ETA 2021 Strategic Plan - Flipbook - Page 71
The Challenge
The term Integrated Energy Systems (IES) broadly
describes a holistic approach to finding coordinated
energy and economic solutions from across a wide
range of energy options. These energy systems
include production (solar, wind, hydro, biofuels),
conveyance (electricity, thermal, hydrogen), storage
(daily and seasonal), and customer-level use (buildings,
transportation, industry). At present, these systems
are linked, but they usually function separately or
respond individually depending on a wide range of
disparate system-operation goals. The inability to link
these systems may limit our ability to find economically
feasible zero-carbon energy solutions across sectors.
We also may underappreciate the future opportunities
available as vehicles, storage, and buildings become
more connected to and interactive with the electricity
grid.
However, mathematical, computational, and hardware
tools that are suitable for the discovery of economically
feasible, zero-carbon, grid-level energy solutions from
across this wide range of energy systems are immature.
Breakthroughs in applied mathematics are needed
that can provide a clear analytical foundation to link
the functions of the electrical grid with near-real-time
consumer use and the vast opportunities provided
by energy storage. Computational breakthroughs are
also needed to optimize and ensure the security and
reliability of this growing connected energy system.
Prototype test simulations are needed to calibrate,
test, and validate the performance of prototype field
demonstrations. These breakthroughs must happen
in the next few years to enable the transformation of
infrastructure between now and 2050 to realize the full
potential of real-time energy controls that balance riskaversion and risk-taking decisions for a future integrated
energy infrastructure. Learning to direct risk-averse and
risk-taking decisions for integrated energy infrastructure
is a grand challenge. This work requires foundational
advances in uncertainty quantification; in particular, to
identify and model unlikely but catastrophic outliers.
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