Simon Malacek | Searcharxiv

arXiv2026

Temperature-Aware Heat Pump Modeling for Large-Scale Energy System Optimization

Heat pumps are expected to dominate the heating sector, substantially increasing peak electricity demand. At the same time, building thermal inertia enables operational strategies, providing temporal flexibility in heat pump operation and short-term demand response. However, this dynamic behavior is not yet represented in large-scale energy system optimization models. To address this gap, we present an innovative formulation of building thermal inertia. The resulting temperature variable is integrated into a novel conic temperature-aware heat pump efficiency formulation, enabling a more precise emulation of smart control strategies. In a case study of the European energy system, we show that the approach captures operational heating flexibility while remaining computationally efficient. The results indicate substantial untapped flexibility potential, enabling up to a 22% reduction in heating-related electricity costs. This potential can be realized through a suitable energy market design that incentivizes coordinated heat pump control, individually or via aggregators.

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arXiv2025

Generating Building-Level Heat Demand Time Series by Combining Occupancy Simulations and Thermal Modeling

Despite various efforts, decarbonizing the heating sector remains a significant challenge. To tackle it by smart planning, the availability of highly resolved heating demand data is key. Several existing models provide heating demand only for specific applications. Typically, they either offer time series for a larger area or annual demand data on a building level, but not both simultaneously. Additionally, the diversity in heating demand across different buildings is often not considered. To address these limitations, this paper presents a novel method for generating temporally resolved heat demand time series at the building level using publicly available data. The approach integrates a thermal building model with stochastic occupancy simulations that account for variability in user behavior. As a result, the tool serves as a cost-effective resource for cross-sectoral energy system planning and policy development, particularly with a focus on the heating sector. The obtained data can be used to assess the impact of renovation and retrofitting strategies, or to analyze district heating expansion. To illustrate the potential applications of this approach, we conducted a case study in Puertollano (Spain), where we prepared a dataset of heating demand with hourly resolution for each of 9,298 residential buildings. This data was then used to compare two different pathways for the thermal renovation of these buildings. By relying on publicly available data, this method can be adapted and applied to various European regions, offering broad usability in energy system optimization and analysis of decarbonization strategies.

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