Quantifying the Gap: Rule-based vs. Smart Energy Management of Residential PV Self-Consumption with Storage

The growing adoption of residential photovoltaic (PV) and battery systems has led to an increasing interest in energy management strategies that optimize performance and maximize savings. These range from simple rule-based methods to advanced optimization and machine learning approaches. Among them, self-consumption maximization (SCM) stands out as a practical, low-cost, rule-based strategy requiring no external infrastructure or forecasting. This work assesses the adequacy of SCM for residential PV and battery systems within collective selfconsumption schemes. SCM performance is benchmarked against a MILP model with perfect foresight to quantify the gap between SCM and the theoretical optimum. A case study in Portugal shows that SCM achieves results within 1- 7% of optimality, even for dynamic tariffs. Achieving optimality would correspond to maximum cost savings of €5 per household per month, when compared to SCM. Given the modest potential gains, the limits imposed by forecast uncertainty on practical performance, and the higher costs of implementing more complex methods, SCM is likely the most cost-effective solution for most residential applications, particularly in sunny climates like Portugal.