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High penetration of Distributed Energy Resources (DERs), fundamental load behavior changes, controllable loads, and significant increase in Electrical Vehicles (EVs) lead to complex dynamic behavior of the electric distribution system. Increasing number of components also means more measurements, more data and more data anomalies. Detecting, classifying and localizing these anomalies are important for situational awareness, and at the same time, very challenging given increasing complexity of the system. Highly accurate and high-resolution analytical techniques are needed to support anomaly detection, classification and localization (AD-C-L) for monitoring, root cause analysis and decision making. This paper provides comprehensive review and analysis of the existing spatio-temporal AD-C-L techniques within the distribution system. Challenges for specific problems in AD-C- L have been also discussed in this paper. Existing AD-C- L techniques have been categorized and synthesized for specific merits and limitations of multiple Machine Learning (ML) methodologies using common developed metrics of performance. The comparative analysis is summarized and presented with the open research challenges and path forward for future research needs.

Power system resiliency has become a key priority for multiple stakeholders, given an observed increase in severe weather events in recent years and impact on the power distribution grid. Distribution network operators need to assess and analyze the resiliency of the system through carefully designed visualization driven by data and model based analytics. Operators require real-time data visualization of system states and resiliency indicators to make correct operational decisions and to control actions to minimize system impact. This paper describes a resilience-driven visualization tool, the Real-Time Resilience Management System Tool (RT-RMS), developed to assist operators in decision making and resilience assessment. RT-RMS utilizes multi-dimensional resilience metrics, geospatial visualization, and data monitors assessing resilience indicators and other key data points. This paper describes the basis of design, design considerations, open-source software components, and use cases demonstrating implementation and discusses the importance of the presented tool, especially in geographically isolated communities, where resilience is valued more than economic operation. The challenge of handling large amounts of data in a web-based application is analyzed by implementing a three-layered architecture and applying RESTful APIs in the back-end. Case studies are presented to highlight key RT-RMS features.

On account of the apparent benefits of microgrids (MGs) and trends in MGs deployment, it is expected that the future distribution grid will consist multiple MGs. The networked microgrid system (NMS) allows network formation with multiple possibilities, while continue to supply energy to critical loads and enhance resiliency of the distribution grid given extreme events. Interconnection architecture and energy management of the NMS will be challenging to meet resiliency requirements. The energy management of a single microgrid (MG) is widely reported in literature, however limited for a NMS. With the increased advantages of DC system, a NMS energy management and control architecture is proposed for a specific configuration of hybrid AC/DC system. The developed hierarchical decentralized droop control scheme can regulate the power exchange, control voltage and also facilitate the demand sharing of the NMS. The seamless ‘plug-in and -out’ of MGs in the NMS are validated using the dynamic modeling and simulation of the distribution feeders using PSCAD/EMTDC. Results are provided for normal operation and operation after adverse events.

