Guidelines for Developing Scheduling Systems

Framiñán, José M.; Leisten, Rainer; Ruíz, Rubén

doi:10.1007/978-1-4471-6272-8_14

Cited by 1 publication

(1 citation statement)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…An extensive review (Fathima and Palanisamy 2015) concludes that there is a vast amount of tools for optimization in hybrid RES, but the flexibility of these tools needs to be increased, especially for control and energy management. Framinan and Ruiz (2009) and Framinan and Ruiz (2010) analyzed the literature on manufactory scheduling and concluded that there is an abundance of different scheduling models and solution procedures but a lack of work on practical implementation.…”

Section: Discussionmentioning

confidence: 99%

An adapter-based architecture for evaluating candidate solutions in energy system scheduling

et al. 2022

View full text Add to dashboard Cite

Increasing shares of volatile generation and non-steerable demand raise the need for automated control of the Energy Systems (ESs). Various solutions for management and schedule-based control of energy facilities exist today. However, the amount and diversity of applications lead to a multitude of different automated energy management solutions. Different optimization algorithms have proven more or less effective for energy management. The multitude of optimization algorithms and energy management solutions require flexible, modular, and scalable integrations. We present a novel Optimization Service (OS) for easily integrating optimization algorithms while evaluating candidate solutions in the context of ESs applications. We propose an adapter-based architecture using metadata and domain knowledge to bridge between clients, e.g. smart grid applications and optimization algorithms. The architecture interfaces different clients with optimizers in a flexible and modular way. The clients provide metadata-based descriptions of optimization jobs translated by OS. OS then interacts with optimizers and evaluates candidate solutions. A consistent definition of interfaces for clients and optimization algorithms facilitates the modular evaluation of candidate solutions. OS’s separation of client and optimization algorithms increases scalability by managing computational resources independently. We evaluate the presented architecture for scheduling a so-called Energy Hub (EH) as a test case describing a simulation scenario of a renewable EH embedded in grid scenarios from an industrial area in Karlsruhe, Germany. OS utilizes an Evolutionary Algorithm (EA) to optimize schedules for cost and strain on the electrical grid. The use case exemplifies OS’s advantages in a proof-of-concept evaluation.

show abstract

Section: Discussionmentioning

confidence: 99%