Authors:
Konstantinos N. Genikomsakis
1
;
Benjamin Bocquier
2
;
Sergio Lopez
3
and
Christos S. Ioakimidis
4
Affiliations:
1
DeustoTech Energy, Spain
;
2
Icam Nantes, France
;
3
University of Deusto, Spain
;
4
University of Mons, Belgium
Keyword(s):
Forecasting, Non-residential Building, Peak Shaving, Photovoltaic, Plug-in Electric Vehicle, Solar Power, Valley Filling.
Related
Ontology
Subjects/Areas/Topics:
Energy and Economy
;
Energy Management Systems (EMS)
;
Energy-Aware Systems and Technologies
;
Load Balancing in Smart Grids
;
Microgeneration
;
Renewable Energy Resources
;
Smart Grids
;
Sustainable Computing and Communications
;
Virtualization Impact for Green Computing
Abstract:
This paper examines the concept of utilizing plug-in electric vehicles (PEVs) and solar photovoltaic (PV)
systems in large non-residential buildings for peak shaving and valley filling the power consumption profile,
given that the energy cost of commercial electricity customers typically depends on both actual
consumption and peak power demand within the billing period. Specifically, it describes a hybrid approach
that combines an artificial neural network (ANN) for solar irradiance forecasting with a MATLAB/Simulink
model to simulate the power output of solar PV systems, as well as the development of a mathematical
model to control the charging/discharging process of the PEVs. The results obtained from simulating the
case of the power consumption of a university building, along with experimental parking occupancy data
from a university parking lot, demonstrate the applicability and effectiveness of the proposed approach.