IEEE Power & Energy Magazine - January/February 2020 - 66

table 1. The existing advanced applications currently being adopted by industry, applications
that will become relevant in the near future, and long-term future applications (continued).
Main Requirements and Characteristics

Application

Network
Model

Communication
Direction

● No
● Simple
● Full

●
●
●
●

No
Unidirectional
Bidirectional
Peer to peer

Location of
Intelligence

Advanced
Analytics

Other Comments

● Centralized
● Distributed
● Local

● Type 1
● Type 2
● Type 3

I: input (measurements)*
O: output (decisions and
controls)

Data quality and data
consistency (enabler)

●/●

●/●

●/●

●

I: dataflow (AMI, and DERMS)
O: data inconsistencies

Autonomous decision
making (enabler)

●/●

●/●

●/●

●

I: local P, Q, and V measurements
O: control P, Q, and V signals

* In addition to knowing the status of the controlled devices.
/: indicates that the corresponding application may employ any of the options that are separated by a /; AMI: advanced
metering infrastructure; DERMS: distributed energy resource management system; DLC: direct load control; P: active power;
PoC: point of connection; Q: reactive power; SA: situational awareness; SDER: complex DER power; Sflow: complex power
flow; Sload: complex load demand; V: voltage.

enhanced resilience in the system from an all-hazards per-
spective. This includes resilience toward natural disasters,
data or cyber issues, and the single-point failure of a central-
ized control system (pursued mostly in stage 2 applications).
In the future, the distribution system will be an autonomous
network, with a mixture of the control hierarchy, a large
number of vulnerable remote measurement and control
units, and heterogeneous and redundant real-time data flow.
To cope with a potential increase in the frequency and sever-
ity of natural disasters, proactive operational and planning
measures are expected to ensure an uninterrupted power
supply. The long-term applications detailed in Table 1 are
early research concepts and targeted to mitigate these future
challenges. The primary drivers for these applications are
likely to be concepts and methods from areas related to com-
puter and network sciences.
This discussion focuses on advanced DMS applications
for system management and operation. We do not include
applications traditionally available in DMSs, such as net-
work analysis functions (short circuit/power flow), monitor-
ing of supervisory control and data acquisition (SCADA)
data, and processing alarms. We also do not include current
or future applications that manage and coordinate distribu-
tion systems markets because market services will be pro-
vided by a separate system. This is consistent with ongoing
discussions worldwide on the requirement for distribu-
tion system operators to manage distribution-level/retail
markets and coordinate them with the wholesale electric
markets. ADMSs, however, will be responsible for the real-
time corrective actions that maintain a reliable network
operation in the presence of these new market paradigms.

Near-Term ADMS Applications
Near-term applications largely rely on model-based opti-
mization methods that require an accurate network model;
66

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the availability of granular, real-time operational data;
and scalable optimization methods. In the following sec-
tions, we detail two examples of near-term applications: 1)
network-level optimization to enhance the performance of
power distribution systems and 2) model-based restoration
to improve the resilience of power distribution systems.

Network-Level Optimization
As the widespread adoption of DERs continues and more
consumers become active participants, managing the effects
of the corresponding reverse power flows is becoming an
increasingly difficult task for distribution companies. Pas-
sive solutions, i.e., standards that impose restrictions on the
installed capacity/output of DERs, are no longer effective
when addressing voltage and congestion issues. Alterna-
tively, next-generation applications of ADMSs that leverage
the enhanced observability and controllability of modern
distribution systems to actively and optimally manage net-
work assets and DER operation can become a preferable
solution. A graphical illustration of these approaches is
shown in Figure 1. For instance, medium-voltage (MV)-
connected wind or photovoltaic (PV) farms could have
their active and reactive power actively managed before
network constraints are breached instead of limiting the
size of their installations based on the worst-case scenario
(i.e., minimum load conditions). A similar concept could
be applied to the PV systems of low-voltage (LV) custom-
ers instead of simply adopting volt-watt settings that will
inevitably penalize customers toward the end of a feeder
(where voltages are expected to be the highest). Control-
lable network assets such as on-load tap changers (OLTCs)
and capacitor banks can also provide extra flexibility if
planned and operated properly.
In practice, addressing these issues effectively requires
consideration of the inherent interactions among customers
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