Search Results (3,626)

With the large-scale integration of renewable energy sources, power electronic components within power grids have surged. Traditional synchronous generator-based power generation is gradually transitioning to renewable energy generation integrated with grid-following (GFL) and grid-forming (GFM) inverters. Furthermore, power grid topology structures are evolving from traditional radial and ring-type configurations toward meshed-type architectures. The impact of grid topology structures on the stability of hybrid systems combining GFL and GFM inverters urgently requires systematic investigation. This paper establishes state-space models of GFM and GFL inverters under three typical grid topology structures and then compares the small signal stability of hybrid systems. First, mathematical models of inverters and transmission lines are established, and a full-order state-space model of the system is accordingly derived. Second, key stability indicators, including eigenvalues, damping ratio, participation factors, and sensitivity indices, are obtained by analyzing the system state matrix. Finally, simulation models for these grid topology structures are implemented in MATLAB/Simulink R2022b to validate the influences of grid topology structures on the stability related to inverters. The results demonstrate that GFL inverters are highly sensitive to grid topology structures, whereas GFM inverters are more influenced by their synchronization control capabilities. Smaller GFL inverters connection impedances and larger GFM inverters connection impedances can enhance system stability. Full article

As the proportion of new energy generation continues to rise, power systems are confronted with novel challenges. Grid-forming converters, which possess voltage source characteristics and can support the grid, typically employ a VSG control strategy during normal operation to emulate the behavior of synchronous generators. This approach enhances frequency response and system stability in modern power systems. This review article systematically examines two typical fault control strategies for grid-forming converters: the switching strategy and the virtual impedance strategy. These different control strategies result in distinct fault response characteristics of the converter. Based on the analysis of fault control strategies for grid-forming converters, this study investigates the impact of the converter’s fault response characteristics on overcurrent protection, pilot protection, distance protection, and differential protection and investigates and prospects corresponding countermeasures. Finally, through simulation modeling, the fault response characteristics under different control strategies and their effects on protection are verified and analyzed. Focusing on grid-forming converters, this paper dissects the influence of their fault control strategies on relay protection, providing strong support for the wide application and promotion of grid-forming converters in new types of power systems. Full article

The optimal selection of conductors (OCS) in radial distribution networks is a critical aspect of system planning, directly impacting both investment costs and energy losses. This paper proposed a mixed-integer convex (MI-Convex) optimization framework to solve the OCS problem under balanced operating conditions, integrating the costs of conductor investment and energy losses into a single convex objective. This formulation leveraged second-order conic constraints and was solved using a combination of branch-and-bound and interior-point methods. Numerical validations on standard 27-, 33-, and 85-bus test systems confirmed the effectiveness of the proposal. In the 27-bus grid, the MI-Convex approach achieved a total cost of $550,680.25, outperforming or matching the best results reported by state-of-the-art metaheuristic algorithms, including the vortex search algorithm (VSA), Newton’s metaheuristic algorithm (NMA), the generalized normal distribution optimizer (GNDO), and the tabu search algorithm (TSA). The MI-Convex method demonstrated consistent and repeatable results, in contrast to the variability observed in heuristic techniques. Further analyses considering three-period and daily load profiles led to cost reductions of up to 27.6%, and incorporating distributed renewable generation into the 85-bus system achieved a total cost of $705,197.06—approximately 22.97% lower than under peak-load planning. Moreover, the methodology proved computationally efficient, requiring only 1.84 s for the 27-bus and 12.27 s for the peak scenario of the 85-bus. These results demonstrate the superiority of the MI-Convex approach in achieving globally optimal, reproducible, and computationally tractable solutions for cost-effective conductor selection. Full article

The integration of variable distributed energy sources (DERs) can reduce overall system inertia, potentially impacting the transient response of both conventional and renewable generators within electrical grids. Although transient stability indicators—for instance, the Critical Clearing Time (CCT), fault-induced short-circuit current ratios, and machine parameters, including subtransient–transient reactances and associated time constants—are influenced by total system inertia, their detailed evaluation remains insufficiently explored. These parameters provide standardized benchmarks for systematically assessing the transient stability performance of conventional and photovoltaic (PV) generators as the penetration level of distributed PV systems (PVD1) increases. This study explores the relationship between conventional stability parameters and system inertia across different levels of PV penetration. CCT, a key metric for transient stability assessment, incorporates multiple influencing factors and typically increases with higher system inertia, making it a reliable comparative indicator for evaluating the effects of PV integration on system stability. To investigate this, the IEEE New England 39-bus system is adapted by replacing selected synchronous machines with PVD1 PV units and adjusting the PV penetration levels. The resulting system behavior is then compared to that of the original configuration to evaluate changes in transient stability. The findings confirm that transient and subtransient reactances, along with their respective time constants under fault conditions, are shaped not only by the characteristics of the generator on the faulted line but also by the surrounding network structure and overall system inertia. The newly introduced sensitivity parameters offer insights by capturing trends specific to conventional versus PV-based generators under different inertia scenarios. Notably, transient parameters show similar responsiveness to inertia variations to subtransient ones. This paper demonstrates that in certain scenarios, the integration of low-inertia PV generators may generate insufficient energy, which is not above critical energy during major disturbances, resulting surviving fault and subsequently an infinite CCT. While the integration of PV generators can be beneficial for their own operational performance, it may adversely impact the dynamic behavior and fault response of conventional synchronous generators within the system. This highlights the need for effective planning and control of DER integration to ensure reliable power system operation through accurate selection and application of both conventional and proposed transient stability parameters. Full article

