## 5 Results
### Industry Devices Optimization, SESS, PV, and IAS Forecasts  
The optimization of the industrial energy assets that are not directly related to the EV's operation are executed using the IAO, DT-IAS, DT-PV, and DT-SESS. These tools enable real-time monitoring, predictive forecasting and operational decision-making, improving cost-effectiveness and minimizing environmental impact. These three DTs are updated every 10 s, extracting real-time data from the database to run the models and refresh their key outputs. Also, following a Rolling Horizon scheme, the IAO optimizer is executed periodically to generate control instructions for the SESS. The sequence of the results to validate the integration of the systems is shown below. To execute the IAO, forecasts for the next 24 h of industrial energy consumption, PV generation, and electricity prices are required. These forecasts are provided by the DT-IAS and DT-PV models. Further details regarding the energy price forecasting methodology can be found in [38]. Additionally, it is important to note that the IAO also requires the aggregated power demand from EV chargers. Therefore, if the fleet charging scheduling is optimized, its execution is carried out after the EVO. The results produced by the DT-IAS and the associated forecasting algorithm are presented in Figure 6.  
Figure 6: Results of DT-IAS and forecast of the consumption profile. Comparison between forecasted and real consumption patterns. The DT-IAS captures and analyzes historical consumption data from the industrial facility. Figure 6 illustrates the results of an execution of this algorithm. The data for the last seven days is successfully extracted using the API, and the forecast algorithm is then executed. The consumption data for the next 24 h, predicted by the ARIMA model, follow a similar trend to the actual consumption data obtained, with the forecast data being within a 95% confidence interval. The industrial consumption forecast will be subsequently aggregated with the power demand from electric vehicle chargers, which are not included in the initial prediction. Once the data for the next 24 h has been obtained and the power demand due to EV charging is known, the necessary conditions for executing the IAO are met. Figure 7 shows the input data for the two study cases, the left image being the baseline consumption scenario and the right image the EV charging optimization scenario. The PV generation forecast is effectively generated by the DT-PV and the forecast tools. In the baseline consumption scenario, where the EV charging scheduling is not optimized, the generation power exceeds the consumption during the hours of maximum irradiance. In contrast, in the EV charging optimization scenario where the EVO is used, the industry consumption covers a higher power demand during the hours of solar generation. With all this data, the IAO is then executed to optimize the power demand of the SESS, using as prioritization variables an economic prioritization \(\alpha=0.75\) and an environmental impact prioritization \(\beta=0.25\), simulating a company that is balancing financial constraints with social responsibility objectives [38]. The performance of the IAO is illustrated in Figure 8, where the two distinct industrial energy consumption scenarios are plotted. The left plot corresponds to a case where the industrial load profile is predominantly nocturnal, while the right plot depicts a scenario with a more diurnal demand pattern, influenced by the use of EVO. In the baseline case, the industry optimizer effectively schedules the charging of the second-life battery during periods of low electricity prices and high PV generation, particularly in the midday hours. This stored energy is then discharged during the evening and night to meet the industry's demand, reducing reliance on grid purchases when electricity prices are elevated. The energy sold to the grid is high in this case due to the surpluses generated during the day, as energy consumption is shifted to nighttime periods.