The major aim of DG optimal placement is obtaining the best DGs units sizes and locations so as to have optimum operation and planning of the distribution network system while considering DG capacity constraint. This paper addresses the issues related to the improvement of voltage profile and power loss reduction by integrating DG units. The technique was applied to optimally placed optimum DG unit size in distribution systems for the improvement of candidate bus voltage and reduction of power loss in the system. The technique proposed was simulated on IEEE-10 bus and IEEE-13 bus standard test system, and the obtained results show that the proposed method is strong and effective for optimal placement of DG units.
Over the years, and despite the energy efficiency measures and possibilities, there has been an increase in energy consumption worldwide. However, the resources and primary energies are limited and short stocked, and the energy production technologies have environmental and social impacts on production and exploration. One alternative is to reuse the energy waste in the processes. In this study, a trigeneration system in a large scale out of grid consumption is analyzed and a technical-economic feasibility is elaborated. The case study is based on an isolated tropical island. For the baseline scenario, two traditional energy production systems that does not contain energy recovery in the engines are assumed to be implemented. For the improved scenario, a trigeneration system absorption chiller is analyzed. An economic analysis of this project was given the indicators obtained, it was possible to conclude that the use of a trigeneration system on an isolated large scale out of grid energy consumption system, is feasible and preferable.
In this work, an efficient solution based on the reducing forms approach is presented to extract the five parameters of the single-diode model of PV generators from their I-V curves. Thus, by reducing the number of the five unknown parameters to two unknowns, the analytical expression of the current based on the LambertW function will then depend only on the ideality factor and the series resistance, as the two unknowns to predict numerically using the non-linear least square technique. The three other parameters are calculated as functions of the two predicted parameters using a linear system of three equations. Two sets of experiments are used for the validation of the proposed approach, which first showed its rapidity and high accuracy compared to the best approaches from the literature. Then, the method was applied for the real-time identification of four PV modules operating outdoors during one reference day at Cocoa (Florida).
Researchers are increasingly using algorithms that are influenced by nature because of its ease and versatility, the key components of nature-inspired metaheuristic algorithms are investigated, involving divergence and adoption, investigation and utilization, and dissemination techniques. Grey Wolf Optimizer (GWO), a relatively recent algorithm influenced by the dominance structure and poaching deportment of grey wolves, is a very popular technique for solving realistic mechanical and optical technical challenges. Half of the recurrence in the GWO are committed to the exploration and the other half to exploitation, ignoring the importance of maintaining the correct equilibrium to ensure a precise estimate of the global optimum. To address this flaw, a Multi-tiered GWO (MGWO) is formulated, that further accomplishes an appropriate equivalence among exploration and exploitation, resulting in optimal algorithm efficiency. In comparison to familiar optimization methods, simulations relying on benchmark functions exhibit the efficacy, performance, and stabilization of MGWO.
To ensure the safe and stable operation of solar photovoltaic system-based power systems, it is essential to predict the PV module output performance under varying operating conditions. In this paper, the interest is to develop an accurate model of a PV module in order to predict its electrical characteristics. For this purpose, an artificial neural network (ANN) based on the backpropagation algorithm is proposed for the performance prediction of a photovoltaic module. In this modeling approach, the temperature and illumination are taken as inputs and the current of the mathematical model as output for the learning of the ANN-PV-Panel. Simulation results showing the performance of the ANN model in obtaining the electrical properties of the chosen PV panel, including I–V curves and P–V curves, in comparison with the mathematical model performance are presented and discussed. The given results show that the error of the maximum power is very small while the current error is about 10-8, which means that the obtained model is able to predict accurately the outputs of the PV panel.
