Architecture of Computing Systems (Lecture Notes in Computer ...

Decentralized Energy-Management to Control Smart-Home Architectures 159
controlling system, we are leaving out the aspect of learning on the controller. In
further work, we will investigate the controller’s aspect of learning, as proposed
in Sect. 3. The observer/controller-architecture is an appropriate framework to
implement learning techniques.
Depending on the appliance’s degree of freedom and the planned schedule,
an optimized schedule can be computed, which is based on the 24h-signal of the
energy supplier. E. g., a dishwasher is often switched on after lunch at 12:10 p. m.
on a certain weekday. It will probably be switched on during the same weekday’s
timeslot in the next week. Since this timeslot is rated with a high demand by
the 24h-signal, it is beneficial to re-schedule the starting time of the dishwasher
to a timeslot, where the demand for energy is as low as possible. The appliance’s
degree of freedom has to be respected. The optimizing process has to determine
the best starting timeslot of each appliance. For that purpose, the function of
the demand-set and the 24h-signal’s rating are considered for each possible starting
timeslot. The appliance’s start is scheduled to the timeslot with the lowest
integral value which is consistent with the operating constraints.
5.4 Integration of an Electric Vehicle
Additionally, the mobile storage in the electric vehicle is used to support the
balancing process of demand and generation. Via the battery’s charge controller,
the SHMD is able to manage the charge- and feed-back-process. Some appliances
in the house with a poor degree of freedom cannot be re-scheduled into a period
of low demand. E. g., a stove is often switched on for cooking before lunchtime,
but this is a period with an extremely high demand. In this case, a vehicle’s
battery can be used to feed-back electrical energy into the grid. The SHMD
can send a control signal to the controller of the electrical vehicle to request
the feed-back of a certain amount of electrical power. The start of the charging
process of the car’s battery can also be scheduledbytheSHMD.Thecharging
of a car battery causes a high electrical load. Thus, it is beneficial to re-schedule
the charging process into periods of low demand.
6 Results
In the upper diagram of Fig. 5, the forecast for the next 24 hours based on
the history data’s analysis is visualized. Each of the intelligent appliances is
scheduled in the time slot interval, where it has been typically powered on during
the considered period. The lower diagram represents the optimized schedule by
the SHMD. The gray curve shows the behavior request from the grid operator
(motivated in Sect. 5). It is shown that, e. g., devices like the breadmaker, the
dishwasher, and the stove are located in periods of high demand, hence they
should be re-scheduled.
The breadmaker and the dishwasher have a sufficient degree of freedom. Thus,
they can be re-scheduled to periods of lower demand, as shown in the lower
diagram of Fig. 5. The stove has a poor degree of freedom. Therefore, it cannot