Download full-text PDF

Cost-minimized combination of wind power, solar power and electrochemical storage, powering the grid up to 99.9% of the time

Article (PDF Available) inJournal of Power Sources 225(3):60–74 · March 2013with371 Reads
DOI: 10.1016/j.jpowsour.2012.09.054
Abstract
We model many combinations of renewable electricity sources (inland wind, offshore wind, and photovoltaics) with electrochemical storage (batteries and fuel cells), incorporated into a large grid system (72 GW). The purpose is twofold: 1) although a single renewable generator at one site produces intermittent power, we seek combinations of diverse renewables at diverse sites, with storage, that are not intermittent and satisfy need a given fraction of hours. And 2) we seek minimal cost, calculating true cost of electricity without subsidies and with inclusion of external costs. Our model evaluated over 28 billion combinations of renewables and storage, each tested over 35,040 h (four years) of load and weather data. We find that the least cost solutions yield seemingly-excessive generation capacity—at times, almost three times the electricity needed to meet electrical load. This is because diverse renewable generation and the excess capacity together meet electric load with less storage, lowering total system cost. At 2030 technology costs and with excess electricity displacing natural gas, we find that the electric system can be powered 90%–99.9% of hours entirely on renewable electricity, at costs comparable to today's—but only if we optimize the mix of generation and storage technologies.
Figures
Cost-minimized combinations of wind power, solar power and electrochemical
storage, powering the grid up to 99.9% of the time
Cory Budischak
a
,
b
,
*
, DeAnna Sewell
c
, Heather Thomson
c
, Leon Mach
d
, Dana E. Veron
c
,
Willett Kempton
a
,
c
,
e
a
Department of Electrical and Computer Engineering, University of Delaware, Newark, DE 19716, USA
b
Department of Energy Management, Delaware Technical Community College, Newark, DE 19713, USA
c
Center for Carbon-Free Power Integration, School of Marine Science and Policy, College of Earth Ocean and Environment, University of Delaware, Newark, DE 19716, USA
d
Energy and Environmental Policy Program, College of Engineering, University of Delaware, Newark, DE 19716, USA
e
Center for Electric Technology, DTU Elektro, Danmarks Tekniske Universitet, Kgs. Lungby, Denmark
highlights graphical abstract
<We modeled wind, solar, and
storage to meet demand for 1/5 of
the USA electric grid.
<28 billion combinations of wind,
solar and storage were run, seeking
least-cost.
<Least-cost combinations have excess
generation (3load), thus require
less storage.
<99.9% of hours of load can be met by
renewables with only 9e72 h of
storage.
<At 2030 technology costs, 90% of
load hours are met at electric costs
below todays.
article info
Article history:
Received 7 June 2012
Received in revised form
13 September 2012
Accepted 15 September 2012
Available online 11 October 2012
Keywords:
Variable generation
Renewable energy
Electrochemical storage
High-penetration renewables
abstract
We model many combinations of renewable electricity sources (inland wind, offshore wind, and
photovoltaics) with electrochemical storage (batteries and fuel cells), incorporated into a large grid
system (72 GW). The purpose is twofold: 1) although a single renewable generator at one site produces
intermittent power, we seek combinations of diverse renewables at diverse sites, with storage, that are
not intermittent and satisfy need a given fraction of hours. And 2) we seek minimal cost, calculating true
cost of electricity without subsidies and with inclusion of external costs. Our model evaluated over 28
billion combinations of renewables and storage, each tested over 35,040 h (four years) of load and
weather data. We nd that the least cost solutions yield seemingly-excessive generation capacitydat
times, almost three times the electricity needed to meet electrical load. This is because diverse renew-
able generation and the excess capacity together meet electric load with less storage, lowering total
system cost. At 2030 technology costs and with excess electricity displacing natural gas, we nd that the
electric system can be powered 90%e99.9% of hours entirely on renewable electricity, at costs compa-
rable to todaysdbut only if we optimize the mix of generation and storage technologies.
Ó2012 Elsevier B.V. All rights reserved.
*Corresponding author. Department of Energy Management, Delaware Technical Community College, 400 Stanton-Christiana Road, Newark, DE 19713, USA. Tel.: þ1 302
453 3099; fax: þ1 302 368 6620.
E-mail address: cbudischak@gmail.com (C. Budischak).
Contents lists available at SciVerse ScienceDirect
Journal of Power Sources
journal homepage: www.elsevier.com/locate/jpowsour
0378-7753/$ esee front matter Ó2012 Elsevier B.V. All rights reserved.
http://dx.doi.org/10.1016/j.jpowsour.2012.09.054
Journal of Power Sources 225 (2013) 60e74
1. Introduction
What would the electric system look like if based primarily on
renewable energy sources whose output varies with weather and
sunlight? Todays electric system strives to meet three require-
ments: very high reliability, low cost [1], and, increasingly since the
1970s, reduced environmental impacts. Due to the design
constraints of both climate mitigation and fossil fuel depletion, the
possibility of an electric system based primarily on renewable
energy is drawing increased attention from analysts. Several studies
(reviewed below) have shown that the solar resource, and the wind
resource, are each alone sufcient to power all humankinds energy
needs. Renewable energy will not be limited by resources; on the
contrary, the below-cited resource studies show that a shift to
renewable powerwill increase the energy available to humanity. But
how reliable, and how costly, will be an electric system reliant on
renewable energy? The common view is that a high fraction of
renewable power generation would be costly, and would either
often leave us in the dark or would require massive electrical storage.
Here we model the hourly uctuations of a large regional grid,
PJM Interconnection, in order to answer these questions. PJM is
a large Transmission System Operator (TSO) in the eastern United
States. It is located geographically in Fig. 1, and described in more
detail in Appendix A. To obtain a multi-year run with constant
system size we analyze calendar years 1999e2002, before its recent
growth, when PJM managed 72 GW of generation, with an average
load of 31.5 GW
a
[2].
To evaluate high market penetration of renewable generation
under a strong constraint of always keeping the lights on, we match
actual PJM load with meteorological drivers of dispersed wind and
solar generation (Fig. 1) for each of the 35,040 h during those four
years. We created a new model named the Regional Renewable
Electricity Economic Optimization Model (RREEOM). Our model is
constrained (required) to satisfy electrical load entirely from
renewable generation and storage, and nds the least cost mix that
meets that constraint. The model is computationally-constrained,
so we did not include additional computing-intensive consider-
ations, such as how much additional transmission is optimum, or
reliability issues not related to renewable resource uctuations.
2. Prior studies
We do not nd the answers to the questions posed above in the
prior literature. Several studies have shown that global energy
demand, roughly 12.5 TW increasing to 17 TW in 2030, can be met
with just 2.5% of accessible wind and solar resources, using current
technologies [3e5]. Specically, Delucci and Jacobson pick one mix
of eight renewable generation technologies,increased transmission,
and storage in grid integrated vehicles (GIV), and show this one mix
is sufcient to provide world electricity and fuels. However, these
global studies do not assess the ability of variable generation to meet
real hourly demand within a single transmission region, nor do they
calculate the lowest cost mix of technologies.
Ekren and Ekren analyzed a small-scale system with batteries,
PV, wind turbines, and auxiliary power [6]. The study assumes
near-constant load (for communications), calculates onlyan energy
capacity for the batteries and not power limits, and optimizes the
conguration for minimum capital cost, not minimum total cost.
Unfortunately, Ekren and Ekren only report their optimized system
cost and area of solar and wind rotor as well as battery size so it is
difcult to analyze these results. In a real grid, we must satisfy
varying load, and with high-penetration renewables, charging and
discharging storage will at times be limited by power limits not just
by stored energy. More typical studies combining wind and solar do
not seek any economic analysis and/or do not look at hourly match
of generation to load (e.g. Markvart, 1996).
Hart and Jacobson determined the least cost mix for California of
wind, solar, geothermal and hydro generation [7]. Because their mix
includes dispatchable hydro, pumped hydro, geothermal, and solar
thermal with storage, their variable generation (wind and photovol-
taic solar) never goes above 60% of generation. Because of these
existing dispatchable resources, California poses a less challenging
problemthan most areasdelsewhere, most orall practical renewable
energy sources are variable generation, and dedicated storage must
be purchased for leveling power output. We cannot draw general
conclusions from the California casesresultsdfor example, one
might plausibly infer fromthis study that it is possibleto have a power
systemwith 60% variable generation, but not a higher fraction; or, we
might conclude that a grid based exclusively on variable generation
would require prohibitively expensive amounts of storage.
We can also compare our model with the HOMER micropower
optimization model [8], which takes hourly load and resource data
and calculates the most cost effective mix of generation. Much like
HOMER, the present work employs a more valid storage cost model
than other studies, because it distinguishes cost per MWh (cost per
stored energy unit) from cost per MW (cost per power transfer
rate). The difference between our study and HOMER is that we
examine a regional power system, whereas HOMER has been used
primarily for small isolated grids such as islands or single resi-
dences or buildings. One of our main objectives is to incorporate
the power-leveling effects of meteorological and resource diversity
on a regional scale.
3. Enough power to meet load
Current electric power systems use fossil fuels as a form of stored
energy, burning fuel at variable rates to generate power matched to
uctuating power demand. The operating principle of fossil gener-
ation is burn when needed, a principle simple enough that it could
be followed withoutcomputers, digital high-speed communications,
or weather forecastingdprecisely the conditions when todays
electric system was created, early in the 20th century.
The ability to reliably meet load will still be required of systems
in the future, despite the variability inherent in most renewable
resources. However, a review of existing literature does not nd
a satisfactory analysis of how to do this with variable generation,
nor on a regional grid-operator scale, nor at the least cost. We need
to solve for all three.
In order to manage variable generation, there are four known
options: geographical expansion, diversifying resources(e.g. solar plus
wind), storage, and fossil backup. All four are employed in this study.
Fig. 1. A map showing the outlines of the current PJM system (blue line) and of the
inland and offshore meteorological stations used for the wind data (pink asterisks).
(For interpretation of the references to color in this gure legend, the reader is referred
to the web or PDF version of this article.)
C. Budischak et al. / Journal of Power Sources 225 (2013) 60e74 61
The rst option is to geographically distribute generation. Wind
from geographically dispersed sites (greater than 1000 km) provides
more consistent power output than generation at nearby locations
with similar weather patterns [9e11] . The current study calculates
the time diversity of generation from geographicallydispersed sites
actual hourly weather (Fig. 1).
The second option, diversifying sources, can similarly level
power production, as has been shown in prior studies of wind and
solar [12,13] for a wider range of renewables [4,7]. These prior
studies showed that combining more diverse renewable resources
produced more level power.