This study investigates the annual carbon emission disparities between privately-owned electric vehicles (EVs) and internal combustion engine vehicles (ICEVs) by developing a usage-phase life cycle assessment (LCA) model, with a focus on the synergistic impacts of grid carbon intensity, driving intensity (e.g., annual mileage), and vehicle energy efficiency. Through scenario analyses and empirical case studies in four Chinese megacities, three key findings are obtained: (1) Grid carbon intensity is the primary factor affecting the emission advantages of EVs. EVs demonstrate significant carbon reduction benefits in regions with low-carbon power grids, even when the annual mileage is doubled. However, in coal-dependent grids under intensive usage scenarios, high-energy-consuming EVs may experience emission reversals, where their emissions exceed those of ICEVs. (2) Higher annual mileage among EV owners (1.5–2 times that of ICEV owners) accelerates carbon accumulation, particularly diminishing per-kilometer emission advantages in regions where electricity grids are heavily reliant on fossil fuels. (3) Vehicle energy efficiency heterogeneity plays a critical role: compact, low-energy EVs (e.g., A0-class sedans/SUVs) maintain emission advantages across all scenarios, while high-energy models (e.g., C-class sedans/SUVs) may exceed ICEV emissions even in regions with low-carbon power grids under specific conditions. The study proposes a differentiated policy framework that emphasizes the synergistic optimization of grid decarbonization, vehicle-class-specific management, and user behavior guidance to maximize the carbon reduction potential of EVs. These insights provide a scientific foundation for refining EV adoption strategies and achieving sustainable transportation transitions. Full article

Droughts are among the most impactful climate extremes in Serbia, with significant socio-economic consequences, particularly in agriculture. The summer of 2012 was one of the most extreme drought events in Serbia’s history, characterized by record-breaking temperatures and prolonged precipitation deficits. In this study, we investigate the meteorological aspects of the 2012 drought, its progression, and its potential recurrence under future climate conditions. Using the high-resolution gridded observational dataset (EOBS) and Single-Model Initial-Condition Large Ensemble (SMILE) simulations from CMIP6—the Max Planck Institute Earth System Model version 1.2 (MPI-ESM 1.2) Grand Ensemble, we analyze precipitation deficits and assess the ability of MPI-GE CMIP6 to reproduce the observed event. We identify analogue events in MPI-GE CMIP6 that resemble the 2012 drought and examine their occurrence across historical and future climate scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5). Our results indicate that MPI-GE CMIP6 effectively captures precipitation deficit extremes and that events comparable to the 2012 drought become more frequent and severe under higher greenhouse gas concentration scenarios. This study underscores the importance of a large ensemble in understanding the full distribution of extreme drought events and provides Serbia-specific insights, which is valuable for regional climate adaptation planning. Full article

This study addresses the issue of resistance plate deterioration in ultra-high voltage energy devices by proposing an improved symplectic geometric mode decomposition-wavelet packet (ISGMD-WP) algorithm that effectively extracts the component characteristics of leakage currents. The extracted features are subsequently input into the I-Informer network, allowing for the prediction of future trends and the provision of early short-term warnings. First, we enhance the symplectic geometric mode decomposition (SGMD) algorithm and introduce wavelet packet decomposition reconstruction before recombination, successfully isolating the prominent harmonics of leakage current. Second, we develop an advanced I-Informer prediction network featuring improvements in both the embedding and distillation layers to accurately forecast future changes in DC characteristics. Finally, leveraging the prediction results from multiple adjacent columns mitigates the impact of power grid fluctuations. By integrating these data with the deterioration interval, we can issue timely warnings regarding the condition of lightning arresters across each column. Experimental results demonstrate that the proposed ISGMD-WP effectively decomposes leakage current, achieving a decomposition ability evaluation index (EIDC) 1.95 under intense noise. Furthermore, in long-term prediction, the I-Informer network yields mean absolute error (MAE) and root mean square error (RMSE) indices of 0.02538 and 0.03175, respectively, enabling the accurate prediction of the energy device’s fault. Full article

