scholarly journals Power flow analysis for low-voltage AC and DC microgrids considering droop control and virtual impedance

2017 ◽  
Author(s):  
Chendan Li ◽  
Sanjay K. Chaudhary ◽  
Mehdi Savaghebi ◽  
Juan C. Vasquez ◽  
Josep M. Guerrero
Keyword(s):  
Power Flow ◽  
Low Voltage ◽  
Flow Analysis ◽  
Droop Control ◽  
Dc Microgrids ◽  
Power Flow Analysis ◽  
Virtual Impedance

scholarly journals Power Flow Analysis for Low-Voltage AC and DC Microgrids Considering Droop Control and Virtual Impedance

2017 ◽  
Vol 8 (6) ◽  
pp. 2754-2764 ◽  
Author(s):  
Chendan Li ◽  
Sanjay K. Chaudhary ◽  
Mehdi Savaghebi ◽  
Juan C. Vasquez ◽  
Josep M. Guerrero
Keyword(s):  
Power Flow ◽  
Low Voltage ◽  
Flow Analysis ◽  
Droop Control ◽  
Dc Microgrids ◽  
Power Flow Analysis ◽  
Virtual Impedance

scholarly journals Stability and reliability of low voltage hybrid AC-DC microgrids power flow model in islanding operation

2020 ◽  
Vol 19 (1) ◽  
pp. 32
Author(s):  
Hasti Afianti ◽  
Ontoseno Penangsang ◽  
Adi Soeprijanto
Keyword(s):  
Power Flow ◽  
Low Voltage ◽  
Flow Analysis ◽  
Steady State Condition ◽  
Dc Microgrids ◽  
Load Power ◽  
Bidirectional Converter ◽  
Islanding Operation ◽  
Stable Frequency ◽  
The Stability

The problem of decreasing and increasing power flow in hybrid AC-DC microgrids in island operations is the concern of this research. This condition arises if one sub-microgrid cannot supply load power requirements, either due to a decrease in power at the source or an increase in load on the sub-microgrid network. As a result, in this research used the bidirectional converter as an interlinking converter, the converter can change the power flow from the ac sub microgrid to the dc sub microgrid and vice versa. With this bidirectional converter, load power requirements can be met even though there is a power change in one of the sub microgrid. The simulation in this research support by Simulink/Matlab software. The model is built in a low voltage system, and the power flow analysis is in steady-state condition with two different cases. The simulation results show the stability and reliability of the power flow in both sub microgrid with stable frequency and voltage when power and load change occur.

Optimal Sizing and Location of Distributed Generators for Power Flow Analysis in Smart Grid Using IAS-MVPA Strategy

2021 ◽  
Author(s):  
Kumar Cherukupalli ◽  
Vijaya Anand N
Keyword(s):  
Smart Grid ◽  
Hybrid Method ◽  
Power Flow ◽  
Low Voltage ◽  
Flow Analysis ◽  
Optimal Location ◽  
Maximum Capacity ◽  
Power Flow Analysis ◽  
Distributed Generators ◽  
Grasshopper Optimization Algorithm

In this paper, the optimal distribution generation (DG) size and location for power flow analysis at the smart grid by hybrid method are proposed. The proposed hybrid method is the Interactive Autodidactic School (IAS) and the Most Valuable Player Algorithm (MVPA) and commonly named as IAS-MVPA method. The main aim of this work is to reduce line loss and total harmonic distortion (THD), similarly, to recover the voltage profile of system through the optimal location and size of the distributed generators and optimal rearrangement of network. Here, IAS-MVPA method is utilized as a rectification tool to get the maximum DG size and the maximal reconfiguration of network at environmental load variation. In case of failure, the IAS method is utilized for maximizing the DG location. The IAS chooses the line of maximal power loss as optimal location to place the DG based on the objective function. The fault violates the equality and inequality restrictions of the safe limit system. From the control parameters, the low voltage drift is improved using the MVPA method. The low-voltage deviation has been exploited for obtaining the maximum capacity of the DG. After that, the maximum capacity is used at maximum location that improves the power flow of the system. The proposed system is performed on MATLAB/Simulink platform, and the effectiveness is assessed by comparing it with various existing processes such as generic algorithm (GA), Cuttle fish algorithm (CFA), adaptive grasshopper optimization algorithm (AGOA) and artificial neural network (ANN).

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