# Reducing Detrimental Communication Failure Impacts in Microgrids by Using Deep Learning Techniques
## 1 Introduction  
The idea of localizing energy generation and consumption is the backbone of microgrid (MG) philosophy. In case of grid-connected MGs, the localization concept can be beneficial for both urban and rural power-systems by reducing energy exchange with the main power-lines while utilizing more renewable energy resources. Minimizing power exchange with the grid during peaks demand or supply results in important benefits [1]:  
* Preventing possible and harmful over-voltage problems especially in weak-grids;
* Avoiding over-designed power-lines due to internal power balancing of the units;
* Improving grid's power quality and contributing to better grid stability. However, due to the inevitable occurrence of internal energy imbalances caused by generation and consumption mismatches, power exchanges with the grid at the point of common coupling (PCC) is unavoidable. Therefore, in most MG architectures energy storage systems (ESS) are included to compensate for power imbalance. These ESSs are controlled by energy management systems (EMS) that regulate the power flow optimally. To achieve a valid operation of such a power system, communication links between these different energy players are required. These links transmit power measurements from different generation/consumption stations and the ESS charge status to the EMS where the calculations are conducted and proper power references for the ESS are set and transmitted [2]. The system's normal functionality is hindered when these links malfunction. As an idealistic prospect, the future power network can be formed by clusters of independent MGs that are able to individually localize their generation and consumptionby design. These MG units will be able to interoperate and exchange energy when excess in one MG can balance a deficit in another. This, inevitably, requires energy awareness between neighboring units through communication links. In other words, the role of communication is not limited to be within an MG and between the individual devices but it also plays a much wider role [3; 4]. In an MG structure, if the communication links fail due to either unintentional reasons such as natural disasters, or intentional purposes such as cyber attacks, the effects are not just limited to poor power regulation. In fact, it can contribute to severe problems such as MG bus voltage and frequency deviation from their reference values or milder effects such as losing the optimal energy balance of the system. In any case, normal operation is no longer possible without an operational communication infrastructure [5]. The communication link loss resulting complications can be mitigated if control structures of power converters that interface the units (for instance an ESS) with the MG buses are able to estimate their operational set-points for the next time steps. Specifically in this case, if consumption and generation forecasting is available for a certain time horizon the future ESS power reference can be calculated by the EMS and transmitted to its controller in advance. These redundant information will be stored on a memory buffer at the controller side and be utilized in case of a communication link loss occurs. This criteria is relatively simple if the generation or consumption follow a known and predictable pattern. For example, in the vehicle-to-grid (V2G) system described in [6] the generation and load are based on an electric train's acceleration and breaking due to arrivals and departures which are precisely scheduled. By contrast, in the type of power-systems that we are interested in this paper, such as residential MGs, it is a quite different story: forecasting the generation that is based on renewable energy supply, which is volatile in nature, or the consumption, that can largely vary based on the users' decisions, is not a trivial problem. However, there are techniques that have proved that even in these circumstances the generation and consumption can be forecasted for a short-term time horizons (one hour with time steps in terms of minutes).