Cyber-Physical Resilience Enhancement for Power Transmission Systems with Energy Storage Systems

In a power system, when extreme events occur, such as ice storm, large scale blackouts may be unavoidable. Such small probability but high risk events have huge impact on power systems. Most resilience research in power systems only considers faults on the physical side, which would lead to overly idealistic results. This paper proposes a two-stage cyber-physical resilience enhancement method considering energy storage (ES) systems. The first stage calculates optimal planning of ES systems, and the second stage assesses resilience and enhancement of ES systems during the disaster. In the proposed model, cyber faults indirectly damage the system by disabling monitoring and control function of control center. As a result, when detection and response process of physical faults are blocked by cyber failures, serious load shedding occurs. Such a cyber-physical coupling mechanism of fault, response, restoration process is demonstrated in the modified IEEE Reliable Test System-79 (RTS-79). Simulation results show compared with the physical-only system, the cyber-physical system has a more accurate but degraded resilient performance. Besides, ES systems setting at proper place effectively enhance resilience of the cyber-physical transmission system with less load Shedding.

Proactive Resilience Enhancement of Power Systems with Link Transmission Model-based Dynamic Traffic Assignment Among Electric Vehicles

The rapid development of electric vehicles(EVs)is strengthening the bi-directional interactions between electric power networks(EPNs)and transportation networks(TNs)while providing opportunities to enhance the resilience of power systems towards extreme events.To quantify the temporal and spatial flexibility of EVs for charging and discharging,a novel dynamic traffic assignment(DTA)problem is proposed.The DTA problem is based on a link transmission model(LTM)with extended charging links,depicting the interaction between EVs and power systems.It models the charging rates as continuous variables by an energy boundary model.To consider the evacuation requirements of TNs and the uncertainties of traffic conditions,the DTA problem is extended to a two-stage distributionally robust version.It is further incorporated into a two-stage distributionally robust unit commitment problem to balance the enhancement of EPNs and the performance of TNs.The problem is reformulated into a mixed-integer linear programming problem and solved by off-the-shelf commercial solvers.Case studies are performed on two test networks.The effectiveness is verified by the numerical results,e.g.,reducing the load shedding amount without increasing the unmet traffic demand.

Reflection on Early Lessons for Urban Resilience and Public Health Enhancement during the COVID-19

With growing impacts on public health systems and economies across the world, as a result of the COVID-19 pandemic outbreak, we need to reflect on some of the early lessons for urban resilience enhancement. In this paper, a brief discussion is made through several recommendations that could make our cities more prepared specially in the probable future waves of this current outbreak or potential spikes in infections or clustered cases. The experiences from global examples highlighted in this study address what has worked in the past few months at the spatial levels of communities and cities. The COVID-19 outbreak highlighted the deficiencies and shortfall across multiple sectors of the urban systems and enabled us to identify risks, challenges, and pathways to better city management. With regard to urban resilience enhancement, the negative impacts of the COVID-19 outbreak are assessed to suggest a checklist of what could be done through early preparedness. The findings are novel in ongoing research related to urban resilience and public health during the COVID-19 pandemic. The early lessons here reflect on the ongoing situation of this pandemic outbreak, but could effectually help to enhance the resilience of our cities and communities, and especially addressing the protection of public health and societal well-being. The findings contribute to major sectors of urban resilience, city management, and public health. The recommendations from this study could be utilised and adapted in any context, allowing for the consideration of all-inclusive decision-making and much-enhanced planning processes.

Optimal decision-making method for equipment maintenance to enhance the resilience of power digital twin system under extreme disaster

Digital twins and the physical assets of electric power systems face the potential risk of data loss and monitoring failures owing to catastrophic events,causing surveillance and energy loss.This study aims to refine maintenance strategies for the monitoring of an electric power digital twin system post disasters.Initially,the research delineates the physical electric power system along with its digital counterpart and post-disaster restoration processes.Subsequently,it delves into communication and data processing mechanisms,specifically focusing on central data processing(CDP),communication routers(CRs),and phasor measurement units(PMUs),to re-establish an equipment recovery model based on these data transmission methodologies.Furthermore,it introduces a mathematical optimization model designed to enhance the digital twin system’s post-disaster monitoring efficacy by employing the branch-and-bound method for its resolution.The efficacy of the proposed model was corroborated by analyzing an IEEE-14 system.The findings suggest that the proposed branch-and-bound algorithm significantly augments the observational capabilities of a power system with limited resources,thereby bolstering its stability and emergency response mechanisms.

