Search Results (39)

Addressing the imperative for energy transition amid depleting fossil fuels, distributed generation (DG) is increasingly integrated into distribution networks (DNs). This integration necessitates low-carbon dispatching solutions that reconcile economic and environmental objectives. To bridge the gap between conventional “electricity perspective” optimization and emerging “carbon perspective” requirements, this research integrated Carbon Emission Flow (CEF) theory to analyze spatiotemporal carbon flow characteristics within DN. Recognizing the limitations of the single-objective approach in balancing multifaceted demands, a multi-objective optimization model was formulated. This model could capture the spatiotemporal dynamics of nodal carbon intensity for low-carbon dispatching while comprehensively incorporating diverse operational economic costs to achieve collaborative low-carbon and economic dispatch in DG-embedded DN. To efficiently solve this complex constrained model, a novel Q-learning enhanced Moth Flame Optimization (QMFO) algorithm was proposed. QMFO synergized the global search capability of the Moth Flame Optimization (MFO) algorithm with the adaptive decision-making of Q-learning, embedding an adaptive exploration strategy to significantly enhance solution efficiency and accuracy for multi-objective problems. Validated on a 16-node three-feeder system, the method co-optimizes switch configurations and DG outputs, achieving dual objectives of loss reduction and carbon emission mitigation while preserving radial topology feasibility. Full article

With the acceleration of global climate warming and agricultural modernization, the energy and carbon emission issues of agricultural parks (APs) have drawn increasing attention. An AP equipped with biogas-based combined heat and power (CHP) generation and photovoltaic systems serves as a prosumer terminal in a distribution network (DN). This paper introduces carbon emission flow (CEF) theory into the coordinated dispatch of APs and DNs. First, a CEF model for APs is established. Then, based on this model, a carbon–energy coordinated dispatch is carried out under bidirectional CEF interaction between the park and DN. A bidirectional carbon tax mechanism is adopted to explore the low-carbon synergy potential between them. Finally, the Stackelberg game approach is employed to address the pricing of electricity purchase/sale and carbon taxes in a DN, and the particle swarm optimization algorithm is used for rapid generating solutions. The case study shows that the proposed CEF model can effectively determine CEF distribution in the park. Moreover, the proposed bidirectional carbon tax mechanism significantly enhances the low-carbon economic benefits of both the AP and the DN. Full article

To improve the consumption rate of distributed energy and enhance the self-healing performance of distribution networks, this paper proposes a distribution network optimization method considering carbon emissions and dynamic reconfiguration. Firstly, various measures such as dynamic reconfiguration and distributed energy scheduling are used in upper-level optimization to reduce the network loss and solar curtailment cost of the system and to realize the optimal economic operation of the distribution network. Secondly, based on carbon emission flow theory in lower-level optimization, a low-carbon demand response model with a dynamic carbon emission factor as the guiding signal is established to promote carbon emission reduction on the user side. Then, the second-order cone planning and improved dung beetle optimization algorithm are used to solve the model. Finally, it is verified on the test system that the method can effectively reduce the risk of voltage overruns and enhance the low-carbonization and economy of distribution network operation. Full article

The imperative of decarbonization in maritime shipping is underscored by the sector’s sizeable contribution to worldwide greenhouse gas emissions. ShipNetSim, an open-source multi-ship simulator created in this study, combines state-of-the-art hydrodynamic modeling, dynamic ship-following techniques, real-time environmental data, and cybersecurity threat simulation to quantify and evaluate marine fuel consumption and CO₂ emissions. ShipNetSim uses well-validated approaches, such as the Holtrop resistance and B-Series propeller analysis with a ship-following model inspired by traffic flow theory, augmented with a novel module simulating cyber threats (e.g., GPS spoofing) to evaluate operational efficiency and resilience. In a case study simulation of the journey of an S175 container vessel from Savannah to Algeciras, the simulator estimated the total fuel consumption to be 478 tons of heavy fuel oil and approximately 1495 tons of CO₂ emissions for a trip of 7 days and 15 h within 13.1% of reported operational estimates. A twelve-month sensitivity analysis revealed a marginal 1.5% range of fuel consumption variation, demonstrating limiting variability for different environmental conditions. ShipNetSim not only yields realistic predictions of energy consumption and emissions but is also demonstrated to be a credible framework for the evaluation of operational scenarios—including speed adjustment, optimized routing, and alternative fuel strategies—that directly contribute to reducing the marine carbon footprint. This capability supports industry stakeholders and policymakers in achieving compliance with global decarbonization targets, such as those established by the International Maritime Organization (IMO). Full article

