Search Results (949)

As modern electricity system evolve toward smart grids, increasing technological and regulatory complexity makes distribution system planning a challenging task. This paper develops new planning and reliability models to help local distribution companies (LDCs) achieve cost-effective, reliable solutions amid these changes. Furthermore, this paper offers a novel iteration-based optimization model incorporating reliability and security considerations. Also, this work proposes the concept of virtual microgrids to enhance distributed generation (DG) penetration in weak areas, thereby improving reliability where it is most needed. The proposed model reduces an LDC’s total planning cost covering substation and feeder upgrades, market energy purchases, payments to DG owners, energy losses, and operations while ensuring that reliability indices and energy not supplied stay within regulatory limits. To ensure compliance with operational security limits as DG penetration rises, the DG capacity is split into normal operating capacity and reserve capacity. The results confirm the proposed algorithm’s superiority relative to current state-of-the-art planning methodologies. Full article

The increasing penetration of electric vehicles (EVs) introduces significant uncertainties into fast-charging station (FCS) planning due to the stochastic nature of EV charging behavior. Accurately representing these uncertainties is essential for making reliable planning decisions in coupled transportation–power networks. This paper proposes a copula-based stochastic planning framework for the optimal allocation of FCSs while accounting for the correlated uncertainties associated with EV charging behavior. A multivariate copula model is employed to capture the dependency structure among key charging variables and generate realistic stochastic charging scenarios, which are subsequently incorporated into the EV charging load forecasting process over the planning horizon. Based on the resulting stochastic charging demand, a multi-objective optimization model is developed to simultaneously minimize investment costs and EV users’ travel distances, improve distribution network performance, and maximize environmental benefits through decarbonization. In addition, distributed generation (DG) units are optimally integrated to improve voltage profiles and reduce power losses. The proposed framework is implemented using MATLAB R2013a and R.4.0.2 and evaluated using both the IEEE 33-bus test system and a realistic 37-bus coupled transportation–power network in Meshgin-Shahr, Iran. The results demonstrate the effectiveness of the proposed stochastic planning framework in addressing uncertainties in EV charging behavior and identifying robust FCS deployment strategies. Full article

As a growing portion of inverters in alternating current (AC) micorgrids (MGs) are utilizing the grid-forming (GFM) control due to its superior active grid support capabilities, the hybrid operation of GFM and grid-following (GFL) converters becomes an imperative scenario, making it more complex to achieve coordination among heterogeneous inverters for grid support. This paper investigates the synergetic operation of GFM and GFL inverters for the support of grid frequency and bus voltages. To address the contradiction between the frequency control and voltage regulation missions, the general roles of GFM and GFL are assigned based on a local node‘s power balance profile. Moreover, to simplify the control system design and achieve unified regulation of two indices, a fixed-time containment control (FTCC) scheme is proposed, which chooses two leader distributed generators (DGs) to form a convex hull shaped by the set points of the control objectives and leads the follower DGs for power sharing. In addition, the fixed-time convergence of the control approach is ensured for accelerating the response speed. Hardware-in-the-loop (HIL) experiments are performed to evaluate the feasibility and performance of the proposed method. The experimental results fully validate the effectiveness and superiority in frequency/voltage regulations and robustness performance. Full article

Modern electrical distribution systems face increasingly complex protection coordination challenges due to variations in power flows under different operating conditions. In this context, this work proposes an adaptive coordination scheme based on deep reinforcement learning (DRL) to reduce the operating times of overcurrent relays while maintaining sensitivity, selectivity, and speed requirements. The methodology was implemented in Python 3.13 using the IEEE 33-bus distribution network with distributed generation (DG), and the coordination problem was addressed using the Deep Deterministic Policy Gradient (DDPG) algorithm, which adjusts the protection settings from previously calculated fault currents. The results show that the DDPG-based approach reduces fault-clearing times compared with the conventional methodology, achieving reductions between 17.01% and 77.5% for three-phase faults and between 18.5% and 74.1% for single-phase-to-ground faults across the analyzed scenarios, without compromising coordination between primary and backup relays. In addition, a comparison with a PSO-based offline optimization approach was included as an additional benchmark, showing that the proposed method provides competitive operating times while preserving its adaptive learning-based nature. These findings show that the proposed methodology is a viable option for adaptive protection coordination in modern distribution networks. Full article

