Sioshansi F.P. Smart Grid: Integrating Renewable, Distributed & Efficient Energy
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methods have been suggested based, for example, on artificial
neural networks and fuzzy controllers [3] and [9].
Planning and Design of Microgrids
The placement of distributed resources into microgrid
topologies is a multi-faceted problem of greater complexity
than may first be evident. Issues of installation cost,
environmental impact, line loss, grid connectivity, reliability,
resource longevity, reuse of waste heat, capacity for
intentional islanding, and physical constraints all affect the
decision-making process. The interaction between these
distinct objectives, the complexity of power flows, and the
relatively small number of existing practical microgrid
installations mean that heuristics or rules-of-thumb are
inappropriate if an optimal or near-optimal system
configuration is required. In a bid to move away from
heuristics, contemporary research [10], [11], [12], [13] and
[14] has examined automating this design process through a
range of computational intelligence techniques.
Irrespective of the planning approach chosen, the need for
reliable and comprehensive data further complicates the task
of planning a microgrid development. Any quality microgrid
plan will be built on knowledge of the types of load profile
that are to be expected, the seasonal characteristics that will
affect renewables, and the specifics of technologies that may
be used, for instance. Where possible, it is likely that much of
this data will need to be generated through models, which
raises concerns both with respect to cost and accuracy.
Moreover, for some microgrids, the exact nature of loads may
not be known, which introduces significant noise into the
planning process.
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Cost of Microgrids
A barrier to the uptake of microgrids is often the perceived
higher financial cost of distributed energy resources
compared to the available generation from centralized power
stations. In particular, the higher per-kilowatt price of
distributed generators is often cited as a drawback, while
doubts remain as to the ongoing maintenance and operation
costs associated with newer distributed technologies. Recent
studies, however, have found that the cost of generating
power in a microgrid is comparable with present electricity
supply, as long as support for photovoltaics is available [15].
At this early stage of the technology's development, it is clear
that the most economic use of microgrids remains in
developing countries without infrastructure, since the
installation cost of the microgrid must be compared with the
cost of installing high-voltage transmission lines [16].
Financial cost features regularly in optimization studies of
microgrids, typically offset against other desired properties,
such as CO
2
reduction. A great deal of effort is being given to
developing microgrid models that will help calculate this
offset. A case study of the hypothetical installation of
distributed generation in a hotel in the United States reveals a
10% cost saving and 8% CO
2
saving [17].
Integrating Renewables into the Microgrid
A central assumption of the microgrid concept is the inclusion
of renewable generation sources. Though such resources
afford the microgrid many advantages, not least in the areas
of environmental impact and fuel costs, they also complicate
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the planning and operation of the microgrid significantly due
to their intermittent production and their need for power
inverters. While Chapter 6, Chapter 10 and Chapter 11 of this
book discuss broader issues around the integration of
renewable generation, the following sections consider this
challenge in the particular context of microgrids.
Inverters
The use of renewables will almost certainly necessitate the
inclusion of inverters, both to convert from direct to
alternating currents where necessary and to provide some
level of frequency control. The key to the successful
integration of inverters into the microgrid is to facilitate
parallel operation of inverters serving potentially
heterogeneous sources without loss of synchronization,
propagation of harmonics, or loss of stability in general. In
the case where a small number of inverters are used, this
presents a challenging, though well-studied, dilemma. If the
inverters use centralized or master-slave control, current
sharing is enabled but high-bandwidth links are required to, at
the least, facilitate the distribution of error signals [18]. In
contrast, distributed, on-board control reduces the bandwidth
but at the cost of synchronization difficulties [18].
For larger microgrids with numerous inverters, the available
literature is less comprehensive, and doubts remain as to the
scalability of techniques proposed for small-scale systems. In
particular, given the complex interactions that may occur
between inverters [18], there is an increasing potential for
problematic emergent phenomena to appear as the size grows.
Predicting and controlling such behavior are difficult and
have not been sufficiently explored in preceding works.
