How demand modelling influences energy market mechanisms in renewable energy communities

Renewable Energy Communities (RECs) aim to maximise local self-consumption by aligning renewable generation and electricity demand. However, Urban Building Energy Modelling (UBEM) typically relies on deterministic schedules, overlooking behavioural variability that is critical for assessing community-level Energy Market Mechanisms (EMMs). This paper investigates how behavioural patterns affect EMM performance in mixed-use districts. Two neighbourhoods in Montreal, Canada, characterised by different morphologies and population structures, are analysed. Electricity demand and rooftop photovoltaic (PV) generation are simulated using an UBEM framework in which deterministic residential schedules are replaced by household-based stochastic profiles. Using modelling outputs, EMMs with different temporal resolutions and remuneration logics are tested. Stochastic modelling produces 30–40% mean hourly demand deviations compared to deterministic models, that smooth out to 4–5% annually. Low-rises with few households show 50–100% hourly variability, whereas high-rises lead to load-averaging effects that reduce deviations. For hourly-resolution EMMs, stochastic modelling reduces energy costs by 1–5.5% in working-age districts and increases revenues by up to 3% in retirement-age districts. Daily net metering achieves self-consumption ratios of 0.96–1.00 and reduces costs by up to 80%, while monthly net metering reaches self-sufficiency between 0.14 and 0.42. These findings confirm the importance of refined demand modelling when evaluating RECs and EMMs.