Existing DG allocation studies commonly rely on static or balanced feeder assumptions, leaving two practical issues insufficiently addressed: the hourly mismatch between renewable outputs and feeder demands and diesel-backup phase-imbalance safety in unbalanced networks. This paper presents a safety-aware allocation framework for hybrid photovoltaic (PV) and wind turbine (WT) systems in unbalanced three-phase feeders. The methodology explicitly accounts for 24 h generation–load coordination and diesel backup operating limits through two post-load-flow indicators: load-following error (LFE), which measures the hourly mismatch between aggregate distributed generation (DG) outputs and a load-proportional target, and power unbalance ratio (PUR), which limits diesel-unit phase-power imbalance to 10% during dispatch. The constrained siting and sizing problem is solved using a genetic algorithm (GA) on the IEEE 37-bus feeder under realistic diurnal load, solar, and wind profiles. While an unconstrained allocation achieves 64.57% active-power loss reduction, it exceeds the adopted diesel PUR screening threshold. Enforcing the PUR constraint yields a feasible operating state with 60.87% loss reduction, retaining 94.27% of the unconstrained benefit. Robustness checks across 30 independent runs and GA/PSO/ACO benchmarking confirm that the adopted GA provides the lowest dispersion and highly repeatable feasible outcomes. The results show that the framework improves energy efficiency, voltage regulation, and daily coordination while satisfying the adopted diesel phase-power screening criterion under severe feeder asymmetry. Full article

The increasing penetration of distributed generation (DG) units and electric vehicles (EVs) has created significant challenges in minimizing real power loss in modern distribution systems. In this paper, a hybrid Genetic Algorithm–Monte Carlo Simulation (GA–MCS) optimization framework is implemented for the optimal sizing and placement of DG units in a 38-bus radial distribution system under stochastic operating conditions. The analysis is carried out for different voltage-dependent load models considering single-, double-, and triple-DG configurations with different EV categories. The proposed framework is implemented using 50 Monte Carlo scenarios and 50 GA iterations under varying operating conditions. The obtained results indicate that DG2 provides the best performance among single-DG cases in terms of real power loss minimization. In coordinated multi-DG cases, the DG1–DG2 combination reduces real power loss by approximately 6.50–10.07% compared with the best-performing single-DG configuration. The DG1–DG2–DG4 configuration under extended-range electric vehicle (EREV) penetration achieves the minimum real power loss with an additional reduction of approximately 11.89–11.96% with respect to the best-performing single-DG configuration. Voltage profile analysis further confirms the improvement in voltage magnitude after coordinated DG integration. Comparative analysis also indicates the improved convergence characteristics of the proposed GA–MCS framework compared with conventional GA and PSO approaches. Full article

Background: Routine use of the chorioallantoic membrane (CAM) tumor model in nuclear imaging studies is hampered by small tumors, embryonic movements and laborious volume-restricted intravenous tracer/drug administration. We sought a workaround by using fast-growing tumors, high-resolution autoradiography and non-intravenous tracer administration. Methods: Dekalb White chicken eggs were grafted with C3H mammary carcinoma fragments or MOC2 oral squamous cell carcinoma fragments from donor mice. The tumor uptake of ¹⁸F-fluorodeoxyglucose (FDG) following in-yolk-sac injection, dripping after CAM scoring or allantoic cavity injection was evaluated using positron emission tomography (PET) and autoradiography. Using in-yolk-sac injection, eggs were administered different tracer mixtures, namely (1) pimonidazole (hypoxia-marker), FDG and ¹⁴C-2-deoxyglucose (¹⁴C-2DG), (2) pimonidazole, FDG and ¹⁴C-acetate or (3) pimonidazole, the hypoxia-selective tracer ¹⁸F-fluoroazomycin-arabinoside (FAZA) and ¹⁴C-2DG. For comparison, tumor-bearing mice were administered FDG/¹⁴C-acetate/pimonidazole. Gross tumor uptake was evaluated using PET. Tumor cryosections were analyzed using dual-tracer autoradiography. Complementary autoradiograms were co-registered, covered by a square grid (0.5 × 0.5 mm). Pearson correlation coefficients (PCC) were calculated from scatterplots. Results: C3H tumors reached a mean weight (with 95% confidence interval) of 0.32 g (0.28–0.37 g), while for MOC2, it was 0.19 g (0.09–0.29 g). In-yolk-sac tracer injection was simple and effective, producing high tracer uptake and contrast 3 h post-administration. Spatial tracer overlap (PCC) was: FDG vs. ¹⁴C-2DG, 0.95–0.97; FAZA vs. ¹⁴C-2DG, 0.71–0.79 and FDG vs. ¹⁴C-acetate, 0.26–0.84 (0.15–0.76 in mice). Pimonidazole revealed tumor hypoxia. Conclusions: Direct-grafting from donor mice generated larger tumors than previously reported. In-yolk-sac tracer administration was practical and allowed larger injected volumes. Autoradiography revealed that: (1) FDG and ¹⁴C-2DG can be used interchangeably, (2) ¹⁴C-2DG was elevated in FAZA-positive areas, suggesting that in some tumors FDG-PET may provide information on the intratumoral distribution of hypoxic areas, and (3) FDG and ¹⁴C-acetate showed variable overlap. We conclude that in-yolk-sac tracer injection and autoradiography simplify and optimize CAM-based nuclear imaging research. Full article

