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Electric Power Systems Research 174 (2019) 105858
Contents lists available at ScienceDirect
Electric Power Systems Research
journal homepage: www.elsevier.com/locate/epsr
Three probabilistic metrics for adequacy assessment of the Pacific Northwest
power system
T
⁎
John Fazio , Daniel Hua
Northwest Power and Conservation Council, 851 SW 6th Avenue, Suite 1100, Portland, OR 97204, USA
A R T I C LE I N FO
A B S T R A C T
Keywords:
Power system
Probabilistic adequacy metrics
Monte Carlo simulation
All regional power system entities within the bulk assessment areas in North America calculate and report
probabilistic adequacy metrics that measure projected shortfall duration and magnitude to the North American
Electric Reliability Corporation (NERC). While no maximum thresholds have been defined for either metric to be
used as an adequacy standard, it is possible that such thresholds could be defined sometime in the future as
planners gain a better understanding of the risks associated with each metric and how they interact. Therefore, it
is prudent to know whether setting a threshold for one metric automatically leads to an equivalent and predictable threshold for the other. To answer this question, this paper examines mathematical relationships among
these two metrics and a third metric (that measures the projected shortfall frequency) for the Pacific Northwest
power supply. Results from a Monte Carlo simulation model show that although in special cases the threshold for
one metric can be used to calculate unique thresholds for the other two, in general this is not the case. Hence, if
these metrics are to be used to define an adequacy standard for the Northwest, threshold values for all three
metrics should be defined independently.
1. Introduction
In order to integrate large amounts of variable generation into the
bulk power system, the North American Electric Reliability Corporation
(NERC) in 2011 recommended to its regional entities of bulk power
systems to calculate probabilistic adequacy metrics loss of load hours
(LOLH) and expected unserved energy (EUE) for comparison to the
traditional adequacy standard of a loss of load expectation (LOLE) of
0.1 day/year [1]. More recently, NERC has also recommended calculating the loss of load events (LOLEV) and the normalized expected
unserved energy (NEUE) metrics, among others, for probabilistic adequacy studies [2]. Definitions of LOLEV, LOLH, EUE and NEUE, and the
equations to calculate them can be found in [2].
A brief description of these metrics are as follows: LOLEV, a frequency metric, is the expected (or average) number of shortfall events
per year, where a shortfall event is defined as a contiguous set of hours
when load exceeds generating capacity. On the other hand, LOLH, a
duration metric, is the expected number of hours per year when load
exceeds generating capacity. Finally, EUE, a magnitude metric, is the
expected amount of unserved energy (or the average sum of the positive
differences between hourly load and generating capacity) per year, in
units of megawatt-hour per year. Closely aligned with EUE is NEUE, a
dimensionless magnitude metric in units of parts-per-million (ppm) per
⁎
year, which is defined as the ratio of EUE to the annual load (in
megawatt-hours) multiplied by one million. Similar to LOLEV and
LOLH, NEUE can be compared directly across power systems serving
vastly different loads.
Currently all assessment areas are required to calculate and report
LOLH and EUE to NERC for publication [3]. However, along with LOLH
and EUE, the duration and magnitude metrics, a more complete adequacy assessment should also include the frequency metric LOLEV,
which allows for a different but also important measure of risk. These
three different statistical measures of power supply shortfall quantify
three different types of risks and enable power system planners to design effective and economical solutions to mitigate only those risks that
are deemed unacceptably severe even as other risks remain tolerable.
Acceptable levels of shortfall risks, or adequacy standards, are set by
planners in the form of thresholds for one or more metric, for example,
the traditional “one-day-in-ten-year” threshold for the loss of load expectation (LOLE) metric (often translated into 0.1 day/year). Although
no assessment area currently uses an adequacy standard based on any
of the three metrics, it is possible that in the future one or more could be
used to define an adequacy standard, especially as the relationship
among these metrics and the risks they quantify become more familiar
from the annually published NERC reports. Therefore, it is prudent to
know if setting a threshold for one metric automatically leads to
Corresponding author.
E-mail address: [email protected] (J. Fazio).
https://doi.org/10.1016/j.epsr.2019.04.036
Received 13 November 2018; Received in revised form 17 April 2019; Accepted 26 April 2019
Available online 09 May 2019
0378-7796/ © 2019 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license
(http://creativecommons.org/licenses/by-nc-nd/4.0/).
Electric Power Systems Research 174 (2019) 105858
J. Fazio and D. Hua
On the demand side, in 2015 the maximum winter and summer
hourly loads were 30,100 MW and 29,000 MW, respectively and were
calculated from the Federal Energy Regulatory Commission Form 714
data for various Balancing Authorities inside the PNW [9].
equivalent and predictable thresholds for the other two, in other words,
whether an adequacy standard can simply be defined by setting a
limiting threshold for just one of these metrics.
To answer this question, this paper calculates the value of these
three metrics under varying resource and load conditions and analyzes
mathematical relationships among them from a set of adequacy studies
for the Pacific Northwest power system. The studies were performed
using an adequacy model based on Monte Carlo chronological hourly
simulations. Analyses in this paper show that under special conditions
any pair of the three metrics can be fitted, with very good accuracy, by
a single linear function. Thus, for these special scenarios, the threshold
for one metric can be used to calculate unique thresholds for the other
two. However, in general, this is not the case. A specified threshold for
one metric will lead to different resulting thresholds for the other two
metrics in separate scenarios depending on how diverse the resource
mixes and loads are between scenarios. Therefore, if these metrics are
to be used to set an adequacy standard for the Pacific Northwest, the
thresholds for all three metrics (frequency, duration and magnitude)
should be assigned independently.
