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        <title>Frontiers in Smart Grids | New and Recent Articles</title>
        <link>https://www.frontiersin.org/journals/smart-grids</link>
        <description>RSS Feed for Frontiers in Smart Grids | New and Recent Articles</description>
        <language>en-us</language>
        <generator>Frontiers Feed Generator,version:1</generator>
        <pubDate>2026-04-08T15:30:17.318+00:00</pubDate>
        <ttl>60</ttl>
        <item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsgr.2026.1623347</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsgr.2026.1623347</link>
        <title><![CDATA[Analysis of theoretical line loss in metro distribution network]]></title>
        <pubdate>2026-03-12T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Ying Zhao</author><author>Zhe Chen</author><author>Zhuo Zhang</author><author>Yuxin Lu</author><author>Yun Zhao</author><author>Xipeng Liu</author>
        <description><![CDATA[IntroductionNovel cable traction power supply can eliminate phase splits and improve regenerative energy utilization in metro systems. However, the cable-based traction network has a relatively complex structure, making internal energy consumption and train-network line loss evaluation insufficiently studied.MethodsTo quantify train-network line losses, an equivalent solid-circuit model of the traction power supply system is established using an external cascade strategy. Combined with actual train operation load modeling, the traction transformer output power and network losses are computed and compared under both traction and regenerative braking operating conditions.ResultsThe proposed modeling-and-calculation framework enables accurate evaluation of power output and line losses across operating modes, and effectively captures the electrical characteristics of the cable traction energy supply system.DiscussionThe developed theoretical line-loss calculation approach provides a quantitative basis for energy-consumption assessment and operational analysis of cable traction networks, supporting performance evaluation and planning of metro traction power supply systems.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsgr.2025.1632546</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsgr.2025.1632546</link>
        <title><![CDATA[Study on a simulation method for photovoltaic power output series based on the headroom model]]></title>
        <pubdate>2025-11-13T00:00:00Z</pubdate>
        <category>Methods</category>
        <author>Hong Dong</author><author>Yuqun Gao</author><author>Liujun Hu</author><author>Yanna Gao</author><author>Yue Xing</author>
        <description><![CDATA[Existing photovoltaic (PV) output simulation methods often rely on artificial neural networks for short-term forecasting, and there has been a struggle to capture long-term patterns and stochastic fluctuations when using Markov Chain Monte Carlo techniques. To address these limitations, this paper proposes an improved headroom model-based approach that enhances traditional methods in three key aspects. First, unlike traditional headroom models that ignore temporal dependencies in output fluctuations, the approach integrates probabilistic distributions with soft sequential constraints to preserve time-dependent patterns. Second, whereas previous studies often overlooked seasonal weather variations, here PV output curves are classified into representative weather types and seasonally adaptive Markov chains are constructed to model radiation dynamics and transition probabilities. Third, to address the oversimplification of sunrise and sunset transitions, the method introduces a specialized statistical correction tailored to these critical periods. The method accurately models PV output patterns and fluctuations, demonstrating < 1% deviation in annual duration (4,121 h) and utilization (1,297 h), with a 7.80%−14.59% lower root mean square error and 10.27%−14.07% reduced mean absolute error vs. conventional methods. It efficiently generates realistic long-term sequences from limited data, enhancing the accuracy and efficiency of PV power sequence simulation.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsgr.2025.1617763</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsgr.2025.1617763</link>
        <title><![CDATA[CatBoost-enhanced convolutional neural network framework with explainable artificial intelligence for smart-grid stability forecasting]]></title>
        <pubdate>2025-11-03T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Stephanie Ness</author>
        <description><![CDATA[IntroductionMobile robots increasingly support inspection and emergency response in smart-grid infrastructure but require accurate, interpretable backend diagnostics. This work is proposing a hybrid model that integrates CatBoost (for tabular features) with a deep 1D-CNN (for spatial feature extraction) and integrates Local Interpretable Model-agnostic Explanations (LIME) to provide transparent, instance-level rationales.MethodsWe evaluate on a synthetic DSGC-based stability dataset (14 features) and externally on the IEEE PES 2018 fault-clearing corpus. The hybrid concatenates CatBoost output probabilities with a three-layer CNN feature vector, followed by dense layers (ReLU and Sigmoid). Models are trained using the Adam optimizer. Performance is reported via Accuracy, Precision, Recall, F1, confusion matrices, ROC-AUC, and LIME explanations.ResultsOn the generated synthetic data, the hybrid achieved 98.23% accuracy (F1 = 97.56%), outperforming ANN, DNN and CNN baselines. External validation on IEEE PES 2018 yielded F1 = 97.6%.DiscussionCombining gradient-boosted trees with deep convolutional features improves discrimination while and it is preserving local explainability. This way it can be supporting both grid operations and stability-aware robotic mission planning. Future work will extend to multiclass/regression settings and compare XAI methods (e.g., SHAP) alongside additional tabular learners (XGBoost/LightGBM).]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsgr.2025.1612770</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsgr.2025.1612770</link>
