## 3 Simulation Results
Figure 16 presents simulation results for 15 August 2023, one of the hottest days of the year. Figure 16 comprises four subfigures illustrating the following: (1) the optimal discharge temperature alongside the intake temperature from the water tank; (2) the total hourly power consumption of the three cooling systems; (3) the hourly electricity cost; and (4) the electricity price on 15 August 2023. The results emphasize that when electricity prices are high, the system reduces cooling intensity to lower power consumption; conversely, during mid-day, when prices decline, the model increases cooling, leading to higher power usage. Although the cost per unit of electricity is low at mid-day, the corresponding increase in power consumption highlights the model's effectiveness in shifting operations from price-agnostic to price-responsive behavior. Ultimately, this flexible strategy yields a total electricity cost of EUR 3245.70, representing a 4.5% reduction compared to the BaU scenario. For a broader daily comparison, Table 4 presents the total electricity costs and  
Figure 15: Total energy consumption and net savings on the 15 th day of each month. percentage savings for both scenarios during the second week of August, a period marked by high seawater temperatures. As shown in Table 4, the proposed method consistently achieves lower operational costs compared to the BaU scenario, particularly on days with elevated water temperatures. Notably, on the first day of the second week of August, after electricity prices reached negative levels, the model strategically increased water cooling to maximize profit. This \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Day** & **Proposed Method (Euro)** & **BAU (Euro)** & **Saving** & **\% of Improvement** \\ \hline
8 August 2023 & \(-187.14\) & \(-168.67\) & \(18.47\) & \(10.95\) \\
9 August 2023 & \(270.26\) & \(289.74\) & \(19.474\) & \(6.72\) \\
10 August 2023 & \(1453.71\) & \(1570.16\) & \(116.45\) & \(7.41\) \\
11 August 2023 & \(2533.28\) & \(2631.00\) & \(97.72\) & \(3.71\) \\
12 August 2023 & \(2157.41\) & \(2188.20\) & \(30.78\) & \(1.40\) \\
13 August 2023 & \(1655.63\) & \(1767.49\) & \(111.85\) & \(6.32\) \\
14 August 2023 & \(2651.10\) & \(2859.40\) & \(208.30\) & \(7.28\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Evaluation of the effectiveness of the various methods in modeling the CO\({}_{2}\) plant. Figure 16: Simulation outputs for 15 August 2023 for the considered use case. clearly demonstrates its ability to operate in a more flexible and cost-effective manner. On other days, the model effectively adjusted the discharge temperature of the water entering the breeding pools, minimizing operational costs while maintaining the required thermal conditions. Overall, implementing the proposed model resulted in a 5.41% reduction in the total operational cost for the week despite limited access to detailed system data. This highlights the value of flexibility, especially during warmer periods. The flexibility analysis also reveals that integrating additional flexible assets within the factory could further reduce operating costs by enhancing the system's responsiveness to electricity prices. Moreover, such flexibility provides technical benefits for grid operators. Specifically, when industrial consumers adapt their behavior in response to dynamic tariff signals, grid operators can better manage demand profiles, mitigate congestion, and improve overall grid reliability. This dual advantage not only delivers financial benefits to industries but also supports the broader objectives of grid stability and efficiency.