Abstract
Abstract To optimally design integrated energy systems a widely used approach is the Energy Hub. The conversion, storage and transfer of different energy vectors is represented by a coupling matrix. Yet, the coupling matrix restricts the configuration of the Energy Hub and the constraints, that can be included. This paper proposes a MILP based optimization framework, which allows a high variability and adaptability and is based on energy flows. The functionality of the developed framework is tested on four use cases depicting different system sizes and Energy Hub configurations. It is shown that the framework is able to simplify the design process of an Energy Hub.
Highlights
To tackle climate change one of the main challenges is to decarbonize the energy generation
The transformation of energy vectors within the Energy Hub is modeled by a coupling matrix representing the converter’s efficiencies and the energy vectors they connect. With this approach the energy flows within the Energy Hub can be calculated and the concept allows an optimization of the planning and operation of an integrated energy system
Despite the great number of publications, five main challenges are identified, which are not solved in a single framework: restricted number of constraints, which can be included in the coupling matrix, no automated building of the coupling matrix, restricted integration of storage systems, no framework exists to simplify the configuration of Energy Hubs and grid fees are not included in the optimization
Summary
To tackle climate change one of the main challenges is to decarbonize the energy generation. The transformation of energy vectors within the Energy Hub is modeled by a coupling matrix representing the converter’s efficiencies and the energy vectors they connect With this approach the energy flows within the Energy Hub can be calculated and the concept allows an optimization of the planning and operation of an integrated energy system. Despite the great number of publications, five main challenges are identified, which are not solved in a single framework: restricted number of constraints, which can be included in the coupling matrix, no automated building of the coupling matrix, restricted integration of storage systems, no framework exists to simplify the configuration of Energy Hubs and grid fees are not included in the optimization. The framework is comparable to common simulation tools with regard to the key feature of object/component-oriented modelling Within this framework, a method is implemented that displays the connections between the Energy Hub’s components based on energy flows instead of a coupling matrix.
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