Analytical reliability evaluation method of smart micro‐grids considering the cyber failures and information transmission system faults

Abstract

The reliability of smart micro‐grids (SMGs), as a cyber‐physical system (CPS), might be influenced by cyber failures and information transmission faults. Several Monte Carlo simulation (MCS)‐based approaches have been reported to assess the reliability of SMGs and smart grids. On the other hand, analytical reliability assessment methods have been presented in some research works, while the cyber system has not been concerned. However, the literature shows a research gap in developing an accurate and fast reliability evaluation method for SMGs based on the unavailability of cyber elements and information transmission faults. This article tries to fill the discussed gap by adding the analytical modelling of cyber‐physical interdependencies and information transmission faults to available analytical methods, focusing on physical uncertainties. Comparing the proposed model with existing MCS‐based and analytical reliability evaluation methods illustrates the advantages of this research. Test results show that less than 5.7% expected energy not supplied (EENS) error occurs by the proposed method, which would be much faster than MCS‐based ones. Moreover, the sensitivity analyses highlight the impacts of the cyber network topologies on the cyber‐physical interdependencies.

Component degradation and system deterioration: An overview of early termination of PV-DG microgrid system

“…Research by [93] confirmed that cyber failures and information transmission faults lead to an occurrence of 5.7 % expected energy not supplied (EENS) error. Moreover, inapt CNC configuration generated up to 70 MW EENS per year [88].…”

“…Whether it is an instant impact, such as the immediate failure of the equipment or is long deteriorated system, such as mishandling the worn-out component [92]. From outside the microgrid, the long-distance operator sometimes causes a ruckus in the cyber setting of the microgrid and disturbs the transmission line or distribution network [93] [94]. The last significant impact comes from the policy sector, whether it is concerning the policy at the beginning of the microgrid project or the ongoing policy for the continuous operation of the microgrid [95][96] [97].…”

“…The microgrid lost the power support Loss of power supply [91] The transmission line setting is not properly conducted Energy supply disturbance Expected energy not achieved [92] Human error in DG operation led to DG Failure Energy production is disturbed Expected energy not achieved [93] The cyber failure led to a fault in the transmission line Disturbance on the transmission network Expected energy not achieved [98] Unsupportive policy on item procurement Acquiring a low-quality item Early microgrid failure…”

Optimized allocation of microgrids’ distributed generations and electric vehicle charging stations considering system uncertainties by clustering algorithms

“…The MCS‐based reliability assessment methods are precise [9, 10]. However, they are not adequately fast [11]. Hence, they cannot be effectively used in developing reliability improvement strategies, particularly if evolutionary algorithms are used [12].…”

“…The precise models have been reported based on MCS for SGs’ reliability evaluation. However, there are some challenges to MCS‐based methods of SGs’ reliability evaluation [11]. The challenges regarding execution time and speed of such approaches limit their applications for real‐time operational decisions and optimal design and planning for SGs, using metaheuristic algorithms.…”