Objectives The primary objectives of the study are to determine the effectiveness of the Kaba Sura Kudineer (KSK) & Nilavembu Kudineer (NVK) along with standard Allopathy Treatment to compared with Placebo (Decaffeinated Tea) with standard Allopathy Treatment in the management of Symptomatic COVID 19 patients and also in reduction of Hospital Stay Time & Changes in Immunological (IL6) and Bio Chemical Markers (Ferritin, CRP, D-Dimer and LDH). The secondary objectives are to evaluate the safety of the trial medicines and their effects in the reduce the risks of the disease. In addition, to document the profile of Symptomatic COVID 19 patients as per Siddha Principles. Trial Design A Double Blinded, Three arm, Single Centre, Placebo Controlled, Exploratory and comparative Randomized Controlled Trial Participants Patients who were admitted to the COVID Care Centre at Govt. Institute of Medical Sciences. Noida in India will be recruited. These will be patients with Mild and Moderate symptoms with laboratory confirmed COVID 19 (RT – PCR Tested Positive) aged 18-65, willing and consenting to participate. Intervention and comparator Arm I: Decaffeinated Tea (Placebo – similar in taste and appearance to the other Two Decoctions), 60 Ml Morning and Night after Food, along with standard Allopathy Treatment for 10 days. Arm II: Nilavembu Kudineer (The Siddha Medicines which is used as a standard Anti-Viral drug for the past Pandemics by Siddha Physicians) 60 Ml Morning and Night after Food, along with standard Allopathy Treatment for 10 days. Arm III: Kaba Sura Kudineer (The Siddha Medicine which is proposed to be used as a Treatment for COVID 19 based on Siddha Literature) 60 Ml Morning and Night after Food, along with standard Allopathy Treatment for 10 days. The investigational drugs are registered products under the Govt.of India and bought from GMP Certified Manufacturing Units. Main Outcomes Primary outcomes: Reduction in Viral load of SARS-CoV-2 at the end of treatment (10 days). 2. Time taken to convert Patient from symptomatic to Asymptomatic based on Reduction in clinical symptoms (10 days). 3. Effect of drugs inflammatory markers (IL6,) at the end of treatment (10 days). 4. Reduction in hospital stay time (20 days follow up). (Based on RT PCR CT Value 3rd, 6th if needed 10th day). (Based on IL 6 Value needed 10th day or IL6 value on turning negative. (entry level/exit level). Secondary outcomes (10 days): Reduction in use of Intensive Supportive Care. 2. Reduction in incidence of complications (Acute Respiratory Distress Syndrome, other systemic complications). 3. MuLBSTA score for viral pneumonia (multinodular infiltration, hypo-lymphocytosis, bacterial co infection, Total Leucocyte Count (TLC ≤ 0.8 x 109/L), smoking history, hyper-tension and age) score. 4. Laboratory markers (Haematological & Biochemical Markers). 5. Adverse events/effects Siddha-based measurements. 6. Siddha Udaliyal assessment by using Yakkai Ilakkanam (YI) Tool to diagnose body condition for covid-19 patients. Randomisation The assignment of the participants into 3 Groups will be allocated in 1:1:1 Ratio through randomization Blocks in Microsoft Excel by a Statistician who is not involved in the study. The allocation scheme will be made by another statistician by using a closed envelope after the assessment of eligibility and Informed consent procedures. The groups will be balanced for age and sex with 3:1 Ratio in each group for mild: severe COVID-19 symptoms. Blinding The Study is Double Blinded. Participants and Investigators were blinded. Numbers to be randomized (Sample size) Sample size could not be calculated, Since there are no prior trials on KSK and NVK as a comparative trial. In addition, there are no prior trials on KSK and NVK in this region. A total Number of 120 Patients, 40 each in 3 groups will be recruited in 1:1:1 Ratio. Trial Status Protocol Number : SCRUND GIMS Noida Study 1,Version: 2.0 Protocol Date : 20.08.2020 The recruitment period is completed for the trial. The Trial started its recruitment on 22.8.2020. We anticipate study including data analysis will finish in January 2021. This is to state that it was a late submission from authors for publication of the protocol to the BMC, after enrolment in the study was over. Trial Registration The trial protocol was registered with CTRI (Clinical Trial Registry of India) and number is CTRI/2020/08/027286 on 21.08.2020 Full Protocol The full Protocol is attached as an additional file, Accessible from the Trials website (Additional file 1). In the interest in expediting dissemination of this material, the familiar formatting has been eliminated. This letter serves as a summary of the key elements of the full protocol. The Study protocol has been reported in accordance with the SPIRIT guidelines.

With the increased penetration of power electronics based distributed energy resources (DERs), its control and automation in traditional grid gives rise to more complex operation of power system. It is difficult to realize such a multidisciplinary power system using a single real-time simulator. Co-simulation is a versatile technique to split the large system models into multiple subsystems and components. In case of limited capability of a single simulator, these components can be modeled in more than one simulators and can be solved in parallel. The technique is also useful to interconnect simulators of different make which utilizes propriety software tools. In this paper, a co-simulation based testbed is developed, which can facilitate a platform for validation of various power engineering algorithms. Two different make of real time simulators, Typhoon Hardware-In-Loop (HIL) and Real Time Digital Simulator (RTDS) have been interfaced using peer-to-peer and VILLASnode based UDP/IP socket communication. In this work, an Ideal Transformer model (ITM) based approach is utilized to couple the simulators where the information is exchanged using the dynamic phasors. An illustrative testbed of CIGRE low voltage distribution system is developed using both types of communication methods and the results are compared in terms of latency, jitter and round trip time. In addition, the effects of packet loss, data corruption and duplication on system transient are also presented in this paper. The results demonstrate the capability of the testbed to evaluate the controllers for microgrid, DERs and protective devices in the distribution network at different levels of deployment.

In recent years, the cyber and physical extreme events have increased and impacted the power system operations. Although there are multiple work reported for improving the resiliency of the power grid systems, there are a limited number of resiliency management tools available to the grid operators. Addressing the data quality issue is critical before feeding the measurements for situational awareness and decision-making using resiliency management tools. In this work, we describe an automated ML-based measurement data anomaly mitigation technique that uses regression, clustering, deep learning techniques as a base detector. Maximum Likelihood Criterion (MLE) based ensemble of these base detectors helps in anomaly detection and mitigation using SyncAED tool and feeding data for enhanced resiliency using a tool: Cyber-Physical Transmission Resiliency Assessment Metric (CP-TRAM). CP-TRAM utilizes real-time power grid data and aims to assist operators in measuring resiliency and ensuring the energy supply to critical loads given a cyber-attack or a natural disaster. This paper discusses the multiple ML algorithms for data anomaly detection, the basis of software design considerations, open-source software components, and use cases for the prototype developed tools.

