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        <title>Frontiers in Smart Grids | Smart Grid Technologies section | New and Recent Articles</title>
        <link>https://www.frontiersin.org/journals/smart-grids/sections/smart-grid-technologies</link>
        <description>RSS Feed for Smart Grid Technologies section in the Frontiers in Smart Grids journal | New and Recent Articles</description>
        <language>en-us</language>
        <generator>Frontiers Feed Generator,version:1</generator>
        <pubDate>2026-05-11T22:42:36.410+00:00</pubDate>
        <ttl>60</ttl>
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        <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.2025.1612770</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.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.2024.1356074</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.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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