IEEE Power & Energy Magazine - November/December 2020 - 38

electric grids, as more DERs are integrated, the number of
active control points will be too much for current control
approaches to effectively manage.
Imagine, for example, the San Francisco Bay Area, which
has a large distribution system with approximately 4.5 million customers. Figure 1 illustrates synthetic distribution
systems of the Bay Area made from actual data that have
been created to replicate properties of the actual systems,
including various voltage levels and both wye and delta connected circuits. What if each customer had a PV system, a
battery energy storage system, an EV, a smart thermostat,
and controllable lighting loads? This would amount to
approximately 10-20 million controllable devices capable
of producing, storing, and consuming electricity. Currently,
there are no control systems capable of ingesting 20 million
data streams and making real-time operation decisions.
In current large-scale grids, such as the Eastern Interconnection in the United States, central station power plants provide power to loads and have on the order of 10,000 points
of control. Current control systems work well when there are
a limited number of active control points in the system, but
to deal with the massive amounts of new DER technologies
and the availability of grid measurements, a new control
framework needs to be developed. The framework needs to

Rural 12.47 k
Rural 25 kV
Urban 12.47 k
Urban 4 kV
Urban Delta

figure 1. This is the San Francisco Bay Area synthetic
distribution system, developed under the ARPA-E GRID
DATA program. Line configurations are mostly wye with
a small amount of delta. (Source: grid data: NREL; map:
OpenStreetMap.org.)
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ieee power & energy magazine

monitor, control, and optimize large-scale grids with significantly high penetration levels of variable generation and
DERs; it needs to process the deluge of data from pervasive
metering; and it needs to implement a variety of new market mechanisms, including multilevel ancillary services. To
handle this highly distributed energy future, we propose the
concept of autonomous energy grids (AEGs).

Autonomous Energy Grids: The Concept
AEGs are multilayer, or hierarchical, cellular-structured
electric grid and control systems that enable resilient, reliable, and economic optimization. Supported by a scalable,
reconfigurable, and self-organizing information and control infrastructure, AEGs are extremely secure and resilient, and they can operate in real time to ensure economic
and reliable performance while systematically integrating
energy in all forms. AEGs rely on cellular building blocks
that can both self-optimize when isolated from a larger grid
and participate in optimal operation when interconnected
to a larger grid. Figure 2 shows how a scalable approach to
control can be built from the lowest level of individual controllable technologies (renewable energy, conventional generation, EVs, storage, and loads) and used to control hundreds of millions of devices through hierarchical cells. In
the figure, the bottom level consists of individual technologies that are aggregated into small cells. Then, each upper
level represents a collection of cells until the entire grid is
covered. Within each layer, distributed controls are used to
optimize energy production and meet system requirements.
There is minimal information passed between layers, and
this hierarchical approach enables the control of hundreds
of millions of devices.
To make this idea a reality, control algorithms for AEGs
will need to be developed and implemented with the following characteristics:
✔✔ Operate in real time: Control algorithms must operate fast enough to ensure real-time operations in
electric grids that balance load and generation every
second.
✔✔ Handle asynchronous data and control actions:
Data need to be used from a variety of asynchronous measurements and sources, whereas distributed decision making leads to asynchronous control
actions.
✔✔ Robustness: This covers both reliability and resilience,
where reliability is fault tolerance, and resilience is the
ability to come back from a failed state. These control
systems also must be robust to communications failures, prolonged communications outages, and largescale disturbances.
✔✔ Scalable: Control algorithms must operate in a scalable fashion to ensure control of hundreds of millions
of devices.
We will discuss these characteristics in detail in the following sections.
november/december 2020

http://www.OpenStreetMap.org

IEEE Power & Energy Magazine - November/December 2020

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