## 5 Results
### Industry Devices Optimization, SESS, PV, and IAS Forecasts
In the EV charging optimization scenario, the IAO optimizer adjusts its strategy accordingly. The SESS is charged in the early morning and, to a lesser extent, during peak PV generation periods. In this case, energy surpluses are lower due to the use of EVO, which allows the electric trucks to be charged during periods of higher solar generation. This strategy improves energy utilization efficiency, leading to an operational cost reduction greater than 6% and a decrease in environmental impact of over 10%. Figure 7: Main forecasted inputs required for the IAO execution. Blue lines (**left axis**) represent power-related variables, while the orange line (**right axis**) reflects the estimated electricity price. (**Left plot**) Inputs for the baseline consumption scenario. (**Right plot**) Inputs for the EV charging optimization scenario. After obtaining the SESS power profile, it is provided to the internal controller in the industry. The measured battery data is then used to run the DT-SESS periodically to extract the battery status. Since the results of the algorithms are exposed in a simulation environment, the IAO output power is used as the measured battery power. Figure 9 shows the results of the DT-SESS, monitoring the power and state of charge of the battery during the simulation period. It is observed that the battery is maintained within safety margins between a maximum and minimum SOC. In addition, by incorporating the battery degradation costs, the battery usage is optimized by minimizing the number of cycles to reduce degradation [38]. To complete the analysis, the economic and environmental performance of the IAO optimizer is evaluated across different scenarios. The results are compared under two configurations: one considering the presence of the SESS and another without it. Obtaining the resulting value of the monetary cost function (\(J_{ec}\)) and the environmental cost function (\(J_{en}\)) after the subsequent IAO execution allows direct quantification of the monetary costs and CO\({}_{2}\) equivalent emissions linked to the energy exchange strategy. In general, using an efficient management of the industry's consumption increases the savings by 6.66% using the SESS with the IAO optimization program, and savings of 6.99% in the case of not using a battery. Similarly, using an efficient management of industry consumption reduces the environmental impact by 10.95% using the SESS with the IAO optimization program and by 10.33% in the case of not using a battery. Figure 8: Main results and forecasts from IAO. Green lines (**left axis**) represent energy-related variables, while orange line (**right axis**) reflects the estimated electricity price. (**Left plot**): Outputs for the baseline scenario. (**Right plot**): Outputs for the EV charging optimization scenario. Figure 9: **Left axis**: SESS power setpoint provided by the IAO. **Right axis**: State of charge calculation of the DT–SESS. In the baseline consumption scenario, the use of the IAO with the second-life battery resulted in a total energy cost of EUR 269.70/day, compared to EUR 273.24/day without the battery, representing a daily cost reduction of EUR 3.54, equivalent to 1.30% savings. From an environmental perspective, however, CO\({}_{2}\) emissions slightly increased from 468.03 kgCO\({}_{2}\)/day (without battery) to 476.72 kgCO\({}_{2}\)/day (with battery), corresponding to a 1.85% increase. In the EV charging optimization scenario, the total daily cost was reduced from EUR 254.13/day (without battery) to EUR 251.72/day (with battery), a EUR 2.41 reduction (0.95% savings). Similarly, emissions increased slightly with battery usage, from 419.65 kgCO\({}_{2}\)/day to 424.51 kgCO\({}_{2}\)/day, a 1.16% rise.