Looking for the unified classification and evaluation approach of SG interface standards for the purposes of ELECTRA IRP

Frascella, Angelo; Świderski, Jacek; Proserpio, Gianluigi; Rikos, Evangelos; Temiz, Armağan; Babś, A.; Uslar, Mathias; Branchetti, Samuele; Graditi, Giorgio

doi:10.1109/sedst.2015.7315228

Cited by 3 publications

(1 citation statement)

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“…One very promising method to increase the efficiency is a detailed use case and requirements engineering. This trend can be observed in a number of recent smart grid projects where use case descriptions of corresponding applications (e.g., voltage control, energy management, demand-response) are in focus [6,7]. Different methodologies and standards exist today for describing such use cases.…”

Section: Introductionmentioning

confidence: 99%

Engineering Smart Grids: Applying Model-Driven Development from Use Case Design to Deployment

2017

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Abstract:The rollout of smart grid solutions has already started and new methods are deployed to the power systems of today. However, complexity is still increasing as focus is moving from a single system, to a system of systems perspective. The results are increasing engineering efforts and escalating costs. For this reason, new and automated engineering methods are necessary. This paper addresses these needs with a rapid engineering methodology that covers the overall engineering process for smart grid applications-from use case design to deployment. Based on a model-driven development approach, the methodology consists of three main parts: use case modeling, code generation, and deployment. A domain-specific language is introduced supporting the use case design according to the Smart Grid Architecture Model. It is combined with the IEC 61499 distributed control model to improve the function layer design. After a completed use case design, executable code and communication configurations (e.g., IEC 61850) are generated and deployed onto compatible field devices. This paper covers the proposed rapid engineering methodology and a corresponding prototypical implementation which is validated in a laboratory experiment. Compared to other methods the proposed methodology decreases the number of engineering steps and reduces the use case design and implementation complexity.

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Section: Introductionmentioning

confidence: 99%