        <title><![CDATA[Research on short-term line loss rate prediction method of distribution network based on RF-CNN-LSTM]]></title>
        <pubdate>2025-08-18T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Lin Jiang</author><author>Chen Li</author><author>Wei Qiu</author><author>Caili Xiang</author><author>Jiawei Yang</author><author>Jun Shu</author>
        <description><![CDATA[Under the background of the new distribution network, the power fluctuation on the line is increasing, which leads to more uncertainties in the predicted line loss rate, thus affecting the economic benefits of the power grid. In order to reduce the prediction error of short-term line loss rate and improve its prediction accuracy, this paper studies a short-term line loss rate prediction method of distribution network based on RF-CNN-LSTM. Firstly, this paper comprehensively considers the influence of various uncertain factors on the accuracy of prediction results. Aiming at the characteristics of high-dimensional time series of line loss rate data, a random forest (RF) algorithm is proposed to analyze the importance of multiple characteristic variables affecting line loss rate. Then, this paper constructs a combined model of convolutional neural network and long short-term memory network (CNN-LSTM) to predict line loss rate. Finally, in order to verify the accuracy of the prediction results, this paper sets up a support vector machine algorithm for synchronous prediction as a comparative experiment. The experimental results show that the prediction results of the proposed prediction method are more accurate.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsgr.2025.1554251</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsgr.2025.1554251</link>
        <title><![CDATA[Electric vehicle scheduling strategy based on dynamic adjustment mechanism of time-of-use price]]></title>
        <pubdate>2025-05-16T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Yang Liu</author><author>Haidong Yu</author><author>Feng Wang</author><author>Min Huang</author><author>Junqing Shi</author><author>Wenbin Liu</author><author>Ying Wu</author><author>Lisheng Li</author><author>Minglin Liu</author>
        <description><![CDATA[As the grid-connected capacity of distributed photovoltaic (PV), energy storage, electric vehicle (EV), and other devices gradually increases, new source-load equipment becomes an important demand response (DR) resource in the distribution network (DN). To fully utilize the DR's capability for EVs and other devices and reduce the system operating costs and line network loss, this article presents a DR scheduling strategy for EVs based on a time-of-use (TOU) price dynamic adjustment mechanism. First, a fuzzy C-mean (FCM) clustering algorithm is used to calculate the typical operating curves of PV and electrical load and their optimal number of classifications. The deterministic scenarios express the PV's output characteristics and the users' electricity consumption characteristics. Second, a dynamic adjustment mechanism of TOU price is proposed based on the load operation curve of the DN, and the interactive price-incentive signal for DR within the DN is formulated. Finally, a DR scheduling strategy for EVs in the DN that considers the economic cost of system operation and line network loss is proposed. CPLEX in MATLAB is employed to simulate the cases. After applying the TOU price dynamic adjustment mechanism proposed, the peak total load and peak–valley load difference decreased by 6.9% and 33.8%, respectively, compared to implementing fixed electricity prices. At the same time, the operating revenue of the distribution network increased by 13.2%, and the line network loss decreased by 12.9%. The analysis results demonstrate that the proposed EV DR scheduling strategy can realize the price guidance and orderly scheduling of EVs and reduce the operation cost and line network loss in the DN.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsgr.2025.1488468</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsgr.2025.1488468</link>
        <title><![CDATA[Integration of electric vehicles in electricity-carbon market toward eco-transport futures]]></title>
        <pubdate>2025-01-30T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Zhidong Wang</author><author>Fuyuan Yang</author><author>Shuying Lai</author><author>Yuechuan Tao</author><author>Jiawei Zhang</author>
