scholarly journals The advancing assessment of power system stability using smart grid technology

Author(s):  
Murat Merekenov ◽  
Karmel Tokhtibakiyev ◽  
Anur Bektimirov ◽  
Rassim Nigmatullin

The Smart grid refers to a next-generation power grid which is a two-way information flow where electricity and information switch over between the service and its customer’s. The power system becomes smart by applying intelligence by way of multidirectional flow of electricity and information to create an extensive distribution network through smart grid technology. It made smarter power system by developing a networking communication, controls, automation, new technologies and tools working together to make the great efficient and more secure environment. For an effective integration and quality of the service to the consumer's smart grid technology is needed due to working with the electrical distribution system and quickly to respond digitally for rapidly changing electric demand. At this point, an electricity disruption such as a blackout can affect a series of failures that can affect several areas such as banking, traffic, and security. To address the power restoration, smart grid makes use of self-healing strategy which will allow automatic switching when equipment failure or outages occur. There have been numerous studies in the last decade or so in to even out the fundamental and mathematical challenges of making a smart self-healing grid a reality. In this paper, we explore the Selfhealing approach for Smart grid Communications likewise discusses the service restoration methods in Distribution Networks of Smart Grid Environment


2019 ◽  
Author(s):  
Deepika Bishnoi ◽  
Harsh Chaturvedi

Global warming, climate change due to rising CO2 emissions, changing load demands from incandescent lamp and induction motor loads to digital loads, emerging electric vehicles and charging stations as well as higher power transmission losses are the factors which are pushing the global power system to make a shift from ‘generation + transmission + distribution’ to ‘distributed renewable generation + storage + localized distribution’. That is why the area of Smart Grids and Microgrids is being scrutinized thoroughly by researchers all over the world and is evolving every day. This research is an attempt to study all the modifications being done in the traditional power grid, to make it more intelligent, resilient, robust, and smarter, with a special focus on India. Smart Grid is a combination of information technology and power transmission, making the power system of the nation smarter. This paper is an attempt to trace the Smart Grid technology from its inception, presenting a comprehensive review of the available communication architecture options, renewable integration policies, targets and protocols and gives the required knowledge to engineers to work for better future of the nation in developing smarter power systems. A prediction of the share of renewables in total electricity production in the year 2030 is also made using linear regression analysis.


This paper mainly focuses the dynamic modeling to overcome the severe challenges of dynamic characteristics of loads of Smart Grid System in Kuching and Samalaju, where facility loads have significant consequences to the power system stability. Appearances of conventional types of loads, which are power-electronic, based or interfaced and it requires operating with increasing non-conventional and intermittent types of generation. These lead to an interest in dynamic modeling. In Sarawak Energy Berhad (SEB) power grid, the instability of the voltage, frequency deviations of the power system can damage the bulk load and important data as well. Dynamic modeling is a technique used to model the system to study the power system stability, including system voltage, frequency, and oscillation of the generation. This paper proposes dynamic modeling that will be done based on the data at the Kuching Bulk Load of SEB. Moreover, this study also assesses the time-domain dynamic simulation by comparing the recorded and simulated response as well as assess the parametric study using the parameter estimation method (Least Square Error). The selected model of the bulk load can be optimized by converging on the data of the Least Square Error Method in this study.


2013 ◽  
Vol 04 (05) ◽  
pp. 391-397 ◽  
Author(s):  
Oshevire Patrick ◽  
Oladimeji Tolulolope ◽  
Onohaebi Sunny

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