This paper presents the design of an energy management system (EMS) capable of forecasting photovoltaic (PV) power production and optimizing power flows between PV system, grid, and battery electric vehicles (BEVs) at the workplace. The aim is to minimize charging cost while reducing 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.

Original languageEnglish
Article number7763845
Pages (from-to)311-320
Number of pages10
JournalIEEE Transactions on Industrial Informatics
Volume14
Issue number1
DOIs
Publication statusPublished - 1 Jan 2018

    Research areas

  • Autoregressive integrated moving average (ARIMA), electric vehicles, energy management system (EMS), forecast, mixed-integer linear programming (MILP), solar carport

ID: 44750067