arXiv subjects

Narayan Bhusal

Publications and source records attributed to Narayan Bhusal.

30 records · Page 2Linked to original sources

arXiv2020

A GA-based Approach to Eco-driving of Electric Vehicles Considering Regenerative Braking

As the deployment of low carbon transportation technologies, specifically electric vehicles (EVs), is increasing, the concept of their eco-driving is gaining significant attention. Contrary to the eco-driving techniques used in conventional internal combustion engine vehicles that do not have the capability of regenerative braking, this paper proposes a genetic algorithm (GA)-based eco-driving technique for EVs considering regenerative braking. In the proposed approach, the optimal or near-optimal combination of variables in the driving cycle of EVs is searched using GA. The proposed approach starts by generating an initial population of chromosomes, where all variables under consideration are encoded in each chromosome. This population of chromosomes is passed through crossover, mutation, and elitist-based selection over a certain number of generations, which results in a driving cycle with the least energy consumption. The proposed method is verified using two case studies. The results of the case studies show the capability of the proposed method in computing the minimum energy driving cycle.

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arXiv2020

Power System Dynamic State Estimation Using Extended and Unscented Kalman Filters

Accurate estimation of power system dynamics is very important for the enhancement of power system reliability, resilience, security, and stability of power system. With the increasing integration of inverter-based distributed energy resources, the knowledge of power system dynamics has become more necessary and critical than ever before for proper control and operation of the power system. Although recent advancement of measurement devices and the transmission technologies have reduced the measurement and transmission error significantly, these measurements are still not completely free from the measurement noises. Therefore, the noisy measurements need to be filtered to obtain the accurate power system operating dynamics. In this work, the power system dynamic states are estimated using extended Kalman filter (EKF) and unscented Kalman filter (UKF). We have performed case studies on Western Electricity Coordinating Council (WECC)'s $3$-machine $9$-bus system and New England $10$-machine $39$-bus. The results show that the UKF and EKF can accurately estimate the power system dynamics. The comparative performance of EKF and UKF for the tested case is also provided. Other Kalman filtering techniques alongwith the machine learning-based estimator will be updated inthis report soon.All the sources code including Newton Raphsonpower flow, admittance matrix calculation, EKF calculation, andUKF calculation are publicly available in Github

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arXiv2020

Spatial Mode Correction of Single Photons using Machine Learning

Spatial modes of light constitute valuable resources for a variety of quantum technologies ranging from quantum communication and quantum imaging to remote sensing. Nevertheless, their vulnerabilities to phase distortions, induced by random media, impose significant limitations on the realistic implementation of numerous quantum-photonic technologies. Unfortunately, this problem is exacerbated at the single-photon level. Over the last two decades, this challenging problem has been tackled through conventional schemes that utilize optical nonlinearities, quantum correlations, and adaptive optics. In this article, we exploit the self-learning and self-evolving features of artificial neural networks to correct the complex spatial profile of distorted Laguerre-Gaussian modes at the single-photon level. Furthermore, we demonstrate the possibility of boosting the performance of an optical communication protocol through the spatial mode correction of single photons using machine learning. Our results have important implications for real-time turbulence correction of structured photons and single-photon images.

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arXiv2020

Optimal Sizing and Siting of Multi-purpose Utility-scale Shared Energy Storage Systems

This paper proposes a nondominated sorting genetic algorithm II (NSGA-II) based approach to determine optimal or near-optimal sizing and siting of multi-purpose (e.g., voltage regulation and loss minimization), community-based, utility-scale shared energy storage in distribution systems with high penetration of solar photovoltaic energy systems. Small-scale behind-the-meter (BTM) batteries are expensive, not fully utilized, and their net value is difficult to generalize and to control for grid services. On the other hand, utility-scale shared energy storage (USSES) systems have the potential to provide primary (e.g., demand-side management, deferral of system upgrade, and demand charge reduction) as well as secondary (e.g., frequency regulation, resource adequacy, and energy arbitrage) grid services. Under the existing cost structure, storage deployed only for primary purpose cannot justify the economic benefit to owners. However, the delivery of storage for primary service utilizes only 1-50\% of total battery lifetime capacity. In the proposed approach, for each candidate set of locations and sizes, the contribution of USSES systems to grid voltage deviation and power loss are evaluated and diverse Pareto-optimal front is created. USSES systems are dispersed through a new chromosome representation approach. From the list of Pareto-optimal front, distribution system planners will have the opportunity to select appropriate locations based on desired objectives. The proposed approach is demonstrated on the IEEE 123-node distribution test feeder with utility-scale PV and USSES systems.

