Sioshansi F.P. Smart Grid: Integrating Renewable, Distributed & Efficient Energy
Подождите немного. Документ загружается.
a brief overview of the modeling framework is presented
below.
Modeling Framework
This chapter provides an estimate of the potential economic
benefits from the integration of smart grids through the
application of CSIRO's Global and Local Learning Model
(GALLM).
2
GALLM is an international and regional
electricity sector model that features endogenous technology
learning via the use of experience curves. GALLM projects
the global uptake of electricity generation technologies under
business as usual and alternative policy environments.
Currently the model is separated into three zones; developed
countries,
3
less developed countries,
4
and Australia [4]. The
model estimates the least-cost mix of different electricity
generation technologies to meet electricity demand in each
region over time, factoring in different initial generation
capacities [5], different resources [6], [7], [8] and [9] and
different electricity demand growth rates [8].
2
The Commonwealth Scientific and Industrial Research Organisation
(CSIRO; http://www.csiro.au) is Australia's national science agency and
one of the largest and most diverse research agencies in the world.
3
Developed countries in the model include Austria, Belgium, Bulgaria,
Canada, Croatia, Czech Republic, Denmark, Finland, France, Germany,
Greece, Hungary, Iceland, Ireland, Italy, Japan, Latvia, Luxembourg,
Netherlands, New Zealand, Norway, Poland, Portugal, Romania, Russia,
Slovenia, Spain, Sweden, Switzerland, United Kingdom, Ukraine, and
United States.
4
381
Less developed countries are countries not included as developed
countries.
In the model, the uptake of a limited set of DG technologies
has been considered by modeling generic combined heat and
power (CHP), generic fuel cells, and rooftop PV. Large-scale
intermittent technologies include wind, solar thermal,
large-scale PV, wave, and ocean/tidal current.
Non-intermittent large-scale technologies in the model are
black and brown coal, pf (pulverized fuel); black and brown
coal combined cycle; black and brown coal with carbon
capture and storage (CCS); gas open and combined cycle; gas
with CCS; nuclear; biomass; hot fractured rocks;
conventional geothermal; and hydroelectric. Table 7.1 lists
the main technology cost and performance assumptions of
GALLM.
Table 7.1
Technology Cost and Performance Assumptions of GALLM
Technology
Capital Cost ($/
kW Sent Out)
Efficiency
HHV (%)
Capacity
Factor
(%)
O&M
($/
MWh)
Learning Rate
(%)
Black coal,
pf
1948 35.1 80 5.48 NA
Black coal,
IGCC
3004 41.0 80 7.78 2.0
Black coal
with CCS
4348 25.2 80 9.83 5.0
Brown coal,
pf
2895 28.0 80 7.34 NA
Source: CSIRO.
Notes: NA: not applicable; HHV: Higher heating value; IGCC: Integrated
gasification combined cycle; O&M: operating and maintenance; BOP: Balance of
plant; DEV: Developed countries; AUS: Australia; LDC: Less developed
countries; inst: installations.
382
Brown coal,
IGCC
3320 41.0 80 8.40 2.0
Brown coal
with CCS
7380 17.0 80 10.55 5.0
Gas open
cycle
449 20.0 20 19.97 NA
Gas
combined
cycle
892 49.0 80 7.70 2.0
Gas with
CCS
2900 40.0 80 25.69 2.2
Nuclear 3971 34.0 80 6.99 3.0
Hydro 3246 NA 20 21.98 NA
Biomass 2924 26.0 45 15.54 5.0
Solar
thermal
5898 NA 25 24.23 14.6
Hot fractured
rocks
4633 NA 80 11.99 8.0 (BOP)
Conventional
geothermal
2878 NA 80 11.99 8.0 (BOP)
Wave 7000 NA 50 32.13 9.0
Ocean
current
5200 NA 35 38.61 9.0
Wind
1518 (DEV);
1742 (AUS);
1389 (LDC)
NA 29 15.33
4.3 (turbine) 11.3
(AUS inst) 19.8
(global inst)
PV rooftop
10529 (DEV);
9960 (AUS);
11858 (LDC)
NA 20 2.14
20.0 (module) 17.0
(BOS all regions)
PV large
scale
6969 (DEV);
6615 (AUS);
7867 (LDC)
NA 20 12.83 As above
CHP 1600 42.1 41 6.53 NA
Fuel cells 12500 50.0 30 55.5 20.0
Source: CSIRO.
