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Spatial and temporal interactions of solar and wind resources in the next generation utility

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The "next generation" electric utility must incorporate variable renewable resources, including wind and solar, in much larger quantities than conventionally thought possible. While resource variability presents a challenge, it should be possible to reduce and manage that variability by geographically distributing renewables, combining them with different renewables, and having more dynamic control of electric loads. This study shows that interconnecting individual solar generation sites into geographically diverse arrays can reduce power output variability, and that including solar generation sites in arrays of geographically diverse wind sites can further reduce the total variability beyond what is possible for either resource type alone. Specifically, optimized portfolios offer an average decrease in variability of 55% below the average of all individual sites. Finally, it was observed that, in the modeled system, only a small subset of the potential sites in an interconnected array need to be included to achieve these variability reductions.
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SPATIAL AND TEMPORAL INTERACTIONS OF SOLAR AND WIND RESOURCES IN THE
NEXT GENERATION UTILITY
Bryan Palmintier
Lena Hansen
Rocky Mountain Institute
1820 Folsom St.
Boulder, CO 80302
bpalmintier@rmi.org
lhansen@rmi.org
Jonah Levine
ECE-Eng-Elecl/Comp Admin
University of Colorado at Boulder
425 UCB
Boulder, CO 80309-0425
jonah.levine@colorado.edu
Abstract
The “next generation” electric utility must incorporate
variable renewable resources, including wind and solar, in
much larger quantities than conventionally thought possible.
While resource variability presents a challenge, it should be
possible to reduce and manage that variability by
geographically distributing renewables, combining them
with different renewables, and having more dynamic control
of electric loads.
This study shows that interconnecting individual solar
generation sites into geographically diverse arrays can
reduce power output variability, and that including solar
generation sites in arrays of geographically diverse wind
sites can further reduce the total variability beyond what is
possible for either resource type alone. Specifically,
optimized portfolios offer an average decrease in variability
of 55% below the average of all individual sites. Finally, it
was observed that, in the modeled system, only a small
subset of the potential sites in an interconnected array need
to be included to achieve these variability reductions.
1 INTRODUCTION
The ever-growing energy demands of the 21st century are
dependent upon a power infrastructure designed for the
early 20th century. Advances in digital communications and
renewable energy technologies could facilitate a transition
to a “next generation utility” that fully integrates both
supply- and demand-side resources in a way that can enable
significantly larger penetrations of variable renewable
energy technologies than conventionally thought possible.
This paper begins with a brief overview of the “next
generation utility” concept, then turns to the ability of the
next generation utility to incorporate solar and wind power
on a large scale, driven by geographical dispersion of both
solar and wind resources at utility and larger scales, cross-
firming of solar and wind resources, and increased grid
flexibility to absorb and mitigate variability.
2 THE NEXT GENERATION UTILITY
A new electric utility paradigm is needed to meet increasing
demands for power quality and reliability and to
significantly reduce global greenhouse gas emissions
generated by electricity production. A new generation of
power technology is developing, however, and can enable
the “next generation utility”, which will involve (see Fig. 1):
Fully capturing the potential of energy efficiency and
demand response;
De-carbonizing electric supply through greatly
increased penetration of renewable and distributed
supply technologies; and
Electrifying or substituting clean, renewable fuels for
loads that would otherwise depend on fossil fuel,
including vehicles.
Fig. 1: The next generation utility will turn generation
infrastructure on its head, with a mix dominated by
efficiency and renewables with minimal coal and nuclear.
A key tenet of the next generation utility concept is that it
should be possible to provide the energy services required
by our modern society using significantly less “baseload”
coal and nuclear power. Doing so requires increased
reliance on variable renewable sources and more dynamic
control of energy demand, and consequently, more focus on
short time scales.
Taken together, the components of the next generation
utility can be thought to interact as seen in the load duration
curve in Fig. 2 below. Specifically, radical gains in building
energy efficiency should reduce the entire demand
significantly. Demand is then met largely through an
intelligently designed portfolio of variable and “firm”
renewable resources. Finally, remaining demand is met
through a combination of distributed generation (combined
heat & power and combined cooling, heat & power),
demand response and plug-in hybrid electric vehicles.
Fig. 2: Conceptual load duration curve for a next generation
utility.
The design of the next generation utility concept is currently
under development by Rocky Mountain Institute. This paper
describes research around new strategies for integration of
large-scale variable renewable resources.
3 BACKGROUND
One of the primary goals of electric utilities is maintaining
the reliability of the electric system—the implication being
that the reliability of any individual generator is only
important in the larger context of system reliability. This
insight also recognizes that all generators, both conventional
and variable, have some probability of failure. The forced
outages of conventional generators result from unplanned
mechanical failures, whereas the effective “forced outages”
of variable generators are due to the risk of “fuel” (i.e., wind
or sun) availability. These two factors lead to the conclusion
that we must evaluate variable renewable generators for
their contribution to overall system reliability, rather than
the reliability of an individual renewable generator.
