Search Results (841)

The increasing penetration of distributed generation (DG) in distribution systems poses significant operational challenges, including increased power losses, voltage profile deviations, and variations in short-circuit currents. These issues may compromise network safety, reliability, and the selectivity of protection schemes under different operating scenarios. This paper proposes a hybrid optimization methodology for the optimal placement and sizing of DG, aiming to minimize active power losses while ensuring voltage regulation and keeping short-circuit currents within permissible limits. An integrated approach is proposed that combines a mesh-to-radial network reconfiguration strategy with a modified Ant Lion Optimization algorithm, known as ALO-DG, enabling the simultaneous optimization of network topology and the allocation of distributed generators at candidate buses. The problem is formulated taking into account power balance constraints, voltage limits, distribution network capacity limits, and short-circuit current limits. The proposed methodology achieved substantial reductions in active power losses in the IEEE 33-bus and 69-bus test systems, reaching 84.42% and 91.56%, respectively. These improvements were accompanied by enhanced voltage profiles while preserving the radial operating structure of the distribution networks. Furthermore, the proposed hybrid methodology serves as a tool for the planning and operation of distribution systems with high DG penetration, particularly in scenarios where grid security and protection coordination are critical considerations. Full article

We introduce a geometric framework for continuous-time quantum walks on graphs by embedding each vertex into a Euclidean space through its time-dependent quantum probability distribution. This construction induces a rich geometry in which quantum transport is characterized by distances, radii, angles, and simplex volumes, allowing interference, localization, and spreading to be analyzed within a unified metric-angular formalism. We prove that, in contrast to classical diffusion, which collapses to a spherical geometry, quantum dynamics generate a generically non-spherical affine geometry with persistent anisotropy. Applying this theory to real-world networks—including transportation systems, semantic graphs, and neuronal connectomes—we show that quantum geometry reveals dynamically meaningful backbones, interference-based “communities”, and vulnerability structures that are invisible to classical random-walk and spectral methods. In particular, angular and radial quantum descriptors isolate functional hubs, control cores, and coherence classes without any topological or dimensionality assumptions. Together, these results demonstrate that quantum-walk-induced geometry provides a powerful new lens for understanding structure and function in complex networks. Full article

Modern smart cities increasingly depend on resilient and intelligent energy infrastructures to maintain critical urban services during large-scale disturbances and multi-fault conditions. Conventional restoration approaches are often limited by centralized operation, delayed response, and inadequate coordination of distributed energy resources (DERs) under emergency conditions. To address these challenges, this paper proposes a Federated AI-Driven Urban Energy Resilience Framework for Smart City Critical Infrastructure Restoration using Virtual Power Plant (VPP) coordination, blockchain-enabled peer-to-peer (P2P) energy trading, and intelligent distributed energy management. The proposed framework is validated on the IEEE 118-bus radial distribution system under severe dual-fault outage conditions, representing urban disaster-induced infrastructure interruptions. Critical urban service zones, including healthcare support systems, emergency loads, smart residential sectors, and EV charging corridors, are considered during the restoration process. The Seagull Optimization Algorithm (SOA) is employed to optimize DER dispatch and improve restoration performance under operational constraints. A progressive restoration strategy comprising conventional outage conditions, VPP-assisted restoration, blockchain-enabled decentralized energy trading, and AI-driven coordinated restoration is analyzed. Simulation results demonstrate that the proposed framework significantly enhances urban energy resilience by increasing load restoration from 55.05% to 94.20%, reducing Energy Not Supplied (ENS), improving voltage stability, and lowering interruption-related economic losses. The minimum bus voltage improves to 0.965 p.u. under the proposed coordinated restoration strategy. The results show that coordinated VPP operation and blockchain-based energy sharing can support reliable restoration of critical urban infrastructure during major outage conditions. The results indicate that integrating AI-assisted VPP coordination with secure decentralized energy trading can effectively support smart city critical infrastructure continuity during extreme outage conditions. The proposed framework provides a scalable and resilient solution for future intelligent urban energy systems and disaster-resilient smart city applications. Full article

