IEEE Power & Energy Magazine - January/February 2020 - 73
and highly stochastic future power distribution systems. The
primary limitation of model-based methods is the need for a
detailed system model and state information to make deci-
sions. For distribution systems, even with the proliferation of
sensing and measurement devices, communication and com-
putational challenges inhibit full observability. This calls for
model-free decision-making concepts for the autonomous
and local control of the grid's decision-making devices with
minimum communication and coordination requirements.
Adaptive control methods are a class of algorithm that
closely relates to the model-free local control paradigm, spe-
cifically perturbation-based methods such as extremum-seek-
ing control algorithms currently being explored by research-
ers. These methods do not require a system model and make
decisions purely based on local measurements. To enforce
coordination among the local agents, the ADMS acts as a
mediator by either resolving the control conflicts or provid-
ing a trajectory or reference signal that all local agents are
required to track. These methods, however, do not retain a
memory of their past decisions and do not have the capabil-
ity to improve their decision making using past data. In this
context, reinforcement learning (RL) methods are a promis-
ing mechanism for model-free autonomous decision making.
RL methods are a class of machine-learning algorithm that
deals with learning decisions or control actions to help the
agent achieve a prespecified goal regardless of the uncertainty
in its environment. The main idea is to learn to make opti-
mal decisions by continuously interacting with the simulated
environment that generates reward and value signals to guide
the learning process. The agent continuously interacts with its
environment to receive a reward that measures the immediate
goodness of its decisions and generates a value to measure the
long-term value of its decisions. Based on these interactions,
the agent learns an optimal decision-making policy.
In the context of power distribution systems, we can
envision control units such as capacitor banks, voltage
Hierarchical
Centralized
Transmission
Transmission
MV
MV
LV
LV
Decentralized
Distributed
Transmission
Transmission
MV
MV
LV
LV
Legend
Intelligence
Data Analytics
Settings (Control Set Points)
Communication
figure 6. The control architectures for future ADMS applications.
january/february 2020
ieee power & energy magazine
73
IEEE Power & Energy Magazine - January/February 2020
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