In order to explore impacts of crowd intervention strategies on indoor respiratory exposure risks during major pandemics, a variety of crowd motion scenarios were established in general indoor conditions based on improved pedestrian dynamics model and respiratory infection probability model. Then, multi-agent simulation technology was utilized to simulate impacts of strategies, including protection optimization, pedestrian flow optimization and route optimization, on the exposure risks. The results show that indoor respiratory exposure risks are mainly determined by total pedestrian flow, individuals’ stay length, movement route planning and duration of stay in contaminated areas. The carryover effect will be formed due to pedestrians’ obedience behavior of social distancing, which will further increase exposure time to contaminated areas. The lower pathogen permeability of masks, and the greater space ventilation are, the lower infection probability the crowd will face.@en

The vigorous expansion of renewable energy as a substitute for fossil energy is the predominant route of action to achieve worldwide carbon neutrality. However, clean energy supplies in multi-energy building districts are still at the preliminary stages for energy paradigm transitions. In particular, technologies and methodologies for large-scale renewable energy integrations are still not sufficiently sophisticated, in terms of intelligent control management. Artificial intelligent (AI) techniques powered renewable energy systems can learn from bio-inspired lessons and provide power systems with intelligence. However, there are few in-depth dissections and deliberations on the roles of AI techniques for large-scale integrations of renewable energy and decarbonisation in multi-energy systems. This study summarizes the commonly used AI-related approaches and discusses their functional advantages when being applied in various renewable energy sectors, as well as their functional contribution to optimizing the operational control modalities of renewable energy and improving the overall operational effectiveness. This study also presents practical applications of various AI techniques in large-scale renewable energy integration systems, and analyzes their effectiveness through theoretical explanations and diverse case studies. In addition, this study introduces limitations and challenges associated with the large-scale renewable energy integrations for carbon neutrality transition using relevant AI techniques, and proposes further promising research perspectives and recommendations. This comprehensive review ignites advanced AI techniques for large-scale renewable integrations and provides valuable informational instructions and guidelines to different stakeholders (e.g., engineers, designers and scientists) for carbon neutrality transition.

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Spatiotemporal energy interaction and sharing are promising solutions to penetrate renewable energy, enhance grid power stability, and improve regional energy flexibility. However, the current literature is restrained in a small-scale neighborhood level, without considering inter-city energy migration through spatiotemporal complementarity between renewable-abundant regions (like suburb or countryside areas) and demand-shortage regions (like city centers). In this study, the energy interaction boundary is extended from a neighborhood scale to an inter-city scale, to maximize the renewable energy penetration, demand coverage, and reduce regional energy imbalance. This study firstly proposes a holistic framework on inter-city transportation-based energy migration, consisting of a residential community with rooftop photovoltaic systems and electrical batteries, an office building, hydrogen vehicles (HVs), a hydrogen (H₂) station, and local power grids, for the energy transmission between building groups in spatially different regions through the daily commuting of HVs. Optimal grid-regulation strategies are thereafter proposed and adopted to stabilize the grid power and reduce energy costs. Parametric analysis on energy trading strategies and prices has been conducted, to improve the participation motivations of different stakeholders. Results indicate that, compared to the reference case with isolated buildings and vehicles, the transportation-based energy migration framework covers 23.2 % of the office energy demand and elevates the community's renewable self-use ratio from 72.7 % to 98.6 %. Meanwhile, the maximum grid-export power in the renewable-abundant region (suburb residential community) and the annual grid-import power in the demand-shortage region (city-center office) are reduced by up to 86.9 % (from 155.7 to 20.4 kW) and 29.4 % (from 49.0 to 34.6 kW), respectively. Moreover, even considering the fuel cell degradation cost of HVs, the transportation-based energy migration framework reduces the operating costs of the office building and HVs (the H₂ cost and the fuel cell degradation cost) by 16.4 % (from $52791.3 to $44154.7) and 1.7 % (from $27172.5 to $26707.4), respectively. Afterward, compared to the reference case, the peak-shaving and load-shaping grid-regulation strategies can decrease the peak grid-export power of the community by about 71.6 % (from 155.7 to 44.2 kW), and the maximum grid-import power of the office by 23.7 % (from 49.0 to 37.4 kW), respectively. Furthermore, the transportation-based energy migration framework is economically feasible, only when the renewable export price for H₂ production is 0.07 $/kWh, the onsite-renewable-generated H₂ lower than 6.5 $/kg for the HV owners, and the vehicle-to-building electricity lower than 0.3 $/kWh for the office building. This study provides a novel inter-city energy migration framework with hydrogen networks to enhance district energy sharing, improve regional energy balance and reduce carbon emission, together with frontier guidelines on energy trading prices to promote participation motivations from different stakeholders.

