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Charalambos Konstantinou

Publications and source records attributed to Charalambos Konstantinou.

At least 55 records · Page 3Linked to original sources

Distributed Energy Resources Cybersecurity Outlook: Vulnerabilities, Attacks, Impacts, and Mitigations

The digitization and decentralization of the electric power grid are key thrusts for an economically and environmentally sustainable future. Towards this goal, distributed energy resources (DER), including rooftop solar panels, battery storage, electric vehicles, etc., are becoming ubiquitous in power systems. Power utilities benefit from DERs as they minimize operational costs; at the same time, DERs grant users and aggregators control over the power they produce and consume. DERs are interconnected, interoperable, and support remotely controllable features, thus, their cybersecurity is of cardinal importance. DER communication dependencies and the diversity of DER architectures widen the threat surface and aggravate the cybersecurity posture of power systems. In this work, we focus on security oversights that reside in the cyber and physical layers of DERs and can jeopardize grid operations. Existing works have underlined the impact of cyberattacks targeting DER assets, however, they either focus on specific system components (e.g., communication protocols), do not consider the mission-critical objectives of DERs, or neglect the adversarial perspective (e.g., adversary/attack models) altogether. To address these omissions, we comprehensively analyze adversarial capabilities and objectives when manipulating DER assets, and then present how protocol and device-level vulnerabilities can materialize into cyberattacks impacting power system operations. Finally, we provide mitigation strategies to thwart adversaries and directions for future DER cybersecurity research.

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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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Volt/VAR Optimization in the Presence of Attacks: A Real-Time Co-Simulation Study

Traditionally, Volt/VAR optimization (VVO) is performed in distribution networks through legacy devices such as on-load tap changers (OLTCs), voltage regulators (VRs), and capacitor banks. With the amendment in IEEE 1547 standard, distributed energy resources (DERs) can now provide reactive power support to the grid. For this, renewable energy-based DERs, such as PV, are interfaced with the distribution networks through smart inverters (SIs). Due to the intermittent nature of such resources, VVO transforms into a dynamic problem that requires extensive communication between the VVO controller and devices performing the VVO scheme. This communication, however, can be potentially tampered with by an adversary rendering the VVO ineffective. In this regard, it is important to assess the impact of cyberattacks on the VVO scheme. This paper develops a real-time co-simulation setup to assess the effect of cyberattacks on VVO. The setup consists of a real-time power system simulator, a communication network emulator, and a master controller in a system-in-the-loop (SITL) setup. The DNP3 communication protocol is adopted for the underlying communication infrastructure. The results show that corrupted communication messages can lead to violation of voltage limits, increased number of setpoint updates of VRs, and economic loss.

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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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Residual-Based Detection of Attacks in Cyber-Physical Inverter-Based Microgrids

This paper discusses the challenges faced by cyber-physical microgrids (MGs) due to the inclusion of information and communication technologies in their already complex, multi-layered systems. The work identifies a research gap in modeling and analyzing stealthy intermittent integrity attacks in MGs, which are designed to maximize damage and cancel secondary control objectives. To address this, the paper proposes a nonlinear residual-based observer approach to detect and mitigate such attacks. In order to ensure a stable operation of the MG, the formulation then incorporates stability constraints along with the detection observer. The proposed design is validated through case studies on a MG benchmark with four distributed generators, demonstrating its effectiveness in detecting attacks while satisfying network and stability constraints.

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A Bi-level Decision Framework for Incentive-Based Demand Response in Distribution Systems

In a growing retail electricity market, demand response (DR) is becoming an integral part of the system to enhance economic and operational performances. This is rendered as incentive-based DR (IBDR) in the proposed study. It presents a bi-level decision framework under the ambit of multiple demand response providers (DRPs) in the retail competition. It is formulated as a multi-leader-multi-follower game, where multiple DRPs, as the DR stakeholders, are strategically interacting to optimize load serving entity cost at the upper level, and individual DRP as the aggregated customers is optimizing its cost at the lower level. The strategic behavior of DRPs is modeled in a game-theoretic framework using a generalized Stackelberg game. Further, the existence and uniqueness of the game are validated using variational inequalities. It is presented as a nonlinear problem to consider AC network constraints. An equilibrium problem with equilibrium constraints is used as a mathematical program to model the multi-leader-multi-follower, bi-level problem, which is simultaneously solved for all DRPs. The diagonalization method is employed to solve the problem. The detailed numerical analyses are conducted on IEEE 33-bus test and Indian-108 bus distribution systems to demonstrate the applicability and scalability of the proposed model and the suggested method.

