Comparative Analysis of Linear Battery Models for Carbon Emission Optimization in Solar Energy Systems

This work addresses the problem of minimizing equivalent carbon emissions in residential photovoltaic-battery energy storage systems (PV-BESS) under uncertainty.

We develop and compare a hierarchy of linear optimization models that differ in their degree of anticipativity and feedback complexity, ranging from a rule-based self-consumption heuristic to fully stochastic formulations with linear feedback control.

The proposed models explicitly incorporate the stochastic variability of household load, solar production, and grid carbon intensity through large scenario sets generated via principal component analysis of real operational data.

Computational experiments on synthetic yet realistic scenarios show that direct stochastic optimization of expected emissions (Programmed Battery model) substantially outperforms heuristic control, achieving emission reductions close to the theoretical lower bound provided by the Omniscient Battery benchmark.

Feedback-based models marginally improve training performance but do not generalize better on unseen data, while incurring higher computational costs.

Overall, results demonstrate that linear stochastic programming provides an effective and tractable framework for emission-aware energy management in distributed PV-BESS systems.