Storage is the third option, typically the most costly. Storage can
ll in supply gaps as well as absorb excess production, and since
storage responds quickly, it can adjust for rapid changes in wind or
solar output. Denholm et al. employed existing spatially dispersed
wind farms in the Midwest along with xed amounts of storage
with the goal of providing level power output, like a baseload
thermal plant [14]. But, the real grid management problem is not to
simulate a single baseload plant by creating constant output;
rather, the problem is to meet uctuating load reliably with uc-
tuating generation, for an entire grid.
A fourth option is to use existing fossil generation for backup.
Although this reintroduces pollution into the system and can only
produce to meet shortfall, not absorb excess electricity, it takes
advantage of existing generation plants, thus costing only fuel and
operations not new plant investment. We model ll-in power from
fossil, not hydro or nuclear power. Hydropower makes the problem
of high penetration renewables too easily solved, and little is
available in many regions, including PJM. We do not simulate
nuclear for backup because it cannot be ramped up and down
quickly and its high capital costs make it economically inefcient for
occasional use. For scenarios in which backup is used rarely and at
moderate fractions of load, load curtailment is probably more
sensible than fossil generation. This could be considered a fth
mechanism, but for simplicity we here conservatively do not
assume load management but ll any remaining gaps of power with
fossil generation.
4. Model parameters
For each of the three power generation technologiesdsolar PV,
offshore wind, and inland winddour input parameters set
a maximum, up to actual resource limits in PJM, and the REEOM
model will try all values from 0 to the maximum, seeking optimum
combinations. The model was run for three storage technologies:
centralized hydrogen, centralized batteries, and grid integrated
vehicles (GIV), the latter using plug-in vehicle batteries for grid
storage when they are not driving (also called vehicle to grid power
or V2G) [15,16]. Wind and solar are parameterized as GW capacity,
storage is parameterized as GW throughputand GWh energy storage
capability. Storage is additionally characterized by losses in storing
or releasing electricity, plus the standby losses while sitting idle. The
models for each individual technology are relatively simple. For
example, we used NRELs program PVWATTS for the solar power
hourly output, and used a typical commercial wind tubinespower
curve to calculate the hourly wind power output with the wind
speed input from NOAA buoys. The method is discussed in more
detail in in Technologiessection of the Appendix. The purpose of
this study is not to model and validate each individual technology in
detail, rather we use accepted and simple models for each tech-
nology, so we can focus on capturing the varying times of generation
and load, and how much storage is needed to level variable
generation.
When running the simulation, for each hour, weather is used to
determine that hours power production. If renewable generation is
insufcient for that hours load, storage is used rst, then fossil
generation. During times of excess renewable generation, we rst
ll storage, then use remaining excess electricity to displace natural
gas. When load, storage and gas needs are all met, the excess
electricity is spilledat zero value, e.g. by feathering turbine
blades. See Fig. 2 for more on the models operation.
In calculating the cost of each combination, we calculate true cost
of electricity without subsidies. In the case of renewable generation,
we exclude current subsidies from the Federal and State govern-
ments. For fossil power, we add in pollutions external costs to third
parties; these are not included in market price, but are borne by
other parties such as taxpayers, health insurers, and individuals.
Here they are included in the costof electricity (see Appendix,Cost
of Electricity). For the cost of renewable energy and storage, we
used published costs for 2008, and published projections for 2030,
all in 2010 dollars. For example, projected capital costs for wind and
solar in 2030 are roughly half of todays capital costs but projected
operations and maintenance (O&M) costs are about the same
(references and explanations of costs are inTables 1 and 2). The 2030
cost projections assume continuing technical improvements and
scaleup, but no breakthroughs in renewable generation nor storage
technologies. For fossil fuels, we use prices plus external coststoday,
without adjustments for future scarcity, pollution control require-
ments, nor fuel shifts. Our cost model is detailed in Appendix,Cost
of Electricity, and as we will show (in Table 4), a simple validation of
the cost model is that unsubsidized renewable energycosts, for 20 08
cost input parameters, are consistent with actual renewable power
costs in recent years. We do not include load growth because we are
comparing the optimum point under differing cost parameters, not
projecting to the power system of 2030. These assumptions have the
advantage that simple and transparent inputs to a complex model
make relationships clearer.
The costs being minimized included the expenses of nancing,
building and operating solar, wind and storage, expressed in cents
per kWh delivered to load. The hours not covered by the system have
an additional cost for fossil electricity; this is tabulated to compute
cost per kWh but it was not part of the cost minimization algorithm.
Separatesimulations were performed for each of the threestorage
technologies, for 2008 costs and for 2030costs, and for three coverage
requirements (30%, 90%, and 99.9% of hours) making a total of 18
RREEOM runs.The coveragerequirement isa constraint on the model,
that is, any one tested combination of generation types and storage
must meet all load that fraction of hours, or we do not evaluate it for
cost. These three percentage requirements allow us to evaluate the
practicality of a range of renewable penetrations. Theyare not system
reliability targetsdas noted subsequently, existing fossil is assumed
to cover part of the load for hours during which renewables were not
sufcient. That is, for example, our 99.9% coverage target does not
imply that the commercial electric reliability requirement of 99.97%
has been reduced, only that the last fraction of a percent above our
99.9% would have to be covered by existing fossil generation (or
demand management, etc.). Each of the 18 REEOM simulations
evaluated about 1.6 billion combinations of technologies to pick the
least cost mix that met each coverage constraint.
We simplify our grid model by assuming perfect transmission
within PJM (sometimes called a copper plateassumption), and
no transmission to adjacent grids. We also simplify by ignoring
reserve requirements, within-hourly uctuations and ramp rates;
these would be easily covered with the amount of fast storage
contemplated here. In addition, we assume no preloading of
storage from fossil (based on forecasting) and no demand-side
management. Adding transmission would raise the costs of the
renewable systems calculated here, whereas using adjacent grids,
demand management, and forecasting all would lower costs. We
judge the latter factors substantially larger, and thus assert
C. Budischak et al. / Journal of Power Sources 225 (2013) 60e7462
(without calculation) that the net effect of adding all these factors
together would not raise the costs per kWh above those we
calculate below.
In order to realistically simulate generation, we use insolation
and wind data from DOE and NOAA for each hour being modeled.
To start with a realistic amount of storage, we run for a 9 month
initialization period to ll storage based on actual generation and
loads. Methods and input values are described in the Appendix.
Our study has some limitations. PJM is a large system operator;
a smaller region would experience less smoothing effect from con-
necting wind across its region. We discounted future renewable
generation at 12%, did not project any increase in fossil fuel prices,
eliminated tax subsidies for renewables but not traditional genera-
tion, and did not project any technology breakthroughs for renew-
ables, all of which raise the comparative cost of renewable power.
5. Results and discussion
Table 3 shows the results from three of the 18 simulations, for
storage using GIV and 2030 technology costs for all three coverage
constraints of 30%, 90% and 99.9%. For other storage technologies
and 2008 technology costs, the relationships are similar to those
shown in Table 3 but quantitative results are not (see Table 8 for all
cases). Results in Table 3 are shown in capacity installed (in GW),
and energy generated or released from storage in average power or
GW
a
.(GW
a
is equivalent to GWh y
1
divided by 8760 h y
1
; using
GW
a
for average power rather than GWh for energy per year makes
it easier to compare among capacity, production, and load.) The
actual PJM system generation capacity and load are shown on the
bottom line. PJM for the years of our study had 72 GW of generation
capacity, which was 230% of its 31.5 GWa average load.
Fig. 2. Flow chart of RREEOM.
C. Budischak et al. / Journal of Power Sources 225 (2013) 60e74 63
Consider rst the power capacity, the leftmost 3 numeric
columns in Table 3. As we move from requiring renewable power to
meet load 30% of hours to 90%, then 99.9% of hours, the capacity of
renewable generators increases and the diversity of renewable
resources increases. For example, at 30%, only the least expensive
renewable, inland wind, is used. But to meet 99.9%, signicant
amounts of all three generation resources are represented, including
higher cost offshore wind and solar; total generation capacity is over
three times that of the current system. Counter-intuitively, when we
increase the requirement from 90% to 99.9%, less storage and
signicantly less fossil backup capacity are needed. This is because,
to meet 99.9% of hours, more renewable generation is required from
more diverse sources.
The energy columns of Table 3 show that the 30% case is rather
different in generated energy from the higher levels of coverage. To
cover 30% of hours with renewables, we generate about equal
amounts of renewable energy and fossil energy, the sum approxi-
mately matching the total need for electricity. That is, 30% coverage
is roughly the equivalent of producing 50% of energy (for the given
GIV storage case), with very little excess generation. By contrast, for
90% or 99.9% of hours, the GW
a
generated exceeds the electrical
energy need of PJM by factors of about 2and 3, respectively!
Table 1
Input parameters 2008 values.
Technology Capital cost per
energy storage
($/kWh)
O&M cost per energy
storage throughput
($/MWh)
O&M net present
cost
a
($/MWh)
Lifetime of energy
equipment (years)
Capital power cost
($/kW capacity)
O&M cost per unit
of power capacity
($/kW/year)
O&M net present cost
a
for 20 years ($/kW)
Photovoltaics 0 N/A 0 N/A 6350
b
12.3
b
91.6
Offshore Wind 0 N/A 0 N/A 4050
b
94.0
b
702
Inland Wind 0 N/A 0 N/A 2022
b
31.8
b
238
GIV 31.8
c,f
247
c,g
1847 15
d
100
c,h
0
c,i
0
Hydrogen 28.1
e,j
0
e
020
e,j
1683
e,k
27.5
e,k
206
Central Batteries
(Lithium titanate)
318
d
0
d
015
d
703
d
12.3
d
91.6
Technology Lifetime power
equipment (years)
Energy cost for
20 years ($/kWh)
Upper energy limit
of resource (GWh)
Power cost for
20 years ($/kW)
Upper power limit
of resource (GW)
Round trip efciency
(fraction)
Storage loss over time
(fraction lost per hour)
Photovoltaics 30
l
N/A N/A 4294
l
186
m
N/A N/A
Offshore wind 30
l
N/A N/A 3168
l
158
n
N/A N/A
Inland wind 30
l
N/A N/A 1507
l
132
o
N/A N/A
GIV 50
p
44.9 382
q
40 239
q
0.81
r
8.33E-05
s
Hydrogen 20
t
28.1 N/A 1889 N0.438
t
1.50E-08
u
Central batteries
(lithium titanate)
15
v
424 N/A 1060 N0.81
r
8.33E-05
s
a
Net present costs were determined using a 12% discount rate over 20 years.
