arXiv subjects

Subhash Lakshminarayana

Publications and source records attributed to Subhash Lakshminarayana.

At least 19 recordsLinked to original sources

arXiv2026

Technical Report on Resilient and Secure Large-Scale Energy Internet Systems

This IEEE PES Task Force report examines the security and resilience of large-scale Energy Internet (EI) systems, in which electricity, information, and market layers are tightly coupled through pervasive digitalization. The report characterizes the EI cyber-physical threat landscape and surveys detection, assurance, and mitigation techniques, presents modeling, control, and decision-making frameworks that capture cyber-physical interdependencies, including storage integration, multi-dimensional resilience, and electricity price forecasting, examines adversarial risks and trustworthy deployment of artificial intelligence, and introduces graph-based, attack-resilient information routing. The report closes with recommendations for research, standardization, and regulatory efforts needed to realize a resilient and secure large-scale EI.

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arXiv2026

TAMS: Task-Aware Multi-View Adaptive Streaming for Wireless Telerobotic Manipulation

Wireless telerobotic manipulation relies on timely multi-view video feedback, but the available uplink bandwidth is often limited and dynamic. This paper presents Task-Aware Multi-View Adaptive Streaming (TAMS), a system that allocates video bitrate according to the current manipulation phase. TAMS infers task phase from lightweight robot-side signals and prioritizes the camera view most relevant to the operator while preserving baseline visibility for secondary views. Experiments on a six-degree-of-freedom (6-DoF) teleoperation testbed under three constrained network conditions show that TAMS improves primary view Structural Similarity Index (SSIM), reduces task completion time, and increases trial success rate compared with equal and static allocation baselines. Under the most constrained bandwidth condition, TAMS reduces mean completion time from 68.9 s to 43.9 s relative to equal allocation and increases trial success rate from 48% to 71%. Code is available at: https://github.com/Dzxx623/TAMS.

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arXiv2025

Distribution System Reconfiguration to Mitigate Load Altering Attacks via Stackelberg Games

The widespread integration of IoT-controllable devices (e.g., smart EV charging stations and heat pumps) into modern power systems enhances capabilities but introduces critical cybersecurity risks. Specifically, these devices are susceptible to load-altering attacks (LAAs) that can compromise power system safety. This paper quantifies the impact of LAAs on nodal voltage constraint violations in distribution networks (DNs). We first present closed-form expressions to analytically characterize LAA effects and quantify the minimum number of compromised devices for a successful LAA. Based on these insights, we propose a reactive defense mechanism that mitigates LAAs through DN reconfiguration. To address strategic adversaries, we then formulate defense strategies using a non-cooperative sequential game, which models the knowledgeable and strategic attacker, accounting for the worst-case scenario and enabling the reactive defender to devise an efficient and robust defense. Further, our formulation also accounts for uncertainties in attack localization. A novel Bayesian optimization approach is introduced to compute the Stackelberg equilibrium, significantly reducing computational burden efficiently. The game-theoretic strategy effectively mitigates the attack's impact while ensuring minimal system reconfiguration.

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arXiv2025

Load-Altering Attacks Against Power Grids: A Case Study Using the GB-36 Bus System Open Dataset

The growing digitalization and the rapid adoption of high-powered Internet-of-Things (IoT)-enabled devices (e.g., EV charging stations) have increased the vulnerability of power grids to cyber threats. In particular, the so-called Load Altering Attacks (LAAs) can trigger rapid frequency fluctuations and potentially destabilize the power grid. This paper aims to bridge the gap between academic research and practical application by using open-source datasets released by grid operators. It investigates various LAA scenarios on a real-world transmission network, namely the Great Britain (GB)-36 Zone model released by the UK's National Electricity System Operator (NESO). It evaluates the threshold of LAA severity that the grid can tolerate before triggering cascading effects. Additionally, it explores how Battery Energy Storage Systems (BESS) based fast frequency response services can mitigate or prevent such impacts. Simulations are conducted using DIgSILENT PowerFactory to ensure realistic system representation. The analysis provides several useful insights to grid operators on the LAA impact, such as the influence of the relative locations of BESS and LAA, as well as how delays in attack execution can influence the overall system response.

