mixed-integer linear program (MILP) and solved using the XpressMP solver. We perform simulations considering a copper plate representation of a large distribution network consisting of 2507 devices (control-lable DERs), including curtailable photovoltaics (PVs), energy storage batteries, EV charging stations, and buildings with heating, ventilation, and air conditioning units (HVACs). We show the effectiveness of the proposed approach in managing DERs interactively for maximum energy trading profit and local supply-demand power balancing. Finally, we demonstrate that the proposed method outperforms other benchmark controllers regarding computation time without compromising operational performance.","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":"125925847","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/PESGM48719.2022.9917136
T. Nguyen, Yu Wang, Q. Duong, Q. Tran, Ha Thi Nguyen, O. Mohammed
Distributed control strategies have been attracted significant attention due to numerous advantages over traditional centralized control strategies. The development of deep reinforcement learning method provides a novel approach to control grid without knowing the system's parameters. The training and validating process with grid simulation as environment have been supported by several toolboxes. In this paper, a platform based on redis NoSQL database is proposed to the deploy the multi-agent system of deep reinforcement learning algorithms for control microgrid in a distributed manner. The accuracy of agent implementation under realistic condition with physical communication network can be evaluated with the proposed platform. The distributed control in islanded DC microgrid using Deep Deterministic Policy Gradient is introduced as an use case to show the operation of the platform.
{"title":"A Platform for Deploying Multi-agent Deep Reinforcement Learning in Microgrid Distributed Control","authors":"T. Nguyen, Yu Wang, Q. Duong, Q. Tran, Ha Thi Nguyen, O. Mohammed","doi":"10.1109/PESGM48719.2022.9917136","DOIUrl":"https://doi.org/10.1109/PESGM48719.2022.9917136","url":null,"abstract":"Distributed control strategies have been attracted significant attention due to numerous advantages over traditional centralized control strategies. The development of deep reinforcement learning method provides a novel approach to control grid without knowing the system's parameters. The training and validating process with grid simulation as environment have been supported by several toolboxes. In this paper, a platform based on redis NoSQL database is proposed to the deploy the multi-agent system of deep reinforcement learning algorithms for control microgrid in a distributed manner. The accuracy of agent implementation under realistic condition with physical communication network can be evaluated with the proposed platform. The distributed control in islanded DC microgrid using Deep Deterministic Policy Gradient is introduced as an use case to show the operation of the platform.","PeriodicalId":388672,"journal":{"name":"2022 IEEE Power & Energy Society General Meeting (PESGM)","volume":"11 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":"123261681","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/PESGM48719.2022.9917018
Md Abul Hasnat, M. Rahnamay-Naeini
Recovering the state of unobservable power system components due to cyber attacks or limited meter availability is a crucial problem to address to enable efficient monitoring and operation of power systems. The graph signal processing (GSP) framework provides new opportunities to improve power system data analysis by capturing the topological information of the system. In this paper, the recovery of the unobservable states in power systems is formulated as a graph signal reconstruction problem in a GSP framework. Specifically, a novel reconstruction technique based on the statistics of the local smoothness of the graph signals along with the global smoothness of the graph signals is casted into an optimization framework. In contrast to many graph signal reconstruction techniques, which assume band-limited signals to be recovered, the proposed technique is applicable to general graph signals irrespective of their bandwidth. The performance evaluation of the proposed method using simulated graph signals for the IEEE 118 bus system show promising reconstruction accuracy.
{"title":"Power System State Recovery using Local and Global Smoothness of its Graph Signals","authors":"Md Abul Hasnat, M. Rahnamay-Naeini","doi":"10.1109/PESGM48719.2022.9917018","DOIUrl":"https://doi.org/10.1109/PESGM48719.2022.9917018","url":null,"abstract":"Recovering the state of unobservable power system components due to cyber attacks or limited meter availability is a crucial problem to address to enable efficient monitoring and operation of power systems. The graph signal processing (GSP) framework provides new opportunities to improve power system data analysis by capturing the topological information of the system. In this paper, the recovery of the unobservable states in power systems is formulated as a graph signal reconstruction problem in a GSP framework. Specifically, a novel reconstruction technique based on the statistics of the local smoothness of the graph signals along with the global smoothness of the graph signals is casted into an optimization framework. In contrast to many graph signal reconstruction techniques, which assume band-limited signals to be recovered, the proposed technique is applicable to general graph signals irrespective of their bandwidth. The performance evaluation of the proposed method using simulated graph signals for the IEEE 118 bus system show promising reconstruction accuracy.","PeriodicalId":388672,"journal":{"name":"2022 IEEE Power & Energy Society General Meeting (PESGM)","volume":"172 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":"123290968","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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