The demand for data center computing and renewable power generation do not always align, which leads to inefficiencies in energy utilization. Flexible coordination of computing demand can address this issue. However, in multi-tenant data centers, users autonomously schedule some tasks, so operators cannot directly dispatch the corresponding loads. This paper proposes a two-stage energy dispatch method for multi-tenant data centers directly supplied by wind and photovoltaic generation. During the day-ahead stage, the operator sets a stochastic Stackelberg game and establishes the internal contract tariff. Contracted tenants act as followers and create load plans that are feasible for their services. During the real-time phase, a rolling model determines the admission of potential external cloud tasks and energy dispatch. The service price follows from the aggregate demand at the optimized admission quantity. Numerical results demonstrate that all contracted tasks are completed. Compared with the baseline without workload coordination, renewable energy utilization rises from 88.46% to 98.76%. The operator’s profit increases by 8.28%, and the cost per compute unit falls by 9.05%. Computing service volume increases, while grid dependence and operational emissions decline. Workload coordination improves profitability and operating flexibility under the tested grid restrictions and renewable-supply disturbances while maintaining contractual service.
Smart textiles have progressed from conventional passive sensing fabrics to advanced cyber-physical systems capable of integrating sensing, actuation, computation, and wireless communication within a single textile platform. With a focus on circular economy principles, this review critically examines intelligent textile ecosystems, from material architectures to AI-enabled wearable platforms. The functional hierarchy involves passive (sensing), active (sensing + response) and ultra-smart (autonomous decision making) textiles. The four classes of materials are metallic conductors, carbonaceous nanomaterials (CNTs and graphene), conductive polymers (PEDOT:PSS) and bio-based alternatives. Integration is achieved through coating, weaving, printing and additive manufacturing. Energy autonomy is achieved by five harvesting mechanisms: piezoelectric (1–10 µW·cm−2), triboelectric (10–100 µW·cm−2), thermoelectric (20–50 µW·cm−2), photovoltaic (100–1000 µW·cm−2) and bio-electrochemical (1–5 µW·cm−2). Hybrid systems can run continuously. Flexible supercapacitors and thin-film batteries are used for energy storage. The combination of AI and IoT allows for multi-tier architectures that encompass edge processing (TinyML), gateway management, and cloud analytics. On-device inference reduces energy consumption by 60–80%, improves privacy and enables real-time anomaly detection using CNNs, LSTMs and autoencoders. Sustainability is considered through life-cycle assessment, which quantifies impacts across extraction, fabrication, use, and end-of-life of materials. Circular strategies such as design for disassembly, harmonization of materials, biodegradable electronics, and recycling routes are all important. We critically evaluate key challenges such as manufacturing scalability, washability, biocompatibility, and regulatory standardization. This review provides a unifying framework that connects materials science, electronics, AI and sustainable design, providing a roadmap for circular, intelligent and energy-autonomous textile systems in line with UN SDGs (3, 9, 12, 13).
The increasing generation of agro-food byproducts and the limited valorization of underutilized biological resources represent significant challenges to the sustainability of food production systems [...]
During coalbed methane production, water plays a crucial role in methane adsorption and diffusion; however, the adsorption and diffusion of methane in water-bearing low-rank coal remain difficult to characterize synergistically within a unified molecular-scale framework, leaving the mechanism by which water affects methane poorly understood. To address this issue, this study focuses on low-rank coal from the Anjialing Mine in the Ningwu Coalfield, China, and integrates experimentally constrained molecular modeling with analyses of adsorption energy, density distribution, and mean square displacement (MSD). Low-pressure CO2 adsorption and low-temperature N2 adsorption were used to characterize the pore structure, while XPS, FTIR, and solid-state 13C NMR were employed to constrain the two-dimensional molecular structure, based on which a three-dimensional macromolecular model and a 1 nm nanopore model were constructed. At 303.15 K and 0–50 MPa, a dry methane single-component system and a methane-water competitive system were compared in terms of adsorption capacity, adsorption-energy probability distribution, spatial density distribution, and MSD. The results show that micropores smaller than 1 nm contribute more than 75% of the pore volume and 91% of the specific surface area of AJL coal. In the dry system, methane adsorption increases rapidly below 10 MPa and then slows gradually; the adsorption energy is mainly concentrated around −10 kcal/mol, and the methane density distribution within the pores is relatively ordered. After water is introduced, methane adsorption decreases by approximately two-thirds, the adsorption-energy distribution broadens to −35 to 0 kcal/mol, and the heterogeneity of the density distribution increases; with increasing pressure, methane adsorption increases whereas water adsorption decreases. Short-time MD comparisons show that water molecules preferentially associate with polar oxygen-containing functional groups and form low-mobility clusters. The resulting decrease in methane MSD, particularly in the 5 MPa high-water-loading configuration, indicates reduced accessible migration space. By linking characterization of the same AJL coal sample with GCMC–MD analysis, this study connects water-induced changes in methane uptake, adsorption-energy distribution, spatial distribution, and short-time mobility within the modeled 1 nm pore, providing a molecular-scale interpretation of methane–water competition.
High penetration of renewable energy poses severe frequency stability challenges to modern power systems, while conventional thermal power units (TPUs) fail to satisfy high-precision frequency regulation demands due to slow dynamic response. A hybrid energy storage system (HESS) with a flywheel and battery has become a mainstream scheme for TPU-HESS coordinated regulation by realizing complementary advantages. However, existing strategies generally adopt rigid state-of-charge (SOC) constraints, lack battery lifespan optimization, and suffer from incomplete power prediction models with weak integration with real-time control. To fill these gaps, this paper proposes a prediction-assisted coordinated control framework. A three-matrix adaptive Markov prediction model corrected by K-means time-series morphological clustering is established, combined with a weighted random sampling scenario tree method. Furthermore, a prediction-driven dynamic SOC interval feedforward-feedback control strategy is designed for HESS power dispatch and SOC management. Simulations verify that the clustering correction reduces prediction RMSE by 22.25%. The proposed strategy increases the optimal SOC operation ratio of batteries by 34.25%, decreases equivalent cycles by 19.80%, and lifts the comprehensive AGC performance index by 160.93%. This study provides technical support for efficient and durable TPU-HESS frequency regulation systems.