This paper is organized as follows: an overview of the resource and
load characteristics of the Pacific Northwest (PNW) power system is
presented in Section 2. Next in Section 3 are three subsections, the first
of which is Section 3.1 which has a brief description of the GENESYS
adequacy model and the Monte Carlo hourly simulations used within
the model; then Section 3.2 presents the equations for calculating the
LOLEV, LOLH and NEUE metrics from GENESYS output; and finally in
Section 3.3 are details of the varying loads and resources of the twelve
scenarios studied (from which the metrics are calculated). Then the
three metrics are calculated for the twelve scenarios and presented in
Section 4, with mathematical relationships between pairs of metrics
analyzed and discussed in Sections 4.1–4.3. Finally, in Section 5 is a
summary and conclusion.
3. Methodology
Both analytical methods and Monte Carlo simulations can be used to
calculate probabilistic adequacy metrics [2], with computation times
for analytical methods being significantly shorter than those for Monte
Carlo simulations. However, for power supplies with energy limited
resources, such as hydroelectric systems, chronological Monte Carlo
simulations are more often used because they can directly account for
time dependency of hydroelectric operations. More specifically, the
available capacity of hydroelectric resources in any time period depends on their operation in previous periods and on time dependent
constraints, such as required minimum off-peak generation to provide
balancing reserves. In spite of not being able to model time dependent
operations, analytical methods can still be used to assess the adequacy
of power supplies with energy limited resources, if the dispatch of those
resources can be reasonably approximated. For example, hydroelectric
systems that have a high ratio of storage volume relative to river flow
(inflow) volume can be modeled such that average monthly hydroelectric energy remains fairly constant from year to year. For hydroelectric systems of this type, monthly hydroelectric energy is assumed
to follow hourly load shapes, adjusted for time dependent constraints.
The resulting approximate hydroelectric dispatch is used to modify
either the load duration curve [10], or the capacity availability curve
[11], which then are convolved to create the surplus probability density
function from which adequacy metrics can be calculated [2]. However,
for hydroelectric systems with a low ratio of storage volume relative to
inflow volume, such as that in the PNW (see Section 2), their monthly
energy can vary significantly from year to year, thus making analytical
methods more difficult. For that reason and the fact that the PNW hydroelectric system accounts for over half of the region's nameplate capacity and has significant time dependent constraints [8], a Monte
Carlo simulation model is used to calculate adequacy metrics.
2. The Pacific Northwest Power System
The Pacific Northwest (PNW) power supply, as defined in the Pacific
Northwest Electric Power Planning and Conservation Act [4], consists
of generating resources committed to serving loads in the geographical
area mainly in the states of Washington, Oregon, Idaho and the part of
western Montana that lies within the Columbia River Basin.
As of 2015, the Pacific Northwest power supply had a nameplate
capacity (operating and under construction) of 62,300 MW, and was
dominated by hydroelectric capacity at 53% (33,200 MW), followed by
natural gas, wind and coal at 16% (9900 MW), 14% (9000 MW) and
12% (7300 MW), respectively. Nuclear and biomass generating capacities each made up 2% (1200 MW), while geothermal, solar and storage comprised the remaining 1% (500 MW) [5]. Although the hydroelectric system includes 60 major U.S. dams with a total of 33,200 MW
of nameplate capacity, it can only provide about 26,000 MW of sustained-peaking capacity (based on the critical water-year 1937 conditions) [6] due to limited storage (U.S. reservoirs can only store about
16% of the annual average river-flow volume as measured at The Dalles
[7]). Hydroelectric sustained-peaking capacity depends on a number of
variables, including river-flow volume, non-power constraints and
peak-load duration. The 26,000 MW of sustained-peaking capacity referenced here was estimated assuming a 10-h peak-load duration and
the lowest river-flow volume on record. Hydroelectric generation simulated in the adequacy model includes 76 regulated projects and a
further set of 71 independent (not regulated within GENESYS) projects,
the vast majority of which is listed in [6]. In addition, the hydroelectric
system operates to satisfy many constraints (many listed in [8]) which
include providing decremental balancing reserves for wind resources by
increasing minimum generation between 1400 MW and 1600 MW
during off-peak hours. It should be noted that a substantial amount of
wind capacity (about 3000 MW out of 9000 MW) is dedicated to serving out-of-region load and does not contribute to regional adequacy.
3.1. The GENESYS adequacy model
The Northwest Power and Conservation Council uses the GENESYS
model [12] to assess resource adequacy. GENESYS is a Monte Carlo
computer program that performs a chronological hourly simulation of
the Pacific Northwest power supply for a single operating year (October
through the following September). Thousands of simulations are run,
with each simulation drawing a different combination of four random
variables; (1) temperature-sensitive loads, (2) temperature-sensitive
wind generation, (3) generator forced outages and (4) unregulated river
flows, each of which is briefly described below.