Cyber-power resiliency analysis of the distribution system is becoming critical with increase in adverse cyberevents. Distribution network operators need to assess and analyze the resiliency of the system utilizing the analytical tool with a carefully designed visualization and be driven by data and model-based analytics. This work introduces the Cyber-Physical Security Assessment Metric (CP-SAM) visualization tool to assist operators in ensuring the energy supply to critical loads during or after a cyber-attack. CP-SAM also provides decision support to operators utilizing measurement data and distribution power grid model and through well-designed visualization. The paper discusses the concepts of cyber-physical resiliency, software design considerations, open-source software components, and use cases for the tool to demonstrate the implementation and importance of the developed tool.

This paper presents a three-phase unbalanced continuation power flow tool (TPCPF) to analyze the voltage stability of distribution systems with high penetration of distributed energy resources (DER). Analyzing distribution system voltage stability with DER will allow high penetration of renewable energy required for sustainability. Developed algorithm can analyze voltage stability for both the meshed and radial system as well as for the balanced and unbalanced three-phase distribution systems. Developed tool allows the voltage stability analysis of the system in order to facilitate the proper planning, operation, control and management of the distribution system. The impact of DER) on the voltage stability of several test cases have been analyzed considering constant power (PQ) and regulated-voltage (PV) modes of operation of DER units. Results demonstrate that developed continuation power flow tool can be used to perform voltage stability analysis for active distribution systems.

Traditionally, electric utilities have used local (non-optimal) or centralized (optimal) approaches for voltage control in electric distribution grids. However, with increased penetration of distributed energy resources (DERs), voltage control has become increasingly challenging for distribution grid operators. This is mainly due to intermittency and variability in power injections of DERs. Digitalization and automation driven by smart grid development provides an opportunity to find balance between non-optimal local and complex but optimal centralized voltage control. Distributed optimization approaches enabled by automation utilize DERs to mitigate such voltage issues in optimal and resilient manner. This paper proposes an optimal distributed voltage control algorithm for three phase unbalanced distribution systems using augmented Lagrangian multiplier theory and primal-dual gradient algorithms. The algorithm requires local voltage measurements and communication among neighbouring buses while meeting hard limits on reactive power injections on voltage control devices and DERs. Developed algorithm is validated for both single phase and three phase unbalanced distribution systems voltage control using OpenDSS.

Due to the high cost of peak hour power generation and a push towards sustainability, the need for demand response (DR) is increasing. Compared to commercial-level DR, residential-level DR is more challenging. Residents are reluctant to participate, and DR controllers lack sufficient real-time activity information to balance energy savings with residents' need for comfort and convenience. To address the above challenges, we propose a sensor data-driven activity-based controller for heating, ventilation, and air conditioning devices. Using our proposed novel strategy, resident activities are recognized in real-time through a random forest machine learning approach. Integrating activity information and forecasted electricity pricing, the proposed controller can simultaneously reduce energy consumption for sustainability and maintain resident constraints for comfort based on recognized activities. Results demonstrate the superiority of the proposed approach.

Phasor Measurement Units (PMUs) are capable of providing high speed and high-resolution wide area measurements typically used for situational awareness and advanced applications. A PMU for distribution systems also known as micro-PMU can provide data even at higher reporting rates. Despite its apparent advantages, reporting at a much higher rate introduces additional communication burden. This constraint will be even more critical with ‘point to wave’ reporting possible in future. Several applications do not require the measurements at that high reporting rate, e.g. state estimation or condition monitoring. Possible congestion in the network with several thousands of these devices may result in either significant delays or packet drops. This work is an efforts towards providing solutions for above challenges with an adaptive PMU, which is capable of adjusting reporting rate based on the communications network bandwidth and/or the application needs.

Event detection and localization is at the heart of all automated system restoration processes. Localizing an event can help in alleviating the root cause behind such disturbances. During an event, the operating states of the power distribution network may undergo significant changes. These variations are more prominent in buses which are close to the source of the event. Thus, events in a distribution system can be localized by analyzing the changes in the system states. This paper proposes an event locator based on the results of a event triggered distribution network state estimator. The state estimation is performed using measurements from a limited number of micro phasor measurement units (μ PMUs). An l 1 regularization based state estimator is designed to estimate the change in system states, using limited number of μ PMU measurements. The results of the state estimation process are further analyzed to locate any event. The algorithm is designed for active distribution networks with radial and meshed topologies. The proposed method is validated on a 13-node test distribution feeder simulated using OPAL-RT real-time simulator.

