IEEE Consumer Electronics Magazine - November/December 2021 - 101

Figure 3. Distributed control scheme for energy
sharing in smart cities.
In terms of the spectral efficiency, noncooperative
direct communication scheme will
require 2 L time slots for the complete information
exchange among L participants. On the
other hand, for the cooperative relay-aided
scheme, for k groups of m participants where
L ¼ km; 2ðm
2 þ L
2Þ time slots will be required.
Thus, the cooperative relay-aided scheme can
offer higher spectral efficiency at the cost of
slightly higher decoding complexities and
increasing number of relay nodes. Also, the
effectiveness of the second communication
scheme will depend on whether the communication
relay nodes can be chosen optimally or not.
Since the market interval is quite large, the communication
speed is not a major concern. However,
the communication speed for information
sharing among participants will be in the range
of 10-20 ms with the proposed communication
schemes if the long-term evolution (LTE) or 4G
technology is used. Finally, the information provided
from this communication platform assists
energy sharing and trading among different facilities
in smart cities which are discussed as
following.
A DISTRIBUTED CONTROL SCHEME
FOR ENERGY SHARING
Energy systems in smart cities mostly include
small- and medium-scale RESs along with diesel
generators, fuel cells, and ESSs. The output
power of these RESs vary significantly due to
November/December 2021
their reliance on environmental conditions (e.g.,
temperature, sunlight, and wind speed) while
loads in smart cities will continuously change.
The random variations in both generation and
loads in smart cities pose new challenges for
real-time operations limiting the integration of
more RESs as these introduce high nonlinearities.
Furthermore, energy sources and loads are
located close to each other in smart cities, i.e.,
generators have direct dynamic interactions
with loads due to their interconnections and any
changes in the output power of these generators
affect prosumers or consumers who will be sharing
power among themselves. Therefore, the
control scheme needs to be designed to minimize
these interactions by tackling all these
nonlinearities and currently, decentralized controllers
are designed for each energy source,
which use local information of generators.9
Hence, these controllers are unable to eliminate
or minimize dynamic interactions for generators
used for other services. Distributed controllers
has the ability to capture information of generators
and controller in neighboring services.10
However, existing distributed controllers have
several problems, among these, parameters and
external disturbances are considered as major
problems.
Figure 3 shows the distributed control structure
for the SEH in smart cities, where all energy
sources are connected with each other via both
power and communication lines. Furthermore,
all controllers for these energy sources communicate
with each other so that energy sharing
can be done without any or with extremely low
interactions. Actually, the set points for these
controllers are defined by the SEH depending on
the energy shortage and excess. The controller
for each energy source then acts to regulate
their output power for sharing energy and it is
worth noting that controllers and P2P energy
trading framework work simultaneously based
on information received from the SGC center. In
this article, a robust nonlinear feedback linearizing
adaptive distributed control scheme is proposed
for the DCC in the SEH, which has the
ability to ensure global operations (i.e., not limited
to operating points) along with robustness
against parametric uncertainties and external
disturbances. By applying the concept of the
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