Beyond net zero energy buildings: Load profile analysis and community aggregation for improved load matching

This study investigates the load matching of electricity consumption and photovoltaic (PV) generation in residential buildings following the net zero energy building (NZEB) framework. Load matching is critical due to the increasing integration of PV systems, driven by policies like the European Green Deal. Utilizing a dataset of 316 smart-metered residential electricity profiles, we conducted a sensitivity analysis to quantify the impact of various factors. Results indicate that the order of factors influencing self-consumption (SC), self-sufficiency (SS), self-production (SP), and grid liability (GL) in a heating-dominated region are annual and intraday consumption patterns, followed by PV tilt angle and finally, azimuth angle. NZEB sizing typically ended in an average SC of only 30.3 % and a GL of 39.5 %, highlighting the need for improved sizing strategies and reducing mismatch. We proposed two alternative PV sizing approaches, maximizing self-production (achieving up to 46.7 % SC) and minimizing grid liability (reducing GL considerably). The study shows that understanding consumption variability and optimizing PV configurations can significantly enhance load-matching outcomes, mainly when aggregated in energy communities, yielding an additional 9 percentage points increase in SC under a reasonable PV penetration. While NZEB communities could exceed original power peaks (200 kW of demand) with feedback periods in more than 10 % of the year, reaching peak feedback of 657 kW, a more reasonable PV penetration suggested (optimizing PV systems for self-production) that only exceeds 200 kW limit in 4 % of the year, with a consolidated peak of 332 kW feedback. Consequently, the study provides practical strategies for better integrating PV into low voltage electricity networks while mitigating adverse grid impacts, aligning with ongoing energy policy reforms. • More than 5000 NZEB concept household electricity profiles were analyzed. • Annual consumption pattern determines load matching of NZEBs the most. • Linear regression model for annual load matching estimation. • A NZEB community exceeds peak loads of a no-PV scenario in 10 % of the year. • Alternative system sizing approaches are suggested by analyzing load matching.