Because of the implications for reliability, capacity credit—
the amount of capacity that can be counted on to contribute
to system reliability—has financial value and can therefore
greatly improve the cost-effectiveness of wind power.
Conventional wisdom holds that capacity credit is given to
an individual site based on the individual site characteristics.
(Milligan 2002) This philosophy generally leads to the
assumption that wind farms have little or no capacity value
because the degree of the resource’s variability is so high at
each individual site. (Kirby, et al 2002)
Similarly, while solar is more predictable than wind, it is
still variable and therefore given little credit for contributing
to system reliability.
However, modern financial portfolio theory offers a
different way of looking at the world. A financial portfolio
consists of a combination of individual stocks. Developed
by Harry Markowitz in 1952, modern portfolio theory
enables the creation of minimum-variance portfolios for a
given level of expected return. This theory is based on
diversification—the lower the correlation between the
individual assets that make up the portfolio, the lower the
portfolio variance, or risk. (Alexander 1996)
Portfolio theory can be easily applied to energy resources.
In this context, a renewable portfolio can comprise a
geographically dispersed set of wind farms and solar electric
systems. This paper seeks to analyze the reliability value,
and therefore capacity value, of a set of wind and solar
generators dispersed across the U.S. Midwest.
4 DATA AND METHODS
4.1 Data Sources
This study attempts to maximize the use of high quality
measured wind speed and solar insolation data. All data
were recorded at hourly intervals. The wind data was
measured at or near a 50-80 meter hub height and the solar
data includes separate direct and diffuse radiation values.
This initial analysis is limited to the Midwest Reliability
Organization (MRO) for the 2004 calendar year. This region
and timeframe were selected from among those previously
analyzed by Hansen & Levine (2008) because they provided
the highest number of corresponding sites for which
measured solar data was available.
The wind data was chosen from the RMI/UC-Boulder wind
database compiled by Levine and Hansen (Levine 2007,
Hansen & Levine 2008). The original source for the MRO
wind data was the University of North Dakota Energy &
Environmental Research Center (EERC) hosted Plains
Organization for Wind Energy (POWER) database.1 Thirty-
five (35) wind sites from MRO were included in this
analysis.
All solar data was taken from the National Solar Radiation
Database (NSRDB) 1991-2005 Update maintained by the
National Renewable Energy Lab (NREL).2 Though this
database contains radiation data for 1,454 sites, only 40 of
these sites include measured data.
For the region and period of interest – MRO in 2004 – three
solar insolation sites were selected with measured data for
90% or more of the time. An additional five modeled sites
were selected to increase the spatial diversity of the dataset.
These modeled sites were carefully selected to be class-I
sites with 100% low data uncertainty during 2004. (NREL
2007)
4.2 Data Preparation
Both wind speed and solar insolation data were first cleaned
to remove any negative, grossly out of range values, or
flagged as invalid points. These removed points were
conservatively set to zero. The measurement times were also
normalized to coordinated universal time (UTC) to ensure
data alignment across time zones.
For wind, the raw wind speed was converted to a consistent
80-meter or greater hub height using the methodology
described in detail in Hansen and Levine (2008). In
summary, all data gathered at lower than 40m were
discarded, data gathered between 40m and 80m were scaled
up to 80m, and all data gathered at or above 80m were left
at the recorded height. Wind speeds were adjusted for height
using the one-seventh-power rule.
For solar, both direct (beam) insolation and diffuse
horizontal collector data was included. Where measured
solar data was not available on an hour-by-hour or site-by-
site basis, modeled data was substituted when possible.
4.3 Wind Power Production Model
As described further in Hansen & Levine (2008), the 2 MW
Vestas V80 was chosen to model power production. The
turbine’s power curve was adjusted for elevation and air
density at each site.
4.4 Solar Power Production Model
Solar power production was modeled for an idealized 1-axis
polar mount tracking photovoltaic system with a maximum
power point (MPP) tracker. Although solar thermal systems
1 Available on line at:
www.undeerc.org/programareas/renewableenergy/wind/default.asp
2 Available on-line at:
http://rredc.nrel.gov/solar/old_data/nsrdb/1991-2005/
are more common for utility scale solar power, a
photovoltaic system was chosen in this analysis because:
The NSRDB-Update modeled direct insolation data
does not adequately capture some frequency
components important for solar thermal analysis
(Renné, et al 2008); and
Concentrating solar power production, including solar
thermal is less suited for areas, such as MRO, where
diffuse radiation comprises a substantial portion of the
total insolation.
The model system was tilted at an angle above horizontal
equal to the site latitude. The Maximum Power Point (MPP)
current was assumed to vary linearly with insolation.
