In modern distribution grids, the access to the growing amount of data from various sources, the execution of complex algorithms on-demand, and the control of sparse actuators require on-demand scalability to support fluctuating workloads. Cloud computing technologies represent a viable solution for these requirements. To ensure that data can be exchanged and shared efficiently, as well as the full achievement of the cloud computing benefits to support the advanced analytic and mining required in smart grids, applications can be empowered with semantic information integration. This paper adopts the semantic web into a cloud-based platform to analyse power distribution grids data and apply a service restoration application to re-energize loads after an electrical fault. The exemplary implementation of the demo is powered by FIWARE, which is based on open-source and customizable building blocks for future internet applications and services, and the SARGON ontology for the energy domain. The tests are deployed by integrating the semantic information, based on the IEC 61850 data model, in the cloud-based service restoration application and interfacing the field devices of the distribution grids. The platform performances, measured as network latency and computation time, ensures the feasibility of the proposed solution, constituting a reference for the next deployments of smart energy platforms.
With the increasing penetration of DC based devices and the improving performance of power electronics converters, more and more advantages foster the deployment of hybrid AC-DC distribution grids. Although the operating control of AC-DC grids is consistently studied in the literature, several protection aspects need further analysis. This paper focuses on the reliability of AC-DC distribution grids, proposing a Service Restoration (SR) algorithm to efficiently re-energize the disconnected nodes in case of electrical faults. To determine the SR solution, the algorithm inspects multiple criteria together: the number of reconnected nodes, the minimization of power losses, the presence of telecontrolled switches, the criticality of the nodes facilities and the restoration feasibility according to power forecasts. The evaluation of reconfiguration topologies is based on the operational limits of AC-DC converters and energy sources; additionally, the power transfer is controlled by setting as optimization objective the losses minimization. The conducted tests, considering different load and generation configurations, prove the applicability of the proposed algorithm in evaluating the feasibility of reconfiguration topologies and computing the near-optimal SR solution.
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