An Empirical Analysis of Smart Grid Deployment System Models Based on Demand Side Perspective

Smart Grids are electricity networks with two-way power & data flow capabilities. This allows them to measure, actuate & repair grid anomalies arising due to usage variation, short-circuits, and other issues. These grids work using multiple small power producers that utilize solar, wind, and biogas, along with other conventional sources of energy. Due to which these grids are decentralized in nature, and include small-scale transmission & regional supply compensation. Thus, these grids work in both directions (from supply to consumer, and consumer to supply), which is facilitated by active participation of consumers. In order to manage such a complex infrastructure, a wide variety of smart grid deployment models are proposed by researchers over the years. These models vary in terms of grid size, capacity, deployment cost, power efficiency, area of application, etc. Furthermore, these models also vary largely in terms of performance, usability features, and internal working operations. Due to such a wide variation, it is difficult for researchers and grid designers to select the most optimum model(s) for their deployments. In order to reduce the complexity of model selection, this text reviews some of the most recently proposed smart grid deployment models, and discusses their advantages, nuances, limitations and future research scopes. This text majorly focusses smart grid design from a demand side perspective, and also compares the reviewed models in terms of statistical parameters including complexity of deployment, cost of deployment, and power efficiency. This statistical comparison will assist readers to select the most optimum model(s) for context specific use. Moreover, this text also recommends various fusion mechanisms which can be utilized by researchers & grid designers to combine internal working architectures of reviewed models. These fusion models are capable of combining best design practices observed from the reviewed models, and assist in further improving smart grid deployments.