Temperature effects and decreased MPP voltage at lower
insolation levels were not included. An isotropic sky is
assumed and implies equal diffuse radiation intensity in all
directions. Reflected radiation is conservatively assumed to
be zero. Other losses, including conversion and inverter
efficiencies were assumed to be constant. Since the system
was scaled to a fixed total AC nameplate power it was not
necessary to quantify these other losses. The resulting
equations for insolation and power production are:
!
I1"axis =IBcos
#
+IDH
1+cos(
$
+
#
)
2
%
&
'
(
)
*
!
P
1"axis =I1"axis #Pnameplate
Where IB=direct (beam) insolation, IDH=horizontal diffuse
insolation, δ=solar declination, and ζ=zenith angle. (adapted
from Masters 2004)
Though this model is very simple, it is adequate to capture
the time variability of the solar resource, which is the
primary concern in this study. Further efforts are underway
to refine this model to both include non-idealities and the
balance of system hardware and to compare other solar
power system designs including fixed photovoltaics and
concentrating solar technologies.
4.5 Scaling and Interconnection
As described in section 3, this study combined multiple
individual generation sites to create portfolios of
geographically and resource (wind vs. solar) diverse
generation. This analysis does not consider the constraints
and losses associated with an interconnecting transmission
system and other infrastructure components.
To facilitate comparisons of results for different scenarios,
all individual wind and solar site date was scaled to a
nameplate power rating of 100 MW AC. For solar, this
scaling was done on the AC power rating at 1-sun (1000
W/m2). When multiple sites were interconnected to form a
portfolio, individual site output power was scaled such that
the total nameplate power for the portfolio was kept at 100
MW. The selection of 100 MW was arbitrary, and the
results can be readily scaled up (or down) as needed. The
use of 100 MW also affords easy conversions to/from
percent of nameplate load.
4.6 Variability and Output Metrics
The variability of site (or portfolio) output was quantified as
the standard deviation, σ, of the (combined) hourly power
production in MW. The standard deviation also has units of
MW. The output was quantified as the arithmetic mean of
the hourly power production in MW. If desired, this average
output measure can be converted to annual energy
production in MWh by multiplying by the number of hours
in a year.
In addition to representing important considerations for
integrating a variable resource into a utility load, the choice
of mean and standard deviation allow for significant
computational savings when optimizing large portfolios.
This is because, rather than having to recalculate the hour-
by-hour power output at each optimization step, it is only
necessary to scale the covariance matrix and mean.
The computation of the portfolio mean power output,
!
pp
,
for n sites is straightforward:
!
pp=aipi
i=1
n
"
where ai is the percent share, or weight, of generating
capacity for an individual site. And
is the mean of the
hourly output series, Pi, of the corresponding site.
The computation of the portfolio standard deviation, σp,
takes advantage of the fact that the variance (σ2) of the sum
of a set of random variables, Xi, is equal to the sum of the
elements in their covariance matrix. Namely,
!
Var(X1+X2KXn)=Cov(Xi,Xj)
j=1
n
"
i=1
n
"
And the property that the covariance of scaled random
variables is equal to the scaled covariance of the original
variables:
!
abCov(X,Y)=Cov(aX,bY )
As a result, the portfolio output power standard deviation is
given by:
!
"
p
2=aiajCov(Pi,Pj)
j=1
n
#
i=1
n
#
or in Matrix form:
!
"
p
2=aTµa
where:
!
a=
a1
M
an
"
#
$
$
$
%
&
'
'
'
!
µ=
Var(P
1)LCov(P
1,P
n)
M O M
Cov(P
1,P
n)LVar(P
n)
"
#
$
$
$
%
&
'
'
'
since Cov(X,X) = Var(X).
4.7 Optimization Methodology
The portfolio variability was minimized using Monte Carlo
methods subject to a constraint on the average output power:
!
minimize(
"
p)
subject to
!
pp"plimit
This portfolio power constraint, plimit, was varied from the
minimum to maximum single site output average power, pi,
for the set of sites in a scenario.
Rather than running a separate optimization for each Plimit,
in which any runs that did not meet the constraint must be
thrown out, the results of each Monte Carlo trial were
binned according to output level. In this way the simulation
lets us run multiple constrained optimizations
simultaneously.
Also, to more fully explore the potential value of sparse
portfolios, at the start of each trial random weights were
assigned not to all n sites, but to a randomized subset, N, of
the available sites. This was necessary since the probability
of multiple zero or near-zero share members existing in a
portfolio of randomly weighted sites drops precipitously
with increasing n.
4.8 Treatment of Constrained Number of Sites
During the analysis, it was noticed that the optimal portfolio
rarely contained all of the sites. Further investigations were
conducted to determine the impacts of restricting the
number of sites included in the portfolio.
