Publications
118 results found
Yan M, Teng F, Gan W, et al., 2023, Blockchain for secure decentralized energy management of multi-energy system using state machine replication, Applied Energy, Vol: 337, ISSN: 0306-2619
Decentralized energy management can preserve the privacy of individual energy systems while mitigating computational and communication burdens. However, most decentralized energy management methods are partially decentralized and cannot ensure information exchange security. Therefore, this paper provides a secure fully decentralized energy management by using blockchain. First, a fully decentralized energy management framework using the optimality condition decomposition (OCD) is provided, in which individual energy system operators only exchange the boundary information with their peers rather than submitting proprietary information to a centralized system operator. Then, an asynchronous mechanism is proposed for updating the information exchange in OCD, enabling the proposed decentralized management to work under potential communication latency or interruption. Furthermore, the blockchain-based framework with state machine replication (SMR) based consensus algorithm is provided to safeguard the information exchange among individual energy systems in a secure and tamper-proof manner. The proposed decentralized energy management is tested on a multi-energy system with seven subsystems and a real-world multi-energy system in North China. The numerical results demonstrate the effectiveness of the proposed method in privacy protection and data security enhancement. The proposed method can prevent the cost increase caused by cheating activities, which in some subsystems can reach 17.6%. Additionally, the proposed fully decentralized method outperforms the partially decentralized method by 37.7% in reducing computation time. Also demonstrated are the computational precision, scalability and adaptability of the proposed method.
Caputo C, Cardin MA, Ge P, et al., 2023, Design and planning of flexible mobile Micro-Grids using Deep Reinforcement Learning, Applied Energy, Vol: 335, ISSN: 0306-2619
Ongoing risks from climate change have significantly impacted the livelihood of global nomadic communities and are likely to lead to increased migratory movements in coming years. As a result, mobility considerations are becoming increasingly important in energy systems planning, particularly to achieve energy access in developing countries. Advanced “Plug and Play” control strategies have been recently developed with such a decentralized framework in mind, allowing easier interconnection of nomadic communities, both to each other and to the main grid. Considering the above, the design and planning strategy of a mobile multi-energy supply system for a nomadic community is investigated in this work. Motivated by the scale and dimensionality of the associated uncertainties, impacting all major design and decision variables over the 30-year planning horizon, Deep Reinforcement Learning (DRL) Flexibility Analysis is implemented for the design and planning problem. DRL based solutions are benchmarked against several rigid baseline design options to compare expected performance under uncertainty. The results on a case study for ger communities in Mongolia suggest that mobile nomadic energy systems can be both technically and economically feasible, particularly when considering flexibility, although the degree of spatial dispersion among households is an important limiting factor. Additionally, the DRL based policies lead to the development of dynamic evolution and adaptability strategies, which can be used by the targeted communities under a very wide range of potential scenarios. Key economic, sustainability and resilience indicators such as Cost, Equivalent Emissions and Total Unmet Load are measured, suggesting potential improvements compared to available baselines of up to 25%, 67% and 76%, respectively. Finally, the decomposition of values of flexibility and plug and play operation is presented using a variation of real options theory, with important impl
Wang D, Zheng K, Teng F, et al., 2023, Quantum Annealing With Integer Slack Variables for Grid Partitioning, IEEE Transactions on Power Systems, Vol: 38, Pages: 1747-1750, ISSN: 0885-8950
Quantum annealing (QA) can be used to efficiently solve quadratic unconstrained binary optimization (QUBO) problems. Grid partitioning (GP), which is a classic NP-hard integer programming problem, can potentially be solved much faster using QA. However, inequality constraints in the GP optimization model are difficult to handle. In this study, a novel solution framework based on QA is proposed for GP problems. The integer slack (IS) and binary expansion methods are applied to transform GP problems into QUBO problems. Instead of introducing continuous variables in traditional slack methods, the proposed IS method can avoid complex iteration processes when using QA. The case study demonstrates that the IS method obtains accurate feasible solutions with less calculation time.