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Hydrogen-based (H₂-based) interactive energy networks for buildings and transportations provide novel solutions for carbon-neutrality transition, regional energy flexibility and independence on fossil fuel consumption, where vehicle fuel cells are key components for H₂-electricity conversion and clean power supply. However, due to the complexity in thermodynamic working environments and frequent on/off operations, the proton exchange membrane fuel cells (PEMFCs) suffer from performance degradation, depending on cabin heat balance and power requirements, and the ignorance of the degradation may lead to the performance overestimation. In order to quantify fuel cell degradation in both daily cruise and vehicle-to-grid (V2G) interactions, this study firstly proposes a two-space cabin thermal model to quantify the ambient temperature of vehicle PEMFCs and the power supply from PEMFCs to vehicle HVAC systems. Afterwards, a stack voltage model is proposed to quantify the fuel cell degradation for multiple purposes, such as daily transportation and V2G interactions. Afterwards, the two models are coupled in a community-level based building-vehicle energy network, consisting of twenty single residential buildings, rooftop PV systems, four hydrogen vehicles (HVs), a H₂ station, community-served micro power grid, local main power grid, and local H₂ pipelines, located in California, U.S.A. Comparative analysis with and without fuel cell degradation is conducted to study the impact of dynamic fuel cell degradation on the energy flexibility and operating cost. Furthermore, a parametrical analysis is conducted on the integrated HV quantity and the grid feed-in tariff to reach trade-off strategies between associated fuel cell degradation costs and grid import cost savings. The results indicate that, in the proposed hydrogen-based building-vehicle energy network, the total fuel cell degradation is 3.16% per vehicle within one year, where 2.50% and 0.66% are caused by daily transportation and V2G interactions, respectively. Furthermore, in the H₂-based residential community, the total fuel cell degradation cost is US$6945.2, accounting for 33.4% of the total operating cost at $20770.61. The sensitivity analysis results showed that, when the HV quantity increases to twenty, the fuel cell degradation of each HV decreases to 2.50%, whereas the total fuel cell degradation cost increases to 42.8% of the total operating cost. Last but not the least, the cost saving by V2G interactions can compensate the fuel cell degradation cost when the grid feed-in tariff is reduced by 40%. Research results can provide basic modelling tools on dynamic fuel cell degradation, in respect to vehicle power supply, vehicle HVAC and V2G interactions, together with techno-economic feasibility analysis, paving path for the development of hydrogen energy for the carbon-neutrality transition.

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La(OH)₃ metal engineered nanoparticles (MENPs) are efficient phosphate binders; however, complex synthesis procedures and purity as well as agglomeration issues impede their development and practical applications. Herein, a green and a one-step method in combination with the spark ablation aerosol technology and electrospinning is proposed for the synthesis of La(OH)₃ MENPs; further, their application as phosphate binders are elucidated as a proof the concept. Material characterization results confirm the successful synthesis of ultrapure La(OH)₃ MENPs, which has not been achieved before via an environmentally friendly one-step procedure. Small angle X-ray scattering and X-ray photoelectron spectroscopy etching results show that La(OH)₃ MENPs loading on the electrospun nanofibers are uniform in both two and three dimensions. The comparative tests revealed a high phosphate adsorption capacity (110.8 mg P/g La) and indicted that the La(OH)₃ MENPs perform well; this was observed even under the interference of coexisting ions (Cl⁻, SO₄ ²⁻, NO₃ ⁻, and F⁻) at different pH values. After three cycles of solution-shaking treatment, the release of La(OH)₃ was less than 1 wt% (0.5 wt%), which was acceptable for an adsorbent. These results indicate that the La(OH)₃ MENP-loaded nanofibers are practical phosphate binders due to the simple production methods, low manufacturing cost, and impressive capacity. The proposed method significantly shortens the loading process and is a promising alternative for not only the synthesis of the adsorbent, but also for other engineering materials where loading is needed.

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