Guest Editorial: Smart Grid Inspired Data Sensing, Processing and Networking Technologies
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The smart grid is the next generation electric grid that enables efficient, intelligent, and economical power generation, transmission, and distribution using modern information and communications technologies, such as advanced sensing, monitoring, and processing technology, and high-speed bi-directional communications and networking. In recent years, the smart grid has attracted significant attentions and become a global trend due to the immense potential benefits including enhanced reliability and resilience, higher operational efficiency, more efficient energy consumption, and better power quality. A long-term strategy to address various challenges introduced or inspired by smart grid shall be a systematic one that brings together the three key aspects of a smart grid communications system that is the core of smart grid, namely, data sensing, data processing and data communications. It is precisely the purpose of this special issue, which serves an integration of these major components or enabling technologies of smart grid.
This special issue features five selected high-quality papers. The first paper entitled “Mobility-aware Vehicle-to-Grid (V2G) Optimization for Uniform Utilization in Smart Grid based Power Distribution Network” proposed a scalable virtual power plant (VPP) based V2G optimization architecture integrated with VANET. Simulations showed that the proposed mobility-aware and scalable V2G optimization algorithm can reduce or significantly postpone the need of expensive upgrade of power distribution infrastructure.
In the second paper with the title “Incorporating FAIR into Bayesian Network for Numerical Assessment of Loss Event Frequencies of Smart Grid Cyber Threats”, the authors proposed a method to incorporate the FAIR’s LEF into Bayesian Network (BN) to derive the numerical assessments to rank the threat severity. The improvements were demonstrated by applying the method to assess cyber threats in a smart grid robustness research project (IRENE).
In the paper entitled “A Decision Function Based Smart Charging and Discharging Strategy for Electric Vehicle in Smart Grid”, the authors proposed an Electric Vehicle Charging and Discharging Strategy (EVCDS) based on a Charging Decision Function (CDF) as well as a Discharging Decision Function (DDF). Comparing with other strategies, EVCDS performs well in terms of reducing the charging demand fluctuations and improving the charging demand balance among charging stations.
The next paper entitled “Efficient Identification of TOP-K Heavy Hitters over Sliding Windows” proposed a new data structure called FCM and its associated algorithms. The key point of this method is that it introduces a novel filter-and-jump mechanism. The experimental results demonstrate that the performance of FCM is superior to the previous related algorithm.
The last paper entitled “LTE Delay Assessment for Real-Time Management of Future Smart Grids” investigated the feasibility of using Long Term Evolution (LTE) cellular networks for the real-time smart grid state estimation. The results show that time-delay prioritized scheduling in combination with flexible PRB assignment greatly reduces the maximum delay when compared to simple random scheduling and fixed PRB assignment.
The guest editors are grateful to the reviewers for their efforts in reviewing the manuscripts. We also thank the Editor-in-Chief, Dr. Imrich Chlamtac, for his support and guidance during the entire process.