Cascaded Optimization of PV and Battery Sizing Under Dynamic Cost and CO Signals

This paper extends a previously developed biobjective Mixed-Integer Linear Programming (MILP) methodology for reducing electricity costs and CO2 emissions in Smart-E-Factory applications. While the earlier approach assumed fixed photovoltaic (PV) and battery capacities, we now propose a cascaded optimization framework to determine the optimal sizing (power rating and orientation of the PV system, battery capacity) while simultaneously optimizing battery dispatch. The cost function combines operational costs with amortized investment costs of both PV and battery systems, embedded in a dynamic scheduling optimization that addresses real-time electricity price and CO2 signals. Numerical results indicate that intermediate capacities and balanced east/west orientation maximize cost-effectiveness and emission reductions. This study underscores the value of coupling parametric design and dispatch optimization to achieve scalable, sustainable solutions for industrial energy systems.