With the development of the socio-economic landscape and the increasing demand for urban power supply, user expectations for power supply reliability have risen significantly. To address this challenge, dual-petal distribution networks, characterized by multiple tie-line structures and inter-regional load transfer paths, have significantly enhanced fault recovery capability and are gradually replacing traditional radial configurations as a key form of modern distribution systems. However, their multi-regional coupling characteristics introduce complex issues such as dynamic changes in load transfer paths and islanded operation, resulting in significant limitations in the accuracy and adaptability of existing reliability assessment methods. To this end, this paper proposes an analytical method for calculating reliability indices of dual-petal distribution networks, considering the characteristics of load transfer zones. First, typical operation modes of dual-petal distribution networks are extracted, and a time-sequential component reliability analysis model is established. Second, a load transfer zone matrix is constructed based on the impact of distribution network faults on load nodes across different regions. Third, based on the fault ride-through capability of distributed generation (DG), a load restoration strategy considering load transfer zone characteristics is formulated, and the DG Island Recovery Matrix (DGIRM) is derived. Finally, by performing algebraic operations among various matrices and reliability parameter vectors, an explicit analytical calculation of reliability indices for dual-petal distribution networks with different DG configurations is achieved. The effectiveness of the proposed method is validated using a typical dual-petal network. The results demonstrate that the proposed method offers high computational efficiency and accuracy, effectively quantifying the impact of DG on the power supply reliability of dual-petal distribution networks, and providing theoretical and methodological support for the reliability assessment and planning of complex distribution systems. Full article

The incipient cable fault in active low-resistance-grounded distribution networks is a typical asymmetrical fault and is difficult to locate because the fault current is weak, short-lasting, and easily affected by distributed generation (DG). To address this typical asymmetrical problem, this paper proposes a fault section location method based on multidimensional information correlation analysis. First, an equivalent incipient fault model is established by combining the Kizilcay arc model with an insulation-defect resistance, so that the intermittent arc behavior and the conductive path of degraded insulation can be represented simultaneously. Then, the generalized S-transform is used to extract three features from the transient zero-sequence current, namely the transient current energy index, group phase-angle polarity, and waveform similarity. On this basis, a multidimensional feature vector and a comprehensive similarity coefficient are constructed to identify the fault section, and an auxiliary downstream energy comparison rule is introduced to distinguish the actual fault section from DG-connected pseudo-fault sections. The method is verified in MATLAB/Simulink R2025a under different fault locations, DG access conditions, penetration levels, noise levels, and key parameter variations. The simulation results under the tested conditions indicate that the proposed method can effectively identify asymmetrical incipient cable fault sections in active low-resistance-grounded distribution networks. Full article

For radial distribution systems (RDSs) to operate efficiently, reliably, and sustainably, distributed generation (DG), capacitor banks (CBs), and network reconfiguration (NR) must be optimally allocated and sized. The main objectives considered in this research are minimizing real power losses, improving voltage profiles, enhancing energy utilization efficiency, and strengthening the operational reliability of distribution networks. To address this challenge, an Improved Frilled Lizard Optimizer (IFLO) is proposed to determine the optimal placement and sizing of DGs, CBs, and NR while satisfying system operational constraints. FLO is inspired by the adaptive survival and movement characteristics of frilled lizards in their natural ecosystem. The optimization mechanism of FLO is driven by hunting behavior for broad exploration and tree-climbing behavior for localized movement, enabling effective search and exploitation of promising regions. The IFLO introduces a defensive strategy phase, mimicking the lizard’s survival responses, and an adaptive local search phase, which models agile movement and stabilization behaviors. These enhancements improve the algorithm’s capability to reduce power losses, improve voltage regulation, increase network efficiency, and facilitate the effective integration of distributed energy resources into modern power distribution infrastructures. Comprehensive simulations on the IEEE 69-bus and the practical large-scale 141-bus RDS evaluate the impacts of DG and CB installation under practical operating constraints. This study investigates six scenarios involving different combinations of DG, CB, and NR to support efficient network planning and operation. Furthermore, recent optimization techniques, including Bezier Curve-Based Optimization (BCO), Horned Lizard Optimization Algorithm (HLOA), Whale Optimization Algorithm (WOA), Jaya Algorithm, and Particle Swarm Optimization (PSO), are implemented on the studied systems, and their results are compared with those of the proposed IFLO. The findings demonstrate that the suggested strategy outperforms existing optimization approaches in terms of convergence speed, solution quality, and network performance enhancement. The IFLO algorithm achieves an active power loss reduction of 92.69% for the large-scale system, while significantly improving voltage stability and operational efficiency. These outcomes contribute to the development of resilient, energy-efficient, and intelligent distribution infrastructures capable of supporting increased penetration of distributed energy resources under diverse operating conditions. Full article

