arXiv subjects

Charalambos Konstantinou

Publications and source records attributed to Charalambos Konstantinou.

At least 37 records · Page 2Linked to original sources

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.

eess.SY↗

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.

cs.CR↗

arXiv2025

Exploring the Effects of Load Altering Attacks on Load Frequency Control through Python and RTDS

The modern power grid increasingly depends on advanced information and communication technology (ICT) systems to enhance performance and reliability through real-time monitoring, intelligent control, and bidirectional communication. However, ICT integration also exposes the grid to cyber-threats. Load altering attacks (LAAs), which use botnets of high-wattage devices to manipulate load profiles, are a notable threat to grid stability. While previous research has examined LAAs, their specific impact on load frequency control (LFC), critical for maintaining nominal frequency during load fluctuations, still needs to be explored. Even minor frequency deviations can jeopardize grid operations. This study bridges the gap by analyzing LAA effects on LFC through simulations of static and dynamic scenarios using Python and RTDS. The results highlight LAA impacts on frequency stability and present an eigenvalue-based stability assessment for dynamic LAAs (DLAAs), identifying key parameters influencing grid resilience.

eess.SY↗

arXiv2025

Admittance Identification of Grid-Forming Inverters Using Time and Frequency-Domain Techniques

The increasing integration of inverter-based resources (IBRs) into the power grid introduces new challenges, requiring detailed electromagnetic transient (EMT) studies to analyze system interactions. Despite these needs, access to the internal firmware of power electronic devices remains restricted due to stringent nondisclosure agreements enforced by manufacturers. To address this, we explore three system identification techniques: sweep frequency response analysis (SFRA), step excitation method (SEM), and eigensystem realization algorithm (ERA). SFRA employs sinusoidal signals of varying frequencies to measure the system's frequency response, while SEM and ERA utilize step functions to derive time-domain responses and transform them into Laplace-domain transfer functions. All three approaches are shown to provide consistent results in identifying the dq admittance of grid-forming inverters (GFM) over a frequency range of 1 Hz to 100 Hz.

eess.SY↗

arXiv2025

ReGraph: A Tool for Binary Similarity Identification

Binary Code Similarity Detection (BCSD) is not only essential for security tasks such as vulnerability identification but also for code copying detection, yet it remains challenging due to binary stripping and diverse compilation environments. Existing methods tend to adopt increasingly complex neural networks for better accuracy performance. The computation time increases with the complexity. Even with powerful GPUs, the treatment of large-scale software becomes time-consuming. To address these issues, we present a framework called ReGraph to efficiently compare binary code functions across architectures and optimization levels. Our evaluation with public datasets highlights that ReGraph exhibits a significant speed advantage, performing 700 times faster than Natural Language Processing (NLP)-based methods while maintaining comparable accuracy results with respect to the state-of-the-art models.

cs.CR↗

arXiv2025

Market-Driven Flexibility Provision: A Tri-Level Optimization Approach for Carbon Reduction

The integration of renewable energy resources (RES) in the power grid can reduce carbon intensity, but also presents certain challenges. The uncertainty and intermittent nature of RES emphasize the need for flexibility in power systems. Moreover, there are noticeable mismatches between real-time electricity prices and carbon intensity patterns throughout the day. These discrepancies may lead customers to schedule energy-intensive tasks during the early hours of the day, a period characterized by lower electricity prices but higher carbon intensity. This paper introduces a novel and comprehensive framework aimed at encouraging customer participation in electricity markets and aligning their flexibility with carbon intensity trends. The proposed approach integrates an incentive-based tariff with a tri-level optimization model, where customers are motivated to submit flexibility bids and, in return, receive financial rewards based on their contributions. The tri-level model ensures a dynamic interaction between the market operation platform (MOP) and end-users. Simulations are performed on a modified IEEE-33 bus system, supported by two scenarios with different RES generations and customer behaviors. Results demonstrate the effectiveness of the proposed framework in guiding the customers' consumption behaviors towards low carbon intensity.

eess.SY↗

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.

