Assessment of smart grid operation under emergency situations

Smart grids constitute a major trend of electrical networks, the operation of which is underpinned by innovative optimization algorithms. Yet, sometimes, their normal operation is challenged by emergencies that require a Decision Support System (DSS) that modifies the Energy Management System (EMS) accordingly, taking into account the disconnected parts. The purpose of this research is to assess the impact of emergencies on smart grids through a novel optimization algorithm. The algorithm comprises an optimizer, which maximizes the autonomy of the smart grid, prioritizing its Renewable Energy Sources (RES), and Artificial Neural Networks (ANN), which provide forecasts related to the intermittent RES production. The assessment of each emergency includes the reduction of the grid's autonomous and sustainable operation, the increase of curtailments, CO2 emissions, etc. The algorithm is applied on a model of an actual smart grid in Spain, investigating a variety of cases. According to the results, an emergency affecting the smart grid's RES during noon might cause up to 46 % reduction of its autonomy, which, in this case, means 31 kWh of remaining autonomy, and an emergency affecting the storage might cause curtailments up to 25 % of RES production, in this case equal to 35 kWh of curtailed energy.