Decision support for strategic microgrid design integrating governance, business, intelligence, communication, and physical perspectives

Microgrids are custom-designed, but their extensive design options hinder their dissemination. Consequently, microgrid-interested parties need strategic support to identify suitable design options for their use case. This paper develops a decision support artifact in the form of a decision tree for recommending the most suitable microgrid design for a project. A multi-step design science-oriented process was used. First, a morphological analysis of academic literature was conducted to deduce all possible microgrid design options and visualize them in a morphological box. Once done, 62 real-world microgrids of diverse types and locations were classified according to the morphological box. The produced dataset was used to derive five microgrid design archetypes algorithmically using cluster analysis. The dataset and their associated archetypes were then fed into a rule-mining algorithm to generate a decision tree that recommends the appropriate microgrid archetype based on up to four questions about, for instance, the microgrid's objective and the connectivity to the main grid. Furthermore, design principles for each microgrid archetype recommendation were formulated. The developed design artifact serves as applicable knowledge and a benchmark framework for researchers, as well as a comprehensive and simultaneously simplified decision-making framework for practitioners.