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Decarbonization requires new energy systems components to mitigate fossil fuel dependency, for instance electric vehicles and heat pumps, forming a sector integrated energy system. Energy management is a promising approach to integrate these devices more efficiently by orchestrating the respective consumption and generation. This study investigates the advantage of an advanced energy management algorithm that is applied to varying energy system scenarios. The energy management algorithm is based on economic principles and the system topology is represented by a rooted tree. Grid elements form parents, which act as auctioneers and devices act according to type specific demand and supply functions. This algorithm is compared to an approach where devices are not coordinated, at a system scale of six households. In order to account for different characteristics of the energy system, the different scenarios are defined according to a morphological analysis and are analysed by means of Monte-Carlo simulation. These scenarios vary the PV generation, heating technology, and building insulation. It is shown that the algorithm reduces peak loads across all scenarios by around 15 kW. Other key performance indicators, such as own consumption and self-sufficiency show a dependency on the scenarios, although the algorithm outperforms the reference in each one, achieving an increase in own consumption of at least 13 p.p. and 22 p.p. in terms of self-sufficiency.

Monte-Carlo Evaluation of Residential Energy System Morphologies Applying Device Agnostic Energy Management