b
Delucchi and Jacobson [3].
c
Kempton and Tomic [15].
d
Burke and Miller [18].
e
Steward [19].
f
The energy cost for the GIV batteries is assumed to be 10% of the cost of standalone Li-ion batteries because of increased cycling, based on our estimate þ$400 divided by
the battery size (24 kWh, the size of the battery in the Nissan Leaf) on board vehicle costs, which will last the w15 year life of vehicle, from Kempton and Tomic [15] Table 5
parameter Cc.
g
This calculation is taken from Table 5 and equations (13) and (15) of [15]. For LiIon, the lifetime (L
C
) is assumed to be 5000 cycles [18].E
s
is assumed to be 24 kWh which is
the size of the battery pack (equivalent to Nissan Leaf). Also, the CYP is the cycles per year which is assumed to be 10. This is updated after the optimum is reached to a more
realistic value. The value is then multiplied by 30% because the depth of discharge is less than 100%, degrading the batteries less. c
d
¼c
bat
L
C
*E
S
*CPY*30% ¼
$247:25
MWh year
h
Capital power costs for GIV were calculated assuming it would cost $1500 for the building connections for a 15 kW battery, which converts to $100,000/MW, with a typical
building lifetime of 50 years. From Kempton and Tomic [15], Table 5.
i
O&M power costs for GIV are considered to be zero because there is no additional maintenance due to GIV power. Maintenance is not increased due to power capability for
GIV, it is calculated as proportional to energy in a separate column. The maintenance costs for controls that are particular to the GIV system, not otherwise required for the
vehicle, are considered negligible.
j
The energy cost is the cost of the steel tank, based on how many kg of hydrogen the tank can hold [20] which is converted to a $/MWh equivalent using the HHV of
hydrogen. It is assumed that the steel tanks last 20 years.
k
The power cost is assumed from the capital cost of the solid oxide fuel cell (SOFC) system and electrolyzer system taking into account the replacement cost of the stack is
30% after ten years (this replacement cost is also discounted at 10 years out). It is assumed that the power systems will last 20 years if this stack replacement is performed. A
SOFC was chosen instead of a proton exchange membrane fuel cell because of the lower cost and higher efciency. The high operating temperature eliminates the need for
precious metals, thus leading to lower cost, and the activation energy decreases, thus leading to higher efciency. This is in contrast to most transportation applications where
SOFC cannot work because of low power density.
l
Delucci and Jacobson [3].
m
This is calculated by scaling up a recent study of capacity potential of south facing rooftop in Newark, DE [21] and extrapolating by population to the PJM region.
n
This is obtained for the PJM region by Baker [22]. For 2008 it is assumed that only the turbines out to 60 m water depth are available.
o
This is obtained for each state from NRELs Wind Powering America Study [23] and then multiplied by the percentage of each state in PJM as shown in the Model
Parameters section. In an NREL capacity study wind sites with less than a 30% bulk capacity factor were discarded, so for an inland wind site to be considered for this simulation
it had to meet the same criterion.
p
The power electronics are mostly considered a part of the regular operation of the vehicle. The only additional electronics are the electronics used to discharge energy back
to the grid. Lifetime is considered to be 50 years.
q
We will limit availability of GIV storage based on the vehicle eet. Although it would make sense to discount the eet by the number who we guess might be participating,
or also the percentage of EVs and plug-in hybrids available, here we assume that 100% of the count of light vehicles in 2002 PJM are available. This is an upper resource limit.
The total vehicle eet per state is from NHTS 2009 survey [24]. 15 kW of storage per vehicle is assumed just as in the cost calculation for GIV.
r
Lund and Kempton [25].
s
Chen et al. [26].
t
Steward [20].
u
Assumed 6.35 mm thick 316 stainless steel at 51.7 MPa at 25
C[27].
v
Burke and Miller [18].
C. Budischak et al. / Journal of Power Sources 225 (2013) 60e7464
This is another important new nding. That is, our cost-minimizing
model of very high penetration (90% and above)dshows that the
least-cost way to cover most or all hours results in producing (or
being capable of producing) 2or more the electrical energy
needed. The 99.9% criterion by denition means fossil would
account for no more than 0.1% ofload; Table 3 shows that the actual
fossil burn required was 0.05% (0.017/31.5).
Fig. 3 shows the four-year simulation for the 99.9% case using
GIV storage, which corresponds to the 99.9% columns in Table 3.
The top graph, in green, is renewable generation; evenwith the mix
of generation types renewable power generation uctuates often,
and can be seen to have lower average output in the summer than
winter. Correspondingly, storage stays mostly lled in the winter
months but discharges periodically during the summer months.
Table 2
Input parameters 2030 values.
Technology Capital cost per
energy storage
($/kWh)
O&M cost per energy
storage throughput
($/MWh)
O&M net present
cost
a
($/MWh)
Lifetime of energy
equipment (years)
Capital power cost
($/kW capacity)
O&M cost per unit of
power capacity
($/kW/year)
O&M net present
cost
a
for 20 years
($/kW)
Photovoltaics 0 N/A 0 N/A 2848
b
12.3
b
91.6
Offshore wind 0 N/A 0 N/A 2128
b
94.0
b
702
Inland wind 0 N/A 0 N/A 1202
b
31.8
b
238
GIV 19.2
c,f
106
c,g
791 15
d
100
c,h
0
c,i
0
Hydrogen 11.2
e,j
0
e
020
e,d
737
e,k
12.2
e,k
91.4
Central batteries
(lithium titanate)
192
d
0
d
015
d
411
d
12.3
d
91.6
Technology Lifetime power
equipment (years)
Energy cost for
20 years ($/kWh)
Upper energy limit
of resource (GWh)
Power cost for
20 years ($/kW)
Upper power limit
of resource (GW)
Round trip
efciency (fraction)
Storage loss over time
(fraction lost per hour)
Photovoltaics 30
l
N/A N/A 1958
l
186
m
N/A N/A
Offshore wind 30
l
N/A N/A 1886
l
248
n
N/A N/A
Inland wind 30
l
N/A N/A 960
l
132
o
N/A N/A
GIV 50
p
26.7 891
q
40.0 239
q
0.81
r
8.33E-05
s
Hydrogen 20
t
11.2 N/A 828 N0.609
t
1.50E-08
u
Central batteries
(lithium titanate)
20
v
256 N/A 503 N0.81r 8.33E-05
s
a
Net present costs were determined using a 12% discount rate over 20 years.
b
Delucchi and Jacobson [3].
c
Kempton and Tomic [15].
d
Gaines and Cuenca [28].
e
Steward [20].
f
The energy cost for the GIV batteries are assumed to be 10% of the cost of standalone Li-ion batteries because of increased cycling, based on our estimate þ$400 divided by
the battery size (56 kWh, the size of the battery in the Tesla) on board vehicle costs, which will last the w15 year life of vehicle, from Kempton and Tomic [15] Table 5,
parameter Cc.
g
This calculation is taken from Table 5 and equations (13) and (15) [15]. For LiIon, the lifetime (L
C
) is assumed to be 5000 cycles [18].E
s
is assumed to be 56 kWh which is the
size of the battery pack (equivalent to Tesla Roadster). Also, the CYP is the cycles per year which is assumed to be 10. This is updated after the optimum is reached to a more
realistic value. The value is then multiplied by 30% because the depth of discharge is less than 100%, degrading the batteries less.c
d
¼c
bat
L
C
*E
S
*CPY*30% ¼
$105:96
MWh year
h
Capital power costs for GIV were calculated assuming it would cost $1500 for the building connections for a 15 kW battery, which converts to $100,000/MW, with
a lifetime of 50 years. From Kempton and Tomic [15], Table 5.
i
O&M power costs for GIV are considered to be zero because there is no additional maintenance due to GIV power. Maintenance is not increased due to power capability for
GIV, it is calculated as proportional to energy in a separate column. The maintenance costs for controls that are particular to the GIV system, not otherwise required for the
vehicle, are considered negligible.
j
The energy cost is the cost of the steel tank, based on how many kg of hydrogen the tank can hold [20] which is converted to a $/MWh equivalent using the HHV of
hydrogen. It is assumed that the steel tanks last 20 years.
k
The power cost is assumed from the capital cost of the SOFC system and electrolyzer system taking into account the replacement cost of the stack is 30% after ten years (this
replacement cost is also discounted at 10 years out). It is assumed that the power systems will last 20 years if this stack replacement is performed.
l
Delucchi and Jacobson [3].
m
This is calculated by scaling up a recent study of capacity potential of south facing rooftop in Newark, DE [21] and extrapolating by population to the PJM region.
n
This is obtained for the PJM region by Baker [22]. For 2030 it is assumed the whole area out to 1 km is available.
o
This is obtained for each state from NRELs Wind Powering America Study [23] and then multiplied by the percentage of each state in PJM as shown in the Model
Parameters section. In an NREL capacity study wind sites with less than a 30% bulk capacity factor were discarded, so for an inland wind site to be considered for this simulation
it had to meet the same criterion.
p
The power electronics are mostly considered a part of the regular operation of the vehicle. The only additional electronics are the controls to regulate charge and discharge.
q
We will limit availability of GIV storage based on the vehicle eet. Although it would make sense to discount the eet by the number who we guess might be participating,
or also the percentage of EVs and plug-in hybrids available, here we assume 100% of the count of light vehicles in PJM in 2002. This is an upper resource limit. The total vehicle
eet per state is from NHTS 2009 survey [24]. 15 kW of storage per vehicle is assumed just as in the cost calculation for GIV.
r
Lund and Kempton [25].
s
Chen et al. [26].
t
Steward [20].
u
Assumed 6.35 mm thick 316 stainless steel at 51.7 MPa at 25
C[27].
v
Gaines and Cuenca [28].
Table 3
Capacity and energy of the cost-minimized mix for 2030 technology costs, using GIV
for storage.
Hours covered Power capacity (GW) Energy produced (GW
a
)
30% 90% 99.90% 30% 90% 99.90%
Solar PV 0 0 16.2 0 0 2.64
Offshore Wind 0 14.4 89.7 0 6.16 38.3
Inland Wind 40.1 126 124 16.3 51.1 50.3
Fossil 61.7 56.9 28.3 15.4 2.18 0.017
Total generation 102 197 258 31.7 59.4 91.3
Storage 27.7 69.2 51.9 1.4 7.99 2.47
PJM 1999e2002 72 31.5 (average load)
C. Budischak et al. / Journal of Power Sources 225 (2013) 60e74 65
This is intuitive because in winter, wind generation is high and
electric load is low, thus storage is kept full. In summer, when wind
generation is low and storage energy is depleted, fossil generators
are run, albeit infrequently.