Chu Z, Teng F, 2023, Voltage Stability Constrained Unit Commitment in Power Systems With High Penetration of Inverter-Based Generators, IEEE Transactions on Power Systems, Vol: 38, Pages: 1572-1582, ISSN: 0885-8950
With the increasing penetration of renewable energy sources, power system operation has to be adapted to ensure the system stability and security while considering the distinguished feature of the Inverter-Based Generator (IBG) interfaced generators. The static voltage stability which is mainly compromised by heavy loading conditions in conventional power systems, faces new challenges due to the large scale integration of IBG units. This paper investigates the static voltage stability problem in high IBG-penetrated system. The analytic criterion that ensures the voltage stability at the IBG buses are derived with the interaction of different IBGs being considered. Based on this, an optimal system scheduling model is proposed to minimize the overall system operation cost while maintaining the voltage stability during normal operation through dynamically optimizing the active and reactive power output from IBGs. The highly nonlinear voltage stability constraints are effectively converted into Second-Order Cone (SOC) form, leading to an overall Mixed-Integer SOC Programming (MISOCP), together with the SOC reformulation of AC power flow and frequency constraints. The effectiveness of the proposed model and the impact of various factors on voltage stability are demonstrated in thorough case studies.
Wang Y, Qiu D, Teng F, et al., 2023, Towards Microgrid Resilience Enhancement Via Mobile Power Sources and Repair Crews: A Multi-Agent Reinforcement Learning Approach, IEEE Transactions on Power Systems, ISSN: 0885-8950
Mobile power sources (MPSs) have been gradually deployed in microgrids as critical resources to coordinate with repair crews (RCs) towards resilience enhancement owing to their flexibility and mobility in handling the complex coupled power-transport systems. However, previous work solves the coordinated dispatch problem of MPSs and RCs in a centralized manner with the assumption that the communication network is still fully functioning after the event. However, there is growing evidence that certain extreme events will damage or degrade communication infrastructure, which makes centralized decision making impractical. To fill this gap, this paper formulates the resilience-driven dispatch problem of MPSs and RCs in a decentralized framework. To solve this problem, a hierarchical multi-agent reinforcement learning method featuring a two-level framework is proposed, where the high-level action is used to switch decision-making between power and transport networks, and the low-level action constructed via a hybrid policy is used to compute continuous scheduling and discrete routing decisions in power and transport networks, respectively. The proposed method also uses an embedded function encapsulating system dynamics to enhance learning stability and scalability. Case studies based on IEEE 33-bus and 69-bus power networks are conducted to validate the effectiveness of the proposed method in load restoration.
Wang C, Yan M, Pang K, et al., 2023, Cyber-Physical Interdependent Restoration Scheduling for Active Distribution Network Via Ad Hoc Wireless Communication, IEEE Transactions on Smart Grid, ISSN: 1949-3053
This paper proposes a post-disaster cyber-physical interdependent restoration scheduling (CPIRS) framework for active distribution networks (ADN) where the simultaneous damages on cyber and physical networks are considered. The ad hoc wireless device-to-device (D2D) communication is leveraged, for the first time, to establish cyber networks instantly after the disaster to support ADN restoration. The repair and operation crew dispatching, the remote-controlled network reconfiguration and the system operation with DERs can be effectively coordinated under the cyber-physical interactions. The uncertain outputs of renewable energy resources (RESs) are represented by budget-constrained polyhedral uncertainty sets. Through implementing linearization techniques on disjunctive expressions, a monolithic mixed-integer linear programming (MILP) based two-stage robust optimization model is formulated and subsequently solved by a customized column-and-constraint generation (C&CG) algorithm. Numerical results on the IEEE 123-node distribution system demonstrate the effectiveness and superiorities of the proposed CPIRS method for ADN.
Toubeau JF, Teng F, Morstyn T, et al., 2023, Privacy-Preserving Probabilistic Voltage Forecasting in Local Energy Communities, IEEE Transactions on Smart Grid, Vol: 14, Pages: 798-809, ISSN: 1949-3053
This paper presents a new privacy-preserving framework for the short-term (multi-horizon) probabilistic forecasting of nodal voltages in local energy communities. This task is indeed becoming increasingly important for cost-effectively managing network constraints in the context of the massive integration of distributed energy resources. However, traditional forecasting tasks are carried out centrally, by gathering raw data of end-users in a single database that exposes their private information. To avoid such privacy issues, this work relies on a distributed learning scheme, known as federated learning wherein individuals' data are kept decentralized. The learning procedure is then augmented with differential privacy, which offers formal guarantees that the trained model cannot be reversed-engineered to infer sensitive local information. Moreover, the problem is framed using cross-series learning, which allows to smoothly integrate any new client joining the community (i.e., cold-start forecasting) without being plagued by data scarcity. Outcomes show that the proposed approach achieves improved performance compared to non-collaborative (locally trained) models, and is able to reach a trade-off between privacy and performance for different architectures of deep learning networks.
