IEEE Power & Energy Magazine - May/June 2015 - 43

of acting in their environment, of which they have partial
representation or none at all; they communicate with each
other; they have a certain level of autonomy, meaning that
they can make decisions without a central controller or commander; and they tend to satisfy certain objectives using
their resources, skills, and services. (See Figure 8.)
A family of MAS algorithms to solve market problems are
the auction algorithms, a type of combinatorial optimization
algorithm that solves assignment and network optimization
problems with linear and convex/nonlinear cost. The main
principle is that the auctioneers submit bids to obtain goods
or services. At the end of an iterative process, the highest
bidder is the winner. For microgrids, goods can be an amount
of energy traded.
Distributed control can also be applied for managing load
recovery during supply restoration following interruptions. The
key challenge is temporary load increase driven by the loss of
diversity and the need for energy recovery. This effect is known
as cold load pickup or payback effect that reduces the natural
diversity of loads, leading to significant demand peaks that may
violate network constraints. To maintain the resilience of the
microgrid's supply, suitable control strategies are required to
mitigate this effect. An example involving electric heat pumps
(EHP) in domestic buildings is presented in Figure 9.
Under normal operating conditions, EHPs maintain the
buildings' indoor temperature at the desired set points without
violating network voltage and thermal constraints. Following
supply interruption and without control of the EHP, however,
a cold load pickup effect emerges at hour 18 when supply is
restored, increasing significantly the demand level and breaching the network's thermal capacity limit. Distributed optimal
control can avoid the violation of the network constraint, while
achieving the minimum loss of comfort for the EHP users;
this loss of comfort will be caused by the fact that the indoor
temperature falls below the desired set point until the energy
not supplied during the interruption is partially recovered.
As observed in Figure 9, the application of optimal control
approach results in prolonged energy recovery period, needed
to maintain the power flow within the network capacity.
MAS-based decentralized control has been performed in
several demonstration sites in the context of EU-funded research
projects (Figure 10). Key findings are that Internet technologies will play a dominant role in the deployment of microgrids.
Existing ICT infrastructure, as well the upcoming technologies,
such as the smart/Wi-Fi-enabled home appliances can be used
to actively control devices in the LV networks.
Another approach being investigated is the use of distributed constraint optimization, particularly for arbitration
and negotiation within decentralized and distributed multiagent control systems, where conflicting control decisions
may arise. Significant further work, however, is required to
develop comprehensive microgrid models and then carry out
the analysis and comparison of distributed intelligent methods for applications such as voltage control, frequency control, thermal constraint management, reconfiguration, and
may/june 2015

control decision arbitration. Such models are needed to test
the robustness and scalability of the self-organizing architecture and carry out a comparison with existing control
philosophies to evaluate the advantages and disadvantages
of the distributed control concept. This would also inform
the operation and control of local community and smart cities energy systems and their integration within the national
level system operation and control.

Summary
The EU megagrid is foreseen to exploit the very large resource
of solar energy in southern Europe and of wind power in
northern Europe. Microgrids with enhanced control capabilities can integrate and coordinate local distributed resources
enhancing the resilience of the EU megagrid and providing
local restoration capabilities. The future modeling challenges
of microgrids and in particular the shift to distributed control,
enhancing further the microgrids resilience, are highlighted.

For Further Reading
European Climate Foundation. (2010, Apr.). Roadmap 2050:
A Practical Guide to a Prosperous, Low-Carbon Europe,
vol. 1. [Online]. Available: http://www.roadmap2050.eu/
project/roadmap-2050
G. Strbac, R. Moreno, I. Konstantelos, D. Pudjianto, and
M. Aunedi. (2014, July). Strategic development of North Sea
grid infrastructure to facilitate least-cost decarbonisation.
[Online]. Available: http://www.e3g.org/docs/NorthSeaGrid_Imperial_E3G_Technical_Report_July_2014.pdf
Directorate-General Energy European Commission.
(2013, July). Benefits of an integrated European energy market. [Online]. Available: http://ec.europa.eu/energy/infrastructure/studies/doc/20130902_energy_integration_benefits.pdf
Microgrids: Architectures and Control, N. Hatziargyriou, Ed. West Sussex, U.K.: IEEE-Wiley, 2014.
J. A. Peças Lopes, C. L. Moreira, and A. G. Madureira,
"Defining control strategies for microgrids islanded operation," IEEE Trans. Power Syst., vol. 21, no. 2, pp. 916-924,
May 2006.
C. L. Moreira, F. O. Resende, and J. A. P. Lopes, "Using
low voltage microgrids for service restoration," IEEE Trans.
Power Syst., vol. 22, no. 1, pp. 395-403, Feb. 2007.

Biographies
Goran Strbac is with Imperial College London, United
Kingdom.
Nikos Hatziargyriou is with the National Technical University of Athens, Greece.
João Pecas Lopes is with Porto University, Portugal.
Carlos Moreira is with Porto University, Portugal.
Aris Dimeas is with the National Technical University of
Athens, Greece.
Dimitrios Papadaskalopoulos is with Imperial College
London, United Kingdom.
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http://www.roadmap2050.eu/ http://www.e3g.org/docs/NorthSea http://ec.europa.eu/energy/infrastruc

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