Journal
ENERGIES
Volume 11, Issue 11, Pages -Publisher
MDPI
DOI: 10.3390/en11112913
Keywords
energy management; machine learning; load forecasting; electric vehicle; energy storage
Categories
Funding
- BMWi, the Federal Ministry for Economic Affairs and Energy [FKZ 03ET6076E]
Ask authors/readers for more resources
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available