The exponential growth of unpredictable renewable power production sources in the power grid results in difficult-to-regulate reactive power. The ultimate goal of optimal reactive power dispatch (ORPD) is to find the optimal voltage level of all the ...
Faraz Bhurt +6 more
doaj +2 more sources
Optimal Reactive Power Dispatch Using a Chaotic Turbulent Flow of Water-Based Optimization Algorithm
In this study, an optimization algorithm called chaotic turbulent flow of water-based optimization (CTFWO) algorithm is proposed to find the optimal solution for the optimal reactive power dispatch (ORPD) problem. The ORPD is formulated as a complicated,
Ahmed M. Abd-El Wahab +4 more
doaj +2 more sources
Improved grey wolf optimizer for optimal reactive power dispatch with integration of wind and solar energy [PDF]
The aim of this paper is to present a new improved grey wolf optimizer (IGWO) to solve the optimal reactive power dispatch (ORPD) problem with and without penetration of renewable energy resources (RERs).
Laouafi, F.
core +3 more sources
Enhanced GSA-Based Optimization for Minimization of Power Losses in Power System [PDF]
Gravitational Search Algorithm (GSA) is a heuristic method based on Newton’s law of gravitational attraction and law of motion. In this paper, to further improve the optimization performance of GSA, the memory characteristic of Particle Swarm ...
Gonggui Chen, Lilan Liu, Shanwai Huang
core +3 more sources
Optimal Reactive Power Dispatch and Demand Response in Electricity Market Using Multi-Objective Grasshopper Optimization Algorithm [PDF]
Optimal Reactive Power Dispatch (ORPD) is a power system optimization tool that modifies system control variables such as bus voltage and transformer tap settings, and it compensates devices' Volt Ampere Reactive (VAR) output. It is used to decrease real
Das D. +4 more
core +2 more sources
An Improved New Caledonian Crow Learning Algorithm for Global Function Optimization. [PDF]
The New Caledonian crow learning algorithm (NCCLA) is a novel metaheuristic algorithm inspired by the learning behavior of New Caledonian crows learning to make tools to obtain food. However, it suffers from the problems of easily falling into local optima and insufficient convergence accuracy and convergence precision.
Wang Y, Song J, Teng Z.
europepmc +2 more sources
Optimization the stochastic optimal reactive power dispatch with renewable energy resources using a modified dandelion algorithm. [PDF]
Improvement performance of transmission systems is crucial task that can be boosted via optimal reactive power dispatch (ORPD). However, the continuous variations of load demand and the power produced by the renewable energy sources (RERs) increases the ...
Agouzoul N +7 more
europepmc +2 more sources
Optimal Siting and Sizing of Multiple DG Units for the Enhancement of Voltage Profile and Loss Minimization in Transmission Systems Using Nature Inspired Algorithms. [PDF]
Power grid becomes smarter nowadays along with technological development. The benefits of smart grid can be enhanced through the integration of renewable energy sources. In this paper, several studies have been made to reconfigure a conventional network into a smart grid.
Ramamoorthy A, Ramachandran R.
europepmc +2 more sources
Multi-objective ORPD Considering Different Load Models for Active Distribution Networks [PDF]
Secure and economical operation of distribution networks needs the management of reactive power resources. Optimal Reactive Power Dispatch (ORPD) optimally manages the reactive power scheduling of generators and distribution generations as well as the ...
Saman Hosseini-Hemati +2 more
doaj +1 more source
An optimized multi-objective reactive power dispatch strategy based on improved genetic algorithm for wind power integrated systems [PDF]
The large uncertainties in wind power generation will bring great challenges to the analysis of optimal reactive power dispatch (ORPD). This paper considers a multi-objective ORPD strategy solved by a heuristic search algorithm that combines the elitist ...
Cetenovic, Dragan +4 more
core +1 more source