Xu W, Jaimoukha IM, Teng F, 2023, Robust Moving Target Defence Against False Data Injection Attacks in Power Grids, IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, Vol: 18, Pages: 29-40, ISSN: 1556-6013
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Garcia-Munoz F, Teng F, Junyent-Ferre A, et al., 2022, Stochastic energy community trading model for day-ahead and intraday coordination when offering DER?s reactive power as ancillary services, SUSTAINABLE ENERGY GRIDS & NETWORKS, Vol: 32, ISSN: 2352-4677
Castiglione L, Hau Z, Ge P, et al., 2022, HA-grid: security aware hazard analysis for smart grids, IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids, Publisher: IEEE, Pages: 446-452
Attacks targeting smart grid infrastructures can result in the disruptions of power supply as well as damages to costly equipment, with significant impact on safety as well as on end-consumers. It is therefore of essence to identify attack paths in the infrastructure that lead to safety violations and todetermine critical components that must be protected. In this paper, we introduce a methodology (HA-Grid) that incorporates both safety and security modelling of smart grid infrastructure to analyse the impact of cyber threats on the safety of smart grid infrastructures. HA-Grid is applied on a smart grid test-bed to identify attack paths that lead to safety hazards, and todetermine the common nodes in these attack paths as critical components that must be protected.
Higgins M, Xu W, Teng F, et al., 2022, Cyber-physical risk assessment for false data injection attacks considering moving target defences Best practice application of respective cyber and physical reinforcement assets to defend against FDI attacks, INTERNATIONAL JOURNAL OF INFORMATION SECURITY, ISSN: 1615-5262
Ge P, Caputo C, Teng F, et al., 2022, A Wireless-Assisted Hierarchical Framework to Accommodate Mobile Energy Resources, Singapore, IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)
Heylen E, Browell J, Teng F, 2022, Probabilistic Day-Ahead Inertia Forecasting, IEEE TRANSACTIONS ON POWER SYSTEMS, Vol: 37, Pages: 3738-3746, ISSN: 0885-8950
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- Citations: 3
O'Malley C, Badesa L, Teng F, et al., 2022, Frequency response from aggregated V2G chargers with uncertain EV connections, IEEE Transactions on Power Systems, Pages: 1-14, ISSN: 0885-8950
Fast frequency response (FR) is highly effective at securing frequency dynamics after a generator outage in low inertia systems. Electric vehicles (EVs) equipped with vehicle to grid (V2G) chargers could offer an abundant source of FR in future. However, the uncertainty associated with V2G aggregation, driven by the uncertain number of connected EVs at the time of an outage, has not been fully understood and prevents its participation in the existing service provision framework. To tackle this limitation, this paper, for the first time, incorporates such uncertainty into system frequency dynamics, from which probabilistic nadir and steady state frequency requirements are enforced via a derived moment-based distributionally-robust chance constraint. Field data from over 25,000 chargers is analysed to provide realistic parameters and connection forecasts to examine the value of FR from V2G chargers in annual operation of the GB 2030 system. The case study demonstrates that uncertainty of EV connections can be effectively managed through the proposed scheduling framework, which results in annual savings of Misplaced &6,300 or 37.4 tCO2 per charger. The sensitivity of this value to renewable capacity and FR delays is explored, with V2G capacity shown to be a third as valuable as the same grid battery capacity.
Graham J, Heylen E, bian Y, et al., 2022, Benchmarking explanatory models for inertia forecasting using public data of the nordic area, 2022 17th International Conference on Probabilistic Methods Applied to Power Systems (PMAPS), Publisher: IEEE, Pages: 1-6
This paper investigates the performance of a day-ahead explanatory model for inertia forecasting based on field data in the Nordic system, which achieves a 43% reduction in mean absolute percentage error (MAPE) against a state-of-the-art time-series forecast model. The generalizability of the explanatory model is verified by its consistent performance on Nordic and Great Britain datasets. Also, it appears that a long duration of training data is not required to obtain accurate results with this model, but taking a more spatially granular approach reduces the MAPE by 3.6%. Finally, two further model enhancements are studied considering the specific features in Nordic system: (i) a monthly interaction variable applied to the day-ahead national demand forecast feature, reducing the MAPE by up to 18%; and (ii) a feature based on the inertia from hydropower, although this has a negligible impact. The field dataset used for benchmarking is also made publicly available.
