Adaptive autotuning mathematical approaches for integrated optimization of automated container terminal

With the development of automated container terminals (ACTs), reducing the loading and unloading time of operation and improving the working efficiency and service level have become the key point. Taking into account the actual operation mode of loading and unloading in ACTs, a mixed integer programming model is adopted in this study to minimize the loading and unloading time of ships, which can optimize the integrated scheduling of the gantry cranes (QCs), automated guided vehicles (AGVs), and automated rail-mounted gantries (ARMGs) in automated terminals. Various basic metaheuristic and improved hybrid algorithms were developed to optimize the model, proving the effectiveness of the model to obtain an optimized scheduling scheme by numerical experiments and comparing the different performances of algorithms. The results show that the hybrid GA-PSO algorithm with adaptive autotuning approaches by fuzzy control is superior to other algorithms in terms of solution time and quality, which can effectively solve the problem of integrated scheduling of automated container terminals to improve efficiency.info:eu-repo/semantics/publishedVersio

Low-carbon Energy Transition and Planning for Smart Grids

With the growing concerns of climate change and energy crisis, the energy transition from fossil-based systems to a low-carbon society is an inevitable trend. Power system planning plays an essential role in the energy transition of the power sector to accommodate the integration of renewable energy and meet the goal of decreasing carbon emissions while maintaining the economical, secure, and reliable operations of power systems. In this thesis, a low-carbon energy transition framework and strategies are proposed for the future smart grid, which comprehensively consider the planning and operation of the electricity networks, the emission control strategies with the carbon response of the end-users, and carbon-related trading mechanisms. The planning approach considers the collaborative planning of different types of networks under the smart grid context. Transportation electrification is considered as a key segment in the energy transition of power systems, so the planning of charging infrastructure for electric vehicles (EVs) and hydrogen refueling infrastructure for fuel cell electric vehicles is jointly solved with the electricity network expansion. The vulnerability assessment tools are proposed to evaluate the coupled networks towards extreme events. Based on the carbon footprint tracking technologies, emission control can be realized from both the generation side and the demand side. The operation of the low-carbon oriented power system is modeled in a combined energy and carbon market, which fully considers the carbon emission right trading and renewable energy certificates trading of the market participants. Several benchmark systems have been used to demonstrate the effectiveness of the proposed planning approach. Comparative studies to existing approaches in the literature, where applicable, have also been conducted. The simulation results verify the practical applicability of this method