arXiv subjects

Anurag Srivastava

Publications and source records attributed to Anurag Srivastava.

10 recordsLinked to original sources

arXiv2026

Quantum Inspired QUBO Assisted ALNS for Reliability Driven Hurricane Restoration of Distribution Networks

Post hurricane distribution system restoration requires rapid repair scheduling subject to feeder topology, field logistics, and electrical feasibility. This paper presents a quantum inspired quadratic unconstrained binary optimization (QUBO) assisted adaptive large neighborhood search (ALNS) framework. At each restoration stage, a local CPU simulated annealing sampler ranks individual repairs and multi job combinations near the energized frontier. A deterministic decoder preserves crew truck logistics, enforces full useful crew utilization, and rejects infeasible batches. Final schedules are validated through OpenDSS replay. The framework is evaluated on the IEEE 123 node test feeder without distributed generation under 80, 90, and 100 m/s wind scenarios. In the 100 m/s stress test, the proposed method reduces mean system average interruption duration index and energy not supplied by 2.24% and restoration makespan by 50.71% relative to classical energized ALNS. Results show that QUBO assistance is most valuable when severe damage creates a larger combinatorial repair space.

eess.SY↗

arXiv2026

An Integrated Two-Stage Deep-Learning Tool for Rapid Post-Hurricane Damage Identification and Repair Scheduling

Post-hurricane damage assessment and repair scheduling can require computationally intensive simulation and optimization. This paper presents an integrated two-stage deep-learning tool for rapid damaged-line identification and repair-schedule computation. An available offline synthetic dataset for the IEEE 9500-node test feeder contains 1,700 hurricane scenarios with exposure features, grid metadata, fragility parameters, OpenDSS outputs, damaged-line labels, and Adaptive Large Neighborhood Search reference schedules. Stage 1 benchmarks MLP, ResMLP, and GraphSAGE, while Stage 2 compares MLP, DeepSets, and Set Transformer. The selected ResMLP-Set Transformer pipeline propagates Stage 1 errors into Stage 2 and achieves a damaged-job F1-score of 0.920, pairwise order agreement of 0.854, and start- and end-time mean absolute errors of 4.349 min and 4.486 min, respectively. The tool provides rapid initial repair-log decision support for new hurricane cases.

eess.SY↗

arXiv2025

Resilience of the Electric Grid through Trustable IoT-Coordinated Assets (Extended version)

The electricity grid has evolved from a physical system to a cyber-physical system with digital devices that perform measurement, control, communication, computation, and actuation. The increased penetration of distributed energy resources (DERs) including renewable generation, flexible loads, and storage provides extraordinary opportunities for improvements in efficiency and sustainability. However, they can introduce new vulnerabilities in the form of cyberattacks, which can cause significant challenges in ensuring grid resilience. We propose a framework in this paper for achieving grid resilience through suitably coordinated assets including a network of Internet of Things (IoT) devices. A local electricity market is proposed to identify trustable assets and carry out this coordination. Situational Awareness (SA) of locally available DERs with the ability to inject power or reduce consumption is enabled by the market, together with a monitoring procedure for their trustability and commitment. With this SA, we show that a variety of cyberattacks can be mitigated using local trustable resources without stressing the bulk grid. Multiple demonstrations are carried out using a high-fidelity co-simulation platform, real-time hardware-in-the-loop validation, and a utility-friendly simulator.

eess.SY↗

arXiv2023

Multi-agent Deep Reinforcement Learning for Distributed Load Restoration

This paper addresses the load restoration problem after power outage events. Our primary proposed methodology is using multi-agent deep reinforcement learning to optimize the load restoration process in distribution systems, modeled as networked microgrids, via determining the optimal operational sequence of circuit breakers (switches). An innovative invalid action masking technique is incorporated into the multi-agent method to handle both the physical constraints in the restoration process and the curse of dimensionality as the action space of operational decisions grows exponentially with the number of circuit breakers. The features of our proposed method include centralized training for multi-agents to overcome non-stationary environment problems, decentralized execution to ease the deployment, and zero constraint violations to prevent harmful actions. Our simulations are performed in OpenDSS and Python environments to demonstrate the effectiveness of the proposed approach using the IEEE 13, 123, and 8500-node distribution test feeders. The results show that the proposed algorithm can achieve a significantly better learning curve and stability than the conventional methods.

eess.SY↗

arXiv2021

Reinventing the Utility for DERs: A Proposal for a DSO-Centric Retail Electricity Market

The increasing penetration of intermittent renewables, storage devices, and flexible loads is introducing operational challenges in distribution grids. The proper coordination and scheduling of these resources using a distributed approach is warranted, and can only be achieved through local retail markets employing transactive energy schemes. To this end, we propose a distribution-level retail market operated by a Distribution System Operator (DSO), which schedules DERs and determines the real-time distribution-level Locational Marginal Price (d-LPM). The retail market is built using a distributed Proximal Atomic Coordination (PAC) algorithm, which solves the optimal power flow model while accounting for network physics, rendering locationally and temporally varying d-LMPs. A numerical study of the market structure is carried out via simulations of the IEEE-123 node network using data from ISO-NE and Eversource in Massachusetts, US. The market performance is compared to existing retail practices, including demand response (DR) with no-export rules and net metering. The DSO-centric market increases DER utilization, permits continual market participation for DR, lowers electricity rates for customers, and eliminates the subsidies inherent to net metering programs. The resulting lower revenue stream for the DSO highlights the evolving business model of the modern utility, moving from commoditized markets towards performance-based ratemaking.

econ.GN↗

arXiv2020

Deep learning architecture LightOCT for diagnostic decision support using optical coherence tomography images of biological samples