The nding noted from Table 3 for the 90% and 99.9% GIV cases,
that more energy is generated than is consumed, is true for the other
two storage technologies as well, and goes against the common
notion that generation output should be matched to load. As
examples of this common notion, Markvart proposed to match the
generation energy each month to the load energy each month,
adding storage as needed to balance hourly variability within the
month [12]. Similarly, Denholm analyzes a combination of renew-
able generation, storage and load but evaluates the mix as better if
over-generation is minimized, accomplished by more storage or by
limiting new renewable generators[17]. Our model, of course, cares
not about overgeneration, it simply makes load at minimum cost.
Which criterion is right? Since our model is cost optimizing, it
demonstrates that matching generation to load via more storage
(per Markvart, Denholm and others) would lead to higher cost of
energy than our models selected mix. Thus, one conclusion of our
study is that over-generation is preferred over more storagebecause
excess generation is more cost-effective.
Fig. 4 shows ner detail than Fig. 3, for one challenging week
starting the evening of Friday, 23 August 2002, and compares the
three storage technologies. The top three graphs of Fig. 4 illustrate
load versus generation without storage. Hourly load, our target, is
the black wavy line, the daily load cycle over these 7 days. Gener-
ation is indicated by the lled in areas, with colors distinguishing
inland wind (magenta), offshore wind (blue), and solar (yellow). The
value of using diverse resources can be seen because offshore wind
and solar are often generating when inland wind is not. The middle
column is the optimum generation mix using central batteries. Since
batteries have the highest storage cost, the cost minimization
selects for higher diversity in renewable generation sources, that is,
the middle top graph has more solar and offshore wind despite their
higher cost, resulting in fewer times of insufcient power (insuf-
cient power is shown as the white spaces below the black load line).
(For interpretation of the references to color in this paragraph, the
reader is referred to the web or PDF version of this article.)
Fig. 4s bottom row of three graphs shows how storage acts to
balance generation with load. Here green shows all renewable
generation that either serves load (green areas below black line) or
generation that lls storage (green above black line). Olive is excess
generation,renewable generation that is not used or stored. There
Table 6
Pearsons linear correlation coefcient among the generation technologies and with
load.
Inland wind Off-shore wind Solar Load
Load 0 0.08 0.28 1
Solar 0.18 0.01 1
Off-shore wind 0.46 1
Inland wind 1
Table 7
Summary of external costs used to calculate cost of fossil electricity.
Coal & lignite Oil Gas Nuclear Hydro Wind
Vcent/kWh 1995 5.71 (ExternE) 5.7 1.79 0.39 0.426 0.15
¢/kWh e1995$ N/A 8.15 2.56 0.558 0.609 0.215
¢/kWh e2010$ 18 (Epstein) 11.7 3.69 0.803 0.877 0.309
PJM % generation 48 0.1 12 36 1.9 <0.01
Total PJM external cost cents/kwh 9.45
Load following contract price cents/kwh 8
Total cost with externalities 17.5
Table 8
Least cost optimization results for each of the 18 RREEOM runs in the text, plus an
additional one with H2 and GIV storage.
2008 Costs 2030 Costs
30% 90% 99.9% 30% 90% 99.9%
Hydrogen storage
PV GW 0 0 8.69 0 0 17.4
Offshore wind GW 0 46 91 0 7 68
Inland wind GW 50 101 111 50 124 115
Hydrogen GW 0 58 87 0 58 58
Hydrogen GWh 0 1232 2971 0 2464 2899
Average power provided
to load (GWa)
19.0 30.7 31.5 19.0 31.0 31.5
Average excess power (GWa) 1.18 29.9 54.0 1.18 22.5 47.0
Central battery storage
PV GW 0 0 32.6 0 0 50
Offshore wind GW 0 80 128 0 90 129
Inland wind GW 53 76 83 50 55 61
Li-titanate batteries GW 0 29 58 0 29 58
Li-titanate batteries GWh 0 145 435 0 145 362
Average power provided
to load (GWa)
19.8 30.0 31.4 19.0 29.1 31.3
Average excess power (GWa) 1.64 35.0 62.4 1.18 31.7 56.9
GIV storage
PV GW 0 0 0 0 0 16.2
Offshore wind GW 0 130 146 0 14 90
Inland wind GW 50 101 120 40 126 124
GIV GW 0 0 49 28 69 52
GIV GWh 0 0 382 90 891 891
Average power provided
to load (GWa)
19.0 30.0 31.4 16.2 31.0 31.5
Average excess power (GWa) 1.18 66.8 79.7 0.09 26.3 59.9
H2 þGIV storage
PV GW 0 0.0 13.8 0 0 27.9
Offshore wind GW 0 85.4 122 0 37.1 95.7
Inland wind GW 50.7 92.7 105 46.5 102 100
Storage GW 0 29.0 64.6 9.23 52.1 55.9
Storage GWh 0 459 1263 30.1 1167 1384
Average power provided
to load (GWa)
19.3 30.2 31.4 18.0 30.4 31.4
Average excess power (GWa) 1.33 43.9 65.4 0.81 26.9 54.6
Table 5
Cost to make load as in Table 4, but also including credit for selling excess electricity
to displace natural gas, (¢/kWh).
Hours covered by
all renewables (%)
Hydrogen Central batteries GIV
2008 2030 2008 2030 2008 2030
30 10 09 10 09 10 11
90 19 08 20 11 23 06
99.9 31 13 39 20 26 11
Table 4
Cost to make load using renewables, storage, and fossil backup, ¢/kWh in 2010
dollars.
Hours covered by
all renewables (%)
Hydrogen Central batteries GIV
2008 2030 2008 2030 2008 2030
30 11 09 11 09 11 11
90 22 10 23 15 28 09
99.9 36 17 45 25 32 17
C. Budischak et al. / Journal of Power Sources 225 (2013) 60e7466
is more generation than needed to meet load at the beginning and
end of the week, but storage has to provide energy in the middle of
the week (gray), and as a last resort load is met with fossil generation
(red). (For interpretation of the references to color in this paragraph,
the reader is referred to the web or PDF version of this article.)
For the three types of storage in Fig. 4, hydrogen, central
batteries, and GIV, the optimized power and energy sizes are:
hydrogen, 58 GW and 2899 GWh, central batteries, 58 GW and
362 GWh, and GIV 52 GW and 891 GWh (Table 8). H2 and central
batteries were at cost-optimized sizes, but GIV could not exceed
891 GWh due to lack of electric vehicles, even assuming all vehicles
would be electried and available. Another measure of storage size
is, how long could storage exclusively provide average summer load
(about 40 GW)dthe cost minimum for the three storage types
correspond to 72 h, 9 h, and 22 h. We nd these to be remarkably
small amounts of storaged9to72hdto run the system at 99.9%
reliability under all conditions encountered over four years.
We selected the week of Fig. 4 because it was one of the most
challenging. During this week, fossil was dispatched to meet load in
all three storage cases. In the hydrogen case load was not met
because of the power limitation from storage (lower left gure,
barely perceptible red tick near the peak of day three), whereas in
the centralized battery and GIV cases it is not met because of the
energy limit of storage, that is, storage was empty (shown as red
lling the gap from green up to load until green goes above load).
Conversely, charging storage can be power limited (seen as height-
limited green above load, with olive above the green), or can be
energy limited, that is, storage full (only olive above the load curve,
no green, until storage is drawn down again). These cases illustrate
that a realistic model of large-scale storage must reect limitations
of both power and energy, as RREEOM does. (For interpretation of
the references to color in this paragraph, the reader is referred to the
web or PDF version of this article.)
Fig. 5 summarizes results from all 18 of our simulations. The
height of bars represents GW capacity. Each cluster of bars shows
one of the three percentage coverage levels, at one of the two cost
years, thus six clusters of bars. Above each cluster of bars is a pie
chart of energy (GWh), with each color showing how much each
Fig. 4. Response of the optimized energy system (99.9% coverage, 2030 costs) to a challenging week for three storage technologies: hydrogen (left column), centralized battery
(center column), and GIV (right column). Top row distinguishes three types of renewable generation compared to load. Bottom row distinguishes generation, storage, and spilled
generation. Abscissa scale is hour of simulation.
Fig. 3. Renewable generation (top), energy in storage (middle), and fossil generation (bottom) for the four-year study period in the 99.9% coverage case with GIV storage and 2030
technology costs. Scales are GW generated and GWh held in storage.
C. Budischak et al. / Journal of Power Sources 225 (2013) 60e74 67
technology serves load. The three storage technologies are aver-
aged to compress the graph, equivalent to deploying a mix of
storage technologies. In brief, each cluster of bars shows the GW
capacity and the pie above it shows the proportions of energy.
Fig. 5 demonstrates that the cost-minimized system at 30%
coverage, with either 2008 or 2030 costs, has roughly equal capacity
of wind and fossil, with wind generating over 60% of energy, and
little or no storage. At 90% and 99.9%, renewable generation and
storage increase, fossil capacity drops 10e20%, fossil energy
production drops to a nearly imperceptible wedge, and virtually all
electric energy comes from wind (offshore plus inland). For 99.9%
coverage, comparing 2008 and 2030 costs, anticipated cheaper
solar in 2030 leads to almost twice the solar capacity, enabling
reduced capacity for all other generation and storage.
We found that, at high fractions of renewables, the cost
minimum species excess renewable generation. As shown in
Fig. 3, the greatest excess is in winter (more precisely, November
through May). This nding leads us to analyze the use of excess
electricity to displace natural gas in the residential and commercial
sectors, where natural gas is mostly used for low-temperature heat.
This heat use evaluation was done ofine, after the cost-
minimization runs, so this second use of electricity was not given
any value in cost-minimization. Specically, we converted elec-
tricity to heat based on energy content to compare the monthly
match. The result, in Fig. 6, shows that monthly excess electricity
corresponds to monthly residential and commercial consumption
of natural gas; the match is remarkably good. This means that
a more efcient economic optimization would not spill excess
electricity, but rather would use it to displace natural gas. In one of
our economic calculations below we credit excess electricity at the
prevailing market price of natural gas. (We equivalence electricity
to gas at its heat value, for example this could be done with low-
capital-cost but inefcient electric resistance heat; see Appendix
Value of Excess Generation.)
