IEEE Power & Energy Magazine - May/June 2014 - 66

across all the gas stations in a town, so the price of
electricity should vary from substation to substation
in a given jurisdiction. in other words, every substation should be able to price its services based on a
host of local parameters such as load congestion,
the demand profile, and the energy available from
the grid and from prosumers. to achieve this, a utility's smart grid integration map should facilitate the
required integration between the energy hub (the
substation) and its termination points (prosumers).

Conclusions
the central theme in all of the examples discussed in this
article is the need to have a forward-looking smart grid
integration map that empowers utilities to add incremental
functionalities to their existing grid, if and when required,
without the need to redo any of their previous investments.
given the examples discussed above-which cannot by any
stretch of the imagination be considered comprehensive-
the utilities have to be extremely careful about the initial
investments they make in this regard. that does not appear
to be always the case, however, as some of the choices that
have already been made in the early stages of the process
have not been encouraging.
the ami model implemented in many jurisdictions
across north america, for example, relies on local data
collection units (often referred to as daUs) as the primary
interface between smart meters and mdm system applications in the back office. in such a model, the local distribution substation will either be totally disconnected from the
ami system that monitors the customers feeding off its feeders or if there is any communication between smart meters
and substation equipment, the data will have to go through
the round robin of being captured by daUs locally, passed
on to the appropriate mdm system in the remote back office,
and handed over to the SCada head end in the back office
before finding its way through the SCada network from the
back office down into the substation.
it goes without saying that such long delays in data and
command communication would make it nearly impossible
to efficiently run any number of smart grid capabilities
that rely on distributed command and control and as such
require local analytics and decision making. Such applications are by default substation-resident, with a stringent
need for unimpeded access to real-time data from smart
meters, sensors, and other termination points associated
with that substation. in other words, smart meters should
ideally be substation's "over-the-fence" intelligent electronic devices (ieds), fully engaged in real-time data and
command exchange with substation-resident functions;
failing this, they are nothing more than an interim solution
for automating billing and revenue management.
Finally, a utility's smart grid integration map must support
the realization of the utility's integrated network domains,

66

ieee power & energy magazine

as depicted in Figure 10, which emphasizes the need for a
distributed command and control system (using a system of
intelligent agents) running across multiple domains of the
utility network and providing end-to-end communication
and data exchange among all utility assets. in that regard,
no single smart grid asset should be planned as fulfilling an
outlying function divorced from the utility's existing and
planned operations and capabilities. if it is, one can seriously
doubt the business justification for acquiring such expensive
assets, as well as that utility's ability to actually implement
cost-effective and efficient smart grid capabilities.

For Further Reading
U.S. department of energy. (2012, dec.). "application of
automated controls for voltage and reactive power management, initial results. [online]. available: https://www.
smartgrid.gov/sites/default/files/doc/files/VVo%20report%20-%20Final.pdf
U.S. department of energy. (2012, mar.). Visioning the
21st century electricity industry: Strategies and outcomes
for america. [online]. available: http://energy.gov/sites/
prod/files/Presentation%20to%20the%20eaC%20-%20Visioning%20the%2021st%20Century%20-%20william%20
Parks.pdf
m. nasri, H. Farhangi, a. Palizban, and m. moallem,
"application of intelligent agents in smart grids for volt/Var
optimization and conservation voltage reduction," in Proc.
IEEE Canada Electrical Power and Energy Conf. london,
ontario, oct. 2012.
m. manbachi, H. Farhangi, a. Palizban, and S. arzanpour, "real-time adaptive optimization engine algorithm for
integrated volt/Var optimization and conservation voltage
reduction of smart microgrids," in Proc. CIGRÉ Canada
Conf., montreal, Sept. 2012.
H. Farhangi, "Smart grid and iCt's role in its evolution," in
Green Communications: Theoretical Fundamentals, Algorithms
and Applications, J. wu, S. rangan, and H. Zhang, eds. boca
raton, Fl: CrC Press, 2012.
g. Stanciulescu, H. Farhangi, a. Palizban, and n.
Stanchev, "Communication technologies for bCit smart
microgrid," in Proc. IEEE PES Innovative Smart Grid Technologies Conf., washington dC, Jan. 2012.
m. manbachi, m. nasri, b. Shahabi, H. Farhangi, a. Palizban,
S. arzanpour, m. moallem, and d. C. lee, "real-time adaptive VVo/CVr topology using multi agent system and ieC
61850-based communication protocol," IEEE Trans. Sustainable Energy, vol. PP, no. 99, p. 1, oct. 2013.

Biography
Hassan Farhangi is with the british Columbia institute of
technology, Vancouver, Canada.

p&e

may/june 2014


https://www http://www.smartgrid.gov/sites/default/files/doc/files/VVo re http://www.energy.gov/sites/

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