Loads. Temperature-sensitive hourly loads for a specific future year
are produced by the Council's econometric load forecasting model,
which uses historical data to project future load growth and energy
efficiency savings. The model creates 77 sets of 8760 temperaturesensitive hourly loads based on 77 years of historical daily average
temperatures at the four major load centers in the region (Seattle,
Portland, Spokane and Boise) [13]. At the beginning of each simulation,
all of the year's hourly loads are fixed by drawing one from the set of 77
possibilities based on 77 years of historical observed temperatures. The
probability density of the temperature-years is uniform since each year
occurred once. Hourly loads are then adjusted for firm out-of-region
contracts (e.g. exported energy is added and imported energy subtracted). Wind generation (see below), which is modeled as a load-reduction resource, is also subtracted from the load.
Wind. Most of the wind generation in the Northwest (located in the
Columbia River Gorge) has been shown to have some correlation to
temperature [14]. More precisely, as temperatures go to extremes (very
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Electric Power Systems Research 174 (2019) 105858
J. Fazio and D. Hua
Fig. 1. Ranked monthly regional temperature vs ranked monthly flows at TDA for 1929–2008.
beginning of each month, the GENESYS model estimates the amount of
hydroelectric energy to be dispatched based on its assigned operating
cost relative to the operating costs of other non-hydroelectric resources.
The dispatched monthly hydroelectric energy is then proportionally
allocated across each day of the month based on daily load shapes. The
hourly hydroelectric generation is limited by total system maximum
sustained-peaking capability and total system off-peak minimum generation limits. Additional hourly hydroelectric generation could be
dispatched, above the allocated amount, if all other resources are fully
dispatched and a shortfall still exists. At the end of each month, the final
amount of hydroelectric energy dispatched is used to adjust initial reservoir elevations for the next month. This method results in 8760 h of
hydroelectric generation.
The four random variables used in GENESYS encompass uncertainties in load forecasting, wind generation, thermal outages and
hydroelectric generation that are consistent with many of those recommended by NERC to consider in adequacy studies [17].
Of the four random variables, past analyses have shown that low
river flows (which affect hydroelectric generation) and extreme temperatures (which affect loads) are the most common causes of shortfall
events. Thus, it is worthwhile to investigate possible correlations between historical temperatures and water conditions. Fig. 1 shows a
scatter plot for ranked monthly average regional temperatures versus
ranked monthly average flows at the Dalles (TDA), here chosen to represent water conditions for the entire Columbia River Basin, for the
historical record from 1929 to 2008.
It is clear from this figure that monthly flows at the Dalles, on
average, are higher for regional temperatures above the 90th percentile
than for those temperatures below the 10th percentile (the two deciles
of extreme temperatures). However, the flow variations within each
temperature percentile group are quite large: they range from 0.1th to
75th percentiles for temperatures below the 10th percentile, while for
temperatures above the 90th percentile, they range from 30th to 90th
percentiles. Therefore, due to the large variations shown in Fig. 1 and
for simplicity, this paper assumes that river flows and temperatures are
not correlated.
Typically, a Monte Carlo analysis would draw a different combination of random variables for each simulation and run a sufficient
number of simulations to ensure convergence of the output parameters
of interest, in this case the three adequacy metrics. However, because
unregulated river flows and temperature-sensitive loads are the dominant uncertainties that determine power system adequacy and because
their respective distributions are uniform, all combinations of these two
random variables are drawn for each study. And because the total
number of combinations of temperature-year (77) and water-year (80)
hot or very cold), wind generation tends to be low. Using historical
temperatures and observed wind generation, bootstrap statistical
methods [14] are used to create synthetic hourly capacity factors for all
77 temperature-year profiles. Furthermore, to better capture the effects
of wind generation uncertainty, 20 possible sets of hourly capacity
factors are created for each temperature-year profile. Thus, at the beginning of each simulation, after the temperature-year profile is chosen,
all 8760 hourly wind capacity factors are determined by drawing from
one of the 20 possible sets for that temperature year. The probability
density of the 20 possible sets of wind capacity factors (for each temperature year) is uniform. Hourly wind generation is obtained by
multiplying the hourly capacity factor by the amount of installed wind
nameplate capacity dedicated to serve regional load and, as mentioned
above, is then subtracted from the hourly loads.
Forced outage. In contrast to load and wind generation whose hourly
values for the entire year are selected at the beginning of each simulation, thermal generator forced outages are accounted for dynamically
during the simulation based on a two-step Markov process. During each
simulation hour the state of each generator is assessed. A generator that
is “on” is assigned a future time to fail by equating a random number
draw to the cumulative probability function of mean-time-to-failure
derived from a log-normal distribution. Similarly a generator that is
“off” is given a future time to return to service by equating a random
number draw to the cumulative probability function of mean-time-torepair, also derived from a log-normal distribution. The log-normal
probability distribution is commonly used to model generator availability in the power industry. References [15,16] contain more details.
This process dynamically determines thermal force outages throughout
the 8760 h of simulation for each game. In contrast, hydroelectric
generator forced outages and maintenance are accounted for by applying fixed monthly availability factors.