The growing penetration of electric vehicles (EVs) and distributed photovoltaic (PV) generation is increasing operational uncertainty in distribution networks and intensifying long-standing challenges such as higher power losses, rising peak demand, and voltage instability. To address these issues, this paper proposes a multi-objective optimization framework for the strategic placement of smart meters equipped with demand response (DR) capability in radial distribution systems. Unlike conventional placement approaches that mainly focus on monitoring or reducing non-technical losses, the proposed method integrates active load control into the planning stage and explicitly considers the stochastic behavior of loads, PV generation, and electric vehicle charging stations (EVCSs). The problem is formulated with four objectives: minimizing total power losses, substation peak demand, voltage deviation penalty, and installation cost. A scenario-based stochastic model is employed to represent operational variability across the network. The resulting nonlinear mixed discrete optimization problem is solved using the Non-dominated Sorting Genetic Algorithm II (NSGA-II), an evolutionary multi-objective optimization technique that generates a set of Pareto-optimal solutions representing trade-offs among conflicting objectives. Smart meters are allowed to curtail a portion of controllable demand during critical loading conditions, which helps reduce feeder loading and improve voltage profiles. The proposed approach is evaluated on the IEEE 33-bus and IEEE 69-bus distribution systems. Simulation results demonstrate significant reductions in power losses and peak demand, with the IEEE 33-bus system achieving up to a 26.2% reduction in power losses and 52.5% reduction in substation peak demand compared with existing metaheuristic approaches. The results also indicate improved voltage stability and effective performance in the IEEE 69-bus system, confirming the importance of topology-aware DR-enabled planning. Overall, the findings show that embedding demand response capability within smart meter allocation can significantly enhance the resilience and operational efficiency of modern distribution networks with high EV and PV penetration. Full article

Submarine channels are critical conduits for sediment transport by turbidity currents, yet the quantitative influence of channel geometry on flow dynamics and sediment segregation remains poorly understood. Based on computational fluid dynamics, we constructed six three-dimensional numerical models of submarine channels with varying curvatures (R1–R3) and axial slopes (R4–R6) using ANSYS Fluent 17.2, with model settings informed by seafloor morphology from the South China Sea. The Eulerian–Eulerian multiphase model coupled with the standard k-ε turbulence model was used to simulate density fields, velocity structures, and sediment distributions. Results show that low-curvature channels exhibit symmetric density evolution and uniform sediment distribution, whereas high curvature induces pronounced asymmetry with a steep outer-bank density front and triggers secondary flow reversal. Increasing curvature also enhances flow thickness and radial mass flux. Increasing axial slope markedly elevates downstream velocity (0.09 to 0.16 m/s), reduces flow thickness, and shifts sediment distribution toward the inner bank without inducing secondary flow reversal. This study provides a parametric comparison of curvature versus slope effects on turbidity current dynamics and sedimentation patterns under fixed-bed, rectangular-channel assumptions. The findings offer a qualitative reference for interpreting sedimentary architectures in deep-water systems such as those in the South China Sea and analogous rift basins. Results are hypothesis-generating, pending further validation with field data and morphodynamic modeling. Full article

This study proposes a rigorous methodology for the optimal placement of voltage regulators in the Guayacanes feeder of the Buena Fe substation, Ecuador, by integrating electrical feeder modeling, exhaustive search, and multi-criteria decision-making. The feeder was modeled in detail by incorporating its radial topology, nodal electrical parameters, and representative operating conditions under minimum- and maximum-load scenarios. Based on this model, a set of technical evaluation criteria was established to quantify the impact of regulator installation, including active power losses, reactive power losses, global voltage deviation, average voltage variation, and voltage imbalance. An exhaustive search strategy was then implemented to evaluate all feasible regulator-location alternatives over the candidate nodes, thereby ensuring a complete exploration of the solution space. The resulting alternatives were ranked using the Weighted Sum Method (WSM) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), allowing the comparison of candidate locations from a multi-criteria perspective. The results indicate that node MTS 108932 provides the most technically favorable overall solution, achieving the greatest improvement in voltage profile quality and the most significant reduction in electrical losses. In addition, a sensitivity analysis was conducted by varying the weighting structure of the decision criteria, confirming the robustness of the selected alternative under different decision-maker preference scenarios. The proposed framework provides a technically sound decision-support methodology for voltage regulation planning in real radial distribution systems. Full article

