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Nested algorithms for optimal reservoir operation and their embedding in a decision support platform (Paperback)
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Nested algorithms for optimal reservoir operation and their embedding in a decision support platform (Paperback)
Series: IHE Delft PhD Thesis Series
Expected to ship within 12 - 17 working days
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Reservoir operation is a multi-objective optimization problem, and
is traditionally solved with dynamic programming (DP) and
stochastic dynamic programming (SDP) algorithms. The thesis
presents novel algorithms for optimal reservoir operation, named
nested DP (nDP), nested SDP (nSDP), nested reinforcement learning
(nRL) and their multi-objective (MO) variants, correspondingly
MOnDP, MOnSDP and MOnRL. The idea is to include a nested
optimization algorithm into each state transition, which reduces
the initial problem dimension and alleviates the curse of
dimensionality. These algorithms can solve multi-objective
optimization problems, without significantly increasing the
algorithm complexity or the computational expenses. It can
additionally handle dense and irregular variable discretization.
All algorithms are coded in Java and were tested on the case study
of the Knezevo reservoir in the Republic of Macedonia. Nested
optimization algorithms are embedded in a cloud application
platform for water resources modeling and optimization. The
platform is available 24/7, accessible from everywhere, scalable,
distributed, interoperable, and it creates a real-time multiuser
collaboration platform. This thesis contributes with new and more
powerful algorithms for an optimal reservoir operation and cloud
application platform. All source codes are available for public use
and can be used by researchers and practitioners to further advance
the mentioned areas.
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