Smart Grid Testing Management Platform (SGTMP)

Schvarcbacher, Martin; Hrabovská, Katarína; Rossi, Bruno; Pitner, Tomáš

doi:10.3390/app8112278

Cited by 13 publications

(4 citation statements)

References 46 publications

(79 reference statements)

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“…Testing in the context of smart grids is extremely complex due to the multi-layer structure of the overall infrastructure [4]. In addition, smart grid testing involves the integration with hardware devices, making use of modeling and simulations highly relevant to discovering integration issues at scale [8], [9]. The testing chain is a series of experiments (from pure simulation to actual field-testing) used to develop a new component reducing the risk of failure or to validate a component with an holistic approach cost effectively.…”

Section: Overview Of the Testing Chain Methodologymentioning

confidence: 99%

Application of a Testing Chain Methodology for Improving Power Converter Controllers

Degefa

Taxt

Pellegrino

et al. 2020

IECON 2020 the 46th Annual Conference of the IEEE Industrial Electronics Society

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The practice of testing power system equipment like converters usually involves simulation, laboratory and field testing in that sequence. Such an approach lacks smooth transition and wide coverage of new services which are being introduced by the smart grid concept. A systematic approach to sequentially test various functionalities may lead to reduced costs and speedup the process from concept to implementation. Testing chain is such an approach where specific capabilities of pure simulation, hardware-in-the-loop, laboratory and field testing are leveraged sequentially to cover wide aspects of the system under investigation. In this study, a test case is exemplary realized aiming to demonstrate the potential of a multi-site testing chain with varied testbeds for generating systematic improvements on the performance of power converter control functions.

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Section: Overview Of the Testing Chain Methodologymentioning

confidence: 99%

Application of a Testing Chain Methodology for Improving Power Converter Controllers

Degefa

Taxt

Pellegrino

et al. 2020

IECON 2020 the 46th Annual Conference of the IEEE Industrial Electronics Society

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“…In our review, we found that 59% of the articles had some form of interaction with hardware devices. We expect that with the explosion of the IoT movement and the large availability of commodity hardware such as Arduinos and Raspberry PIs, such trend will increase, allowing hybrid simulations to take place in this context, as recent research has already shown-Schvarcbacher & Rossi (2017); Schvarcbacher et al (2018), (Nannen et al, 2015).…”

Section: Research Directionsmentioning

confidence: 99%

Smart Grids Co-Simulations: Survey & Research Directions

Mihal,

Schvarcbacher,

Rossi

et al. 2021

Preprint

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The integration of renewable sources, communication and power networks with information and communication technologies is one of the main challenges in Smart Grids (SG) large-scale testing. For this reason, the coupling of simulators is commonly used to dynamically simulate several aspects of the SG infrastructure, in the so-called cosimulations. In this paper, we provide a scoping review of research of co-simulations in the context of Smart Grids: i) research areas and research problems addressed by co-simulations, ii) specific co-simulation aspects focus of research, iii) typical coupling of simulators in co-simulation studies. Based on the results, we discuss research directions of future SG co-simulation research in each of the identified areas.

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“…The complexity of smart grid and smart city architectures brings many challenges and issues, especially when it comes to reliability, resilience, integration and security of the whole infrastructure. Therefore, the approach of software modelling and simulation can be highly beneficial in process of testing of such applications and mechanisms before the actual deployment to ensure proper operation in all scenarios without high costs [38,39]. Data-driven Architecture for Energy-Efficient Smart Cities…”

Section: Simulation Toolmentioning

confidence: 99%

Data-driven framework for energy-efficient smart cities

Petrović

Kocic

2020

Serb J Electr Eng

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Energy management is one of the greatest challenges in smart cities. Moreover, the presence of autonomous vehicles makes this task even more complex. In this paper, we propose a data-driven smart grid framework which aims to make smart cities energy-efficient focusing on two aspects: energy trading and autonomous vehicle charging. The framework leverages deep learning, linear optimization, semantic technology, domain-specific modelling notation, simulation and elements of relay protection. The evaluation of deep learning module together with code generation time and energy distribution cost reduction performed within the simulation environment also presented in this paper are given. According to the results, the achieved energy distribution cost reduction varies and depends from case to case. [Projects of the Serbian Ministry of Education, Science and Technological Development, Grant no. III44006 and Gran no. III47003]

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Smart Grid Testing Management Platform (SGTMP)

Cited by 13 publications

References 46 publications

Application of a Testing Chain Methodology for Improving Power Converter Controllers

Application of a Testing Chain Methodology for Improving Power Converter Controllers

Smart Grids Co-Simulations: Survey & Research Directions

Data-driven framework for energy-efficient smart cities

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