This introduced an additional constraint to the optimization:
!
length(N)"nlimit
Separate simulations were run for each value of nlimit.
In these scenarios, the subset of sites with nonzero output
shares was randomly selected for each trial from the entire
appropriate set of power data (e.g. all wind sites). In this
way, the members in the subset of sites was allowed to vary
to achieve the optimal results across a spectrum of output
levels. The sites represented at low output levels for a given
nlimit would typically be different than those included in a
higher output portfolio for the same nlimit.
5 RESULTS AND DISCUSSION
5.1 Wind alone
Given the growing body of literature on the subject (Archer
& Jacobson 2007, for example) and the results of prior
studies by the group using different optimization methods
(Hansen & Levine 2008), it was not surprising to find that
the power production for an optimized portfolio of wind
assets was less variable than that for its sites individually.
Specifically, optimized wind portfolios for MRO in 2004
reduced output variability an average of 45%3 compared to
the individual sites and increased the capacity factor from
0.19 to 0.254. The 80/90/95/99% available output level also
increased from an average of 1.5/0/0/0MW to 9/6/4/1MW5.
5.2 Solar alone
Similar to wind, combining solar generating assets into an
optimal portfolio reduced the output variability compared to
that of the individual sites. Optimized solar only portfolios
for MRO in 2004 reduced output variability an average of
15% compared to the individual sites and increased the
capacity factor from 0.23 to 0.25. Because the sun sets, the
power output for individual solar sites is zero at least half of
the time. When combined into a portfolio, this increased to
8 MW of firm output capacity available 50% of the time.
A major factor in this reduced variability comes from the
range of longitudes included in a portfolio. Increasing
longitudinal spans makes it possible for the sun to be
available to some collector in the portfolio for more hours of
3 All portfolio averages include the two middle quartiles of the set
of optimal portfolios.
4 CF for portfolio with moderately high output and standard
deviations (bin 15/20) vs site average.
5 Increased guaranteed output levels for portfolio with moderately
high output and standard deviations (bin 15/20.)
the day. Spatial diversity of solar also reduces the impact of
patchy clouds covering the sun, since it is likely that the sun
will be unobscured at one of the other sites.
In this analysis, the variability reduction was less dramatic
than for wind, largely because solar radiation is more
correlated between sites than wind speed. In fact, the
minimum covariance between individual solar sites is 50x
higher than that for wind sites.
5.3 Solar & Wind Together
When combined, solar and wind resources provide optimal
portfolios which offer further decreases in power variability
beyond that of either alone.
Fig. 3: (top) Load-duration-style output curves for optimal
portfolios. A high, flat line that is never at zero is best.
(bottom) Zoom in on the lower right showing significantly
improved firmness of output for portfolios. 6
In this analysis, both the wind-only and solar-only
covariance matrices were strictly positive, indicating that
the resource specific power production was more or less
correlated. In the combined solar & wind scenario, negative
elements appear corresponding to an anti-correlation
between the solar and wind resources which is a powerful
indicator for the potential of cross-firming.
Optimized portfolios offer an average decrease in variability
of 55% below the average of individual sites. This
represents a 13% lower average variability than the optimal
for wind only and 60% lower than the optimal solar. The
6 The portfolios depicted as optimal in these figures are those with
moderately high output and standard deviations. (bin 15/20). See
section 5.4 for further discussion.
combined optimal capacity factor was 0.25 and the
80/90/95/99% available output increased to 11/7/4/2 MW.
The top chart in Fig. 3 compares the output duration
improvements for the optimal combined portfolios with
those of the individual technology portfolios and those of an
arbitrary subset of the individual sites. The upper plot shows
that all of the optimal portfolios and the combined
wind+solar and the wind-only profiles in particular, have a
relatively flatter profile, illustrating that a narrower range of
output levels is produced for a majority of the time. The
combined portfolio produces the flattest profile, illustrating
its further variability reductions. The flat regions of the
curve are also higher than those of the individual sites,
indicating an increase in reliable output power during these
periods of reduced variability.
The bottom chart in Fig. 3. shows that the optimal combined
and wind-only portfolios eliminate the amount of time with
zero output. This represents a significant improvement
above the roughly 15% of the time the wind sites in this
analysis have zero output. The optimal solar-only portfolio
also shows a large reduction in zero output from 50% to
40% of the time.
Some of the ways in which the solar and wind resources
compliment each other are illustrated in Fig. 4. At night, the
wind generators provide power when the sun can’t. During
the afternoon of May 27th and all day on May 28th solar
output is able to compensate for low wind power output to
produce a lower variability output.
Fig. 4: Generation profile of optimal portfolios.
5.4 Trade-offs
For each scenario there is a set of optimal portfolios that
represent a trade-off between variability (standard
deviation) and power output (
!
pp
).