This research introduces a unique optimization framework centered on the Geese V-Formation Algorithm to enhance the technical planning of distributed energy resources in renewable microgrid-oriented radial distribution systems. The proposed methodology addresses the optimal placement and sizing of photovoltaic panels, wind turbines, battery energy storage systems, and capacitor banks to provide comprehensive voltage support, minimize active power losses, and refine overall grid functionality. Drawing inspiration from the aerodynamic efficiency of migratory geese, the Geese V-Formation Algorithm integrates dynamic leader-follower coordination, adaptive role rotation, and cooperative information exchange mechanisms. These features allow the algorithm to effectively balance global exploration and local exploitation, making it uniquely suited to address the complex, nonlinear, and multi-objective nature of modern microgrid design. The effectiveness of this approach was evaluated through rigorous simulations on the IEEE-33 and IEEE-69 bus distribution systems utilizing the Python programming language. The empirical results indicate that the Geese V-Formation Algorithm achieves substantial power loss reductions, reaching 91.62% and 92.45%, respectively, when integrating solar and wind resources with energy storage and reactive power compensation. Furthermore, the optimized configurations significantly improved bus voltage profiles and enhanced substation power factors, confirming the technical effectiveness of the framework under the considered benchmark constraints. By providing a technical decision-support approach for engineers and utility planners, this framework facilitates the deployment of reliable, decentralized renewable energy systems that align with global energy transition objectives and promote sustainable infrastructure development. Full article

The minimal geometric deformation method is applied on Einstein–Maxwell field equations in this study to obtain two novel exact anisotropic solutions for polytropic configurations. A static spherically symmetric seed structure penetrated by the anisotropic fluid distribution is taken into consideration in order to accomplish this goal. The gravitational interaction of the new Lagrangian density is then coupled with the initial fluid configuration, representing an additional matter source. We obtain the field equations that correspond to the associated charged fluid sources. Two separate decoupled systems are developed when the field equations are subjected to a radial transformation. By applying the distinct constraints, each system’s solution is determined individually. The entire fluid configuration is then generated by combining these solutions via a certain linear combination. The constraints needed to determine the integration constants in the internal solutions are provided by junction conditions at the interface between the interior and exterior geometry. The suggested models are then verified by comparing them graphically under the observational data from the $CenX-3$ candidate star. In conclusion, for certain values of the decoupling parameter, our derived relativistic solutions satisfy established physical acceptability requirements. Full article

This study presents an Ant Colony Optimization (ACO)-based methodology for the optimal placement of lithium-ion battery energy storage systems (BESSs) in radial electrical distribution networks. The proposed framework integrates base-case power-flow assessment, critical-bus identification, discrete BESS siting, technical–economic objective evaluation, and post-optimization validation. The methodology is applied to the IEEE 33-bus radial distribution test system, where the initial operating condition is characterized in terms of nodal voltage profile, voltage deviation, voltage-stability index, active-power losses, and annual loss cost. The optimization process identifies buses 13 and 31 as the most suitable locations for two identical BESS units, with the reported validation case evaluating each unit at upper admissible capacity limits of $1000\mathrm{kW}$ and $4000\mathrm{kWh}$. The obtained results show that the optimized BESS allocation increases the minimum voltage profile to values above $0.94\mathrm{p}.\mathrm{u}.$, raises the voltage-stability index to more than 0.88, reduces active-power losses to approximately $0.0166\mathrm{p}.\mathrm{u}.$, and decreases the annual cost associated with active-power losses by more than $66\%$ relative to the base case. Additional validation through sensitivity analysis, repeated stochastic runs, operating-mode evaluation, and comparison against a genetic algorithm confirms the consistency and robustness of the proposed ACO-based methodology. The results demonstrate that the proposed framework provides a technically consistent and computationally accessible solution for improving voltage regulation, reducing feeder losses, and lowering loss-related operating costs in radial distribution systems. Full article

