Frontiers in Smart Grids最新文献

The role of data-driven methods in power system security assessment from aggregated grid data 数据驱动方法在利用电网汇总数据进行电力系统安全评估中的作用

Frontiers in Smart Grids Pub Date : 2024-05-20 DOI: 10.3389/frsgr.2024.1385367

F. De Caro, G. Giannuzzi, C. Pisani, Silvia Iuliano, Alfredo Vaccaro

{"title":"The role of data-driven methods in power system security assessment from aggregated grid data","authors":"F. De Caro, G. Giannuzzi, C. Pisani, Silvia Iuliano, Alfredo Vaccaro","doi":"10.3389/frsgr.2024.1385367","DOIUrl":"https://doi.org/10.3389/frsgr.2024.1385367","url":null,"abstract":"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.","PeriodicalId":480489,"journal":{"name":"Frontiers in Smart Grids","volume":"14 22","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141119997","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Bidding strategies for multi-microgrid markets taking into account risk indicators 考虑风险指标的多微网市场竞标策略

Frontiers in Smart Grids Pub Date : 2024-04-25 DOI: 10.3389/frsgr.2024.1353732

Xiangyv Lv, Chenglong Qi, Xiu Ji, Jiqing Yv, Hui Wang, Huanhuan Han

{"title":"Bidding strategies for multi-microgrid markets taking into account risk indicators","authors":"Xiangyv Lv, Chenglong Qi, Xiu Ji, Jiqing Yv, Hui Wang, Huanhuan Han","doi":"10.3389/frsgr.2024.1353732","DOIUrl":"https://doi.org/10.3389/frsgr.2024.1353732","url":null,"abstract":"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.","PeriodicalId":480489,"journal":{"name":"Frontiers in Smart Grids","volume":"57 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140656429","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A review of machine learning applications in power system protection and emergency control: opportunities, challenges, and future directions 回顾机器学习在电力系统保护和紧急控制中的应用：机遇、挑战和未来方向

Frontiers in Smart Grids Pub Date : 2024-04-09 DOI: 10.3389/frsgr.2024.1371153

Gayashan Porawagamage, K. Dharmapala, J. S. Chaves, Daniel Villegas, Athula Rajapakse

{"title":"A review of machine learning applications in power system protection and emergency control: opportunities, challenges, and future directions","authors":"Gayashan Porawagamage, K. Dharmapala, J. S. Chaves, Daniel Villegas, Athula Rajapakse","doi":"10.3389/frsgr.2024.1371153","DOIUrl":"https://doi.org/10.3389/frsgr.2024.1371153","url":null,"abstract":"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.","PeriodicalId":480489,"journal":{"name":"Frontiers in Smart Grids","volume":"61 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140722644","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Identification of harmonic sources in smart grid using systematic feature extraction from non-active powers 利用非有功功率的系统特征提取识别智能电网中的谐波源

Frontiers in Smart Grids Pub Date : 2024-01-31 DOI: 10.3389/frsgr.2024.1338774

S. Joga, P. Sinha, Kaushik Paul, Satyabrata Sahoo, Samita Rani Pani, Geetanjali Dei, T. Ustun

{"title":"Identification of harmonic sources in smart grid using systematic feature extraction from non-active powers","authors":"S. Joga, P. Sinha, Kaushik Paul, Satyabrata Sahoo, Samita Rani Pani, Geetanjali Dei, T. Ustun","doi":"10.3389/frsgr.2024.1338774","DOIUrl":"https://doi.org/10.3389/frsgr.2024.1338774","url":null,"abstract":"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.","PeriodicalId":480489,"journal":{"name":"Frontiers in Smart Grids","volume":"85 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140478343","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Model predictive control–based robust-control strategy of distribution control for a grid-connected AC microgrid 基于模型预测控制的并网交流微电网鲁棒控制配电控制策略

Frontiers in Smart Grids Pub Date : 2023-12-11 DOI: 10.3389/frsgr.2023.1188074

James Xorse Halivor

{"title":"Model predictive control–based robust-control strategy of distribution control for a grid-connected AC microgrid","authors":"James Xorse Halivor","doi":"10.3389/frsgr.2023.1188074","DOIUrl":"https://doi.org/10.3389/frsgr.2023.1188074","url":null,"abstract":"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.","PeriodicalId":480489,"journal":{"name":"Frontiers in Smart Grids","volume":"53 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138979242","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0