The integration of distributed generation (DG) changes the fault current path, magnitude, direction, and transient characteristics of distribution networks, which increases the difficulty of fault type identification. In particular, weak fault features and high-frequency transient components may reduce the reliability of traditional feature-based diagnosis methods. To improve the representation and classification capability of fault signals, this paper proposes a fault type identification method based on wavelet packet transform and an improved CNN-BiGRU model with a channel attention mechanism. First, three-phase voltage, three-phase current, and zero-sequence voltage signals are decomposed by wavelet packet transform, and the corresponding time–frequency matrices are constructed. Then, these matrices are integrated and converted into time-frequency images, so that multi-source fault information can be represented in a unified form. On this basis, CNN is used to extract local spatial features from the time-frequency images, while BiGRU is employed to capture bidirectional dependency information of fault features. Furthermore, a channel attention mechanism is introduced to enhance informative feature channels and suppress redundant information, thereby improving the fault classification performance. Simulation results based on a 10 kV DG-integrated distribution network show that the proposed method achieves high recognition accuracy under different DG capacities and access configurations. Compared with CNN, BiGRU, and CNN-BiGRU models, the proposed CNN-BiGRU-Attention model shows better classification accuracy and adaptability, demonstrating its effectiveness for fault type identification in active distribution networks. Full article

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

Cross-scene Hyperspectral Image (HSI) classification faces substantial domain shifts caused by sensor heterogeneity, acquisition variation, and scene diversity. While benchmark annotations are assigned to individual center pixels, local patches often contain implicit multi-label semantics due to spectral mixing and spatial overlap. This mismatch distorts prediction structure, exacerbates generalization errors, and limits the effectiveness of standard domain generalization (DG) techniques focused solely on feature or prediction invariance. We propose Class-Aware Semantic Calibration (CASC), a systematic semantic structure calibration framework that addresses three complementary distortions induced by mismatched patch supervision: (i) Balance corrects class frequency bias via reweighted supervision; (ii) Separability enhances boundary decision stability through margin-based logit calibration; and (iii) Independence reduces domain-specific spurious co-occurrence via prediction covariance decorrelation. To preserve calibrated semantics under pseudo-source shift, we further introduce a complementary DualAlign (DA) module, which jointly aligns feature statistics and prediction distributions, enforcing consistency at both representation and semantic levels. Extensive experiments on three cross-scene benchmarks (Houston, Pavia, and WHU-Hi) demonstrate that CASC-DA consistently improves performance over strong baselines, achieving an average gain of 3.0% in overall accuracy and 4.9% in Kappa coefficient compared with the best-performing baseline on each dataset. These results underscore the importance of semantic structure calibration for domain-generalized HSI classification. Full article

In this work, a multi-objective quantum particle swarm optimization (QPSO) algorithm is proposed to address the optimal scheduling of non-dispatchable sources in a microgrid energy management system (MGEMS) for residential areas under utility-induced demand-side management (DSM) programs. While taking economic and environmental aspects into account, the goal is to maximize energy management by integrating a variety of distributed generation (DG) units with an energy storage device. Using real-time meteorological data, two case studies were analyzed and simulated using MATLAB/Simulink R2025b. The simulation results reveal that the optimum optimization outcome among the case studies is obtained at a higher DSM load participation level of 10%. Without the involvement of DSM, MG’s producing units in the first case had the highest carbon emissions of 797.110 kg and an overall operating cost of 267.10 €. Similarly, with the involvement of DSM, the second case had the lowest overall operating cost of 155.01 € and the lowest carbon emissions of 748.731 kg. The second case, which has optimal DG scheduling, is the suggested way to improve microgrid efficiency and provide a dependable power supply with low operating costs and emission reduction. 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