Authors:
Joerg Bremer
1
and
Sebastian Lehnhoff
2
Affiliations:
1
Department of Computing Science, University of Oldenburg, Uhlhornsweg, Oldenburg and Germany
;
2
R&D Division Energy, OFFIS – Institute for Information Technology, Escherweg, Oldenburg and Germany
Keyword(s):
χ-shapes, Decoder, Flexibility Modeling, CMA-ES, Constraint-handling.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Computational Intelligence
;
Evolutionary Computing
;
Representation Techniques
;
Soft Computing
Abstract:
A steadily rising share of small, distributed, and volatile energy units like wind energy converters solar panels, co-generation plants, or similar assigns new tasks and challenges to the smart grid regarding operation and control. The growing complexity of the grid also imposes a growing complexity of constraints that restrict the validity of solutions for operation schedules, resource capacity utilization or grid compliance. Using surrogate models as an abstraction layer has recently become a promising approach for constructing algorithms independently from any knowledge about the actual device or operation restricting constraints. So called decoders as a special constraint handling technique allow for systematically generating feasible solutions directly from a learned surrogate model. Some decoder approaches based on support vector machines have already been implemented, but suffer from performance issues and a sensible parametrization. We propose a new type of decoder based on a
cascade of χ-shapes to overcome these problems. The applicability is demonstrated with a simulation study using different types of flexible energy units.
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