Interface debonding between the casing and cement sheath (CC interface) is a major cause of wellbore failure in hot dry rock geothermal wells. By adding iron filings to cement and varying their distribution along the radial direction, a cement sheath with gradient mechanical properties is obtained. This sheath is called a functional gradient cement sheath. In this paper, a theoretical mechanical model of the functional gradient cement sheath is established. Its mechanical parameters are obtained from laboratory experiments. Analytical solutions for the stress and displacement fields of the casing–functional gradient cement sheath–formation system are derived using elastic thick-walled cylinder theory. The effectiveness of the functional gradient cement sheath in preventing CC interface debonding is then studied. The results indicate the following: (1) cement block samples containing iron filings were prepared with particle sizes of 0.5 mm, 1 mm, and 2 mm and with iron filing-to-cement mass ratios of 0%, 10%, 20%, 30%, and 40%. The compressive strength and elastic modulus of these samples both varied with the iron filing content. As the iron filing content increases, the compressive strength and elastic modulus generally increase, but they decrease under certain conditions. (2) With the total mass of iron filings fixed, the influence of different elastic modulus distributions (exponential, linear, quadratic parabolic, and uniform) on the functional gradient cement sheath was investigated. It was found that the quadratic parabolic distribution of the elastic modulus yields the best mechanical properties. (3) The influence law of the size and dosage of iron filings on the functional gradient cement sheath was studied. Based on the experimental data (0%, 10%, 20%, 30%, 40%), three representative contents (15%, 30%, 45%) were selected for theoretical analysis. It was found that when the iron filing size was 0.5 mm and the dosage was 15%, the stress and displacement on the inner wall of the functional gradient cement sheath were the minimum. Full article

The UHV dry-type bushing plays a critical role in power transmission by enabling electrical connection, electrical insulation, and mechanical support, making it a core component for ensuring the safe and stable operation of UHV direct current (DC) transmission projects. Epoxy resin, serving as the fundamental insulating material for the bushing core, undergoes significant residual strain during high-temperature curing due to chemical shrinkage and thermal strain, which directly affects the molding quality and service reliability of the component. This paper investigates the curing process of a large-thickness epoxy material, which is on the same scale as a UHV bushing. An in situ monitoring system combining fiber Bragg grating (FBG) sensors and thermocouples, together with COMSOL Multiphysics simulations, is employed to systematically study the evolution of the temperature field and residual strain throughout the entire curing process, considering the demolding effect. The results show that during the curing stage, the internal temperature distribution is non-uniform, with a maximum temperature difference of 65 °C between the center and the edge. The residual strain is dominated by chemical shrinkage (accounting for 73.25%) and exhibits a pronounced radial gradient. Mold constraint and demolding cause abrupt changes in the strain. The developed thermo-chemo-mechanical coupled model shows good agreement between simulations and experimental measurements. Thermal cycling relaxes the residual stress, achieving a reduction of 3.89–5.77%. This study provides support for process optimization and defect prevention in large-scale epoxy insulation components. Full article

The integration of renewable-based distributed units into distributed systems has been aided by recently developed technologies based on renewable energy, changes to utility infrastructure, and progressive government regulations. In this paper, an improved version of the golden jackal optimization (IGJO) is implemented to incorporate distributed generators (DGs) and capacitor banks (CBs) into the distribution system. The existing studies give only DG unit insertion, but in this work, simultaneous integration of different kinds of DG with a capacitor bank is used to analyze the impact. The main emphasis of this study is to minimize power loss along with the upgradation of the voltage profile. Improvement in voltage stability index and minimization of total voltage deviation (TVD) were also achieved by placing the DG and CB units in a suitable position. Load modeling is also considered here to validate the results. Seven types of loading, including constant power (half load and heavy load), constant current, constant impedance, residential, industrial, and commercial loads, are used to show the effect of integration of DG and capacitor bank into a 33-bus and 118-bus radial distribution system. Comparison of the proposed method with previous studies shows the better performance of the implemented method over other techniques. Full article

