• Energy Research
• 2025-2025close
• Transport Research close
• Energy Research close

Scenarios to stabilize global climate and meet international climate agreements require rapid reductions in human carbon dioxide (CO2) emissions, often augmented by substantial carbon dioxide removal (CDR) from the atmosphere. While some ocean-based removal techniques show potential promise as part of a broader CDR and decarbonization portfolio, no marine approach is ready yet for deployment at scale because of gaps in both scientific and engineering knowledge. Marine CDR spans a wide range of biotic and abiotic methods, with both common and technique-specific limitations. Further targeted research is needed on CDR efficacy, permanence, and additionality as well as on robust validation methods—measurement, monitoring, reporting, and verification—that are essential to demonstrate the safe removal and long-term storage of CO2. Engineering studies are needed on constraints including scalability, costs, resource inputs, energy demands, and technical readiness. Research on possible co-benefits, ocean acidification effects, environmental and social impacts, and governance is also required.

The growing demand for decarbonization, coupled with the development of intelligent transportation systems (ITS), has driven the emergence of eco-driving technologies for electric vehicles (EVs). However, existing eco-driving technologies rarely integrate path and velocity planning while neglecting macro traffic flow and environmental impacts, resulting in less practical and less precise planning outcomes. Therefore, this study proposes a hierarchical eco-driving model that establishes a high-dimensional system incorporating macro traffic flow, micro vehicle model, and road environments. First, a traffic network model is constructed based on the real road topology. Next, a high-precision vehicle energy consumption model and a database of typical driving cycles are established to calculate the edge costs of the road network. Then, an energy-efficient route is efficiently planned using the proposed multi-heuristic A* algorithm. Finally, based on the route information from the upper level, along with traffic, kinematic, and road information, a convex optimization algorithm is employed to achieve accurate and efficient velocity planning. Experimental results demonstrate that the proposed method computes in less than 2 s for most scenarios and can effectively save energy and time by over 10%. The proposed framework offers a new solution for eco-driving and has significant practical implications.

Abstract Future 6th Generation (6G) networks will rely on Terahertz (THz) wireless communication as their main enabler for delivering both ultra-high data speed and minimal delay. THz wireless systems become crucial for upcoming communications by using Unmanned Aerial Vehicles (UAVs) together with Intelligent Reflecting Surfaces (IRS) while improving reliability and efficiency. In UAV-IRS-assisted networks, minimizing mission completion time and energy consumption is critical. However, achieving rapid mission execution often requires UAVs to operate at higher speeds, increasing energy usage and creating a trade-off that demands optimization. This paper addresses the challenge of optimizing UAV-IRS trajectories in THz networks to reduce mission time while adhering to energy constraints. Given the non-convex and NP-hard nature of the problem, traditional optimization methods are insufficient. To tackle this, we propose a Multi-Agent Deep Reinforcement Learning (MADRL) algorithm, which provides an efficient, low-complexity solution for trajectory optimization. MADRL dynamically adapts UAV-IRS paths, balancing mission efficiency and energy savings. Simulation results demonstrate that the proposed MADRL-based approach outperforms existing benchmarks, achieving shorter mission times and near-optimal energy consumption across varying scenarios. By leveraging cooperative learning, the algorithm effectively handles complex environments with multiple users and IRS elements. This work highlights the potential of MADRL for UAV-IRS trajectory optimization, offering a scalable solution for energy-efficient and high-performance THz communication systems.

This study explores the intersection of two pivotal interventions aimed at achieving carbon neutrality: the electric vehicles (EVs) adoption and the renewable energy (RE) electricity generation. Focusing on a Renewable Energy-Dominated (RED) electricity system, the research examines the interdependence between these interventions and their collective impact on economic dispatch. The study's objective is to determine optimal economic dispatch strategies that meet hourly electricity demand, considering two distinct supply scenarios across eight supply options. The first scenario assesses the maximum possible supply, while the second contemplates the minimum possible supply from each option. Additionally, the study delves into the influence of social cost of emissions on these economic dispatches. Employing an experimental design, the study generates representative load curves that incorporate EV charging demands for varied levels of EV penetration, alongside regular electricity demand. Data from Karnataka's RED electricity system provides a basis for the supply-side analysis. The economic dispatch for each supply scenario is formulated as a Mixed Integer Linear Program (MILP), aiming to minimize both costs for generation and social costs of emissions, while adhering to operational constraints of the supply options. Key findings from this approach, highlight several critical insights: the significant role of incorporating social costs in economic dispatch decisions, the tangible impact of EV demand on supply shortages, and the importance of maintaining supply capacity to minimize these shortages.