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arXiv2020

A Spanning Tree-based Genetic Algorithm for Distribution Network Reconfiguration

This paper presents a spanning tree-based genetic algorithm (GA) for the reconfiguration of electrical distribution systems with the objective of minimizing active power losses. Due to low voltage levels at distribution systems, power losses are high and sensitive to system configuration. Therefore, optimal reconfiguration is an important factor in the operation of distribution systems to minimize active power losses. Smart and automated electric distribution systems are able to reconfigure as a response to changes in load levels to minimize active power losses. The proposed method searches spanning trees of potential configurations and finds the optimal spanning tree using a genetic algorithm in two steps. In the first step, all invalid combinations of branches and tie-lines (i.e., switching combinations that do not provide power to some of loads or violate the radiality and connectivity conditions) generated by initial population of GA are filtered out with the help of spanning-tree search algorithm. In the second step, power flow analyses are performed only for combinations that form spanning trees. The optimal configuration is then determined based on the amount of active power losses (optimal configuration is the one that results in minimum power losses). The proposed method is implemented on several systems including the well-known 33-node and 69-node systems. The results show that the proposed method is accurate and efficient in comparison with existing methods.

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arXiv2020

A Sensitivity-based Approach for Optimal Siting of Distributed Energy Resources

This paper presents a sensitivity-based approach for the placement of distributed energy resources (DERs) in power systems. The approach is based on the fact that most planning studies utilize some form of optimization, and solutions to these optimization problems provide insights into the sensitivity of many system variables to operating conditions and constraints. However, most of the existing sensitivity-based planning criteria do not capture ranges of effectiveness of these solutions (i.e., ranges of the effectiveness of Lagrange multipliers). The proposed method detects the ranges of the effectiveness of Lagrange multipliers and uses them to determine optimal solution alternatives. Profiles for existing generation and loads, and transmission constraints are taken into consideration. The proposed method is used to determine the impacts of DERs at different locations, in the presence of a stochastic element (load variability). This method consists of sequentially calculating Lagrange multipliers of the dual solution of the optimization problem for various load buses for all load scenarios. Optimal sizes and sites of resources are jointly determined in a sequential manner based on the validity of active constraints. The effectiveness of the proposed method is demonstrated through several case studies on various test systems including the IEEE reliability test system (IEEE RTS), the IEEE 14, and 30 bus systems. In comparison with conventional sensitivity-based approaches (i.e., without considering ranges of validity of Lagrange multipliers), the proposed approach provides more accurate results for active constraints.

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arXiv2020

Cybersecurity of Electric Vehicle Smart Charging Management Systems

In concept, a smart charging management system (SCMS) optimizes the charging of plug-in vehicles (PEVs) and provides various grid services including voltage control, frequency regulation, peak shaving, renewable energy integration support, spinning reserve, and emergency demand response. These functionalities largely depend upon data collected from various entities such as PEVs, electric vehicle supply equipment (EVSE), service providers, and utilities. SCMS can be susceptible to both cyber and physical threats (e.g. man-in-the-middle attack, data intrigued attack, denial of charging, physical-attack) due to interactions of and interdependencies between cyber and physical components. Cyber-physical threats through highly connected malware vectors raise various concerns including public safety hazards to vehicle operators and those in the immediate vicinity as well as disruptions to electric grid operations. This paper describes the concept of SCMS and provides a comprehensive review of the cybersecurity aspects of EVSEs and SCMSs with their possible impacts on the power grid and society. It also contributes to the development of cybersecurity measures to the SCMSs. Various functions of SCMS are reviewed in detail including peak shaving, demand charge reduction, frequency regulation, spinning reserve, renewable integration support, distribution congestion management, reactive power compensation, and emergency demand response with unidirectional PEVs charging. Also, a critical literature survey on current practices of SCMS cybersecurity is provided to explore major impacts and challenges of cyber-physical attacks and to identify research gaps and vulnerabilities in currently available SCMSs technologies.