The optimal reconfiguration of electrical distribution feeders is a fundamental strategy for reducing active power losses and improving voltage profiles, yet it remains a challenging mixed-integer nonlinear programming (MINLP) problem due to the combinatorial explosion of radial topologies and the nonlinearities introduced by power flow equations. This paper proposes a novel master–slave methodology that integrates a Chu and Beasley genetic algorithm (CBGA) with a minimum spanning tree (MST)-based repair mechanism to address these challenges. In the master stage, the CBGA explores the binary space of switching decisions via steady-state population management, duplicate elimination, and stagnation restart policies. A key contribution lies in the MST-based repair procedure, which ensures that every individual generated by crossover and mutation is projected onto a feasible radial and connected configuration, effectively confining the search to the constrained solution space without recourse to penalty functions. A systematic weight-design rule preserves the Hamming distance between infeasible offspring and repaired solutions, minimizing the distortion of genetic information. The slave stage evaluates each candidate topology using a successive approximations power flow solver, assessing electrical feasibility and computing active power losses. The proposed methodology is validated on multiple test feeders, ranging from small 9- and 24-bus networks to large-scale benchmarks including 33-, 69-, 84-, 136-, and 415-bus systems. A comparison against the deterministic sequential switch opening method (SSOM) and a specialized tabu search demonstrates that the CBGA-MST consistently matches the best-known optima in the literature, achieving loss reductions of up to 9.63% compared to SSOM on the 415-bus system. A statistical analysis over 100 independent runs confirms the algorithm’s robustness, with zero standard deviation for networks of up to 69 buses and a standard deviation of only 2.99 kW (0.51%) for the 415-bus system. The findings confirm that the proposed approach offers superior scalability, robustness, and solution quality, positioning it as a practical and effective tool for distribution system operators seeking to enhance network efficiency under peak load conditions. Full article

Efficient feed management is essential in aquaculture, especially for bottom-feeding species such as shrimp that require feed delivery at the tank bottom. Most commercial automated feeders are designed for surface-feeding fish and are unsuitable for benthic organisms, leading to feed waste and uneven distribution. This study developed and evaluated an automatic bottom feeder capable of dispensing sinking pellets directly to the substrate. The system integrated a 3D-printed auger for precise feed metering and a closed-type centrifugal impeller positioned at the water surface to achieve radial dispersion of feed. An Arduino Uno microcontroller operated the impeller speed (285.98–586.85 rpm), feed mass (95.23–285.68 g), and dispersion time (2–8 s). A Box–Behnken response surface methodology was used to analyze the influence of these parameters on the mean radius spread of feed, supported by image-based uniformity assessment using OpenCV. Results identified impeller speed as the most significant factor (p = 0.010), with optimal dispersion observed at moderate speeds and longer spread durations. The system demonstrated reliable mechanical performance and precise control, providing a novel, programmable solution for uniform feed delivery in shrimp aquaculture and a promising foundation for scalable, automated bottom-feeding technologies. Full article

The intermittent nature of distributed generators based on renewable energy sources (DGs-RESs), together with the time-varying behavior of load demand, introduces significant uncertainty into the planning and operation of active distribution networks. These uncertainties make the optimal siting and sizing of DGs-RESs and D-STATCOMs a challenging multi-objective optimization problem. This paper proposes a multi-objective Coati Optimization Algorithm (MOCOA) for the coordinated allocation of DGs-RESs and D-STATCOMs in radial distribution networks under uncertainty. The proposed framework aims to minimize total active power losses (TAPLs) and enhance the voltage stability index (VSI) while satisfying the operational constraints of the distribution system. First, the load sensitivity factor (LSF) is employed to identify the most suitable candidate buses, thereby reducing the search space and improving the computational efficiency of the optimization process. Then, MOCOA is applied to determine the optimal placement and sizing of DGs-RESs and D-STATCOMs. The uncertainties associated with load demand, solar irradiance, and wind speed are modeled using probabilistic representations, and reduced representative scenarios are considered to evaluate system performance under uncertain operating conditions. The proposed method is validated using modified IEEE 33-bus and IEEE 69-bus radial distribution networks. The simulation results demonstrate that the coordinated integration of DGs-RESs and D-STATCOMs significantly reduces TAPLs, improves the VSI, and enhances the voltage profile. In particular, increasing the number of DG/D-STATCOM units and using wind energy reduces the TAPL by 26.95% and increases the 24 h cumulative VSI from 20.16781 p.u. to 20.4162 p.u. Comparative results with other optimization techniques confirm the effectiveness, robustness, and superior performance of the proposed MOCOA for uncertainty-aware planning of active distribution networks. Full article