Increased power demands, push for economics and limited investment in grid infrastructure have led utilities to operate power systems closer to their stability limits. Voltage instability may trigger cascade tripping, wide-area voltage collapse, and power blackouts. Real-time voltage stability monitoring possible with deployment of phasor measurement units (PMUs) is essential to take proactive control actions and minimise the impact on the system. This study presents a novel online algorithm for (a) hybrid perturbation analysis based voltage stability monitoring (HPVSM), (b) including Q-limit in voltage stability index, and (c) real-time security analysis using voltage stability index. HPVSM-based voltage stability index is computed using the data obtained from linear state estimator and PMU measurements. Typically measurement-based schemes ignore the impact of generator Q-limits and security analysis is not feasible. The proposed HPVSM-based index considers the impact of generator Q-limits violations by anticipating the critical generators using real-time PMU measurements. Contingencies are ranked using the proposed voltage stability index for security analysis. Results simulated for the 9-bus WECC, IEEE 14, 57, and 118 bus systems highlight the superiority of the proposed method in real-time voltage stability and security analysis.

Demand response (DR) has emerged as a very important part of the future smart grid to enhance the power system reliability and frequency stability. This study presents the benefits of DR control and battery energy storage (BES) on frequency regulation in wind farm integrated power system. In this study, a generalised load frequency control model of a hybrid hierarchical DR control mechanism is developed by incorporating the inherent system parameters such as frequency dead-band, aggregated generations/devices and communication time delay and so on. The mathematical formulation for frequency stability and sensitivity analysis are carried out with respect to the frequency change coefficient for switching OFF the DR devices and frequency regulation service shared by conventional supplementary control, DR and BES. The extensive simulation results are presented for system frequency response (SFR) to support the theoretical analysis. It is found that DR has a significant effect to stabilise SFR. The observations drawn from analysis can be helpful for system operators/regional transmission organisation to investigate or plan for deciding the frequency threshold, minimum turn OFF time for individual DR devices, frequency coefficient factor and percentage share of frequency regulation services to restore the frequency quickly.

Integration of distributed energy resources has been significantly increasing in the electric power grid. One of the predominant renewable energy sources, wind energy, possess a significant challenge for grid operations, due to its intermittency, uncertainity, and variability. In changing operating conditions with wind uncertainity, part of wind may need to be curtailed for the safe utilization of the transmission system. In order to minimize the wind power curtailment and to keep the power flow on a transmission line under limits, a cyber resilient distributed remedial action scheme (DRAS) is developed in this paper. The remedial action scheme (RAS) is formulated as an optimization problem, considering the real-power flow constraints of the transmission lines and given wind variability. In this paper, DRAS is developed to operate even with failures in the computing nodes to make it cyber-resilient. Distributed simplex algorithm is used for optimization in the distributed RAS. The DRAS is implemented in a decentralized platform called Resilient Information Architecture Platform for Smart Grid to enable cyber resiliency of DRAS. The effectiveness of the proposed approach is validated through offline simulations of the New England 39-bus system and online simulations of IEEE 14-bus test system. A cyber–physical testbed utilizing real time digital simulator, phasor measurement units, and BeagleBones has been used for the online simulation and validation of the developed cyber-resilient RAS.

The development of more resilient grids is an on-going effort that has attracted multiple participants. Within this context, Distributed Energy Resources, along with transactive energy mechanisms are being considered as the key driving technologies. Yet their under-laying communication capabilities might introduce additional cybersecurity risks that must be analysed. This paper proposes a cyber-physical microgrid testbed using OpenDSS, Mininet and IEEE 2030.5 that can be used to study the grid’s cyber-resilience under various scenarios. For critical microgrid installations, it is essential that the critical loads are served in spite of multiple contingencies. A resiliency analysis is proposed for a military microgrid to study its performance with these contingencies and the results are analyzed.

The push to automate and digitize the electric grid has led to widespread installation of Phasor Measurement Units (PMUs) for improved real-time wide-area system monitoring and control. Nevertheless, transforming large volumes of high-resolution PMU measurements into actionable insights remains challenging. A central challenge is creating flexible and scalable online anomaly detection in PMU data streams. PMU data can hold multiple types of anomalies arising in the physical system or the cyber system (measurements and communication networks). Increasing the grid situational awareness for noisy measurement data and Bad Data (BD) anomalies has become more and more significant. Number of machine learning, data analytics and physics based algorithms have been developed for anomaly detection, but need to be validated with realistic synchophasor data. Access to field data is very challenging due to confidentiality and security reasons. This paper presents a method for generating realistic synchrophasor data for the given synthetic network as well as event and bad data detection and classification algorithms. The developed algorithms include Bayesian and change-point techniques to identify anomalies, a statistical approach for event localization and multi-step clustering approach for event classification. Developed algorithms have been validated with satisfactory results for multiple examples of power system events including faults and load/generator/capacitor variations/switching for an IEEE test system. Set of synchrophasor data will be available publicly for other researchers.

