# NFV-Enabled Efficient Renewable and Non-Renewable Energy Management: Requirements and Algorithms
## 9 Evaluation
### Case Studies  
The strategies are evaluated in six case studies, which are summarized in Table 5. These cases studies correspond to small and large-scale scenarios. The differentiation in the scenario sizes is given by the value of \(N\), because this parameter has a direct impact on complexity, as discussed in Section 5.3 (Equation (30)) and Section 7.2 (Equation (33)). Thus, case studies with \(N>20\) services are categorized as large-scale scenarios; otherwise, they are classified as small scenarios. The first four cases in Table 5 correspond to the small-scale scenarios in which both OptTs and FastTs are evaluated. The fifth case in Table 5 is used to analyze FastTs and the modification of partitions in large-scale scenarios. Finally, although case F is a small-scale scenario, only the evaluation of FastTs is carried out due to the number of services and \(T_{s}^{k}\) that is not feasible for OptTs. In this regard, the consumption information for the HEMS scenario of case F is adapted from [33] and detailed in Table 6.  
For the case F, the priorities of the energy demands are detailed in Table 6, for the rest of cases there is a single priority level (i.e., \(l=1\)). Additionally, for all cases, \(W\) is given by Equation (19). In the simulations, four generation and consumption profiles are used. Figure 12 illustrates the profiles used in the evaluation of FastTs in HEMS, the rest of profiles are depicted in Figure 13. Although in Figure 13, for simplicity in the analysis, a flat-energy profile is used, this does not mean a restriction for algorithmic strategies, which are capable of working with any demand and supply profile. The selected profiles allow for the analysis of the operation of the strategies in: (i) shortage power states, due to a high concentration of demands, as shown in Figure 13a, where the time-shifting of some demands enable full energy utilization; (ii) periodic transitions between periods of scarcity and abundance of power, as shown in Figure 13b, where energy is only harnessed if all demands are shifted in time; (iii) more realistic situations where the services have different (random) values of \(P_{d}^{k}\) and \(T_{d}^{k}\), as depicted in Figure 13c; and, (iv) consumption peaks due to efficient load distribution and that  
\begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline
**Scenario** & \(P_{ES}\) & \(T_{wait}^{P_{HS}}\) & \(m\) & \(N\) & \(T_{MH}^{k}\) & \(T_{d}^{k}\) & \(P_{d}^{k}\) \\ \hline Case A: Demands & 3 constant & \(3\), 2, 1, for & given by & & & & \\ within \(P_{ES}\) & within \(m\) & \(T_{d}^{k}=1,2,3\) & Equation (26) & 9 & \(4,\forall\text{S}_{k}\) & 1–3 & \(1,\forall\text{S}_{k}\) \\ \hline Case B: Demands & 2 constant & \(T_{d}^{k}\) & \(\begin{matrix}\text{given by}\\ \textproduce an extra effort in generation as shown in Figure 11(a). The simulations have been repeated 50 times, when considering a confidence interval of 95%, in order to ensure the quality and stability of results for the random scenarios.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Load Description** & **Demanded Power [W]** & **Quantity** & \(I\) & \(T_{init}^{k}\) & \(T_{d}^{k}\) \\ \hline Freezer & 210 & 1 & 1 & 0 & 24 \\ Refrigerator & 650 & 1 & 1 & 0 & 24 \\ Oven & 1800 & 1 & 3 & 16 & 3 \\ Lighting & 25 & 9 & 1 & 17 & 7 \\ TV & 140 & 1 & 1 & 18 & 5 \\ Laptop & 90 & 1 & 1 & 17 & 5 \\ PC & 140 & 1 & 1 & 18 & 6 \\ Vacuum Cleaner & 600 & 1 & 1 & 19 & 3 \\ Water Heater & 2000 & 1 & 2 & 17 & 6 \\ Air-Conditioner & 1280 & 1 & 2 & 14 & 7 \\ Washing Machine & 1350 & 1 & 3 & 19 & 3 \\ Dishwasher & 1250 & 1 & 3 & 18 & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Description of Home Energy Management Systems (HEMS) scenario that corresponds to the Case F in Table 5.  
Figure 12: Performance evaluation of FastTs in the HEMS scenario. Parameters: \(N=20\) and \(numPart=2\), the rest of parameters according to Case F in Table 5.