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while
battery not charged
do
if
P
(
k
)
< P
(
k
)
then
c
i
(
k
+1)=
c
i
(
k
)+
α.ΔT
else
generate uniform random number,
p
if
p<p
i
then
c
i
(
k
+1)=
β
(1)
.c
i
(
k
)
else
c
i
(
k
+1)=
β
(2)
.c
i
(
k
)
end
end
end
Here, c
i
(
k
) is the charge rate of the
i
th EV,
P
(
k
) is the total power demanded
by all the EVs connected and
P
(
k
) maximum power available to charge EVs at
the
k
th time instant.
α
is the additive constant value in kW/s,
β
(1)
,
β
(2)
are the
multiplicative constants, which are selected at random with probability
p
i
and
ΔT
is the time interval between EV charge rate updates. During operation each
active EV charge point additively increases its charge rate until a capacity event
occurs at which point it applies a multiplicative decrease to the charge rate. A
capacity event occurs when the power
P
(
k
) demanded by the active EV charger
points exceeds the maximum available power
P
(
k
). Here
P
(
k
) is computed as:
N
(
k
)
P
(
k
)=
c
i
(
k
)
,
(1)
i
=1
where
N
(
k
) is the number of active chargers at the
k
th time instant. The
P
(
k
)
< P
(
k
) capacity event condition is monitored by a central monitoring
station (server) which broadcasts a message to the charge points when events
occur. As disscussed in [5], this algorithm guarantees an equitable 'average' dis-
tribution of the power if each charge point chooses the same
α
,
β
and
p
, while
requiring a minimum of communication infrastructure.
2.3 Formation of the Smart Charging Strategy
In this section a new charging strategy is formulated based on the decentral-
ized AIMD method. The objective is to achieve benefits for both utilities and
customers. In order to do this we incorporate power system constraints on volt-
ages and load balance into the AIMD implementation so that all EVs can share
the maximum amount of available power fairly while ensuring that the dis-
tribution network continues to operate within acceptable limits. In addition,
we modulate the available power signal in response to a varying electricity
price [11] to affect a shift in EV loads away from periods of high demand,
thereby reducing peak-power capacity requirements and ultimately the cost to
the consumer.
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