        <description><![CDATA[Integrating electric vehicles (EVs) into the electricity and carbon markets presents a promising pathway toward sustainable transportation futures. This article proposes a comprehensive framework that synergizes the operations of the electricity and carbon markets with the growing adoption of EVs. The proposed framework includes a low-carbon transmission network operation model that integrates the electricity and carbon markets, facilitating optimal energy dispatch while minimizing carbon emissions. In addition, the framework extends to distribution network operations, incorporating a double carbon taxation mechanism to address emissions at both the generation and consumption levels. A carbon emission flow model is employed to meticulously trace carbon emissions across the supply chain, enhancing transparency and accountability. The framework also introduces an EV-integrated traffic flow model that captures the interactions between transportation networks and energy demand, influencing traffic dynamics and EV charging behaviors. Furthermore, a planning and pricing model for EV charging stations is developed, incorporating carbon costs into the pricing strategy to incentivize eco-friendly practices. The multilevel solution algorithm ensures an iterative convergence of decision variables across transmission, distribution, and transportation networks, ultimately fostering an integrated eco-transport system. This work contributes to the development of sustainable transport systems by promoting efficient EV integration and supporting decarbonization efforts in both the energy and transportation sectors.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsgr.2024.1505351</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsgr.2024.1505351</link>
        <title><![CDATA[A multi-stage balancing scheduling method for flexible loads of large-scale electric vehicles]]></title>
        <pubdate>2024-12-03T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Songling Pang</author><author>Hailong Zhao</author><author>Meiyi Huo</author>
        <description><![CDATA[To address the degradation of grid quality and charging efficiency associated with the large-scale integration of electric vehicles (EVs), a multi-stage balanced flexible load scheduling method is proposed. This approach is designed to facilitate peak shaving and valley filling, balance intermittent energy fluctuations, and provide auxiliary services, thereby significantly altering system load characteristics, smoothing energy fluctuations, reducing operational costs, and enhancing the regulatory capabilities of power grid dispatching operations. A multi-objective optimization mathematical model is developed, focusing on key indicators that impact the scheduling process, including network loss, operational cost, and user satisfaction. A multi-stage flexible load scheduling framework is introduced within the competitive swarm optimization (CSO) algorithm, resulting in the design of an advanced CSO algorithm. This algorithm is distinguished from traditional methods by the implementation of advanced learning based on grouping after a random competitive learning phase, which enhances the efficiency of particle swarm learning while ensuring stable population convergence throughout the optimization process. Furthermore, the CSO framework is maintained to ensure effective population diversity, greatly improving the optimization performance. Simulation results indicate that the voltage fluctuation index of the proposed algorithm is 1.8% lower than that of the standard CSO algorithm, while network loss and operational costs are reduced by 2.83 and 5.81%, respectively, thereby validating the effectiveness and efficiency of the proposed approach.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsgr.2024.1476695</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsgr.2024.1476695</link>
        <title><![CDATA[Study on improved control strategy of virtual synchronous generator]]></title>
        <pubdate>2024-11-05T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Ji Li</author><author>Fan Yang</author><author>Huanmin Wang</author><author>Bingqing Chu</author>
        <description><![CDATA[Virtual synchronous generator (VSG) control technology for photovoltaic, energy storage, wind power, and other new energy to provide flexibility in the grid interface characteristics, is conducive to improving the stability of the power system, and has been widely considered by many scholars. Firstly, an improved VSG control method is proposed through simulation and analysis, which realizes the complete decoupling of the frequency response time constant and the inertia quantity of the active power control loop and reduces the complexity of the parameter design of the VSG system. Secondly, to avoid the frequent action of the VSG system caused by small-scale frequency change perturbation, this study proposes a VSG frequency optimization control method for VSG frequency control with rated angular velocity ωset feedforward composed of multivariate factors when considering a primary frequency regulation dead zone. Thirdly, the impact of VSG parameter design on the system is investigated through the system response characteristics of power scheduling, primary frequency regulation at the grid connection, and the small-signal dynamic characterization of the improved VSG. Finally, Simulation and experimental verification yielded an active power overshoot of 7% and a maximum frequency deviation of 0.17 Hz for the improved system. The improved control method resulted in an improvement of 0.1 s in the frequency response time and a reduction of 0.15 Hz in the oscillation amplitude. The response speed of the improved control method is much better, while the oscillation amplitude is reduced to meet the grid's regulation requirements. The simulation and experimental analysis verify the feasibility of the improved VSG control method.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsgr.2024.1462460</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsgr.2024.1462460</link>
