Optimal Power Flow using a Novel Harris Hawk Optimization Algorithm to Minimize Fuel Cost and Power loss

2019 ◽  
Author(s):  
Mohammad Zohrul Islam ◽  
Noor Izzri Abdul Wahab ◽  
Veerapandiyan Veerasamy ◽  
Hashim Hizam ◽  
Nashiren Farzilah Mailah ◽  
...  
Keyword(s):  
Optimization Algorithm ◽  
Power Loss ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Fuel Cost ◽  
Optimal Power

scholarly journals Application of the Multiverse Optimization Method to Solve the Optimal Power Flow Problem in Direct Current Electrical Networks

2021 ◽  
Vol 13 (16) ◽  
pp. 8703
Author(s):  
Andrés Alfonso Rosales-Muñoz ◽  
Luis Fernando Grisales-Noreña ◽  
Jhon Montano ◽  
Oscar Danilo Montoya ◽  
Alberto-Jesus Perea-Moreno
Keyword(s):  
Objective Function ◽  
Successive Approximation ◽  
Direct Current ◽  
Optimization Algorithm ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Flow Problem ◽  
Distributed Generators ◽  
Optimal Power ◽  
Power Flow Problem

This paper addresses the optimal power flow problem in direct current (DC) networks employing a master–slave solution methodology that combines an optimization algorithm based on the multiverse theory (master stage) and the numerical method of successive approximation (slave stage). The master stage proposes power levels to be injected by each distributed generator in the DC network, and the slave stage evaluates the impact of each power configuration (proposed by the master stage) on the objective function and the set of constraints that compose the problem. In this study, the objective function is the reduction of electrical power losses associated with energy transmission. In addition, the constraints are the global power balance, nodal voltage limits, current limits, and a maximum level of penetration of distributed generators. In order to validate the robustness and repeatability of the solution, this study used four other optimization methods that have been reported in the specialized literature to solve the problem addressed here: ant lion optimization, particle swarm optimization, continuous genetic algorithm, and black hole optimization algorithm. All of them employed the method based on successive approximation to solve the load flow problem (slave stage). The 21- and 69-node test systems were used for this purpose, enabling the distributed generators to inject 20%, 40%, and 60% of the power provided by the slack node in a scenario without distributed generation. The results revealed that the multiverse optimizer offers the best solution quality and repeatability in networks of different sizes with several penetration levels of distributed power generation.

Improved Pseudo-Gradient Search Particle Swarm Optimization for Optimal Power Flow Problem

2016 ◽  
pp. 177-207 ◽  
Author(s):  
Jirawadee Polprasert ◽  
Weerakorn Ongsakul ◽  
Vo Ngoc Dieu
Keyword(s):  
Particle Swarm Optimization ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Voltage Stability ◽  
Stability Index ◽  
Gradient Search ◽  
Fuel Cost ◽  
Swarm Optimization ◽  
Optimal Power ◽  
Voltage Deviation

This paper proposes an improved pseudo-gradient search particle swarm optimization (IPG-PSO) for solving optimal power flow (OPF) with non-convex generator fuel cost functions. The objective of OPF problem is to minimize generator fuel cost considering valve point loading, voltage deviation and voltage stability index subject to power balance constraints and generator operating constraints, transformer tap setting constraints, shunt VAR compensator constraints, load bus voltage and line flow constraints. The proposed IPG-PSO method is an improved PSO by chaotic weight factor and guided by pseudo-gradient search for particle's movement in an appropriate direction. Test results on the IEEE 30-bus and 118-bus systems indicate that IPG-PSO method is superior to other methods in terms of lower generator fuel cost, smaller voltage deviation, and lower voltage stability index.

scholarly journals Optimal Power Flow Controller for Grid-Connected Microgrids using Grasshopper Optimization Algorithm

Electronics ◽  
2019 ◽  
Vol 8 (1) ◽  
pp. 111 ◽  
Author(s):  
Touqeer Jumani ◽  
Mohd Mustafa ◽  
Madihah Rasid ◽  
Nayyar Mirjat ◽  
Mazhar Baloch ◽  
...  
Keyword(s):  
Dynamic Response ◽  
Power Quality ◽  
Optimization Algorithm ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Harmonic Distortion ◽  
Optimal Power ◽  
Grasshopper Optimization Algorithm ◽  
Grasshopper Optimization ◽  
Power Flow Controller

Despite the vast benefits of integrating renewable energy sources (RES) with the utility grid, they pose stability and power quality problems when interconnected with the existing power system. This is due to the production of high voltages and current overshoots/undershoots during their injection or disconnection into/from the power system. In addition, the high harmonic distortion in the output voltage and current waveforms may also be observed due to the excessive inverter switching frequencies used for controlling distributed generator’s (DG) power output. Hence, the development of a robust and intelligent controller for the grid-connected microgrid (MG) is the need of the hour. As such, this paper aims to develop a robust and intelligent optimal power flow controller using a grasshopper optimization algorithm (GOA) to optimize the dynamic response and power quality of the grid-connected MG while sharing the desired amount of power with the grid. To validate the effectiveness of proposed GOA-based controller, its performance in achieving the desired power sharing ratio with optimal dynamic response and power quality is compared with that of its precedent particle swarm optimization (PSO)-based controller under MG injection and abrupt load change conditions. The proposed controller provides tremendous system’s dynamic response with minimum current harmonic distortion even at higher DG penetration levels.

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