The costs of electricity for these 18 optimized REEOM mixes are
given in Tables 4 and 5.Table 4 gives the cost of providing all
electric power from renewables, storage and fossil, with no credit
for excess electricity. The ¢ kWh
1
in both tables can be compared
with current power costs as all are expressed in 2010 dollars. The
electricity product we have modeled is load followingpower, that
is, power provided from a diversity of generators with the power
supplier required to continuously match load; this is more valuable
than the commonly-cited baseload power costs, which cannot
cover peak loads. We estimate from bilateral contracts that load
following power has a wholesale market price in PJM of 8¢ kWh
1
for the electricity only, or total cost of 17¢ kWh
1
including exter-
nalities (calculated in Section A6). Comparing todays costs of
17¢ kWh
1
with Table 4,wend that at 2008 technology costs, 30%
renewable coverage costs less than todays electricity plus exter-
nalities, whereas at 90% or 99.9% coverage, renewables cost more
than today. But at 2030 technology costs, with inexpensive storage
(hydrogen or GIV), renewables are at price parity at 99.9%, and are
less expensive than today at 90%.
Table 5 incorporates the sale of excess electricity at times of
natural gas need. Even though the electricity is sold when gas is
needed, at the lower value of gas, and only at its heat value (not
using a heat pump), the economics are notably improved over
spilling or giving it away (per Table 4). For example, consider the
low-cost case in Table 5, GIV storage and 2030 technology costs:
renewables can cover 90% of the hours at only 6¢ kWh
1
,
substantially lower than todays cost of 17¢ kWh
1
. For the GIV,
2030 prices case, one might ask, why is 90% the cost minimum? The
answer is that costs are higher at 99.9% because substantially more
renewable equipment must be purchased, and costs are higher at
30% because fossil backup costs more than renewables at 2030
costs, with all subsidies removed. On the other hand, costs for
a non-optimal mix can be high. This is seen even in Tables 4 and 5
for the high-cost case of central batteries. Not shownin these tables
are a very large number of more expensive ways to build a high-
penetration renewables system that failed our cost-minimum tests.
We make four policy observations from Tables 4 and 5: First, at
2008 equipment costs, todays cost of electricity would be lowered
by renewables covering 30% of hours (60% of energy). That is, the
true cost of electricity is 1/3 lower (dropping from 17¢ to 10¢ or 11¢)
at 2008 technology costs. At 2030 technology costs, we nd that
90% coverage of hours (96% of energy) is the least cost for most
cases. If storage is inexpensive (the GIV case) 90% coverage is much
less expensive than lower fractions of renewables. The second
policy observation is that aiming for 90% or more renewable energy
in 2030, in order to achieve climate change targets of 80%e90%
Fig. 6. Natural gas consumption for the PJM region (black) compared with excess
renewables generation (gray), for the GIV 2030 99.9% case. Bars are energy per month.
Fig. 5. Power capacity of generation and storage (bars) and source of energy used to
meet load (pie charts) in all 18 RREEOM runs. Each clusterof bars (e.g. the 4 bars labeled
2008 90%) and pie chart above are an average of the three storage technologies.
C. Budischak et al. / Journal of Power Sources 225 (2013) 60e7468
reduction of CO
2
from the power sector, leads to economic savings,
not costs. Third, the 2008 and 2030 differences show we can seek
an intermediate 30% target now, and seek a 90% target later, and
with the right mix, at each step the target will move toward lower
costs than todays system. And fourth, noting that we nd the cost-
minimum using unsubsidized prices, todays market will not move
to the least-cost system with current policies, because todays
market is distorted by tax subsidies for renewables and nuclear, and
by larger cross-sector subsidies for fossil.
6. Conclusions
Here we simulated uctuating power input to a large regional
electric system, seeking the least-cost combinations of renewable
generation and storage to provide sufcient power for load. Unlike
many prior studies, we do not employ storage in order to balance
generation capacity more closely to loaddwe only care about
reliably making load at the least cost.
We nd that 90% of hours are covered most cost-effectively by
a system that generates from renewables 180% the electrical energy
needed by load, and 99.9% of hours are covered by generating
almost 290% of need. Only 9e72 h of storage were required to cover
99.9% of hours of load over four years. So much excess generation of
renewables is a new idea, but it is not problematic or inefcient, any
more than it is problematic to build a thermal power plant
requiring fuel input at 250% of the electrical output, as we do today.
At 2008 technology costs, 30% of hours is the lowest-cost mix
we evaluated. At expected 2030 technology costs, the cost-
minimum is 90% of hours met entirely by renewables. And 99.9%
of hours, while not the cost-minimum, is lower in cost than todays
total cost of electricity.
Over-generation is cost-effective at 2030 technology costs even
when all excess is spilled. If excess generation displaces heating
fuels, the cost is lowered further. Todays electricity is rarely used for
heating because fuel cost dominates electric generation costs and
energy is lost in generating electricity, so when heat is desired it is
cheaper to burn fuel on site where the heat is needed. By contrast,
renewable generations primary costs are capital and the fuel is
freedonce built, we will want to run renewable generators when-
ever electricity has any value at all. Again, the cost-optimization
model forces us to think about system design differently. Today
we build dispatchable generation, and design for enough capacity to
meet peak load plus a reserve margin. If we applied the ndings of
this article, in the future we would build variable generation,
designing for enough capacity to make electric load for the worst
hours, and as a side effect we will have enough extra electricity to
meet thermal loads.
In the 99.9% case, using fossil generation to ll the gaps in the
remaining 0.1% of hours (9 h year
1
) requires maintaining less than
half of todays legacy generation capacity, with that capacity
producing only 0.017% of the energy needed for load. Thus, further
pollution-reduction will provide scant motivation to retire old fossil
generation. However, maintaining old fossil plant may be uneco-
nomic if rarelyused, in which case, other existingmechanismsdsuch
as demand management, interruptible rates, or preloading storage
from lower capacity fossildcould be used to retire old fossil plants.
Disclosure statement
CB declares equity interest in a solar education startup. WK
declares equity interest in a GIV startup. Other authors declare no
conict of interest.
Acknowledgments
For help and advice we thank Andrew Levitt, Mark Jacobson,
Mark Delucchi and Claus Nygaard Rasmussen. Authors Sewell,
Thomson and Kempton were partly supported by US DOE grant DE-
EE0003535, Advanced Offshore Wind EnergydAtlantic Consor-
tium.Kempton was also supported by the Otto Mønstad Gœst
Professorship at Danish Technical University.
Appendix A
RREEOM model
The Regional Renewable Electricity Economic Optimization
Model (RREEOM) takes as inputs the costs of each type of genera-
tion or storage technology, a constraint to fully meet load for a set %
of hours (percent coverage), a maximum limit for each resource,
and hourly weather and load. The output is a single most cost-
effective combination of renewable types and storage capacity, to
meet the percentage coverage of hours with the given type of
storage. The model does not prohibit over capacity; the capability
for excess generation above load and lling storage has no negative
or positive value in the optimization other than its effect on the
cost. (In the real-time management of a power system, excess
generation is avoided simply by turning down fuel input to thermal
generators, feathering the blades of wind turbines, or switching off
solar inverters; excess capacity is not a management problem, only
a potential economic problem.) The model calculates renewable
generation each hour from the given inputs (solar photovoltaics,
offshore wind, inland wind) and subtracts that from the same
hours load. If there is excess generation it is put into storage. If
instead load exceeds generation, then the model draws energy out
of storage. If load exceeds generation and there is not enough in
storage, the hour is counted as failing to make load from renew-
ables. The standby losses from storage are also calculated and
subtracted from energy in storage each hour. A ow chart of the
simulation is in Fig. 2.
RREEOM was run using the enumerative method with 70 equally
spaced-divisions per input variable i.e. all inputs were linearly
sampled 70 times and all combinations of these samples were run
with RREEOM. There are 5 input variables (power capacity for each
of three renewable generation types, energy capacity of storage, and
power capacity of storage), so 70
5
, or 1,680,700,000 combinations
were run. Since testing each of the 70
5
combinations against 4 years
of data took approximately0.1 s, parallel processing was essential. A
3000-processor cluster was employed, reducing each of the 18 input
RREEOM cases to 168,070,000 s/300 0 processors¼56,023 s ¼15.5 h,
times 18 different combinations ¼11.6 days with 3000 processors.
The electricity cost of each combination was calculated in ¢ kWh
1
,
using 2010 dollars, as described below. Then the four years were
simulated, using hourly weather, to determine if the given mix of
technologies was sufcient to make hourly load the required
percentage of hours. (Processing time was reduced by not running
the four year simulation if the costs were higher than that of a prior
successful run.)
After the optimization is run, excess generation was used to
displace natural gas as a post-processing step that generated
revenue and thus reduced the cost of electricity. However, the cost
minimization only minimized the cost of electricity, so natural gas
savings were not considered in the cost optimization.
PJM interconnection
We examine the service area of PJM Interconnection, a Trans-
mission System Operator (TSO) spanning part of the US Eastern
C. Budischak et al. / Journal of Power Sources 225 (2013) 60e74 69
Interconnect. PJMs area includes land area in 13 states and the
District of Columbia, specically: NJ (100%), PA (95%), DE (100%), MD
(100%), VA (95%), WV (100%), IN (15%), OH (65%), MI (5%), IL (25%),
NC (15%), KY (10%) and DC (100%). These land areas are for the PJM
region today, not for our study period in 1999e2002. The earlier
study period was used for load records in order to have a longer
period of time (4 years) without the complication of system
expansion during the model run years of our simulation. We used
the contemporary PJM land areas for our renewable power output
calculations, because we are using, and developing, data matched to
the current PJM system, and because PJM already draws on many
wind projects outside its territory, from adjacent states. None of
inland wind, solar, or offshore wind were limited by resources, so
the use of a wider area had no effect of increased resource size but
would have increased our models inland wind diversity slightly
over a territorial cutoff at the historic boundary. As of this writing
(2012), the system has expanded to 178 GW of generation, with
0.65 GW of wind and 0.02 GWof solar operating, and applications in
process to add 37.8 GW of wind and 3.6 GW of solar.
1
Constant input values
For each technology, and for the cost years of 2008 and 2030, we
provide input values for each technology in 2010 dollars. Input
values are shown in Tables 1 and 2. The values in those tables are
described as follows:
BCapital cost per energy storage ($/kWh) eThis is zero for gener-
ation technologies, but is important for storage technologies, for
which cost per energy storage size can be the dominant cost.
BO&M cost per energy storage throughput ($/MWh) eThis
expresses a MWh throughput wear factor that degrades life-
time further, on top of the standard $/MW O&M based on
capacity. (This second O&M value is needed only for GIV, whose
base O&M per MW is not counted since its cost is attributed to
the driving function [15]; we attribute GIV O&M cost to the
electric system only proportionally to the additional energy
moved through the batteries to serve as grid storage.
BLifetime of energy equipment (years) eApplies to storage only.
