Abstract

By the end of 2022, the Chinese government had completely phased out subsidies for the purchase of electric vehicles and introduced replacement subsidies instead, aiming to promote the electrification of automobiles by incentivizing consumers to replace fuel vehicles with electric ones. This paper employs evolutionary game theory to construct a tripartite evolutionary game model of the government, automakers, and consumers. It analyzes the evolutionary strategies of automaker and consumer decisions under both proactive and passive subsidy attitudes of the government, coupled with system dynamics simulations. The study investigates the evolutionary stable states of the government, automakers, and consumers, as well as the impact of external variables on the strategic choices of the involved parties. Simulation results indicate that the government’s strategic choices at the initial stages of new energy vehicle development are crucial for the system to reach a positive stable state; operational revenues from charging stations and the utility derived from new energy vehicles are the primary drivers for automakers and consumers to adopt proactive strategies, respectively. This research provides a quantitative analysis of government subsidies to inform the optimization of new energy vehicle subsidy policies.

Abstract

Green bonds are a rapidly expanding and important green financing tool, but their impact on carbon emission reduction remains in question, particularly at the more granular level of cities. This study examines the relationship between green bonds and emission mitigation at the city level, using data on green bond issuance in 226 Chinese cities from 2006 to 2019. The findings reveal a positive correlation between the issue of green bonds and urban decarbonization, while this positive correlation is not uniformly observed in regions with relatively poor economic development, limited technological innovation and outdated industrial structure. This indicates that green bonds do not help those most in need. Furthermore, no significant mediating or moderating effects of urban industrial structures and technological innovation have been observed, suggesting that the current size and quality of green bond market is not enough to bring about changes in the industrial structure and low-carbon innovation. We also identify urban energy structure and related green finance policy such as Low-Carbon City pilot policy can jointly enhance the mitigation effect of green bond. In this regard, policymakers should take into account the variations among regions and the coordination between policies. Green bonds need to improve their role in reducing financing costs and risks for green projects, allowing funds to be directed to areas where they are most needed.

Abstract

A novel design of different arrangements of micro pin-fins is proposed and comparative analysis has been done to study the heat transfer enhancement evaluated by pressure drop, dimensionless temperature hoist at the exit and Nusselt number. Furthermore, the variables that material of heat sink, five different shapes and three different arrangement of micro pin fins are investigated to determine the MCHS with optimum heat transfer capacity. The predicted results demonstrate that the amount of Nu for the novel deign 59.9% larger than unfinned structure at Re=1000. The addition of micro pin-fins increases heat transfer surface area as well as interrupts and makes thermal boundary layer redevelop, resulting effective heat transfer. Different arrangements improve the effect and show better thermal performance. The results also illustrate that the pin-fin with triangular shaped cross-section yield better performance.

Abstract

Urban aquaponics farms, which combining recirculating aquaculture systems with hydroponics, are an innovative and eco-friendly way to meet the animal protein needs of urban populations with limited agricultural land and water resources. Aquaponics farms spend a large percentage of their total operating costs on electricity due to the wide variety of electrical equipment and a single energy supply method. This study proposes a demand response-based method for joint dispatch of greenhouse aquaponics PV output and load that can optimize the unit operation scheme and the battery storage charging/discharging activities to reduce operating costs and maximize the consumption of renewable energy. The case study demonstrates that the proposed method reduces the overall electricity consumption of the farm and improves sustainability and economic efficiency. The proposed approach promotes the sustainable growth of urban aquaponics farms.

Abstract

To achieve carbon neutrality while maintaining economic stability, it is imperative to promote the widespread adoption of clean energy and further reduce emissions from fossil fuel use. With the decreasing cost of clean energy and the increasing penetration of electric vehicles (EV), utilizing EVs as the primary energy storage for photovoltaic (PV) systems can play a crucial role in constructing low-carbon cities. In this study, we conducted a technical and economic analysis of a combined rooftop PV generation and EV energy storage system in Qingdao, China. Our findings reveal that leveraging EV energy storage to address the bottleneck in urban distributed PV development holds immense potential. By 2030, carbon emissions in the electricity and vehicles part could be reduced by 73%, while energy costs could decrease by 35%. This study can provide a reference for the decarbonization in Qingdao and provide a research framework for smaller scale studies.

