IEEE Signal Processing - July 2018 - 107

allowing the SFCs of the community to jointly share their
energy storage devices and provide demand response services
in the P2P energy-trading market. When there is a large number of RUs and SFCs in the system, the reservation and bidding prices will vary significantly. Each RU is assumed to be a
group between five and 25 households, where each household
is equipped with a 25-kWh capacity storage device. The
required electricity storage for each SFC is assumed to be
within the range of 100-500 kWh. The required storage space
for sharing could be different if the participants' usage patterns change, and, since the type of energy storage (and its
associated costs) used by different RUs can vary significantly,
the choices of reservation prices to share their storage space
with the SFCs can fluctuate considerably as well.
Note that, once all of the participating RUs offer their free
storage space to the auction market, they are distributed according to the allocation rule described in the "P2P Energy Trading in Service Domains" section. Figure 7 investigates how the
average utility of each RU varies as the total storage amount
required by the SFCs changes. In this case, the total energy storage requirements of the SFCs are assumed to be 100, 150, 200,
250, 300, 350, 400, 450, 500, 550, and 600. Generally speaking, the average utility cost for each resident initially increases
with the increased requirements of the SFCs and eventually
becomes saturated, resulting in a stable value (Figure 7). This
is because, as the required amount of storage space increases,
the RU can share more of its reserved energy storage that it put
into the market with the SFCs at the determined auction price;
hence, its utility costs increase. However, each RU has a particular fixed storage amount that it can put on the market to share,

Average Benefit Achieved by the RUs

of available energy for sale increases, leading to a drop in the
trading price and decreasing the suppliers' revenue.
Moreover, Figure 6(a) shows how different kinds of generated
energy may affect the monthly revenue of the suppliers and EUs.
Such a scenario refers to the case in which participants are able
to choose the type of generation they would like to use for trading, as observed in the BMP. We demonstrate in Figure 6(a) that,
when the number of suppliers is lower than 24, the buyers and
sellers both prefer solar generation over wind generation because
of its higher monthly revenue. Similarly, both parties prefer wind
generation when the number of energy suppliers is larger than
72. However, if the number of energy suppliers is between 24
and 72, EUs prefer solar generation, whereas energy suppliers
prefer wind generation. Therefore, in this case, the mixed usage
of wind generators and solar generators is advisable.
Figure 6(b) demonstrates the monthly monetary profit of
EUs and energy suppliers for different percentages of solar
generators used by energy suppliers. According to this figure, an optimal percentage of solar generators exists that
maximizes the total revenue of EUs and energy suppliers.
For example, when the number of suppliers is 30, the total
revenue is maximized when 60% of all of the suppliers are
solar generators. It can also be seen from Figure 6(b) that the
optimal percentage of solar generators approaches zero as the
number of energy suppliers increases, which is supported by
the findings in Figure 6(a).
Based on the results in the DER and storage domain, as discussed previously, we can summarize our insights accordingly.
■ Participant cooperation within a P2P energy network is
always beneficial because it provides a platform for trading
energy without involving the main
grid pricing scheme, which is not as
attractive as the P2P scheme [36].
1,100
Note that this may also be affected by
1,000
how the pricing scheme is designed.
■ When the number of energy suppli900
ers in the market becomes very large,
the revenue to the energy suppliers
800
reduces, subsequently increasing the
revenue of the P2P energy-trading
700
network EUs.
600
■ Depending on the number of energy
suppliers within the market, the dif500
ferent percentage mixture of solar
and wind generations would be
400
optimal for the energy suppliers to
maximize their revenue from the
300
energy trading.

Service domains
Finally, in this section, we will discuss
and illustrate some of the findings of a
game-theoretic approach in the service
domain, based on [35]. In this study,
the authors consider a number of RUs
at different blocks who are interested in

200
100

η0.001 > 0
η0.01 > 0
Supply > Demand
150

η0.001 > 0
η0.01 = 0

η0.001 = 0
η0.01 = 0
Supply < Demand

200 250 300 350 400 450 500
Required Storage Space by the SFCs

550

600

α = 0.001 (More Willing to Share)
α = 0.01 (Less Willing to Share)

FIGURE 7. An illustration showing how the average benefit received by an RU may differ across various
storage demands of the SFCs within the P2P energy network [35].
IEEE Signal Processing Magazine

July 2018

107

Table of Contents for the Digital Edition of IEEE Signal Processing - July 2018