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2020
Conference Paper
Titel
Bridging the Gap between Scheduling and Control of Distributed Energy Resources: Dynamic Parametrization of a Metaheuristic Method
Abstract
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.