IEEE Consumer Electronics Magazine - November/December 2021 - 104

Special Section on Advances in Smart Cities
Figure 6. Case study for the energy trading.
Figure 5. Error performance of the different
communication schemes under different channel
conditions. (a) Error probability. (b) Decoded energy
excess/deficit.
scheme performs slightly better than the direct
scheme when the CGs are high. When the direct
links between the participant and control center
are weak, the relay-aided scheme can significantly
outperform the direct scheme. Figure 5(b) shows
how the energy excess/deficit information is
decoded at the control center for different communication
schemes. When the noise power is
high, the decoded information is significantly
different from the actual one. For the direct
communication scheme, there can be 1-2 dB
signal-to-noise ratio (SNR) penalty compared
to the relay-aided scheme when the channel
conditions between the participant and the
control center worsen. However, it can be
said that all three parts of the proposed SEH
are practically feasible when channel conditions
are moderate to good.
The distributed controller regulates the
switching signal of converters used with different
energy sources depending on the requirement.
The P2P energy trading framework is
implemented for a test case with six houses for
which the generation and demand data are
obtained from the Ausgrid solar home electricity
dataset for the first day of July 2012. Here, the
first four houses are equipped with the solar PV
unit with the maximum generated power on that
day as 2.16 kW, 1.02 kW, 1.02 kW, and 1.05 kW,
respectively, while the last two houses do not
have any solar panel. In this work, the range for
the bidding is considered from $0.113/kWh to
104
$0.20/kWh. Figure 6 shows the amount of energy
sold by different houses at different time intervals
of the day. House 2 did not sell any energy as it did
not have any energy excess on that specific day. It
can be seen that House 1 sells energy most of the
time due to its higher generation capacity. Houses
3 and 4 participate as sellers mostly before 10 AM
and after 1 PM. In this case study, the market clearing
prices vary indifferent market intervals ranging
from$0.16/kWh to $0.18/kWh. The revenues generated
by Houses 1, 3, and 4 on the market clearing
prices are determined as $4.80, $0.07, and $0.05,
respectively. Houses 3 and 4 generate less revenues
as the amount of their shared energy is much
lower than House 1. Hence, the proposed SEH enables
energy sharing and trading among different
houses while assisting to generate some revenues.
CONCLUSIONS AND FUTURE
DIRECTIONS
A SEH is presented for smart cities, which has
its own communication platform while having
the ability to communicate with existing communication
facilities. The smart grid communication
scheme ensures the reliability as it offers
different communication options (with existing
ICT services, directly, and indirectly using inactive
users) for consumers and prosumers. The
nonlinear distributed controller in this hub
ensures stable and secure operation of system
under any conditions and it does not matter how
citizens in smart cities are changing their energy
usage patterns. The energy trading framework
provides benefits to consumers and prosumers
for maximizing their economic incentives while
considering the physical properties of energy
systems including the profitability analysis for
both individual and shared generation systems.
IEEE Consumer Electronics Magazine
IEEE Consumer Electronics Magazine - November/December 2021

Table of Contents for the Digital Edition of IEEE Consumer Electronics Magazine - November/December 2021
