arXiv subjects

Charalambos Konstantinou

Publications and source records attributed to Charalambos Konstantinou.

At least 19 recordsLinked to original sources

arXiv2026

Iterative Single-Loop Coordination of Transmission and Distribution Systems and DER Aggregators with Limited Information Sharing

The growing integration of DERs underscores the importance of unified coordination across TSO, DSO, and aggregator levels. This paper proposes a coordination method between TSO, DSOs and DER aggregators, based on a transparent, well-scalable, and decentralized single-loop iterative mechanism with limited information sharing. The approach enables iterative price-based coordination between adjacent levels, where the higher-level entities provide price signals and the lower-level ones respond with desired power schedules. The proposed method uses the proximal-gradient-based iterative co-optimization algorithm extended to a three-level case, which enjoys convergence characteristic of proximal algorithms. The performance of the proposed approach is compared with both a centralized formulation and a sequential reference formulation based on fixed transmission-level energy prices. The results demonstrate that the proposed coordination achieves outcomes very close to those obtained with the centralized formulation, while significantly improving performance relative to the sequential reference, particularly at higher levels of DER flexibility.

eess.SY↗

arXiv2026

Learning Without Adversarial Training: A Physics-Informed Neural Network for Secure Power System State Estimation under False Data Injection Attacks

Power System State Estimation (PSSE) converts geographically distributed measurements into the voltage magnitudes and phase angles needed for grid monitoring and control. Learned estimators can perform this mapping rapidly, but model-aware False Data Injection Attacks (FDIAs) may corrupt their inputs while retaining AC plausibility and residual-based stealth. Physics-Informed Neural Networks (PINNs) limit candidate states through power-flow consistency; however, their robustness depends on balancing supervised and physics losses whose scales and gradient contributions evolve during training. This paper proposes a PINN that jointly learns homoscedastic uncertainty parameters and uses them to adapt the two objectives. The formulation assigns trainable log-uncertainties to the active-power, reactive-power, voltage, and angle losses while safeguarding against an underweighted physics objective. The estimator is trained only on clean steady-state data and is evaluated, without adversarial retraining, under baseline state-distortion and stricter residual-profile-matching regimes on the IEEE~118-bus system. Accuracy is measured against the uncompromised system state. Relative to a fixed-weight PINN, dynamic weighting reduces overall Mean Absolute Error (MAE) by $55\%$ while also improving voltage- and angle-estimation accuracy. The results show that learning the physics/data balance from clean data improves robustness to unseen FDIAs.

cs.LG↗

arXiv2026

Exposing the Invisible: Detecting Stealthy Parameter-Based Cyber-Attacks on Inverter Synchronization Loops

The increasing integration of Internet-of-Things (IoT) technologies for monitoring and control of inverter-based resources (IBRs) has expanded the attack surface, enabling stealthy manipulation of controller parameters through vulnerable supervisory control interfaces. Phase-locked loops (PLLs) emerge as prime targets, as they interact with all control loops and critically influence the dynamic response of grid-following (GFL) converters. This paper analyzes the underlying threat model to elucidate the mechanisms enabling such stealthy behavior and conducts a thorough stability and transient response analysis to characterize how PLL tampering can degrade system performance without necessarily destabilizing the system. The results reveal critical interactions among frequency estimation, control, and synchronization that can significantly reduce stability margins. To counter the stealthy nature of these attacks, a modified PLL is proposed that exposes gain variations through shifts in its equilibrium points while preserving conventional PLL performance. Experimental results validate the effectiveness of the proposed approach in detecting PLL cyber-attacks.

eess.SY↗

arXiv2026

Shift or curtail? How much data-center flexibility is worth depends on the host power grid

Data-center growth risks overbuilding power grid infrastructure and stranding capital. Flexible data-center operation can defer infrastructure investments, but its value depends on the flexibility mechanism and the host power grid characteristics. We classify data-center load as firm, flexible or interruptible, and embed them in capacity expansion applied to market-organized, fossil-heavy PJM and carbon-capped, centrally coordinated Korea. In PJM, the flexibility value is spatial: shifting workloads between zones reduces system cost by 6% in 2028 and 19% in 2038, avoiding 4.4 GW and 8.9 GW of gas and nuclear generation. In Korea, it is temporal: shifting load into midday solar hours makes 0.5 GW of additional solar worth building in 2028 and avoids 1.2 GW of gas and 0.3 GW of batteries in 2038. In both, realistic event-shape limits diminish the value of curtailment. The results show that flexibility procurement and its value are driven by grid characteristics and policy objectives.

