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[en] In this work, we present a tunable alloying strategy to prepare high entropy alloy (HEA) thin films with FCC/BCC dual-phase structure. The dual-phase HEA films consist of uniformly equiaxed grains with average size about 40 nm. Comparing with single-phase FCC HEA, the dual-phase HEA has higher hardness up to 10.4 GPa. The relative large atomic radius of Al leads to severe lattice distortion, resulting in a stronger solid solution strengthening behavior in the dual-phase HEA. Moreover, high dense heterogeneous phase interfaces effectively blocking dislocation motion enhance the strain hardening ability further.
[en] In this study, an inexact coupled coal and power management (ICCPM) model was developed for planning coupled coal and power management systems through integrating chance-constrained programming (CCP), interval linear programming (ILP) and mixed integer linear programming (MILP) techniques. The ICCPM model can effectively handle uncertainties presented in terms of probability density functions and intervals. It can also facilitate dynamic analysis of capacity expansions, facility installation and coal inventory planning within a multi-period and multi-option context. Complexities in coupled coal and power management systems can be systematically reflected in this model, thus applicability of the modeling process would be highly enhanced. The developed ICCPM model was applied to a case of long-term coupled coal and power management systems planning in north China. Interval solutions associated with different risk levels of constraint violations have been obtained, which can be used for generating decision alternatives and helping identify desired policies. The generated results can also provide desired solutions for coal and power generation, capacity initiation and expansion, and coal blending with a minimized system cost, a maximized system reliability and a maximized coal transportation security. Tradeoffs between system costs and constraint-violation risks can also be tackled.
[en] Management of energy resources is crucial for many regions throughout the world. Many economic, environmental and political factors are having significant effects on energy management practices, leading to a variety of uncertainties in relevant decision making. The objective of this research is to identify optimal strategies in the planning of energy management systems under multiple uncertainties through the development of a fuzzy-random interval programming (FRIP) model. The method is based on an integration of the existing interval linear programming (ILP), superiority-inferiority-based fuzzy-stochastic programming (SI-FSP) and mixed integer linear programming (MILP). Such a FRIP model allows multiple uncertainties presented as interval values, possibilistic and probabilistic distributions, as well as their combinations within a general optimization framework. It can also be used for facilitating capacity-expansion planning of energy-production facilities within a multi-period and multi-option context. Complexities in energy management systems can be systematically reflected, thus applicability of the modeling process can be highly enhanced. The developed method has then been applied to a case of long-term energy management planning for a region with three cities. Useful solutions for the planning of energy management systems were generated. Interval solutions associated with different risk levels of constraint violation were obtained. They could be used for generating decision alternatives and thus help decision makers identify desired policies under various economic and system-reliability constraints. The solutions can also provide desired energy resource/service allocation and capacity-expansion plans with a minimized system cost, a maximized system reliability and a maximized energy security. Tradeoffs between system costs and constraint-violation risks could be successfully tackled, i.e., higher costs will increase system stability, while a desire for lower system costs will run into a risk of potential instability of the management system. Moreover, multiple uncertainties existing in the planning of energy management systems can be effectively addressed, improving robustness of the existing optimization methods
[en] Highlights: ► Analyze mutual interactions and restrictions within energy management systems. ► Tackle uncertainties expressed as fuzzy sets, and regular and radial intervals. ► Obtain optimal solutions under preferred satisfaction degrees and system benefits. ► Use protection level to reflect tradeoffs between constraint-violation and system reliability. ► Provide decision makers with effective energy management schemes. - Abstract: In this study, a fuzzy radial interval linear programming (FRILP) model was developed for supporting robust planning of energy management systems with environmental and constraint-conservative considerations, facilitating the reflecting of multiple uncertainties that are existing in energy activities and environmental emissions and could be expressed as fuzzy sets, and regular and radial intervals. Particularly, it could ensure the generation of robust solutions that would be feasible with high probability under input data variations, reflecting tradeoffs between the conservatism levels of solutions and probability levels of constraint violation. Specifically, 24 radial intervals associated with the electricity generation efficiency and electricity demands under different protection levels based on the natural and technologic conditions, as well as decision makers’ expectation were determined. Totally, 30 scenarios under the combinations of five protection levels were analyzed. Through solving the developed model, the results showed that decision variables would be rising with the increase of protection levels and higher radii fluctuation levels of radial intervals would cause higher system cost and lower satisfaction degree. The generated solutions could offer detail energy management plans (e.g., energy conversion technology capacity expansions) for decision makers, and thus could guarantee optimal economic and environmental benefits under desirable system reliability.