Laboratory infrastructure driven key performance indicator development using the smart grid architecture model

Abstract

This study presents a methodology for collaboratively designing laboratory experiments and developing key performance indicators for the testing and validation of novel power system control architectures in multiple laboratory environments. The contribution makes use of the smart grid architecture model as it facilitates the integration of individually developed control functions into a consolidated solution for laboratory validation and testing. The experimental results obtained across multiple laboratories can be efficiently compared, when the proposed methodology is adopted and thus the study offers means of support for improved cooperation in smart grid validation and round robin testing.

Grid of the future and the need for a decentralised control architecture: the web-of-cells concept

“…In order to systematically evaluate the WoC control schemes in ELECTRA IRP, system modelling and simulation tools as well as realistic, near real-world laboratory conditions are used to prove the system performance. In order to plan, design, implement and compare simulation results and lab experiments from different ELECTRA IRP partners, a Key Performance Indicator (KPI)-based validation methodology is proposed [10]. The goal is to define KPIs (addressing WoC integration and technical issuesan example is provided in Table 1) which can be implemented and measured in corresponding simulation and labbased testing scenarios.…”

Applying the Smart Grid Architecture Model for Designing and Validating System-of-Systems in the Power and Energy Domain: A European Perspective

“…Finally, for the proof-of-concept, the SGAM was used in order to identify suitable laboratories for testing selected control schemes and functions [43]. Therefore, lab capabilities have been mapped to the different SGAM layers in a first step.…”

Smart grid architecture model for control, optimization and data analytics of future power networks with more renewable energy

“…Within the SGAM framework, various aspects of the smart grid have been described e.g. peer to peer energy trading (Zhang et al, 2016), integration of distributed energy resources (McGranaghan et al, 2016), distributed control , laboratory infrastructure (Syed et al, 2017), e-mobility for electric cars (Schuh, Fluhr, Birkmeier and Sund, 2013), power quality (Gustavsson, Hussain and Saleem, 2013), security management (Kammerstetter, Langer, Skopik and Kastner, 2014), distributed automation for distribution networks (Angioni et al, 2017), advanced metering infrastructure (Dänekas and González, 2013), railway energy management (Khayyam et al, 2016), virtual power plant (Etherden, Vyatkin and Bollen, 2016), substation automation (Leonardi, Mathioudakis, Wiesmaier and Zeiger, 2014), wireless communication (López et al, 2014), softwaredefined networking (Molina et al, 2015) etc. However, this paper focuses on establishing the mapping of the components to conduct simulation studies for control, optimization, and data analytics to highlight the concept of SGAM model on a broader context.…”