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A power distribution network is a critical infrastructure in any society and any disruption has an enormous impact on the economy and daily lives. Therefore, the objective of this study is to transform the conventional power distribution systems into resilient autonomous microgrid networks by optimally sizing and siting the distributed generators (DGs). First, N main DGs are placed to transform an existing network into an autonomous microgrid network. Second, all the possible combinations of the initially deployed DGs are made and then the outage of 1 to N  − 1 DGs is considered. Considering the outage of DGs in each combination (one at a time), the resiliency of the network is analysed. Amount of load shedding, total power loss in the network, and voltage limits are analysed in this step. Finally, based on the resiliency analysis, additional DGs are placed to enhance the resiliency of the transformed network. Heuristic methods (particle swarm optimisation and genetic algorithm) are used for both sizing and siting of DGs during the first and the second steps. The objective of the formulation is to minimise load shedding, total power loss (active and reactive), and voltage deviations in the network during DG outages.

This work presents a self-healing system designed for smart distribution networks, integrating fault detection, isolation, and power restoration with optimal network reconfiguration. Leveraging the Backward/Forward Sweep method for load flow analysis. The Particle Swarm Optimization algorithm for distributed generation sizing and location is used to improve the voltage profile for buses. The proposed design is validated on the IEEE 33-bus radial distribution system using MATLAB/Simulink and CYME software. Simulation results demonstrate a swift service restoration time, a reduction in power losses post-fault with two distributed generators, and an improved minimum voltage. This work enhances network reliability for the customers, voltage stability, and operational efficiency, offering a feasible and practical solution for investment in modern smart grids.

The rapid growth of distributed photovoltaic (PV) resources is transforming distribution networks into active systems with highly variable net loads, while the rising frequency and severity of extreme weather events is increasing outage risk and restoration challenges. In this context, utilities require reliability assessment tools that jointly represent operational variability and climate-driven stressors beyond stationary assumptions. This paper presents a weather-aware probabilistic framework to quantify the reliability of active distribution networks with high PV penetration. The approach synthesizes realistic residential demand and PV time series at 15-min resolution, models extreme weather as a low-probability/high-impact escalation of component failure rates and restoration uncertainty, and computes IEEE Std 1366–2022 indices (SAIFI, SAIDI, ENS) through Monte Carlo simulation. The methodology is validated on a modified IEEE 33-bus feeder with parameter values representative of urban/suburban overhead networks. Compared with classical reliability modeling, the proposed framework captures in a unified pipeline the joint effects of load/PV stochasticity, weather-dependent failure escalation, and repair-time dispersion, providing a consistent statistical interpretation supported by kernel density estimation and convergence diagnostics. The results show that (i) extreme weather shifts the distributions of SAIFI, SAIDI and ENS to the right and thickens upper tails (higher exceedance probabilities); (ii) PV penetration yields a non-monotonic response with measurable improvements up to intermediate levels and saturation/partial degradation at very high penetrations; and (iii) compound risk is nonlinear, as the mean ENS surface over (r[sub.PV],P[sub.ext]) exhibits a valley at moderate PV and a ridge for large storm probability. A tornado analysis identifies the base failure rate, storm escalation factor and storm exposure as dominant drivers, in line with resilience literature. Overall, the framework provides an auditable, scenario-based tool to co-design DER hosting and resilience investments.

This study presents a multi-objective optimization of a hybrid microgrid (HMG) targeting the energy trilemma goals—energy security, affordability, and sustainability—using the Slime Mould Algorithm (SMA). The proposed HMG integrates renewable energy sources, diesel generators, and electric vehicle (EV) batteries as distributed energy resources (DERs) with bidirectional vehicle-to-grid (V2G) capabilities. Compared to conventional metaheuristic such as Particle Swarm Optimization (PSO) and Genetic Algorithm (GA), the SMA achieves a power loss reduction of 12.3% and a levelized cost of energy (LCOE) improvement of 9.8%. The loss of power supply probability (LPSP) is reduced to 0.012, outperforming benchmark results from HOMER and Salp Swarm Algorithm (SSA), which reported LPSP values of 0.021 and 0.017, respectively. The superior performance of SMA is attributed to its dynamic balance between exploration and exploitation, leading to faster convergence and enhanced computational efficiency. The novel integration of EV batteries as DERs, with explicit modeling of bidirectional V2G operations, distinguishes this work from previous studies that considered only unidirectional or static EV participation. While the proposed approach demonstrates significant improvements, scalability to larger microgrid networks and the computational demands of SMA in real-time applications remain challenges for future research.

The potential availability of renewable energy sources is unquestionable and the government is setting steep targets for renewable energy usage. Renewable‐based DGs, reduce dependence on fossil fuels, mitigate global climate change, ensure energy security, and reduce emissions of CO2 and other greenhouse gases. This study addresses microgrid system analysis with hybrid energy sources and reconfiguration simultaneously for efficient operation of the system. Microgrid zones are formulated categorically with the existing distribution system. In this study, wind, solar and small hydro‐based DGs are considered. Uncertainties of renewable power generation and load are also taken care in the optimization problem. A multi‐objective optimisation method proposed in this paper for optimal integration of renewable‐based DGs and reconfiguration of the network to minimise power loss and maximise annual cost savings. Optimal location and sizes of DG units are determined using gravitational search algorithm and general algebraic modelling system respectively. Optimal reconfiguration of the microgrid system is obtained using genetic algorithm. Simulation results are obtained for the IEEE 33‐bus system and compared with existing methods as available in the literature. Furthermore, this study has been carried out with a 24‐hr time‐varying distribution system. The simulation results show the efficiency and accuracy of the proposed technique.

Integrating Distributed Generation (DG) at appropriate location in distribution system can reduce its real power losses and can also increase the voltage regulation. Optimal allocation of DG has two parts, i) location identification of DG, ii) capacity determination of DG. This article uses loss sensitivity methods for fixing of optimal place of DG and then Particle Swarm Optimization (PSO) technique is implemented for determination of optimal size of DG at that location. During optimization, effect of inertia weight of PSO on the optimal placement of DG is demonstrated in this paper. For detailed study, IEEE 33-bus system is considered and impact of integration DG in the system is also shown.

This study focuses on the cyber‐physical system (CPS) consisting of interdependent electrical distribution and communication networks, where the two networks are mutually dependent. A small disturbance in either of them can trigger a cascade of faults within the entire network. To investigate the failure mechanism, first, two features that affect topological resilience (TR) are defined in this study: adaptation and recovery abilities. Second, the authors model the process of cascading failures that occur in this coupled system by introducing and developing the infrastructure interdependencies simulator. The process of cascading failures is based on percolation theory, and they present a detailed analysis of cascading failure in a standard IEEE 33‐bus system coupled with the 33‐node communication system. This study proves that the adaptation ability of a coupled system is even lower than a single system. This is due to the interdependencies between systems, and the study of the failure mechanisms helps planers to make a better decision in the recovery process. Finally, the modified shortest path search is used to optimise the repair sequence. Their numerical results validate that the recovery ability of the coupled system is increased through the optimisation, which contributes to the TR enhancement.