2024 4th International Conference on Smart Grid and Renewable Energy (SGRE)最新文献

A Novel Droop Control For Enhanced Performance of Grid-Forming Distributed Energy Resources 一种用于提高分布式能源并网性能的新型降压控制方法

2024 4th International Conference on Smart Grid and Renewable Energy (SGRE) Pub Date : 2024-01-08 DOI: 10.1109/SGRE59715.2024.10428956

Salman Harasis, Irfan Khan, Ahmad Massoud

{"title":"A Novel Droop Control For Enhanced Performance of Grid-Forming Distributed Energy Resources","authors":"Salman Harasis, Irfan Khan, Ahmad Massoud","doi":"10.1109/SGRE59715.2024.10428956","DOIUrl":"https://doi.org/10.1109/SGRE59715.2024.10428956","url":null,"abstract":"The growing demand for reliable distributed generation (DG) systems promoted multiple advancements in grid-forming (GFM) control strategies applied to distributed energy resources (DERs). This paper proposes a novel “logistic function-based droop control”, designed to improve the performance of GFM DERs. By utilizing the distinctive features of the logistic function that include an S-shaped curve, flexibility, and gradual ramp-down, the proposed control is designed to enable the flexible and reliable operation of DERs integrated into a microgrid. This control utilizes the inherent benefits of logistic functions to address the challenges associated with conventional droop control with minimal control complexity, particularly, during transient conditions and sudden load changes. Through comprehensive analysis and simulations, the efficacy of the proposed “Logistic function-based droop control” is demonstrated in comparison to the traditional droop control. The results show the effectiveness of the proposed droop in substantially enhancing the operation of GFM DERs.","PeriodicalId":518478,"journal":{"name":"2024 4th International Conference on Smart Grid and Renewable Energy (SGRE)","volume":"18 5","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2024-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140531609","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 Solid State Transformer-based Charging Station for Catering Heavy/Medium and Light Electric Vehicles 基于固态变压器的轻型/重型电动汽车充电站

2024 4th International Conference on Smart Grid and Renewable Energy (SGRE) Pub Date : 2024-01-08 DOI: 10.1109/SGRE59715.2024.10428728

Ruvini De Seram, Kushan Tharuka Lulbadda, Tarlochan S. Sidhu, Sheldon S. Williamson

{"title":"A Solid State Transformer-based Charging Station for Catering Heavy/Medium and Light Electric Vehicles","authors":"Ruvini De Seram, Kushan Tharuka Lulbadda, Tarlochan S. Sidhu, Sheldon S. Williamson","doi":"10.1109/SGRE59715.2024.10428728","DOIUrl":"https://doi.org/10.1109/SGRE59715.2024.10428728","url":null,"abstract":"Incorporating solid-state transformers into EV charging stations can greatly improve their power density, reliability, efficiency, etc. This paper presents an solid state transformer (SST)-based EV charging station that can cater to three different types of vehicles according to the charging power requirement; heavy, medium, and light electric vehicles. Also, the proposed charging station provides both AC on-board charging and DC off-board charging ports. A MATLAB model of the proposed system was developed and simulated to validate the feasibility. The simulation results verifies that the proposed 10 kV, 1 MVA charging station is capable of charging the all categories of electric vehicles from 20% state of charge (SoC) to 80% SoC within 17.8 mins using constant current charging strategy. Furthermore, one heavy duty electric vehicle which is 30% of the charging station capacity is disconnected to demonstrate a sudden EV demand drop and the total harmonic distortion (THD) of grid current is observed to be laid below 2.6% before and after the event of disconnecting ensuring the stability of the proposed system.","PeriodicalId":518478,"journal":{"name":"2024 4th International Conference on Smart Grid and Renewable Energy (SGRE)","volume":"32 1-2","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2024-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140531767","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

Forecasting EV Charging Demand: A Graph Convolutional Neural Network-Based Approach 预测电动汽车充电需求：基于图卷积神经网络的方法

2024 4th International Conference on Smart Grid and Renewable Energy (SGRE) Pub Date : 2024-01-08 DOI: 10.1109/SGRE59715.2024.10428726