This concept is represented graphically with the efficient
frontier shown in Fig. 5 This plot shows the trade-off
between risk (variability) and reward (output). Individual
sites appear as points, while optimal portfolios lie along a
curve. Moving toward the left (lower variability) and up
(higher output) represent desired trajectories. A utility can
pick from along the curves to select the best-suited balance
of output and variability.
Fig. 5: Tradeoff of output power vs variability. Upper left is
best.
In the figure it is clear that in all cases – wind-alone, solar-
alone, combined solar and wind – the optimal portfolios
offer decreased variability (standard deviation) for a given
output level and/or increased average power output for a
given variability compared to their associated individual
sites alone. This figure also clearly shows the added value of
cross-firming wind with solar to allow a few percentage
points of increased output or decreased variability.
5.5 Effect of Number of Site Constraints
This study conducted preliminary analysis on the impacts of
limiting the number of sites selected for a portfolio. As seen
in Fig. 6, including only a few of the available sites can
achieve the majority of reductions in variability (or
increases in output). A marked improvement in variability is
achieved by interconnecting portfolios as small as two sites
and portfolios of only six optimally chosen sites are nearly
indistinguishable from the unconstrained optimums.
Furthermore, the actual number sites that make up the
optimal portfolios for less-constrained simulations is
observed to be much lower than nlimit as seen in Table 1.
This could plausibly be due to the difficulty of finding
optimal solutions from the extremely large number of
combinations of sites and weights for high nlimit scenarios.
However, increasing the number of trials, which should
increase the odds of locating an optimal portfolio with a
high number of sites, has instead been observed to further
reduce the number of sites in optimal portfolios. This
observation taken together with the trend that increasing
nlimit beyond a certain point does not significantly affect the
variability, provides support for the theory that the optimal
portfolio for a given output level does not contain all of the
sites.
Fig. 6: Improvement in variability for a given output can be
had with only a few optimally selected sites.
This analysis shows rapidly diminishing returns for
decreasing variability by increasing nlimit for a given
geographic region. Others, including Archer and Jacobson
(2007), have shown seemingly contradictorily results that
the variability of power output tends to decrease
monotonically with the number of sites interconnected in an
array with only gradually diminishing returns. One possible
explanation is that the number of sites available to draw
from when creating a portfolio, rather than the actual
number of interconnected sites is the key to reducing
variability Further investigation is required to better
understand this phenomenon.
TABLE 1: OPTIMAL PORTFOLIO RESULTS
(WIND+SOLAR)
Max Sites
Constraint
Avg # Sites in
Best Portfolios
Average Drop in
Std. Dev.7
2
2.0
9.0 MW
4
4.0
13.0
6
5.5
13.4
12
8.8
13.7
20
9.8
13.8
43 (all)
8.18
13.8
7 Relative to the average std. dev. of individual sites of 25MW
8 The full portfolio is the result of 5-10x as many simulations as
the other sites.
6 SOLAR AND WIND IN A NEXT GENERATION
UTILITY
While geographical dispersion of variable resources and the
combination of different variable resources can significantly
reduce portfolio variability, as described in this paper, the
remaining variability must be managed in order to balance
demand and supply on the hourly, minute, and second
scales.
This balancing currently happens through the use of
automated generation control and ancillary services.
However, with greatly increased penetrations of variable
renewables, more flexible capacity will be required. Given
advances in communications and control technologies,
much of this remaining variability could be met effectively
through the dynamic use of:
Responsive Loads—demand response has traditionally
been used to clip and shift on-peak demand to off-peak
periods in order to defer building new generation
capacity. Increasing the magnitude and duration of
demand response contributes to controlling absolute
demand growth. Furthermore, developing demand
response techniques that can operate at more than just
peak periods should allow demand response to provide
ancillary grid services and help manage renewable
variability. Previous pilot projects in California and
Nevada have shown that automated technologies with
two-way digital communications can successfully drive
demand response;
Energy Storage— powerful system performance
synergies can be derived from the integration of the
electric and transportation sectors through the use of
plug-in hybrid electric vehicles and full electric
vehicles. For the electric utility, PHEVs and EVs
(collectively xEVs) offer responsive off-peak load, the
potential for dispatchable on-peak capacity from
vehicle-to-grid (V2G) connections, and the prospect of
economic electric storage, since the high capital costs
of batteries would be shared with drivers; and
Intelligent Grid Communications—Increased use of
responsive load and xEVs requires advanced grid
communications technologies. Utilities must be able to
communicate in real-time with loads and xEVs to make
most effective use of the firming capabilities of those
resources. Such capabilities are being explored in on-
going research into “smart grid” technologies.
