2019 North American Power Symposium (NAPS)最新文献

PV Penetration Optimization with WAM Smart Inverters through Reduction of Power System Distribution Losses 基于WAM智能逆变器的光伏渗透优化研究

2019 North American Power Symposium (NAPS) Pub Date : 2019-10-01 DOI: 10.1109/NAPS46351.2019.9000234

J. A. Beuning, H. Salehfar

{"title":"PV Penetration Optimization with WAM Smart Inverters through Reduction of Power System Distribution Losses","authors":"J. A. Beuning, H. Salehfar","doi":"10.1109/NAPS46351.2019.9000234","DOIUrl":"https://doi.org/10.1109/NAPS46351.2019.9000234","url":null,"abstract":"The continued decrease in pricing of photovoltaic (PV) systems has led to sustained growth in installations of residential and commercial arrays. Many of these residential arrays are behind the meter (BTM) and can influence the power flow, voltage levels and the characteristics of lines and buses in the distribution system. The addition of PV systems into the grid can help reduce losses for utility companies, but due to the inherent intermittent nature of PV it can also pose new challenges. These challenges are apparent when designing the voltage regulating devices and the ideal locations of the new distributed generation sources. The goal of this study is to develop an algorithm in MATLAB coupled with Electric Power Research Institute's (EPRI) simulation tool OpenDSS to determine the optimal locations in the distribution networks for the installation of residential or commercial grade PV systems to reduce power system losses while monitoring the bus voltages and system frequency to ensure stability of the system. A wide-are a-monitoring (WAM) smart inverter is implemented in the algorithm to determine optimized PV locations to aid with frequency and voltage stability. The IEEE 123 bus system is used as the test distribution network for analysis, but the algorithm will not be wed to this network. Any future network implemented by users in the OpenDSS system will be able to be optimized for PV installations with the algorithm. This system will provide distribution utility companies a tool to support designing optimal locations for the PV systems in their respective networks to minimize the power system losses as well as to allow for future growth of distributed generation (DG).","PeriodicalId":175719,"journal":{"name":"2019 North American Power Symposium (NAPS)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120968945","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

Energy Management of a Battery Combined with PV Generation 电池与光伏发电相结合的能量管理

2019 North American Power Symposium (NAPS) Pub Date : 2019-10-01 DOI: 10.1109/NAPS46351.2019.9000314

Mohamed Abuagreb, Hussain Beleed, B. Johnson

引用次数: 1

A Novel Wide-Area Storage-Based Control for Transient Stability Enhancement 一种基于广域存储的暂态稳定增强控制方法

2019 North American Power Symposium (NAPS) Pub Date : 2019-10-01 DOI: 10.1109/NAPS46351.2019.9000342

Wenzong Wang, Zhangxin Zhou, Garng M. Huang

引用次数: 3

Data Fusion Based Hybrid Deep Neural Network Method for Solar PV Power Forecasting 基于数据融合的混合深度神经网络太阳能光伏发电功率预测方法

2019 North American Power Symposium (NAPS) Pub Date : 2019-10-01 DOI: 10.1109/NAPS46351.2019.9000331

D. A. R. de Jesús, P. Mandal, M. Velez-Reyes, S. Chakraborty, T. Senjyu

{"title":"Data Fusion Based Hybrid Deep Neural Network Method for Solar PV Power Forecasting","authors":"D. A. R. de Jesús, P. Mandal, M. Velez-Reyes, S. Chakraborty, T. Senjyu","doi":"10.1109/NAPS46351.2019.9000331","DOIUrl":"https://doi.org/10.1109/NAPS46351.2019.9000331","url":null,"abstract":"This paper proposes a new Hybrid Deep Neural Network (HDNN) based fusion method to predict short-term solar photovoltaic (PV) power output. The HDNN is the combination of Fully Convolutional Network (FCN) and Long Short-Term Memory (LSTM) networks that fuses the output of two individual forecast models, i.e., Autoregressive Moving Average with Exogenous Inputs (ARMAX) and Adaptive Neuro Fuzzy Inference System (ANFIS). The Deep Neural Network (DNN) based parts, which are stemmed from the idea that individual predictions obtained by several models, add value to the final forecasting process. The major advantage of the fusion component in the proposed method is that it allows the salient feature extraction through the HDNN model by identifying sequential dependencies in historical trends using different forecasting models' perspectives to predict solar PV power output. The prediction accuracy of the proposed HDNN-Fusion model is validated by comparing its performance to other techniques through several soft computing models. Simulation results demonstrate the suitability of the proposed fusion method to obtain accurate short-term PV power forecasts for multiple seasons of the year.","PeriodicalId":175719,"journal":{"name":"2019 North American Power Symposium (NAPS)","volume":"87 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127049660","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: 6

Realistic Microgrid Test Bed for Protection and Resiliency Studies 现实微电网保护与弹性研究试验台

2019 North American Power Symposium (NAPS) Pub Date : 2019-10-01 DOI: 10.1109/NAPS46351.2019.9000214

P. Gadde, S. Brahma

引用次数: 12

Two-Stage Stochastic Power Grid Expansion Considering Multiple N-1-1 Contingencies 考虑多个N-1-1事件的两阶段随机电网扩展

2019 North American Power Symposium (NAPS) Pub Date : 2019-10-01 DOI: 10.1109/NAPS46351.2019.9000326

Daniel A. Zuniga Vazquez, Neng Fan

引用次数: 1

Chance-constrained Unit Commitment via the Scenario Approach 通过情景方法的机会约束单元承诺

2019 North American Power Symposium (NAPS) Pub Date : 2019-10-01 DOI: 10.1109/NAPS46351.2019.9000192

Xinbo Geng, Le Xie

引用次数: 7

Feasibility Study of Solar Energy System at the University of North Dakota 北达科他大学太阳能系统可行性研究

2019 North American Power Symposium (NAPS) Pub Date : 2019-10-01 DOI: 10.1109/NAPS46351.2019.9000206

Bo K. Yesel, Jonathan J. Eslinger, Michael Nord, D. Selvaraj, P. Ranganathan

引用次数: 0

CT Instrumentation Channel Error Correction Using Dynamic State Estimation 基于动态估计的CT仪器通道误差校正

2019 North American Power Symposium (NAPS) Pub Date : 2019-10-01 DOI: 10.1109/NAPS46351.2019.9000191

Emeka Obikwelu, A. Meliopoulos

引用次数: 3

Synchronized Operating Point Stability of Multimachine Power System using Holomorphic Embedding in Kuramoto Framework 基于Kuramoto框架全纯嵌入的多机电力系统同步工作点稳定性研究

2019 North American Power Symposium (NAPS) Pub Date : 2019-10-01 DOI: 10.1109/NAPS46351.2019.9000266

K. Sonam, S. Wagh, N. Singh

引用次数: 1