IEEE Power & Energy Magazine - July/August 2016 - 36
based on the information from TaG experts, researchers
at JhU came up with six more "plug-hole" scenarios to cover
the full range of uncertain variables. This set of 20 scenarios
allowed us to compare the impact on the results of considering 1) no uncertainty (base case only), 2) a restricted set of
scenarios (base case plus the other 2013 study cases), and 3)
the full set of scenarios (figure 4); we discuss these later in
this article. In an actual stakeholder-based planning process,
the final scenario set would be reviewed and approved by the
stakeholder group; however, for the purposes of our methodology demonstration, this was not necessary.
To conclude the scenario-creation step of the process,
we assigned a probability to each of the 20 scenarios. This
probability is used in the objective function to weight the
costs occurring in each of the future scenarios. one set of
probabilities was based on a "moment-matching" procedure
that chose scenario probabilities so that the mean and standard deviation of the uncertain variables were consistent
with the ranges originally assigned by the TaG members.
as a sensitivity analysis, we also considered equal probabilities for the scenarios.
figure 5 shows in greater detail the interactions among
the components of JhsmIne (on the left side of the figure)
and the uncertainties embodied in the scenarios (right side).
for instance, uncertainties regarding whether states will
adopt more ambitious renewable portfolio standards will
affect the model's constraints upon generation mix, with different renewable targets in different scenarios. as another
example, uncertain carbon tax/permit price levels will result
in variation among the scenarios in the objective function's
net fuel costs. a third example arises from the lengthy permit licensing times for transmission lines, which result in the
possibility of a failure to launch for some proposed individual lines. This type of uncertainty is represented by distinct
scenarios, some of which omit particular planned lines from
the model's grid representation.
The diagram shows many such linkages among the various long-run uncertainties and the optimization's model
objectives, decision variables, and constraints. however,
it is not an exhaustive list of possible sources of uncertainty, and additional ones could be inserted in the blocks
on the right. This figure also omits short-term variations
and risks, such as load/wind/solar variations or n-1 security constraints.
a simplified network based on the real transmission
network is necessary for JhsmIne to run on a university
workstation. Thus, in the third major step of the analysis,
with the help of colleagues yujia Zhu and dan Tylavsky at
arizona state University, we simplified the western Interconnection system to a 300-bus dc load-flow approximation
that preserved the major interregional paths (figure 6). such
a simplification makes it practical to optimize placement
of transmission and generation investments over multiple
years, considering numerous wind/solar/load conditions
within each year.
july/august 2016
table 1. The variables and their ranges.
Fuel and
carbon prices
Net energy
for load
Coincident
peak demand
Capital cost
Variables
Low
High
Natural gas (US$/MMBtu)
3.86
14.5
Carbon (US$/ton)
25.9
87.5
Coal (US$/MMBtu)
2.24
3.50
Total WECC load
growth (%/yr)
1.0
1.9
Energy reductions (%/yr)
0.3
4.0
Electrification (%/yr)
0.3
1.8
System peak growth
rate (%/yr)
1.3
1.9
Policy-driven peak
reductions (%y/r)
0.1
3.3
Policy-driven peak
electification (%/yr)
0.2
0.8
Onshore wind (US$/kW)
1,569
2,064
Geothermal (US$/kW)
5,015
6,490
Solar PV (US$/kW)
2,320
4,233
Solar thermal (US$/kW)
3,560
4,519
Deterministic
(a)
Stochastic (5)
Stochastic (20)
Base Case
(or Other Single
Scenario)
Base Case
Study Case 1: Econ Recovery
Study Case 2: Clean Energy
Study Case 3: Short-Term
Consumer Costs
Study Case 4: Long-Term
Societal Costs
(b)
Base Case
2013 Study Cases (4)
Technical Advisory Cases (9)
"Gap" Scenarios (6)
(c)
figure 4. The schematic for the change node of stochastic
planning under (a) one, (b) five, or (c) 20 scenarios.
It Is Possible-and Better-to
Consider All Scenarios in One Run?
we now highlight some results of the JhsmIne application
to the weCC data that illustrate the value and insights to be
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