Platform for optimization and sizing of hybrid photovoltaic-battery systems for industrial decarbonization

Abstract

Battery Energy Storage Systems (BESS) have been experiencing accelerated growth worldwide, driven by electrification, the expansion of intermittent renewable energy sources, and the increasing need for operational flexibility in the power sector. In this context, this work develops and implements a computational platform for the sizing and optimization of hybrid photovoltaicbattery systems (PV-BESS), aimed at industrial decarbonization. The methodology is based on a Mixed-Integer Linear Programming (MILP) optimization model, implemented in Python using the Pyomo library. The tool was validated using real data from a medium-sized industry located in the state of Ceará, Brazil, classified as a Group A consumer (high voltage), with an annual electricity consumption of approximately 1.4 GWh. The model simultaneously considers energy, operational, and economic aspects, including capital investment costs (CAPEX), electricity consumption tariffs, grid energy injection/compensation, and peakhour operation. As the main methodological differentiator, the sizing can be conducted according to two distinct operational profile approaches, associated with the user’s desired level of risk aversion. The f irst approach is based on a critical day, using the 95th percentile (P95) to model demand and restricting photovoltaic generation to the 10th percentile (P10) of historical solar irradiance, with an additional safety margin, resulting in a solution oriented toward maximum operational robustness. The second approach uses average hourly load and irradiance profiles, optimizing the project for typical day-to-day operating conditions while reducing implementation costs. For the analyzed case study, the sizing based on the average profile resulted in an 800 kW photovoltaic system combined with a 3,916 kWh BESS, providing 92.7% self-sufficiency and an estimated reduction of 608.37 tons of CO2 avoided annually. Additionally, the comparison between the “critical day” and “average day” strategies highlighted the tradeoff between operational robustness, energy resilience, and economic viability, reinforcing the practical applicability of the tool for energy transition and industrial decarbonization projects.