The Bridge - Issue 3, 2020 - 13

THE FUTURE OF RENEWABLE ENERGY TRANSMISSION: An Autonomous Energy Grid

Feature

AEGs: The Concept [1],[2]
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 1 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 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 use to meet system requirements.
Minimal information is needed to be passed between
layers, and this hierarchical approach enables 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
A real-time optimization framework has been
developed at the National Renewable Energy
Laboratory (NREL) [3],[4] that can model welldefined objectives and constraints of DERs within
each cell as well as consistency constraints for
electrical quantities that pertain to the cell-to-cell
connections. By using measurements in the system as
a feedback mechanism and tracking optimal solution
trajectories, the resultant feedback-based online
optimization methods can cope with inaccuracies in
the representation of the AC power flow and avoid
having to measure all the noncontrollable resources.
The algorithms enable DERs to track given performance
objectives while adjusting their power to respond to
services requested by grid operators and to maintain
electrical quantities within engineering limits.

Figure 1. AEGs form a distributed hierarchical control system that
integrates individual technologies in a cellular structure to the bulk power
system. The scale on the left side indicates the number of controllable
technologies seen along the bottom level. The lowest level shows
locations of various generation, storage, and loads. [2]

Hierarchical Communications
and Asynchronous Data
To enable real-time optimization of AEGs with millions
of controllable devices, a hierarchical communications
architecture that includes cell-to-cell and cell-tocustomer message passing can be formulated to
manage these devices [5],[6]. Mathematically, to obtain
consistency among cells, constraints are added to the
optimization problem to ensure adjacent cells agree on
the power flows from one cell to another. This is known
as consensus-based optimization. Overall, the resultant
feedback-based online optimization methods need to
provably track the solution of the convex optimization
problems by modeling well-defined objectives and
constraints for each cell, as well as the consistency
constraints for electrical quantities pertaining to the
cell-to-cell connections. The feedback-based method
also works for nonconvex problems; however, analytic
proof of convergence for the feedback-based method
is very tricky and not well established. These cell
connections can be geographically co-located or based
on aggregators such as smart home aggregators. In

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The Bridge - Issue 3, 2020

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