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arXiv2025

VAE-GAN Based Price Manipulation in Coordinated Local Energy Markets

This paper introduces a model for coordinating prosumers with heterogeneous distributed energy resources (DERs), participating in the local energy market (LEM) that interacts with the market-clearing entity. The proposed LEM scheme utilizes a data-driven, model-free reinforcement learning approach based on the multi-agent deep deterministic policy gradient (MADDPG) framework, enabling prosumers to make real-time decisions on whether to buy, sell, or refrain from any action while facilitating efficient coordination for optimal energy trading in a dynamic market. In addition, we investigate a price manipulation strategy using a variational auto encoder-generative adversarial network (VAE-GAN) model, which allows utilities to adjust price signals in a way that induces financial losses for the prosumers. Our results show that under adversarial pricing, heterogeneous prosumer groups, particularly those lacking generation capabilities, incur financial losses. The same outcome holds across LEMs of different sizes. As the market size increases, trading stabilizes and fairness improves through emergent cooperation among agents.

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arXiv2025

A Distributed Local Energy Market Clearing Framework Using a Two-Loop ADMM Method

The diversity of prosumers' resources in energy communities can provide significant technical and economic benefits to both prosumers and the distribution system operator (DSO). To maximize these benefits, a coordination framework is required to address all techno-economic constraints as well as the objectives of all agents. This paper presents a fully distributed market-clearing scheme to coordinate the strategies of agents within a local energy community. In the proposed framework, prosumers, the DSO, and the local market operator (LMO) are the participating agents. The framework addresses the preferences and techno-economic constraints of all actors while preserving their privacy. The proposed model is based on a modified alternating direction method of multipliers (ADMM) method with two outer and inner loops; the outer loop models the interactions between the LMO and prosumers, while the inner loop addresses the interactions between the LMO and the DSO. The model is demonstrated on IEEE-69bus test network, showcasing its effectiveness from various perspectives.

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arXiv2025

Cybersecurity Issues in Local Energy Markets

Local Energy Markets (LEMs), though pivotal to the energy transition, face growing cybersecurity threats due to their reliance on smart grid communication standards and vulnerable Internet-of-Things (IoT)-enabled devices. This is a critical issue because such vulnerabilities can be exploited to manipulate market operations, compromise participants' privacy, and destabilize power distribution networks. This work maps LEM communication flows to existing standards, highlights potential impacts of key identified vulnerabilities, and simulates cyberattack scenarios on a privacy-preserving LEM model to assess their impacts. Findings reveal how attackers could distort pricing and demand patterns. We finally present recommendations for researchers, industry developers, policymakers, and LEM stakeholders to secure future LEM deployments.

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arXiv2025

Survey of Load-Altering Attacks Against Power Grids: Attack Impact, Detection and Mitigation

The growing penetration of IoT devices in power grids despite its benefits, raises cybersecurity concerns. In particular, load-altering attacks (LAAs) targeting high-wattage IoT-controllable load devices pose serious risks to grid stability and disrupt electricity markets. This paper provides a comprehensive review of LAAs, highlighting the threat model, analyzing their impact on transmission and distribution networks, and the electricity market dynamics. We also review the detection and localization schemes for LAAs that employ either model-based or data-driven approaches, with some hybrid methods combining the strengths of both. Additionally, mitigation techniques are examined, focusing on both preventive measures, designed to thwart attack execution, and reactive methods, which aim to optimize responses to ongoing attacks. We look into the application of each study and highlight potential streams for future research.

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arXiv2025

Moving Target Defense Against Adversarial False Data Injection Attacks In Power Grids

Machine learning (ML)-based detectors have been shown to be effective in detecting stealthy false data injection attacks (FDIAs) that can bypass conventional bad data detectors (BDDs) in power systems. However, ML models are also vulnerable to adversarial attacks. A sophisticated perturbation signal added to the original BDD-bypassing FDIA can conceal the attack from ML-based detectors. In this paper, we develop a moving target defense (MTD) strategy to defend against adversarial FDIAs in power grids. We first develop an MTD-strengthened deep neural network (DNN) model, which deploys a pool of DNN models rather than a single static model that cooperate to detect the adversarial attack jointly. The MTD model pool introduces randomness to the ML model's decision boundary, thereby making the adversarial attacks detectable. Furthermore, to increase the effectiveness of the MTD strategy and reduce the computational costs associated with developing the MTD model pool, we combine this approach with the physics-based MTD, which involves dynamically perturbing the transmission line reactance and retraining the DNN-based detector to adapt to the new system topology. Simulations conducted on IEEE test bus systems demonstrate that the MTD-strengthened DNN achieves up to 94.2% accuracy in detecting adversarial FDIAs. When combined with a physics-based MTD, the detection accuracy surpasses 99%, while significantly reducing the computational costs of updating the DNN models. This approach requires only moderate perturbations to transmission line reactances, resulting in minimal increases in OPF cost.