S. Fahim, R. Atat, Cihat Keçeci, Abdulrahman Takiddin, Muhammad Ismail, Katherine R. Davis, E. Serpedin

{"title":"Forecasting EV Charging Demand: A Graph Convolutional Neural Network-Based Approach","authors":"S. Fahim, R. Atat, Cihat Keçeci, Abdulrahman Takiddin, Muhammad Ismail, Katherine R. Davis, E. Serpedin","doi":"10.1109/SGRE59715.2024.10428726","DOIUrl":"https://doi.org/10.1109/SGRE59715.2024.10428726","url":null,"abstract":"Electric vehicles (EVs) are expected to revolutionize the global transportation sector by promoting sustainability and eco-friendliness. The continuous proliferation of EVs requires an expansion of the existing charging infrastructure to meet the corresponding increase in electricity demand. Such an expansion requires an accurate forecasting of charging demand in both space and time domains for a well-planned allocation of charging stations (CSs). This paper proposes a Graph Convolutional Neural Networks (GCNN) based approach combined with a Long Short-Term Memory (LSTM) to predict the future charging demands of EVs. The proposed architecture fuses the benefits of both GCNN and LSTM to extract the underlying spatio-temporal features from the collected dataset. The training dataset reflects the coupling between the power and transportation systems, and thereby it helps the proposed deep learning architecture to capture the spatio-temporal patterns of the inter-connected environment. A comparative analysis is conducted with other state-of-the-art EV charging load prediction models to assess the prediction performance of the proposed load forecasting strategy.","PeriodicalId":518478,"journal":{"name":"2024 4th International Conference on Smart Grid and Renewable Energy (SGRE)","volume":"27 12","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2024-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140531950","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

Low-cost Digital Twin Design for Power Electronics using Deep Neural Networks 利用深度神经网络进行电力电子低成本数字孪生设计

2024 4th International Conference on Smart Grid and Renewable Energy (SGRE) Pub Date : 2024-01-08 DOI: 10.1109/SGRE59715.2024.10428716

N. Kamal, Ali Sharida, Sertac Bayhan, Hussein Alnuweiri, H. Abu-Rub

引用次数: 0

Scalable Light-weight Anomaly Detection for Data of Individual Smart Meters 针对单个智能电表数据的可扩展轻量级异常检测

2024 4th International Conference on Smart Grid and Renewable Energy (SGRE) Pub Date : 2024-01-08 DOI: 10.1109/SGRE59715.2024.10429010

Ahmad Al-Khateeb, N. Kamal, Hussein Alnuweiri, Sertac Bayhan, Mohammad B. Shadmand

引用次数: 0

Optimal Depth of Discharge for Electric Batteries with Robust Capacity-Shrinkage Estimator 利用稳健的容量收缩估计器优化蓄电池放电深度

2024 4th International Conference on Smart Grid and Renewable Energy (SGRE) Pub Date : 2024-01-08 DOI: 10.1109/SGRE59715.2024.10428801

Thomas A. Weber

{"title":"Optimal Depth of Discharge for Electric Batteries with Robust Capacity-Shrinkage Estimator","authors":"Thomas A. Weber","doi":"10.1109/SGRE59715.2024.10428801","DOIUrl":"https://doi.org/10.1109/SGRE59715.2024.10428801","url":null,"abstract":"A user’s benefit from the energy stored in a battery over its lifetime depends on the time-varying characteristics of the battery, which are in turn affected by the chosen usage behavior. Both the capacity shrinkage and the number of lifetime cycles are strongly impacted by the depth of discharge as a key decision variable. Available models of battery lifetime are rather complex and depend on many factors, such as temperature and other physical particularities, which complicate the user’s decision problem. We propose a simple, relatively robust approach for determining an optimal robust depth of discharge, based on a cycle-discharge curve and an unknown exponential capacity-shrinkage curve. We characterize the optimal robust depth of discharge and describe the implied performance guarantees. A relative cost of robustness is obtained as the boundary of the uncertainty set varies. The method provides an example of parameter estimation based on minimizing the relative regret caused by the implied decisions. In the special case where the cycle-discharge curve is exponential as well, we find a closed-form solution.","PeriodicalId":518478,"journal":{"name":"2024 4th International Conference on Smart Grid and Renewable Energy (SGRE)","volume":"40 3","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2024-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140532009","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

ANN-LSTM Based Tool For Photovoltaic Power Forecasting. 基于 ANN-LSTM 的光伏功率预测工具。

2024 4th International Conference on Smart Grid and Renewable Energy (SGRE) Pub Date : 2024-01-08 DOI: 10.1109/SGRE59715.2024.10428969

