Research on Power Grid Resilience and Power Supply Restoration during Disasters-A Review

Electric power system plays an indispensable role in modern society, which supplies the energy to residential, commercial, and industrial consumers. However, the high-impact and low-probability natural disasters (i.e., windstorm, typhoon, and flood) come more frequent because of the climate change in the recent years, which may sequentially cause devastating damages to the infrastructure of power systems. The aim of this paper is mainly to explore and review the resilience of power grid system during the disaster and the power supply management strategies to recover the power grid. Firstly, the category of natural disasters and different influences on power grid are discussed. Then, the definition of power grid resilience is explored and the supply management strategies copying with disasters are introduced, such as microgrids and distributed generation systems. Specially, the electric vehicles (EVs) equipped with large-capacity battery pack in the transportation network can also be considered as the distributed power sources with mobility. Thus, the conceptual frameworks of integrating large-scale EVs into the power grid to fasten restoration of the power systems in the pre-disaster/post-disaster are emphatically investigated in this paper. Finally, the opportunities and challenges in further research on employing EVs for emergency power supply in the extreme weather events are also discussed

A Review of the Role of Natural Clay Minerals as Effective Adsorbents and an Alternative Source of Minerals

The minerals with unique properties such as natural clay minerals (NCMs) have promising approach in environmental and industrial sphere. In fact, under some specific conditions the NCMs could be used either as effective adsorbent material or alternative source of minerals. This chapter presents an outline of a general review of factors that affect the application ability of NCMs and a descriptive analysis of NH4+ and REE adsorption behavior and extraction of rare earth elements (REE) by an ion-exchange with NH4+ ions onto NCMs. Clays and NCMs both effectively remove various contaminants from aqueous solution and serve as alternative sources of minerals, as extensively discussed in this chapter. This review compiles thorough literature of current research and highlights the key findings of adsorption (NH4+ and REE) that use different NCMs as adsorbents or alternative sources of minerals (i.e., REE). The review confirmed that NCMs excellently remove different cations pollutants and have significant potential as alternative source of REE. However, modification and further development of NCMs applications for getting the best adsorption and the best extraction of REE onto NCMs, which would enhance pollution control and leaching system is still needed

Real-time energy optimization of HEVs under-connected environment: a benchmark problem and receding horizon-based solution

[EN] In this paper, we propose a benchmark problem for the challengers aiming to energy efficiency control of hybrid electric vehicles (HEVs) on a road with slope. Moreover, it is assumed that the targeted HEVs are in the connected environment with the obtainment of real-time information of vehicle-to-everything (V2X), including geographic information, vehicle-to-infrastructure (V2I) information and vehicle-to-vehicle (V2V) information. The provided simulator consists of an industrial-level HEV model and a traffic scenario database obtained through a commercial traffic simulator, where the running route is generated based on real-world data with slope and intersection position. The benchmark problem to be solved is the HEVs powertrain control using traffic information to fulfill fuel economy improvement while satisfying the constraints of driving safety and travel time. To show the HEV powertrain characteristics, a case study is given with the speed planning and energy management strategy.Xu, F.; Tsunogawa, H.; Kako, J.; Hu, X.; Eben Li, S.; Shen, T.; Eriksson, L.... (2022). Real-time energy optimization of HEVs under-connected environment: a benchmark problem and receding horizon-based solution. Control Theory and Technology. 20:145-160. https://doi.org/10.1007/s11768-022-00086-y14516020Zhou, Q., Zhao, D., Shuai, B., Li, Y., Williams, H., & Xu, H. (2021). Knowledge implementation and transfer with an adaptive learning network for real-time power management of the plug-in hybrid vehicle. IEEE Transactions on Neural Networks and Learning Systems, 32(12), 5298–5308. https://doi.org/10.1109/TNNLS.2021.3093429Xu, F., & Shen, T. (2021). Decentralized optimal merging control with optimization of energy consumption for connected hybrid electric vehicles. 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IEEE Transactions on Control Systems Technology, 20(3), 817–825.Rezaei, A., Burl, J. B., Solouk, A., Zhou, B., et al. (2017). Catch energy saving opportunity (CESO), an instantaneous optimal energy management strategy for series hybrid electric vehicles. Applied Energy, 208, 655–665.Xie, S., Hu, X., Qi, S., & Lang, K. (2018). An artificial neural network-enhanced energy management strategy for plug-in hybrid electric vehicles. Energy, 163, 837–848.Zhang, J., & Shen, T. (2016). Real-time fuel economy optimization with nonlinear MPC for PHEVs. IEEE Transactions on Control Systems Technology, 24(6), 2167–2175.Sciarretta, A., Serrao, L., Dewangan, P. C., et al. (2014). A control benchmark on the energy management of a plug-in hybrid electric vehicle. Control Engineering Practice, 29, 287–298.Lars, E. (2019). An overview of various control benchmarks with a focus on automotive control. Control Theory and Technology, 17(2), 121–130.Moura, S. J., Fathy, H. K., Callaway, D. S., & Stein, J. L. (2010). A stochastic optimal control approach for power management in plug-in hybrid electric vehicles. IEEE Transactions on Control Systems Technology, 19(3), 545–555.Sun, C., Hu, X., Moura, S. J., & Sun, F. (2014). Velocity predictors for predictive energy management in hybrid electric vehicles. IEEE Transactions on Control Systems Technology, 23(3), 1197–1204.Xiang, C., Ding, F., Wang, W., & He, W. (2017). Energy management of a dual-mode power-split hybrid electric vehicle based on velocity prediction and nonlinear model predictive control. Applied Energy, 189, 640–653.Sun, C., Sun, F., & He, H. (2017). Investigating adaptive-ECMS with velocity forecast ability for hybrid electric vehicles. Applied Energy, 185, 1644–1653.Zhang, F., Hu, X., Langari, R., & Cao, D. (2019). Energy management strategies of connected HEVs and PHEVs: Recent progress and outlook. Progress in Energy and Combustion Science, 73, 235–256.Yang, C., Zha, M., Wang, W., Liu, K., & Xiang, C. (2020). Efficient energy management strategy for hybrid electric vehicles/plug-in hybrid electric vehicles: Review and recent advances under intelligent transportation system. IET Intelligent Transport Systems, 14(7), 702–711. https://doi.org/10.1049/iet-its.2019.0606Zhang, J., Xu, F., Zhang, Y., & Shen, T. (2019). ELM-based driver torque demand prediction and real-time optimal energy management strategy for HEVs. Neural Computing and Applications, 32: 14411C14429.Zhang, B., Zhang, J., Xu, F., & Shen, T. (2020). Optimal control of power-split hybrid electric powertrains with minimization of energy consumption. Applied Energy, 266, 114873.Zhang, F., Xi, J., & Langari, R. (2016). Real-time energy management strategy based on velocity forecasts using V2V and V2I communications. IEEE Transactions on Intelligent Transportation Systems, 18(2), 416–430.Li, J., Zhou, Q., He, Y., et al. (2019). 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Interleukin (IL)-2 Is a Key Regulator of T Helper 1 and T Helper 2 Cytokine Expression in Fish : Functional Characterization of Two Divergent IL2 Paralogs in Salmonids