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arXiv2020

Sizing of Movable Energy Resources for Service Restoration and Reliability Enhancement

The frequency of extreme events (e.g., hurricanes, earthquakes, and floods) and man-made attacks (cyber and physical attacks) has increased dramatically in recent years. These events have severely impacted power systems ranging from long outage times to major equipment (e.g., substations, transmission lines, power plants, and distribution system) destruction. Distribution system failures and outages are major contributors to power supply interruptions. Network reconfiguration and movable energy resources (MERs) can play a vital role in supplying loads during and after contingencies. This paper proposes a two-stage strategy to determine the minimum sizes of MERs with network reconfiguration for distribution service restoration and supplying local and isolated loads. Sequential Monte Carlo simulations are used to model the outages of distribution system components. After a contingency, the first stage determines the network reconfiguration based on the spanning tree search algorithm. In the second stage, if some system loads cannot be fed by network reconfiguration, MERs are deployed and the optimal routes to reach isolated areas are determined based on the DSPA. The traveling time obtained from the DSPA is incorporated with the proposed sequential Monte Carlo simulation-based approach to determine the sizes of MERs. The proposed method is applied on several distribution systems including the IEEE-13 and IEEE-123 node test feeders. The results show that network reconfiguration can reduce the required sizes of MERs to supply isolated areas.

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arXiv2020

Modeling of Natural Disasters and Extreme Events in Power System Resilience Enhancement and Evaluation Methods

The frequency of disruptive and newly emerging threats (e.g. man-made attacks--cyber and physical attacks; extreme natural events--hurricanes, earthquakes, and floods) has escalated dramatically in the last decade. Impacts of these events are very severe ranging from long power outage duration, major power system equipment (e.g. power generation plants, transmission and distribution lines, and substation) destruction, and complete blackout. Accurate modeling of these events is vitally important as they serve as mathematical tools for the assessment and evaluation of various operations and planning investment strategies to harden power systems against these events. This paper provides a comprehensive and critical review of current practices in the modeling of extreme events, system components, and system response for resilience evaluation and enhancement, which is a very important stepping stone toward the development of complete, accurate, and computationally attractive modeling techniques. The paper starts with reviewing existing technologies to model the propagation of extreme events and then discusses the approaches used to model impacts of these events on power system components and system response. This paper also discusses the research gaps and associated challenges, and potential solutions to the limitations of the existing modeling approaches.

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arXiv2020

Optomechanical entanglement at room temperature: a simulation study with realistic conditions

Quantum entanglement is the key to many applications like quantum key distribution, quantum teleportation, and quantum sensing. However, reliably generating quantum entanglement in macroscopic systems has proved to be a challenge. Here, we present a detailed analysis of ponderomotive entanglement generation which utilizes optomechanical interactions to create quantum correlations. We numerically calculate an entanglement measure -- the logarithmic negativity -- for the quantitative assessment of the entanglement. Experimental limitations, including thermal noise and optical loss, from measurements of an existing experiment were included in the calculation, which is intractable to solve analytically. This work will play an important role in the development of ponderomotive entanglement devices.

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arXiv2020

Quantum-Limited Squeezed Light Detection with a Camera

We present a technique for squeezed light detection based on direct imaging of the displaced-squeezed-vacuum state using a CCD camera. We show that the squeezing parameter can be accurately estimated using only the first two moments of the recorded pixel-to-pixel photon fluctuation statistics, with accuracy that rivals that of the standard squeezing detection methods such as a balanced homodyne detection. Finally, we numerically simulate the camera operation, reproducing the noisy experimental results with low signal samplings and confirming the theory with high signal samplings.

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arXiv2019

Identification of Light Sources using Machine Learning

The identification of light sources represents a task of utmost importance for the development of multiple photonic technologies. Over the last decades, the identification of light sources as diverse as sunlight, laser radiation and molecule fluorescence has relied on the collection of photon statistics or the implementation of quantum state tomography. In general, this task requires an extensive number of measurements to unveil the characteristic statistical fluctuations and correlation properties of light, particularly in the low-photon flux regime. In this article, we exploit the self-learning features of artificial neural networks and naive Bayes classifier to dramatically reduce the number of measurements required to discriminate thermal light from coherent light at the single-photon level. We demonstrate robust light identification with tens of measurements at mean photon numbers below one. Our work demonstrates an improvement in terms of the number of measurements of several orders of magnitude with respect to conventional schemes for characterization of light sources. Our work has important implications for multiple photonic technologies such as LIDAR and microscopy.

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