Smart City Gnosys
Smart city article details
| Title | Optimization Classification And Techniques Of Wsns In Smart Grid |
|---|---|
| ID_Doc | 40586 |
| Authors | Naeem M.; Iqbal M.; Anpalagan A.; Ahmad A.; Obaidat M.S. |
| Year | 2016 |
| Published | Smart Cities and Homes: Key Enabling Technologies |
| DOI | http://dx.doi.org/10.1016/B978-0-12-803454-5.00015-8 |
| Abstract | The emergence of smart city that is enabled primarily through the use of communication and networking technologies with sensors requires better use of available resources. Smart grid is one such application that helps monitor, control, and make efficient use of power in a smart city. However, due to the interconnected nature of the smart grid with other technologies, it becomes a challenge to design a fully optimized wireless sensor network (WSN) for smart grid. Wireless sensors and actuators are major components in smart grid. These sensing devices are used for real-time detection and monitoring of smart grid operations. One important role of WSN is real-time identification of outages in the smart grid based power system. An efficient deployment and optimized operational framework is required for WSN in smart grid to reduce the cost, increase reliability, security, and smooth working of the next generation power systems/networks.In this chapter, we present the optimization classification of WSNs for smart grids with respect to different types of renewable energy sources, different modes of operations, types of optimization, and different geographical areas. We present a general optimization framework for WSN in smart grid and will specify different possibilities for input, output, objective function, and constraints. In addition, we investigate different objectives used in defining the optimization problems. We also present different types of linear and nonlinear optimization algorithms used to solve these types of optimization problems in WSN-assisted smart-grid applications. Finally, we will discuss future directions and research challenges related to WSNs for smart grid applications. © 2016 Elsevier Inc. |
| Author Keywords | Optimization classification; Simulation tools for smart grid; Smart grid optimization; Wireless sensor network |
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