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Impact Assessment of Data Integrity Attacks in MVDC Shipboard Power Systems

The development of power electronics-based medium voltage direct current (MVDC) networks has revolutionized the marine industry by enabling all-electric ships (AES). This technology facilitates the integration of heterogeneous resources and improves efficiency. The independent shipboard power system (SPS) is controlled by exchanging measurements and control signals over a communication network. However, the reliance on communication channels raises concerns about the potential exploitation of vulnerabilities leading to cyber-attacks that could disrupt the system. In this paper, a notional 12 kV MVDC SPS model with zonal electrical distribution system (ZEDS) architecture is considered as an exemplary model. As the system stability is closely linked to the transient performance, we investigate how to determine the operational status of the system under potential data integrity attacks on the governor and exciter of the power generation modules (PGMs). Further, the impact of these attacks on the stability of rotor speed and the DC link voltage is derived and discussed. The simulation of the system is carried out in MATLAB/Simulink environment.

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Lost at Sea: Assessment and Evaluation of Rootkit Attacks on Shipboard Microgrids

Increased dependence of the maritime industry on information and communication networks has made shipboard power systems vulnerable to stealthy cyber-attacks. One such attack variant, called rootkit, can leverage system knowledge to hide its presence and allow remotely located malware handlers to gain complete control of infected subsystems. This paper presents a comprehensive evaluation of the threat landscape imposed by such attack variants on Medium Voltage DC (MVDC) shipboard microgrids, including a discussion of their impact on the overall maritime sector in general, and provides several simulation results to demonstrate the same. It also analyzes and presents the actions of possible defense mechanisms, with specific emphasis on evasion, deception, and detection frameworks, that will help ship operators and maritime cybersecurity professionals protect their systems from such attacks.

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Resilient Distributed Integral Control for Multimachine Power Systems with Inherent Input Constraint Satisfaction

In this paper, a novel distributed controller for multimachine power systems is proposed to guarantee grid frequency restoration and accurate real and reactive power sharing among the generator units, while maintaining the generator inputs (mechanical torque and field excitation voltage) within given bounds. The boundedness of the controller outputs (generator inputs) is rigorously proven using vector field theory. It is additionally shown that even if one generator input reaches its upper/lower limit, the remaining units can still accomplish the desired control tasks without modifying the controller structure or dynamics; hence introducing enhanced system resilience using the proposed approach. This has been accomplished in a unified control structure while using neighbour-to-neighbour communication, thus maintaining the distributed nature of the controller. An example of a 10-bus, 4-machine power system is simulated to verify the proposed controller performance under sudden changes of the load demand.

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A Bi-Level Stochastic Game Model for PMU Placement in Power Grid with Cybersecurity Risks

Phasor measurement units (PMUs) provide accurate and high-fidelity measurements in order to monitor the state of the power grid and support various control and planning tasks. However, PMUs have a high installation cost prohibiting their massive deployment. Minimizing the number of installed PMUs needs to be achieved while also maintaining full observability of the network. At the same time, data integrity attacks on PMU measurements can cause mislead power system control and operation routines. In this paper, a bi-level stochastic non-cooperative game-based placement model is proposed for PMU allocation in the presence of cyber-attack risks. In the first level, the protection of individual PMU placed in a network is addressed, while considering the interaction between the grid operator and the attacker with respective resource constraints. In the second level, the attacker observes the placement of the PMUs and compromises them, with the aim of maximizing the state estimation error and reducing the observability of the network. The proposed technique is deployed in the IEEE-9 bus test system. The results demonstrate a 9% reduction in the cost incurred by the power grid operator for deploying PMUs while considering cyber-risks.