Unregulated river flows. Finally, unregulated inflows at all hydroelectric projects in GENESYS are drawn from a set of 80 historical
water-year profiles. Similar to the loads based on the 77 historical
temperature-years, the probability density of the 80 historical wateryears is also uniform. Also, just as the hourly loads and wind generation
for the entire year are set at the beginning of each simulation by the
temperature-year selection, unregulated monthly inflows at each hydroelectric project for the year are determined by the selected historical
water-year profile. For each project, the selected inflow data also includes monthly operating guidelines such as maximum (flood control)
and minimum end-of-month elevation limits, ramping rates, maximum
and minimum outflow limits and other operating constraints. From
these data, monthly generation at each project is calculated and then
summed to get the total hydroelectric system generation. At the
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Electric Power Systems Research 174 (2019) 105858
J. Fazio and D. Hua
the loss of load probability (LOLP) where LOLP = N/Ntotal, to determine
adequacy for which LOLP ≤ 0.05. The LOLP as defined has a shortcoming in that simulations with different shortfall events, durations and
magnitudes all contribute equally.
variables is relatively small at 77 × 80 = 6160, it is still practical to
perform all these simulations (years), totaling 54 million hours of dispatch, and requiring about 3 hours run time on a typical business
desktop personal computer.
The LOLEV, LOLH and NEUE metrics are calculated in Section 4 for
multiple scenarios for differing loads and resources. For these scenario
analyses, resource and load variations are selected to reflect forecast
uncertainties, details of which are discussed in Section 3.3. Then in
Sections 4.1–4.3, mathematical relationships among the three metrics
for the scenarios are analyzed to determine if one of the three metrics
can be used to calculate (i.e. predict) the other two. However, the
equations for calculating the metrics from GENESYS output are presented first in the next section.
3.3. Scenarios
LOLEV, LOLH and NEUE can be calculated directly from GENESYS
output using Eqs. (1)–(4), respectively. Mathematical relationships between pairs of the three metrics are investigated in Section 4 for 12
scenarios used for the Pacific Northwest Power Supply Adequacy Assessment for 2022, prepared by the Northwest Power and Conservation
Council [19].
The scenarios form a set of sensitivity studies which are comprised
of combinations of three load levels and four resource levels to reflect
forecast uncertainties, details of which are discussed below.
The three forecasted load levels are, high (H), medium (M) and low
(L). The M hourly loads are the forecasted PNW regional hourly loads
for 2022 with the H loads being 3% higher and the L loads being 3%
lower. More specifically, if {Mi} is the set of hourly loads for the M
level, then {1.03Mi} is the set of hourly H-loads, while {0.97Mi} is the
set of hourly L-loads. The three hourly loads {Hi}, {Mi} and {Li} can be
expressed as α{Mi} where α = 1.03, 1.00, and 0.97, respectively. It
should be noted that as defined all three load levels have the same
hourly load shape.
The 2022 forecasted M loads, which incorporate 77 years of historical temperature variations, have winter peak hour loads (in MW) in
the range (29,900, 43,200) and summer peak hour loads in the range
(27,500, 30,300) [19]. These loads include projected cumulative peak
hour energy efficiency savings of 2600 MW during winter and
1680 MW during summer since 2015. In comparison, the observed peak
winter and summer loads for 2015 (from Section 2) were 30,100 MW
and 29,000 MW respectively.
On the other hand, the four forecasted resource levels A, B, C and D
represent increasing amounts of available import capacity (modeled as
a very high heat rate thermal resource) expressed as a percentage of
L̄ max = 35, 000 MW, the average winter peak-hour load (calculated
from the set of forecasted M winter peak hour loads discussed in the
previous paragraph). The relative increase of import capacity, relative
to resource A, for resources A, B, C and D are equal to 0.0%, 1.4%, 2.9%
and 4.0% of the average peak-hour load L̄ max , respectively. The hourly
resource {Ai} contains the forecasted available generation for the year
2022 with 2000 MW of import capacity. The four hourly resources {Ai},
{Bi}, {Ci}, {Di} can then be expressed as {Ai} + R × Lmax where
R = 0.000, 0.014, 0.029 and 0.040 respectively. For example, hourly
resource {Bi} = {Ai} + (0.014)Lmax. As defined, it is clear that the four
resource levels also have the same hourly shape.
Compared to resources in 2015 discussed previously in Section 2
and excluding the import capacity, the 2022 forecasted generating resources have a net reduction of approximately 500 MW of nameplate
capacity. The net change is due to actual and planned retirement of
approximately 2500 MW generation of which the majority, 2300 MW,
is coal, and approximately 2000 MW of additional installed and
planned generation of which 49% is wind, 28% solar and 23% natural
gas [20], [21].
The three metrics, in Eqs. (1)–(4) then can be symbolically expressed as outputs of a vector function G of hourly loads and resources
represented in the GENESYS model,
3.2. Equations for LOLEV, LOLH and NEUE
For each scenario, GENESYS records all relevant data for each hour
when generation fails to meet load. All three adequacy metrics can be
calculated from a subset of this data, which lists rows of (i, hij, cij),
where i ∈ (1, …, 6160) is the simulation index, hij is the jth curtailment
hour and cij is the jth curtailment magnitude for simulation i. Let
= {i1, …, iN } be the set of N simulation indices that contain at least one
curtailment. For each simulation index i ∈ , let i = {hi1, …, hiji} represent the curtailment hours and i = {ci1, …, ciji} the corresponding
curtailment magnitudes, where ji is the number of curtailment hours for
simulation i. Calculations for the metrics LOLEV, LOLH and NEUE,
previously defined in Section 1, are presented below.