Notes: NA: not applicable; HHV: Higher heating value; IGCC: Integrated
gasification combined cycle; O&M: operating and maintenance; BOP: Balance of
plant; DEV: Developed countries; AUS: Australia; LDC: Less developed
countries; inst: installations.
383
To encourage the development of CHP, heat credits
5
have
been added into the objective function of the model. The CHP
heat credit was initially 31.08 AU$/MWh, and this is reduced
by 0.025% per year to reflect the fact that there may be an
oversupply of heat.
6
European feed-in-tariffs have also been
included for rooftop PV, assuming 0.19 AU$/kWh and
reduced by 8% per year over 20 years. Table 7.2 provides a
list of the fuel cost and CO
2
emission rate assumptions used
in the modeling.
5
This is an allowance for heat production [10].
6
At the time of writing the Australian dollar (AU$) is roughly at parity
with US$.
Table 7.2
Fuel Cost and Emissions Assumptions of GALLM
Fuel Type Cost of Fuel (AU$/GJ) Emissions (kgCO
2
/GJ)
Brown coal 0.5 93.6
Black coal 1.0 95.29
Natural gas 0.9–30.0 62.9
Biomass fuel 0.6–10.0 0
Uranium 0.7–30.0 0
Source: CSIRO.
In GALLM, most technological development occurs as a
result of global technology deployment such that all countries
benefit from the spill-over effects of other countries investing
in new technologies. Wind and PV are exceptions. Both wind
and PV were assigned two experience curves: a global curve
for the prime mover; and a local curve for installation and
balance of system (BOS), where BOS only applies to PV
384
costs. The twin experience curves for wind turbines and their
installation in developed countries are shown in Figure 7.3.
Figure 7.3
Experience curves for wind turbines and their installation in developed
countries.
Source: CSIRO, Note: International turbine wind turbine data with the experience curve and
international installation data with the experience curve. Each data point represents a year where
the first data point for turbines is from 1998 and the first data point for installation is from 2000.
Another feature of GALLM is that capital cost reductions can
differ from that expected by the learning curve. For example,
before the global economic crisis in 2008, the capital cost of
energy technologies was extremely high. In regard to wind
energy, the price rise was due to high demand and the
resultant increased profit margins and higher materials prices
that this allowed [11]. These market forces have been
included in GALLM as a “penalty” constraint; if demand for
one technology exceeds one third of total required new
installed capacity, then a premium is placed on the price of
385
that technology based on historical data for wind turbines [8].
One effect of implementing the penalty constraint in the
model is that it creates a disincentive for too rapid an uptake
of any single energy technology [4].
Scenario Definition
The purpose of this chapter is to estimate the potential
benefits of greater renewable and distributed generation that
may be facilitated by the roll out of smart grids on a broad
scale. To capture this, a new constraint is implemented in
GALLM. It constrains the uptake of technology by imposing
a variable constraint or cap on generation from intermittent
renewable technologies including wind, solar thermal, PV,
wave, and ocean current. Four scenarios that vary this
constraint were considered in the current modeling:
• Case A: a base case with a maximum 20% cap on
generation of intermittent renewable technologies by
region;
• Case B: a maximum 30% cap in 2030 on generation of
intermittent renewable technologies by region that
increases linearly from 20% in 2020;
• Case C: a maximum 40% cap in 2030 on generation of
intermittent renewable technologies by region that
increases linearly from 20% in 2020;
• Case D: a maximum 50% cap in 2030 on generation of
intermittent renewable technologies by region that
increases linearly from 20% in 2020.
