Date of Award

9-2013

Embargo Period

2-19-2014

Degree Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Electrical and Computer Engineering

Advisor(s)

Marija Ilic

Abstract

Well-designed demand response is expected to play a vital role in operating
power systems by reducing economic and environmental costs. However,
the current system is operated without much information on the benefits of
end-users, especially the small ones, who use electricity. This thesis proposes a
framework of operating power systems with demand models including the diversity
of end-users’ benefits, namely adaptive load management (ALM). Since
there are a large number of end-users having different preferences and conditions
in energy consumption, the information on the end-users’ benefits needs
to be aggregated at the system level. This leads us to model the system in
a multi-layered way, including end-users, load serving entities, and a system
operator. On the other hand, the information of the end-users’ benefits can be
uncertain even to the end-users themselves ahead of time. This information is
discovered incrementally as the actual consumption approaches and occurs. For
this reason ALM requires a multi-temporal model of a system operation and
end-users’ benefits within. Due to the different levels of uncertainty along the
decision-making time horizons, the risks from the uncertainty of information
on both the system and the end-users need to be managed. The methodology
of ALM is based on Lagrange dual decomposition that utilizes interactive communication
between the system, load serving entities, and end-users. We show
that under certain conditions, a power system with a large number of end-users
can balance at its optimum efficiently over the horizon of a day ahead of operation
to near real time. Numerical examples include designing ALM for the
right types of loads over different time horizons, and balancing a system with a large number of different loads on a congested network. We conclude that
with the right information exchange by each entity in the system over different
time horizons, a power system can reach its optimum including a variety of
end-users’ preferences and their values of consuming electricity.

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