        <title><![CDATA[Editorial: Horizons in smart grids]]></title>
        <pubdate>2024-10-30T00:00:00Z</pubdate>
        <category>Editorial</category>
        <author>Nikos Hatziargyriou</author>
        <description></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsgr.2024.1449152</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsgr.2024.1449152</link>
        <title><![CDATA[Smooth control strategy for emergency switching of multi-port flexible interconnected distribution system modes]]></title>
        <pubdate>2024-08-20T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Dan Pang</author><author>Yu Yin</author><author>Zhipeng Wang</author><author>Jinming Ge</author><author>Wei Wang</author><author>Zhenhao Wang</author><author>Hongyin Yi</author><author>Yan Zhuang</author>
        <description><![CDATA[IntroductionWith the rise of distributed energy resources, the interconnection of distribution networks and Flexible Multi-State Switch (FMSS) has become a key technology in the construction of new distribution networks. FMSS plays a significant role in enhancing the reliability and flexibility of the system.MethodsThis paper investigates the impact of mode-switching in FMSSs on voltage shocks, current shocks, and power fluctuations in the event of a feeder fault in a multi-port flexible interconnected distribution system. Firstly, an improved state-tracking control method is proposed to effectively mitigate these impacts. Secondly, for feeder faults connected to the fixed DC bus voltage (Udc-Q) control port, a reselection method for the Udc-Q control port at the main station is introduced, aiming to select the optimal port to maintain the stability of the DC bus voltage.ResultsSimulation experiments have validated the effectiveness of the proposed control method in reducing voltage and current shocks as well as power fluctuations. Additionally, the proposed control strategy has demonstrated an enhancement in the safe and stable operation of the system under emergency fault conditions.DiscussionThe control strategy presented in this paper is capable of addressing the challenges posed by feeder faults and ensuring the stability of the DC bus voltage and reliable power supply to feeder loads, thereby enhancing the overall performance and reliability of the flexible interconnected distribution system. The research findings have been verified on the MATLAB/Simulink simulation platform.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsgr.2024.1385367</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsgr.2024.1385367</link>
        <title><![CDATA[The role of data-driven methods in power system security assessment from aggregated grid data]]></title>
        <pubdate>2024-05-20T00:00:00Z</pubdate>
        <category>Methods</category>
        <author>Fabrizio De Caro</author><author>Giorgio Maria Giannuzzi</author><author>Cosimo Pisani</author><author>Silvia Iuliano</author><author>Alfredo Vaccaro</author>
        <description><![CDATA[Data-driven techniques have been considered as an enabling technology for reducing the computational burden of both static and dynamic power system security analysis. Anyway, the studies reported in the literature mainly focused on inferring from historical data the mapping between the bus variables before and after a certain contingencies set, while, to the best of the Author's knowledge, limited contributions have been devoted to try and classify the power system security state by processing aggregated grid data. This is a relevant issue to address for a Transmission System Operator since it could allow a sensible decrease in the computational burden and, considering that aggregated grid data can be reliably predicted from several hours to one day ahead, it may enable the evolution of security assessment to security forecasting. In trying and filling this research gap, this paper explores the role of machine learning and feature selection algorithms. A realistic case study involving 2 years of synthetic grid data simulated on the Italian power system model against future potential operational scenarios characterized by a high share of renewables is presented and discussed to identify the most promising computing paradigms, analyzing the criticality of tuning the feature selection and classifier algorithms.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsgr.2024.1397380</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsgr.2024.1397380</link>
        <title><![CDATA[Cyber resilience methods for smart grids against false data injection attacks: categorization, review and future directions]]></title>
        <pubdate>2024-05-03T00:00:00Z</pubdate>
        <category>Mini Review</category>
        <author>Andrew D. Syrmakesis</author><author>Nikos D. Hatziargyriou</author>
        <description><![CDATA[For a more efficient monitoring and control of electrical energy, the physical components of conventional power systems are continuously integrated with information and communication technologies, converting them into smart grids. However, energy digitalization exposes power systems into a wide range of digital risks. The term cyber resilience for electrical grids expands the conventional resilience of power systems, which mainly refers to extreme weather phenomena. Since this is a relatively new term, there is a need for the establishment of a solid conceptual framework. This paper analyzes and classifies the state-of-the-art research methodologies proposed for strengthening the cyber resilience of smart grids. To this end, the proposed work categorizes the cyberattacks against smart grids, identifies the vulnerable spots of power system automation and establishes a common ground about the cyber resilience. The paper concludes with a discussion about the limitations of the proposed methods in order to extract useful suggestions for future directions.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsgr.2024.1353732</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsgr.2024.1353732</link>