In Case A, for example, the model does not force the
deployment of intermittent renewable technologies in each
386
region to 20%. Rather, the 20% is a maximum share of
electricity demand in each region that can be met by
intermittent renewable technologies if it is economic to do so.
This 20% cap, while quite arbitrary, represents a value similar
to limits often quoted in industry discussion (e.g., UKERC
[12] and Myers et al. [13]). The 20% cap also represents an
aspirational renewable target in some countries (e.g.,
Australia ORER [14] and the EU-25 ECCA [15]) and some
U.S. states [16], to be reached by the year 2020. Figure 7.4
shows the current technology mix of world power generation.
It shows that intermittent renewables, labeled as “other
renewable” in Figure 7.4, represent a small proportion of
current global power production.
Figure 7.4
Percentage of world power generation by fuel type.
Source: IEA [17]
387
While some regions may have higher or lower targets, it is
worth noting that such targets are generally set at a high level
that simply assumes the intermittent sources can be
accommodated without problems or constraints. Ultimately
the actual amount that can be accommodated will be highly
dependent on the type and size of generation technology, the
location of the source of generation, the topology of the
network, and market trading structures. In the analysis
presented here, it is assumed that the same limit applies
everywhere. The modeling then considers a future where the
percentage of intermittent generation is able to increase on the
presumption that smart grid technologies and techniques
discussed in this book enable an increase in their use.
For results displayed in this chapter, GALLM was operated
under a business-as-usual (BAU) case and two different
carbon price scenarios in which the price varied within
regions in GALLM as shown in Figure 7.5. The first case, the
550 ppm, represents a lower price path consistent with a
target of 550 parts per million (ppm) CO
2
equivalent (CO
2
-e)
atmospheric concentration corresponding to 2.8–3.2°C
average global temperature increase by 2050, consistent with
other studies by the International Energy Agency (IEA) and
the UN's Intergovernmental Panel on Climate Change or
IPCC [18] and [19]. The second case, the 450 ppm, represents
a higher price path with a target of 450 ppm CO
2
-e
corresponding to 2.0–2.4°C temperature increase. As a point
of comparison, the target ranges for the United States, the
European Union, and Japan all correspond to entitlements for
a global agreement between 450 ppm and 550 ppm
atmospheric concentration of CO
2
-e [20].
388
Figure 7.5
Carbon price trajectories used in the modeling for the 450 ppm and 550 ppm
cases.
Source: Commonwealth of Australia [21]
The corresponding carbon prices associated with the 450 ppm
and 550 ppm targets are shown in Figure 7.5, taken from the
Commonwealth of Australia [21] report examining the
impacts of carbon pricing on the Australian economy. This
report showed that higher carbon prices facilitated greater
uptake of intermittent renewable technologies. For the 550
ppm scenario the prices start at AU$20 per ton of CO
2
-e and
increase by 4% each year. For the 450 ppm case the starting
price is AU$43 per ton of CO
2
-e rising at 4% per year. For
modeling purposes it is assumed that a global emissions
trading scheme (ETS), similar in nature to the one adopted in
the EU, is adopted by all developed countries on
commencement in 2013 and that developing countries begin
trading in 2025.
389
It should be noted that there is currently no national or global
commitment to implement any of the modeled schemes.
Hence these two carbon price trajectories are only a guide to
possible future carbon prices were such schemes to be agreed
and implemented some time in the future.
Other groups, such as the International Energy Agency, in
their modeling use an emissions reduction target scenario, the
“BLUE Map,” consistent with reaching 450 ppm of
atmospheric concentration of CO
2
-e, rather than assigning a
carbon price [5] and [19]. This chapter uses the carbon prices
in Figure 7.5, rather than an emissions reductions target used
by the IEA. This is because GALLM is a partial equilibrium
model and thus only covers electricity generation, not other
emitting sectors such as transport and agriculture. Figure 7.6
shows the estimated emissions reduction under the 550 ppm
and 450 ppm scenarios and the IEA “BLUE Map” scenario.
Figure 7.6
390