Reliability Assessment of Microgrid-Integrated Electrical Distribution System

2022 ◽  
pp. 958-977
Author(s):  
Baseem Khan ◽  
Mesfin Fanuel
Keyword(s):  
Renewable Energy ◽  
Distribution System ◽  
Renewable Energy Sources ◽  
Reliability Assessment ◽  
Smart Structure ◽  
Energy Sources ◽  
Reliability Indices ◽  
Micro Grid ◽  
Distribution System Reliability ◽  
Renewable Energy Generation

Due to the transition of traditional power system to smart structure, integration of renewable energy sources is of great importance. It is performed by using distributed generators and Micro grid. Integration of renewable sources is very useful for the reliability enhancement of distribution system as energy can be supplied locally at distributed level. Therefore, this chapter provides an overview of various reliability methods which are utilized for finding the impact of renewable energy generation on distribution system reliability assessment. These renewable energy sources are integrated in the distribution system at distributed and Micro grid level. Various characteristics of renewable energy sources are discussed to model them. A general problem of reliability assessment in terms of reliability indices is also discussed.

Reliability Assessment of Microgrid-Integrated Electrical Distribution System

2020 ◽  
pp. 136-156
Author(s):  
Baseem Khan ◽  
Mesfin Fanuel
Keyword(s):  
Renewable Energy ◽  
Distribution System ◽  
Renewable Energy Sources ◽  
Reliability Assessment ◽  
Smart Structure ◽  
Energy Sources ◽  
Reliability Indices ◽  
Micro Grid ◽  
Distribution System Reliability ◽  
Renewable Energy Generation

Due to the transition of traditional power system to smart structure, integration of renewable energy sources is of great importance. It is performed by using distributed generators and Micro grid. Integration of renewable sources is very useful for the reliability enhancement of distribution system as energy can be supplied locally at distributed level. Therefore, this chapter provides an overview of various reliability methods which are utilized for finding the impact of renewable energy generation on distribution system reliability assessment. These renewable energy sources are integrated in the distribution system at distributed and Micro grid level. Various characteristics of renewable energy sources are discussed to model them. A general problem of reliability assessment in terms of reliability indices is also discussed.

scholarly journals A Review on DC Microgrid and Decentralized Approach

2021 ◽  
Vol 9 (8) ◽  
pp. 1719--1723
Author(s):  
Mahesh Abdare
Keyword(s):  
Renewable Energy ◽  
Distribution System ◽  
Renewable Energy Sources ◽  
Energy Generation ◽  
Energy Sources ◽  
Dc Microgrid ◽  
Renewable Energy Generation ◽  
The Given ◽  
Day By Day

Abstract: DC Microgrid is going to be a very important part of the Distribution system soon. The given circumstances have forced us to find how to utilize renewable energy sources in the integration to increase its reliability in our day-to-day life. This paper gives a good idea of the DC Microgrid and various methods being used for the controlling part of it. As day by day cost incurred in renewable energy generation is decreasing, we need to find out significant parts where this kind of DC Microgrid can be utilized to provide electricity in all parts of the country. Keywords: DGUs, ImGs, DMA, OXD, DC Microgrid.

scholarly journals Development of a Micro grid with Renewable Energy Sources with Feedback Controller

2019 ◽  
Vol 8 (11) ◽  
pp. 2014-2018
Keyword(s):  
Renewable Energy ◽  
Electricity Generation ◽  
Low Cost ◽  
Renewable Energy Sources ◽  
Electrical Power ◽  
Energy Sources ◽  
Military Operations ◽  
Urban Centers ◽  
Industrial Projects ◽  
Micro Grid

To make micro grid with renewable energy and to over come the technical challenges and economy base and policy and regulatory challenges . From the natural wastage we can generate the Electricity. Thus, the Electrical Power or Electricity is available with a low cost and pollution free to anyplace in the world at all times. This process divulge a unequaled step in electricity generation and this type of generation is maintain the ecological balance. We can have an uninterrupted power supply irrespective of the natural condition without any kind of environmental pollution. More influence this process relent the less production cost for electricity generation. Micro grids have long been used in remote areas to power off-grid villages, military operations or industrial projects. But increasingly they are being used in cities or towns, in urban centers. Here we try a proto type of micro grid with renewable energy sources.

The Forecasting of Renewable Energy Generation for Turkey by Artificial Neural Networks and a Auto-Regressive Integrated Moving-Average Model -2023 Generation Targets by Renewable Energy Resources

2021 ◽  
Author(s):  
özlem karadag albayrak
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Renewable Energy ◽  
Moving Average ◽  
Renewable Energy Sources ◽  
Energy Generation ◽  
Energy Sources ◽  
Ann Model ◽  
Artificial Neural ◽  
Renewable Energy Generation

Abstract Turkey attaches particular importance to energy generation by renewable energy sources in order to remove negative economic, environmental and social effects caused by fossil resources in energy generation. Renewable energy sources are domestic and do not have any negative effect, such as external dependence in energy and greenhouse gas, caused by fossil resources and which constitute a threat for sustainable economic development. In this respect, the prediction of energy amount to be generated by Renewable Energy (RES) is highly important for Turkey. In this study, a generation forecasting was carried out by Artificial Neural Networks (ANN) and Autoregressive Integrated Moving Average (ARIMA) methods by utilising the renewable energy generation data between 1965-2019. While it was predicted by ANN that 127.516 TWh energy would be generated in 2023, this amount was estimated to be 45.457 TeraWatt Hour (TWh) by ARIMA (1.1.6) model. The Mean Absolute Percentage Error (MAPE) was calculated in order to specify the error margin of the forecasting models. This value was determined to be 13.1% by ANN model and 21.9% by ARIMA model. These results suggested that the ANN model provided a more accurate result. It is considered that the conclusions achieved in this study will be useful in energy planning and management.

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