Sioshansi F.P. Smart Grid: Integrating Renewable, Distributed & Efficient Energy
Подождите немного. Документ загружается.
accommodate the technical performance characteristics of the
generation mix as it changes in time.
One example of a smart grid response to intermittency is the
semi-scheduled rules for large intermittent plant in Australia
[2]. In this case the market operator (AEMO) established
rules for the operation of large intermittent plant that
constrains the divergence of output of these plants from their
nominated dispatch levels. By constraining large-scale
fluctuations, the market operator is able to maintain system
balance more readily than it could if these sources were left
uncontrolled. In a related activity AEMO also commissioned
a wind forecasting system to improve efficiency of overall
dispatch and pricing, and to permit better network stability
and security management [2]. It is expected that solar
forecasting will be built in the near future. If non-renewable
large-scale centralized plant begin to be dominated by slow
reacting facilities such as nuclear, then mechanisms such as
those used by AEMO may become even more important to
maintain system balance.
Much of the context for current smart grid discussion relates
to distribution networks. A major reason for this is that
historically they have acted passively, with system balance
provided by large centralized plant matching aggregated
demand to supply delivered through the transmission network
as noted above. These large-scale operations can be
considered “smarter” than their distribution counterparts as
they already contain aspects of measurement and control. In
many ways the emergence of smart grid technologies in
distribution networks is driven by a need to bring these
systems into line with the automated measurement and
control of the large high-voltage networks.
401
One emerging response in distribution networks is demand
management through automated control of individual
devices—such as air conditioners, pool pumps, refrigerator
compressors, and so on—and consumer response to varying
tariff structures, such as dynamic pricing.
7
In the future this
is likely to play a significant role in controlling local demand
as covered in Chapter 2 and Chapter 9; demand management
may make it more easy to balance supply, particularly if it is
supplied by more variable sources.
7
Chapter 8 and Chapter 12 describe these options in more detail.
The role of storage in buffering the impacts of intermittency
is an area of growing interest in both transmission and
distribution systems, as further described in Chapter 5. There
are many different types of storage devices, such as batteries,
thermal, compressed air, flywheels, ultra-capacitors, and
superconducting magnetic devices. These devices can store
and release energy at significantly different timescales and
efficiencies. This variation in performance affects their
potential application and arbitrage opportunities in the energy
market. In contrast to these statically located devices, the
emergence of electric vehicles, covered in Chapter 18 and
Chapter 19, provides both a temporally and spatially varying
storage device that may contribute both to the use and supply
of energy, with its effects most pronounced in distribution
networks.
A critical aspect of the modeling presented here is how the
energy mix changes over time under different scenarios. The
results show the emergence and dominance of fundamentally
different types of generation technology, which vary in time
and by end-use sector. In an earlier analysis [22] of the
402
Australian energy market and the potential impact of DG, the
modeling showed there is a large potential for gas-fired co-
and tri generation to economically reduce emissions in the
near future. In time, this potential is taken over by renewable
technologies as their price becomes more favorable from
increased learning and higher carbon prices. These findings
are repeated here in the global modeling for rooftop PV and
to a lesser extent CHP.
In this case there are important implications for distribution
network planning due to the manner in which the generation
devices are connected into the grid. Taking the Australian
case as an example, the increased use of large gas-fired co-
and trigeneration plant is more ideally suited in the
commercial and industrial sectors where waste heat can be
well utilized and it is predicted to be more economically
viable in the near term. PV on the other hand is predicted to
be predominantly installed in the residential sector in later
years after it becomes more economically attractive.
Gas-fired CHP units are typically connected to the grid
through a generator as a synchronous machine. These
machines can produce and sustain large fault currents. A fault
current is an abnormal current in a circuit due to a fault
(usually a shortcircuit). The maximum (or making) fault
current occurs in the first 20 ms while the steady-state fault
current follows after approximately 40–60 ms. To protect a
circuit, the fault current must be high enough to operate a
protective device as quickly as possible, and the protective
device must be able to withstand the fault current. A
calculation of fault currents in a system determines the
maximum current at a particular location, and this value
determines the appropriate rating of breakers and fuses.
403
Changes to fault currents from this type of installation are
already posing challenges to some network operators
particularly in central business districts (CBDs). Considerable
effort will be required to manage their potential impact by
appropriately locating control equipment such as
superconducting fault current limiters, one example of an
emerging smart grid technology.
Intermittent renewable generators on the other hand are
connected to the grid via inverters that change DC output
from the generating device (e.g., solar panel) to an AC
waveform for export to the grid. These devices do not sustain
large fault currents but can add harmonic distortions to the
network, change voltage profiles on feeders, and be
disconnected if voltage levels exceed preset bounds. In this
case the installation of an integrated voltage control system
including software and hardware will be required as part of a
smart grid solution to ensure the successful integration of DG.