Conventional approaches for designing distribution grids are often time-consuming and computationally expensive. To minimize power harmonics in a low-voltage network, there is a dire need of in-depth mathematical and technical calculations for each electrical equipment involved in the modeling of a distribution grid. In this study, a time- and resource-efficient distribution grid model is proposed, which is capable of improving power-quality impact of electric vehicle charging infrastructure. The proposed method uses mathematical equations, field measurement, data from equipment manufacturers, and distribution network operators to develop precise distribution grid model for the integration of bidirectional electric vehicle charging infrastructure. To prove the effectiveness of the proposed model, power-quality analysis of electric vehicle charging stations is conducted in the MATLAB/Simulink environment. As a result, the grid voltage THD has improved to 0.05% while the grid-connected current THD obtained is 0.88%. This signifies that by varying selection of technical parameters of electrical components of a distribution grid, power losses resulting in the form of harmonics can be improved. Full article

The collapse of Baltimore’s Francis Scott Key Bridge in March 2024 has stressed the need for urban traffic network optimization within smart city initiatives. This paper utilizes the ARIMA model to forecast what traffic would have been like if the bridge had not collapsed, giving us a benchmark to assess the impact. It then identifies the roads most affected by comparing these forecasts with the actual post-collapse traffic data. To address the increased demand for efficient public transport, we propose an intelligent bus network model. This model uses principal component analysis and grid segmentation to inform decisions on increasing bus stations and adjusting bus frequencies on key routes. It aims to satisfy stakeholders by enhancing service coverage and reliability. The research also presents a comprehensive traffic model that leverages principal component analysis, genetic algorithms, and KD-tree to evaluate overall and directional traffic flow, providing strategic insights into congestion mitigation. Furthermore, it examines traffic safety issues, including accident-prone areas and traffic signal intersections, to offer recommendations. Finally, the study evaluates the effectiveness, stability, and benefits of the proposed intelligent traffic network model, aiming to improve the city’s traffic infrastructure and safety. Full article

The surge in electric vehicles (EVs) in Denmark challenges the country’s residential low-voltage (LV) distribution system. In particular, it increases the demand for home EV charging significantly and possibly overloads the LV grid. This study analyzes the impact of EV charging integration on Denmark’s residential distribution networks. A residential grid comprising 67 households powered by a 630 kVA transformer is studied using DiGSILENT PowerFactory. With the assumption of simultaneous charging of all EVs, the transformer can be heavily loaded up to 147.2%. Thus, a voltage-droop based autonomous control approach is adopted, where the EV charging power is dynamically adjusted based on the point-of-connection voltage of each charger instead of the fixed rated power. This strategy eliminates overloading of the transformers and cables, ensuring they operate within a pre-set limit of 80%. Voltage drops are mitigated within the acceptable safety range of ±10% from normal voltage. These results highlight the effectiveness of the droop control strategy in managing EV charging power. Finally, it exemplifies the benefits of intelligent EV charging systems in Horizon 2020 EU Projects like SERENE and SUSTENANCE. The findings underscore the necessity to integrate smart control mechanisms, consider reinforcing grids, and promote active consumer participation to meet the rising demand for a low-carbon future. Full article

Accurate day-ahead photovoltaics (PV) power forecasting results are significant for power grid operation. According to different weather modes, the existing research has established a classification forecast framework to improve the accuracy of day-ahead forecasts. However, the existing framework still has the following two problems: (1) weather mode prediction and power forecasting are highly dependent on the accuracy of numerical weather prediction (NWP), but the existing classification forecasting framework ignores the impact from NWP errors; (2) the validity of the classification forecasting framework comes from the accurate prediction of weather modes, but the existing framework lacks the analysis and decision-making mechanism of the reliability of weather mode prediction results, which will lead to a significant decline in the overall accuracy when weather modes are wrongly predicted. Therefore, this paper proposes a day-ahead PV power forecasting method based on irradiance correction and weather mode reliability decision. Firstly, based on the measured irradiance, K-means clustering method is used to obtain the daily actual weather mode labels; secondly, considering the coupling relationship of meteorological elements, the graph convolutional network (GCN) model is used to correct the predicted irradiance by using multiple meteorological elements of NWP data; thirdly, the weather mode label is converted into one-heat code, and a weather mode reliability prediction model based on a convolutional neural network (CNN) is constructed, and then the prediction strategy of the day to be forecasted is decided; finally, based on the weather mode reliability prediction results, transformer model are established for unreliable weather and credible weather respectively. The simulation results of the ablation experiments show that classification prediction is an effective strategy to improve the forecasting accuracy of day-ahead PV output, which can be further improved by adding irradiance correction and weather mode reliability prediction modules. Full article