        <title><![CDATA[Bidding strategies for multi-microgrid markets taking into account risk indicators]]></title>
        <pubdate>2024-04-25T00:00:00Z</pubdate>
        <category>Methods</category>
        <author>Xiangyv Lv</author><author>Chenglong Qi</author><author>Xiu Ji</author><author>Jiqing Yv</author><author>Hui Wang</author><author>Huanhuan Han</author>
        <description><![CDATA[A large proportion of new energy generation is integrated into the power grid, making it difficult for the power grid system to maintain reliable, stable, and efficient operation. To address these challenges, this article proposes a multiple microgrid hierarchical optimization structure based on energy routers as the core equipment for energy regulation within microgrids. Considering the uncertainty of renewable energy generation within microgrids, a two-layer energy management bidding strategy based on risk indicators is further proposed. In the process of trading, with the goal of maximizing a comprehensive economy, the energy trading model of the distribution network center and energy routers is divided into two sub-objectives for solving. In the first stage, based on the interests and energy supply and demand relationships of each microgrid, a risk assessment model considering wind and solar uncertainty is established to determine the risk preferences and expected returns of each microgrid. In the second stage, the original problem is decomposed into two sub-problems: the minimum cost sub-problem and the maximum transaction volume sub-problem. An asymmetric bargaining mechanism is adopted to determine the production and sales payment of the microgrid containing energy routers based on the contribution values of energy routers in each microgrid. Finally, the rationality and effectiveness of energy routers as an intelligent decision-maker in energy optimization are verified in a three-node system.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsgr.2024.1356074</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsgr.2024.1356074</link>
        <title><![CDATA[A supervisory Volt/Var control scheme for coordinating voltage regulators with smart inverters on a distribution system]]></title>
        <pubdate>2024-04-16T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Valliappan Muthukaruppan</author><author>Yue Shi</author><author>Mesut Baran</author>
        <description><![CDATA[This paper concentrates on the efficient utilization of smart inverters for Volt/Var control (VVC) within a distribution system. Although new smart inverters possess Var support capability, their effective deployment necessitates coordination with existing Volt/Var schemes. To address this, a novel VVC scheme is proposed to facilitate such synchronization. The proposed scheme bifurcates the issue into two levels. The initial level involves utilizing Load Tap Changer (LTC) and Voltage Regulators (VRs), coordinating their control with smart inverters to regulate the circuit's voltage levels within the desired range. The subsequent level determines the Var support required from smart inverters to minimize overall power loss in the circuit. The supervisory control results are communicated to the respective devices equipped with their local controllers. To minimize frequent dispatch, smart inverters are supervised by adjusting their Volt/Var characteristics as necessary. This approach enables the smart inverters to operate near their optimal control while meeting the limited communication prerequisites in a distribution system. A case study employing the IEEE 34 bus system illustrates the efficacy of this supervisory control scheme in contrast to traditional Volt/Var schemes.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsgr.2024.1371153</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsgr.2024.1371153</link>
        <title><![CDATA[A review of machine learning applications in power system protection and emergency control: opportunities, challenges, and future directions]]></title>
        <pubdate>2024-04-09T00:00:00Z</pubdate>
        <category>Review</category>
        <author>Gayashan Porawagamage</author><author>Kalana Dharmapala</author><author>J. Sebastian Chaves</author><author>Daniel Villegas</author><author>Athula Rajapakse</author>
