Spatiotemporal D2D Small Cell Allocation and On-Demand Deployment for Microgrids

Abstract

Microgrids in smart grid development create a new era towards resilient energy supply. In turn, They also render complicated cyber-physical system development towards high scalability and comprehensiveness. Heterogeneous Small Cell based Networks (HSCNs) endow a suitable cyber infrastructure for microgrid based cyber-physical systems. To tackle this emerging research area, this paper proposes a Spatiotemporal Device-to-device communication based HSCN Deployment scheme for Microgrid based Smart grid Development (3xSD), to achieve high energy efficiency with high qualityof-service, chronically and dynamically. Concretely, a joint optimization for long-term energy efficiency and achievable data rate maximization with interference mitigation is formulated for spatial small cell positioning, which models both cyber and physical characteristics of Smart Grid User Equipment (SGUE). A Temporal On-demand Small cell Powering and work-Offloading scheme (TOSPO) is proposed on top of the determined small cell positioning. TOSPO considers the real-time heterogeneous features and demands of SGUEs to maximize energy and cost efficiency, for both communication and computation perspectives. To evaluate the performance of 3xSD, spatial analysis, temporal analysis, and selected case study are simulated. Numerical results showcase that 3xSD is capable of providing both long-term and on-demand optimal energy efficiency and quality-of-service solution for microgrids, outperforming existing benchmarks in the literature.INDEX TERMS Small cells, smart grid, on-demand, energy efficiency, cyber-physical systems.

AI based controller optimization for VSC-MTDC grids

“…PSO algorithm has a great potential to optimize parameter settings (Behera et al, 2016). Apart from industrial processes the applications relating to power system can be found in the literature this research is focused on the power system application and optimization (Ehsan et al, 2021;Shehzad et al, 2021;Chi et al, 2021;Autonomous, 2021;Guasti Junior and Santos, 2021;Lau and Lim, 2018;Fan et al, 2019;Hu and Eberhart, 2002;Dashtdar et al, 2022;Soliman et al, 2021).…”

“…Going forward, the format used to indicate the weights and biases associated with a neuron is as follows W layer number (neuron number) Input number b layer number Bias number for that input Here, we have two inputs and four neurons in each hidden layer. So, the corresponding weighted matrixes can be written as (Chi et al, 2021),…”