Renewable microgrids are new solutions for enhanced security, improved reliability and boosted power quality and operation in power systems. By deploying different sources of renewables such as solar panels and wind units, renewable microgrids can enhance reducing the greenhouse gasses and improve the efficiency. This paper proposes a machine learning based approach for energy management in renewable microgrids considering a reconfigurable structure based on remote switching of tie and sectionalizing. The suggested method considers the advanced support vector machine for modeling and estimating the charging demand of hybrid electric vehicles (HEVs). In order to mitigate the charging effects of HEVs on the system, two different scenarios are deployed; one coordinated and the other one intelligent charging. Due to the complex structure of the problem formulation, a new modified optimization method based on dragonfly is suggested. Moreover, a self-adaptive modification is suggested, which helps the solutions pick the modification method that best fits their situation. Simulation results on an IEEE microgrid test system show its appropriate and efficient quality in both scenarios. According to the prediction results for the total charging demand of the HEVs, the mean absolute percentage error is 0.978, which is very low. Moreover, the results show a 2.5% reduction in the total operation cost of the microgrid in the intelligent charging compared to the coordinated scheme.

One of the many barriers to decarbonization and decentralization of the energy sector in developing countries is the economic uncertainty. As such, this study scrutinizes economics of three grid-independent hybrid renewable-based systems proposed to co-generate electricity and heat for a small-scale load. Accordingly, the under-study systems are simulated and optimized with the aid of HOMER Pro software. Here, a 20-year average value of discount and inflation rates is deemed a benchmark case. The techno-economic-environmental and reliability results suggest a standalone solar/wind/electrolyzer/hydrogen-based fuel cell integrated with a hydrogen-based boiler system is the best alternative. Moreover, to ascertain the impact of economic uncertainty on optimal unit sizing of the nominated model, the fluctuations of the nominal discount rate and inflation, respectively, constitute within the range of 15–20% and 10–26%. The findings of economic uncertainty analysis imply that total net present cost (TNPC) fluctuates around the benchmark value symmetrically between $478,704 and $814,905. Levelized energy cost varies from an amount 69% less than the benchmark value up to two-fold of that. Furthermore, photovoltaic (PV) optimal size starts from a value 23% less than the benchmark case and rises up to 55% more. The corresponding figures for wind turbine (WT) are, respectively, 21% and 29%. Eventually, several practical policies are introduced to cope with economic uncertainty.