Summary
The utilization of renewable energy resources (RERs) in the traditional power system has gained a global recognition owing to their technical, economic and environmental benefits. The techno‐economic analysis of a microgrid system that consists of diesel generator (DG), methanol generator (MG), photovoltaic (PV) and battery system (BS) is implemented in this study to evaluate the performance of the power system. The feasibility study of the power system is implemented by using HOMER application tool and meteorological data provided by the National Aeronautics and Space Administration. The analysis indicates that PV‐DG‐MG‐BS microgrid system is the most optimized configuration based on the net present cost (NPC) of $213 405.4, cost of energy (COE) of $0.256/kWh, renewable fraction of 88.6%, diesel fuel of 3055 L/y and DG operating hours of 1037 h/y. The results obtained from the optimized configuration translate to a substantial reduction in NPC, COE, diesel fuel and operating hours of DG when compared to the base case study. This indicates that the combination of DG, PV, MG and BS in a microgrid system is the most economical configuration to achieve a feasible result. Moreover, sensitivity analysis is carried out to investigate the impacts of solar radiation, load demand, fuel cost and inflation rate on the performance of the power system. The results obtained from the study clearly prove the effectiveness of using RERs to increase the sustainability and performance of the power system. This improves the standard of living and economic activities in areas where the microgrid systems are sited.
The reliability of the power supply depends on the reliability of the structure of the grid. Grid networks are exposed to varying weather events, which makes them prone to faults. There is a growing concern that climate change will lead to increasing numbers and severity of weather events, which will adversely affect grid reliability and electricity supply. Predictive models of electricity reliability have been used which utilize computational intelligence techniques. These techniques have not been adequately explored in forecasting problems related to electricity outages due to weather factors. A model for predicting electricity outages caused by weather events is presented in this study. This uses the back-propagation algorithm as related to the concept of artificial neural networks (ANNs). The performance of the ANN model is evaluated using real-life data sets from Pietermaritzburg, South Africa, and compared with some conventional models. These are the exponential smoothing (ES) and multiple linear regression (MLR) models. The results obtained from the ANN model are found to be satisfactory when compared to those obtained from MLR and ES. The results demonstrate that artificial neural networks are robust and can be used to predict electricity outages with regards to faults caused by severe weather conditions.
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