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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.

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The ambitious targets for the reduction of GHG emissions force the enhanced integration and installation of RES. Furthermore, the increased reliance of multiple sectors on electrical energy additionally challenges the electricity grid with high volatility from the demand side. In order to keep the transmission system operation stable and secure, the present approach adds local flexibility into the distribution system using the modular EHG concept. For this concept, two different test cases are configured and evaluated. The two configured EHG demonstrate the ability to provide flexibility and adaptability by reducing the difference between maximal and minimal load in the surrounding grid infrastructure by 30% in certain time periods. Furthermore, the average energy exchange is reduced by 8%. Therefore, by relieving the grid infrastructure in the local surroundings, the additional potential of RES is enabled and the curtailment of existing ones can be reduced.