IEEE Power & Energy Magazine - July/August 2016 - 32

The results from the JHU-WECC studies show that it is practical to
identify economically optimal grid additions considering multiple
scenarios simultaneously using stochastic programming.

Combined cooptimization-stochastic-multistage models for
implementing adaptive grid planning have great potential in
identifying transmission investments to make today that will
best position the grid to maximize economic and sustainability benefits under the full range of future possibilities.

Existing Optimization Methods for
Economic Planning of Transmission
optimization-based planning tools commonly used for
transmission planning studies have a number of widely
acknowledged limitations. Two of these shortcomings are
related to the three key features already discussed: 1) supply resources and transmission investments are optimized
independently, while 2) the effect of long-run technological,
economic, and policy uncertainties on transmission costs is
either ignored or assessed through sensitivity analyses illequipped to identify the mix of transmission investments
that optimize probability-weighted costs and benefits. a
third potential limitation is that the impact of variable generation on the need for operational flexibility is often greatly
simplified. we will briefly summarize available software
and their limitations. (Interested readers are referred to the
detailed reviews by lumbreras and ramos and by Krishnan
et al. listed under "for further reading.")
The most common approach planners use is detailed production-cost modeling tools to assess the economic performance of predefined transmission and generation configurations. These tools optimize generation dispatch to simulate
how energy markets utilize transmission and can successfully capture the rich diversity of constraints and costs in an
actual system. examples of such tools include pss®e, GridView, sddp, and promod IV. however, these commercial
modeling packages do not optimize network topology and
do not automatically suggest the most economic transmission investments.
In contrast, a few commercial models such as netplan
have topology optimization capabilities made in a chronological way. but these methods assume a fixed scenario of
generation build-out (i.e., they are unable to represent how
the generator-siting and investment mix responds to transmission investment). further, they do not consider the uncertainties in market and regulatory conditions that are the
drivers for generation investment. a notable exception is the
long-run planning tool from the western electricity Coordinating Council (weCC), which provides insights on the
interactions among generation and transmission investments
july/august 2016

that could be made ten to 20 years in the future; the tool
does so by iterating between new generation capacity evaluation (using a levelized-cost methodology) and transmission
investment optimization. other exceptions include energy
exemplar's pleXos and psr's opTGen, which perform
simultaneous generation and transmission cooptimization
but do not consider long-run uncertainties except through
sensitivity analyses.
Thus, current transmission planning methods are limited
in their ability to represent uncertainty. Under scenario planning, a range of scenarios is defined, each of which represents one possible combination of future drivers of generation
investment, such as load growth, fuel prices, or environmental
policies. for each of these scenarios, a separate transmission
plan is developed using either deterministic optimization (as
in netplan) or, more often, by testing various predefined plans
using production-costing models.
In some studies, investments that are selected in all or
most of the scenario plans are identified as "robust" decisions. examples of this type of planning approach include
the "multivalue projects" identified by the mid-Continent
Independent system operator (Iso) and the "least-regret
investments" by the California Iso. The central assumption
of these heuristic approaches is that investments selected in
all or most scenarios provide a hedge against uncertainty and
are, therefore, attractive for development.
however, it has been proven mathematically that optimal
stochastic investment strategies (i.e., ones that minimize
probability-weighted costs across scenarios, considering
adaptability) cannot be constructed through such heuristics.
Indeed, a robustness heuristic, like the examples described
previously, can perform considerably worse than a deterministic plan if the plans have few overlapping lines, resulting
in underinvestment in transmission. plans that are optimal
under uncertainty may not be best for any individual deterministic scenario.
for example, a particular transmission investment might
perform well in many scenarios because it gives the system
some flexibility, e.g., to develop any of several renewable
energy zones. but that investment might never be the very
best choice in any single scenario of renewable development.
however, when considered stochastically, that line would
provide a hedge against uncertainty and could be optimal
overall. for this reason, scenario planning and heuristics are
unable to quantify the full value of alternatives that increase
the adaptability of transmission plans. (we give examples
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Table of Contents for the Digital Edition of IEEE Power & Energy Magazine - July/August 2016