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.
In recent decades, earth’s sharp population growth followed by increasing demand for energy has turned the energy and its current and future sources into much debated issues. Given the well-known consequences of excessive reliance on fossil energy sources, this study is concentrated on wind-powered hydrogen production by desalination of sea water and then subjecting the product to electrolysis. For this purpose, a coastal city was selected from each Iranian coastal province, and then the wind energy generation potential in these cities was evaluated by Weibull distribution function. The amount of energy to be generated by three commercially available wind turbines and the amount of desalinated water and hydrogen to be produced in each area were then evaluated. The results showed that the port of Anzali on the coast of the Caspian Sea has an average annual wind power density of 327 w/m2, and thus enjoys the best wind energy generation potential among the studied coastal areas. The annual energy generation to be achieved by one EWT direct wind 52/900 turbine installed in this port was found to be 2315.53 MWh, which is equivalent to 1804 tons of net annual CO2 emission reduction. The total energy output of the said turbine could be used to produce 439,950.7 m3 of treated water or 35,973.49 kg of hydrogen a year. Thus, a wind farm containing 55 of these turbines could provide enough power to produce the hydrogen needed to fuel all private cars in Anzali.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.