Effect of EV Movement Schedule and Machine Learning-Based Load Forecasting on Electricity Cost of a Single Household

Stefan Arens, Karen Derendorf, Frank Schuldt, Karsten Von Maydell and Carsten Agert
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Stefan Arens: DLR Institute for Networked Energy Systems, Carl-von-Ossietzky-Str. 15, 26129 Oldenburg, Germany
Karen Derendorf: DLR Institute for Networked Energy Systems, Carl-von-Ossietzky-Str. 15, 26129 Oldenburg, Germany
Frank Schuldt: DLR Institute for Networked Energy Systems, Carl-von-Ossietzky-Str. 15, 26129 Oldenburg, Germany
Karsten Von Maydell: DLR Institute for Networked Energy Systems, Carl-von-Ossietzky-Str. 15, 26129 Oldenburg, Germany
Carsten Agert: DLR Institute for Networked Energy Systems, Carl-von-Ossietzky-Str. 15, 26129 Oldenburg, Germany

Energies, 2018, vol. 11, issue 11, 1-19

Abstract: An energy management system (EMS) for a household energy system is proposed in this paper, which is composed of a photovoltaic (PV) generator , a home energy storage (HES), an electric vehicle (EV), an electrical household load and a grid connection, with 24 h operation horizon. The EMS objective is to reduce the electricity cost of the household by using a linear optimization algorithm. Two different EV schedules are utilized for simulations. One mainly describes rides to work and the other describes rides in a domestic context, such as rides to a supermarket. A forecast algorithm for the electrical load of the household, based on k-means clustering and an artificial neural network, is evaluated and integrated into the EMS to realistically represent the household’s load profile. It is shown that the developed forecast algorithm performs better than two of the benchmarks. Another finding is that the more storage is available at PV-production intervals, the higher the effect of forecast uncertainties and the lower the electricity cost of the household, disregarding the investment cost.

Keywords: energy management; machine learning; load forecasting; electric vehicle; energy storage (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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