Risk-Averse Transmission Path Selection for Secure State Estimation in Power Systems

He, Jiasheng; Chen, Cailian; Zhu, Shanying; Yang, Bo; Guan, Xinping

doi:10.1109/jiot.2018.2879110

Cited by 8 publications

(5 citation statements)

References 29 publications

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“…[177] investigates the optimal profile for communication technologies for smart grid networks based on these network classifications. This paper [178] works on risk-averse transmission path selection to improve data communication's reliability and resiliency in Advanced metering infrastructure (AMI) applications for smart grids. Finally, in [179], a communication interface for end-use applications outlined in the 2030 standard is used in a hierarchical multi-agent management system for EV integration with the grid.…”

Section: Ieee Std 2030: Guide For Control and Automation Installation...mentioning

confidence: 99%

A Review of Microgrid Energy Management and Control Strategies

Ahmad

Shafiullah

Ahmed³

et al. 2023

IEEE Access

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Several issues have been reported with the expansion of the electric power grid and the increasing use of intermittent power sources, such as the need for expensive transmission lines and the issue of cascading blackouts, which can adversely affect critical infrastructures. Microgrids (MG) has been widely accepted as a viable solution to improve grid reliability and resiliency, ensuring continuous power supply to loads. However, to ensure the effective operation of the Distributed Energy Resources (DER), Microgrids need to have Energy Management and Control Systems (EMCS). Therefore, considerable research has been conducted to achieve smooth profiles in grid parameters during operation at optimum running cost. This paper aims to provide a review of EMCS techniques that have evolved in recent years. Firstly, the fundamentals of microgrids are discussed for a general overview of the field. Then, a critical literature review is undertaken for the various methods applied for EM optimization in microgrid applications. Multiple factors have been explored in the objective functions throughout this review, including MG daily operational costs, energy storage degradation, revenue through trading with the grid or other parties, and Greenhouse Gas (GHG) emissions. A review of control systems has been conducted next by categorizing them based on the different applications in MGs for stable operation. This paper also focuses on IEEE standards related to MG operation and control to facilitate other researchers to build upon a standardized set of rules and to enhance the interoperability of the diverse EMCS techniques.

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Section: Ieee Std 2030: Guide For Control and Automation Installation...mentioning

confidence: 99%

A Review of Microgrid Energy Management and Control Strategies

Ahmad

Shafiullah

Ahmed³

et al. 2023

IEEE Access

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“…An important aspect is cyber-security since cyber attacks can make best designed controls to malfunction or degrade their performances. Several works dealt with this problem [80][81][82][83][84][85][86][87][88]. Reference [80] considered cyber-physical systems security from systems and control perspective in general, and shortly discussed possibilities to use RL and DRL to this purpose.…”

Section: Power System Control-related Considerationsmentioning

confidence: 99%

“…Test results revealed if even a few substations are attacked a voltage collapse with its consequences can happen in the system. Power system state estimation under cyber attacks was considered in [82,84,85]. Secure state estimation with assumption that measurements are sent over a wireless networks under jamming attacks was dealt in [82] and the antijamming game framework for secure state estimation using multi-agent reinforcement learning to determine optimal path against an intelligent attacker.…”

Section: Power System Control-related Considerationsmentioning

confidence: 99%

“…Secure state estimation with assumption that measurements are sent over a wireless networks under jamming attacks was dealt in [82] and the antijamming game framework for secure state estimation using multi-agent reinforcement learning to determine optimal path against an intelligent attacker. Reference [85] considered secure state estimation with risk-averse transmission path selection method that is based on RL idea and demonstrated how proposed approach can improve secure state estimation robustness. DRL method (DQN) was proposed in [84] to defend against data integrity attacks in power systems state estimation.…”

Section: Power System Control-related Considerationsmentioning

confidence: 99%

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(Deep) Reinforcement learning for electric power system control and related problems: A short review and perspectives

Glavić

2019

Annual Reviews in Control

111

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This paper reviews existing works on (deep) reinforcement learning considerations in electric power system control. The works are reviewed as they relate to electric power system operating states (normal, preventive, emergency, restorative) and control levels (local, household, microgrid, subsystem, wide-area). Due attention is paid to the control-related problems considerations (cyber-security, big data analysis, short-term load forecast, and composite load modelling). Observations from reviewed literature are drawn and perspectives discussed. In order to make the text compact and as easy as possible to read, the focus is only on the works published (or "in press") in journals and books while conference publications are not included. Exceptions are several work available in open repositories likely to become journal publications in near future. Hopefully this paper could serve as a good source of information for all those interested in solving similar problems.

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“…The antijamming game framework was used to determine the optimal path against an intelligent attacker. He et al [ 13 ] considered secure-state estimation with risk-averse transmission path selection method that is based on RL concept. They demonstrated how the proposed approach can improve secure-state estimation robustness.…”

Section: Introductionmentioning

confidence: 99%

Resiliency Assessment of Power Systems Using Deep Reinforcement Learning

Ibrahim

Alsheikh

Elhafiz

2022

Computational Intelligence and Neuroscience

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Evaluating the resiliency of power systems against abnormal operational conditions is crucial for adapting effective actions in planning and operation. This paper introduces the level-of-resilience (LoR) measure to assess power system resiliency in terms of the minimum number of faults needed to produce a system outage (blackout) under sequential topology attacks. Four deep reinforcement learning (DRL)-based agents: deep Q-network (DQN), double DQN, the REINFORCE (Monte-Carlo policy gradient), and REINFORCE with baseline are used to determine the LoR. In this paper, three case studies based on IEEE 6-bus test system are investigated. The results demonstrate that the double DQN network agent achieved the highest success rate, and it was the fastest among the other agents. Thus, it can be an efficient agent for resiliency evaluation.

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Risk-Averse Transmission Path Selection for Secure State Estimation in Power Systems

Cited by 8 publications

References 29 publications

A Review of Microgrid Energy Management and Control Strategies

A Review of Microgrid Energy Management and Control Strategies

(Deep) Reinforcement learning for electric power system control and related problems: A short review and perspectives

Resiliency Assessment of Power Systems Using Deep Reinforcement Learning

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