IEEE Power & Energy Magazine - January/February 2020 - 48

The various advanced application functions automatically build the
sets of equations to be solved based upon the electrical equipment
characteristics and connectivity descriptions in the DNOM.
model) and a limited set of input parameters, such as
system loading and some specific measured or assumed
voltages. The solution of this large and complex group of
interrelated mathematical equations is a comprehensive
set of voltage values for the terminals of every piece of
equipment represented in the model. With this set of solution voltages, corresponding flows and losses in each piece
of conducting equipment (the power flow solution) can be
calculated. State estimation refers to a type of power flow
function where measurements are included in the input
parameters. Short circuit is again a type of power flow function, specifically to study the flows resulting in pieces of
equipment connected to the live distribution network and
ground, usually by accident.
Network optimization functions solve similar sets of
mathematical equations but have the additional goal of
proactively suggesting how to adjust controllable distribution network equipment to meet desired network operating
conditions. Common examples of desired network operating
conditions include
✔ minimal operating limit violations for equipment
loading and customer service voltages
✔ restoring as many disconnected customers as possible
after an unplanned outage
✔ minimum customer energy consumption by lowering
their delivery voltage as much as possible within acceptable operating limits.
Controllable distribution network equipment includes
✔ tap-changing transformers
✔ capacitors that can be opened and closed
✔ switchgear that can be used to reconfigure circuits
✔ DERs with advanced inverters that can accept desired
control settings from an ADMS network optimization
function.
The distribution network optimization functions include
✔ switching reconfiguration, which proposes switching
plans for fault isolation and service restoration (FISR)
as well as planned maintenance work or circuit-load
balancing/shifting
✔ IVVO, which involves demand management and loss
minimization within voltage operating limits using
taps, capacitors, and DERs, such as photovoltaics and
storage coupled with advanced inverters.
The difficulties and challenges in implementing the
advanced applications usually have to do with the problem of
consistently obtaining those simultaneous equation solutions,
commonly referred to as solution convergence. The various
48

ieee power & energy magazine

advanced application functions automatically build the sets
of equations to be solved based upon the electrical equipment
characteristics and connectivity descriptions in the DNOM.
The number of equations is related to the number of pieces of
conducting equipment. This means that the number required
to solve a power flow analysis of the feeders energized from
just a single distribution substation is typically on the order
of thousands, the same magnitude as a power flow analysis
for an entire utility transmission network. The equations
themselves are only engineering approximations of the relationships of terminal voltages and flows through the equipment. The DNOM data inevitably contain some minor errors
about equipment attributes; for example, there may be a small
misprint on the manufacturer's specifications sheet as well
as a larger number of mistakes made on the GIS surveys or
through data entry. The dynamic nature of equipment connectivity, such as manual switching activity in the field, often
leads to some errors or changes not captured accurately into
the DNOM.
All of these potential errors create a challenge for
advanced application algorithms in converging on a suitable solution. The algorithms must be numerically robust,
or forgiving, to some level of inaccurate data. Additionally,
the ADMS should have a comprehensive and automated
model validation component to detect problems in the
DNOM data before being passed to the advanced applications. This is especially true during the initial rounds of
an advanced application deployment. If converged solutions can be obtained, even when there are data errors,
those solutions can then be very helpful in diagnosing
and pinpointing their source for subsequent correction.
If solutions fail to converge, they are much less helpful
for finding the root difficulties. On the positive side, most
initial problems in the equipment models and parameters
tend to be systematic and can be cleaned up rapidly in an
automated fashion, once identified. To have a successful
advanced application implementation, however, in addition to robust algorithms and automated model validation,
the utility must have a commitment and business process to
stay on top of the model data errors that will continuously
creep into the system with new construction, repairs, and
dynamic switching activity.

Utility Practices Resulting
in Successful Implementations
So far, we have discussed architectural, functional, and algorithmic aspects of an ADMS that relate to a successful
january/february 2020



IEEE Power & Energy Magazine - January/February 2020

Table of Contents for the Digital Edition of IEEE Power & Energy Magazine - January/February 2020

Contents
IEEE Power & Energy Magazine - January/February 2020 - Cover1
IEEE Power & Energy Magazine - January/February 2020 - Cover2
IEEE Power & Energy Magazine - January/February 2020 - Contents
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