Predicting vehicles parking behaviour in shared premises for aggregated EV electricity demand response programs

• URL: http://arxiv.org/abs/2109.09666v1
• Date: Mon, 20 Sep 2021 16:33:17 GMT
• Title: Predicting vehicles parking behaviour in shared premises for aggregated EV electricity demand response programs
• Authors: Vinicius Monteiro de Lira, Fabiano Pallonetto, Lorenzo Gabrielli, Chiara Renso
• Abstract summary: We propose a methodology to predict an estimation of the parking duration in shared parking premises. We formalize the prediction problem as a supervised machine learning task to predict the duration of the parking event. This predicted duration feeds the energy management system that will allocate the power over the duration reducing the overall peak electricity demand.
• Score: 3.448121798373834
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The global electric car sales in 2020 continued to exceed the expectations climbing to over 3 millions and reaching a market share of over 4%. However, uncertainty of generation caused by higher penetration of renewable energies and the advent of Electrical Vehicles (EV) with their additional electricity demand could cause strains to the power system, both at distribution and transmission levels. Demand response aggregation and load control will enable greater grid stability and greater penetration of renewable energies into the grid. The present work fits this context in supporting charging optimization for EV in parking premises assuming a incumbent high penetration of EVs in the system. We propose a methodology to predict an estimation of the parking duration in shared parking premises with the objective of estimating the energy requirement of a specific parking lot, evaluate optimal EVs charging schedule and integrate the scheduling into a smart controller. We formalize the prediction problem as a supervised machine learning task to predict the duration of the parking event before the car leaves the slot. This predicted duration feeds the energy management system that will allocate the power over the duration reducing the overall peak electricity demand. We structure our experiments inspired by two research questions aiming to discover the accuracy of the proposed machine learning approach and the most relevant features for the prediction models. We experiment different algorithms and features combination for 4 datasets from 2 different campus facilities in Italy and Brazil. Using both contextual and time of the day features, the overall results of the models shows an higher accuracy compared to a statistical analysis based on frequency, indicating a viable route for the development of accurate predictors for sharing parking premises energy management systems

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