Dynamic Optimization of CO₂ Emissions and Electricity Costs in Smart Factories

This paper presents a robust methodology for optimizing CO<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> emissions and electricity costs in industrial applications, with the aim of developing a flexible and dynamic energy management strategy that balances sustainability and cost-efficiency. Addressing the growing need for sustainable and economically viable energy solutions amidst the global urgency of climate change mitigation, the proposed approach is based on dynamic energy management techniques that minimize dependence on grid electricity, which can fluctuate between energy import and export. A flexible cost function is developed to simultaneously account for CO<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> emissions and electricity prices, enabling a balance between environmental impact and operational costs. The optimization framework employs Mixed-Integer Linear Programming (MILP) to derive the optimal energy management strategy, showcasing significant potential for reducing both CO<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> emissions and electricity costs. Although the methodology is demonstrated in a specific industrial setting, its flexible design ensures applicability across various energy profiles and operational scenarios, making it relevant for a wide range of industrial applications.