The conversion and storage of renewable energy into thermal energy is an important part of the low carbon economy. The goaf of a deep mine offers the possibility of large-scale thermal energy storage due to its sufficient underground space. Since the repositories are built inside the goaf backfill and there are few studies on their heat storage capacity and effectiveness, this paper builds an experimental platform based on the similarity theory and selects the geometric similarity ratio of 1:15 to study the phase change heat storage performance of the backfill mine heat storage. Under the typical operating conditions, the temperature distribution of the PCM inside the cylindrical storage unit was analyzed. At the end of heat storage, the temperature distribution of the PCM was 0.93–0.98, but at the end of heat release, the temperature distribution of the PCM was not uniform. At the same time, the heat is reasonably corrected, so that the thermal energy charging effectiveness is increased to 0.98, and the total effectiveness of thermal energy charge and discharge remains 0.87. The parameters of the storage unit are analyzed in detail by changing the water temperature, the flow velocity and the ratio of heat storage and release time of the circulating medium. The experimental results show that when the heat release water temperature is constant and only the heat storage water temperature is changed, the higher the water temperature, the higher the total effectiveness of thermal energy charge and discharge. On the contrary, when the heat storage water temperature is constant and the heat release water temperature is reduced to 14 °C, the total effectiveness of the heat release is increased by 7.5%. When the flow state is in transition, the total effectiveness decreases. The longer the heat storage/release time, the smaller the TSTD_ave inside the PCM and the more uniform the temperature distribution. By restoring the experimental data to the engineering prototype, the repositories installed in the goaf were able to store and extract 422.88 GJ and 375.97 GJ of heat, respectively. Finally, the environmental assessment of the C-LHSU showed that the carbon emissions per unit heating area of the CFB, GWHF and GHF were reduced by 88.1%, 84.2% and 83.0%, respectively. The experimental results show that the cylindrical phase change heat reservoir has higher heat transfer energy efficiency, which provides a theoretical basis and engineering reference for efficient heat storage and utilization in deep mine goafs. Full article

In the context of integrating renewable energy sources such as wind and solar energy sources into distribution networks, this paper proposes a proactive low-carbon dispatch model for active distribution networks based on carbon flow calculation theory. This model aims to achieve accurate carbon measurement across all operational aspects of distribution networks, reduce their carbon emissions through controlling unit operations, and ensure stable and safe operation. First, we propose a method for measuring carbon emission intensity on the source and network sides of active distribution networks with network losses, allowing for the calculation of total carbon emissions throughout the operation of networks and their equipment. Next, based on the carbon flow distribution of distribution networks, we construct a low-carbon dispatch model and formulate its optimization problem within a Markov Decision Process framework. We improve the Soft Actor–Critic (SAC) algorithm by adopting a Gaussian-distribution-based reward function to train and deploy agents for optimal low-carbon dispatch. Finally, the effectiveness of the proposed model and the superiority of the improved algorithm are demonstrated using a modified IEEE 33-bus distribution network test case. Full article

