|Source:||Yang, K.Leung, V.C.M.Zhang, Y.Yang, S.Hu, J.Gao, J., 1st International Conference on Smart Grid Inspired Future Technologies, SmartGIFT 2016. 19 May 2016 through 20 May 2016, 175, 204-213|
|Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST|
Benchmarking · Electric power transmission networks · Resource allocation · Smart power grids · State estimation · Different granularities · Key performance indicators · Measurement reports · Modulation and coding schemes · Monitoring and control · Real-time management · System level simulation · Wide area monitoring · Long Term Evolution (LTE) · ICT · NTW - Networks · TS - Technical Sciences
This study investigates the feasibility of using Long Term Evolution (LTE), for the real-time state estimation of the smart grids. This enables monitoring and control of future smart grids. The smart grid state estimation requires measurement reports from different nodes in the smart grid and therefore the uplink LTE radio delay performance is selected as key performance indicator. The analysis is conducted for two types of measurement nodes, namely smart meters (SMs) and wide area monitoring and supervision (WAMS) nodes, installed in the (future) smart grids. The SM and WAMS measurements are fundamental input for the real-time state estimation of the smart grid. The LTE delay evaluation approach is via ‘snap-shot’ system level simulations of an LTE system where the physical resource allocation, modulation and coding scheme selection and retransmissions are modelled. The impact on the LTE delay is analyzed for different granularities of LTE resource allocation, for both urban and suburban environments. The results show that the impact of LTE resource allocation granularity on delay performance is more visible at lower number of nodes per cell. Different environments (with different inter-site distances) have limited impact to the delay performance. In general, it is challenging to reach a target maximum delay of 1 s in realistic LTE deployments (This work is partly funded by the FP7 SUNSEED project, with EC grant agreement no: 619437.). © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2017.