IEEE Power & Energy Magazine - May/June 2015 - 85

Grid resiliency depends on many factors,
but a fundamental element is being able to forecast
what is going to happen in advance.

An added value of the IGREENGrid analysis is providing
key performance indicators (KPIs) to compare and objectively
evaluate different proposals. In Figure 3, the KPI of voltage
quality allows for a comparison of how much of the voltage
tolerance range (dashed red lines) becomes a safety band and
consequently the KPI is a measurement of resiliency (the size
of the green area).
A KPI close to one in a certain smart grid implementation indicates high resiliency, given that the voltage is always
close to the target, and most of the band is ready to be used
for contingencies. In Figure 3(c), the voltage goes out of the
limits, and the KPI become negative.
MetaPV is one of the projects analyzed by IGREENGRID
that provides interesting information about the differences
between local and centralized voltage control. In the first case,
each generator injects a given amount of reactive power into the
grid, depending on the locally measured rms voltage value (droop
control). In the second case, a central controller sends reactive
power set points (computed by an optimization algorithm) to the
distributed generators. According to the numerical results, the
adopted centralized control maintains the voltage of the entire
network very close to its nominal value, showing a greater performance with respect to the local control of generators.
The results of this experience have been processed and
computed in terms of IGREENGrid KPIs. Having considered the performance indicator relative to the voltage quality
improvement, it is evident that centralized control is more
effective than local. In fact, comparing the performed voltage quality with the one related to the business as usual
case, the KPI results in 0.35 for local and 0.51 for centralized control.

Predictability and Local Balancing

stochastic by nature, and the "unbalancing" problem may
appear at any place throughout the grids that could have an
impact on the system reliability.
For medium-sized plants, the issue has been faced from
different perspectives at research and development level,
like storage or virtual power plants (VPPs). Another more
adaptable approach to large, medium, and small DRESs
would be forecasting.
The German demonstration ("Zukunftsnetze," grids for
the future) proposed the combined work of a gas storage plant
with a solar farm to make the total generation on the distribution network predictable, allowing for balancing and avoiding
grid operation risks. The idea consists of storing power generated during peak production periods for later moments of high
demand. They store biogas produced by fermentation of agricultural waste. Normally the biogas is directly used to generate electrical or thermal energy by combined heat and power
(CHP). In this case, the biogas is stored in the moments of high
PV generation or when there is a low demand. In times of higher
demand, the stored gas is used to buffer these peaks. Additionally, information from the grid is used to generate a timetable
for the CHP operation. In cases of 100% filled storage, the CHP
operates regardless of high PV generation or low demand.
This approach to support grid resiliency allows for
exploiting the total capacity of solar generation, making the
output power predictable. The tests have shown successful
results, but it is not cost effective when applied to small-size
PV plants.
The VPP proposal is also a valid approach to minimize the
impact of the environment's unpredictability. Additionally,
it doesn't need to implement new generation infrastructures
meant to correct the deficiencies of the renewable ones. The proposal employs the existing medium- and small-sized generation
plants in a certain area, combining their generation capacities to

Given the nature of wind and solar power, there is an associated unpredictability and consequently a problem of accurately
determining the available power
VQ
VQ
to schedule the generation. This
+10%
VQ
difficulty is becoming combined
with the geographical dispersion
-10% Voltage Profile
of nonprogrammable RESs and
the reduction of size of these
(a)
(b)
(c)
generators.
DG is increasing the necesfigure 3. A graphical explanation of the voltage performance evaluation adopted by
sity for balancing loads and genthe IGREENGrid project. (a) Voltage quality 0 < VQ < 1. (b) Voltage quality VQ ~ 1.
(c) Voltage quality VQ < 0.
eration at local level. Both are
may/june 2015

ieee power & energy magazine

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

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IEEE Power & Energy Magazine - May/June 2015 - Cover3
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