Resilience Augmentation in Unmanned Weapon Systems via Multi-Layer Attention Graph Convolutional Neural Networks

The collective Unmanned Weapon System-of-Systems(UWSOS)network represents a fundamental element in modern warfare,characterized by a diverse array of unmanned combat platforms interconnected through hetero-geneous network architectures.Despite its strategic importance,the UWSOS network is highly susceptible to hostile infiltrations,which significantly impede its battlefield recovery capabilities.Existing methods to enhance network resilience predominantly focus on basic graph relationships,neglecting the crucial higher-order dependencies among nodes necessary for capturing multi-hop meta-paths within the UWSOS.To address these limitations,we propose the Enhanced-Resilience Multi-Layer Attention Graph Convolutional Network(E-MAGCN),designed to augment the adaptability of UWSOS.Our approach employs BERT for extracting semantic insights from nodes and edges,thereby refining feature representations by leveraging various node and edge categories.Additionally,E-MAGCN integrates a regularization-based multi-layer attention mechanism and a semantic node fusion algo-rithm within the Graph Convolutional Network(GCN)framework.Through extensive simulation experiments,our model demonstrates an enhancement in resilience performance ranging from 1.2% to 7% over existing algorithms.

Resilient power grid for smart city

Modern power grid has a fundamental role in the operation of smart cities.However,high impact low probability extreme events bring severe challenges to the security of urban power grid.With an increasing focus on these threats,the resilience of urban power grid has become a prior topic for a modern smart city.A resilient power grid can resist,adapt to,and timely recover from disruptions.It has four characteristics,namely anticipation,absorption,adaptation,and recovery.This paper aims to systematically investigate the development of resilient power grid for smart city.Firstly,this paper makes a review on the high impact low probability extreme events categories that influence power grid,which can be divided into extreme weather and natural disaster,human-made malicious attacks,and social crisis.Then,resilience evaluation frameworks and quantification metrics are discussed.In addition,various existing resilience enhancement strategies,which are based on microgrids,active distribution networks,integrated and multi energy systems,distributed energy resources and flexible resources,cyber-physical systems,and some resilience enhancement methods,including probabilistic forecasting and analysis,artificial intelligence driven methods,and other cutting-edge technologies are summarized.Finally,this paper presents some further possible directions and developments for urban power grid resilience research,which focus on power-electronized urban distribution network,flexible distributed resource aggregation,cyber-physical-social systems,multi-energy systems,intelligent electrical transportation and artificial intelligence and Big Data technology.

Resilience-oriented Restoration Strategy by Offshore Wind Power Considering Risk

Global climate changes have created intense naturaldisasters such as typhoons, which may cause serious damage topower systems. As an emerging renewable energy resource, offshore wind power has great potential in power systems resilienceenhancement with its rapid start-up capability and developmentof anti-typhoon technology. In this paper, a restoration strategyby offshore wind power considering risk is proposed to speedup the restoration process and enhance system resilience. Specifically, a failure risk model of an individual wind turbine andthen the whole wind farm is built for predicting severe weather’simpact, with focus on failure probability. Further, a quantificationmodel of resilience enhancement and risk cost, based on customerinterruption cost assessment method, is introduced. Then, a twostage optimized decision-making model is proposed to solve thescheme of offshore wind power and conventional power unitsin load restoration process. Case studies are undertaken on amodified IEEE RTS-79 system and results indicate the proposedrestoration strategy can shorten duration of restoration andreduce customers’ economic losses meanwhile ensuring systemsafety.

Resilient Power Systems Operation with Offshore Wind Farms and Cloud Data Centers

To enhance the resilience of power systems with offshore wind farms(OWFs),a proactive scheduling scheme is proposed to unlock the flexibility of cloud data centers(CDCs)responding to uncertain spatial and temporal impacts induced by hurricanes.The total life simulation(TLS)is adopted to project the local weather conditions at transmission lines and OWFs,before,during,and after the hurricane.The static power curve of wind turbines(WTs)is used to capture the output of OWFs,and the fragility analysis of transmission-line components is used to formulate the time-varying failure rates of transmission lines.A novel distributionally robust ambiguity set is constructed with a discrete support set,where the impacts of hurricanes are depicted by these supports.To minimize load sheddings and dropping workloads,the spatial and temporal demand response capabilities of CDCs according to task migration and delay tolerance are incorporated into resilient management.The flexibilities of CDC’s power consumption are integrated into a two-stage distributionally robust optimization problem with conditional value at risk(CVaR).Based on Lagrange duality,this problem is reformulated into its deterministic counterpart and solved by a novel decomposition method with hybrid cuts,admitting fewer iterations and a faster convergence rate.The effectiveness of the proposed resilient management strategy is verified through case studies conducted on the modified IEEERTS 24 system,which includes 4 data centers and 5 offshore wind farms.