eess.SY↗

arXiv2025

Event-Triggered Islanding in Inverter-Based Grids

The decentralization of modern power systems challenges the hierarchical structure of the electric grid and necessitates automated schemes to manage adverse conditions. This work proposes an adaptive isolation methodology that can divide a grid into autonomous islands, ensuring stable and economical operation amid deliberate or unintentional abnormal events. The adaptive isolation logic is event-triggered to prevent false positives, enhance detection accuracy, and reduce computational overhead. A measurement-based stable kernel representation (SKR) triggering mechanism initially inspects distributed generation controllers for abnormal behavior. The SKR then alerts an ensemble classifier to assess whether the system behavior remains within acceptable operational limits. The event-triggered adaptive isolation framework is evaluated using IEEE RTS-24 and 118-bus systems. Simulation results demonstrate that the proposed framework detects anomalous behavior with 100% accuracy in real-time, i.e., within 22msec. Supply-adequate partitions are identified outperforming traditional islanding detection and formation techniques while minimizing operating costs.

eess.SY↗

arXiv2025

Electricity Market-Clearing With Extreme Events

Extreme events jeopardize power network operations, causing beyond-design failures and massive supply interruptions. Existing market designs fail to internalize and systematically assess the risk of extreme and rare events. Efficiently maintaining the reliability of renewable-dominant power systems during extreme weather events requires co-optimizing system resources, while differentiating between large/rare and small/frequent deviations from forecast conditions. To address this gap in both research and practice, we propose managing the uncertainties associated with extreme weather events through an additional reserve service, termed extreme reserve. The procurement of extreme reserve is co-optimized with energy and regular reserve using a large deviation theory chance-constrained (LDT-CC) model, where LDT offers a mathematical framework to quantify the increased uncertainty during extreme events. To mitigate the high additional costs associated with reserve scheduling under the LDT-CC model, we also propose an LDT model based on weighted chance constraints (LDT-WCC). This model prepares the power system for extreme events at a lower cost, making it a less conservative alternative to the LDT-CC model. The proposed market design leads to a competitive equilibrium while ensuring cost recovery. Numerical experiments on an illustrative system and a modified 8-zone ISO New England system highlight the advantages of the proposed market design.

eess.SY↗

arXiv2024

An MDP-Based Approach for Distribution System Control with PV Generation and Battery Storage

This paper proposes a decision-making approach for the control of distribution systems with distributed energy resources (DERs) equipped with photovoltaic (PV) units and battery energy storage systems (BESS). The objective is to minimize the total operational cost of the distribution system while satisfying the system operating constraints. The method is based on the discrete-time finite-horizon Markov Decision Process (MDP) framework. Different aspects of the distribution system operation are considered, such as the possibilities of curtailment of PV generation, managing battery storage, reactive power injection, load shedding, and providing a flexibility service for the transmission system. The model is tested for the IEEE 33-bus system with two added DERs and the study cases involve various unexpected events. The experimental results show that this method enables the attainment of relatively low total cost values compared to the reference deterministic approach. The benefits of applying this approach are particularly evident when there is a significant difference between the predicted and actual PV power generation.

eess.SY↗

arXiv2024

Decentralized Bus Voltage Restoration for DC Microgrids

Regulating the voltage of the common DC bus, also referred to as the load bus, in DC microgrids is crucial for ensuring reliability and maintaining the nominal load voltage, which is essential for protecting sensitive loads from voltage variations. Stability and reliability are thereby enhanced, preventing malfunctions and extending the lifespan of sensitive loads (e.g., electronic devices). Voltage drops are caused by resistances of feeders connecting converters to the common DC bus, resulting in a reduced DC bus voltage compared to the nominal/desired value. Existing techniques to restore this voltage in DC microgrids are mainly centralized and rely on secondary control layers. These layers sense the common DC bus voltage, compare it to the nominal value, and utilize a PI controller to send corrections via communication links to each converter. In this paper, a local and straightforward approach to restoring the bus voltage in DC microgrids is presented, ensuring regulation in a decentralized manner. Voltage drops across resistances of feeders are compensated by an additional control loop feedback within each converter, based on the converter output current and feeder resistance. The proposed approach is verified through simulation and hardware-in-the-loop results, eliminating the need for communication links and hence increasing reliability and reducing cybersecurity threats.

eess.SY↗

arXiv2024

Advanced Resilience Planning for Distribution Systems

Climate change has led to an increase in the frequency and severity of extreme weather events, posing significant challenges for power distribution systems. In response, this work presents a planning approach in order to enhance the resilience of distribution systems against climatic hazards. The framework systematically addresses uncertainties during extreme events, including weather variability and line damage. Key strategies include line hardening, backup diesel generators, and sectionalizers to strengthen resilience. We model spatio-temporal dynamics and costs through a hybrid model integrating stochastic processes with deterministic elements. A two-stage stochastic mixed-integer linear approach is developed to optimize resilience investments against load loss, generator operations, and repairs. Case studies on the IEEE 15-bus benchmark system and a realistic distribution grid model in Riyadh, Saudi Arabia demonstrate enhanced system robustness as well as cost efficiency of 10% and 15%, respectively.