Potential equipment includes automatic load tap changers,
switched capacitors, medium voltage sensors, customer
meters, and inverters able to operate at lagging or leading
power factor.
Detecting and isolating faults, restoring operation, controlling
voltage, and controlling real and reactive power flows will be
some of the significant issues for smart grids to address if
these generators are to reach their full economic and
environmental potential. Since the type of technology and
location in which it is installed will vary in time, the
development of the smart grid will need to be well planned
and flexible accommodate this complex evolution in supply
and demand. This has implications both for technological
development and associated policy and regulation. In
404
countries such as Australia where the once vertically
integrated system has been separated for economic efficiency
reasons, this coordination may be potentially more difficult.
Furthermore the disaggregation may make it harder to
measure and attribute the value of individual actions to the
system as a whole, which could inhibit the uptake of the most
efficient solutions [22].
As noted above, the present analysis does not attempt to
specify how the smart grid should evolve to meet the potential
change in energy supply. Instead the focus is on the savings
that might be achieved by allowing intermittent renewable
technologies to reduce greenhouse gas emissions. Below the
discussion considers how the savings may compare to costs of
smart grids already identified by organizations such as EPRI.
Value of Potential Benefits
While the previous figures show how the mix in generation
technologies could vary in time, Table 7.5 provides a
summary of the undiscounted savings that may be achieved
by increasing the amount of intermittent generation in the
grid. This analysis has not specified how the intermittency
may be accommodated as many of these technological issues
are examined elsewhere within this book. Instead it focuses
on the relative savings that could be realized from changes to
capital expenditure, operation and maintenance, fuel and
carbon prices, and revenues generated from feed-in-tariffs and
heat credits for centralized and decentralized generation.
From the table it is obvious that by 2050 very significant
savings—potentially up to AU$20 trillion—could be achieved
worldwide if the amount of intermittent supply can be
405
increased in response to the world's reduction of greenhouse
gas emissions.
Table 7.5
Undiscounted Cumulative Savings AU$ Billion from Increasing Intermittency
Relative to the 20% Base Case (Case A) by 2050
Case B (Max 30%) Case C (Max 40%) Case D (Max 50%)
BAU 2680 2678 2695
550 ppm 9577 8688 10522
450 ppm 12024 18997 20763
Source: CSIRO.
The value of AU$20 trillion does not include the costs
actually spent on upgrading the network to a smart grid to
enable intermittent sources to reach their potential. To
estimate this cost, estimates from EPRI [1] for the United
States have been extrapolated to a global scale. EPRI [1]
estimates that upgrading the U.S. network to a fully
functioning smart grid will cost between US$338 billion and
476 billion by 2030. Assuming that this level of expenditure
allows the modeled levels of intermittency to be achieved,
and extrapolating the upper end cost ($476 billion) using IEA
data on U.S. and world demand, by 2050 roughly AU$6.4
trillion will be needed to upgrade the global electricity
networks to a smart grid, all else being equal. When this is
combined with the savings in generation from utilizing more
distributed generation and large-scale intermittent
technologies, it means that the total global cumulative savings
from installing a smart grid, assuming it allows 50%
intermittent generation onto networks, could be as high as
AU$14 trillion by 2050 if the world acts to reduce
atmospheric concentration of CO
2
e to 450 ppm.
406
It is important to note that the modeling indicates most of
these savings come in later years, from 2040 onwards, after
current long-lived stock with sunk costs have retired and large
amounts of new generation come online and are able to
become profitable; that is, run for sufficient time to recover
initial investment. It is also important to note that the result is
formed through comparison to a base case where there is an
assumed intermittency limit of 20%. This limit is quite
arbitrary for the purpose of this high-level study and in reality
will vary by location due to the types and size of generation
assets, installation location of generation within the grid,
topologies of the electrical network, and market mechanisms.
While this examination is a simple high-level assessment, the
outcomes show that it is an important consideration as the full
benefits of smart grids will only be captured by examining the
long-term changes that can occur through advanced operation
of the electricity network, which takes into account all costs
and benefits. The study also highlights the complexity in
designing a smart grid that takes into account all potential
changes in an evolving electricity system as consumers react
to rising prices and the challenge of reducing emissions.
This study has shown that substantial savings could be
obtained by allowing an increase in the use of centralized and
distributed intermittent renewable generation. These savings
are above and beyond those typically noted in cost-benefit
assessments of smart grids in which network-specific aspects
such as increased reliability, customer engagement, and asset
performance are considered. While these factors are
exceptionally important, the high-level analysis presented
here shows that even further benefit can be captured by taking
into account the role of smart grids in providing an increase in
407
the amount of intermittent renewable generation able to
participate in electricity markets.
