For power grids with high penetration of distributed energy resources (DERs), microgrids can prov... more For power grids with high penetration of distributed energy resources (DERs), microgrids can provide operation and control capabilities for clusters of DERs and load. Furthermore, microgrids enhance resilience of the hosting bulk power grid if they are enabled to serve critical load beyond the jurisdiction of the microgrids. For widespread deployment of microgrids, a modular and standardized Microgrid Building Block (MBB) is essential to help reduce the cost and increase reliability. This paper proposes the conceptual design of an MBB with integrated features of power conversion, control, and communications, resulting in a systemwide controller for the entire microgrid. The results of a feasibility study indicate that, in a utility-connected mode, MBB-based microgrids can exchange power with the hosting power grid while serving regulation and optimal dispatch functions. In a resiliency (islanded) mode when the microgrid is disconnected from the utility system, the MBB control system acts to stabilize the system frequency and voltage under small or large disturbances. The microgrid controller is supported by a communication system that meets the latency requirements imposed by the microgrid dynamics as well as data acquisition time. The extended IEEE 13-node system is used as a microgrid model to validate the proposed MBB design and functionality.
Machine learning (ML)-based intrusion detection system (IDS) approaches have been significantly a... more Machine learning (ML)-based intrusion detection system (IDS) approaches have been significantly applied and advanced the state-of-the-art system security and defense mechanisms. In smart grid computing environments, security threats have been significantly increased as shared networks are commonly used, along with the associated vulnerabilities. However, compared to other network environments, ML-based IDS research in a smart grid is relatively unexplored although the smart grid environment is facing serious security threats due to its unique environmental vulnerabilities. In this paper, we conducted an extensive survey on ML-based IDS in smart grids based on the following key aspects: (1) The applications of the ML-based IDS in transmission and distribution side power components of a smart power grid by addressing its security vulnerabilities; (2) dataset generation process and its usage in applying ML-based IDSs in the smart grid; (3) a wide range of ML-based IDSs used by the surv...
Distributed energy resource management systems (DERMS) are increasingly deployed to manage potent... more Distributed energy resource management systems (DERMS) are increasingly deployed to manage potential adverse impacts of DERs on distribution feeder voltages. However, communication channels used by DERMS controllers, along with the unmanned substations where they are deployed, provide a target for cyber attackers to cause severe voltage disturbances. The voltage problems can damage equipment, trip inverters, and cause undesirable operations of voltage regulating devices. In this paper, using detailed cyber-physical models, the various cyberattack paths an adversary may use to conduct falsified control command cyberattacks are presented. Robust and fast acting centralized cyber layer and de-centralized inverter intrusion detection are also proposed. This two-tiered system can detect and mitigate these cyberattacks before the power system layer is impacted, even if the DERMS controller itself has been compromised. The cyberattack models and proposed intrusion detection techniques have been validated using IEEE test feeders.
The rapid escalation in plug-in electric vehicles (PEVs) and their uncoordinated charging pattern... more The rapid escalation in plug-in electric vehicles (PEVs) and their uncoordinated charging patterns pose several challenges in distribution system operation. Some of the undesirable effects include overloading of transformers, rapid voltage fluctuations, and over/under voltages. While this compromises the consumer power quality, it also puts on extra stress on the local voltage control devices. These challenges demand a well-coordinated and power network-aware charging approach for PEVs in a community. This paper formulates a realtime electric vehicle charging scheduling problem as a mixed-integer linear program (MILP). The problem is to be solved by an aggregator that provides charging service in a residential community. The proposed formulation maximizes the profit of the aggregator, enhancing the utilization of available infrastructure. With prior knowledge of load demand and hourly electricity prices, the algorithm uses a moving time horizon optimization approach, allowing an unk...
The rapid escalation in plug-in electric vehicles (PEVs) and their uncoordinated charging pattern... more The rapid escalation in plug-in electric vehicles (PEVs) and their uncoordinated charging patterns pose several challenges in distribution system operation. Some of the undesirable effects include overloading of transformers, rapid voltage fluctuations, and over/under voltages. While this compromises the consumer power quality, it also puts on extra stress on the local voltage control devices. These challenges demand for a well coordinated and power network-aware charging approach for PEVs in a community. This paper formulates a real-time electric vehicle charging scheduling problem as an mixed-integer linear program (MILP). The problem is to be solved by an aggregator, that provides charging service in a residential community. The proposed formulation maximizes the profit of the aggregator, enhancing the utilization of available infrastructure. With a prior knowledge of load demand and hourly electricity prices, the algorithm uses a moving time horizon optimization approach, allowi...
Solar based electricity generations have experienced a strong and impactful growth in the recent ... more Solar based electricity generations have experienced a strong and impactful growth in the recent years. The regulation, scheduling, dispatching and unit commitment of intermittent solar power is dependent on the accuracy of the forecasting methods. In this paper, a robust Expanded Extreme Learning Machine (EELM) is proposed to accurately predict the solar power for different time horizon and weather condition. The proposed EELM technique has no randomness due to the absence of random input layer weights and takes very less time to predict the solar power efficiently. The performance of the proposed EELM is validated through historical data collected from National Renewable Energy Laboratory (NREL) through various performance metrics. The efficacy of the proposed EELM method is evaluated against basic ELM and Functional Link Neural Network (FLNN) for 5 minute and 1 hour ahead time horizon.
