Authors: Dennis van der Meer ; Gautham Ram Chandra Mouli ; Germán Morales-España Mouli ; Laura Ramirez Elizondo ; Pavol Bauer

Extended Abstract:

The number of battery electric vehicles (BEVs) increases rapidly. Some countries, such as Norway and Denmark, are considering banning internal combustion vehicles in the coming decade and the BEV is typically seen as their replacement. However, uncontrolled charging of BEVs poses challenges to the operational performance of the electricity grid while the improvement in greenhouse gas emissions that BEVs offer could be nullified when these are charged with electricity generated from coal or gas fired power plants. To that end, we present an energy management system (EMS) capable of forecasting photovoltaic (PV) power production of a solar carport and optimizing power flows between the solar carport, grid, and BEVs at the workplace. The aim is to minimize the charging cost while reducing the energy demand from the grid by increasing PV self-consumption and consequently increasing sustainability of the BEV fleet. The developed EMS consists of two components: An autoregressive integrated moving average model to predict PV power production and a mixed-integer linear programming framework that optimally allocates power to minimize charging cost. The results show that the developed EMS is able to reduce charging cost significantly, while increasing PV self-consumption and reducing energy consumption from the grid. Furthermore, during a case study analogous to one repeatedly considered in the literature, i.e., dynamic purchase tariff and dynamic feed-in tariff, the EMS reduces charging cost by 118.44% and 427.45% in case of one and two charging points, respectively, when compared to an uncontrolled charging policy.

2019 Best paper award for IEEE Transactions on Industrial Informatics

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