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Experimental Impact Analysis of Cyberattacks in Power Systems using Digital Real-Time Testbeds

Smart grid advancements and the increased integration of digital devices have transformed the existing power grid into a cyber-physical energy system. This reshaping of the current power system can make it vulnerable to cyberattacks, which could cause irreversible damage to the energy infrastructure resulting in the loss of power, equipment damage, etc. Constant threats emphasize the importance of cybersecurity investigations. At the same time, developing cyber-physical system (CPS) simulation testbeds is crucial for vulnerability assessment and the implementation and validation of security solutions. In this paper, two separate real-time CPS testbeds are developed based on the availability of local research facilities for impact analysis of denial-of-service (DoS) attacks on microgrids. The two configurations are implemented using two different digital real-time simulator systems, one using the real-time digital simulator (RTDS) with a hardware-in-the-loop (HIL) setup and the other one using OPAL-RT with ExataCPS to emulate the cyber-layer infrastructure. Both testbeds demonstrate the impact of DoS attacks on microgrid control and protection operation.

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A Resource Allocation Scheme for Energy Demand Management in 6G-enabled Smart Grid

Smart grid (SG) systems enhance grid resilience and efficient operation, leveraging the bidirectional flow of energy and information between generation facilities and prosumers. For energy demand management (EDM), the SG network requires computing a large amount of data generated by massive Internet-of-things sensors and advanced metering infrastructure (AMI) with minimal latency. This paper proposes a deep reinforcement learning (DRL)-based resource allocation scheme in a 6G-enabled SG edge network to offload resource-consuming EDM computation to edge servers. Automatic resource provisioning is achieved by harnessing the computational capabilities of smart meters in the dynamic edge network. To enforce DRL-assisted policies in dense 6G networks, the state information from multiple edge servers is required. However, adversaries can "poison" such information through false state injection (FSI) attacks, exhausting SG edge computing resources. Toward addressing this issue, we investigate the impact of such FSI attacks with respect to abusive utilization of edge resources, and develop a lightweight FSI detection mechanism based on supervised classifiers. Simulation results demonstrate the efficacy of DRL in dynamic resource allocation, the impact of the FSI attacks, and the effectiveness of the detection technique.

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Blockchain for Unmanned Underwater Drones: Research Issues, Challenges, Trends and Future Directions

Underwater drones have found a place in oceanography, oceanic research, bathymetric surveys, military, surveillance, monitoring, undersea exploration, mining, commercial diving, photography and several other activities. Drones housed with several sensors and complex propulsion systems help oceanographic scientists and undersea explorers to map the seabed, study waves, view dead zones, analyze fish counts, predict tidal wave behaviors, aid in finding shipwrecks, building windfarms, examine oil platforms located in deep seas and inspect nuclear reactors in the ship vessels. While drones can be explicitly programmed for specific missions, data security and privacy are crucial issues of serious concern. Blockchain has emerged as a key enabling technology, amongst other disruptive technological enablers, to address security, data sharing, storage, process tracking, collaboration and resource management. This study presents a comprehensive review on the utilization of Blockchain in different underwater applications, discussing use cases and detailing benefits. Potential challenges of underwater applications addressed by Blockchain have been detailed. This work identifies knowledge gaps between theoretical research and real-time Blockchain integration in realistic underwater drone applications. The key limitations for effective integration of Blockchain in real-time integration in UUD applications, along with directions for future research have been presented.

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CPES-QSM: A Quantitative Method Towards the Secure Operation of Cyber-Physical Energy Systems

Power systems are evolving into cyber-physical energy systems (CPES) due to the integration of modern communication and Internet-of-Things (IoT) devices. CPES security evaluation is challenging since the physical and cyber layers are often not considered holistically. Existing literature focuses on only optimizing the operation of either the physical or cyber layer while ignoring the interactions between them. This paper proposes a metric, the Cyber-Physical Energy System Quantitative Security Metric (CPES-QSM), that quantifies the interaction between the cyber and physical layers across three domains: electrical, cyber-risk, and network topology. A method for incorporating the proposed cyber-metric into operational decisions is also proposed by formulating a cyber-constrained AC optimal power flow (C-ACOPF) that considers the status of all the CPES layers. The cyber-constrained ACOPF considers the vulnerabilities of physical and cyber networks by incorporating factors such as voltage stability, contingencies, graph-theory, and IoT cyber risks, while using a multi-criteria decision-making technique. Simulation studies are conducted using standard IEEE test systems to evaluate the effectiveness of the proposed metric and the C-ACOPF formulation.