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Intermittency of Renewable Generation and the Need for
Storage
Given the intermittency of power supply from wind and solar
generators, the obvious response is to add energy storage to
intermittent sources, allowing for stored reserves to be
accessed when environmental conditions are unfavorable. The
fundamental challenge with such an approach lies in selecting
a suitable amount of storage and in optimally using that
storage.
For an isolated microgrid, the storage must be sufficient to
satisfy gaps between generation and load across both small
and large time-scales. Thus, in order to correctly size the
storage devices in advance, accurate and reliable behavioral
models for intermittent sources are required. The use of such
models inevitably raises concerns about quality, and the
fidelity of intermittent source models is seldom high.
Moreover, even if the models are accurate, selecting storage
that minimizes cost while satisfying both short- and long-term
commitments is a complex optimization task.
Dispatch of stored energy is complicated by a similar need to
serve both short- and long-term goals. In order to assess
which stores should be accessed and at what rate they should
be discharged, it will be necessary to implement intelligent
control systems that are capable of handling noisy and
dynamic data. Such systems must also correctly assess when
energy storage devices should charge and how. Beyond the
complexity of building such intelligent controls, it is also
likely that the controllers will require high-speed
communications between microgrid devices in order to
capture system state information.
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Islanding
A central benefit of the connected microgrid is the capacity to
ride-through failures that occur on the utility grid with limited
loss of localized service. By rapidly disconnecting from a
faulting utility grid system (intentional islanding), adjusting
local generation, and shedding non-priority loads, particularly
high levels of reliability can be guaranteed for priority
resources residing on the microgrid. Achieving this goal
though is not as straight forward as the high-level description
may imply.
• First, the fault condition must be detected, which is quite
a challenge given the significantly different fault levels
between the utility grid and microgrid, and the two-way
power flows across the PCC.
• Second, the entire microgrid must be coordinated to take
collective action, including disconnecting from the utility
grid, and either black-starting (shutting down and
re-starting in island mode, the simplest path of action), or
maintaining supply (which requires very fast response
times from loads and generators on the microgrid and
sophisticated interfacing hardware between the microgrid
and utility grid).
• Third, all controllers must change their logic to support
operating in this different mode, which may require new
operating parameters for inverters, and fault detection
states for protection equipment.
• Fourth, the loads in the microgrid must be adjusted to
match the available generation capacity, which is almost
certainly reduced. In its simplest form, this will require a
prioritized table of loads to drop when generation capacity
is limited; however, ideally an intelligent load management
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scheme would be used to dynamically and continuously
match load with available supply.
• Fifth, when the fault is cleared, the microgrid must be
able to make the transition back to normal service, which
will require re-synchronization of all components in the
microgrid with the operating state of the wider utility grid.
Microgrid Modeling
Microgrids are expensive to install and set up for the purposes
of research, and the first approach will usually be to derive
some kind of model. Comprehensive models exist for
traditional rotating generators, but models for distributed
generation are much more problematic, as these devices rely
on power electronics interfaces such as chargers and inverters.
Models of the operation of microgrids and their component
devices are required to analyze issues such as microgrid
stability, fault behavior, interaction with the utility grid, and
the transition between grid-connected and island modes of
operation. As with all models, there are important issues
relating to system calibration, scope of assumptions, and
result validation. Though it is a practical reality that all
microgrid models will only ever be an approximation of
real-world behavior, accuracy is paramount to success (as
discussed by Jayawarna et al. [19]), and care must be taken to
identify and consider the impact of simplifications on model
fidelity. Currently, there is a distinct scarcity of commonly
accepted comprehensive models for microgrid analysis.
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Energy Management
To achieve goals such as the minimization of fuel
consumption, an energy management system (EMS) is
expected to be responsible for generator dispatch and for
adjusting generator set-points and parameters. However, this
adjustment has implications for the stability of the microgrid,
and therefore the stable limits of parameters must be well
known for a range of operating conditions [20] and [21].
The importance of the EMS is underlined by the fact that it
may be responsible for bidding on a deregulated market, the
performance of which may determine whether or not the
microgrid is able to meet its economic goals. The EMS may
also be responsible for reconfiguration of the microgrid
during transition to and from island mode [22] and [23].