Abstract

Wind-powered hydrogen production is a promising method to enhance dispatch flexibility for renewable energy. However, frequent fluctuations of the wind power cause switching of electrolyzer states, resulting in instabilities in the hydrogen production process. Furthermore, the prolonged cold start leads to low operation temperature which thus leads to low energy utilization rate in the wind-powered hydrogen production system. For these concerns, this study proposes a solution for stabilizing the voltage output with variable system temperatures within different power ranges, so as to effectively minimize the frequency of start-stop cycles and enhance the efficiency of hydrogen production. Combined with computational fluid dynamics (CFD), a research model for the polarization characteristic curve of an alkaline electrolyzer (AE) under variable working temperature conditions, as well as a model of wind-powered hydrogen production system considering heat transfer are established. In addition, through non-dominated sorting genetic algorithm, the voltage output schemes, setting efficient and stable hydrogen production in different power intervals as the objective function, are optimized. The proposed strategy is applied to a wind power generation process with a design condition of 1 MW capacity under specific climatic conditions in Northeast China. The system energy efficiency analyses show that, compared with the traditional wind-powered hydrogen production system, the temperature compensation strategy is able to effectively improve the system energy utilization rate by 0.61%. Meanwhile, full-condition operation analyses demonstrate that the system start-stop frequency could be significantly reduced. This study provides a valuable reference program for efficient hydrogen production under variable temperature conditions.

Abstract

Hydrogen production by methanol steam reforming (MSR) is one of the promising solutions for mobile hydrogen sources, particularly in applications like proton exchange membrane fuel cells (PEMFCs) and hydrogen refueling stations. However, due to the endothermic characteristics of MSR, its hydrogen production efficiency is greatly affected by thermal management, such as temperature gradient and cold/hot spot. Considering portability and integrated applications, miniaturizing the reaction system to achieve efficient continuous hydrogen production is also a significant challenge. Using fractal geometry in engineering applications can produce larger surface area and lower mass devices. In order to improve the heat and mass transfer as well as the chemical reaction performance of the reforming system, this study uses the Sierpinski carpet model, which integrates four iterations of fractal geometry, into the design of the MSR microreactor. The fluid flow, heat transfer, and chemical reaction processes in MSR fractal microreactor are simulated by the finite element method. A comparison is made with a flat plate type reactor (0th order). The results indicate that the heat and mass transfer performance of the fractal microreactors is higher than that of the flat plate type reactor. Compared with the 0th order reactor, the 4th order fractal microreactor can effectively improve the utilization efficiency of H by about 8.33% and improve the H2 production rate by 7.91%. In addition, a higher steam/CH3OH (S/C) mole ratio results in a higher H utilization efficiency, the relative concentration of CO at the outlet, and the H2 production rate, while a higher reforming temperature results in a lower H2 production rate. This study can provide a theoretical basis and technical support for the industrial application of microreactors in mobile hydrogen production and fuel cells.

Abstract

With the rapid increase in building energy consumption during the urbanization process, the analysis of building energy consumption and response potential needs to consider the impact of urban microclimate. This paper explores the factors and pathways influencing the energy consumption of Building Energy Cells under urban microclimatic conditions and constructs a model of Building Energy Cells affected by urban microclimate, aiming to reveal how these microclimatic conditions affect the energy use behavior and response potential of Building Energy Cells. This study provides a scientific basis for urban planners and architects, promotes a deeper understanding of building energy consumption under urban microclimatic conditions, and offers new perspectives and methods for achieving sustainable use of building energy.

Abstract

The widespread adoption of distributed photovoltaic (PV) systems highlights the need for sophisticated segmentation technologies that can accurately identify PV panels, essential for calculating potential capacity and informing development strategies. Although artificial intelligence has significantly advanced the accuracy and reliability of PV panel segmentation, real-world complexities such as diverse panel types, installation methods, and varied backgrounds pose challenges to model adaptability and generalization. This research introduces a method that enhances PV panel segmentation by employing the enhanced Segment Anything Model, which has been extensively pre-trained using a comprehensive real-world dataset to incorporate multimodal semantic information, thus improving generalization. Additionally, a fine-tuning process has been integrated to better absorb critical features from the training data, increasing the model’s sensitivity to the unique characteristics of specific PV installations. Field tests in Heilbronn, Germany, confirm the method’s superior performance and flexibility, underscoring its potential to support strategic planning for large-scale PV deployment.

Abstract

As an increasing number of areas are turning to renewable energy sources to meet the growing energy demand of buildings, the intermittent generation times of renewable energies present a significant challenge. These sources often fail to provide sufficient energy during peak consumption periods. Vehicle-to-Building systems (V2B), serving as flexible energy storage solutions within buildings, have the capability to overcome these intermittency issues. This work focuses on applying deep reinforcement learning (DRL) to control the complex building energy management system. Our algorithm leverages historical photovoltaic data, building energy consumption profiles, and the State of Charge (SOC) along with the entry and exit times of electric vehicles. It strategically sets the charging and discharging power of each EV in real-time to optimize energy usage and manage the unpredictability of renewable energy. We integrated the reinforcement learning framework with a city-level energy simulation platform and conducted on various urban forms in Shenzhen, China as case studies. A series of experiments were carried out, demonstrating the effectiveness and practicality of our approach compared with Model Predictive Control (MPC) methods in peak shaving and load leveling.