We demonstrate Kronos, a framework and system that automatically extracts highly dynamic knowledge for complex event analysis in Cyber-Physical systems. Kronos captures events with anomaly-based event model, and integrates various events by correlating with their temporal associations in realtime, from heterogeneous, continuous cyber-physical measurement data streams. It maintains a lightweight highly dynamic knowledge base, enabled by online, window-based ensemble learning and incremental association analysis for event detection and linkage, respectively. These algorithms incur time costs determined by available memory, independent of the size of streams. Exploiting the highly dynamic knowledge, Kronos supports a rich set of stream event analytical queries including event search (keywords and query-by-example), provenance queries ("which measurements or features are responsible for detected events?"), and root cause analysis. We demonstrate how the GUI of Kronos interacts with users to support both continuous and ad-hoc queries online and enables situational awareness in Cyber-power systems, communication, and traffic networks.

Operation and management of unbalanced distribution network (UDN) is challenging and complex for optimum utilization of active resources such as distributed generation and battery energy storage systems (BESSs). In order to exploit the inherent unbalancing of UDNs as an advantage in exploiting the most benefits from BESS, a phase-sensitive day-ahead dispatch strategy for BESS is proposed. The objectives incorporated in the proposed multiobjective, multiperiod optimization include a) maximizing peak shaving, b) improving voltage profile, c) loss minimization, d) reducing tap changer operations of voltage regulators, and e) minimizing discharge–recharge cycles. A two-stage fuzzy multiobjective optimization platform is developed to 1) represent uncertainty in load demand, 2) incorporate a human-friendly linguistic interface, and 3) provide flexibility to operator for changing priorities as a function of time. A novel application of max-average and max-product compositions in fuzzy systems is employed for multiperiod optimization. The effectiveness of the proposed approach is tested on an IEEE 37-node standard test feeder with comparative results of phase-balanced and phase-sensitive BESS dispatch. It is further validated on a feeder in Northwest USA with an existing BESS. Single-objective and multiobjective optimization results with multiple Pareto solutions demonstrate the stability and robustness of the proposed approach.

In this paper, a multihypothesis-based centralized Failure Diagnosis in Protection System (FDProSys) tool using phasor measurement units (PMU) data has been proposed to systematically and automatically detect malfunctions or failures in protective devices. The proposed diagnostic algorithm and FDProSys tool employs synchrophasor measurements to select a section of the network called ProNet, with possible device malfunction. Multiple hypotheses have been generated automatically based on node incidence matrices and multihypothesis theory. The malfunction of the protective devices in the presence of fault and relay operation is identified by comparing the five-digit message of the real event with multiple hypotheses. Trustworthiness of PMU data caused by common failures, such as communication outage and GPS problems, has also been considered by the proposed algorithm using the PMU status flag. The developed tool FDProSys can also work with supervisory control and data acquisition data assisted by manual analysis with less accuracy and is robust enough to several PMU/relay failures. Simulation results using the IEEE 14-bus, IEEE 57-bus, and IEEE 300-bus systems demonstrate the accuracy of the developed algorithm.

Network operators and utilities are challenged with increasing extreme weather conditions, resulting in interrupted power supply to critical loads. Resiliency metrics, which can capture the level of preparedness to resist adverse impact of extreme conditions on a distribution system, can be leveraged in multiple ways to provide better operation of the network and design of the future systems. In this paper, a methodology to quantify resiliency and maintain power supply to critical loads (CLs) during extreme contingencies has been proposed. Resiliency evaluation of power distribution system has been defined as a multi-criteria decision making problem and quantified using graph theoretic approach and Choquet integral. The algorithm proposed in this paper to calculate the resiliency for all feasible network configurations supplying CLs in a network is useful in planning as well as operation of the distribution network. The application of the proposed algorithm is demonstrated through several case studies using two proximal CERTS microgrids and IEEE 123 node distribution system. Simulation studies are also provided for planning of resilient network, by placing additional switches in the considered distribution systems with microgrid.

