In the optimization of technical systems focused on a specific functional purpose (reliability, safety, and availability) with the use of simulation methods, an important parameter is the digital simulation time of the research subject. With the complexity of the issue, the digital simulation time increases. The aim of the article is to present a method (combination of parallel computing and variance reduction techniques) of reducing the computer simulation time of the research technical object. An example of the application of the developed method was presented as a result of an experiment conducted for decision making and control processes aimed at optimizing the process of operating overhead cranes in critical conditions. In this paper, selecting parallel batch jobs computation and stratified sampling, we exponentially decreased the simulation time, finding fast and practical solutions and eliminating the time constraint in the search of solutions.
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A simulation model to evaluate risks in Power Systems including green Energy sources to generate electricity for electro mobility use is presented in the paper. The model allows to calculate risk indicator that characterize the performance of the Power Systems. The model considers the additional risks of wind and solar variability in the Power Systems, through wind farms and PV farms, respectively. Also, in the recent years, the number of electric vehicles (EVs) on the road have been rapidly increasing. Charging this increasing number of EVs is expected to have an impact on the power grid especially if high charging powers and opportunistic charging are used. Multiple papers have observed that the charging stations are used by multiple users during the day. In a context where electric mobility is gaining increasing importance as a more sustainable solution for urban environments, this work presents the optimization of charging profiles of the potential users of these charging stations. We analyzed the charging profiles in a power grid with renewables sources of energy and we determine the optimal charging profiles for the power grid based on maximizing the energy delivered by renewable sources of energy.
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