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arXiv2025

Cybersecurity Threats to Power Grid Operations from the Demand-Side Response Ecosystem

This article focuses on cyber security threats from IoT-enabled energy smart appliances (ESAs) such as smart heat pumps, electric vehicle chargers, etc., to power grid operations. It presents an in-depth analysis of the demand side threats, including (i) an overview of the vulnerabilities in ESAs and the wider risk from the demand-side response (DSR) ecosystem, (ii) key factors influencing the attack impact on power grid operations, (iii) measures to improve the cyber-physical resilience of power grids, putting them in the context of ongoing efforts from the industry and regulatory bodies worldwide.

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arXiv2024

Survey of Moving Target Defense in Power Grids: Design Principles, Tradeoffs, and Future Directions

Moving target defense (MTD) in power grids is an emerging defense technique that has gained prominence in the recent past. It aims to solve the long-standing problem of securing the power grid against stealthy attacks. The key idea behind MTD is to introduce periodic/event-triggered controlled changes to the power grid's SCADA network/physical plant, thereby invalidating the knowledge attackers use for crafting stealthy attacks. In this paper, we provide a comprehensive overview of this topic and classify the different ways in which MTD is implemented in power grids. We further introduce the guiding principles behind the design of MTD, key performance metrics, and the associated trade-offs in MTD and identify the future development of MTD for power grid security.

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arXiv2024

A Game-Theoretic Approach for PMU Deployment Against False Data Injection Attacks

Phasor Measurement Units (PMUs) are used in the measurement, control and protection of power grids. However, deploying PMUs at every bus in a power system is prohibitively expensive, necessitating partial PMU placement that can ensure system observability with minimal units. One consequence of this economic approach is increased system vulnerability to False Data Injection Attacks (FDIAs). This paper proposes a zero-sum game-based approach to strategically place an additional PMU (following the initial optimal PMU deployment that ensures full observability) to bolster robustness against FDIAs by introducing redundancy in attack-susceptible areas. To compute the Nash equilibrium (NE) solution, we leverage a reinforcement learning algorithm that mitigates the need for complete knowledge of the opponent's actions. The proposed PMU deployment algorithm increases the detection rate of FDIA by 36% compared to benchmark algorithms.

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arXiv2024

The Impact of Load Altering Attacks on Distribution Systems with ZIP Loads

Load-altering attacks (LAAs) pose a significant threat to power systems with Internet of Things (IoT)-controllable load devices. This research examines the detrimental impact of LAAs on the voltage profile of distribution systems, taking into account the realistic load model with constant impedance Z, constant current I, and constant power P (ZIP). We derive closed-form expressions for computing the voltages of buses following LAA by making approximations to the power flow as well as the load model. We also characterize the minimum number of devices to be manipulated in order to cause voltage safety violations in the system. We conduct extensive simulations using the IEEE-33 bus system to verify the accuracy of the proposed approximations and highlight the difference between the attack impacts while considering constant power and the ZIP load model (which is more representative of real-world loads).

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arXiv2024

Cybersecurity for Modern Smart Grid against Emerging Threats

Smart Grid is a power grid system that uses digital communication technologies. By deploying intelligent devices throughout the power grid infrastructure,from power generation to consumption, and enabling communication among them, it revolutionizes the modern power grid industry with increased efficiency, reliability, and availability. However, reliance on information and communication technologies has also made the smart grids exposed to new vulnerabilities and complications that may negatively impact the availability and stability of electricity services, which are vital for people's daily lives. The purpose of this monograph is to provide an up-to-date and comprehensive survey and tutorial on the cybersecurity aspect of smart grids. The book focuses on the sources of the cybersecurity issues, the taxonomy of threats, and the survey of various approaches to overcome or mitigate such threats. It covers the state-of-the-art research results in recent years, along with remaining open challenges. We hope that this monograph can be used both as learning materials for beginners who are embarking on research in this area and as a useful reference for established researchers in this field.