        <description><![CDATA[Modern power systems, characterized by complex interconnected networks and renewable energy sources, necessitate innovative approaches for protection and control. Traditional protection schemes are often failing to harness the vast data generated by modern grid systems and are increasingly found inadequate and challenging for some applications. Recognizing the need to address these issues, this paper explores data-driven solutions, focusing on the potential of machine learning (ML) in power system protection and control. It presents a comprehensive review highlighting various applications which are challenging to address from conventional methods. Despite its promise, the integration of ML into power system protection introduces unique challenges. These challenges are examined in the paper, and suggestions are provided to overcome them. Furthermore, the paper identifies potential future research directions, reflecting the progressive trends in ML and its relevance to power system protection and control. This review thereby serves as an essential resource for practitioners and researchers working at the intersection of ML and power systems.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsgr.2024.1338774</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsgr.2024.1338774</link>
        <title><![CDATA[Identification of harmonic sources in smart grid using systematic feature extraction from non-active powers]]></title>
        <pubdate>2024-01-31T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>S. Ramana Kumar Joga</author><author>Pampa Sinha</author><author>Kaushik Paul</author><author>Satyabrata Sahoo</author><author>Samita Rani Pani</author><author>Geetanjali Dei</author><author>Taha Selim Ustun</author>
        <description><![CDATA[The paper introduces a novel method for identifying the location of harmonic-generating sources in smartgrids. The method utilizes a Dual-Tree Complex Wavelet Transform (DTCWT) of voltage and current signals measured at a specific point in the network. By applying DTCWT Transform, the signals are decomposed, and three non-active power quantities are extracted to represent the harmonic components within the system exclusively. These chosen non-active power quantities serve as indicators of the presence of harmonics in the system. Through analysis and comparison of these quantities, the method enables determining the precise location of the dominant harmonic generating source. This information is valuable for effectively addressing and mitigating harmonic issues in the network. Leveraging DTCWT and focusing on non-active power quantities provides a valuable tool for power system engineers and operators to diagnose and mitigate harmonic issues, ultimately improving power quality and system performance. This study presents a new feature extraction method to compute Non-active power quantities based on DTCWT due to its shift-invariant property.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsgr.2023.1188074</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsgr.2023.1188074</link>
        <title><![CDATA[Model predictive control–based robust-control strategy of distribution control for a grid-connected AC microgrid]]></title>
        <pubdate>2023-12-11T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>James Xorse Halivor</author>
        <description><![CDATA[The challenge of ensuring the reliable running of power systems has gotten more difficult in recent years due to the rising complexity of power system networks. The decreasing accessibility of fossil fuels has necessitated a greater dependence on renewable energy sources, such as solar systems, wind power, and hydroelectric power, by the international community. As a result, there is an increasing demand for AC microgrids to offer an effective approach for distributing power. The power system networks that consist of microgrids frequently have a significant number of failures, surpassing 80%. These failures occur because microgrids are susceptible to unexpected changes in different distributed generating sources. The variations greatly impair the operating efficiency of the microgrid and have negative consequences for the distribution system. The microgrid consists of numerous dispersed generation units and local loads. The load in a microgrid exhibits parametric uncertainty, which adds to the fluctuation observed in its performance. The formulated control strategy is Model Predictive Control, which aims to achieve robust performance even in the presence of unaccounted-for loads, dynamic loads, harmonic loads, and both balanced and unbalanced loads. The authors of this paper have developed a control approach that utilizes model predictive control (MPC) and is characterized by its robustness and optimality. MPC has the ability to predict the future behavior of a certain system. The controller successfully mitigates and reduces any disruptions that may occur within the power distribution system by taking into account its healthy characteristics. The model is implemented in the MATLAB Simulink environment, where it produces an accurate and appropriate total harmonic distortion value. The model was compared to previous efforts and significantly improved by increasing some crucial parameters by up to 90%. The value functions as a measure of the controller's performance quality and the improved efficiency of the microgrid system.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsgr.2023.1241963</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsgr.2023.1241963</link>
        <title><![CDATA[Control and optimization algorithm for lattice power grids with multiple input/output operation for improved versatility]]></title>
        <pubdate>2023-08-17T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Daniel Zhang</author><author>Jingyang Fang</author><author>Stefan Goetz</author>