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Mitigation of Cyberattacks through Battery Storage for Stable Microgrid Operation

In this paper, we present a mitigation methodology that leverages battery energy storage system (BESS) resources in coordination with microgrid (MG) ancillary services to maintain power system operations during cyberattacks. The control of MG agents is achieved in a distributed fashion, and once a misbehaving agent is detected, the MG's mode supervisory controller (MSC) isolates the compromised agent and initiates self-healing procedures to support the power demand and restore the compromised agent. Our results demonstrate the practicality of the proposed attack mitigation strategy and how grid resilience can be improved using BESS synergies. Simulations are performed on a modified version of the Canadian urban benchmark distribution model.

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A Secure and Trusted Mechanism for Industrial IoT Network using Blockchain

Industrial Internet-of-Things (IIoT) is a powerful IoT application which remodels the growth of industries by ensuring transparent communication among various entities such as hubs, manufacturing places and packaging units. Introducing data science techniques within the IIoT improves the ability to analyze the collected data in a more efficient manner, which current IIoT architectures lack due to their distributed nature. From a security perspective, network anomalies/attackers pose high security risk in IIoT. In this paper, we have addressed this problem, where a coordinator IoT device is elected to compute the trust of IoT devices to prevent the malicious devices to be part of network. Further, the transparency of the data is ensured by integrating a blockchain-based data model. The performance of the proposed framework is validated extensively and rigorously via MATLAB against various security metrics such as attack strength, message alteration, and probability of false authentication. The simulation results suggest that the proposed solution increases IIoT network security by efficiently detecting malicious attacks in the network.

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Load-Altering Attacks Against Power Grids under COVID-19 Low-Inertia Conditions

The COVID-19 pandemic has impacted our society by forcing shutdowns and shifting the way people interacted worldwide. In relation to the impacts on the electric grid, it created a significant decrease in energy demands across the globe. Recent studies have shown that the low demand conditions caused by COVID-19 lockdowns combined with large renewable generation have resulted in extremely low-inertia grid conditions. In this work, we examine how an attacker could exploit these {scenarios} to cause unsafe grid operating conditions by executing load-altering attacks (LAAs) targeted at compromising hundreds of thousands of IoT-connected high-wattage loads in low-inertia power systems. Our study focuses on analyzing the impact of the COVID-19 mitigation measures on U.S. regional transmission operators (RTOs), formulating a plausible and realistic least-effort LAA targeted at transmission systems with low-inertia conditions, and evaluating the probability of these large-scale LAAs. Theoretical and simulation results are presented based on the WSCC 9-bus {and IEEE 118-bus} test systems. Results demonstrate how adversaries could provoke major frequency disturbances by targeting vulnerable load buses in low-inertia systems and offer insights into how the temporal fluctuations of renewable energy sources, considering generation scheduling, impact the grid's vulnerability to LAAs.

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Behind Closed Doors: Process-Level Rootkit Attacks in Cyber-Physical Microgrid Systems

Embedded controllers, sensors, actuators, advanced metering infrastructure, etc. are cornerstone components of cyber-physical energy systems such as microgrids (MGs). Harnessing their monitoring and control functionalities, sophisticated schemes enhancing MG stability can be deployed. However, the deployment of `smart' assets increases the threat surface. Power systems possess mechanisms capable of detecting abnormal operations. Furthermore, the lack of sophistication in attack strategies can render them detectable since they blindly violate power system semantics. On the other hand, the recent increase of process-aware rootkits that can attain persistence and compromise operations in undetectable ways requires special attention. In this work, we investigate the steps followed by stealthy rootkits at the process level of control systems pre- and post-compromise. We investigate the rootkits' precompromise stage involving the deployment to multiple system locations and aggregation of system-specific information to build a neural network-based virtual data-driven model (VDDM) of the system. Then, during the weaponization phase, we demonstrate how the VDDM measurement predictions are paramount, first to orchestrate crippling attacks from multiple system standpoints, maximizing the impact, and second, impede detection blinding system operator situational awareness.

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