The importance of strongly coupling human activity, appliance use and electricity tariffs for load curve simulation in the residential sector

Our research focuses on load curve simulation in the residential sector and its application to short- and long-term prospective studies. The post-Covid, post-energy-crisis era highlights an increasing interaction between human behaviors (e.g., more flexible and energy-saving practices) and new electricity tariffs aimted at supporting renewable energy integration and grid stability. These shifts are part of a broader electrification trend, driven by the expanding adoption of electric mobility, heat pumps, and self-consumption.

Activity-based approaches for modeling residential energy consumption are increasingly explored. Initiatives such as IEA EBC Annex 79 have highlighted several challenges regarding the link between activities and appliance use in a changing technical, societal, and economic context. For example, can the model adjust to new, dynamic incentives to shift consumption to specific hours? Recent literature typically categorizes energy consumption into active consumption (directly tied to an activity) and passive consumption (e.g., controlled appliances with set points). However, even passive consumption often result from occupant decisions or preferences that can change over time. Therefore we argue that models must be flexible enough to account for how contextual changes (such as increased prices or incentives for improved grid balancing) influence behaviors.

Our previous work suggests that an approach where activities, appliances, and electricity tariffs are strongly coupled is necessary to conduct prospective studies. Tariff modeling should reflect the behavioral and technical impacts of peak and off-peak hours temporal placement, which aims to incentivize shifting household practices to lower-priced periods.

Our aim is to demonstrate the capability and scalability of a multi-agent model of human activity, built on multi-sourced data and strongly coupled with appliance and tariff modeling. The model is designed to evaluate load curves and their variations under different future contexts. The presented research focuses on the model’s ability to represent how tariffs and their temporal placement influence human activities, behaviors, appliance use, and appliance control.

We demonstrate how the model can be used to evaluate the impact of changes in tariff structures. We present an industrial case study on the impact of structural changes in the off-peak hours distribution at the national scale.