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arXiv2023

Charge Manipulation Attacks Against Smart Electric Vehicle Charging Stations and Deep Learning-based Detection Mechanisms

The widespread deployment of "smart" electric vehicle charging stations (EVCSs) will be a key step toward achieving green transportation. The connectivity features of smart EVCSs can be utilized to schedule EV charging operations while respecting user preferences, thus avoiding synchronous charging from a large number of customers and relieving grid congestion. However, the communication and connectivity requirements involved in smart charging raise cybersecurity concerns. In this work, we investigate charge manipulation attacks (CMAs) against EV charging, in which an attacker manipulates the information exchanged during smart charging operations. The objective of CMAs is to shift the EV aggregator's demand across different times of the day. The proposed CMAs can bypass existing protection mechanisms in EV communication protocols. We quantify the impact of CMAs on the EV aggregator's economic profit by modeling their participation in the day-ahead (DA) and real-time (RT) electricity markets. Finally, we propose an unsupervised deep learning-based mechanism to detect CMAs by monitoring the parameters involved in EV charging. We extensively analyze the attack impact and the efficiency of the proposed detection on real-world EV charging datasets. The results highlight the vulnerabilities of smart charging operations and the need for a monitoring mechanism to detect malicious CMAs.

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arXiv2023

Optimal Placement and Power Supply of Distributed Generation to Minimize Power Losses

An increasing number of renewable energy-based distribution generation (DG) units are being deployed in electric distribution systems. Therefore, it is of paramount importance to optimize the installation locations as well as the power supply of these DGs. The placement of DGs in the grid can decrease the total distance that power is transmitted and thus reduce power losses. Additionally, the reactive power supply from the DGs can further reduce power losses in the distribution grid and improve power transmission efficiency. This paper presents a two-stage optimization strategy to minimize power losses. In the first stage, the DG locations and active power supply that minimize the power losses are determined. The second optimization stage identifies the optimal reactive power output of the DGs according to different load demands. The proposed approach is tested on the IEEE 15-bus and the IEEE 33-bus systems using DIgSILENT PowerFactory. The results show that the optimized power losses can be reduced from 58.77 kW to 3.6 kW in the 15-bus system, and from 179.46 kW to around 5 kW in the 33-bus system. Moreover, with the proposed optimization strategy, voltage profiles can be maintained at nominal values enabling the distribution grid to support higher load demand.

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arXiv2023

Communication Reduction for Power Systems: An Observer-Based Event-Triggered Approach

The management of distributed and heterogeneous modern power networks necessitates the deployment of communication links, often characterized by limited bandwidth. This paper presents an event detection mechanism that significantly reduces the volume of data transmission to perform necessary control actions, using a scalable scheme that enhances the stability and reliability of power grids. The approach relies on implementing a linear quadratic regulator and the execution of a pair of Luenberger observers. The linear quadratic regulator minimizes the amount of energy required to achieve the control actions. Meanwhile, the Luenberger observers estimate the unmeasured states from the sensed states, providing the necessary information to trigger the event detection mechanism. The effectiveness of the method is tested via time-domain simulations on the IEEE 13-node test feeder interfaced with inverter-based distributed generation systems and the proposed observed-based event-triggered controller. The results demonstrate that the presented control scheme guarantees the bounding of the system states to a pre-specified limit while reducing the number of data packet transmissions by 39.8%.

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arXiv2023

Uncovering Load-Altering Attacks Against N-1 Secure Power Grids: A Rare-Event Sampling Approach

Load-altering attacks targetting a large number of IoT-based high-wattage devices (e.g., smart electric vehicle charging stations) can lead to serious disruptions of power grid operations. In this work, we aim to uncover spatiotemporal characteristics of LAAs that can lead to serious impact. The problem is challenging since existing protection measures such as $N-1$ security ensures that the power grid is naturally resilient to load changes. Thus, strategically injected load perturbations that lead to network failure can be regarded as \emph{rare events}. To this end, we adopt a rare-event sampling approach to uncover LAAs distributed temporally and spatially across the power network. The key advantage of this sampling method is the ability of sampling efficiently from multi-modal conditional distributions with disconnected support. Furthermore, we systematically compare the impacts of static (one-time manipulation of demand) and dynamic (attack over multiple time periods) LAAs. We perform extensive simulations using benchmark IEEE test simulations. The results show (i) the superiority and the need for rare-event sampling in the context of uncovering LAAs as compared to other sampling methodologies, (ii) statistical analysis of attack characteristics and impacts of static and dynamic LAAs, and (iii) cascade sizes (due to LAA) for different network sizes and load conditions.

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