The electric power grid which has been an indispensable part of our
society is being put under increasing pressure to meet the demands
of a major surge in global energy consumption. As a result, the
power grid needs to undergo transformations to meet the new
challenges for a more sustainable society. One of these
transformations is the need for continuous optimization in the
smart grid so that it can react rapidly to dynamic situations in
presence of fluctuating demands and uncertain renewable energy. In
the past, the operations of power grid relied on careful a-priori
planning, under the assumptions of static demands and predictable
circumstances. In the era of dynamic smart grid, self-optimization
with adaptive control is more crucial to its operations. Many of
the ideas developed by the theoretical computer science community
can be applied in such cases. This monograph establishes an
interdisciplinary bridge between power systems engineering and
theoretical computer science by relating the practical and
challenging problems in electric power systems with the modern
theoretical tools from computer science. The proper understanding
of these hard problems in electric power systems can advance the
frontiers of both communities. This monograph introduces Power
System Engineers to the concepts and results of approximation
algorithms, and applies them to solve electric power systems
problems as well as providing Computer Scientists with an
exposition of a class of challenging combinatorial problems in
electric power systems.
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