Access to resources can contribute to social progress in extremely impoverished communities. The introduction of cyber-physical systems for electricity, water, and irrigation facilitates greater fulfillment of needs. Yet, the availability of resources may be inconsistent or lacking. The social dynamics of the community can provide insight into how the available resources support well-being. Thus, the cyber-physical system requires the addition of a social consideration to become cyber-physical-social systems. However, the social considerations typically include qualitative parameters. This prompts the need for integrating qualitative and quantitative information. In this paper, we present a method for mathematically representing qualitative and quantitative relationships. This is achieved by connecting Bond Graph Modeling and System Dynamics. The Bond Graph model is used to mathematically represent relationships between qualitative and quantitative elements. These relationships are used in the System Dynamics analysis. The method is anchored in expanding cyber-physical to cyber-physical-social systems through incorporating both qualitative and quantitative information in the systems analysis. The mathematical connectivity of qualitative and quantitative information is a key feature of this approach. A test problem in resource allocation is used to demonstrate the function and flexibility of the method. This is anchored in connecting qualitative and quantitative information in the analysis.

Electrification can act as a catalyst in social progress. In some communities, grid connection is not possible. As such, microgrids are a viable alternative to provide access to electricity. Yet, progress can be impacted by challenges with insufficient energy supply. In such scenarios, it is important to understand the relationships between electricity supply and social development in managing available resources. We propose a framework to relate quality of life with power management, such that progress is not hindered when available energy is insufficient. In this paper, electrical loads for pumping water, powering streetlights, and powering household devices are examined. A compromise decision support problem (cDSP) is developed to balance the produced and consumed energy. We develop a set of power management options by exploring the solution space developed from performing the cDSP, anchored in quality of life. Organizations engaged in sustainable development can select the solution most appropriate for the community. A salient feature of the framework is the versatility. The formulation can be modified for different requirements, communities, and time periods. A test problem is used to illustrate the flexibility of the approach. This framework is constructed to support decision making for microgrid operation to continue to uplift communities.

Microgrid systems can provide distributed power to communities that lack basic access to electricity. Scalability, maintenance, and power management are prominent challenges associated with microgrid resilience. These obstacles can be limiting in the continued development of the community. As progress drives an increase in energy demand, the microgrid needs to continue to meet that demand. Designing systems with the appropriate maintenance levels is another obstacle that impacts resiliency. There are many design tools that analyze system performance and cost. One goal of this work is to review a set of these technical models. HOMER Energy, System Advisor Model (SAM), and the Microgrid Design Toolkit (MDT) were reviewed. However, technical and economic performance is just one aspect of design. In the design procedure, social parameters also need to be considered. Few, if any, microgrid design tools consider societal dynamics. This work also aims to evaluate social indicators in the context of sustainable microgrid design. Accordingly, relationships are established between common societal indicators and the microgrid challenges of scalability, maintenance, and power management.

The interaction between technology and people is characterized by sociotechnical models. In the context of design, these types of systems are analyzed to increase productivity. The level of productivity is expected to increase as the technology evolves. Still, a lack of focus on adaptive design hinders the success of sociotechnical systems. The problem is evident in the relationship between microgrid technology and the residents of developing communities. An analysis of this type of sociotechnical system is analyzed in this paper. Rural villages in the developing world often lack access to the power grid. However, microgrids can provide electrical power in these locations. Power can be harnessed from renewable resources such as wind, solar, geothermal, and hydropower. Large batteries are used to store energy and buffer the electrical supply with the demand. The system powers security lighting, water pumps, and purification systems. Microgrids also power small machines that sustain agriculture in developing communities. The access to energy uplifts the developing community socially and economically. Still, as the community evolves, energy demand increases and the microgrid is unable to provide sufficient energy. A challenge in microgrid design involves the scalability of the system. Currently, there is no method for adapting the microgrid system to the increases in demand that occur over time. Accordingly, a mathematical framework is needed to support design decisions that could otherwise support adaptability. A demand model to predict the energy use for a composite rural village is presented. The predicted demand requirements are configured using a design optimization simulation model. These configurations are studied, and adaptive design techniques are devised through the process. The outcome of this study identifies a basic design methodology for microgrid design that is cognizant of scalability. Moreover, it identifies key attributes and relationships for the mathematical framework that supports the overarching goal of adaptable design.