This work was funded by the Biotechnology and Biological Sciences Research Council (BBSRC, BB/N024052/1) under the Newton Fund RCUK-CONICYT Research Partnerships call. YH was supported by a PhD Studentship from the Ministry of Education, Republic of China (Taiwan). EW was supported financially by the Faculty of Technology, Mahasarakham University Grant Year 2018. FL was supported by a Newton International Fellowship funded by the Academy of Medical Sciences, UK (AMS, NIF004\1036). ML and QX were supported financially by the National Scholarship Council of China. This work was partially supported financially by European Commission contract No. 311993 (TargetFish).Peer reviewedPublisher PD

Ultra-Wideband Dual-Polarized Patch Antenna With Four Capacitively Coupled Feeds

A novel dual-polarized patch antenna for ultra-wideband(UWB) applications is presented. The antenna consists of asquare patch and four capacitively coupled feeds to enhance theimpedance bandwidth. Each feed is formed by a vertical isoscelestrapezoidal patch and a horizontal isosceles triangular patch. Thefour feeds are connected to the microstrip lines that are printedon the bottom layer of the grounded FR4 substrate. Two taperedbaluns are utilized to excite the antenna to achieve high isolationbetween the ports and reduce the cross-polarization levels. In orderto increase the antenna gain and reduce the backward radiation,a compact surface mounted cavity is integrated with the antenna.The antenna prototype has achieved an impedance bandwidth of112% at (|S11 | ≤ -10 dB) whereas the coupling between thetwo ports is below -28 dB across the operating frequency range.The measured antenna gain varies from 3.91 to 10.2 dBi for port 1and from 3.38 to 9.21 dBi for port 2, with a 3-dB gain bandwidthof 107%

MPC-Based Optimal Control for Diesel Engine Coupled with Lean NOx Trap System

In this paper, an on-board optimal control problem for diesel engines with lean NOx trap (LNT) is investigated. First, a two-order LNT model based on mass conservation and energy conservation is constructed. Then, the optimal control problem is formulated as a continuous receding horizon problem under dynamical model constraint and descretized into a nonlinear programming problem by using the multiple shooting method. A sequential quadratic programming approach is employed to derive a numerical solution. Finally simulations are conducted under a standard driving cycle and a random driving cycle with comparison to a dynamic programming based control scheme in MATLAB/Simulink platform. Simulation results verify the effectiveness of the proposed control scheme

A Learning-Powered Model Predictive Control for Hybrid Electric Vehicles with Real-World Driving Data

International audienceIn this paper, the energy management strategy for hybrid electric vehicle by using large-scale historical traffic data to improve the energy efficiency is explored. Firstly, the modeling of HEV powertrain is built. Then, an optimization problem is formulated to minimize the fuel consumption under the constraints of driver’s demand torque and physical limitations of powertrain. To derive the optimal solution for real-time application, an economic model predictive control algorithm is employed, where the learning concept is introduced to identify the feedback controller. Finally, the verification of the proposed algorithm is conducted in a high-performance simulator

Real-time energy management based on ECMS with stochastic optimized adaptive equivalence factor for HEVs

For both globally suboptimal solution and implementable strategy, a real-time energy management strategy, based on equivalent consumption minimization strategy (ECMS), is proposed for commuter hybrid electric vehicles (HEVs) running on fixed routes. The determination of the adaptive equivalence factor is a focus. By the statistical characteristics deriving from historical driving data, the infinite-horizon stochastic dynamic programming (SDP) optimization with a discount factor is first formulated for finding proper equivalence factor according to uncertain driving cycles on a fixed route. And then, a mapping of equivalent factor on the system state is established off-line by stochastic optimal solution deriving from SDP policy iteration algorithm. In the power splits online, the equivalence factor of the implemented adaptive ECMS is obtained from the mapping according to the real time driving condition to achieve the near global optimal control objective that fuel consumption is minimized and the battery state of charge (SOC) is maintained within the boundaries over the whole driving route. Based on the HEV test platform established by specialized GT-Suite, simulation results and comparisons in some real driving cycles are presented to verify the effectiveness of the proposed strategy and to evaluate the advantages over other strategies