physics.soc-ph↗

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.

eess.SY↗

arXiv2026

Impedance-Based VSC Unit Commitment with STATCOM Support under High IBG Penetration

The large-scale replacement of synchronous machines with inverter-based generation (IBG) introduces critical challenges to both voltage and frequency stability. This work builds on a mixed-integer second-order cone programming (MISOCP) framework that co-optimizes unit commitment (UC) model which embeds frequency-nadir constraints through synthetic inertia (SI) dispatch and an SOC voltage stability boundary for IBG buses. The formulation extends by modeling a STATCOM as a reactive-power decision variable in the same MISOCP model. A modified IEEE 30-bus system is used to assess three scheduling strategies: (i) baseline UC with SI only, (ii) voltage-stability-constrained (VSC) UC with SI, and (iii) the joint UC with SI and reactive power support from IBGs. The impact of incorporating a 30~MVAr STATCOM at a weak grid location near the IBG buses is investigated. Simulation results show that the proposed framework enhances voltage security, maintains frequency-nadir compliance, and reduces operating cost, while STATCOM integration further improves dispatch feasibility under high IBG.

eess.SY↗

arXiv2026

A Reality Check on SBOM-based Vulnerability Management: An Empirical Study and A Path Forward

The Software Bill of Materials (SBOM) is a critical tool for securing the software supply chain (SSC), but its practical utility is undermined by inaccuracies in both its generation and its application in vulnerability scanning. This paper presents a large-scale empirical study on 2,414 open-source repositories to address these issues from a practical standpoint. First, we demonstrate that using lock files with strong package managers enables the generation of accurate and consistent SBOMs, establishing a reliable foundation for security analysis. Using this high-fidelity foundation, however, we expose a more fundamental flaw in practice: downstream vulnerability scanners produce a staggering 92.0\% false positive rate in our case study. We pinpoint the primary cause as the flagging of vulnerabilities within unreachable code. We then demonstrate that function call analysis can effectively prune 61.9\% of these false alarms. Our work validates a practical, two-stage approach for SSC security: first, generate an accurate SBOM using lock files and strong package managers, and second, enrich it with function call analysis to produce actionable, low-noise vulnerability reports that alleviate developers' alert fatigue.

cs.CR↗

arXiv2026

When Market Prices Drive the Load: Modeling, Grid-Security Analysis, and Mitigation of Data Center Workload Scheduling

Data centers (DCs) are emerging as large, geographically distributed, controllable loads whose participation in electricity markets can significantly affect grid operation, especially when cloud platforms shift workloads across sites to exploit energy-arbitrage opportunities. This paper analyzes and seeks to mitigate the grid impacts of geographically distributed multi-site DCs under exogenous electricity prices. It develops a detailed job-level scheduling framework for market-driven DCs, formulated as a mixed-integer model that preserves execution logic and captures a unified set of implementable control actions. It also incorporates service-side quality-of-service (QoS) constraints and penalty terms to improve fidelity. Case studies on a modified IEEE 14-bus system, complemented by a more realistic network based on Travis County, Texas, show that purely price-driven scheduling improves economic performance, but also increases voltage-security risk and congestion exposure by inducing localized demand concentration and sharp site-level load variation. To mitigate these effects, this work introduces load-redistribution policies that curb extreme load shifting and support grid operators in managing such conditions.

eess.SY↗

arXiv2026

HyMGP: A Customized MILP-Based Tool for Techno-Economic Planning of Islanded Microgrids

This paper presents a customized microgrid planning algorithm and tool, HyMGP, for remote sites in arid regions, which is formulated as a Mixed Integer Linear Programming (MILP) problem. HyMGP is compared with HOMER Pro to evaluate its performance in optimizing the sizing of microgrid components, including photovoltaic panels (PVs), vertical axis wind turbines (VAWTs), and battery energy storage systems (BESS), for remote and off-grid applications. The study focuses on a standalone microgrid in the Saudi Arabia, considering high solar irradiance, limited wind availability, and a constant load profile composed of continuous cathodic protection and daytime cooling. In the simulation environment, comparisons with HOMER solutions demonstrate the advantages of HyMGP, which provides optimal and more flexible solutions by allowing user-defined component specifications and strictly enforcing all constraints. Further analysis shows that incorporating wind turbines reduces the Net Present Cost (NPC) by decreasing the required PV and battery capacities. Increasing battery autonomy leads to a higher NPC in both PV-only and hybrid systems due to the need for larger storage. Finally, lithium iron phosphate (Li-ion LFP) batteries are found to be more cost effective than lead acid, offering lower NPCs due to their longer lifespan, deeper discharge capability, and fewer replacement cycles.