Caputo C, Cardin M-A, Korre A, et al., 2022, Energy System Evolution Strategies for Mobile Micro-grids using Deep Reinforcement Learning Flexibility Analysis, Espoo, Finland, 32nd European Conference on Operational Research (EURO 2022)
Teng F, Chhachhi SAURAB, Ge PUDONG, et al., 2022, Balancing privacy and access to smart meter data: an Energy Futures Lab briefing paper
Digitalising the energy system is expected to be a vital component of achieving the UK’s climate change targets. Smart meter data, in particular, is seen a key enabler of the transition to more dynamic, cost-effective, cost-reflective, and decarbonised electricity. However, access to this data faces a challenge due to consumer privacy concerns. This Briefing Paper investigates four key elements of smart meter data privacy: existing data protection regulations; the personal information embedded within smart meter data; consumer privacy concerns; and privacy-preserving techniques that could be incorporated alongside existing mechanisms to minimise or eliminate potential privacy infringements.
Hou X, Sun K, Zhang N, et al., 2022, Priority-driven self-optimizing power control scheme for interlinking converters of hybrid AC/DC microgrid clusters in decentralized manner, IEEE Transactions on Power Electronics, Vol: 37, Pages: 5970-5983, ISSN: 0885-8993
Hybrid AC/DC microgrid clusters are key building blocks of smart grid to support sustainable and resilient urban power systems. In microgrid clusters, the subgrid load-priorities and power quality requirements for different areas vary significantly. To realize optimal power exchanges among microgrid clusters, this paper proposes a decentralized self-optimizing power control scheme for interlinking converters (ILC) of hybrid microgrid clusters. A priority-driven optimal power exchange model of ILCs is built considering the priorities and capacities in subgrids. The optimization objective is to minimize the total DC-voltage/AC-frequency state deviations of subgrids. To realize the decentralized power flow control, an optimal-oriented quasi-droop control strategy of ILCs is introduced to not only achieve a flexible self-optimizing power flow management, but also provide an ancillary function of voltage support. Consequently, as each of ILCs only monitors the local AC-side frequency and DC-side voltage signals, the whole optimal power control of the wide-area microgrid clusters is achieved in a decentralized manner without any communication link. Thus, the proposed control algorithm has the features of decreased cost, increased scalability, reduced geographic restrictions and high resilience in terms of communication faults. Finally, the proposed method is validated by case studies with four interconnected microgrids through hardware-in-loop tests.
Bellizio F, Xu W, Qiu D, et al., 2022, Transition to Digitalized Paradigms for Security Control and Decentralized Electricity Market, PROCEEDINGS OF THE IEEE, ISSN: 0018-9219
Hua W, Jiang J, Sun H, et al., 2022, Consumer-centric decarbonization framework using Stackelberg game and Blockchain, APPLIED ENERGY, Vol: 309, ISSN: 0306-2619
Ge P, Teng F, Konstantinou C, et al., 2022, A resilience-oriented centralised-to-decentralised framework for networked microgrids management, Applied Energy, Vol: 308, ISSN: 0306-2619
This paper proposes a cyber–physical cooperative mitigation framework to enhance power systems resilience against power outages caused by extreme events, e.g., earthquakes and hurricanes. Extreme events can simultaneously damage the physical-layer electric power infrastructure and the cyber-layer communication facilities. Microgrid (MG) has been widely recognised as an effective physical-layer response to such events, however, the mitigation strategy in the cyber lay is yet to be fully investigated. Therefore, this paper proposes a resilience-oriented centralised-to-decentralised framework to maintain the power supply of critical loads such as hospitals, data centres, etc., under extreme events. For the resilient control, controller-to-controller (C2C) wireless network is utilised to form the emergency regional communication when centralised base station being compromised. Owing to the limited reliable bandwidth that reserved as a backup, the inevitable delays are dynamically minimised and used to guide the design of a discrete-time distributed control algorithm to maintain post-event power supply. The effectiveness of the cooperative cyber–physical mitigation framework is demonstrated through extensive simulations in MATLAB/Simulink.
