scenario generation
To ensure brevity, the main manuscript provides an abridged version of the scenario evaluation methodology and results. First, this supplementary material provides a discussion of the DM-test procedure employed and its validity. A detailed discussion and specific references related to the procedure applied to test the statistical significance in the improvement of the multivariate scores are presented. Then, the CC-ERCO formulation details, related SG evaluation procedure, and additional results are presented and outlined in more detail, supplementing the main article.
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An efficient artificial scenerio generator for EV load simulation modeling has been developed acquiring probabilistic method for characterizing the stochastic nature of EVs and generate the schedule of EVs charging to ultimately achieve the EV load profile for impact study of EVs on distribution network. Model has been tested under different settings and by generating different scenarios to make it viable, realistic and adaptable to any defined characteristics.
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