Before calculating LOLEV, it is necessary to obtain the total number
of curtailment events for simulation i by grouping all the curtailment
hours in i of simulation i into ki subsets ({hi1, …}1, …, {…,hiji} ki) , where
all hours in each subset are contiguous, and hours from different subsets
are not contiguous. ki then is the number of curtailment events for simulation i. Then LOLEV is the expected (or average) number of curtailment events for all simulations,
iN
LOLEV =
∑ ki/Ntotal
(1)
i = i1
where Ntotal = 6160 is the total number of simulations.
Next, LOLH, the expected number of curtailment hours for all simulations, is calculated as
iN
LOLH =
∑ ji /Ntotal
(2)
i = i1
where ji is the number of curtailments hours for simulation i, defined
previously.
In contrast to the simple calculations for LOLEV and LOLH, two
intermediate quantities are required for calculating NEUE. The first is
the total unserved energy Ei for simulation i, obtained by summing of all
the hourly curtailment magnitudes in i of simulation i,
ji
Ei =
∑ ci j
j=1
and the second is EUE, the expected unserved energy over all simulations,
iN
EUE =
∑ Ei/Ntotal.
(3)
i = i1
(LOLEV, LOLH, NEUE) = G (α {Mi}, {Ai } + RL max )
Then NEUE is just EUE expressed as a fraction of annual load energy
(in units of parts per million or ppm), and calculated as,
NEUE = [EUE ×
106]/[L¯
× 8760]
(5)
where various other inputs of GENESYS that remain constant among
the scenarios are not shown explicitly in G. The metrics are calculated
for the twelve scenarios by varying over combinations of α and R. It
should be noted that variations among the values of α are small, as are
those of R, which as scenario inputs for G in Eq. (5) can determine the
type of mathematical relationships that exist among the metrics, which
(4)
where L̄ is the annual average load averaged over all 77 temperature
years.
Currently the PNW region uses threshold on a single metric [18]:
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Electric Power Systems Research 174 (2019) 105858
J. Fazio and D. Hua
are varied (e.g. family H contains scenarios HA, HB, HC and HD all with
load level H and varying resources A, B, C, and D). Obviously because of
the small number of scenarios, using higher order polynomials in leastsquares fitting will result in perfect fit since all the RMSE's would be
zero. However, as discussed in the previous section, the excellent
(though not perfect) linear fit for each family is not unexpected because
resource variations, expressed by values of R in Table 1, are relatively
small.
It is worthwhile to discuss the linearity between LOLEV and LOLH
further. By adding a large enough import capacity, for example, having
an R = 1.0, scenarios for all three families of loads (H, M, L) will
converge to the completely adequate (but impractical) scenario O
plotted at the origin in Fig. 2, where LOLEV = 0 = LOLH = NEUE. It
should be obvious that it is not necessary to run GENESYS for scenario
O to calculate these three metrics since when one is zero the other two
must also equal zero. It should be noted in Fig. 2 that none of the fitted
lines, if extrapolated, would intersect scenario O at the origin since
none of their intercepts is close to zero. This leads to the conclusion that
as resource variations become large, as between R = 1 and R ∈ (0.000,
0.014, 0.029, 0.040), defined previously in Section 3.3, the relationship
between these two metrics, for fixed load, can no longer be accurately
approximated by a linear function.
In addition, the fitted line for each fixed-load family in Fig. 2 can be
expressed as NEUE = aj × [LOLEV] + bj for j = (L, M, H), where
parameters aj and bj are obtained from least-squares analyses of NEUE
and LOLEV values in each family. Hence aj and bj are functions of the
metrics, and by Eq. (5), also functions of loads and resources. However,
Fig. 2 implies that aj and bj remain constant for each family of fixed
loads and small variations in resources.
An interesting observation from Fig. 2 is that the metrics for the H
family are much higher than those for the M and L families even though
the change in loads from M to H and L is the same at ± 3%, respectively. For NEUE, the behavior is due to the distribution of unserved
energy magnitudes as shown in Fig. 3 for scenarios HB, MB and LB,
which shows that there are overwhelmingly more unserved energies of
smaller magnitudes than those of larger magnitudes (note the log scale
for the frequency axis). From regression analysis, histograms for the
frequency of unserved energy can be roughly represented by f ∝ e−βE
for fitting parameter β. Furthermore, since the vast majority of shortfalls occur in winter, the ± 3% hourly load difference can be roughly
approximated by applying the percentage to the average M winter
peak-hour load of 35,000 MW, which results in ∼ ± 1000 MW. Thus,
in Fig. 3 as suggested by the arrow, the histogram in the (8000, 9000)
MWh bin for HB (blue histogram) can be reasoned to shift leftward to
become approximately the histogram in the (7000, 8000) MWh bin for
MB (brown histogram). The same reasoning also results in all histograms shifting leftward by ∼1000 MW from HB to MB and from MB to
LB. Therefore the three sets of histograms can be approximately represented by fH ∝ e−βE, fM ∝ e−β(E+▵) and fL ∝ e−β(E+2▵) where
▵≈1000 MW. Finally, the total unserved energy for each set of loads
can be approximated by a sum-product of the histograms and the bins,
T = ∑i [Ei × f (Ei )],
which
leads
to
(TH − TM) ≈ (TM − TL)
eβ▵ ≫ (TM − TL), which confirms that NEUE for the H scenarios are
much higher than those for M and L scenarios in Fig. 2. Similarly,
analysis of histograms of shortfall events per simulation also explains
large differences in LOLEV in Fig. 2.
