No abstract

This research performed a techno-economic analysis of diesel-biogas hybrid microgrid system. The paper modeled, designed, and simulated the microgrid system using MAT-LAB/SIMULINK and performed system optimization using HOMER software. The anaerobic digestion (AD) processes were designed and simulated with the aid of Simulink to obtain the methane yield from the reactor. Results show that the methane yield is 95.04 kg/day at a reactor temperature of 55 • C. The synchronous generator was modeled and simulated for the application of both diesel fuel and biogas fuel system. The HOMER software was used to optimize the hybrid micro-grid system with the diesel system taken as the base case. Biogas production was varied between 1 and 5 tons while the calculated energy demand of the village was 271925 kWh. At a biomass production of 4 tons and above, the hybrid system became powered by only the biogas system for total energy production. The energy produced by biogas is 452820 kWh and a cost of energy (COE) of $0.0484. The net present cost (NPC) of the base case system is $1141292 while that of the hybrid system is $176600 and that of the biogas system is $170085 which shows the saving cost of 84.5% and 85.1%, respectively, compared to the base case system over the project lifetime.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

The growing gap between electric power generated and that demanded is of utmost concern especially in developing economy, hence calling for measures to argument the existing power generated of which DG is a more viable aspect to explore in curtailing this challenges; although been confronted with issue of location and sizing. This research applied Adaptive neuro fuzzy logic technique to optimize DG location and size. A 24 bus radial network was used to demonstrate this process and having a suitable location and size at optimal position reduces power losses and also improves the voltage profile at the buses. The method was simulated using ANFIS toolbox MATLAB R2013b (8.2.0.701) 64-bit software and tested using Gwagwalada injection sub-station feeder 1 system. The results obtained were compared to that obtained using ANN. It was observed that adaptive neuro fuzzy logic technique performed better in terms of reducing power losses compared to ANN technique. The percentage reduction in the power loss at the buses cumulatively is 48.96% for ANN while adaptive neuro fuzzy logic technique is 49.21%. The voltage profile of the networks after optimizing the DG location and sizes using adaptive neuro fuzzy logic technique were also found to be much improved with the lowest bus voltage improved from 0.9284 to 1.05pu.

No abstract

This provides an overview of ferroresonance phenomena, modeling aspects and practical experience in recognizing and mitigating the effect of  ferroresonance in the power network. In particular, we will present symptoms of ferroresonance. In this study, ferroresonance phenomenon are investigated under the modelling of the underground cable of 15 MVA, 33/11/0.415 kV with four feeders supply over 31 distribution transformers of  Garki 1 service unit, Abuja, conducted using MATLAB/SIMSCAPE software package. The network is subjected to switching resulting in the formation of a series LC combination prone to ferroresonance. The waveforms and total harmonic distortion charts obtained are observed, analyzed and then  used to design Harmonic filters which have been implemented to mitigate ferroresonance in the network. The simulated ferroresonance  phenomenon was suppressed using Shunt Active Harmonic Filter connected in parallel to the network. A reduced harmonic content and dampened event when compared to a non-filtered event was observed. Application of Shunt Active Harmonic filter reduces ferroresonance effect by 67.743%. Keywords: Ferroresonance, Transformer, MATLAB/Simscape, Harmonic Distortion, Power system Modelling, Saturable inductor, Capacitance

No abstract

No abstract

No abstract