scholarly journals Modeling Uncertainty in Support Vector Surrogates of Distributed Energy Resources - Enabling Robust Smart Grid Scheduling

2016 ◽  
Author(s):  
Jörg Bremer ◽  
Sebastian Lehnhoff
Keyword(s):  
Smart Grid ◽  
Energy Resources ◽  
Distributed Energy Resources ◽  
Support Vector ◽  
Distributed Energy ◽  
Grid Scheduling ◽  
Modeling Uncertainty

scholarly journals Smart Grid: ICT Control for Distributed Energy Resources

2016 ◽  
Vol 12 (5) ◽  
pp. 1329421 ◽  
Author(s):  
Jose Ignacio Moreno ◽  
Manel Martínez-Ramón ◽  
Pedro S. Moura ◽  
Javier Matanza ◽  
Gregorio López
Keyword(s):  
Smart Grid ◽  
Energy Resources ◽  
Distributed Energy Resources ◽  
Distributed Energy

scholarly journals Internet of Things Technologies for Smart Grid

2022 ◽  
pp. 805-832
Author(s):  
Imed Saad Ben Dhaou ◽  
Aron Kondoro ◽  
Syed Rameez Ullah Kakakhel ◽  
Tomi Westerlund ◽  
Hannu Tenhunen
Keyword(s):  
Internet Of Things ◽  
Smart Grid ◽  
Smart Grids ◽  
Reference Model ◽  
Energy Resources ◽  
Distributed Energy Resources ◽  
Reference Architecture ◽  
Distributed Energy ◽  
Research Gaps ◽  
Utility Company

Smart grid is a new revolution in the energy sector in which the aging utility grid will be replaced with a grid that supports two-way communication between customers and the utility company. There are two popular smart-grid reference architectures. NIST (National Institute for Standards and Technology) has drafted a reference architecture in which seven domains and actors have been identified. The second reference architecture is elaborated by ETSI (European Telecommunications Standards Institute), which is an extension of the NIST model where a new domain named distributed energy resources has been added. This chapter aims at identifying the use of IoT and IoT-enabled technologies in the design of a secure smart grid using the ETSI reference model. Based on the discussion and analysis in the chapter, the authors offer two collaborative and development frameworks. One framework draws parallels' between IoT and smart grids and the second one between smart grids and edge computing. These frameworks can be used to broaden collaboration between the stakeholders and identify research gaps.

scholarly journals Data-Driven Fault Localization in Distribution Systems with Distributed Energy Resources

Energies ◽  
2020 ◽  
Vol 13 (1) ◽  
pp. 275 ◽  
Author(s):  
Zhidi Lin ◽  
Dongliang Duan ◽  
Qi Yang ◽  
Xuemin Hong ◽  
Xiang Cheng ◽  
...  
Keyword(s):  
Fault Detection ◽  
Distribution Systems ◽  
Fault Localization ◽  
Energy Resources ◽  
Data Driven ◽  
Distributed Energy Resources ◽  
Support Vector ◽  
Support Vector Data Description ◽  
Distributed Energy ◽  
P Values

The integration of Distributed Energy Resources (DERs) introduces a non-conventional two-way power flow which cannot be captured well by traditional model-based techniques. This brings an unprecedented challenge in terms of the accurate localization of faults and proper actions of the protection system. In this paper, we propose a data-driven fault localization strategy based on multi-level system regionalization and the quantification of fault detection results in all subsystems/subregions. This strategy relies on the tree segmentation criterion to divide the entire system under study into several subregions, and then combines Support Vector Data Description (SVDD) and Kernel Density Estimation (KDE) to find the confidence level of fault detection in each subregion in terms of their corresponding p-values. By comparing the p-values, one can accurately localize the faults. Experiments demonstrate that the proposed data-driven fault localization can greatly improve the accuracy of fault localization for distribution systems with high DER penetration.

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