Averaging favors MPC: How typical evaluation setups overstate MPC performance for residential battery scheduling

TL;DR

This paper reveals that common evaluation methods overstate Model Predictive Control's advantages over Rule-Based Control in residential battery management, especially when finer time resolutions are considered, affecting cost assessments.

Contribution

It demonstrates how typical averaging in evaluations inflates MPC performance and proposes more accurate assessment methods for residential battery scheduling.

Findings

Averaging overestimates MPC's performance by 69% on average.

Finer, minute-level evaluation can reverse the perceived advantage.

Simple RBC can outperform MPC with perfect foresight in some cases.

Abstract

Residential prosumers with PV-battery systems increasingly manage their electricity exchange with the power grid to minimize costs. This study investigates the performance of Model Predictive Control (MPC) and Rule-Based Control (RBC) under 15/30/60 minute averaging commonly used in research, when Net Billing and battery degradation are considered. We simulate five consecutive months for 15 buildings in northern Germany, generating costs at up to 1-minute resolution while scheduling at 15/30/60 minutes. We find that time-averaged evaluations make MPC look consistently better than RBC, yet when costs are recomputed at minute-level ground-truth, the reported advantage shrinks by 69\% on average for hourly schedulers. For individual buildings, the finer evaluation can reverse conclusions, and simple RBC can achieve lower total costs than an MPC with perfect foresight. These findings caution against drawing conclusions from coarse averages and show how a fair assessment of battery scheduling approaches can be obtained.