Younes Zahraoui, Tarmo Korõtko, Saad Mekhilef, A. Rosin

{"title":"ANN-LSTM Based Tool For Photovoltaic Power Forecasting.","authors":"Younes Zahraoui, Tarmo Korõtko, Saad Mekhilef, A. Rosin","doi":"10.1109/SGRE59715.2024.10428969","DOIUrl":"https://doi.org/10.1109/SGRE59715.2024.10428969","url":null,"abstract":"Renewable energy resources are currently being utilized in different sectors in order to meet the demands of energy, reduce environmental pollution, decrease the usage of fossil fuels, and achieve social and economic benefits for sustainable development. As the integration of renewable energy resources into electrical energy systems is expanding. Meanwhile, The development of applications for forecasting renewable energy output has also been progressing rapidly. For example, forecasting the power dispatch from the solar photovoltaic (PV) system. Therefore, an accurate forecast of the main factors of power generation became highly important in the case of large-scale power systems for intelligent match the power generation with the power demand for efficient energy management. Many applications for forecasting have been performed using artificial intelligence techniques. This paper proposed a framework for predicting PV power output using artificial neural network (ANN) and long short-term memory (LSTM) models-based tools. In this study, the meteorological and power parameters are applied as input variables. In addition, two levels of deep learning are applied to allow a deeper and more comprehensive exploration of the learning mechanism. The proposed framework is evaluated using a centralized unit for a real-time hardware-in-the-Ioop (HIL) test platform. The objective of this research, as indicated by the findings, is to enhance the practical engineering application of deep learning-based intelligent models for predicting PV power generation.","PeriodicalId":518478,"journal":{"name":"2024 4th International Conference on Smart Grid and Renewable Energy (SGRE)","volume":"12 5","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2024-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140531998","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

Analyzing Controller Performance in Single-Phase Inverters under Variable Conditions - Part I: Proportional-Integral, Sliding Mode and Model Predictive Control Approaches 分析变化条件下单相逆变器的控制器性能 - 第一部分：比例-积分、滑动模式和模型预测控制方法

2024 4th International Conference on Smart Grid and Renewable Energy (SGRE) Pub Date : 2024-01-08 DOI: 10.1109/SGRE59715.2024.10428847

Samet Ayik, Şevki Demirbaş, Ugur Fesli

引用次数: 0

Three-Phase Quaternary Multilevel Inverter for Two-Stage Grid-Connected Solar PV Application 用于两级并网太阳能光伏应用的三相四级多电平逆变器

2024 4th International Conference on Smart Grid and Renewable Energy (SGRE) Pub Date : 2024-01-08 DOI: 10.1109/SGRE59715.2024.10428688

Mohsin Karim Ansari, N. Tak, S. Chattopadhyay

引用次数: 0

Assessment of Offshore Wind Turbine Controls for Integration with Offshore Oil & Gas Platforms 评估近海风力涡轮机控制装置与近海油气平台的整合情况

2024 4th International Conference on Smart Grid and Renewable Energy (SGRE) Pub Date : 2024-01-08 DOI: 10.1109/SGRE59715.2024.10428829

João M. T. Amaral, J. S. Ramos, Emanuel L. van Emmerik, Robson F. S. Dias

{"title":"Assessment of Offshore Wind Turbine Controls for Integration with Offshore Oil & Gas Platforms","authors":"João M. T. Amaral, J. S. Ramos, Emanuel L. van Emmerik, Robson F. S. Dias","doi":"10.1109/SGRE59715.2024.10428829","DOIUrl":"https://doi.org/10.1109/SGRE59715.2024.10428829","url":null,"abstract":"This paper introduces a Controller Hardware-in-the-Loop (CHIL) setup to explore the integration of an Offshore Wind Power Plant (WPP) with an Oil & Gas platform. The platform is represented through a model of its power generation system, which comprises a Gas Turbine (GT) employing the Rowen Model. The loads are modeled using a passive diode rectifier and a controllable load. The WPP incorporates a Type-4 Wind Turbine Generator (WTG) equipped with a full-scale Back-to-Back (BTB) Converter and two distinct control philosophies. The first control philosophy follows the Grid-Following (GFL) approach, reminiscent of older control systems that tend to degrade power quality within power grids. The second philosophy employs a Grid-Forming (GFM) strategy, which can help to mitigate the adverse effects of integrating Renewable Energy Sources (RES). The power system’s behavior is simulated using the Real-Time Simulator (RTS) HIL 602+ from Typhoon-HIL, with the control strategies implemented in the Digital Signal Processor (DSP) TMS320F28379D – F28379D LaunchPad of Texas Instruments (TI). The validity of the CHIL setup is verified through load step tests, assessing the performance of the control systems. The results from the tests reveal that the GFM control strategy can enhance the system stability while also providing valuable ancillary services such as frequency and voltage regulation.","PeriodicalId":518478,"journal":{"name":"2024 4th International Conference on Smart Grid and Renewable Energy (SGRE)","volume":"20 4","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2024-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140531583","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