IEEE Electrification Magazine - March 2014 - 89

Electric Load Shift Within the Field Trial

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Figure 5. The load shift in the field test achieved by variable tariffs (February 2011).

consumption is lower than an ordinary house in Greece.
however, the ecological awareness of its habitants and
the electrical structure [all houses are connected to the
same medium voltage/low voltage (Mv/lv) transformer]
of the settlement make it ideal for use as a test bed for
functions related to emergency and critical grid situations. the installation of distributed generation (dGs),
including a 40-kva diesel generator, 4.5-kW Pv panels,
and small residential wind turbines, can partially support the Meltemi campground in a microgrid operation.
the MaGic system installed allows the dGs and the
household to negotiate to decide the next sequence of
actions. this system is a Java-based software that implements intelligent agents. a critical component of the
MaGic system is the intelligent load controller (figure 6)
based on an embedded processor that runs linux and can
be used to monitor the status of a power line providing
voltage, current, and frequency measurements. the controller is expandable with several serial ports and a universal serial bus port and has the ability not only to
control but also to monitor several appliances. it is
designed for indoor installation and is equipped with a
display to present messages directly to the consumer. in
addition, the consumers are informed online about the
status of the system as well as their consumption and
energy costs. this information is also available through a
Web portal. this information is critical since consumers
accept them only when they visualize the potential for
energy savings cost benefits.

Measurements and Results
congestion management is based on the monitoring of
the transformer that feeds the camping site. the idea is
that when the transformer is close to its critical level of
overloading, the dso requests that the aggregator proceed
with load shedding.
the MaGic system deals with this request in two
steps. during the first step, the agents monitor the system
and provide to the other agents and the aggregator information about its status, i.e., production, consumption, and
voltage measurements. the system provides a list of loads
that can be shed, as well as the units that are capable of
providing extra energy.
the second step concerns the time period just before
the transformer overload. the system predicts the overload using its forecasting functions and proceeds with
intelligent load shedding. advanced load-shedding algorithms are used ensuring that loads of the consumers are
curtailed in a fair way.
to evaluate this function, a simple algorithm that
simply reacts to the various disturbances was considered. the difference between the two approaches is
shown in figure 7. during the disturbance, the simple
algorithm reacts when the system detects the overload.
this stimulates load shedding at the Mv substation. on
the other hand, the MaGic system has the ability to predict the incoming event and act faster. it has been discovered that 15 min early warning is enough time to
cope with the disturbance.

IEEE Electrific ation Magazine / MARCH 2 0 1 4

Table of Contents for the Digital Edition of IEEE Electrification Magazine - March 2014