IEEE Power & Energy Magazine - May/June 2015 - 42

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Power Flow on Feeder (MVA)

✔ further enhancements should
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0

Time (h)
No Fault, No Control
Fault, Optimal Control

Fault, No Control
Feeder Capacity

figure 9. Power flow on a feeder with electric heat pumps.

settings of network control devices while considering condition of distribution circuits and switching
equipment; this would be a multiobjective optimization model that balances network security margin, distributed generation export levels, power quality, and
power losses
✔ models to integrate the contribution that electric
vehicles, either as controllable loads or storage elements, would bring to improve the operation of the
microgrid and contribute to its autonomy when in islanding mode
✔ real-time, post-fault microgrid reconfiguration/restoration including optimizing settings of network
control sources (active reactive power control, voltage control) the response of distributed generation,
demand-side response, and distributed storage

figure 10. The Meltemi holiday camp in Greece with
DERs and intelligent LCs has been used for implementing
MAS technologies.
42

ieee power & energy magazine

include risk-constrained approaches to directly deal
with uncertainties associated with demand and
generation predictions, including the impact of delays,
inaccuracies, and losses of
real-time measurements;
there will be uncertainties
associated with post-fault
actions need to be taken into
account.

Shift from the
Centralized to
Distributed Control of
Microgrids

Microgrids operating in a market environment might require
that the competitive actions of each unit's controller have a
certain degree of independence and intelligence. Furthermore,
local DER owners might have different objectives, i.e., next
to selling power to the network, they might produce heat for
local installations, keep the voltage locally at a certain level,
or provide backup for local critical loads in case of main system failure. Some microgrid customers might seek their own
energy cost minimization and have diverse needs, although
they all might benefit from the common objective of lowering
feeder operating costs. Moreover, microgrids might have dozens of households with several installed DERs so the dimension of the problem can be very high. An approach that limits
the amount of data transfer is essential. The availability of high
computing facilities or dedicated operators in LV grids is also
highly unlikely. These are factors that impose decentralized
solutions to the overall operation problem of microgrids.
The shift from a centralized to a fully decentralized
operation paradigm will open new opportunities for enhancing cost-effectiveness and security performance of future
microgrids, with the objective of delivering truly integrated
self-controlling, self-optimizing, self-protecting electricity and self-healing networks. In stark contrast to the present network control standard, control algorithms deployed
within future microgrids will be meeting dynamically changing objectives while the network topology, network conditions, and control infrastructure are also changing. The key
driver for enhancing real-time control of microgrids is the
need to improve supply resilience and quality of service
delivered to end consumers.
A significant paradigm for building such distributed systems is multiagent systems (MASs). The core idea is that an
autonomous control process is assumed by each local intelligent controller, namely MCs and LCs. The MAS theory
describes the coordination algorithms, the communication
between the agents and the organization of the entire system
including the energy service company. Agents are capable
may/june 2015



Table of Contents for the Digital Edition of IEEE Power & Energy Magazine - May/June 2015

IEEE Power & Energy Magazine - May/June 2015 - Cover1
IEEE Power & Energy Magazine - May/June 2015 - Cover2
IEEE Power & Energy Magazine - May/June 2015 - 1
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IEEE Power & Energy Magazine - May/June 2015 - Cover3
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