The maritime shipping sector is a major contributor to greenhouse gas emissions, particularly in coastal regions. In response, the adoption of electric ferries powered by renewable energy and supported by battery storage technologies has emerged as a viable decarbonization pathway. This study investigates the operational impacts of coordinated electric ferry charging on a medium-voltage distribution network at Gladstone Marina, Queensland, Australia. Using DIgSILENT PowerFactory integrated with MATLAB Simulink and a Python-based control system, four proposed ferry terminals equipped with BESSs (Battery Energy Storage Systems) are simulated. A dynamic model of BESS operation is optimized using a balanced hybrid metaheuristic algorithm combining GA-PSO-BFO (Genetic Algorithm-Particle Swarm Optimization-Bacterial Foraging Optimization). Simulations under 50% and 80% transformer loading conditions assess the effects of charge-only versus charge–discharge strategies. Results indicate that coordinated charge–discharge control improves voltage stability by 1.0–1.5%, reduces transformer loading by 3–4%, and decreases feeder line loading by 2.5–3.5%. Conversely, charge-only coordination offers negligible benefits. Further, quasi-dynamic analyses validate the system’s enhanced stability under coordinated energy management. These findings highlight the potential of docked electric ferries, operating under intelligent control, to act as distributed energy reserves that enhance grid flexibility and operational efficiency. Full article

Smart-grid technologies are essential to achieving sustainable high-level grid resilience. Integrating sensors and monitoring devices throughout grid infrastructure provides additional data on weather-related parameters in real-time, enabling the smart grid to respond appropriately to inclement weather and its associated challenges. The recording of all these data associated with each extreme weather event helps in the study and development of methodological tools for decision-making on issues of restoration and modification of the electricity network, with a view to enhancing its resilience and consequently ensuring the uninterrupted supply of electricity, even during the occurrence of these weather phenomena. This article focuses on enabling the utilization of meteorological data archives of past events, which demonstrate that natural disasters and extreme weather phenomena nowadays require network designs that can cope with the more frequent occurrence (high frequency) of events that have a significant impact (high impact) on the smooth operation of the network. Full article

As electric vehicle (EV) adoption grows, efficient and accessible charging infrastructure is essential. This paper introduces a modular simulation environment to evaluate charging point configurations and operational strategies. The simulation incorporates detailed models of electrical consumers and user behaviour, leveraging real-world data to simulate charging scenarios. A rule-based control strategy is applied to assess six configurations for a supermarket parking lot charging point. Key findings include the highest profit being achieved with two fast chargers. In scenarios with a 50 kW grid connection limit, combining fast chargers with stationary battery storage proves effective. Conversely, mobile charging robots generate lower revenue, though grid peak limitations have minimal impact. The study highlights the potential of the simulation environment to optimise charging layouts, refine operational strategies, and develop energy management algorithms. This work demonstrates the utility of the simulation framework for analyzing diverse charging solutions, offering insights into cost efficiency and user satisfaction. The results emphasise the importance of tailored strategies to balance grid constraints, profitability, and user needs, paving the way for intelligent EV charging infrastructure development. Full article

This study provides a comparative analysis of the environmental and economic performance of Oman, Egypt, and Morocco, focusing on the critical interplay between their economic structures, governance frameworks, and energy policies. Morocco stands out as a regional leader in renewable energy, driven by significant investments in solar, wind, and hydroelectric projects, positioning itself as a model for clean energy transition. Egypt, despite its rapid industrialization and urbanization, faces mounting environmental pressures that challenge its economic diversification efforts. Oman, heavily dependent on hydrocarbons, confronts significant sustainability risks due to its reliance on fossil fuels, despite the political stability that could support renewable integration. The research underscores that while these nations share common challenges, including regulatory weaknesses and energy policy inconsistencies, their distinct economic contexts demand tailored approaches. Morocco’s path to energy leadership must focus on integrating renewables across all sectors, enhancing grid infrastructure, and expanding green technology innovations to maintain momentum. Egypt should prioritize scaling up renewable infrastructure, reducing dependency on fossil fuels, and investing in clean technology to address its carbon footprint. For Oman, the strategic diversification of its economy, combined with aggressive renewable energy integration, is critical to reducing CO₂ emissions and mitigating climate impacts. This study contributes novel insights by highlighting the role of political stability, institutional quality, and policy coherence as critical enablers of long-term sustainability. It also identifies the importance of regional cooperation and knowledge sharing to overcome shared challenges like data limitations, geopolitical complexities, and methodological gaps in sustainability assessments. The findings advocate for a multi-method approach, integrating economic modeling, life-cycle analysis, and policy evaluation, to guide future sustainability efforts and foster resilient, low-carbon economies in the MENA region. Full article