Ge P, Chen B, Teng F, 2022, Cyber-Resilient Self-Triggered Distributed Control of Networked Microgrids Against Multi-Layer DoS Attacks, IEEE Transactions on Smart Grid, ISSN: 1949-3053
Networked microgrids with high penetration of distributed generators have ubiquitous remote information exchange, which may be exposed to various cyber security threats. This paper, for the first time, addresses a consensus problem in terms of frequency synchronisation in networked microgrids subject to multi-layer denial of service (DoS) attacks, which could simultaneously affect communication, measurement and control actuation channels. A unified notion of Persistency-of-Data-Flow (PoDF) is proposed to characterise the data unavailability in different information network links, and further quantifies the multi-layer DoS effects on the hierarchical system. With PoDF, we provide a sufficient condition of the DoS attacks under which the consensus can be preserved with the proposed edge-based self-triggered distributed control framework. In addition, to mitigate the conservativeness of offline design against the worst-case attack across all agents, an online self-adaptive scheme of the control parameters is developed to fully utilise the latest available information of all data transmission channels. Finally, the effectiveness of the proposed cyber-resilient self-triggered distributed control is verified by representative case studies.
Li K, Guo H, Fang X, et al., 2022, Market Mechanism Design of Inertia and Primary Frequency Response with Consideration of Energy Market, IEEE Transactions on Power Systems, ISSN: 0885-8950
The shortage of inertia and primary frequency response (IPFR) will be more severe in future power systems since conventional fossil-based synchronous generators are gradually being replaced by variable renewable energy (VRE) generators. To relieve the shortage of IPFR, corresponding market mechanisms should be designed and incorporated to motivate appropriate provision from various sources. The mechanism of IPFR provision from different types of generators and its tight relation with energy production should receive particular attention. This paper proposes a novel IPFR market mechanism in which the energy market is taken into joint consideration. The virtual inertia and droop factor provided by VRE generators are defined and introduced, considering its dominant share in future power systems. A differentiated pricing scheme is designed towards incentive compatibility, considering provision from different types of generators with different quality levels and opportunity costs. Then, the proposed IPFR market mechanism is formulated, and a modified piecewise linearization method is utilized to simplify the non-linear nadir constraints. Finally, the model is tested on a modified IEEE 30-bus system according to historical data in CAISO. The results indicate the proposed mechanism could increase the utilization of VREs, decrease system operation costs, and guarantee reasonable payback for various types of generators.
Xu W, Higgins M, Wang J, et al., 2022, Blending Data and Physics Against False Data Injection Attack: An Event-Triggered Moving Target Defence Approach, IEEE Transactions on Smart Grid, ISSN: 1949-3053
Fast and accurate detection of cyberattacks is a key element for a cyber-resilient power system. Recently, data-driven detectors and physics-based Moving Target Defences (MTD) have been proposed to detect false data injection (FDI) attacks on state estimation. However, the uncontrollable false positive rate of the data-driven detector and the extra cost of frequent MTD usage limit their wide applications. Few works have explored the overlap between these two areas. To fill this gap, this paper proposes blending data-driven and physics-based approaches to enhance the detection performance. To start, a physics-informed data-driven attack detection and identification algorithm is proposed. Then, an MTD protocol is triggered by the positive alarm from the data-driven detector. The MTD is formulated as a bilevel optimisation to robustly guarantee its effectiveness against the worst-case attack around the identified attack vector. Meanwhile, MTD hiddenness is also improved so that the defence cannot be detected by the attacker. To guarantee feasibility and convergence, the convex two-stage reformulation is derived through duality and linear matrix inequality. The simulation results verify that blending data and physics can achieve extremely high detection rate while simultaneously reducing the false positive rate of the data-driven detector and the extra cost of MTD. All codes are available at https://github.com/xuwkk/DDET-MTD.
Chen Y, Sun M, Chu Z, et al., 2022, Vulnerability and Impact of Machine Learning-based Inertia Forecasting Under Cost-Oriented Data Integrity Attack, IEEE Transactions on Smart Grid, ISSN: 1949-3053
With the increasing penetration of renewables, the power system is facing unprecedented challenges of low-inertia levels. The inherent ability of the system to defense disturbance and power imbalance through inertia response is degraded, and thus, system operators need to make faster and more efficient scheduling operations. As one of the most promising solutions, machine learning (ML) methods have been investigated and employed to realize effective inertia forecasting with considerable accuracy. Nevertheless, it is yet to understand its vulnerability with the growing threat of cyberattacks. To this end, this paper proposes a methodological framework to explore the vulnerability of ML-based inertia forecasting models, with a special focus on data integrity attacks. In particular, a cost-oriented false data injection attack is proposed, for the first time, with the primary objective to significantly increase the system operation cost while retaining the stealthiness of the attack via minimizing the differences between the pre-perturbed and after-perturbed inertia forecasts. Moreover, we propose four vulnerability assessment metrics for the ML-based inertia forecasting models. Case studies on the GB power system demonstrate the vulnerability and impact of the ML-based inertia forecasting models, as well as the stealthiness and transferability of the proposed cost-oriented data integrity attacks.
