Due to the increase of volatile renewable energy resources, additional flexibility will be necessary in the electricity system in the future to ensure a technically and economically efficient network operation. Although home energy management systems hold potential for a supply of flexibility to the grid, private end users often neglect or even ignore recommendations regarding beneficial behavior. In this work, the social acceptance and requirements of a participatively developed home energy management system with focus on (i) system support optimization, (ii) self-consumption and self-sufficiency optimization, and (iii) additional comfort functions are determined. Subsequently, the socially-accepted flexibility potential of the home energy management system is estimated. Using methods of online household survey, cluster analysis, and energy-economic optimization, the socially-accepted techno-economic potential of households in a three-community cluster sample area is computed. Results show about a third of the participants accept the developed system. This yields a shiftable load of nearly 1.8 MW within the small sample area. Furthermore, the system yields the considerably larger monetary surplus on the supplier-side due to its focus on system support optimization. New electricity market opportunities are necessary to adequately reward a systemically useful load behavior of households.

To achieve the ambitious targets of net-zero greenhouse gas emissions by 2050, there is a need for change in all parts of society, industry, and mobility, as well as in all energy sectors. For this purpose, sector coupling plays a crucial role, e.g., in the form of coupling the electricity with the heat sector using power-to-heat systems. In this article, the effects of the integration of intermittent wind energy via a direct cable, as well as the integration of a boiler into district heating systems powered by a biomass plant and/or a gas boiler, are investigated. Sector coupling in the district heating networks is achieved via the integration of a boiler connected to a local grid station and the use of two air-to-water and two water-to-water heat pumps, which are solely powered by electricity produced by local wind turbines. Furthermore, this work evaluates the economic impacts of the exploding energy prices on the sustainability of district heating systems. Our analysis shows that despite high electricity prices, a reduction in fossil-fuel-based energy generators in the winter season can be determined, and thus a sustainable heat supply can be ensured.

The ongoing miniaturization in electronics poses various challenges in the designing of modern devices and also in the development and optimization of the corresponding fabrication processes. Computer simulations offer a cost- and time-saving possibility to investigate and optimize these fabrication processes. However, modern device designs require complex three-dimensional shapes, which significantly increases the computational complexity. For instance, in high-resolution topography simulations of etching and deposition, the evaluation of the particle flux on the substrate surface has to be re-evaluated in each timestep. This re-evaluation dominates the overall runtime of a simulation. To overcome this bottleneck, we introduce a method to enhance the performance of the re-evaluation step by calculating the particle flux only on a subset of the surface elements. This subset is selected using an advanced multi-material iterative partitioning scheme, taking local flux differences as well as geometrical variations into account. We show the applicability of our approach using an etching simulation of a dielectric layer embedded in a multi-material stack. We obtain speedups ranging from 1.8 to 8.0, with surface deviations being below two grid cells (0.6–3% of the size of the etched feature) for all tested configurations, both underlining the feasibility of our approach.

Large language models (LLMs) are increasingly used to support the analysis and interpretation of building energy data through natural language interaction and explanatory reasoning. However, their integration into operational energy systems raises significant challenges related to data minimisation, access control, auditability, and trust, particularly when high resolution metering data is involved. This paper presents a fully local, policy enforced system architecture for LLM assisted building energy analytics that embeds governance guarantees at the system level rather than relying on model behaviour. All access to energy and contextual data is mediated through structured tools based on the Model Context Protocol (MCP), which provides explicit, task specific context to the model. Identical MCP tools are reused across multiple stakeholder roles, while role-aware permission enforcement at a dedicated model gateway and an external policy engine ensures that the scope, granularity, and content of tool outputs are adapted to each user’s authorisation level. Using a real-world office building with high frequency electrical metering, the proposed architecture is validated with respect to data locality, data minimisation by design, role-aware reuse of analytical logic, and auditability of context construction. The results demonstrate that raw high resolution energy data is never directly exposed to the LLM and that access constraints are enforced independently of prompt formulation or model behaviour. Beyond its architectural contribution, the approach has practical implications for building energy management by enabling flexible, question driven interaction with energy data for diverse stakeholders while preserving strict governance guarantees. The proposed architecture provides a practical and deployable foundation for trustworthy LLM assisted energy analytics in operational environments.