Lifetime of the energy (not power) components.
BCapital power cost ($/kW capacity) eA standard cost measure
of generation, also used here for power capacity of storage.
Note that capacity factor is not calculated for generation here,
because the actual power output is determined hourly by
measured wind speed or insolation.
BO&M cost per unit of power capacity ($/kW year
1
)eThe cost
is per power capacity as larger facilities require more mainte-
nance. Most data resources give O&M costs per MW capacity,
so we apply it to facilities based on their MW capacity.
BLifetime of power equipment (years) eApplies to generation
and storage elifetime of generation, or for storage, lifetime of
power conversion components. Counted as years to replace-
ment, at which time the capital cost must be expended again.
BUpper power limit of resource (GW) eMaximum power
resource for solar and wind. For GIV, total numberof cars times
max power per car. (No practical power resource limit on H2 or
central batteries, since the model builds as much power
conversion for these as is economical.)
BUpper energy limit of resource (GWh) eApplies only to GIV. The
total number of cars times batterysize per car. There is no energy
resource limit for H2 and central batteries, as more can be built.
BRound trip efciency (dimensionless fraction) eapplies only to
storage
BStorage lost over time (h
1
)efraction of remaining energy lost
per hour
Technologies
Inland wind eMost inland wind is generated by utility-scale
(1e3 MW) turbines placed in areas of high wind speeds. The cost
of wind power capacity, including the capital equipment, installa-
tion, and maintenance, were obtained from Delucchi and Jacobson
[3], there is no per MWh energy production cost, as maintenance
cost is calculated per power capacity per month of operation. We
calculated three quantities: annual capacity factor from measured
wind speed to determine likely development sites, hourly capacity
factor to determine hourly power output, and upper wind resource
limit for the region. All 135 meteorology stations with wind data
available for the 4-year time period was collected from the National
Climate Data Center [29]. Wind data at measurement height were
extrapolated to 80 m hub height and power output was calculated
via a commercial turbine power curve (a REpower 5M). To select
stations, annual average capacity factor (CF) was calculated for each
site, and stations with CF below 30% were eliminated as less likely
for wind development. The resulting stations are shown in Fig. 1.
The remaining stationshourly CFs are then calculated, yielding
a single CF for each hour, for the aggregate of stations. Using a single
inland wind CF for each hour tremendously speeds up the iterative
calculation of four years for each of the 70
5
combinations of
renewables and storage, and a single hourly CF is justied since we
have already simplied the problem by assuming perfect trans-
mission. However, the stations with hourly wind speed are not
adequate to know the total wind resource, which requires inter-
polation between weather stations and consideration of excluded
land areas. The total resource calculation is available from DOEs
state wind resource data [23]. We added the DOE-calculated wind
capacity for each state in PJM. When appropriate we took a fraction
of the state proportional to the amount of land in PJM. This yielded
a total resource, expressed in MW of capacity, to use as the
maximum value for RREEOM. This process resulted in a high
capacity factor, about 40%, because the less economic sites were
eliminated. We judge this a bit high for large-scale deployment of
inland wind in this region; a more accurate approach would be to
decrease the capacity factor of subsequent wind farms, after the
best initial sites are developed.
Offshore wind eOffshore wind employs turbines installed on
the continental shelf. Installation and maintenance costs are higher
in the marine environment, but the wind is stronger and steadier.
The turbines are larger (5 MW, with 10 MW planned) to reduce
costs. As with inland wind, energy costs were set at zero, power
capacity and O&M costs were obtained from Delucchi and Jacobson
[3], also checked against Levitt et al. [30]. The hourly wind data was
from NOAA buoys [29], locations shown in Fig. 1. The total resource
size for the PJM region is from Baker [22]. The calculation methods
are the same as for inland wind; in this case it is more reasonable to
assume a uniform CF no matter how much resource is developed.
Solar eSolar photovoltaics (PV) convert light into electricity. As
with wind power, capital costs and O&M costs are taken from
Delucchi and Jacobson [3], cost per energy unit produced is set at
zero. Calculations of generation potential draw from a study of
rooftop potential for Newark, DE [21] and extrapolate to the PJM
region. Hourly solar irradiation from NREL [31] for Wilmington, DE,
was used to calculate solar output. Wilmington is roughly mid-
latitude in the PJM region. Output power was calculated from
solar inputs using NRELs PVWatts program [32].
1
From Renewable Energy Dashboard,accessed May 2012 at www.green.pjm.
com.
C. Budischak et al. / Journal of Power Sources 225 (2013) 60e7470
Hydrogen storage ePower into hydrogen storage is via elec-
trolysis and compression. Power out is via a Solid Oxide Fuel Cell
(SOFC). Storage is in a constructed high-pressure tank. The energy
and power costs were both obtained from Steward [20].The
capital power cost in $/kW is the capital cost of the SOFC system
and electrolyzer taking into account the replacement cost of the
stack, which is 30% after ten years (this replacement cost is also
discounted at 10 years out). A SOFC system is used because it has
a lower capital cost per energy unit stored, lower maintenance
cost, and higher efciency than the other dominant fuel cell
technology, protein exchange membrane. Compared to batteries,
tank-based hydrogen storage also has the advantage that there are
lower losses of stored energy. The disadvantages of SOFC are its
low power density which makes it unt for transportation appli-
cations (not relevant here) and the high cost of conversion
equipment for moving in and out of storage. It is assumed that the
power systems will last 20 years if this stack replacement is per-
formed. The capital cost of storage is calculated from the cost per
kg capacity of a hydrogen storage tank, which is converted to
a $/kWh equivalent using the Higher Heating Value of hydrogen.
As the technology is essentially a large steel tank, there is no size
restriction assumed, and it is assumed that the steel tanks will last
20 years.
Central batteries eLithium titanate batteries were specied
because they have the longest cycle life (>5000 cycles), and the
ability to charge quickly. For 2008 the costs were obtained from
Burke and Miller [18] while 2030 costs were obtained from Gaines
and Cuenca [28]. Power electronics prices and life-time were
assumed to be the same as solar inverter costs for 2008 and 2030
taken from NREL [33]. No maximum amount of battery storage was
assumed as more batteries can be built.
Grid Integrated Vehicles (GIV) eIn a GIV system, an electric
vehicle (here assuming lithium ion batteries) has controls to
regulate the rates of charge from the grid and discharge back to the
grid. Because the batteries and power electronics would already
have been bought and maintained to drive the car, the costs are
controls added for GIV and communications, plus increased wear
on the batteries due to extra cycling. Costs are calculated according
to Kempton and Tomi
c[15]. Energy costs are here set at 10% of the
cost of standalone batteries (assuming 10% added wear due to GIV),
plus $400 (on board incremental cost of controls) divided by the
battery size (see below). Power costs are the cost of upgrading the
building electrical system to accommodate higher power than
might be used only for charging. Calculations are shown in the
footnotes to Tables 1 and 2. The maximum energy capacity of the
GIV storage is determined by the size of the battery and the size of
the vehicle eet in 2002 PJM. We assume that 100% of light vehicles
will be EVs or PHEVs and will be made available for grid storage at
times of greatest need. Assuming all cars available yield
a maximum resource estimate, which could be scaled down
depending on expectations of policy, market penetration and
program participation. As batteries become less expensive, car
buyers will want larger battery packs, so we assume per-vehicle
energy storage will grow from 24 kWh for the 2008 cost to
56 kWh in 2030 (respectively, the sizes of batteries in the Nissan
Leaf and the Tesla Roadster today). For the purpose of comparison,
we assume the same 2002 eet size for both cost years, because our
years compare different technology costs, not the actual circum-
stances in each year. We do vary the size of the battery in the two
years, to be realistic about vehicles for 2030. Vehicle data per state
is from an NHTS 2009 survey [24] and the percentage of vehicles
available in each state is given above in the PJM interconnection
section. This gives a total of 15.9 million GIVs.15 kW power capacity
is assumed per vehicle for power available and for cost calculations
for GIV. This power level assumes some upgrade to the building
wiring, part of the per kW cost of GIV, assuming a 50 year life
consistent with building life.
In the model, whenever energy is taken out of storage, the
energy available is obtained by the following: E
L
¼S*Rwhere E
L
is
the energy from storage that can meet load, Sis the energy in
storage to be taken out, and Ris the round trip efciency of the
storage technology (can be found in Tables 1 and 2). Also each
storage technology losses some of its energy content each hour.
This is quantied by the following: E
tþ1
¼E
t
*L, where E
tþ1
is the
new value of the energy that is stored, E
t
is the current energy in
storage, and Lis one minus the storage loss over time (can be found
in Tables 1 and 2).
Model operation
The differing mixes of generation and storage technologies are
all run attempting to meet the load for the entire PJM region from 1
April 1998 through 31 December 2002, with the rst months used
only to initialize storage at a realistic level. The different generation
resources have different time proles. As an example, Fig. 4
suggests visually that solar has a better correlation with load than
either offshore or inland wind. The load-matching advantage of
solar is conrmed by the Pearson correlation coefcients seen in
Table 6. Offshore wind produces more consistent output than either
of the other two sources, as can also be seen in Fig. 4. The consis-
tency of offshore wind is not visible in the correlation coefcient,
although it has been previously demonstrated for the Atlantic coast
[11]. These characteristics of solar and offshore wind can also be
seen in the present studys cost-minimization model results, in
which adding solar and offshore wind both reduce the need for
storage.
We constrained the model to require that renewable generation
plus storage are sufcient to provide all load for three percentages
of hoursd30%, 90%, and 99.9% of hours. From each of the mixes of
generation and storage that successfully meet the given percentage
coverage requirement, we calculate the total cost of the renewable
system used to meet load. The output for each case is the least cost
mix that meets the required percentage of hours.
The 99.9% criterion corresponds to 9 h per year when not all load
would be covered. This is a less stringent criterion than the tradi-
tional target of one day in 10 yearsor roughly 0.03% of the time.
We used 99.9% rather than 99.97% or 100% because to make a claim
of 100% coverage would require a simulation run of more than 4
years, because cost may go up asymptotically as we require all
hours of load for a longer sequence of years, and because we
suspect it would be more cost efcient to use demand management
(see below) for the few hours of shortage than to build more
generation and storage. Of course, an electric system meeting our
criterion of 99.9% from renewables and storage would not have
lower reliability than todays electric system, because we assume
that a subset of existing fossil plants will be used to meet load when
the new renewables and storage are not sufcient. (In fact, by
installing storage in distributed locations with appropriate
switching, our proposed system would have higher reliability,
because distribution failures are the most common cause of power
loss.) We conservatively did not assume any use of demand
management at all, whereas most large distribution utilities today
have many ways of dealing with generation or transmission
shortfallsdthey can shed load, activate direct load reduction
programs, dispatch old fossil power plants, bring in power by
transmission, invoke critical peak pricing, etc. Those are likely to be
more cost effective ways to meet the last 0.1% in our 99.9% case.