Conclusions
This chapter presents results focused on the potential net
benefits that may occur by allowing a greater proportion of
global energy supply to be met by intermittent renewable and
DG resources out to 2050. It is assumed that the introduction
of smart grids will enable a larger proportion of generation to
be provided by intermittent and local generation devices.
These savings come from reductions in capital expenditure,
fuel costs, operation and maintenance costs, and carbon costs,
and revenue from feed-in-tariffs and heat credits for DG.
Modeling presented here clearly shows that savings from
allowing an increased proportion of intermittent renewable
and distributed generation can be very significant when
considering how the world may meet the dual challenge of
reducing emissions of greenhouse gases while
accommodating the ongoing growth in demand. These
savings are only realized by considering the long-term change
to energy supply because of the lifetimes of the assets
involved. This has important implications for smart grid use,
planning, and development, which will be needed to ensure
these renewable technologies reach their full potential.
When coupled with more traditionally noted benefits such as
increased reliability, security, and consumer awareness, the
development of a smart grid appears to be a very favorable
mechanism to help the world reduce its greenhouse gas
emissions while maintaining current levels of supply enjoyed
in many of the world's developed countries.
408
References
[1] EPRI, Estimating the Costs and Benefits of the Smart
Grid. A Preliminary Estimate of the Investment
Requirements and the Resultant Benefits of a Fully
Functioning Smart Grid. 1022519 Final Report, March
2011.
[2] AEMO, Australian Wind Energy Forecasting System
(AWEFS). http://www.aemo.com.au/electricityops/
awefs.html, 2011 (accessed 30.03.11).
[3] AEMO, Semi-Dispatch of Significant Intermittent
Generation: Proposed Market Arrangements.
http://www.aemo.com.au/electricityops/140-0091.html,
2010 (accessed 20.03.11).
[4] J.A. Hayward, P.W. Graham, P.K. Campbell, Projections
of the Future Costs of Electricity Generation
Technologies: An Application of CSIRO's Global and
Local Learning Model (GALLM). CSIRO Report
EP104982, http://www.csiro.au/resources/
GALLM-report.html, 2011.
[5] IEA, Energy Technology Perspectives 2008. (2008) IEA,
Paris.
[6] Jacobson, M.Z., Review of solutions to global warming,
air pollution, and energy security, Energy Environ. Sci. 2
(2009) 148–173.
[7] Carnegie Wave Energy, Wave Energy as a Global
Resource. http://www.carnegiecorp.com.au, 2010
(accessed 07.01.11).
[8] IEA, IEA Wind Energy, Annual Report 2007. (2008) IEA,
Paris; http://www.ieawind.org.
409
[9] Fridleifsson, I.B.; Bertani, R.; Heunges, E.; Lund, J.W.;
Ragnarsson, A.; Rybach, L., The possible role and
contribution of geothermal energy to the mitigation of
climate change, In: (Editors: Hohmeyer, O.; Trittin, T.)
IPCC Scoping meeting on Renewable Energy Sources
(2008) Lübeck, Germany.
[10] IEA, Projected Costs of Generating Electricity: 2010
edition. (2010) IEA, Paris.
[11] Milborrow, D., Dissecting wind turbine costs, WindStats
Newsletter 21 (2008) 2.
[12] UKERC, The Costs and Impacts of Intermittency: An
Assessment of the Evidence on the Costs and Impacts of
Intermittent Generation on the British Electricity
Network, Imperial College London, 2010, ISBN 1 90314
404 3.
[13] Myers, K.; Klein, S.; Reindl, D., Assessment of high
penetration of solar photovoltaics in Wisconsin, Energy
Policy 38 (11) (2010) 7338–7345.
[14] ORER, LRET/SRES – The Basics.
http://www.orer.gov.au/publications/
lret-sres-basics.html, 2011 (accessed 15.05.11).
[15] ECCA, The EU Climate and Energy Package.
http://ec.europa.eu/clima/policies/package/index_en.htm,
2011 (accessed 30.03.11).
[16] Jansson, P.M.; Michelfelder, R.A., Integrating
renewables into the U.S. grid: is it
sustainable?Electricity J. 21 (6) (2008) 9–21.
[17] IEA, World Energy Outlook 2010. (2010) IEA, Paris.
[18] IPCC, Climate Change 2007: Mitigation of Climate
Change, In: (Editors: Metz, B.; Davidson, O.R.; Bosch,
P.R.; Dave, R.; Meyer, L.A.) Contribution of Working
410