A voltage source inverter (VSI) is the key component of grid-tied AC Microgrid (MG) which requires a fast response, and stable, robust controllers to ensure efficient operation. In this paper, a fuzzy logic controller (FLC)-based direct power control (DPC) method for photovoltaic (PV) VSI was proposed, which was modelled by modulating MG’s point of common coupling (PCC) voltage. This paper also introduces a modified grid synchronization method through the direct power calculation of PCC voltage and current, instead of using a conventional phase-locked loop (PLL) system. FLC is used to minimize the errors between the calculated and reference powers to generate the required control signals for the VSI through sinusoidal pulse width modulation (SPWM). The proposed FLC-based DPC (FLDPC) method has shown better tracking performance with less computational time, compared with the conventional MG power control methods, due to the elimination of PLL and the use of a single power control loop. In addition, due to the use of FLC, the proposed FLDPC exhibited negligible steady-state oscillations in the output power of MG’s PV-VSI. The proposed FLDPC method performance was validated by conducting real-time simulations through real time digital simulator (RTDS). The results have demonstrated that the proposed FLDPC method has a better reference power tracking time of 0.03 s along with reduction in power ripples and less current total harmonic distortion (THD) of 1.59%.

Demand for continuous and reliable power supply has significantly increased, especially in this Industrial Revolution 4.0 era. In this regard, adequate planning of electrical power systems considering persistent load growth, increased integration of distributed generators (DGs), optimal system operation during N-1 contingencies, and compliance to the existing system constraints are paramount. However, these issues need to be parallelly addressed for optimum distribution system planning. Consequently, the planning optimization problem would become more complex due to the various technical and operational constraints as well as the enormous search space. To address these considerations, this paper proposes a strategy to obtain one optimal solution for the distribution system expansion planning by considering N-1 system contingencies for all branches and DG optimal sizing and placement as well as fluctuations in the load profiles. In this work, a hybrid firefly algorithm and particle swarm optimization (FA-PSO) was proposed to determine the optimal solution for the expansion planning problem. The validity of the proposed method was tested on IEEE 33- and 69-bus systems. The results show that incorporating DGs with optimal sizing and location minimizes the investment and power loss cost for the 33-bus system by 42.18% and 14.63%, respectively, and for the 69-system by 31.53% and 12%, respectively. In addition, comparative studies were done with a different model from the literature to verify the robustness of the proposed method.

This paper introduces a novel metaheuristic technique, a COOT‐based algorithm, to determine the optimal Distributed Generation (DG) allocation within a loop‐configured network. This method significantly narrows the optimization gap by leveraging a COOT‐based algorithm, ensuring accelerated convergence and resultant global optima. The core incentive for employing this technique is to substantially mitigate power loss, curtail voltage deviation, and bolster system stability in a loop distribution network. To attain optimal outcomes, the elaborated COOT and improved grey wolf optimization improved grey wolf optimization (IGWO) algorithms were executed on IEEE bus‐33 and 69 mesh distribution networks (MDNs) under varying power factors. The derived mathematical results effectively underscore accomplishing the stipulated objectives: a marked reduction in voltage deviation and power loss coupled with an augmentation in system stability. Notably, at unity, incorporating DGs resulted in a paramount reduction in power loss, attaining a decrease of 78% and 85% for bus‐33 and 69 MDNs, respectively. Moreover, an impressive decrease in power loss by 94% and 98% was observed at the optimal power factor for both MDNs. A comparative evaluation of the results accentuates that the proposed COOT and IGWO algorithms eclipse other documented research in performance, showcasing superior efficiency on a techno‐economic basis.

In recent decades, flash floods have become more common because of climate change and are considered a substantial risk for many cities worldwide. This catastrophic natural hazard presents a significant threat to critical infrastructure in urban areas, particularly the power distribution system. As modern societies are much more dependent on electrical energy these days, it is essential and imperative to make existing distribution systems resilient against flash flooding. Although researchers in this area have proposed various algorithms to impart resilience to a distribution system, however, the focus in these works is on wind‐related events such as hurricanes, cyclones, and windstorms. Therefore, here, the spatiotemporal effects of a flash flood on the distribution system are modelled using a grid‐based hydrodynamic model. The evolving line faults are then included in the proposed resilience‐oriented time horizon‐based service restoration model that also considers dynamic load demand, heavy uncertainties related to renewable generation, and interdependence among critical loads. Finally, the resilience of the distribution system's response is assessed using an operational resilience metric. The efficacy of the proposed framework is evaluated on IEEE 33‐bus and 69‐bus systems and the results show that the model provides an efficient restoration solution despite increased complexity caused by varying conditions.

Power distribution systems face increasing threats from high-impact, low-probability (HILP) events caused by extreme weather conditions such as floods, typhoons, droughts, and heatwaves. These events often lead to power outages worldwide, highlighting the need for effective strategies to mitigate their impact. This work proposes an emergency response strategy that integrates network reconfiguration (NR) with the deployment of mobile emergency generators (MEGs) to enhance system resilience. The objective is to maximize power supply availability following HILP events. To achieve this, mixed-integer quadratic constraint programming (MIQCP) is used to optimize MEG deployment and network restoration. Additionally, improved quantitative resilience metrics are introduced to assess system degradation, pre-recovery, and recovery phases, enabling continuous resilience measurement and informed decision-making. Furthermore, an optimal capacity deployment strategy (OCDS) is proposed to ensure that MEGs are deployed with suitable capacities based on the specific needs of outage-affected areas. The effectiveness of the proposed strategy is demonstrated through tests on the IEEE 118 bus system. The results show a significant improvement of up to 100% in system resilience, reducing power outages and accelerating restoration. The findings confirm that integrating NR with optimized MEG deployment enhances service restoration, providing an effective approach for utilities to manage power disruptions.