Abstract The technology of battery storage is fast developing, owing to the increasing need for effective, sustainable, safe, and economical energy storage systems. This review provides an in-depth evaluation of the current state of the art in lithium-ion batteries (LIBs), solid-state batteries, flow batteries, and alternative battery chemistries with specific focus on energy density, charging and discharging capability, cycle life, thermal management, and operational safety. Recent developments in artificial intelligence, machine learning, and smart battery management systems (BMSs) are also explored for state estimation, predictive maintenance, aging evaluation, and real-time performance optimization. The comparative research suggests that state-of-the-art LIB systems can reach round-trip efficiencies of about 90%–95%, while upcoming solid-state technologies provide the possibility of much enhanced energy density and safety over traditional battery structures. The analysis also shows that enhanced BMS, thermal management, fast-charging techniques, and data-driven health prediction are emerging as key enablers for prolonging battery service life and ensuring dependable high-power operation. Real-world examples demonstrating the increasing use of battery storage in renewable energy integration, grid stabilization, electric mobility, and distributed energy systems are shown. Technological advancement is critically assessed with economic, environmental, recycling, and life-cycle issues. Finally, the review points out resource availability, battery deterioration, recycling efficiency, thermal safety, standardization, and large-scale commercialization as difficulties still to be addressed. Future research should thus concentrate on sustainable battery chemistries, intelligent aging-aware BMSs, enhanced recycling, second-life applications, and digitally connected storage systems to enable reliable and low-carbon energy infrastructure.
Abstract To address the limited interpretability and adaptability of conventional power grid dispatch risk identification, this paper proposes an interpretable framework integrating domain knowledge with reinforcement learning. Grid topology, operational constraints, dispatch rules, and risk propagation knowledge are embedded into state representation and policy evaluation to guide risk-sensitive learning. A knowledge-driven explanation mechanism traces risk sources, affected equipment, and potential consequences. Case studies show that the improved model reaches a mean Average Precision of ~0.72–0.73, while the optimal learning-rate configuration achieves 0.77 accuracy and 0.72 AUC (Area Under Curve), demonstrating effective and interpretable risk identification.
Abstract As China’s core policy instrument for achieving its dual-carbon goals (carbon peaking by 2030 and carbon neutrality by 2060), the dual control system imposes structural constraints on manufacturing supply chain efficiency (SPC) through energy and carbon emission limits, creating a potential efficiency trade-off between environmental targets and economic growth in the digital era. Overly aggressive emission reduction measures may exacerbate growth constraints. Using panel data covering 13 manufacturing subsectors from 2007 to 21, this study adopts a dynamic two-way fixed-effects regression model to analyze the impact of the dual control system transition on the supply chain efficiency of China’s manufacturing sector and systematically investigates its transmission mechanisms. The key findings are as follows: (i) although policy implementation generally improves the efficiency of China’s manufacturing supply chains and boosts total manufacturing output, factor endowment constraints lead to efficiency losses under energy–carbon dual control. (ii) The effects exhibit substantial industry heterogeneity. (iii) The multichannel transmission framework operates through six pathways: technological innovation, digital transformation, global value chain positioning, labor allocation, capital effects, and operating income. This study enriches the literature on SPC by integrating policy constraints and resource allocation perspectives, providing theoretical and practical insights to optimize China’s dual-carbon strategy and promote the high-quality development of the manufacturing sector.
Abstract This study presents a techno-economic analysis of grid-connected solar photovoltaic (PV) systems for public senior high schools in Ghana, comparing monofacial and bifacial technologies using a 190-kW system at Presbyterian Boys’ Senior High School, Accra. Simulations were conducted in PVsyst using site-specific load and meteorological data. Results show that the bifacial system outperforms the monofacial configuration, generating 324 MWh/year compared to 293 MWh/year. The bifacial system achieves a lower levelized cost of electricity of $0.22/kWh versus $0.28/kWh and a shorter payback period of 6.9 years (monofacial: 7.4 years), despite a higher initial investment of $138 011. Environmental analysis indicates annual CO₂ savings of 46.01 tCO₂ for bifacial systems compared to 40.03 tCO₂ for monofacial systems. Sensitivity analysis highlights the influence of tariffs, emission levies, and subsidies on project viability. Overall, bifacial PV systems demonstrate superior techno-economic and environmental performance, offering a more cost-effective solution for Ghana’s public education sector.
Abstract Against the backdrop of the “dual carbon” goals, exploring the collaborative empowerment of artificial intelligence (AI) technology and green finance (GF) in promoting urban pollution and carbon emission reduction (PCER) has become an important path to promote green transformation and high-quality development. This study, taking liquid, solid, and gaseous pollutants into account, measures the level of PCER by constructing a comprehensive environmental pollutant index and calculating carbon emissions. Based on the panel data of 212 Chinese cities from 2008 to 2023, it applies spatial econometric models to examine the specific impact mechanisms of AI technology application and its synergistic effect with GF on urban PCER. The findings are as follows: (i) The application of AI technology not only directly promotes the level of PCER, but also exerts an indirect effect by improving green total factor productivity (GTFP), accelerating green innovation (GI), and reducing energy consumption intensity (ECI). (ii) GF can work in synergy with AI technology application to boost urban PCER efforts. This collaborative effect can also be realized through mechanisms linked to GTFP, GI, and ECI, and it generates a notable positive spatial spillover effect. (iii) Heterogeneity analysis reveals that AI-driven PCER exerts a more pronounced impact on western cities, non-provincial capital cities, and resource-based cities. The results of this study provide valuable theoretical insights and practical direction for formulating policies that promote urban PCER, while also advancing green and high-quality development.