        <description><![CDATA[With the proliferation of alternative energy sources, power grids are increasingly dominated by grid-tied power converters. With this development comes the requirement of grid-forming, but current architectures exclude high-voltage applications through serial connectivity. Lattice power grids allow for the generation of both higher voltages and currents than their individual modules by marrying the advantages of serial and parallel connectivity, which include reduced switching and conduction losses, sensorless voltage balancing, and multiport operation. We use graph theory to model lattice power grids and formalize lattice generation processes for square, triangular, and hexagonal lattice grids. This article proposes depth-first-search based algorithms for the control and efficient operation of lattice power grids, achieving voltage and current objectives while minimizing switching losses. Furthermore, we build upon previous algorithms by harnessing multiple input/output operation. The algorithm allows for sequential operation (in which loads are added one by one), simultaneous operation (in which several loads are added at the same time), and combined sequential-simultaneous operation. These methods were applied to a variety of lattice structures, and simulations of dc analysis and pulse train generation were performed. These modeled results validate the proposed algorithms and improve versatility in the operation of lattice power grids in both grid-connected and standalone applications. The potential of applying this method in transcranial magnetic stimulation (TMS) is discussed.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsgr.2023.1129541</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsgr.2023.1129541</link>
        <title><![CDATA[Research on the flow characteristics identification of steam turbine valve based on FCM-LSSVM]]></title>
        <pubdate>2023-03-09T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Xiaoguang Hao</author><author>Fei Jin</author><author>Bin Wang</author><author>Qinghao Zhang</author><author>Chuang Wu</author><author>Hao Sun</author>
        <description><![CDATA[Due to aging and deformation of the through-flow path and system modifications, the flow characteristics of the turbine inlet valve often deviate from the design value, which affects the unit load control accuracy and operational stability. In order to obtain the actual valve flow characteristics of the turbine and thus improve the FM performance, an FCMLSSVM model is proposed in this paper to identify the valve flow characteristics. First, FCM clustering is proposed to classify the historical operating data of the plant and obtain a wide range of variable operating conditions. Then, using least squares support vector machine (LSSVM), the relationship between turbine input and output variables was modeled in each condition cluster, with integrated valve position command, speed, and real power generated as input variables and actual steam inlet flow as output variables. Using a 330 MW turbine unit as an application example, the established FCM-LSSVM model was validated for the valve flow characteristics of the turbine. The results show that the model can obtain accurate valve flow characteristics without conducting tests on the turbine. The method can save a lot of labor and material resources in doing the characteristic test, and after comparison, the proposed method can identify the flow characteristics more accurately among the existing neural network identification methods, which can provide technical support to improve the unit frequency regulation characteristics and improve the accuracy of valve operation.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsgr.2022.1110871</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsgr.2022.1110871</link>
        <title><![CDATA[Optimal configuration of grid-side energy storage considering static security of power system]]></title>
        <pubdate>2023-01-12T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Xin Tian</author><author>Long Zhao</author><author>Chenjie Tong</author><author>Xiangfei Meng</author><author>Qibin Bo</author><author>Yubing Chen</author><author>Nian Liu</author>
        <description><![CDATA[The large-scale access of distributed sources to the grid has brought great challenges to the safe and stable operation of the grid. At the same time, energy storage equipment is of great importance to effectively enhance the consumption of renewable energy and ensure the safe and stable operation of the grid. This paper proposes a method for optimal allocation of grid-side energy storage considering static security, which is based on stochastic power flow analysis under semi-invariant method. Firstly,according to the load, wind power and photovoltaic probability model, a system stochastic power flow model is constructed. Furthermore, the fault probability and fault severity indicators are established from two dimensions of branch power flow and node voltage. And combine the fault probability and severity indicators to establish a static security assessment indicators system. Then, a grid-side energy storage planning model is constructed from the perspective of energy storage operators. Finally, an improved genetic algorithm is used to solve the two-stage planning and operation problem proposed in this paper, and simulation analysis is conducted based on the IEEE-30 node system. The results show that the energy storage configuration considering static security constraints can effectively reduce the fault probability and the severity of fault overlimit. The simulation and case study verify that the proposed energy storage allocation method can effectively improve the static security of the system.]]></description>
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