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The DC microgrid (DC MG) concept enables the hosting of DC-type renewable energy resources. However, their intermittent nature means that a high penetration of renewables can jeopardize supply adequacy and voltage provision during islanding. The work presented in this paper was therefore directed at developing a probabilistic graphical approach bas...
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Context 1
... To demonstrate the generalization of the proposed BN-based method, we have simulated a 15-bus islanded DC microgrid whose one-line diagram is depicted in Fig. 9. The 15-bus microgrid comprises five droop-based DGs, three PVs, and one DS. The failure rates and operating states of the system components are the same as those of the 6-bus microgrid. The base kW and voltage were taken as 50 kW and 600 V, respectively. The complete system description, e.g., i.e., DER types and parameters, load ...
Citations
... The growing prevalence of DERs and the consequent decentralization of power generation have fostered the concept of MG in Electricity Distribution Systems (EDS) as autonomous entities capable of functioning independently from the primary grid [46]. Furthermore, MGs are rapidly gaining attention as a method of improving reliability and security [47]. They have the advantage of employing RES and DGs in the grid. ...
... The DC characteristics of DER technologies have driven the development of DC microgrids (DC-MGs), leading to their rapid adoption [48,49]. The reliability analysis of DC-MGs that are mostly powered by renewables is investigated by [47]. In theory, MG reliability specifies that the local power supply must be enough to fulfill local-load reliability requirements during islanding [50]. ...
... Also, reliability analysis for IBR-integrated power systems based on BN-structure was the goal of [45]. The authors in [47] aimed to create a BNbased probabilistic graphical method for the reliability analysis of DC-MGs that are mostly powered by renewables. ...
Decarbonization is driving power systems toward more decentralized, self-governing models. While these technologies improve efficiency, planning, operations, and reduce the carbon footprint, they also introduce new challenges. In modern grids, particularly with the integration of power electronic devices and high penetration of Renewable Energy Sources (RES) and Inverter-Based Resources (IBRs), traditional reliability concepts may no longer ensure adequate performance due to systemic restructuring. This shift necessitates new or significantly modified reliability indices to capture the characteristics of the evolving power system. Ensuring converter reliability is essential for effective planning, which requires precise, component-to-system-level modeling, as different converters impact system performance indicators. However, the existing literature in this field faces a significant limitation, as most studies focus on a singular perspective. Some examine reliability at the device-level, others at the component-level, while broader reviews in power systems often emphasize system-level analysis. In this paper, we aim to bridge these gaps by comprehensively reviewing the interconnections between these levels and analyzing the mutual influence of power converter and system reliability. A key point to highlight is that, with the rapid evolution of modern power grids, decision-makers must adopt a multi-level approach that incorporates insights from all levels to enable more accurate and realistic planning and operational strategies. Our ultimate goal is to provide an in-depth investigation of studies addressing the unique challenges posed by modern power grids. Finally, we will highlight the gaps in the literature and suggest directions for future research.
... Microgrids (MGs), as an emerging resource of smart grids, have been the center of attention of researchers in recent years. Several studies were performed in the literature to evaluate the reliability of MGs concerning their different properties, namely intermittence of RE sources and loads [8,9], the impact of energy storage systems (ESSs) [10], protection systems [11], and control systems [12]. The major issue of these studies is that they are mainly focused on the physical layer of MGs, and the cyber layer, including cyber elements and route paths, is neglected in their assessments, which can adversely affect the reliability of the MGs due to the interdependency of physical and cyber layers. ...
... In this study, graph theory and related functions [15] are used to model the electricity and heat transmission channels between production and consumption units after identifying the state of physical sub-systems. Therefore, the CPEH interdependence matrix (CPEHIM) is defined according to (8) at each time slot. This matrix includes power-heat (PHPH), power-heat-cyber (PHC), cyber-power-heat (CPH), and cybercyber (CC) interconnections. ...
In this paper, the reliability of cyber-physical energy hubs (CPEHs) is evaluated based on the Monte Carlo simulation (MCS) method. Previous works in the related literature are generally focused on the physical layer of energy hubs (EHs). However, the security of EHs heavily relies on the reliable and safe performance of the cyber system. Therefore, this study considers the failure of cyber-physical components, data packet errors, and information transmission delay to improve the reliability assessment accuracy. The physical layer of the proposed CPEH is divided into two sections, namely heat and power sections, and reliability indices are calculated for both sections. Various scenarios are suggested to precisely analyze the effects of the cyber layer consideration on the CPEH reliability results. Sensitivity analysis on the failure rate of the components is also conducted to understand better the performance of the CPEH in situations with a higher risk of heat components failure. The notable impacts of the cyber layer disturbances on the CPEH reliability evaluation are demonstrated through the simulation results.
... The study developed a probabilistic approach based on Bayesian networks to conduct reliability analysis. The model was verified using Monte Carlo simulations and suggested that different renewables have different impacts on microgrid reliability [7]. ...
Around 300 remote communities in Canada rely on diesel for their energy needs, a situation associated with high costs, high emissions, and accessibility problems. Various studies have addressed this problem by proposing renewable energy microgrids, which have a lot of potential due to the abundance and availability of renewable sources. However, there is a lack of studies regarding remote communities in Newfoundland and combined heat and power microgrids. This study chose Cartwright remote community based on consumption and available resources. Both distributed and centralized microgrid components were designed using MS Excel, Polysun, HOMER, and BEOPT. The final system included solar thermal, PV, wind energy, hydroelectric energy and fuel cells for energy generation, and hydrogen as an energy carrier for storage. The solar thermal distributed system reduced the thermal load by 35%. The microgrid reduced diesel consumption by 71% and CO2 emissions by 9000 tons. Renewable sources provided 100% of the electric load and 63.5% of the thermal load. The microgrid achieved a Levelized cost of -0.0245 $/kWh, which is only possible for combined heat and power systems.
The normal operation of the grounding grid is related to the safety of transmission lines and even the power grid. Due to its buried underground location, wide distribution, and harsh operating environment, the grounding body is prone to corrosion. Therefore, the corrosion problem of the grounding grid has always been a problem that troubles the power sector. Therefore, in-depth research on the methods of grounding grid corrosion diagnosis is of great significance for ensuring the safe operation of transmission lines. To effectively improve the accuracy of detecting the location and type of corrosion faults in substation grounding grids, a grounding grid fault diagnosis method based on Naive Bayes Classifier (NBC) impulse impedance feature spectrum is proposed. First, CDEGS software simulation is used to obtain the corresponding spectrum maps under various fault conditions, and then the waveform fitting degree and amplitude difference between each frequency band and the standard spectrum map are input into the Naive Bayes Classifier as characteristic parameters, and the expected results are obtained. The test results show that the identification accuracy of the model for fault types is as high as 93.33%, and can accurately distinguish Single point of failure and regional fault.
