A Framework for Improving Rural Microgrid Sustainability Through Integrated Socio-technical Considerations

With the advancement of the engineering science there are increased capabilities for engineering models that consider the complex relationships among multidisciplinary phenomena. Cyber-physical systems are a technology that can be implemented in model-based design. An example of these systems can be found in smart microgrids that are now being installed in rural villages. The inability to regulate microgrid power often leads to power losses. Moreover, these losses have a deleterious effect on the quality of life, and hence, the progress that rural electrification aims to promote. In this paper, we present a computational framework for integrating quality of life and power management to promote sustainability in this cyber-physical system. Using the framework, we elucidate quantifiable relationships that exist between these domains in the context of sustainable rural electrification. In the context of power management, we achieve the same by balancing supply and demand. User demand is examined and related to quality of life. This is realized by identifying the roles of powered devices in daily life. Our main contribution in this paper is a framework to incorporate quality of life in power management for a rural microgrid. The foundational mathematical construct for decision support in the framework is the compromise decision support problem (cDSP). The cDSP is a mathematical construct used to formulate decision support problems. The cDSP is executed for different scenarios and the solution space is explored to identify satisficing solutions. In this paper, we demonstrate the utility of the framework and design constructs presented using a rural microgrid design problem. Our focus in this problem is to balance energy loads and battery storage. The key functionalities of the framework tested are the flexibility and adaptability, both of which are crucial in creating sustainable solutions. We are interested in understanding the quality of life through a system dynamics perspective, exploring multi-resource dependent allocation problems, and developing models to enhance the framework established in the current paper.