# Reducing Detrimental Communication Failure Impacts in Microgrids by Using Deep Learning Techniques
## 6 Proposed EMS and ESS Architecture
### The Benefit of the Utilization of Forecast Data in the Case of a Transmission Link Loss  
This study started with the conceptualization of MG communication systems, followed by demonstrating link loss effects on the performance of a typical grid-connected MG in terms of PCC power exchange. After that, a possible solution was proposed that was equipping the EMS with predicted values of consumption and generation. It was demonstrated how these predictions can be achieved by using DL techniques and after that, by transmission latency analysis it was proven that enhanced messages with a larger payload will not considerably affect the latency. Finally, a proposed EMS and ESS structure was proposed to create and manage enhanced messages to mitigate link-loss failures earlier in Section 6. In this part, the attempt is to show by using the proposed architecture how the effects of transmission link-loss can be mitigated in a quantifiable manner. For this purpose, the short term load/ PV-generation forecasting results from Sections 4.2 and 4.3 were employed and  
Figure 12: Control structure of grid-feeding converters. a comparison of MG power performance in normal and faulty (communication link-losses) situations is conducted. In other words, the power exchange pattern with the grid at the PCC is analyzed when the ESS is operating with accurate EMS power-references originated from real-time measurements or when the EMS calculations are based on forecasted values. In order to achieve this goal, one-hour of load and PV-generation values with 5-min resolution of the dataset used for experiments in Section 3.1 is selected and the differences between consumption and generation values of the same time steps are calculated. By this the internal power imbalance can be worked out. This process has also been conducted for the forecasted values of the same time steps that are calculated by the methods explained in Sections 4.2 and 4.3. The aforementioned calculated values represent the ESS power references assuming that the instantaneous ESS-SoC value permits for a valid charge/discharge operation. The results are provided in Figure 13. In this selected time horizon, since the PV-generation values are consistently larger than the consumption there is power excess inside the MG which is represented by the black curve in the figure. On the other hand, it can be observed that the forecast curve follows the pattern of the actual values in an acceptable manner. For this time period the total internal energy imbalance inside the MG is (0.4367 kWh) which means if an operational ESS is present in the system, (0.4367 kWh) energy will be stored while using accurate Prefs. This is only possible if an operational communication link between the EMS and ESS exists. Needless to say, if forecasted values are used in the case of communication link loss, the ESS cannot compensate fully for the internal power imbalance at every time-interval and power exchanges with the main grid are necessary. In the case of this test, using the forecast values by the ESS results to the storage of (0.3742 kWh) of the aforementioned energy value, which means even if some is lost (injected into the grid) still the result is much better compared to a situation where all of it is exported in the case of an internal imbalance when the transmission link is down. This is the situation indicated by red arrows in our aforementioned architecture, Figure 11.  
In addition, similar results are also provided with actual and forecast data for two and four time periods and the results are illustrated in Figures 14 and 15. It should be noted that, in the case of these tests, the LSTM-RNN network state was updated by using forecasted values at each time step (as explained in Section 4.2) so errors will accumulate when the time horizon is expanding.