Next, Fig. 4 shows the same twelve LOLEV and NEUE data points as
plotted in Fig. 2 but in contrast, linear least-squares fits are performed
over families of fixed resource levels while loads are varied (e.g. family
A contains scenarios HA, MA, LA, all with fixed resource level A and
varying loads H, M and L). All four linear fits have NEUE RMSE values
of 0.67 ppm, 0.33 ppm, 0.27 ppm and 0.07 ppm for the family of resource level A, B, C and D, respectively. The dotted linear fit lines in
Fig. 4 show the good fit between LOLEV and NEUE for the four families.
Similar to the discussion of Fig. 2, the excellent (though not perfect)
linear fit for each family is due to load variations, expressed by α in
Table 1
Adequacy metrics for 12 scenarios as a combination of loads (L, M, H) and
Resources (A, B, C, D).
Scenario
α
R
LOLEV
LOLH
NEUE
LA
LB
LC
LD
MA
MB
MC
MD
HA
HB
HC
HD
0.97
0.97
0.97
0.97
1.00
1.00
1.00
1.00
1.03
1.03
1.03
1.03
0.000
0.014
0.029
0.040
0.000
0.014
0.029
0.040
0.000
0.014
0.029
0.040
0.07
0.05
0.04
0.03
0.20
0.16
0.13
0.12
0.93
0.80
0.73
0.70
0.84
0.61
0.44
0.32
2.12
1.64
1.32
1.16
9.64
8.13
7.31
6.98
7.6
4.9
3.1
2.1
16.7
11.6
8.2
6.2
57.5
45.0
37.5
33.7
are explored in the next section.
4. Simulation results and analysis
The LOLEV, LOLH and NEUE metrics for all twelve scenarios are
calculated using Eqs. (1)–(4) from GENESYS output data and summarized in Table 1. These scenarios cover a very wide range of adequacy levels, with LOLEV values ranging from 0.03 to 0.93 events per
year (or equivalently 0.3 to 9.3 events per 10 years). Mathematical
relationships between the pairs (LOLEV, NEUE), (LOLEV, LOLH) and
(LOLH, NEUE) are explored in Sections 4.1–4.3, respectively using
least-squares analysis.
4.1. Relationship between NEUE and LOLEV
In this section, least-squares analyses are performed on the scenarios
listed in Table 1 to explore the mathematical relationship between
NEUE and LOLEV.
Fig. 2 shows the correlation between NEUE and LOLEV, with each of
the twelve data points labeled by load and resource level according to
Table 1. It is easily seen that both metrics display expected behavior, in
that, as load is lowered or as resource is added, both metrics decrease,
as they should. For example, for fixed load level H, both metrics decrease as resources are added to the system, as seen from scenarios HA
to HD. Similarly, for fixed resource level A, both metrics decrease as
load is lowered, as evident from scenarios HA to MA to LA.
It also turns out that linear least-squares fits over subsets (families)
of scenarios with fixed load (e.g. the three load levels H, M and L) all
have small root-mean-square errors (RMSE) of 0.40 ppm, 0.28 ppm and
0.16 ppm, respectively for NEUE. The dotted linear least-squares fit
lines in Fig. 2 show the very accurate fit between LOLEV and NEUE for
the three families of fixed load levels (H, M, L) as resources (A, B, C, D)
Fig. 2. NEUE vs. LOLEV. Dotted lines are linear least-squares fits for the three
families of scenarios with fixed load (H, M, L) and varying resources (A, B, C,
D).
5
Electric Power Systems Research 174 (2019) 105858
J. Fazio and D. Hua
Fig. 3. Histogram of Unserved Energy for scenarios HB (blue), MB (brown) and LB (light blue). As hourly loads decrease from in HB to MB to LB, the respective
unserved energy histograms shift leftward. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
Fig. 4. NEUE vs. LOLEV. Dotted lines are linear least-squares fits for the four
families of scenarios with fixed resource (A, B, C, D) and varying loads (H, M,
L). Scenario A1 was found from iterative GENESYS runs with fixed resource
level A and varying loads to obtain LOLEV = 0.51.
Fig. 5. NEUE vs. LOLEV for families L and M. Dotted lines are linear leastsquares fits for the two families. Scenarios L1 and M1 are iterated from LA and
MD respectively with appropriate resources to achieve LOLEV = 0.1 (see the R
values in Table 2).
Table 1, being relatively small.
