A digital twin framework for efficient electric power restoration and resilient recovery in the aftermath of hurricanes considering the interdependencies with road network and essential facilities

The community’s resilience in the face of natural hazards relies heavily on the rapid and efficient restoration of electric power networks,which plays a critical role in emergency response,economic recovery,and the func-tionality of essential lifeline and social infrastructure systems.Leveraging the recent data revolution,the digital twin(DT)concept emerges as a promising tool to enhance the effectiveness of post-disaster recovery efforts.This paper introduces a novel framework for post-hurricane electric power restoration using a hybrid DT approach that combines physics-based and data-driven models by utilizing a dynamic Bayesian network.By capturing the complexities of power system dynamics and incorporating the road network’s influence,the framework offers a comprehensive methodology to guide real-time power restoration efforts in post-disaster scenarios.A discrete event simulation is conducted to demonstrate the proposed framework’s efficacy.The study showcases how the electric power restoration DT can be monitored and updated in real-time,reflecting changing conditions and facilitating adaptive decision-making.Furthermore,it demonstrates the framework’s flexibility to allow decision-makers to prioritize essential,residential,and business facilities and compare different restoration plans and their potential effect on the community.

Cyber-physical Collaborative Restoration Strategy for Power Transmission System Considering Maintenance Scheduling

In order to improve the ability of power transmission system to cope with compound faults on the communication side and power side,a cyber-physical collaborative restoration strategy is proposed.First,according to the information system’s role in fault diagnosis,remote control of equipment maintenance and automatic output adjustment of generator restoration,a cyber-physical coupling model is proposed.On this basis,a collaborative restoration model of power transmission system is established by studying interactions among maintenance schedule paths,information system operation,and power system operation.Based on power flow linearization and the large M-ε method,the above model is transformed into a mixed integer linear programming model,whose computational burden is reduced further by the clustering algorithm.According to the parameters of IEEE39 New England system,the geographic wiring diagram of the cyber-physical system is established.Simulation results show the proposed restoration strategy can consider the support function of the information system and space-time coordination of equipment maintenance at both sides comprehensively to speed up load recovery progress.

Power system restoration:a literature review from 2006 to 2016

Power system restoration has attracted more attention and made great progress recently. Research progress of the power system restoration from 2006 to 2016 is reviewed in this paper, including black-start, network reconfiguration and load restoration. Some emerging methods and key techniques are also discussed in the context of the integration of variable renewable energy and development of the smart grid. There is a long way to go to achieve automatic self-healing in bulk power systems because of its extreme complexity. However, rapidly developing artificial intelligence technology will eventually enable the step-by-step dynamic decision-making based on the situation awareness of supervisory control and data acquisition systems(SCADA) and wide area measurement systems(WAMS) in the near future.

Parallel power system restoration planning using heuristic initialization and discrete evolutionary programming

This paper proposes a sectionalizing planning for parallel power system restoration after a complete system blackout.Parallel restoration is conducted in order to reduce the total restoration process time.Physical and operation knowledge of the system,operating personnel experience,and computer simulation are combined in this planning to improve the system restoration and serve as a guidance for system operators/planners.Sectionalizing planning is obtained using discrete evolutionary programming optimization method assisted by heuristic initialization and graph theory approach.Set of transmission lines that should not be restored during parallel restoration process(cut set)is determined in order to sectionalize the system into subsystems or islands.Each island with almost similar restoration time is set as an objective function so as to speed up the resynchronization of the islands.Restoration operation and constraints(black start generator availability,load-generation balance and maintaining acceptable voltage magnitude within each island)is also takeninto account in the course of this planning.The method is validated using the IEEE 39-bus and 118-bus system.Promising results in terms of restoration time was compared to other methods reported in the literature.

Multi-time Collaborative Restoration for Integrated Electrical-gas Distribution Systems Based on Rolling Optimization

This paper proposes a multi-time collaborative restoration model for integrated electricity-gas distribution sys-tems(IEGDSs)considering multiple resources after extreme weather events.Based on the linearized power flow constraints of the unbalanced electrical distribution system(EDS)and gas distribution system(GDS),this problem can be formulated as a mixed-integer linear programming(MILP)model.To improve the efficiency and veracity of the solution,a rolling optimiza-tion based two-stage method is developed with the first stage solved by a linear approximation model,and the second stage solved by real-time updated rolling optimization.By solving the MILP problem using rolling optimization,the proposed model and solution method achieve efficient and reliable collaborative restoration of IEGDS considering multiple resources and unbal-anced operation characteristics of EDS.The effectiveness of the proposed model and method is validated by using an IEGDS made of a 37-bus unbalanced EDS and 11-node GDS.Index Terms-Electricity-gas system,mix-integer linear programming,power system restoration.

Efficient background removal based on two-dimensional notch filtering for polarization interference imaging spectrometers

A background removal method based on two-dimensional notch filtering in the frequency domain for polarization interference imaging spectrometers（PIISs） is implemented. According to the relationship between the spatial domain and the frequency domain, the notch filter is designed with several parameters of PIISs, and the interferogram without a background is obtained. Both the simulated and the experimental results demonstrate that the background removal method is feasible and robust with a high processing speed. In addition, this method can reduce the noise level of the reconstructed spectrum, and it is insusceptible to a complicated background, compared with the polynomial fitting and empirical mode decomposition（EMD） methods.

A Tabu-search-based Algorithm for Distribution Network Restoration to Improve Reliability and Resiliency

Fault restoration techniques have always been crucial for distribution system operators(DSOs).In the last decade,it started to gain more and more importance due to the introduction of output-based regulations where DSO performances are evaluated according to frequency and duration of energy supply interruptions.The paper presents a tabu-searchbased algorithm able to assist distribution network operational engineers in identifying solutions to restore the energy supply after permanent faults.According to the network property,two objective functions are considered to optimize either reliability or resiliency.The mathematical formulation includes the traditional feeders,number of switching operation limit,and radiality constraints.Thanks to the DSO of Milan,Unareti,the proposed algorithm has been tested on a real distribution network to investigate its effectiveness.