Algorithm developed for smart grid autonomy during emergencies

Researchers from Greece’s Centre for Research and Technology (CERTH) have developed an algorithm to maximise the autonomy of the smart grid under emergency conditions.

The algorithm, developed by CERTH as part of the EU-funded microgrid project TIGON, prioritises renewable energy sources (RES) and utilises artificial neural networks to provide forecasts related to intermittent RES production.

According to the researchers in their paper, Assessment of smart grid operation under emergency situations, the normal operation of smart grids is challenged by emergencies that require a decision support system that modifies the energy management system accordingly, considering various disconnected components.

Such emergencies include partial or total blackouts and disconnection from the main grid, necessitating tools be developed as part of the wider decision support system that is used by operators. Once such an emergency is detected, the faulty part of the smart grid needs to be disconnected.

With the algorithm, an optimiser is automatically modified for such emergencies, excluding the faulty component from the model, and uses current smart grid measurements as input, including components such as PV and wind generator production, load, state of charge of the battery energy storage system (BESS), etc.

According to the paper, the output of the modified optimal energy management system comprises the optimal decisions regarding the charge/discharge of each BESS, fuel-based production and RES curtailments, if required.

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The algorithm stemmed from the project’s research, which found that an emergency affecting a smart grid’s RES at noon has the potential to cut the grid’s autonomy by 46% and an emergency affecting storage might cause curtailments of up to 25% in RES production.

The assessment of each emergency includes the reduction of autonomy and sustainability of smart grid operation, with respect to curtailments and CO2 emissions, among other factors.

Investigating a variety of cases to highlight the impact of each component’s disconnection on the smart grid at different time intervals, the algorithm was applied on a model of a smart grid in Spain, including PVs, a wind generator, two BESSs, a small diesel generator, residential load and EVs as different components.

According to the research, the loss of PV systems have a higher impact than the loss of a wind generator, as the PV system produces more energy daily, even though the wind generator produces consistently.

However, the most important finding, they add, is that the loss of RES during noon hours, either entirely or partially, affects the post emergency capabilities of the smart grid. Specifically, such emergencies cause the discharge of the BESS systems, which would otherwise be charged with surplus RES.

Additionally, the study states the importance of having a rich mix of energy sources and storage systems, which may fully or partially cover the demand in case one component needs to be disconnected.

It also notes that, regarding CO2 emissions, even though the diesel generator is beneficial for smart grid autonomy, it may be more harmful for the environment in comparison to the energy injected from the main grid if the latter incorporates enough RES production to have lower emissions per kWh.