The Bridge - Issue 3, 2020 - 15

THE FUTURE OF RENEWABLE ENERGY TRANSMISSION: An Autonomous Energy Grid

back up to Level 2 (e.g., via monitoring the aggregate
power of the neighborhood) to indicate this, and the
optimization is repeated until each agent in the cell
has reached a feasible solution that achieves the global
objective, as well as individual satisfaction.

Evaluations in the NREL Energy Systems
Integration Facility (ESIF)
To evaluate if the software algorithms would work
when integrating many real controllable devices, we
set up a large experiment at NREL's ESIF. NREL's work
on the ARPA-E NODES program helped develop the
first implementation of the algorithms in hardware and
successfully demonstrate the real-time optimization of
a single AEG cell. The experiment included simulation
of a real distribution feeder from California with 366
single-phase connection points, more than 100
controllable assets at power (inverters, EVs, and
batteries; see Figure 3 and Figure 4), and hundreds
of simulated devices. The distributed algorithms were
implemented in cost-effective microcontrollers that selfoptimize and communicate to the central coordinator
to attain system-wide goals (voltage regulation,
frequency response).

Feature

(Figure 5) are a pilot for an altogether new approach
to the grid. These homes optimize energy for residents
and their neighbors, but the principles behind Basalt
Vista go much further. Within homes, each new
connected device or energy resource-such as a
residential battery, water heater, or solar PV system-
can be controlled for unprecedented energy efficiency.
At a larger scale, entire communities could rapidly share
power, creating reliable energy for everyone.
HCE had been searching for a solution to managing
new devices on its system. This has included a mix
of customer energy technologies and bulk generation
resources, since decreasing costs of connected
customer-owned devices have made these systems
much more affordable. HCE's grid has seen 10 to 15
rooftop solar installations per week, and it has been
increasing its solar base for years, planning for a 150MW summer peaking system through 2030.

Figure 5. Smart homes in Basalt, Colorado (USA).

Conclusion

Figure 3. Fleet of EVs under distributed
control in the NODES experiment at
NREL's ESIF.

Figure 4. Inverters under
test in the ARPA-E NODES
experiment.

Real-World Applications
We have now started to move out of the laboratory to
demonstrate the deployment of AEGs in the real world.
The team has been working with Holy Cross Energy
(HCE), a utility cooperative near Aspen, Colorado, to
deploy the AEG technology in a group of smart homes
in Basalt, Colorado. The smart homes in Basalt Vista

AEGs of the future will need to control and optimize
millions of controllable devices in real time. A traditional
central optimization approach to this problem
is infeasible because of the computational cost;
therefore, robust, scalable, and predictive hierarchical
and distributed control algorithms with provable
convergence are needed to optimize the grid in real
time. NREL has developed these scalable algorithms to
enable the proliferation of DERs on a massive scale.
A fundamental underpinning of AEGs is the ability to
accurately model the cellular building blocks and their
interactions with the rest of the systems so that control,
optimization, and forecasting methods might be
applied in operation. NREL has taken the preliminary

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The Bridge - Issue 3, 2020

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