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Bridging the Gap between Scheduling and Control of Distributed Energy Resources: Dynamic Parametrization of a Metaheuristic Method

: Vasconcelos, M.; Hechenrieder, H.; Zocher, J.; Andres, M.


Institute of Electrical and Electronics Engineers -IEEE-:
9th International Conference on Power Science and Engineering, ICPSE 2020 : October 23-25, 2020 London, UK
Piscataway, NJ: IEEE, 2020
ISBN: 978-1-7281-8454-8
ISBN: 978-1-7281-8453-1
ISBN: 978-1-7281-8452-4
International Conference on Power Science and Engineering (ICPSE) <9, 2020, Online>
Fraunhofer FIT ()

Bridging the gap between market-oriented operational scheduling and grid-oriented control of distributed energy resources entails the formulation of a nonlinear optimization problem solvable with metaheuristic methods. Despite the generally advantageous computing time of metaheuristics compared to complex nonlinear problems, metaheuristic methods require a time-consuming parametrization of each new instance of the underlying optimization problem. Considering the time constraints of control strategies for distributed energy resources, the time-consuming parametrization poses a significant challenge. This paper introduces dynamic parametrization approaches based on machine learning, which can increase the performance of the parametrization process.