eess.SY↗

arXiv2024

Observer-Based Discontinuous Communication in the Secondary Control of AC Microgrids

This paper proposes an observer-based event-driven approach to decrease the overuse of communication networks. The suggested approach aims to estimate the required data for sharing between units in line with as much communication reduction as possible. In other words, the proposed approach effectively determines which state variables should be shared (observer concept) among the units during specific time intervals (event-triggered concept). This strategy significantly reduces the overall communication load. It is shown that the estimation error remains bounded and Zeno behavior, characterized by an endless number of transmissions occurring within a limited time frame, does not occur. The proposed methodology can be systematically applied to any communication-based secondary controller in alternating current (AC) microgrids. Simulation results demonstrate a high degree of precision in estimating the states under the proposed approach. Also, the secondary controller performance under the proposed method is evaluated in MATLAB/Simulink environment.

eess.SY↗

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.

eess.SY↗

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).

eess.SY↗

arXiv2024

Design and Evaluation of a DC Microgrid Testbed for DER Integration and Power Management

This paper presents a DC microgrid testbed setup that consists of various Distributed Energy Resources (DERs) including solar Photovoltaics (PV), supercapacitors for voltage regulation, and Battery Energy Storage Systems (BESS). The DC microgrid accommodates both non-flexible and flexible loads which can be dynamically adjusted based on PV power availability. The integration of the setup with the Hyphae Autonomous Power Interchange System (APIS) framework automates energy transfer within the BESS, ensuring efficient power management and optimizing the overall efficiency of the DC microgrid. Furthermore, the setup is validated in terms of the efficacy of the proposed model via real-time simulation, facilitated by the Speedgoat baseline real-time target Hardware-in-the-Loop (HIL) machine. The results demonstrate the model's adeptness in efficiently managing power sharing, emphasizing the capabilities of the DC microgrid setup in terms of performance and reliability in dynamic energy scenarios as well as enhancing the resilience of the grid amidst PV uncertainties.

eess.SY↗

arXiv2024

A Novel Observer-Centric Approach for Detecting Faults in Islanded AC Microgrids with Uncertainties

Fault detection is vital in ensuring AC microgrids' reliable and resilient operation. Its importance lies in swiftly identifying and isolating faults, preventing cascading failures, and enabling rapid power restoration. This paper proposes a strategy based on observers and residuals for detecting internal faults in grid-forming inverters with power-sharing coordination. The dynamics of the inverters are captured through a nonlinear state space model. The design of our observers and residuals considers $H_{-}/H_{\infty}$ conditions to ensure robustness against disturbances and responsiveness to faults. The proposed design is less restrictive than existing observer-based fault detection schemes by leveraging the properties of quadratic inner-boundedness and one-sided Lipschitz conditions. The internal faults considered in this paper include actuator faults, busbar faults, and inverter bridge faults, which are modeled using vector-matrix representations that modify the state space model of the inverters. One significant advantage of the proposed approach is its cost-effectiveness, as it does not require additional sensors. Experiments are conducted on an islanded AC microgrid with three inductive lines, four inductive loads, and four grid-forming inverters to validate the merits of the proposed fault detection strategy. The results demonstrate that our design outperforms existing methods in the field.

eess.SY↗

arXiv2023

Physics-Informed Neural Networks for Accelerating Power System State Estimation

State estimation is the cornerstone of the power system control center since it provides the operating condition of the system in consecutive time intervals. This work investigates the application of physics-informed neural networks (PINNs) for accelerating power systems state estimation in monitoring the operation of power systems. Traditional state estimation techniques often rely on iterative algorithms that can be computationally intensive, particularly for large-scale power systems. In this paper, a novel approach that leverages the inherent physical knowledge of power systems through the integration of PINNs is proposed. By incorporating physical laws as prior knowledge, the proposed method significantly reduces the computational complexity associated with state estimation while maintaining high accuracy. The proposed method achieves up to 11% increase in accuracy, 75% reduction in standard deviation of results, and 30% faster convergence, as demonstrated by comprehensive experiments on the IEEE 14-bus system.

cs.LG↗