Authors:
Joerg Bremer
and
Sebastian Lehnhoff
Affiliation:
University of Oldenburg and OFFIS – Institute for Information Technology, Germany
Keyword(s):
Flexibility Modeling, Folded Distributions, Simulated Annealing, Predictive Scheduling.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Artificial Intelligence and Decision Support Systems
;
Computational Intelligence
;
Enterprise Information Systems
;
Evolutionary Computing
;
Informatics in Control, Automation and Robotics
;
Intelligent Control Systems and Optimization
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Machine Learning
;
Soft Computing
;
Symbolic Systems
Abstract:
Smart grid control demands delegation of liabilities to distributed, rather small energy resources in contrast to
todays large control power units. Distributed energy scheduling constitutes a complex task for optimization
algorithms regarding the underlying high-dimensional, multimodal and nonlinear problem structure. Additionally,
the necessity for abstraction from individual capabilities is given while integrating energy units into
a general optimization model. For predictive scheduling with high penetration of renewable energy resources,
agent-based approaches using classifier-based decoders for modeling individual flexibilities have shown good
performance. On the other hand, such decoder-based methods are currently designed for single entities and
not able to cope with ensembles of energy resources. Combining training sets randomly sampled from individually
modeled energy units, results in folded distributions with unfavorable properties for training a decoder.
Neverthel
ess, this happens to be a quite frequent use case, e. g. when a hotel, a small business, a school or
similar with an ensemble of co-generation, heat pump, solar power, and controllable consumers wants to take
part in decentralized predictive scheduling. We use a Simulated Annealing approach to correct the unsuitable
distribution of instances in the aggregated ensemble training set prior to deriving a flexibility model. Feasibility
is ensured by integrating individual flexibility models of the respective energy units as boundary penalty
while the mutation drives instances from the training set through the feasible region of the energy ensemble.
Applicability is demonstrated by several simulations using established models for energy unit simulation.
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