7 CONCLUSIONS
This study shows that, as is the case for wind,
interconnecting individual solar generation sites into
geographically diverse arrays can reduce the variability of
the power output. It also shows that including solar
generation sites into arrays of geographically diverse wind
sites can further reduce the total variability beyond what is
possible for either resource type alone. Finally, it was
observed that, at least in the modeled system, only a small
subset of the potential sites in an interconnected array need
to be included to achieve these variability reductions.
8 NEXT STEPS
To expand and enhance this analysis for incorporation into
the next generation utility concept, there are several
additional elements of analysis that will be addressed,
including:
Other geographic areas—this analysis covers only the
Midwest Reliability Organization (MRO). As with the
wind-only analysis conducted by Hansen & Levine in
2008, this analysis will be expanded into the Southwest
Power Pool (SPP) and the Electric Reliability Council
of Texas (ERCOT). Additionally, both the wind-only
analysis and the wind and solar analysis will be
expanded into the Western Electric Coordinating
Council (WECC). Once these regions have been
analyzed, the majority of good wind and solar sites
within the continental United States will have been
addressed.
Longer time periods—this analysis comprises only the
year 2004. To more accurately capture the variability
over time of both wind and solar power, hourly data
over at least three years should be analyzed. This
expanded analysis will be conducted as possible given
the availability of hourly data in a consecutive three-
year period.
Match to load shape—as discussed at the beginning of
this paper, renewable resource variability is only
important in the context of system load. Therefore, a
complete analysis includes the covariance of
renewables with load over the same time period. This
type of analysis, frequently referred to as the effective
load carrying capability (ELCC) of a renewable
resource, is dependent in part on the ability to acquire
accurate hourly load data.
Integration with demand-side resources—finally, the
next generation utility project will analyze the
interactions between variable renewable resources and
demand-side resources, including responsive load and
xEVs. The ability of these resources to manage
renewable variability largely depends on the duration
and possible rate of change of each resource.
Economic drivers—the viability of the next generation
utility concept is dependent on the cost-effectiveness of
the system and its components. The theory put forward
in this paper is that the intelligent combination of
resources can reduce the cost of the portfolio. However,
this and other economic drivers, including the cost of
various technologies and of the transmission capacity
needed to connect them, must be explicitly addressed.
9 ACKNOWLEDGEMENTS
The authors would like to thank Joel Swisher, Ph.D., P.E.,
and the Energy & Resources Team of the Rocky Mountain
Institute for their collective work on the next generation
utility concept. This research is funded through the generous
support of the William and Flora Hewlett Foundation.
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... Connecting CRE production utilities to transport grids smooths such medium frequency variations, as long as their space-time co-variability is weak enough over the connected domain (e.g. [21]). Wind and solar energy production may experience large and sudden variations called "ramps" linked, respectively, to wind turbulence and cloud circulation [22,23]. ...
... Most studies concern actual or potential wind-and solar-power sources. Sparse meteorological stations over Central US, considered as a portfolio of solar and wind energy plants (8 and 26 respectively), show that i) the statistical distribution of the portfolio production is much smoother than the distribution of individual plants, reducing notably the probability of no production, ii) both the total production and its standard-deviation vary by a factor of three depending on the fraction of equipment put on each individual plant, and, iii) at a given level of production, the standard deviation of the portfolio production is half the standard deviation of individual plants, where the standard-deviation of the portfolio depends on the covariance between plants [21]. These conclusions are confirmed in similar studies. ...
... Since climatic variables driving CRE production and energy demand are weakly correlated, the space integration and the combination of different CRE sources are expected to first contribute to the base load. They can be adapted to intermediate and peak loads by adding more dynamic control of the demand [21]. Using a weather model over Europe for the 2000-2007 period (ca. ...
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A major part of renewable electricity production is characterized by a large degree of intermittency driven by the natural variability of climate factors such as air temperature, wind velocity, solar radiation, precipitation, evaporation, and river runoff. The main strategies to handle this intermittency include energy-storage, -transport, -diversity and -information. The three first strategies smooth out the variability of production in time and space, whereas the last one aims a better balance between production and demand. This study presents a literature review on the space-time variability of climate variables driving the intermittency of wind-, solar- and hydropower productions and their joint management in electricity systems. A vast body of studies pertains to this question bringing results covering the full spectrum of resolutions and extents, using a variety of data sources, but mostly dealing with a single source. Our synthesis highlights the consistency of these works, and, besides astronomic forcing, we identify three broad climatic regimes governing the variability of renewable production and load. At sub-daily time scales, the three considered renewables have drastically different pattern sizes in response to small scale atmospheric processes. At regional scales, large perturbation weather patterns consistently control wind and solar production, hydropower having a clearly distinct type of pattern. At continental scales, all renewable sources and load seem to display patterns of constant space characteristics and no indication of marked temporal trends.
... To supplement our discussion we summarize a few recent results on systemic solutions for this problem. Systemic solutions combine different forms of renewable energy (54,55) and different locations of energy generation in a portfolio approach to minimize outage times (14,56). ...