This is a complex problem, and it is clear that traditional
industrial automation techniques are unlikely to meet these
needs. There is therefore a need for the development of a new
generation of management systems [24]. In response, a
redundant hierarchical tree structure was proposed in Ishida et
al. [25], while [26] develops a full-scale small-signal model
for the entire microgrid and obtains settings via dynamic
analysis. A less traditional approach to an intelligent EMS is
using an artificial neural network [9]. Though such
approaches are promising, it is clear that more investigation is
needed.
Finally, a related issue is the need for prediction of generation
required. Though a host of works exist in this area, including
[27] who use a neural network approach, the fidelity of the
prediction will invariably be drawn into question, while
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inevitable short- and long-term inaccuracies leave any
intelligent system prone to sub-optimal performance.
Policy, Regulation, and Standards
The microgrid concept is relatively new, and it is not
surprising therefore that the regulatory framework for
integration of microgrids into the wider grid is still
developing [28]. However, getting this framework right is
essential as it deeply affects the economic benefits of
microgrids [29]. It is clear that the current framework poses
some barriers for the uptake of the microgrid [15]. For
example, IEEE Standard 1547 requires that grid-connected
power inverters can detect a grid fault and shut down in that
event. Consequently, commercial inverters have been
designed to do that, and there is no incentive for them to offer
island transition and support of uninterruptible supply in
microgrids. This standard is driving research into the static
switch, which can disconnect and reconnect in sub-cycle
times [30].
At the operational level, there is no agreed policy relating to
how microgrids will behave under different operating
conditions, such as with light or heavy loads, with or without
grid connection, or after communication failure (see, for
example, Phillips [4]). Research is being conducted into
standards for the operation of microgrids [5] and a new
generation of energy management systems are being designed
to meet the challenges, in particular to meet standard
EEC61970 [24]. The work, however, is only in its infancy.
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Existing Installations with Microgrid Features
Although the microgrid concept has only been formalized in
recent years, there are numerous real-world networks that
satisfy at least some of the requirements of a functional
microgrid. These practical installations provide valuable
insight into both the operational issues that must be addressed
and the advantages that are offered within this new power
paradigm. To understand the general trends in deployed
microgrids, we reviewed the information available in this
area. While the area is so immature that it is difficult to obtain
direct references detailing particular installations, our research
has uncovered a variety of information on today's existing
microgrids, uncovering approximately 40 significant
microgrid installations worldwide. A sample of these is listed
in Table 8.3.
Currently, many of the reviewed systems are relatively
simple—based on bi-generation setups (most often
wind-diesel), with a non-renewable source used to offer
baseline support to an intermittent renewable technology.
These generation sources are seldom co-located with loads
and instead mimic the prevailing centralized generation and
distribution approach, albeit with a more interesting mix of
outputs, a smaller range, and lower capacity. Since most such
systems are used to service small remote communities, such
as those on Cape Verde Island and Flores Island, to name but
a few, it also seems unlikely that they offer any level of
control over loads, be it through demand management or
otherwise.
In reviewing the general trends across existing microgrid
installations, we analyzed the types of generation most
commonly used in existing deployments worldwide. Figure
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8.3 shows that wind turbines are by far the most popular
renewable generation technology used in today's microgrids.
Somewhat unexpected is the prevalence of fuel cells, which
are used in significant numbers of the microgrids we have
seen, despite its fledgling nature as a technology and relative
expense. Indeed, fuel cells are used more frequently than the
much more mature microturbine technology. It is possible
that the value of fuel cells in CHP systems, where they can
provide impressive thermal capacity, and their flexibility in
size make them a viable alternative generation source in this
context.
Figure 8.3
Characteristics of existing microgrid installations.
Following our generation analysis, we then considered the
total peak capacity of today's installed microgrids, detailed in
Figure 8.4. Considering this information, there is a reasonably
even distribution of microgrid sizes across the reviewed
systems, covering microgrids that generate less than 20kW to
those that produce more than 60MW.
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