Enhanced Dynamic Optimization for CO2 Reduction and Cost Savings through Load Shifting in Smart Factories

The previous methodology for optimizing CO2 emissions and electricity costs in industrial applications is extended by integrating dynamic load shifting with battery energy storage. Building on earlier work that employed Mixed-Integer Linear Programming (MILP) to manage a stationary battery based on real-time electricity prices and CO2 intensity signals, two industrial machines and one electric vehicle (EV) are now incorporated as additional shiftable loads. These new elements introduce further operational constraints while enhancing energy management flexibility. The framework employs an adjustable weighting factor λ to balance environmental impact and cost, and comparative analyses across three scenarios—battery-only, load-shifting-only, and combined—demonstrate nearly additive CO2 reductions alongside non-additive cost improvements, underscoring the synergistic potential for environmental benefits despite diminishing cost returns. Moreover, validation against dynamic programming confirms the MILP approach’s accuracy and computational efficiency.