Figs. 2 and 4 show that for a family of scenarios with either fixed
loads or fixed resources, a linear least-squares fit produces a function of
LOLEV from which reasonably accurate values of NEUE can be calculated using existing data from Table 1. For example, in Fig. 4, the blue
dotted fit line for family A suggests that for LOLEV = 0.51,
NEUE = 33.6. Indeed, from iterative GENESYS runs it was found that
using input parameters α = 1.02 and R = 0.000 (a member of family
A), produces scenario A1 with metrics (LOLEV, NEUE) = (0.51, 34.4),
and plotted in Fig. 4 as the solid blue diamond. Relevant parameters for
scenario A1 are listed in Table 2. The difference between the simulation
value (34.4) and fit-line interpolation (33.6) for NEUE is reasonably
close to the RMSE value 0.67 for the family-A fit line. Even though only
one example has been shown, this paper assumes that scenarios exist
with LOLEV and NEUE close to interpolated values on the fit lines.
For adequacy assessments in general, scenarios with both varying
loads and resources should be considered. An example is the combined
scenarios of families M and L in Fig. 2, from which a magnified region is
shown in Fig. 5.
To explore mathematical relationships between LOLEV and NEUE
for the combined families, a potential common region at LOLEV = 0.1
(represented by the dotted red line) is chosen by extrapolating the two
dotted fit lines. The extrapolated blue dotted fit line for the M family
suggests a scenario with metrics (LOLEV, NEUE) = (0.1, 4.0) while the
extrapolated black dotted fit line for the L family suggests another
scenario with metrics of (0.1, 11.9). However, in contrast to interpolated values, it is less certain that scenarios exist with LOLEV or
NEUE close to extrapolated values on the fit lines. Fortunately, the two
extrapolations were supported by scenarios M1 and L1 (obtained from
iterative GENESYS runs) for which LOLEV = 0.1 and NEUEs of 3.5 and
11.0 respectively, which are somewhat close to the extrapolated values.
Table 2 lists the input parameters for loads and resources, α and R,
respectively along with the three metrics for L1 and M1. The slightly
larger deviations of NEUE for L1 and M1 from the extrapolated fit lines
suggest that nonlinearity is beginning to show due to the larger range in
R values with the additional scenario for each family. Nevertheless,
Fig. 5 shows that since scenarios L1 and M1 are members of families L
and M respectively, then NEUE for each scenario can be reasonably
calculated from just LOLEV using the corresponding linear fit line.
However, Fig. 5 also shows that at LOLEV = 0.1, NEUE takes on two
Table 2
Adequacy metrics for interpolated and extrapolated scenarios.
Scenario
α
R
LOLEV
LOLH
NEUE
A1
L1
M1
L2
1.02
0.97
1.00
0.97
0.000
−0.012
0.069
−0.013
0.510
0.100
0.099
0.103
5.21
1.13
0.86
1.16
34.4
11.0
3.5
11.3
6
Electric Power Systems Research 174 (2019) 105858
J. Fazio and D. Hua
possible values from the two families of fixed loads, demonstrating that
LOLEV by itself is not sufficient to calculate a unique NEUE value.
Furthermore, if an adequacy standard only includes an LOLEV
threshold of 0.1 events per year, then both scenarios L1 and M1 would
be considered adequate, yet L1 has a NEUE value nearly three times
higher than that for M1, which might be too high to be considered
adequate.
On the other hand, for NEUE = 7 in Fig. 5, there are two (interpolated) values for LOLEV: 0.066 from the L-family fit line, and 0.124
from the M-family fit line. Hence NEUE by itself is not sufficient to
determine a unique value for LOLEV.
Finally, in Fig. 4 for the overlap region at LOLEV = 0.6 there are
four interpolated values for NEUE, and conversely, for NEUE = 30
there are four interpolated values of LOLEV, from the four families of
fixed resources. Hence neither NEUE nor LOLEV by itself is sufficient to
determine a unique value for the other. In subsequent sections, analyses
of other pairs of metrics for families with constant resources follow the
same reasoning and lead to the same conclusion, and thus will not be
presented again.
Analysis in this section has shown that for scenarios where both
loads and resources vary, neither LOLEV nor NEUE by itself is sufficient
to calculate a unique value for the other, thus a threshold for one does
not automatically lead to a unique threshold for the other.
Fig. 7. LOLH vs. LOLEV for families L and M. Dotted lines are linear leastsquares fits for the two families. Values for the load and resource input parameters, and metrics for scenarios L1 and M1 are listed in Table 2.
possible values, 0.86 for scenario M1 and 1.13 for scenario L1 (see
Table 2). Therefore, an LOLEV value by itself is not sufficient to determine a unique value for LOLH. LOLH values for L1 and M1, however,
do show larger deviations from the linear fit lines, due to the larger
variations in resources (R values) for each family with the inclusion of
the extrapolated scenario.
Fig. 7 also shows that for LOLH = 1.0, and there are two interpolated values of LOLEV from the two families of fixed loads. Hence
LOLH by itself is not sufficient to determine a unique LOLEV.
It has been demonstrated here that for scenarios where both loads
and resources vary, neither LOLEV nor LOLH by itself is sufficient to
determine a unique value for the other, and thus setting an adequacy
threshold for one does not automatically lead to a unique threshold for
the other.
4.2. Relationship between LOLH and LOLEV
In this section, least-squares fits are performed on the same twelve
scenarios listed in Table 1 to explore mathematical relationships between LOLH and LOLEV, very similar to the analysis done previously
for NEUE and LOLEV. Hence, only a brief description and summary are
presented.