It is essential to improve the resiliency of power distribution systems (PDS) given the increase in extreme weather events, number of malicious threats, and consumers’ need for higher reliability. This paper provides a formal approach to evaluate the operational resiliency of PDS and quantify the resiliency of a system using a code-based metric. A combination of steady state and dynamic simulation tools is used to determine the resiliency metric. Dynamic simulation tools help analyzing the impact of short-term events, which might affect operational resiliency in the long term. A dynamic optimization algorithm for changing operating criteria to increase the sustainability of the most critical loads has been proposed. The proposed theoretical approach is validated using a simple PDS model and simulation results demonstrate the ability to quantify the resiliency using the proposed code-based metric. The time-dependent quantification of resiliency has been demonstrated on a test system of two connected Consortium of Electric Reliability Technology Solutions (CERTS) microgrids.

Commonly, voltage regulators and capacitor banks are used as control devices for voltage and reactive power (volt/VAr) control in distribution system. A specific mechanism can be developed using volt/VAr control for energy savings known as conservation voltage reduction (CVR). Active distribution system with high penetration of distribution generations (DG's) offers additional mechanism for volt/VAr control. The unique contribution of this paper is development of a computational algorithm for intelligent volt/VAr optimization and control of complex distribution networks with active participation from DG's to maximize overall network energy savings through CVR. Proposed volt/VAr optimization problem is solved to find the optimal voltage regulator settings, switched capacitor states, and voltage magnitude of DG controlled bus. This work presents a unique coordinated volt/VAr architecture that can find application in distribution management systems. Particle swarm optimization has been used given compatibility with combinatorial variables, robustness in solving nonlinear optimization problems, and ease of implementation. The results have been validated against an exhaustive search strategy for the IEEE 13 bus and IEEE 37 bus distribution system, and also using commercial distribution software SynerGEE. Results are also presented for a utility feeder with detailed analysis.

Several techniques have been developed to estimate the load parameters in power systems. Most of the existing algorithms mainly focus on estimating the parameters for offline studies. With on-going smart grid development, high-resolution data at faster rates are available to allow estimation of load parameters in real time. This paper addresses the challenges in online estimation of the load parameters using phasor measurement unit data. A novel adaptive search-based algorithm to estimate load model parameters is presented here. In this paper, a static load model is used with the Z (constant impedance), I (constant current), and P (constant power) components of the load. Developed estimation algorithms for the ZIP parameter estimation are validated using the IEEE 14-bus system and data provided by the industry collaborators. Simulation results demonstrate the accurate estimation of the ZIP load model using the developed method. Also, various techniques to eliminate anomalies in the input data for accurate estimation of the load parameters have been presented in this paper.

Intermittency of wind energy poses a great challenge for power system operation and control. Wind curtailment might be necessary at the certain operating condition to keep the line flow within the limit. Remedial action scheme (RAS) offers quick control action mechanism to keep reliability and security of the power system operation with high wind energy integration. In this paper, a new RAS is developed to maximize the wind energy integration without compromising the security and reliability of the power system based on specific utility requirements. A new distributed linear state estimation (DLSE) is also developed to provide the fast and accurate input data for the proposed RAS. A distributed computational architecture is designed to guarantee the robustness of the cyber system to support RAS and DLSE implementation. The proposed RAS and DLSE is validated using the modified IEEE-118 Bus system. Simulation results demonstrate the satisfactory performance of the DLSE and the effectiveness of RAS. Real-time cyber-physical testbed has been utilized to validate the cyber resiliency of the developed RAS against computational node failure.

Electric power systems are going through a major upgrade with the integration of advanced technologies to enable the smarter electric grid (SEG). The SEG will use information and communications technology to have enhanced controllability and will become more interactive. This ongoing change also necessitates educating professionals and future generation of engineers to manage evolving complexity of the electric grid. This paper presents experiences in the design and teaching of a unique multidisciplinary team-taught course on smart grid cyber infrastructure to provide a new generation of engineers with a solid foundation of smart grid concepts and their associated challenges. This paper identifies the course topics covered, learning objectives, and assessment activities for the class as well as lessons learned based on course evaluations obtained from multiple offerings. All of the course material is available in public domain and can be easily adopted at another educational institution.

In an active distribution network with increasing penetration of Distributed generators (DGs), it is critical to model DGs and keep the model updated for analysis. However, the generator parameter provided by the manufacturer changes because of aging and errors. Modeling parameters are typically provided by manufacturers and routinely adjusted with offline field testing. With availability of the high resolution real time measurement data such as $\mu$-PMUs, parameter can be estimated without taking generator offline. Model validation and calibration allows non-intrusive estimation of the parameters in distributed generators. This work aims at estimating the state and transient reactances of a distributed synchronous generator based combined heat and power generation unit connected to a medium voltage distribution network. The state and parameter estimation is performed on-line using phasor data collected by micro-phasor measurement unit ($\mu$-PMU) connected at the terminals of the machine. A constrained least-squares algorithm is used in recursion to track the changes in the state and parameter of the synchronous generator. The parameters are estimated considering different switching and dynamic events like faults, islanding, and load/capacitor switching. Results show the superiority of the proposed approach.

