Multi-Year Time-Series-Based Power System Planning with Hybrid Optimization and Supervised Learning Methods
Produktform: Buch / Einband - flex.(Paperback)
The increasing share of renewable energy sources in the power
system necessitates new planning methods for power systems.
On the one hand, flexible operational measures must be included
in planning. On the other hand, conventional measures have to
be considered. In this thesis, a multi-year planning strategy for
meshed high voltage (HV) systems is proposed considering operational
flexibility as well as conventional planning measures. The
defined optimization problem is solved by a hybrid optimization
algorithm combining the advantages of heuristic and mathematical
programming approaches. A reduction of the high computational
effort of time series simulations is achieved by several
strategies, which are integrated into the open-source tool pandapower.
Furthermore, several machine learning algorithms are
compared. The developed hybrid optimization method is a combination
of the Iterated Local Search metaheuristic and a linear optimization
model. This combination increases convergence while
reducing simulation time in comparison to the existing methods.
Finally, two case studies show the applicability of the developed
planning framework for a real HV power system model.weiterlesen
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