A comprehensive review of cyber-attacks and defense mechanisms for improving security in smart grid energy systems: Past, present and future

Ghiasi, Mohammad; Niknam, Taher; Wang, Zhanle; Mehrandezh, Mehran; Dehghani, Moslem; Ghadimi, Noradin

doi:10.1016/j.epsr.2022.108975

Cited by 236 publications

(99 citation statements)

References 132 publications

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“…The reason this organ is so large in this marine mammal is that it plays an important role in storing oxygen when diving in deep areas. They are so intelligence and they are living in a huge colony 32 . They could discover fishes and lead them to surface water, then the fishes go near to the shore or at sea surface.…”

Section: Methodsmentioning

confidence: 99%

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Predicting building energy utilization with energy plus simulation and advanced sea lion optimization algorithm based on Elman neural network

Al-Fadheeli

Bouteraa

Mohammed

et al. 2023

Concurrency and Computation

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There are different methods for modeling a structure and the building with the heat from the exterior and interior sources for evaluating a good performance, predicting energy utilization. Various methods, differing from simple regression to techniques, which are according to the physical ideology, could be utilized in simulation. There is a prevalent explanation for whole these methods, in which the input parameters are according to an actual database when they are accessible, or else, the energy utilization assessment would be underestimated or over-estimated. In this investigation, two procedures have been presented that one is according to Elman neural network based on advanced sea lion optimization algorithm and the other one is according to physical ideology, that is, energy plus, which is prediction equipment for predicting building energy utilization. It can be seen that both methods can be proper for energy utilization prediction. In addition, an analysis that is parametric is implemented for the reflected structure on energy plus for evaluating the impact of some variables like the occupation of the building profile and weather database on predicting.

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Section: Methodsmentioning

confidence: 99%

“…They are so intelligence and they are living in a huge colony. 32 They could discover fishes and lead…”

Section: Sea Lion Optimization Algorithmmentioning

confidence: 99%

Predicting building energy utilization with energy plus simulation and advanced sea lion optimization algorithm based on Elman neural network

Al-Fadheeli

Bouteraa

Mohammed

et al. 2023

Concurrency and Computation

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“…The functioning of the smart power grids can be improved in terms of efficiency and failure avoidance via the application of intelligent approaches based on ML techniques. The network can be more robust if its users are aware of the various cyberattack techniques that may be used against power systems and how to counter them [ 15 ].…”

Section: Introductionmentioning

confidence: 99%

Security Analysis of Cyber-Physical Systems Using Reinforcement Learning

Ibrahim

Elhafiz

2023

Sensors

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Future engineering systems with new capabilities that far exceed today’s levels of autonomy, functionality, usability, dependability, and cyber security are predicted to be designed and developed using cyber-physical systems (CPSs). In this paper, the security of CPSs is investigated through a case study of a smart grid by using a reinforcement learning (RL) augmented attack graph to effectively highlight the subsystems’ weaknesses. In particular, the state action reward state action (SARSA) RL technique is used, in which the agent is taken to be the attacker, and an attack graph created for the system is built to resemble the environment. SARSA uses rewards and penalties to identify the worst-case attack scenario; with the most cumulative reward, an attacker may carry out the most harm to the system with the fewest available actions. Results showed successfully the worst-case attack scenario with a total reward of 26.9 and identified the most severely damaged subsystems.

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“…Generally, PEVs are expected to be recharged overnight at home; however, the limited driving range of the PEVs will require public charging stations to be used for long journeys. Since charging the PEVs at home highly depends on the behaviour of vehicle owners, if no control procedure is considered for the household chargers, regional and local overloads might be created over the power grid [11, 12]. Since long‐term trips are not possible due to the limited electrical capabilities of the PEVs, public charging infrastructure plays a considerable role in the acceptance of PEVs.…”

Section: Introductionmentioning

confidence: 99%

New optimal planning strategy for plug‐in electric vehicles charging stations in a coupled power and transportation network

Keleshteri

Niknam

Ghiasi

et al. 2023

The Journal of Engineering

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The use of plug-in electric vehicles (PEV) and their developing technology can create new challenges to the smart power system. The type, method, and time of charging electric vehicles are also other issues. Allocating and determining the optimal capacity of electric vehicle charging stations (EVCS) is related to the technical requirements of the distribution network. This is economically important for the construction of charging stations. This paper proposes a new approach for optimal siting and sizing of PEV charging stations in a coupled electrical and transportation network. This work presents the problem from a techno-economic point of view of the electrical network as a multi-objective problem with the objectives of simultaneously reducing the cost of building EVCSs and active power losses. The Pareto method is used to solve the problem and to display optimal points. In order to carry out the simulation, the proposed method is tested on a case study of the standard IEEE 37-bus network with a 25-node transport system and the proposed solution in the subject environment. The Floyd-Warshall method is utilized to determine the shortest travel routes for PEVs. The obtained results confirm the effectiveness of the optimal planning of PEV charging stations.

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A comprehensive review of cyber-attacks and defense mechanisms for improving security in smart grid energy systems: Past, present and future

Cited by 236 publications

References 132 publications

Predicting building energy utilization with energy plus simulation and advanced sea lion optimization algorithm based on Elman neural network

Predicting building energy utilization with energy plus simulation and advanced sea lion optimization algorithm based on Elman neural network

Security Analysis of Cyber-Physical Systems Using Reinforcement Learning

New optimal planning strategy for plug‐in electric vehicles charging stations in a coupled power and transportation network

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