Design, Management and Optimization of a Distributed Energy Storage System with the presence of micro generation in a smart
house
The owners of a house in today’s society do not know in real-time how much electricity
they use. It could be beneficial for any residential consumer to have more
control and overview in real-time over the electricity consumption. This could be
done possible with a system that monitors the consumptions, micro renewables
and the electricity prices from the grid and then makes a decision to either use
or sell electricity to reduce the monthly electricity cost for the household and
living a "Greener" life to reduce carbon emissions. In this thesis, estimations are
made based on artificial neural network (ANN). The predictions are made for air
temperature, solar insolation and wind speed in order to know how much energy
will be produced in the next 24 hours from the solar panel and from the wind
turbine. The predictions are made for electricity consumption in order to know
how much energy the house will consume. These predictions are then used as an
input to the system. The system has 3 controls, one to control the amount of sell
or buy the energy, one to control the amount of energy to charge or discharge the
fixed battery and one to control the amount of energy to charge or discharge the
electric vehicle (EV). The output from the system will be the decision for the next
10 minutes for each of the 3 controls.
To study the reliability of the ANN estimations, the ANN estimations (S_ANN)
are compared with the real data (S_real ) and other estimation based on the mean
values (Smean) of the previous week. The simulation during a day in January gave
that the expenses are 0.6285 BC if using S_ANN, 0.7788 BC if using S_mean and 0.5974
BC if using S_real . Further, 3 different cases are considered to calculate the savings
based on the ANN estimations. The first case is to have the system connected with
fixed storage device and EV (S_con,batt ). The second and third cases are to have the
system disconnected (without fixed battery) using micro generation (S_discon,micro)
and not using micro generation (Sdiscon) along with the EV. The savings are calculated
as a difference between S_con,batt and S_discon, also between S_discon,micro and
Sdiscon. The saving are 788.68 BC during a year if S_con,batt is used and 593.90 BC
during a year if S_discon,micro is used. With the calculated savings and the cost for
the equipment, the pay-back period is 15.3 years for Scon;batt and 4.5 years for
S_discon,micro. It is profitable to only use micro generation, but then the owner of
the
Hannes Eliasstam
2012
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Senast uppdaterad: 2021-11-10
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