Fig. 6 shows very accurate linear least-squares fits between LOLEV
and LOLH, for the three families of scenarios with fixed load levels (H,
M, L), as resources (A, B, C, D) are varied. Figs. 6 and 2 look similar and
share the following properties discussed previously: the excellent linear
fit for each family of fixed loads with small variations in resources, the
disparity in metric values between family H and families M and L, and if
scenario O is added to each family, a linear function can no longer
provide a good fit.
The same 12 LOLEV and LOLH data points presented in Fig. 6 can be
alternatively plotted to show the excellent linear least-squares fits for
the four families of scenarios with fixed resource levels (A, B, C, D) and
varying loads (H, M, L), similar to Fig. 4 for LOLEV and NEUE. However, the linear fit lines for this case are very densely packed together,
and such a plot would not be able to show enough distinguishing details
to be useful.
Finally, Fig. 7 is a magnified region of Fig. 6 for families L and M
and shows that in the overlap region LOLEV = 0.1, LOLH takes on two
4.3. Relationship between NEUE and LOLH
In this section, some of the mathematical relationships between
NEUE and LOLH can be deduced from results in the previous two sections without the need to explicitly perform least-squares analysis.
Specifically, from Section 4.1, NEUE and LOLEV were shown to be well
correlated and have an excellent linear fit for families of scenarios with
fixed loads or fixed resources, and the same properties also exist between LOLEV and LOLH, as discussed in Section 4.2. It follows then that
NEUE and LOLH also share the same excellent linear least-squares fits
for families with either fixed loads or fixed resources and that NEUE can
be calculated from a linear function of just LOLH under those circumstances.
LOLH and NEUE values from Table 1 for load families L and M are
plotted in Fig. 8 for varying resource (A, B, C, D), along with scenario
L2, obtained by iteratively decreasing R in scenario LA in the GENESYS
model to reach the same LOLH of 1.16 as scenario MD. Fig. 8 shows that
Fig. 6. LOLH vs. LOLEV. Dotted lines are linear least-squares fits for the three
families of scenarios with fixed load (H, M, L) and varying resources (A, B, C,
D).
Fig. 8. NEUE vs. LOLH for families L and M. Scenario L2 is iterated from LA with
appropriate resources to achieve LOLH = 1.16 (see the R values in Table 2).
7
Electric Power Systems Research 174 (2019) 105858
J. Fazio and D. Hua
for family L, extrapolation of the black dotted fit line at LOLH = 1.16
results in NEUE = 10.9, which is reasonably close to the simulation
value NEUE = 11.3 for scenario L2 (see Table 2). Fig. 8 also shows that
at the overlap region LOLH = 1.16, NEUE takes on two possible values,
11.3 from L2 and 6.2 from MD. It follows that LOLH by itself is not
sufficient to calculate a unique NEUE, very similar to the conclusions
for the other two pairs of metrics discussed previously for Figs. 5 and 7 .
Conversely, Fig. 8 also shows that for NEUE = 8, there are two interpolated values of LOLH from families L and M. Therefore, NEUE by
itself is not sufficient to determine a unique LOLH.
Thus, results in this section show that in general neither NEUE nor
LOLH by itself can be used to calculate a unique value for the other, and
setting a threshold for one does not automatically lead to a unique
threshold for the other.
References
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5. Conclusion
In summary, least-squares analyses show that for the Pacific
Northwest power supply each of the three adequacy metrics (LOLEV,
NEUE and LOLH) can be used in a linear function to calculate with very
good accuracy the other two metrics, but only for families of scenarios
with fixed load or fixed resources (and small variations in the other
input), as seen previously in Figs. 2, 4 and 6 . However, in general,
when both loads and resources vary, even in small amounts, the value
of one metric does not automatically determine unique values for the
other two, as presented in Figs. 5, 7 and 8 . For the more general cases,
additional load or resource data are needed to determine which family,
if any, the scenarios belong to, and thus the unique fitting function that
relate the pair of metrics. To assess power supply adequacy, it is prudent to use metrics that measure different dimensions of risk, and
manage those risks by setting tolerable limits for each metric. LOLEV,
LOLH and NEUE, which measure shortfall frequency, duration and
magnitude, form a good set of risk measures for a power supply.
Moreover, as both LOLH and NEUE have been calculated by all regional
power system entities and reported annually to NERC for publication
for the past few years, many power system planners might be familiar
with the risks associated with these two metrics. Analyses in previous
sections suggest that if the three metrics were to be used to determine
power supply adequacy, then thresholds for all three metrics should be
set independently. On the other hand, if only a single metric is used to
set the adequacy standard for the PNW, for example setting a 0.1 events
per year threshold for the LOLEV, what might have been acceptable
associated values for LOLH and EUE could become unacceptably large
with future changes in resources and loads, even while the LOLEV remains at or below 0.1 events per year.
Finally it should be noted that using numerical probabilistic convolution methods (in contrast to Monte Carlo simulations), near-linear
mathematical relationships were also discovered among similar metrics
[22].
Conflicts of interest
None.
Acknowledgment
The authors would like to thank Dr. B. Bagen at Manitoba Hydro for
helpful discussions on the use of analytical methods in adequacy analysis.
8
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