For power grids with high penetration of distributed energy resources (DERs), microgrids can prov... more For power grids with high penetration of distributed energy resources (DERs), microgrids can provide operation and control capabilities for clusters of DERs and load. Furthermore, microgrids enhance resilience of the hosting bulk power grid if they are enabled to serve critical load beyond the jurisdiction of the microgrids. For widespread deployment of microgrids, a modular and standardized Microgrid Building Block (MBB) is essential to help reduce the cost and increase reliability. This paper proposes the conceptual design of an MBB with integrated features of power conversion, control, and communications, resulting in a systemwide controller for the entire microgrid. The results of a feasibility study indicate that, in a utility-connected mode, MBB-based microgrids can exchange power with the hosting power grid while serving regulation and optimal dispatch functions. In a resiliency (islanded) mode when the microgrid is disconnected from the utility system, the MBB control system acts to stabilize the system frequency and voltage under small or large disturbances. The microgrid controller is supported by a communication system that meets the latency requirements imposed by the microgrid dynamics as well as data acquisition time. The extended IEEE 13-node system is used as a microgrid model to validate the proposed MBB design and functionality.
Machine learning (ML)-based intrusion detection system (IDS) approaches have been significantly a... more Machine learning (ML)-based intrusion detection system (IDS) approaches have been significantly applied and advanced the state-of-the-art system security and defense mechanisms. In smart grid computing environments, security threats have been significantly increased as shared networks are commonly used, along with the associated vulnerabilities. However, compared to other network environments, ML-based IDS research in a smart grid is relatively unexplored although the smart grid environment is facing serious security threats due to its unique environmental vulnerabilities. In this paper, we conducted an extensive survey on ML-based IDS in smart grids based on the following key aspects: (1) The applications of the ML-based IDS in transmission and distribution side power components of a smart power grid by addressing its security vulnerabilities; (2) dataset generation process and its usage in applying ML-based IDSs in the smart grid; (3) a wide range of ML-based IDSs used by the surv...
Distributed energy resource management systems (DERMS) are increasingly deployed to manage potent... more Distributed energy resource management systems (DERMS) are increasingly deployed to manage potential adverse impacts of DERs on distribution feeder voltages. However, communication channels used by DERMS controllers, along with the unmanned substations where they are deployed, provide a target for cyber attackers to cause severe voltage disturbances. The voltage problems can damage equipment, trip inverters, and cause undesirable operations of voltage regulating devices. In this paper, using detailed cyber-physical models, the various cyberattack paths an adversary may use to conduct falsified control command cyberattacks are presented. Robust and fast acting centralized cyber layer and de-centralized inverter intrusion detection are also proposed. This two-tiered system can detect and mitigate these cyberattacks before the power system layer is impacted, even if the DERMS controller itself has been compromised. The cyberattack models and proposed intrusion detection techniques have been validated using IEEE test feeders.
The rapid escalation in plug-in electric vehicles (PEVs) and their uncoordinated charging pattern... more The rapid escalation in plug-in electric vehicles (PEVs) and their uncoordinated charging patterns pose several challenges in distribution system operation. Some of the undesirable effects include overloading of transformers, rapid voltage fluctuations, and over/under voltages. While this compromises the consumer power quality, it also puts on extra stress on the local voltage control devices. These challenges demand a well-coordinated and power network-aware charging approach for PEVs in a community. This paper formulates a realtime electric vehicle charging scheduling problem as a mixed-integer linear program (MILP). The problem is to be solved by an aggregator that provides charging service in a residential community. The proposed formulation maximizes the profit of the aggregator, enhancing the utilization of available infrastructure. With prior knowledge of load demand and hourly electricity prices, the algorithm uses a moving time horizon optimization approach, allowing an unk...
The rapid escalation in plug-in electric vehicles (PEVs) and their uncoordinated charging pattern... more The rapid escalation in plug-in electric vehicles (PEVs) and their uncoordinated charging patterns pose several challenges in distribution system operation. Some of the undesirable effects include overloading of transformers, rapid voltage fluctuations, and over/under voltages. While this compromises the consumer power quality, it also puts on extra stress on the local voltage control devices. These challenges demand for a well coordinated and power network-aware charging approach for PEVs in a community. This paper formulates a real-time electric vehicle charging scheduling problem as an mixed-integer linear program (MILP). The problem is to be solved by an aggregator, that provides charging service in a residential community. The proposed formulation maximizes the profit of the aggregator, enhancing the utilization of available infrastructure. With a prior knowledge of load demand and hourly electricity prices, the algorithm uses a moving time horizon optimization approach, allowi...
Solar based electricity generations have experienced a strong and impactful growth in the recent ... more Solar based electricity generations have experienced a strong and impactful growth in the recent years. The regulation, scheduling, dispatching and unit commitment of intermittent solar power is dependent on the accuracy of the forecasting methods. In this paper, a robust Expanded Extreme Learning Machine (EELM) is proposed to accurately predict the solar power for different time horizon and weather condition. The proposed EELM technique has no randomness due to the absence of random input layer weights and takes very less time to predict the solar power efficiently. The performance of the proposed EELM is validated through historical data collected from National Renewable Energy Laboratory (NREL) through various performance metrics. The efficacy of the proposed EELM method is evaluated against basic ELM and Functional Link Neural Network (FLNN) for 5 minute and 1 hour ahead time horizon.
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Papers by Nitasha Sahani