Chu Z, Lakshminarayana S, Chaudhuri B, et al., 2022, Mitigating Load-Altering Attacks Against Power Grids Using Cyber-Resilient Economic Dispatch, IEEE Transactions on Smart Grid, ISSN: 1949-3053
Large-scale Load-Altering Attacks (LAAs) against Internet-of-Things (IoT) enabled high-wattage electrical appliances pose a serious threat to power system security and stability. This paper investigates, for the first time, the optimal mitigation strategy from a system perspective against such attacks. In particular, a Cyber-Resilient Economic Dispatch (CRED) concept is proposed and seamlessly integrated with attack detection and identification to form a cyber resiliency enhancement framework. Instead of only relying on local resources, CRED coordinates the frequency droop control gains of Inverter-Based Resources (IBRs) in the system to mitigate the destabilizing effect of LAAs while minimizing the overall operational cost. To achieve this, the LAA-inclusive system frequency dynamics is formulated and the corresponding system stability constraints are explicitly derived based on parametric sensitivities, which are further incorporated into the system scheduling model with minimum error through a novel recursive linearization method. In addition, a distributionally robust approach is proposed to account for the uncertainty associated with system dynamics driven by the LAA detection/parameter estimation errors. The overall performance of the proposed CRED model is demonstrated through extensive simulations in a modified IEEE reliability test system.
Graham J, Heylen E, Bian Y, et al., 2022, Benchmarking Explanatory Models for Inertia Forecasting using Public Data of the Nordic Area, 17th International Conference on Probabilistic Methods Applied to Power Systems (PMAPS), Publisher: IEEE, ISSN: 2642-6730
Xu W, Jaimoukha IM, Teng F, 2022, Physical Verification of Data-Driven Cyberattack Detector in Power System: An MTD Approach
Stealthy false data injection attacks (FDIAs) have been shown to compromise power system state estimation. The data-driven detector is a promising way to counter FDIAs. However, it suffers from low interpretability and thus introduces uncontrollable false alarms, which has been overlooked by the literature. This paper proposes to utilise moving target defence (MTD) as an additional physics layer to verify the decisions made by the data-driven detector. First, the data-driven anomaly detector is extended to identify part of the attack vector through iterative normality projection. Second, a novel MTD algorithm is formulated to maintain the high detection rate of the data-driven detector on the identified attack vector with minimum usage. The proposed algorithm is thoroughly tested under the IEEE bus-14 system.
Zografopoulos I, Karamichailidis P, Procopiou AT, et al., 2022, Mitigation of Cyberattacks through Battery Storage for Stable Microgrid Operation, Pages: 238-244
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.
Chen Y, Lakshminarayana S, Teng F, 2022, Localization of Coordinated Cyber-Physical Attacks in Power Grids Using Moving Target Defense and Deep Learning, Pages: 387-392
As one of the most sophisticated attacks against power grids, coordinated cyber-physical attacks (CCPAs) damage the power grid's physical infrastructure and use a simultaneous cyber attack to mask its effect. This work proposes a novel approach to detect such attacks and identify the location of the line outages (due to the physical attack). The proposed approach consists of three parts. Firstly, moving target defense (MTD) is applied to expose the physical attack by actively perturbing transmission line reactance via distributed flexible AC transmission system (D-FACTS) devices. MTD invalidates the attackers' knowledge required to mask their physical attack. Secondly, convolution neural networks (CNNs) are applied to localize line outage position from the compromised measurements. Finally, model agnostic meta-learning (MAML) is used to accelerate the training speed of CNN following the topology reconfigurations (due to MTD) and reduce the data/retraining time requirements. Simulations are carried out using IEEE test systems. The experimental results demonstrate that the proposed approach can effectively localize line outages in stealthy CCPAs.
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