As Isolated DC microgrids (IDCMG) rely on renewable energy resources (RES) to reduce carbon emissions, running cost, etc., the reliability become serious issue due to their intermittency and uncertainty. Diesel generators (DG) are used as prevalent alternatives to suppress this issue. However, they suffer from startup delay, frequent switching, and uneven loading when embedded with RES that results in increased maintenance and carbon footprint. Besides, their fuel efficiency and emission characteristic vary with loading since most of DGs are driven by constant speed engines. Effective power management techniques play crucial role in conquering above issues. Hence, an exhaustive power management scheme (PMS) is proposed in this paper by considering the hybrid energy storage system. Further, this strategy includes the extreme scenarios like DG failure/scheduled maintenance, low power generation and battery charge. This paper considers AC bus coupled IDCMG as study system so that it can enclose both AC and DC loads/buses simultaneously. This makes system more comprehensive to resemble various practical isolated DC grids behavior. Simulation of the system is explored in real time digital simulator (RTDS) platform to show efficacy of proposed strategy.

Demand response analysis in smart grid deployment substantiated itself as an important research area in recent few years. Two-way communication between utility and users makes peak load reduction feasible by delaying the operation of deferrable appliances. Flexible appliance rescheduling is preferred to the users compared to traditional load curtailment. Again, if users’ preferences are accounted into appliance transferring process, then customers concede a little discomfort to help the utility in peak reduction. This paper presents a novel Utility-User Cooperative Algorithm (UUCA) to lower total electricity cost and gross peak demand while preserving users’ privacy and preferences. Main driving force in UUCA to motivate the consumers is a new cost function for their flexible appliances. As a result, utility will experience low peak and due to electricity cost decrement, users will get reduced bill. However, to maintain privacy, the behaviors of one customer have not be revealed either to other customers or to the central utility. To justify the effectiveness, UUCA is executed separately on residential, commercial and industrial customers of a distribution grid. Harmony search optimization technique has proved itself superior compared to other heuristic search techniques to prove efficacy of UUCA.

Natural calamities all over the globe have emphasized the need for operative solutions that can improve the resilience of Power Distribution Networks (PDN) under the scenario of the adverse effects. PDN is comprised of various loads, sources and storage system. Mixed-Integer linear programming (MILP) is used to schedule the battery as well as loads to improve the resiliency of the power system. This work aims at maintaining energy balance between sources and loads, subject to the constraints of serving critical loads for a possibly longer time. The enhancement of high critical loads' resiliency is achieved by managing the battery storage capacity in steps of allotted slabs. A resiliency metric, which is a qualitative index of the resiliency of the considered network is also determined. To test the effectiveness of the optimization technique on the considered network, it is tested for different restoration periods on MATLAB environment.

This paper provides the comparative analysis including concepts, validation and limitations of four different synchrophasor measurement based corridor voltage stability indices (VSI) namely: a) Thevenin based equivalent, b) Area Equivalent, c) Power Equivalent and d) Radial Equivalent and Independent (REI) equivalent. To validate these four approaches, continuation power flow solutions are used as a benchmark and equivalent 2-port network is developed for the corridor buses. Moreover, a threshold methodology is proposed for Area, Power and REI based equivalents to facilitate better operator awareness. An enhanced algorithm is proposed to take into account impact of nearest Q-limits for hybrid approach using measurement from phasor measurement units (PMUs) and minimal model information for corridor VSI.

The High Voltage Direct Current (HVDC) technology is typically used for power transfer over longer distances or asynchronous back-to-back connections of the ac networks, or connecting cluster of high power renewable energies to the grid. The performance of the power system can be impacted by the HVDC control and operations. Functionality of the HVDC controllers depend on the information and communication technologies and are vulnerable to cyber-attacks. Hence, the cyber-physical security analysis of the HVDC system needs to be investigated. This study is a step towards cyber-physical analysis for grid with HVDC system by analyzing: 1) the cyber system vulnerabilities of the HVDC system, 2) the integrated cyber-physical system model developement with HVDC technology, 3) the effect of cyber-attacks on the HVDC and impact on the dynamic voltage stability of the power system, and 4) the cyber-attack detection and mitigation. Also, different cyber-attack scenarios on the HVDC system are developed for analyzing the developed approach. The proposed analysis are validated by simulation of the several case studies on the integrated cyber-physical model of the IEEE 9-bus system with the HVDC technology.

