## 3 Simulation Results
### Simulation Results
Forecasted voltage profiles, before the application of the LVC. Figure 13: Simulation results. Forecasted voltage profiles, after the application of the LVC. Figure 16: Control action. HEMS connected to node 28. Figure 17: Control action. HEMS connected to node 40. Figure 18: Control action. HEMS connected to node 66. Figure 15: Control action. DSO-owned storage device connected to node 117. As can be seen from Figures 16-21 and Table 5, the algorithm uses only a small amount of the total flexibility provided by the HEMS in order to perform the active management of the LV network. Simulation results for one year, based on the four representative days, estimate that the total flexibility \begin{table}
\begin{tabular}{l c c c c} \hline \hline  & **Contracted** & **Total** & **Flexibility** & **Flexibility** \\  & **Power, [kVa]** & **Flexibility, [kVh]** & **Used, [kWh]** & **Used, [\%]** \\ \hline Node 28 & 3.45 & 16.45 & 0.06 & 0.35\% \\ Node 40 & 4.60 & 22.17 & 0.67 & 3.02\% \\ Node 66 & 6.90 & 33.14 & 0.60 & 1.81\% \\ Node 88 & 4.60 & 22.09 & 1.40 & 6.32\% \\ Node 115 & 3.45 & 16.70 & 1.07 & 6.41\% \\ Node 132 & 6.90 & 33.12 & 1.54 & 4.64\% \\ \hline Total & - & 143.67 & 5.33 & 3.71\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Control action. Flexibility requested to consumers with HEMS. Figure 21: Control action. HEMS connected to node 132. Figure 19: Control action. HEMS connected to node 88. Figure 20: Control action. HEMS connected to node 115.  
required from domestic consumers is 1821.50 kWh, 3.47% of the total flexibility available. From the analysis of Figure 15, it can be seen that the LVC algorithm uses the energy storage device, once the overvoltage is registered at around 11:45, respecting the pre-established merit order. However, since the DSO-owned device is electrically far from the violation node (see Figure A1), it is unable to fully solve the voltage constraint. Once this resource is exhausted, the LVC selects the next DER, in this case the HEMS that is electrically closer to the voltage violation. The process continues until the voltage constraint is solved. From Figure 15 it can also be seen that the LVC algorithm tries to regularize the SoC of the storage device, in order not to jeopardize the availability of the resource for future time instants. To counteract the consumption peak registered at the peak solar production period of the day, the storage device slowly discharges during periods where it does not disturb the normal operation of the network, thus maintaining the SoC level at a safe point for the next control period. For comparison purposes, in Figures 22-24 the control actions required to maintain the voltage magnitude within the limits are presented, if only generation curtailment was considered instead of using available flexibility. As it can be seen from Figures 22-24, a total of three generators would need to be curtailed, in order to maintain the voltage magnitude within the limits. The total amount of energy curtailment required to securely operate the network is 2.42 kWh. With the proposed method, no curtailment is required. The presented results show the potential of the proposed tool to be applied to highly stressed networks, allowing for their secure operation in scenarios of high DG penetration. Furthermore, from the DSO point-of-view, the LVC tool would allow to defer (or even avoid) large investments in network reinforcement and/or DSO-owned DER, such as ES devices, to maintain the secure and efficient operation of the network. Taking into account the current deployment state of AMI, it is reasonable to assume that the proposed solution could be deployed to real networks in the near future. The tool will be demonstrated on a smart grid pilot in Portugal, where the proposed functionalities will be tested in a real testbed to assess the robustness and efficiency of the proposed framework.