A SGAM-Based Test Platform to Develop a Scheme for Wide Area Measurement-Free Monitoring of Smart Grids under High PV Penetration

• Published in: Energies (Basel) 2019-04, Vol.12 (8), p.1417
• Main Authors: Estebsari, Abouzar, Barbierato, Luca, Bahmanyar, Alireza, Bottaccioli, Lorenzo, Macii, Enrico, Patti, Edoardo
• Format: Article
• Language: English
• Subjects: Algorithms; Communication; Communications networks; Computer engineering; Computer simulation; Current distribution; Distributed generation; distribution systems; Electrical engineering; Electricity distribution; Energy; Energy resources; Infrastructure; Innovations; Internet of Things; Interoperability; Measurement; PV penetration; Real time; real time simulation; Security management; Simulation; Simulators; smart grid architecture model; Smart grid technology; Software; State estimation; State variable; wide area monitoring
• ISSN: 1996-1073; 1996-1073
• DOI: 10.3390/en12081417

In order to systematically shift existing control and management paradigms in distribution systems to new interoperable communication supported schemes in smart grids, we need to map newly developed use cases to standard reference models like Smart Grid Architecture Model (SGAM). From the other side, any new use cases should be tested and validated ex-ante before being deployed in the real-world system. Considering various types of actors in smart grids, use cases are usually tested using co-simulation platforms. Currently, there is no efficient co-simulation platform which supports interoperability analysis based on SGAM. In this paper, we present our developed test platform which offers a support to design new use cases based on SGAM. We used this platform to develop a new scheme for wide area monitoring of existing distribution systems under growing penetration of Photovoltaic production. Off-the-shelf solutions of state estimation for wide area monitoring are either used for passive distribution grids or applied to the active networks with wide measurement of distributed generators. Our proposed distribution state estimation algorithm does not require wide area measurements and relies on the data provided by a PV simulator we developed. This practical scheme is tested experimentally on a realistic urban distribution grid. The monitoring results shows a very low error rate of about 1 % by using our PV simulator under high penetration of PV with about 30 % error of load forecast. Using our SGAM-based platform, we could propose and examine an Internet-of-Things-based infrastructure to deploy the use case.