Model validation and calibration of generating units are critical for using the most updated models in planning and operation of power grid. Typically, validation and calibration is done manually in couple of years at substations, which are not effective due to the test expenses and the requirement to take generator out of service for several days. However, high resolution data from Phasor Measurement Unit (PMU)s possible through automation in ongoing smart grid activities can help in validation and calibration process. Variations of signal-playback form the power grid dynamic simulator and comparing the results with PMU measurements for different events is one of the possible approach for model validation. Calibration requires optimizing generator parameters to fit with response obtained by field PMU measurements. This is a challenging problem to solve given multiple parameters to tune in generators and given limited PMU measurements. This paper automates the process of model validation and parameter calibration by using a commercial dynamic simulator, PSS/E and developing all the interfacing with Ensemble of Kalman Filter tool. Additionally, the advantage, and limitations of existing optimization methods for calibration, specially Ensemble Kalman Filter (EnKF) are discussed. A sensitivity test is devised to diminish the subset of to-be-calibrated parameters and our framework in PSS/E is proposed with simulation results. Advantage and limitations of the approach are discussed and guidelines for future works are presented.

Enhanced automation and control requires advanced sensors in the smart electric power grid. Phasor Measurement Unit (PMU) is one of the sensors with the fastest streaming data enabling advanced automation and control algorithms in the electric grid. After PMUs are initially tested, installed, and brought into service in the field, there are no established procedure for in-field calibration testing following the IEEE Synchrophasor Measurement Test Suite Specification (TSS). Hence, in-field remote testing of PMU is necessary to guarantee that PMU performs accurately when needed and are not affected with any software/hardware error that develops with time or hidden failure that was not captured before commissioning. This paper proposes a feasible architecture for in-field remote PMU testing using an FPGA platform and PPA software platform, as well as presents an architecture and accuracy requirement of a typical PMU calibrator. An algorithm implemented in the FPGA platform using a low-cost National Instrument compact RIO (NI-cRIO) is also proposed in this paper. The generated signal using the proposed method is demonstrated to be suitable for PMU static test cases. The accuracy of the proposed method and the in-built signal generation module of NI-cRIO is compared based on the frequency uncertainty and total harmonic distortion. The compliance results of a sample PMU for static test cases using the proposed method is also presented in this paper.

The complexity and vulnerability of the bulk power system are well known. Among all the factors that are important for maintaining the continuity and security of the power system, the importance of the power system operator lies at the top in the hierarchy. It is important to ascertain that the operator has the critical tools, knowledge, access to training, cognitive and technical skills and the experience for extreme contingency condition that may arise. In this paper, decision making ability of power system operators in extreme events with and without advanced power system tools has been analyzed by relating to their measured cognitive flexibility.

High penetration of renewable energy (RE) is highly expected for sustainable green power system. Photovoltaic (PV) is the most suitable form of renewable generation in present distribution system. However, in an existing feeder, the amount of PV accommodation is limited because of utility-established acceptable voltage limit, voltage unbalance, transformer rating, line thermal overloading limit, regulation equipment, protection co-ordination, feeder configuration, load profile and more. It is important for feeder operation and planning to calculate the amount of PV that can be hosted inside an existing feeder subject to satisfy voltage limit, thermal limit, and protection criteria - often referred to as feeder hosting capacity (FHC) or PV hosting capacity. PV has uncertainty due to inherent nature and further, PV ramp rate is much faster than regulator response time. Therefore, it is common practice to consider worst-case scenario. Usually FHC is a complex power system optimization problem using steady state calculations. It is not possible to explore all possible scenarios in a practical timeframe. Therefore, multiple pre-defined scenarios are generated from random Monte Carlo simulation. However, the authors propose a swarm based intelligent scenario (location) selection from local and global search experiences for faster and better solution. Simulation results show effectiveness of the proposed method.

This paper investigates control methodologies based on first principle model of an integrated AC/DC microgrid utilizing the dq-framework. The model shows high non-linearity and captures the dynamic interaction between the AC and DC subsystems of the microgrid for different types of controllers and disturbances. Simulation results show that the AC and DC subsystems are tightly dynamically coupled so that any disturbance in one subsystem induces transients in the other. It is shown that separate controllers in AC and DC subsystems can easily suppress disturbances originating in any subsystem of the grid. PSCAD simulation was used to further validate the results. The proposed method can be extended in modeling islanded or grid-connected microgrid with multiple generators and various types of DC and AC loads and disturbances.

The contemporary power distribution system is facing an increase in extreme weather events, cybersecurity threats and even physical threats such as terrorism. Therefore there is a growing interest towards resiliency estimation and improvement. In this paper the resiliency enhancement strategy by means of Distributed Energy Resources and Automated Switches is presented. Resiliency scores are calculated using Analytical Hierarchy Process. The developed algorithm was validated on the modified IEEE 123 node system. It provides the most resiliency feasible network that satisfies the primary goal of serving the critical loads.