Occam's Razor in Residential PV-Battery Systems: Theoretical Interpretation, Practical Implications, and Possible Improvements

TL;DR

This paper provides a theoretical interpretation of Occam's control for residential PV-battery systems, introduces an improved algorithm based on online convex learning, and demonstrates its practical benefits over traditional methods.

Contribution

It offers the first theoretical upper bound for Occam's control and proposes a superior alternative algorithm with similar complexity.

Findings

Online learning methods outperform classical optimization in residential PV-BSS systems.

Theoretical upper bounds are established for Occam's control.

Improved algorithms enhance economic and operational performance.

Abstract

This paper presents a theoretical interpretation and explores possible improvements of a widely adopted rule-based control for residential solar photovoltaics (PV) paired with battery storage systems (BSS). The method is referred to as Occam's control in this paper, given its simplicity and as a tribute to the 14th-century William of Ockham. Using the self-consumption-maximization application, it is proven that Occam's control is a special case of a larger category of optimization methods called online convex learning. Thus, for the first time, a theoretical upper bound is derived for this control method. Furthermore, based on the theoretical insight, an alternative algorithm is devised on the same complexity level that outperforms Occam's. Practical data is used to evaluate the performance of these learning methods as compared to the classical rolling-horizon linear/quadratic programming. Findings support online learning methods for residential applications given their low complexity and small computation, communication, and data footprint. Consequences include improved economics for residential PV-BSS systems and mitigation of distribution systems' operational challenges associated with high PV penetration.