The RREEOM model can be thought of as calculating how much
renewable electricity will be produced each hour, and how much
goes to load, to storage, and out of storage. The use of renewable
C. Budischak et al. / Journal of Power Sources 225 (2013) 60e74 71
electricity is sorted into the following cost categories, used for the
subsequent cost calculations:
Renewable electricity uses and values:
1. Meets load immediately ecredited at electricity price
2. Fills storage and replacing leaks from storage eno value
3. Drawn from storage to meet load ecredited at electricity price
4. Excess generation above load and above lling of storage
a Displaces natural gas enot used to nd cost minimum, but
credited at natural gas price when calculating cost of elec-
tricity in Table 5
b Spilled or sent by transmission outside region eno value
Cost of electricity calculation
Equipment cost is one input to the model, and because the costs
of these new technologies are changing rapidly, we calculate costs
for two time periods. We pick the years 2008 and 2030 because the
rst reects known installed costs and the second represents a time
of more mature industry, with costs based on mass-production. We
chose 2030 as a future comparison year also so that we could draw
from, and compare results with, prior published work, specically
Delucchi and Jacobson [3].
In both 2008 and 2030 cases, the net present cost of the
renewable generation plus storage is calculated as: the installation
cost of all generation and storage technologies used, plus the
discounted present cost (12%) of all operations and maintenance
costs and equipment replacement costs (battery and power elec-
tronics) over a 20 year horizon. From present cost, we calculate the
cost of electricity in $/kWh by calculating the present energy
value. That is, the lifetime amount of energy production is dis-
counted to present energy value, also at 12%. Discounting of
future energy production in kWh is unconventional, but allows us
to discount energy benets in the future prior to knowing the
future price of energy. In counting energy value, we count only
kWh delivered to load, because production not delivered to load is
conservatively assumed to have no value. In the cost-
minimization, we did not attribute any value to remaining elec-
tricity, and the values (¢/kWh) in Table 4 are based on this
calculation. After the cost minimization we separately calculate
the value of selling excess electricity to displace natural gas, using
the methods explained in the next section, even though realisti-
cally it could also be sold to adjacent electricity markets, or used
for new non-electric end-uses such as electric cars, both with
higher value than natural gas. These assumptions tend to under-
value excess electricity production in the cost minimization, so if
excess electricity was given value in the cost minimization, our
optimum mixes would have somewhat higher generation capacity
than shown.
Cost-minimization by RREEOM is based on the above-calculated
cost of renewable electricity delivered to load, including that
delivered to load from storage. For the hours not fully covered by
renewables or storage, some of the kWhs needed to make load are
supplied by fossil generation and are charged the fossil kWh rate.
To calculate the external cost of that fossil generation, we use the
existing PJM generation mix, which in fact is partially nuclear, thus
in PJM, our use of the word Fossilfor traditional generation is not
quite accurate. Because there is so little energy from traditional
generation in our high penetration models, ll-in power probably
would in fact be fossil, not nuclear. This is due to fossil generations
lower capital carrying cost and faster ramping, which are more
important than fossils disadvantages of high fuel costs and high
externality costs. Although the cost of fossil electricity is not used
in the optimization and we assume zero capital cost for fossil, the
cost of fossil ll-in power is included in Tables 4 and 5, as it is part
of the total cost per kWh of providing electricity to meet 100%
of load.
The cost per delivered MWh of fossil electricity is simply
calculated as present market price plus present external cost. Our
comparisons with the cost of renewable electricity discount future
renewable generation at 12%, and compare to the present cost and
externalities of fossil fuels, a comparison that probably disadvan-
tages renewables. Market electricity most comparable to our
output is a bilateral contract price for load-following power (in
which the power provider matches the needed load), not the
hourly locational marginal price (LMP). Our model output matches
load (at some expense) like a load-following contract, whereas LMP
is primarily the cheaper baseload, and does not match load uc-
tuations. From familiarity with a few bilateral contracts in PJM, we
take $80 MWh
1
as a reasonable load-following contract price. For
the higher penetration cases (90% and 99.9%), less fossil capacity is
needed, so the market would shift to fewer legacy generators in
operation, and we would expect attrition from the eet of gener-
ators. Those which are not easily maintained would likely be the
rst to be decommissioned. Such a market might have higher costs
per kWh for electricity, but lower externalities because they would
be run far less frequently. As the model results show, these legacy
generators will be running within a future generation mix that
includes substantial storage, sodcontrary to conventional
thinkingdfast ramp rates may not be needed as much as the ability
to stay shut down efciently for months. What we are describing is
a different market for electricity, but again for simplicity, we
assume the same market prices and the same externalities per
kWh. We would speculate that in the 99.9% system, demand
management will be more economical to cover the 9 h year
1
of
shortfall, rather than retaining old fossil generators. Whether in the
future we use old fossil or demand management has no effect on
the least-cost system results, since our model considers only the
cost of renewables and storage in minimizing cost to meet load.
For external costs for coal-generated electricity, we used the
recent and thorough Epstein et al. study. Including all coal life cycle
impacts (mining, transport, combustion products, climate change),
the total mid-range external value in 2008$ is, 17.8¢ kWh
1
[34].
For other fuels, we used the earlier EU report ExternE[35], and
averaged differing country values to develop a single value for each
fuel, the top row in Table 7. ExternE monetizes the external costs
associated with human health, ecosystems, crop output, climate
change and other factors, but the values are lower than those from
more thorough analyzes such as Epstein et al. [34]. (For example,
ExternE calculated coal external cost as 5.71 Euro cents, or in
US$2010, 11.75¢ kWh
1
, versus Epstein et al. 17.8¢ kWh
1
.) Table 7
shows the calculation for each fuel, weighted by the percentage in
PJM. The coal value is based on Epstein et al., the other fuel values
are from ExternE, converted to US 2010$ [35]. This calculation
yields an external cost of PJM electricity of 9.45¢ kWh
1
, a cost born
by other parties not paying for the electricity, in addition to our
estimated 8¢ kWh
1
bilaterial price paid by the buyer, for a total
cost of PJM electricity of 17.45¢ kWh
1
.
Adding the total cost of renewables and storage to the total cost
of fossil provides the total cost of electricity shown in Table 4 of the
main text.
We conclude this cost section with a perspective on future cost
estimation. To summarize our technology cost inputs in Tables 1
and 2the projected 2030 capital costs are roughly half of todays
capital costs, and O&M costs are roughly the same. These values are
based on literature review, cited in the table notes. Of course, the
precise 2030 values are not known. Based on the present rate of
technical advancement and cost-reductions, and general principles
of industrialization, scale-up, and learning curves, we consider the
C. Budischak et al. / Journal of Power Sources 225 (2013) 60e7472
cited 2030 costs to be reasonable estimates, not optimistic. In fact
the 2030 costs do not include technology breakthroughs, which we
would judge to be likely before 2030. The point of runningour cost-
minimization model is not to calculate a precise cost of electricity in
the future, the accuracy of which is limited by uncertainties in input
values. Rather, our model shows thatdgiven lower equipment
costs for renewable generation and for storage, both inevitableda
cost-optimized electricity system will have substantially more
generation than needed by load, and can meet required load with
storage measured in a few days rather than measured in months or
seasons. Those general ndings are unlikely to be changed by
renements in future cost estimates. For perspective on our nding
of over-capacity being optimum, an analogy would be that, at 1980
personal computer costs, a professional worker would have one
computer on his or her desktop; for those few workers who had
a second computer at home, all members of the household shared
it. At 2012 computer costs, the same worker may have a desktop
computer at the ofce, a laptop in the briefcase, a cell phone in
a purse or pocket, and younger members of the family may have
a tablet, each of these devices a computer. Furthermore, each
computer would have three orders of magnitude more computing
power than the 1980 computer from which a one computer per
familyprojection might have been made. Our point is that for
rapidly-evolving technologies, we must use some estimate of
future costs, because projecting on the basis of present costs will
obscure our understanding of the future systems conguration.
Value of excess generation
In the RREEOM cost-minimization, excess generation was not
given any value. The main article explained that excess generation
is well matched to natural gas demand, so as an alternative calcu-
lation of the price of electricity, we credit the excess as displacing
natural gas in the commercial and residential sector. It is credited at
the current retail price of natural gas in these sectors, without
externalities. The displacement rate is one energy unit of electricity
to directly replace one energy unit of natural gas. Technically, there
are two ways of doing this. For space heating, natural gas furnaces
could be replaced by resistive heating units at the point of use.
While we could have assumed the more efcient electric heat
pump technology for this region, resistive heat produces high
temperature which can be stored for several days of heating
duration using inexpensive and compact high-temperature ceramic
heat storage [36]. A second technical means would be to bleed off
the hydrogen storage system in order to use the hydrogen as
a gaseous fuel, mixed with or displacing natural gas. Use of the
hydrogen as a fuel would take advantage of the electrolyzers, not
usable when storage is full, and would make good use of the
buffering capacity of the hydrogen tanks, mostly full at the time of
year that natural gas is mostly used. Our use of one-to-one energy
unit displacement is accurate for the use of resistive heaters; the
heat pump will achieve 2to 3the natural gas displacement per
unit of electricity, whereas direct use of hydrogen would displace
only w1/2 due to inefciency of the electrolyzer and pump
conversions.
In order to determine how much natural gas the excess gener-
ation could displace, monthly excess generation was compared
with monthly natural gas usage. An example of this comparison is
shown in Fig. 6, for the 99.9% coverage case with GIV storage and
2030 costs. It can be seen that in the winter months the excess
generation can replace most of the natural gas while in the summer
months excess generation exceeds natural gas consumption and
thus would be spilled.
Once we calculated the amount of displaced natural gas, the
revenue from this displaced natural gas was calculated. The 2010
average of the residential and commercial price for the United
States, $10.2 per thousand cubic feet [37] was used in this calcu-
lation and all revenue for our 20 year study period was discounted
(using 12% discount rate) to the current year. This does not include
the recent (2011 and 2012) drop in price, assumes no price
increases for natural gas, and discounts future natural gas revenues.
Arguably, we should have used wholesale natural gas prices and
included externalities rather than retail and not including exter-
nalities. This present value of gas revenue was subtracted from the
total cost of the renewable generation and storage system which
was then used to recalculate the $/kWh of electricity (Table 5 in
main text).
