Urban Building Energy Modeling (UBEM) has become a critical tool for developing local heating and cooling plans, as required by the European Union. Despite growing interest, the reproducibility and reliability of UBEM studies remain limited due to data scarcity and workflow complexity. This paper presents a comprehensive framework to evaluate the data pipeline in UBEM, with a particular focus on data acquisition, enrichment, simulation, calibration, and information application. The approach applies three distinct UBEM workflows (CityEnergyAnalyst, DistrictGenerator, and SimStadt) to the Mierendorffinsel district in Berlin, Germany. We compare the based on quantitative performance metrics and qualitative framework criteria. The results highlight the influence of data sources, archetype definitions, and geometric preprocessing on simulation outcomes. The CEA models consider between 365 and 646 buildings, depending on the scenario. The study provides guidelines for practitioners to enhance model transparency, reproducibility, and accuracy in urban energy modeling. Although official data provide more accurate building functions, the geometries need extensive preprocessing. The residential archetypes are far more refined, e.g., in the status of renovation, which reflects the amount of residential buildings compared to nonresidential buildings. We show that evaluation threshold criteria for the district level are scarce and evaluate multiple metrics. Results depend on the selected evaluation method, but observed differences are generally higher in case of nonresidential buildings, with differences of more than 300 kWh/m2 for several nonresidential building types. The heated area considered differs up to a factor of 1.9, because of different buildings, metadata, and calculation approaches.

In view of the ongoing integration of distributed energy resources (DERs) and energy storage into the energy system, conventional consumers are transitioning into prosumers and flexumers. Local energy markets (LEMs) enables these end users to trade electricity directly with each other in order to obtain lower energy prices and to increase the local self-consumption. Since bidding strategies have a decisive impact on efficient trading in auction-based LEMs, the comparison and consistent evaluation of bidding strategies is an important research area. As novelty we propose an uniform evaluation methodology keeping all market features equal except the used bidding strategy. For the first time we evaluate a device-oriented (DO) strategy including an incentive price signal with a zero-intelligence (ZI) strategy, a learning-intelligence (LI) strategy employing the modified Roth–Erev learning algorithm. As evaluation we analyze the resulting market trading with various key performance indicators (KPIs), which consider both cost savings and local energy supply. Our findings reveal that the chosen bidding strategy has a decisive impact on cost savings and the distribution of gains between the buyer and seller side. In a photovoltaic (PV) and combined heat and power (CHP) scenario with different technology penetrations, the end users’ gains attain the highest values for the DO bidding strategy with 7909 € and 16454 €. Also the average market clearing prices are for the DO bidding strategy the highest with 0.269 €/kWh for the and 0.3032 €/kWh, which implies that the seller side predominantly obtains the gains.