The construction of a usable, formal, and extensible modeller and simulator for Smart Energy Grids is of a paramount importance in the industrial settings. Final users are interested in deploying effective smart-home configurations able to satisfy energy requests in the most economical way. Hence, a tool able to forecast both energy consumption and related costs of a smart-home configuration is needed. In this paper, the μGRIMOIRE (micro GRId MOdelling envIRonmEnt) toolset is presented. This tool is based on the well-known model-driven paradigm and its successful applications in the generation of formal/quantitative models for complex systems. By using a Domain Specific Modelling Language, a final user can define a smart-home system configuration and energy saving logics. Then, the tool offers the possibility of evaluating the desired user metrics by translating the model into a Fluid Stochastic Petri Net model representing both discrete and continuous variables.

µGRIMOIRE: A Tool for Smart Micro Grids Modelling and Energy Profiling

Nardone, Roberto
2016-01-01

Abstract

The construction of a usable, formal, and extensible modeller and simulator for Smart Energy Grids is of a paramount importance in the industrial settings. Final users are interested in deploying effective smart-home configurations able to satisfy energy requests in the most economical way. Hence, a tool able to forecast both energy consumption and related costs of a smart-home configuration is needed. In this paper, the μGRIMOIRE (micro GRId MOdelling envIRonmEnt) toolset is presented. This tool is based on the well-known model-driven paradigm and its successful applications in the generation of formal/quantitative models for complex systems. By using a Domain Specific Modelling Language, a final user can define a smart-home system configuration and energy saving logics. Then, the tool offers the possibility of evaluating the desired user metrics by translating the model into a Fluid Stochastic Petri Net model representing both discrete and continuous variables.
2016
Fluid Stochastic Petri Nets, Model-Driven Techniques, Hybrid Systems Design, Energy Consumption Evaluation, Smart Grid Modelling Language
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.12318/58576
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