Scalability Considerations in the Design of Microgrids to Support Socioeconomic Development in Rural Communities

Abstract

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.