Integrating AHP and Fuzzy TOPSIS for Adaptive Early-Stage Microgrid Resource Selection

Abstract

Secure energy is vital for the operation of all industries and sectors, particularly as there is increased reliance on technology. Microgrids are gaining attention for their islanding ability and use of diversified generation and storage resources. Many researchers have focused on optimal methods of selecting these resources, with multicriteria decision making (MCDM) models as one such category to handle diverse criteria. In this paper, a novel decision support framework is introduced using an analytical hierarchy process (AHP) and Fuzzy technique for order of preference by similarity to ideal solution (TOPSIS) that is adaptable to user-selected mission types and climates. This framework provides ranks and scores for three chosen user configurations at the overall and subcriteria levels. The results demonstrate that an accessible, adjustable, and dynamic framework tailored to mission type and climate can provide valuable insight for customized microgrid design. This framework lays the groundwork for further expansion and optimization, while offering practical early-stage analysis in its current implementation. Ultimately, this framework encourages decision makers to consider more diverse distributed energy resources (DERs) and to fortify their energy landscape using microgrid designs.