Summary table of output values
Table 8 shows the outputs of the 18 RREEOM simulations dis-
cussed in the text, organized by one table section per storage
technology. The forth table section at the bottom is a run with both
GIV and H2 as options to bring in, not discussed in the text; this had
to be run at lower increment resolution or it would have required
impractical amounts of computer time.
References
[1] R.F. Hirsh, Power Loss: The Origins of Deregulation and Restructuring in the
American Electric Utility System, MIT Press, Cambridge, MA and London, 1999.
[2] PJMs Board of Managers, PJM Annual Report 2002: Working to Perfect the
Flow of Energy, 2002.
[3] M.A. Delucchi, M.Z. Jacobson, Energy Policy (Dec. 2010). http://dx.doi.org/
10.1016/j.enpol.2010.11.045.
[4] M.Z. Jacobson, M.A. Delucchi, Energy Policy, No. 2009, Dec. 2010. http://dx.doi.
org/10.1016/j.enpol.2010.11.040.
[5] L. Brown, Plan B 4.0: Mobilizing to Save Civilization, Earth Policy Institute,
2009, 109e209.
[6] O. Ekren, B.Y. Ekren, Applied Energy 87 (2) (Feb. 2010) 592e598. http://
dx.doi.org/10.1016/j.apenergy.2009.05.022.
[7] E.K. Hart, M.Z. Jacobson, Renewable Energy 36 (8) (Aug. 2011) 2278e2286.
http://dx.doi.org/10.1016/j.renene.2011.01.015.
[8] Homer Energy, HOMER, Boulder, CO, 2011.
[9] C.L. Archer, M.Z. Jacobson, Journal of Applied Meteorology and Climatology 46
(11) (Nov. 2007) 1701e1717. http://dx.doi.org/10.1175/2007JAMC1538.1.
[10] E. Kahn, Electric Power Systems Research 2 (1) (Mar. 1979) 1e14. http://
dx.doi.org/10.1016/0378-7796(79)90021-X.
[11] W. Kempton, F.M. Pimenta, D.E. Veron, B.A. Colle, Proceedings of the National
Academy of Sciences of the United States of America 107 (16) (Apr. 2010)
7240e7245. http://dx.doi.org/10.1073/pnas.0909075107.
[12] T. Markvart, A. Fragaki, J.N. Ross, Solar Energy 80 (1) (Jan. 2006) 46e50. http://
dx.doi.org/10.1016/j.solener.2005.08.011.
[13] A. Sahin, Energy Sources, Part A: Recovery, Utilization, and Environmental
Effects 22 (9) (Oct. 2000) 845e850. http://dx.doi.org/10.1080/
009083100300001645.
[14] P. Denholm, G.L. Kulcinski, T. Holloway, Environmental Science & Technology
39 (6) (Mar. 2005) 1903e1911. http://dx.doi.org/10.1021/es049946p.
[15] W. Kempton, J. Tomi
c, Journal of Power Sources 144 (1) (Jun. 2005) 268e279.
http://dx.doi.org/10.1016/j.jpowsour.2004.12.025.
[16] W. Kempton, J. Tomi
c, Journal of Power Sources 144 (1) (Jun. 2005) 280e294.
http://dx.doi.org/10.1016/j.jpowsour.2004.12.022.
[17] P. Denholm, Enabling technologies for high penetration of wind and solar, in:
Proceedings of the ASME 2011 5th International Conference on Energy
Sustainability, 2011.
[18] A. Burke, M. Miller, The UC Davis Emerging Lithium Battery Test Project, 54,
(June 2009) 24. UC Davis, Institute for Transportation Studies.
[19] D.M. Steward, Scenario development and analysis of hydrogen as a large-scale
energy storage medium NREL/PR-560-45873, in: RMEL Meeting, 2009.
[20] D.M. Steward, Scenario development and analysis of hydrogen as a large-scale
energy storage medium NREL/PR-560-45873, in: RMEL Meeting, 2009.
[21] Center for Energy and Environmental Policy, Creating a Solar City:
Determining the Potential of Solar Rooftop Systems in the City of Newark,
Newark, DE 2009.
[22] S.D. Baker, The Atlantic Offshore Wind Power Potential in PJM: A
Regional Offshore Wind Power Resource Assessment, Thesis, Master of
Marine Policy, University of Delaware, 2011.
[23] U.S. Department of Energy, Wind and Water Power Program, 2011. [Online].
Available: http://www.windpoweringamerica.gov/docs/wind_potential_
80m_30percent.xls (accessed 09.05.11).
[24] US Department of Transportation: Federal Highway Administration, 2009
National Household Travel Survey.
C. Budischak et al. / Journal of Power Sources 225 (2013) 60e74 73
[25] H. Lund, W. Kempton, Energy Policy 36 (9) (Sep. 2008) 3578e3587. http://
dx.doi.org/10.1016/j.enpol.2008.06.007.
[26] H. Chen, T.N. Cong, W. Yang, C. Tan, Y. Li, Y. Ding, Progress in Natural
Science 19 (3) (Mar. 2009) 291e312. http://dx.doi.org/10.1016/
j.pnsc.2008.07.014.
[27] R. Schefer, W. Houf, C. Sanmarchi, W. Chernicoff, L. Englom, International
Journal of Hydrogen Energy 31 (9) (Aug. 2006) 1247e1260. http://dx.doi.org/
10.1016/j.ijhydene.2005.09.003.
[28] L. Gaines, R. Cuenca, Costs of Lithium-ion Batteries for Vehicles, 2000. http://
dx.doi.org/10.2172/761281.
[29] NOAA Satellite and Information Service, NNDC Climate Data Online, National
Climatic Data Center. [Online]. Available: http://cdo.ncdc.noaa.gov/pls/
plclimprod/cdomain.abbrev2id.
[30] A.C. Levitt, W. Kempton, A.P. Smith, W. Musial, J. Firestone, Energy Policy
39 (10) (Oct. 2011) 6408e6421. http://dx.doi.org/10.1016/j.enpol.2011.
07.044.
[31] National Renewable Energy Lab, Solar Prospector, 2011. [Online]. Available:
http://maps.nrel.gov/node/10/ (accessed 09.05.11).
[32] National Renewable Energy Lab, PVWatts, 2011. [Online]. Available: http://
www.nrel.gov/rredc/pvwatts/version2.html (accessed 09.05.11).
[33] National Renewable Energy Lab, A Review of PV Inverter Technology Cost and
Performance Projections, 2006. NREL/SR-620-38771.
[34] P.R. Epstein, et al., Annals of the New York Academy of Sciences 1219 (1) (Feb.
2011) 73e98. http://dx.doi.org/10.1111/j.1749-6632.2010.05890.x.
[35] CIEMAT and ES, National Implementation, in: ExternE Externalities of Energy,
vol. XX (1999).
[36] G.T. Bellarmine, Load management techniques, in: Proceedings of the IEEE
SoutheastCon 2000. Preparing for The New Millennium(Cat.No. 00CH37105),
2000, pp. 139e145. http://dx.doi.org/10.1109/SECON.2000.845449.
[37] Energy Information Administration, Natural Gas, Independent Statistics and
Analysis, 2010. [Online]. Available: http://www.eia.gov/naturalgas/data.
cfm#prices.
C. Budischak et al. / Journal of Power Sources 225 (2013) 60e7474
  • ... There of course remains (some high profile) debate regarding the extent to which energy portfolios can incorporate renewables such as wind, solar and hydroelectric and the costs of doing so ( Clack et al., 2017;Mooney, 2017). For example, Budischak et al. (Budischak et al., 2013) showed that a large-grid (72 gigawatts) relying on a portfolio of 90% renewables is the least-cost system in most cases (using 2030 technology prices), while MacDonald et al. (2016) showed that a portfolio of 38% wind, 21% natural gas, 17% solar, 16% nuclear and 8% hydroelectric could reduce CO 2 levels by 80% below 1991 levels without an increase in the levelized cost of electricity. Regardless of their cost all such transitions would require drastic changes to not only the grid, but to our regulatory, commercial and legal systems ( MacDonald et al., 2016). ...
  • ... 3.59 P [126] Ireland 2050 125 13/2 0.24 10 0.19 PHM [127] India j 2030 2597+1620 31/45 208 115 4.93 PH [128] Japan 2100 1400 70/30 402.86 P [73] MENA j 2030 1756+3874 48/48 296 593 5.26 PH [129] Morocco 2050 28 37/63 1.13.93 P [130] North America j 2030 6059+2596 58/31 221 442 2.55 PH [131] North East Asia j 2030 9877+1245 51/33 407.6 452 3.66 PH [74] SE Asia j 2030 1629+608 22/44 43.1 118 1.93 PH [132] South America j 2030 1813+663 17/49 42.7 131 1.72 PH [133] Sub-Saharan Africa j 2030 866+199 31/50 24.3 54 2.28 PH [134] UK 2030 900 55/6 27 35 3.00 PHM [135] US (Region) 2030 276 97/3 2.9 58 1.05 P [84] World 2050 44,000 75/25 16.50.04 P [43] a Ratio between useful energy produced from wind and solar. ...
  • ... An estimate for the quantity of storage required to meet a supply-demand balance for a given period can be derived from studies with a high penetration of solar PV. However, much of the electricity system scenario literature avoids the problem of large-scale storage by maintaining a significant share of legacy thermal generation capacity at low capacity factor ( Budischak et al., 2012), or by assuming the ready availability of large-scale biomass-fueled thermal generation ( Lenzen et al., 2016). In the context of energy transition feasibility assessment, we note that studies that reduce emissions while retaining legacy generation capacity involve fundamentally different goals to those focused on transition to 100% renewable electricity supply. ...
Project
The project is to study different dimensions of Electric cars industry : Design ; VtoG...
Project
PhD thesis on its final stages; studies history of copper smelting technologies to understand how the perception of constraints to technological solutions ("can't do that because X is limited") has…" [more]
Article
January 2005
    Offshore wind now appears to be a huge resource--sufficient to provide most of the electricity for many of the world's heavily populated coastal states. This long-term resource promise may be constrained because wind's power fluctuations lead to grid integration problems. At small scales of wind implementation, existing mechanisms of grid regulation are sufficient. However, existing mechanisms... [Show full abstract]
    Article
    October 2017 · Renewable Energy · Impact Factor: 3.48
      This study addresses resident attitudes and visual and auditory impacts from nearby electricity generation. Unlike most prior studies, questions allowing bidirectional answers are used, allowing positive or negative responses, and matched questions are applied in paired communities, one community proximate to utility-scale wind generation and the second proximate to fossil generation. At least... [Show full abstract]
      Discover more