Optical coherence tomography (OCT) is being increasingly adopted as a label-free and non-invasive technique for biomedical applications such as cancer and ocular disease diagnosis. Diagnostic information for these tissues is manifest in textural and geometric features of the OCT images, which are used by human expertise to interpret and triage. However, it suffers delays due to the long process of the conventional diagnostic procedure and shortage of human expertise. Here, a custom deep learning architecture, LightOCT, is proposed for the classification of OCT images into diagnostically relevant classes. LightOCT is a convolutional neural network with only two convolutional layers and a fully connected layer, but it is shown to provide excellent training and test results for diverse OCT image datasets. We show that LightOCT provides 98.9% accuracy in classifying 44 normal and 44 malignant (invasive ductal carcinoma) breast tissue volumetric OCT images. Also, >96% accuracy in classifying public datasets of ocular OCT images as normal, age-related macular degeneration and diabetic macular edema. Additionally, we show ~96% test accuracy for classifying retinal images as belonging to choroidal neovascularization, diabetic macular edema, drusen, and normal samples on a large public dataset of more than 100,000 images. The performance of the architecture is compared with transfer learning based deep neural networks. Through this, we show that LightOCT can provide significant diagnostic support for a variety of OCT images with sufficient training and minimal hyper-parameter tuning. The trained LightOCT networks for the three-classification problem will be released online to support transfer learning on other datasets.

physics.med-ph↗

arXiv2018

Switching mechanism of CO2 by alkaline earth atoms decorated on g-B4N3 nanosheet

The adsorption and desorption of carbon dioxide (CO2) molecule by alkaline earth metal (AEM) (Mg+2, Ca+2, Sr+2 and Ba+2) functionalized on graphitic boron nitride (g-B4N3) nanosheet have been analyzed by using density functional theory (DFT) approach includes long-range correlation (DFT+D). This method has been implemented in such a way to understand the switchable or capture/release mechanism of CO2 molecule by computing the electron mobility, electronic properties, charge accumulation, charge transfer (e-) and adsorption energy (Kcal/mol). The g-B4N3 nanosheet yields high carrier mobility (8020 cm2 V-1s-1) at 300 K. The positive alkaline earth adatoms on the nanosheet of g-B4N3 has been provided external energy to do the capture/release process of greenhouse gas CO2. Here, Mg positive ion work as adatom which confirms physisorption while others show chemisorption behaviors. Therefore, due to the weak absorption of CO2, it makes possible to discharge from the g-B4N3 nanosheet and shows instantaneous switching mechanism. Briefly, the negatively charged g-B4N3 nanosheets are highly sensitive for CO2.

cond-mat.mes-hall↗

arXiv2018

A Diversity-based Substation Cyber Defense Strategy utilizing Coloring Games

Growing cybersecurity risks in the power grid require that utilities implement a variety of security mechanism (SM) composed mostly of VPNs, firewalls, or other custom security components. While they provide some protection, they might contain software vulnerabilities which can lead to a cyber-attack. In this paper, the severity of a cyber-attack has been decreased by employing a diverse set of SM that reduce repetition of a single vulnerability. This paper focuses on the allocation of diverse SM and tries to increase the security of the cyber assets located within the electronic security perimeter(ESP) of a substation. We have used a graph-based coloring game in a distributed manner to allocate diverse SM for protecting the cyber assets. The vulnerability assessment for power grid network is also analyzed using this game theoretic method. An improved, diversified SMs for worst-case scenario has been demonstrated by reaching the Nash equilibrium of graph coloring game. As a case study, we analyze the IEEE-14 and IEEE-118 bus system, observe the different distributed coloring algorithm for allocating diverse SM and calculating the overall network criticality.

cs.CR↗

arXiv2016

Defect-engineered graphene for bulk supercapacitors with high energy and power densities

The development of high-energy and high-power density supercapacitors (SCs) is critical for enabling next-generation energy storage applications. Nanocarbons are excellent SC electrode materials due to their economic viability, high-surface area, and high stability. Although nanocarbons have high theoretical surface area and hence high double layer capacitance, the net amount of energy stored in nanocarbon-SCs is much below theoretical limits due to two inherent bottlenecks: i) their low quantum capacitance and ii) limited ion-accessible surface area. Here, we demonstrate that defects in graphene could be effectively used to mitigate these bottlenecks by drastically increasing the quantum capacitance and opening new channels to facilitate ion diffusion in otherwise closed interlayer spaces. Our results support the emergence of a new energy paradigm in SCs with 250% enhancement in double layer capacitance beyond the theoretical limit. Furthermore, we demonstrate prototype defect engineered bulk SC devices with energy densities 500% higher than state-of-the-art commercial SCs without compromising the power density.

cond-mat.mtrl-sci↗

arXiv2012

Integrated Routing Protocol for Opportunistic Networks

In opportunistic networks the existence of a simultaneous path is not assumed to transmit a message between a sender and a receiver. Information about the context in which the users communicate is a key piece of knowledge to design efficient routing protocols in opportunistic networks. But this kind of information is not always available. When users are very isolated, context information cannot be distributed, and cannot be used for taking efficient routing decisions. In such cases, context oblivious based schemes are only way to enable communication between users. As soon as users become more social, context data spreads in the network, and context based routing becomes an efficient solution. In this paper we design an integrated routing protocol that is able to use context data as soon as it becomes available and falls back to dissemination based routing when context information is not available. Then, we provide a comparison between Epidemic and PROPHET, these are representative of context oblivious and context aware routing protocols. Our results show that integrated routing protocol is able to provide better result in term of message delivery probability and message delay in both cases when context information about users is available or not.

cs.NI↗