Abstract Transit is a crucial mode of transportation, especially in urban areas and for urban and rural disadvantaged communities. Because extreme temperatures often pose threats to the elderly, members of the disability community, and other vulnerable populations, this study seeks to understand the level of influence that extreme temperatures may have on transit users across different demographic groups. In this case study for Atlanta, GA, heat stress is predicted for 2019 transit riders (using transit rider activity survey data) and for three future climate scenarios, SSP245, SSP370, and SSP585, into the year 2100. The ThermoRoute Analyzer and TransitSim 4.0 models were applied to predict cumulative heat exposure and trip-level risk for 35 999 trip equivalents for an average Atlanta area weekday in the summer of 2019. The analyses show that under 2019 weather conditions, 8.33% of summer trips were estimated to be conducted under extreme heat. With the projected future climate conditions, the percentage of trips under extreme heat risk grows steadily. By 2100, 37.1%, 56.1%, and 76.4% are projected to be under extreme heat risk for low, medium, and high future climate projections (scenarios SSP245, SSP370, and SSP585), respectively. Under current weather conditions, Atlanta transit riders that own no vehicles and transit riders that are African American are disproportionately influenced by extreme heat. The disparity between these two groups and other groups of transit riders becomes wider as climate change continues to exacerbate. The findings of the study highlight an urgent need to implement heat mitigation and adaptation strategies in urban transit networks.

As electric mobility gains popularity, Electric Vehicles (EVs) and their batteries are becoming more attractive due to their size and energy density advantages. However, the electric grid has not undergone similar improvements, potentially impacting power stability and affecting EV energy usage and availability. The key challenge lies in managing increasing power demands from a fully EV fleet. To address this, efforts are needed to analyze the integration of EVs into the grid and optimize power distribution. In this paper, an innovative Energy Management Strategy (EMS) is proposed to effectively control energy loads, energy sources, and EVs, incorporating Vehicle-to-Grid (V2G) capability. The EMS optimizes energy flow and storage based on time of day, potential energy production, and the cost of grid electricity. The integration of this EMS results in significant benefits, with approximately 12% savings in electricity bills compared to a reference strategy.

This report examines pathways for advancing renewable energy and decarbonization in Brazil, analyzing three scenarios: Business as Usual, expanded Distributed Generation Photovoltaic (DGPV) adoption, and increased electric vehicle (EV) use. Key findings highlight the importance of financial incentives for DGPV expansion, with a 35% cost reduction in solar technology expected to have a significant impact by 2042. A projected 20% increase in EVs by 2050 would contribute to a 20% emissions reduction with minimal strain on the electricity system. The report recommends long-term policy planning, integrated renewable energy and transportation policies, and further research on EV adoption and implementation challenges.

Integrating Electric Vehicles (EVs) into power grid presents critical energy management challenges, especially in microgrid systems powered by renewable energy sources. This study introduces a novel energy management strategy for EV charging stations utilizing an Adaptive Neuro-Fuzzy Inference System (ANFIS) controller. This system dynamically optimizes the coordination of renewable energy sources solar PhotoVoltaic (PV) panels and wind turbines energy storage, and EV chargers. By leveraging real-time data and predictive algorithms, the ANFIS controller adapts to fluctuations in energy supply and demand, ensuring optimal performance. The innovation of this work lies in combining fuzzy logic with neural network-based learning to enhance decision-making under uncertain and variable renewable energy conditions. The proposed approach employs a robust design methodology, integrating neural network training with fuzzy logic system development, to create an adaptive and intelligent control system. Simulation results using MATLAB/Simulink demonstrate a 92% increase in energy efficiency and an 89% enhancement in load-handling capacity compared to conventional methods. The system effectively manages renewable energy variability, battery state-of-charge, and load demand, maintaining stable electrical characteristics even under dynamic wind and solar conditions. This work underscores the importance of advanced AI-driven control strategies in enabling sustainable EV charging infrastructure within microgrid environments.