IEEE Electrification - December 2021 - 39

scenarios that might be considered include the imposition
of a tax on greenhouse gases (GHGs), the construction
of a major new transmission line, or alternative cost
assumptions. Each scenario is run through the capacity
expansion and production cost models, resulting in multiple
candidate portfolios that may then each be subjected
to further risk analysis by stochastically modeling
how they perform under varying levels of customer load,
fuel prices, and so on.
The objective of this process is to identify a preferred
portfolio: the combination of investments that minimizes
cost and risk across various possible futures. A preferred
portfolio does not represent a definitive plan for future
investments but, rather, a snapshot capturing the utility's
best estimate of future needs based on current information.
Utilities typically prepare IRPs every two to three
years, and preferred portfolios may significantly differ
from one cycle to the next as circumstances change. To
guide the utility's actions between IRP cycles, each plan
will usually contain an action plan outlining the steps
that will be taken to meet near-term needs. Figure 1 presents
a diagram of this process.
For each scenario, the capacity expansion model
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potential generation resources and a price forecast for
energy purchases.
x Load forecast: To identify the future demand of its customers,
utilities employ econometric models to project
customer usage based on economic, social, and
technological factors.
x Capacity expansion: To build out the utility's system to
meet future demand, a capacity expansion model considers
various resource options (based on the cost and
performance assumptions that the utility builds into
the model) to identify the least-cost portfolio of generation
and demand-side resources to meet that demand.
x Production cost: Where a capacity expansion model
identifies the cost of building a resource portfolio, a
production cost model simulates operation of that
portfolio for a representative year to verify that it can
meet reliability requirements and determine its annual
operational cost.
To account for different possible futures, the utility
will develop various scenarios for study. Example
must build a portfolio that maintains a constant balance
between load and generation. For each time
interval that the model considers, this means performing
a complex calculation to find the optimal combination
of demand-side resources, energy generation, and
market purchases to meet the demand forecast for
that interval. Because these calculations must be done
thousands of times for every scenario, utility planners
make several simplifying assumptions to keep the process
manageable.
Our previous work detailed how those simplifying
assumptions create barriers for energy storage, which can
be summarized in the following two types:
x Temporal barriers: To limit the calculations that they
must perform, capacity expansion and production
cost models operate at hourly granularity and use
reserve margins to represent ancillary service needs
(rather than modeling the optimal provision of those
needs). But these simplifications prevent models from
identifying the need for and value of flexible resources
like energy storage.
x Spatial barriers: To simplify the representation of
the utility's system and maintain the focus on generation
needs, IRP models focus only on the generation
system. This prevents recognition of the
values that storage could provide to the transmission
and distribution systems as well as recognition
of the value that aggregated distributed energy
resources could provide to the generation system
(Cooke et al. 2019).
The previous work reviewed 21 IRPs from around the
United States to measure the degree to which utilities are
including energy storage in their planning processes. Two
IEEE Electrification Magazine / DECEMBER 2021
39
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IEEE Electrification - December 2021

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