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Scheduling of wind-photovoltaic-hydropower systems is a complex multi-objective collaborative problem which requires multi-criteria decision making (MCDM) to assist in formulating the most-preferred scheduling scheme. There are uncertainties existing in criteria weights (CWs) due to the limitations of the knowledge level of decision makers and the subjectivity of preferences. This paper proposes a two-stage stochastic multi-criteria group decision making model considering the uncertainties in the decision making environment and the progression of the decision making process. For the prophase decision making (the first stage), the stochastic multi-criteria acceptability analysis (SMAA) theory and the MCDM model VIKOR are combined to construct a SMAA-VIKOR model for inverse weight space analysis on the situation that the initial CW information is completely unknown. In the second stage of decision making, the decision makers are allowed to express their fuzzy preferences based on the information provided in the first stage by coupling the intuitionistic fuzzy analytic hierarchy process (IFAHP) into the SMAA-VIKOR model to obtain the final scheme. The wind-photovoltaic-hydropower system in Yalong River Basin is selected as the case study. Results show that among the 12 alternative schemes, scheme A2 is ranked first with the probability of 0.69 in the first stage while the probability of taking the first ranking in the second stage is increased to 1. More explicit ranking results are obtained based on the results of the first stage, which effectively enhances the confidence of decision makers and improves decision making efficiency.

Recent years have witnessed significant advances in all-solid-state lithium batteries (ASSLBs). However, soft breakdown hidden in ASSLBs has been overlooked in most previous research. Moreover, existing assessment criteria are insensitive to detecting soft breakdown. Here, we first discuss the current status of ASSLBs and highlight the challenges of evaluating the soft breakdown phenomenon with the existing evaluation method. A simple but effective strategy—cyclic voltammetry—is then proposed to diagnose soft breakdown in all-solid-state symmetric cells. To establish a standard testing protocol, several critical parameters that have not been well emphasized thus far, including areal capacity, thickness, and porosity of solid electrolytes, are numerically analyzed to understand their significant effect on the energy density of practical all-solid-state pouch cells. With these understandings, we establish a definitive testing benchmark with the aim of guiding the research efforts toward in-depth scientific understanding and practical engineering design.

Solid electrolytes are considered as strong alternatives for conventional liquid electrolytes to overcome the safety issues of next-generation high-energy-density lithium metal batteries (LMBs). Although Li1.5Al0.5Ge1.5(PO4)3(LAGP) has satisfied ionic conductivity at room temperature (∼10–4S cm–1), high stability in air, and can be easily sintered, it still suffers from instability of the lithium metal. Moreover, the large interfacial resistance between solid electrolytes and solid electrodes and the stress generated by the volumetric change of lithium metal anodes during cycling would deteriorate the performance of LMBs. Here, we report an effective solution to overcome the abovementioned problems by introducing a three-dimensional gel polymer electrolyte at the interface between LAGP pellets and lithium metal anodes, achieving stable cycling of LiFePO4//Li cells at room temperature for 300 cycles. Besides, the degeneration mechanisms of the interfaces of LAGP pellets under different conditions are compared, and peculiar properties different from their counterparts were found.

Lithium-rich materials represented by xLi2MnO3·(1 – x)LiMO2(M = Mn, Co, Ni) are attractive cathode materials for lithium-ion battery due to their high specific energy and low cost. However, some drawbacks of these materials such as poor cycle and rate capability remain to be addressed before applications. In this study, a thin polyimide (PI) layer is coated on the surface of Li1.2Ni0.13Mn0.54Co0.13O2(LNMCO) by a polyamic acid (PAA) precursor with subsequently thermal imidization process. X-ray diffraction (XRD), scanning electron microscopy (SEM), and high-resolution transmission electron microscopy (HR-TEM) results confirm the successful formation of a PI layer (∼3 nm) on the surface of LNMCO without destruction of its main structure. X-ray photoelectron spectroscopy (XPS) spectra show a slight shift of the Mn valence state from Mn(IV) to Mn(III) in the PI-LNMCO treated at 450 °C, elucidating that charge transfer takes place between the PI layer and LNMCO surface. Electrochemical performances of LNMCO including cyclic stability and rate capability are evidently improved by coating a PI nanolayer, which effectively separates the cathode material from the electrolyte and stabilizes their interface at high voltage.

Forecast errors of multiple information sources of a cascade hydropower system cause risks of water and energy supply in real‐time operation. Mechanisms minimizing multiple risks with budgeted cost under oscillations of multiple forecast uncertainties through robust operation are not yet well‐investigated. This study proposed a multiobjective robust optimization (RO) and decision‐making framework comprising series of models for risk analysis, robust control, and decision making. The risk analysis model identifies and analyzes dependent risks that stem from forecast errors of supply and demand‐side information by Copula functions. A RO model was established for minimizing risk probabilities, vulnerabilities of ecological protection, water and energy supply, as well as revenue loss from energy. Thereafter, a multiattribute decision‐making model was incorporated for determining the compromise solution from competing non‐dominated solutions. The proposed framework was applied to the cascade hydropower system of the Xiluodu, Xiangjiaba, Three Gorges project and Gezhouba reservoirs on the Yangtze River, China. The principal findings were as follows. (a) Addressing the singular forecast uncertainty of streamflow is inadequate to obtain robust solutions. (b) In a normal year, RO can reduce the average conditional value‐at‐risk of ecological water shortfall, consumptive water shortfall, and energy shortfall by 25.1%, 35.3%, and 16.5% than that of chance‐constrained programming. (c) Multiple risks are dispersed through risk avoidance and hedging that reduces outflow variance and homogenizes mean outflow, lowering down efficiencies of energy production and water usage as a tradeoff. The proposed model framework could be applied to inform operation of cascade hydropower systems. Holistic multiobjective robust optimization model framework for reservoir operation under supply and demand uncertainties was developedConflicts of reducing risk probabilities, vulnerabilities, and revenue loss for ecological protection, water supply, and energy were quantifiedMechanisms of tradeoffs in multiobjective robustness with energy production through risk hedging and avoiding were discovered Holistic multiobjective robust optimization model framework for reservoir operation under supply and demand uncertainties was developed Conflicts of reducing risk probabilities, vulnerabilities, and revenue loss for ecological protection, water supply, and energy were quantified Mechanisms of tradeoffs in multiobjective robustness with energy production through risk hedging and avoiding were discovered