IEEE Power & Energy Magazine - Grid Edge 2023 - 89

Operate in Real Time
One key challenge with AEGs is the development and implementation
of real-time optimization and control methods.
We use the term real time to indicate that power set points of
the DERs are updated within each cell on a second or subsecond
timescale. Electric grids must maintain energy balance
at every time instance. This is required to maximize the
operational and economic objectives while coping with the
variability of ambient conditions and noncontrollable energy
assets and achieving intercell coordination to ensure reliable
systemwide operation. Solving optimization problems to convergence
every second or every few seconds, however, has
been impractical because of the following challenges:
✔ Complexity and convergence analysis. For largescale
grids, the computat ional complexity of a
centrally defined system could prevent the solution of
optimization problems at the required timescales. When
an optimization problem is solved in a distributed and/or
hierarchical fashion (e.g., with device-to-device or cellto-cell
communications as well as intracellular message
passing), multiple communications rounds are necessary
to converge to (possibly optimal)
solutions. Note that the
optimization tasks related to
AEGs are markedly different
from traditional settings in
which energy systems are optimized
at the wholesale level
using economic- and market-based
objectives. In the
traditional operation of bulk
systems, a few large-scale
generators are dispatched,
and the noncontrollable net
load varies slowly. Such operation
is incompatible with
AEGs that include a massive
integration of DERs or whose
optimization models require
accurate representations of
ac power flows within the
DERs' controllability region.
In traditional bulk systems,
optimization problems are
nonconvex, nondeterministic,
and polynomial-time hard
(NP-hard); therefore, they
may be infeasible to solve
at the envisioned timescale
with hundreds of millions
of control points. To address
these challenges and facilitate
the development of provably
stable and optimal distributed
solution methods for AEGs, a
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first step is to develop convex relaxations and linear approximations
of pertinent nonconvex problems.
✔ Model inaccuracy. Approximate linear models or
convex relaxation methods might be leveraged to derive
convex problems that facilitate the design of computationally
affordable solutions. However, approximate/relaxed
convex problems might involve only
an approximate representation of a system's physics
and constraints; therefore, the optimal solutions of the
convex problem might not be feasible for the original
problem. To begin to address this issue, distributed
optimization algorithms have been developed to use
measurement information directly, which is known
as online optimization with feedback. Measurementbased
(or feedback-based) algorithms address the feasibility
issue, and they can be distributed or centralized.
The design of a distributed version is certainly
more challenging than the centralized one, but the distributed
version can be implemented on a more flexible
communications architecture, which can enhance
cyber robustness.
1-100 s Renewable Conventional EV Industrial Commercial Residential
figure 2. The AEGs form a distributed hierarchical control system that integrates
individual technologies in a cellular structure to the bulk power system. The scale
on the side indicates the number of controllable technologies seen along the bottom
level. The lowest level depicts the locations of various generation, storage, and loads.
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IEEE Power & Energy Magazine - Grid Edge 2023

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