... Connecting several locations into a geographically diverse array decreases given outage times and buffers against the variability of individual sites (7,(54)(55)(56)(57). Connecting the four time zones of the U.S. extends the time available for solar electricity generation by four hours, while a north-south connecting network decreases the impact of reduced daylight hours in northerly latitudes during winter and allows the south to benefit from the much longer daytimes during summer in the north. ...
... Palmintier et al. (54) determine optimized portfolios out of 8 U.S. solar and 35 wind sites. They find a 45% reduction in wind energy output variability and a 15% reduction for solar energy compared to the average of individual sites. ...
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Several forms of renewable energy compete for supremacy or for an appropriate role in global energy supply. A form of renewable energy can only play an important role in global energy supply if it fulfills several basic requirements. Its capacity must allow supplying a considerable fraction of present and future energy demand, all materials for its production must be readily available, land demand must not be prohibitive, and prices must reach grid parity in the nearer future. Moreover, a renewable energy technology can only be acceptable if it is politically safe. We supply a collection of indicators which allow assessing competing forms of renewable energy and elucidate why surprise is still a major factor in this field, calling for adaptive management. Photovoltaics (PV) are used as an example of a renewable energy source that looks highly promising, possibly supplemented by solar thermal electricity production (ST). We also show why energy use will contribute to land use problems and discuss ways in which the right choice of renewables may be indispensible in solving these problems.
... This issue has already been investigated on the grounds of empirical data in e.g. Palmintier et al. (2008), Jónsson et al. (2010) and Grams et al. (2017). Moreover, our numerical example supports the findings of these papers. ...
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In this paper we study the impact of errors in wind and solar power forecasts on intraday electricity prices. We develop a novel econometric model which is based on day-ahead wholesale auction curves data and errors in wind and solar power forecasts. The model shifts day-ahead supply curves to calculate intraday prices. We apply our model to the German EPEX SPOT data. Our model outperforms both linear and non-linear benchmarks. Our study allows us to conclude that errors in renewable energy forecasts exert a non-linear impact on intraday prices. We demonstrate that additional wind and solar power capacities induce non-linear changes in the intraday price volatility. Finally, we comment on economical and policy implications of our findings.
... Research groups and large industrial consortia have proposed several continental and transcontinental solar networks, including Desertec EUMENA (connecting Europe, North Africa, and the Middle East) (13,16), an Asian−Australian energy infrastructure (14,15), and the "US Solar Grand Plan" (12,20), a predominantly renewable energy supply system using high-insolation areas in the US Southwest. These networks all still need large amounts of overcapacity and storage, even if solar, wind, and geothermal are combined (19,(21)(22)(23)(24). A recent study designed to meet 1/5 of the US electricity demand from solar and wind includes overcapacity at up to 3 times the load (19,21). ...
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Significance The recent sharp drop in the cost of photovoltaic (PV) electricity generation accompanied by globally rapidly increasing investment in PV plants calls for new planning and management tools for large-scale distributed solar networks. We found that pairs of electricity generation capacity G and storage S , such that S is minimal to provide a given dispatchable electricity capacity for a given G , exhibit a smooth relationship of mutual substitutability between G and S . These G − S isolines support the solution of several tasks. This includes optimizing the size of G and S for dispatchable electricity, optimizing connections between solar parks across time zones for minimizing intermittency, and management of storage in situations of far below average insolation.
... Distributed solar electricity generation across large geographic areas [1][2][3][4][5][6][7][8][9][10] can significantly reduce the intermittency of solar energy [2,6,[10][11][12][13][14]. In ever more regions such schemes become competitive due to rapidly decreasing costs of photovoltaics (PV). ...
... This would result in a more consistent level of output over a longer time frame, which could reduce the cost of wind integration " (Hurlbut 2009). For additional analysis of spatial diversity, see also Palmintier et al. 2008. 37 In 2008 than 8.5 GW of wind was installed in the United States, reaching a total capacity of about 25.3 GW by the end of the year (AWEA 2009). ...
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Renewable energy sources, such as wind and solar, have vast potential to reduce dependence on fossil fuels and greenhouse gas emissions in the electric sector. Climate change concerns, state initiatives including renewable portfolio standards, and consumer efforts are resulting in increased deployments of both technologies. Both solar photovoltaics (PV) and wind energy have variable and uncertain (sometimes referred to as intermittent) output, which are unlike the dispatchable sources used for the majority of electricity generation in the United States. The variability of these sources has led to concerns regarding the reliability of an electric grid that derives a large fraction of its energy from these sources as well as the cost of reliably integrating large amounts of variable generation into the electric grid. In this report, we explore the role of energy storage in the electricity grid, focusing on the effects of large-scale deployment of variable renewable sources (primarily wind and solar energy).