eess.SY↗

arXiv2026

Stochastic Power-Water Coordination: Unlocking Flexibility in Hybrid RO Desalination Plants via Variable-Speed Pumps and Tank Mixing

Water desalination plants (DPs) are among the most critical infrastructures and largest electricity loads in water-scarce regions worldwide. Although reverse osmosis (RO) desalination is the most energy-efficient and dominant technology, it remains energy-intensive but can offer substantial flexibility potential for power systems. This paper proposes a coordinated operation framework for power systems and DPs that explicitly accounts for both systems' operational constraints and fully unlocks DP flexibility. To achieve this, a detailed DP model is developed, incorporating the characteristics of an actual high-pressure pump with variable-speed operation, on-off operation with flushing requirements, water quality constraints, and water dynamics and salt mixing in the storage tank. By proactively managing freshwater storage and tank salinity in a closed-loop coordinated scheduling framework, the operational flexibility of the DP is significantly enhanced. With appropriate simplification and linearization, the resulting coordinated scheduling problem is formulated as a tractable mixed-integer linear programming (MILP) model, and a two-step decomposed commitment-scheduling stochastic optimization (TDCSO) is proposed to efficiently address uncertainties. Case studies validate the proposed approach and demonstrate up to a 6% operating cost reduction.

eess.SY↗

arXiv2026

Impact Assessment of Heterogeneous Grid Support Functions in Smart Inverter Deployments

The decarbonization of the energy sector has led to a significant high penetration of distributed energy resources (DERs), particularly photovoltaic (PV) systems, in low-voltage (LV) distribution networks. To maintain grid stability, recent standards (e.g., IEEE 1547-2018) mandate DERs to provide grid-support functionalities through smart inverters (SIs), which typically operate autonomously based on local measurements. However, as DER penetration increases, uncoordinated control modes of SIs can lead to adverse interactions, compromising system efficiency, voltage regulation, and overall stability. While previous studies have demonstrated the benefits of coordinated inverter control and optimal dispatch strategies, the system-wide impacts of heterogeneous SI groups operating under different control modes remain largely unexamined. This paper addresses this gap by assessing the dynamic interactions among multiple SI groups with varying control strategies, namely: Constant Power Factor (CPF), Volt-VAR, and Volt-Watt modes. Furthermore, the analysis covers both resistive and inductive feeder types. The validation is performed using a real-time setup. The CIRGE low-voltage (LV) distribution network is simulated in the Opal-RT platform as the test network, enabling realistic and high-fidelity evaluation of SI control interactions under practical grid conditions.

eess.SY↗

arXiv2025

A Cyber Insurance Policy for Hedging Against Load-Altering Attacks and Extreme Load Variations in Distribution Grids

Uncertainties in renewable energy resources (RES) and load variations can lead to elevated system operational costs. Moreover, the emergence of large-scale distributed threats, such as load-altering attacks (LAAs), can induce substantial load variations, further exacerbating these costs. Although traditional defense measures can reduce the likelihood of such attacks, considerable residual risks remain. Thus, this paper proposes a cyber insurance framework designed to hedge against additional operational costs resulting from LAAs and substantial load variations in renewable-rich grids. The insurance framework determines both the insurance coverage and premium based on the Value at Risk (VaR) and Tail Value at Risk (TVaR). These risk metrics are calculated using the system failure probability and the probability density function (PDF) of the system operation cost. The system failure probability is assessed through a semi-Markov process (SMP), while the cost distribution is estimated through a cost minimization model of a distribution grid combined with a Monte Carlo simulation to capture load variability. Furthermore, we employ a bi-level optimization scheme that identifies the specific load distribution leading to the maximum system cost, thereby enhancing the accuracy of the operation cost PDF estimation. The effectiveness and scalability of the proposed cyber insurance policy are evaluated considering a modified IEEE 118-bus test system and the IEEE European low-voltage (LV) test feeder model. The case study shows that with a relatively low premium, the network operator can hedge against additional operational costs caused by malicious load manipulations.

eess.SY↗

arXiv2025

A Dynamic Recurrent Adjacency Memory Network for Mixed-Generation Power System Stability Forecasting

