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On the improvement of the reliability of the restoration processes of power grids

The grids that are examined under the SDN-microSENSE include both low voltage and high voltage networks with real time monitoring capabilities that include power generation from DERs such as photovoltaics and wind turbines. The issue with such grid networks is that the optimization of their restoration planning process requires knowledge of actual consumption patterns and fluctuations in the generation. These fluctuations are usually caused by massively deployed photovoltaic sources. In order to address these issues, the distributed system operators will need to know i)the unused capacity that can be used for new devices that either consume or produce, ii)the available service that can be provided to customers who attach a new consumption or production device to the grid, iii) the bottlenecks that may be caused by electric vehicle charging at specific nodes. Finally, it is worth mentioning that besides the aspects of planning and monitoring, electric vehicles and photovoltaic systems require energy scheduling and control on the operational level [1].

The improvement of the reliability of such grid networks is directly dependent to the enhancement of their operation flexibility options. The main goal of such flexibility options is to create power reserves that will be able to be activated when needed according to the demands of the network during certain time periods, or critical events (i.e. during the restoration process) within the day. The majority of such flexibility options are located in active distribution systems [2], thus, the collaboration between the distribution system operators and the transmission system operator is necessary for the management of these flexible resources. This cooperation, along with the supportive services that the distribution system operators can provide to the transmission system operator can be characterized in terms of active or reactive power reserves of an active distribution system. Additionally, these supportive services can also include participation in unforeseen overload congestion management, voltage profile support (by dispatching the reserves of reactive power of active distribution systems) or enhancement of insufficient spinning reserves to face N-1 emergencies of conventional generators [3].

Another aspect for consideration which is relevant to power grids which SDN-microSENSE is examining is the management of the generated disturbances in the network due to the photovoltaic sources. The efficient management of such sources can be crucial for the optimization of the restoration processes, thus, smart control mechanisms are essential. Guillermo Domínguez-López et al. proposes in [4] a microgrid manager that was tested in a pilot experiment in “la Graciosa’’ island of the Canary Islands. This microgrid manager consists of a microgrid optimizer and a supervisory control and data acquisition (SCADA) application. The microgrid optimizer is used for the management of the existing flexible assets on the grid in order to enhance its reliability. The optimizer runs in a 15-minute step and produces an optimization plan of 24 hours. The SCADA application enables both local and remote system management and collects data from the devices in the grid in real-time. This allows the distribution system operators to detect grid events (such as anomalies that may lead to malfunctions and/or blackouts) at distribution level. Finally yet importantly, the proposed SCADA enables the remote operation of DERs and hybrid energy storage systems by allowing turn on, turn off or reset functionalities in case of malfunctions during real-time management. This micromanagement strategy can be beneficial both for blackout prevention as it can isolate problematic devices, and for the post-blackout restoration process as it can allow efficient planning and prioritization for the power distribution of the recovering grid [5].


[1] “Groenbaek, J., Bessler, S., Wallentin, L., & Sitter, H. (2013). Use of available power in the LV grid for energy balancing. 2013 IEEE Grenoble Conference PowerTech, POWERTECH 2013.”.
[2] “A. Keane, L. Ochoa, C. Borges, G. Ault, A. Alarcon-Rodriguez, R. Currie, F. Pilo, C. Dent, and G. Harrison, State-of-the-Art Techniques and Challenges Ahead for Distributed Generation Planning and Optimization, IEEE Trans. Pow. Syst., vol. 28, no. 2, 2013”.
[3] “Capitanescu, F. (2018). OPF integrating distribution systems flexibility for TSO real-time active power balance management. IET Conference Publications, 2018(CP759).”.
[4] “Domínguez-López, G., Paradell-Solà, P., Domínguez-García, J. L., Rodríguez-Rivero, J., & Sánchez-Cifuentes, J. (2018). Demonstration of a microgrid manager in a distribution grid with high PV penetration and Storage: La Graciosa case study. E3S Web of Con”.
[5] “Alharbi, T., & Bhattacharya, K. (2017). Optimal Scheduling of Energy Resources and Management of Loads in Isolated/Islanded Microgrids Planification optimale des ressources énergétiques et gestion des charges dans les micro-réseaux isolés/insulaires. Cana”.