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Previously, we quantified a decline in the marginal economic value of wind and PV with increasing penetration levels based on a long-run equilibrium investment and dispatch model that accounted for operational constraints for conventional generation. We use the same model and data, based loosely on California in 2030, to evaluate several options to stem the decline in value of these technologies. The largest increase in the value of wind at high penetration levels comes from increased geographic diversity. The largest increase in the value of PV at high penetration levels comes from assuming that low-cost bulk power storage is an investment option. Other attractive options, particularly at more modest penetration levels, include real-time pricing and technology diversity.
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Investments in renewable energy were at US$211 billion in 2010 and developing economies overtook developed ones for the first time in terms of new financial investments in renewable energy. Photovoltaics for generation of electricity from sunlight has the highest growth rate among the competing forms of renewable energy and has now begun to achieve grid parity in some regions. If these trends of investments continue, solar energy will play a major economic role. We analyze these developments and assess the ensuing amounts of investment and employment for a range of sizes of the sector of solar energy. We find that by 2050 electricity from photovoltaics could cover up to 90% of total global energy demand, with a then global capital investment in our main scenario in photovoltaic manufacturing capacity at 500 billion US$2010 by around 2030 and 1,500 billion by 2050. Employment in photovoltaic manufacturing is predicted to rise to 6 million by 2050. Sensitivity analysis with respect to the core parameters of assumptions is supplied.
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Wind is the world's fastest growing electric energy source. Because it is intermittent, though, wind is not used to supply baseload electric power today. Interconnecting wind farms through the transmission grid is a simple and effective way of reducing deliverable wind power swings caused by wind intermittency. As more farms are interconnected in an array, wind speed correlation among sites decreases and so does the probability that all sites experience the same wind regime at the same time. The array consequently behaves more and more similarly to a single farm with steady wind speed and thus steady deliverable wind power. In this study, benefits of interconnecting wind farms were evaluated for 19 sites, located in the midwestern United States, with annual average wind speeds at 80 m above ground, the hub height of modern wind turbines, greater than 6.9 m s-1 (class 3 or greater). It was found that an average of 33% and a maximum of 47% of yearly averaged wind power from interconnected farms can be used as reliable, baseload electric power. Equally significant, interconnecting multiple wind farms to a common point and then connecting that point to a far-away city can allow the long-distance portion of transmission capacity to be reduced, for example, by 20% with only a 1.6% loss of energy. Although most parameters, such as intermittency, improved less than linearly as the number of interconnected sites increased, no saturation of the benefits was found. Thus, the benefits of interconnection continue to increase with more and more interconnected sites.
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In this chapter we present an overview of the development of today’s electric power industry, including the regulatory and historical evolution of the industry as well as the technical side of power generation. Included is enough thermodynamics to understand basic heat engines and how that all relates to modern steam-cycle, gas-turbine, combined-cycle, and cogeneration power plants. A first-cut at evaluating the most cost-effective combination of these various types of power plants in an electric utility system is also presented.
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This manual describes how to obtain and interpret the data products from the updated 1991-2005 National Solar Radiation Database (NSRDB). This is an update of the original 1961-1990 NSRDB released in 1992.
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As the worldwide use of wind turbine generators in utility-scale applications continues to increase, it will become increasingly important to assess the economic and reliability impact of these intermittent resources. Although the utility industry appears to be moving towards a restructured environment, basic economic and reliability issues will continue to be relevant to companies involved with electricity generation. This article is the second in a two-part series that addresses modelling approaches and results that were obtained in several case studies and research projects at the National Renewable Energy Laboratory (NREL). This second article focuses on wind plant capacity credit as measured with power system reliability indices. Reliability-based methods of measuring capacity credit are compared with wind plant capacity factor. The relationship between capacity credit and accurate wind forecasting is also explored. Published in 2000 by John Wiley & Sons, Ltd.
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  • J Levine
Levine, J. (2007). Pumped hydroelectric energy storage and spatial diversity of wind resources as methods of improving utilization of renewable energy sources. MS Thesis. University of Colorado at Boulder.
California Renewable Portfolio Standard Renewable Generation Integration Cost Analysis, Phase III: Recommendations for Implementation. California Energy Commission
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Kirby, B., et al. (2004). California Renewable Portfolio Standard Renewable Generation Integration Cost Analysis, Phase III: Recommendations for Implementation. California Energy Commission
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Hansen. L. & Levine, J. (2008) Intermittent Renewables in the Next Generation Utility. POWER-GEN Renewable Energy & Fuels 2008, February 19-21, 2008.
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Renné, D., George, R. Wilcox, S., Stoffel, T., Myers, D., & Heimiller, D. (2008). Renewable Systems Interconnection: Solar Resource Assessment. National Renewable Energy Lab: Golde, CO. October 30, 2007 Draft.