IEEE Consumer Electronics Magazine - November/December 2021 - 103

loss for transferring energy from sellers to
buyers. It is worth noting that most of the existing
trading frameworks do not consider this loss
during the energy trading though it plays a significant
role in decision making. The bidding process
continues until both buyer and seller agree
on a price that satisfies their desired utilities
and reach the equilibrium point as indicated in
Figure 4. This equilibrium point needs to be
achieved within a market interval (Dt), i.e., the
auction needs to be cleared within Dt period.
Since both buyers and sellers participate in
the bidding process, this framework is called a
double-auction mechanism where the SEH process
the bidding based on the mechanism as discussed
by Akter et al.12 During the bidding
process, the SEH creates a priority ranking for
both buyers and sellers. It rank buyers based on
the highest to lowest utilities and similarly, sellers
based the lowest to highest utilities. The
equilibrium point is achieved based on the bidding
strategy as presented by Akter et al.12 The
energy trading model is adaptive in nature,
which basically optimizes cost functions for
both buyers and sellers to minimize the energy
purchase cost and maximize energy selling
price. In this work, the market interval is considered
as 30 min as the energy sharing is considered
for this interval. However, this can be
adjusted to any values. Finally, the feasibility of
trading the energy is confirmed by sharing the
information with the DCC using the SGC framework
and the excess energy is traded with feasible
buyers by regulating the control action
through the DCC. All these information can be
formulated as a noncooperative multiplayer
game as indicated by Opadokun et al.11
Finally, the proposed trading framework calculates
benefits of energy trading for each service
based on the traded energy and settlement
price from the auction. There are chances of having
lots of shared facilities in smart cities for
which a group of citizens can jointly invest, e.g.,
residents in apartment building. The energy
trading framework in the SEH also calculates the
benefits for such facilities using the contribution
factor as discussed by Akter et al.12 The proposed
energy trading framework is scalable and
adaptable as any strategies can be incorporated
in order to fulfill the requirements of citizens in
November/December 2021
a smart city. The double auction can be performed
in any online platform based on the collected
information from field devices where
these devices are basically sensors collecting
energy information.
APPLICATIONS OF THE SEH AND
SIMULATION RESULTS
The proposed SEH can be implemented on
any existing infrastructure with very little modification.
The low-cost sensors can be placed at different
points (particularly with generators and
loads or smart meters) within the smart city.
The proposed SGC framework can then be used
to exchange information among different entities
as discussed earlier on. All calculations related
to the set points for controllers, energy sharing,
and trading can be performed using a cloudbased
system where any existing secured payment
gateway used for online transactions can
be incorporated for handling the payment or
bills can be issued to participants while providing
different payment options. Hence, the existing
smart grid infrastructure can easily adopt
the proposed SEH.
The benefits of energy sharing without using
any communication platform can be seen from
Akter et al.12 which shows that the energy sharing
and trading save electricity bills for both contributing
and noncontributing apartments in a
multidwelling building. Moreover, the contributing
apartments get their investments on DERs
back within seven years, which seem to be a reasonable
investment and their grid dependencies
reduce by up to 80%.12 Similarly, the role of distributed
controllers in terms of minimizing
dynamic interactions can be found by Mahmud
et al.10 The impact of communication errors on
the information received about energy excess/
deficit from participants is illustrated here. A
certain interval is considered when a participant
tries to communicate an energy excess/deficit of
1926 W.
Figure 5(a) shows the error probability of the
relay-aided communication scheme when channel
gains (CGs) between the participants and the
relay node are considered as 200. It also shows
the performance of the direct communication
scheme for CGs of 100 and 10. The relay-aided
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