Synthesizing interpreted domain-specific models to manage smart microgrids

The increase in prominence of model-driven software development (MDSD) has placed emphasis on the use of domain-specific modeling languages (DSMLs) during the development process. DSMLs allow for domain concepts to be conceptualized and represented at a high level of abstraction. Currently, most DSML models are converted into high-level languages (HLLs) through a series of model-to-model and/or model-to-text transformations before they are executed. An alternative approach for model execution is the interpretation of models directly without converting them into an HLL. These models are created using interpreted DSMLs (i-DSMLs) and realized using a semantic-rich execution engine or domain-specific virtual machine (DSVM). In this article we present an approach for model synthesis, the first stage of model interpretation, that separates the domain-specific knowledge (DSK) from the model of execution (MoE). Previous work on model synthesis tightly couples the DSK and MoE reducing the ability for implementations of the DSVM to be easily reused in other domains. To illustrate how our approach to model synthesis works for i-DSMLs, we have created MGridML, an i-DSML for energy management in smart microgrids, and an MGridVM prototype, the DSVM for MGridML. We evaluated our approach by performing experiments on the model synthesis aspect of MGridVM and comparing the results to a DSVM from the user-centric communication domain.