IEEE Power & Energy Magazine - November/December 2017 - 88

complementary generation and transmission (G&T) infrastructure will likely lead to a more flexible and reliable system with less fossil fuel-based backup.
Siting wind and solar plants to achieve both high AEP and
high value, while considering the corresponding transmission
capacity, will likely result in an energy mix that reduces the
variability caused by localized weather. The siting of wind and
solar plants based on atmospheric science is more complex
than what is suggested in the hydro analogy because changes in
wind and solar resources occur across much smaller temporal
periods and spatial regions than do hydro resources. In addition,
the deployment of each new wind power plant changes both the
nearby resource and net demand and hence the optimal location
of future wind plants.

New Tools
Cooptimization of G&T
Traditionally, planning new G&T resources has occurred
sequentially, with the more expensive generation planning
occurring first followed by transmission planning. Today's
computer hardware coupled with efficient optimization solvers enables cooptimized expansion planning (CEP), in which
investment decisions regarding G&T are made within a single optimization. Doing so captures G&T interdependencies
and, as a result, identifies lower cost solutions than those
identified by sequential planning.
As illustrated in Figure 2, CEP may be conceptualized as
follows: for each possible investment plan (as specified by technology, timing, amount, and location for G&T investments), a
PCS is run over the entire planning horizon (e.g., between ten
and 40 years), and the investment plan is selected that minimizes the total cost of investing in and operating the power
system. The PCS is simplified to maintain computational tractability; some models reduce the number of time steps, while
others use a reduced form of operations in terms of the production cost (linear relaxation of the integer unit-commitment
problem) based on hourly time steps.

CEP identifies investment and retirement decisions to
minimize the net present value of costs incurred over the
planning horizon associated with G&T investments, fixed operating and maintenance (O&M), variable O&M, fuel, planning
and operating reserves, environmental impacts (e.g., costs imposed
on air quality influences or CO2 emissions), and retirements,
subject to constraints on
✔ network operating conditions (e.g., supplying the demand, satisfying Kirchhoff's laws, and respecting limits
on circuit flows and generator outputs)
✔ G&T investments (e.g., renewable investments exceeding minimum regional or state limits)
✔ environmental targets (e.g., limiting CO2 emissions).
Decision variables represent choices for generation (renewable and fossil based) and transmission (ac and dc) in terms
of technology, location, capacity, and timing of construction.
Some or all of the system design can be "enforced" by the
analyst, an approach that enables exploration of various proposed future systems.

G&T Planning Including Flexibility Sufficiency

Generation and flexibility investors, such as EDF, decide asset
investments based on estimates of revenues over the duration
of the project's life. The increase of variable generation (VG) in
the mix requires adapting the electricity system infrastructure
in terms of the network and the remaining (non-VG) generation mix. Decisions regarding energy policy, network investment, and generation and flexibility investment are made by
agents having imperfect knowledge of decisions taken by
other investment agents. The representation of weather uncertainties will become increasingly important for investment
decisions, and variability for different time scales (short-term
to interannual variations) will need to be considered. Climate
change effects have received increased consideration as one of
the uncertainties to include in investment models.
From a system planning perspective, addressing this variability and uncertainty raises two issues: 1) ensuring system
capacity to respect the desired adequacy level and 2) ensuring
that the system develops flexibility
levers to handle short-term variability and uncertainty without
Select Investment
compromising system security.
Strategy
From an investment perspective,
this translates into receiving the
T-L
T-Te
appropriate signals to make the
G-Loc
o
chn
c
ation
atio
olog
Run Production
n
y
necessary investment decisions
Cost Simulation
nt
ount
(in generation capacity and flexmou
m
Over
Planning
A
A
y
G
T
G-T
og
imin
ibility). These two issues have
Horizon
nol
h
c
g
T-Timin
e
g
G-T
traditionally been addressed separately, the first being a planning
problem and the second an operaIs Total Cost < Best Plan So Far?
tions problem.
Addressing capacity and flexibility sufficiency within a single optifigure 2. The cooptimization concept. G: generation; T: transmission.
mization is very computationally

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november/december 2017



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