Modern power systems with high penetration of inverter-based resources exhibit complex dynamic behaviors that challenge the scalability and generalizability of traditional stability assessment methods. This paper presents a dynamic recurrent adjacency memory network (DRAMN) that combines physics-informed analysis with deep learning for real-time power system stability forecasting. The framework employs sliding-window dynamic mode decomposition to construct time-varying, multi-layer adjacency matrices from phasor measurement unit and sensor data to capture system dynamics such as modal participation factors, coupling strengths, phase relationships, and spectral energy distributions. As opposed to processing spatial and temporal dependencies separately, DRAMN integrates graph convolution operations directly within recurrent gating mechanisms, enabling simultaneous modeling of evolving dynamics and temporal dependencies. Extensive validations on modified IEEE 9-bus, 39-bus, and a multi-terminal HVDC network demonstrate high performance, achieving 99.85%, 99.90%, and 99.69% average accuracies, respectively, surpassing all tested benchmarks, including classical machine learning algorithms and recent graph-based models. The framework identifies optimal combinations of measurements that reduce feature dimensionality by 82% without performance degradation. Correlation analysis between dominant measurements for small-signal and transient stability events validates generalizability across different stability phenomena. DRAMN achieves state-of-the-art accuracy while providing enhanced interpretability for power system operators, making it suitable for real-time deployment in modern control centers.

eess.SY↗

arXiv2025

Real-Time Co-Simulation for DC Microgrid Energy Management with Communication Delays

The growing integration of renewable energy sources (RESs) in modern power systems has intensified the need for resilient and efficient microgrid solutions. DC microgrids have gained prominence due to their reduced conversion losses, simplified interfacing with DC-based RESs, and improved reliability. To manage the inherent variability of RESs and ensure stable operation, energy management systems (EMS) have become essential. While various EMS algorithms have been proposed and validated using real-time simulation platforms, most assume ideal communication conditions or rely on simplified network models, overlooking the impact of realistic communication delays on EMS performance. This paper presents a novel real-time cyber-physical system (CPS) testbed for evaluating EMS performance in DC microgrids under realistic communication delays. The proposed testbed integrates a DC microgrid modeled in OPAL-RT with an EMS controller implemented on a Raspberry Pi (RPi). The communication network is emulated using EXataCPS, enabling the exchange of actual power system traffic and the replication of realistic latency conditions. This comprehensive setup captures the interplay between power system dynamics, EMS control, and communication network behavior.

eess.SY↗

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.

eess.SY↗

arXiv2025

Cyber-Physical Co-Simulation of Load Frequency Control under Load-Altering Attacks

Integrating Information and Communications Technology (ICT) devices into the power grid brings many benefits. However, it also exposes the grid to new potential cyber threats. Many control and protection mechanisms, such as Load Frequency Control (LFC), responsible for maintaining nominal frequency during load fluctuations and Under Frequency Load Shedding (UFLS) disconnecting portion of the load during an emergency, are dependent on information exchange through the communication network. The recently emerging Load Altering Attacks (LAAs) utilize a botnet of high-wattage devices to introduce load fluctuation. In their dynamic form (DLAAs), they manipulate the load in response to live grid frequency measurements for increased efficiency, posing a notable threat to grid stability. Recognizing the importance of communication networks in power grid cyber security research, this paper presents an open-source co-simulation environment that models the power grid with the corresponding communication network, implementing grid protective mechanisms. This setup allows the comprehensive analysis of the attacks in concrete LFC and UFLS scenarios.

eess.SY↗

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.

cs.LG↗

arXiv2025

Robust Power System State Estimation using Physics-Informed Neural Networks

Modern power systems face significant challenges in state estimation and real-time monitoring, particularly regarding response speed and accuracy under faulty conditions or cyber-attacks. This paper proposes a hybrid approach using physics-informed neural networks (PINNs) to enhance the accuracy and robustness, of power system state estimation. By embedding physical laws into the neural network architecture, PINNs improve estimation accuracy for transmission grid applications under both normal and faulty conditions, while also showing potential in addressing security concerns such as data manipulation attacks. Experimental results show that the proposed approach outperforms traditional machine learning models, achieving up to 83% higher accuracy on unseen subsets of the training dataset and 65% better performance on entirely new, unrelated datasets. Experiments also show that during a data manipulation attack against a critical bus in a system, the PINN can be up to 93% more accurate than an equivalent neural network.

cs.LG↗