IEEE Power & Energy Magazine - November/December 2014 - 86

and adjacent control areas. in the high gas demand scenario,
the simplifying assumption has been made that new gas-fired
generation resources would likely be located at or around
deactivated generation stations in order to more fully utilize
existing electric transmission infrastructure. in the low gas
demand scenario, the simplifying assumption has been made
that the additional renewable resources would likely be sited
near the existing renewable resource locations across the
study region. these assumptions allow for the identification
of gas constraints when utilizing the existing and planned
electric infrastructure to serve different levels of resources;
this approach helps keep the focus of the analysis on gas
infrastructure adequacy. the location of the specific new generation will ultimately be determined by generation providers
that will take into account a number of factors, one of which
will be the existing and planned gas infrastructure.
For each of the gas demand scenarios, the aUroraxmp
multizonal electricity price forecasting model produced
forecasts of the gas requirements of all gas-capable electric
generating units within the study region for the peak winter
and summer seasons of 2018 and 2023. the corresponding
gas demand forecasts for the rci sector were based on forecasts and regulatory filings by the Ldcs operating in the
study region, where publicly available. By necessity, the rci
forecasts encompass statistical analysis performed by Lai to
promote standardized results. these data sources were augmented with information available in various Ldc and pipeline company financial reports, as well as forecasts available
from government or private sources, e.g., canada's national
energy Board (neB), the doe, the U.s. energy information
administration (eia), and industry trade associations. state
or city programs oriented around accelerated Ldc customer
conversion from oil to natural gas for space heating were
also incorporated.
other industry data in the public domain reflecting past
usage provided additional insight into the level and profile of
rci gas demand. in the event that forecast data were not available for a particular Ldc, future demand was 1) estimated
based on historical demand trends from a database of pipeline
deliveries, 2) adjusted for generator gas demand using ePa
emissions data, and 3) escalated using gas demand growth
rates from eia's Annual Energy Outlook 2013. historical data
were also used to bracket the high and low demand scenarios
relative to the reference gas demand scenario, in the event
that alternate cases were not available from the other forecast
sources. Within each gas demand scenario, both winter and
summer peak day forecasts were developed for each modeled
year. incremental demands resulting from new programs and
initiatives that are not yet adequately reflected in the historical
data trends were calculated separately as adders to extrapolated historical demand in order to fully estimate future rci
demand. one example of this type of initiative is former new
York city mayor Michael Bloomberg's expanded clean heat
program to convert housing authorities and other city buildings from heavy heating oils to natural gas.
86

ieee power & energy magazine

With the forecasts of electric and rci sector gas demands in
hand, gPcM was utilized to evaluate infrastructure adequacy
to meet the combined customer demands. For purposes of this
analysis, the seasonal peak day was defined as the day with the
highest electric sector gas demand based on the aUroraxmp
model results. the study made the conservative assumption that
rci peak demand coincided with the peak electric sector gas
demand day. the gPcM database was modified as necessary
to include planned gas infrastructure expansions, as well as any
additional expansions required to meet forecast increases in
rci gas demands. the "planned" gas infrastructure expansions
incorporated in gPcM for model year 2018 included all projects with executed precedent agreements, as indicated by prefiling or filing a certificate application before Ferc or in press
releases or other news articles. not included in the reference gas
demand scenario pipeline additions were those projects on the
drawing board, even those that ostensibly enjoy strong political support. these projects were instead tested in a sensitivity
analysis focused on increased gas transportation out of the Marcellus shale.
the gPcM model results were evaluated for each scenario (defined by gas demand scenario, season, and year)
to identify those segments for which the full gas demand
at downstream nodes cannot be delivered. For each such
constrained segment, the primary cause of the capacity constraint was identified, i.e., the compressor station, discrete
pipeline segment, or other facility that sets the throughput
capacity of the gPcM arc. to determine the frequency and
duration of the constraint, the unserved demand was compared with seasonal load duration curves based on aUroraxmp results and historical data for the electric and rci
sectors, respectively, to determine the number of days during
which that segment was likely to be constrained.
Based on the constraints that were identified, we then tested
demand reductions and capacity expansions to determine
opportunities for mitigation. the demand reductions test simulated one or more generators' switching from gas to an alternate fuel. if the number of generators with existing dual-fuel
capability was not sufficient to relieve the constraint, we identified additional generators that would need to install dual-fuel
capability in order to maintain fuel assurance. on the gas delivery side, for constraints that were not aligned with previously
announced proposed pipeline or storage projects, we formulated
an infrastructure expansion that would alleviate the constraint,
with benchmark cost estimates associated with the expansion.
Perhaps equally important to the identification of constrained
pipeline segments, Lai also identified areas with slack deliverability, either currently or based on projected changes in gas
flow patterns as a result of continuing shale gas development,
including the reversal of traditional flow to accommodate shale
gas dynamics in the PJM, Miso, tVa, and ieso regions.

Target 3: Contingency Analysis
Based on the results and findings of the target 2 analysis,
a list of potential contingencies was developed for each gas
november/december 2014



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