In order to clearly determine the carbon emission distribution of regions, lines or nodes in the power grid, this paper applies cloud theory to the evaluation of the distribution of carbon emissions in the power grid. Based on the theory of carbon emission flow in the whole life cycle, five indicators that can reflect the spatial and temporal distribution of carbon emissions are constructed from the two dimensions of space and time. Cloud theory is used to establish the standard cloud of the carbon emission distribution level to quantify the randomness and fuzziness of the data to be evaluated. The bilateral constraint cloud theory and data-driven cloud transformation are combined to construct five comprehensive standard clouds of excellent, good, medium, poor and inferior, which are used as the evaluation interval of the evaluation index of carbon emission distribution. The reverse cloud is used to convert multiple sets of data into cloud droplets. Through the similarity measurement algorithm based on cloud model overlap, the comprehensive evaluation level of carbon emission distribution state in the time dimension is determined. Taking the IEEE 39 system as the research object, the spatial and temporal distribution of carbon emissions is evaluated, and the rationality and effectiveness of the proposed model are verified. Finally, the influence of the new energy penetration rate and power supply structure on the carbon emission distribution of the power grid is discussed by using cloud computing. Based on this, the targeted carbon reduction strategies for different types of nodes and the method of measuring the optimal new energy penetration rate are proposed and can provide a decision-making reference for optimizing the carbon emissions of the power grid. Full article

The possible contribution of phonon drag effect to the thermoelectrically sustained potential of a heated nanoisland on a semiconductor surface was estimated in a first principal consideration. We regarded electrons and phonons as interacting particles, and the interaction cross-section was derived from the basic theory of semiconductors. The solution of the equation of motion for average electrons under the simultaneous action of phonon drag and electric field gave the distributions of phonon flux, density of charge carriers and electric potential. Dimensional suppression of thermal conductance and electron-phonon interaction were accounted for but found to be less effective than expected. The developed model predicts the formation of a layer with a high density of charge carriers that is practically independent of the concentration of dopant ions. This layer can effectively intercept the phonon flow propagating from the heated nanoisland. The resulting thermoEMF can have sufficient magnitudes to explain the low-voltage electron emission capability of nanoisland films of metals and sp²-bonded carbon, previously studied by our group. The phenomenon predicted by the model can be used in thermoelectric converters with untypical parameters or in systems for local cooling. Full article

The decarbonization of power systems plays a great part in the carbon neutrality goal. Currently, researchers have explored reducing carbon in power systems in terms of the optimization of energy supply structure and operation strategies, but ignored the carbon reduction potential of users. To investigate the carbon reduction capability of users and further promote power system decarbonization through the active response of electricity loads, this paper proposes a carbon-reduction-oriented demand response (CRODR) technology based on generalized nodal carbon emission flow theory. First, the framework of the CRODR mechanism is established to provide an interaction baseline for users facing carbon reduction guiding signals. Secondly, the generalized nodal carbon emission flow theory is introduced to provide a calculation method for the guiding signals, considering dynamic electricity carbon emission factors with various spatiotemporal resolutions. Then, a matrix-based method is proposed to efficiently solve the carbon emission flow and obtain the guiding signals. On this basis, an optimal load-regulating model to help users meet their carbon reduction goals is built, and a carbon reduction benefit-evaluation method is proposed. Case studies on China’s national power system and a textile company verify that CRODR technology can realize efficient carbon reduction through load shifting while maintaining the total power consumption of users. The proposed CRODR technology is expected to provide a theoretical basis and guiding mechanism for promoting carbon reduction throughout the entire power system. Full article

Power systems are the main source of carbon emissions. Currently, coordinated operation strategies of the source–grid–load–storage model considering carbon emissions is primarily expanded from the generation side. For practical power systems, where multiple types of generating units coexist at a single node, it is difficult to develop unit combination strategies that simultaneously consider carbon emission factors and power flow constraints. Therefore, a new power flow calculation method based on connectivity matrix theory was proposed, aiming to address the issues of existing approaches that are too coarse and unable to accurately represent the operating states of multiple units under each node. Furthermore, a new method for dynamic calculation of regional carbon emission based on connectivity matrix and carbon emissions flow was introduced to improve the accuracy of carbon emission measurements. Firstly, a simulation model for a coordinated optimization operation based on the minimum system cost for the source–grid–load was established and an optimal flow calculation method using a connectivity matrix was introduced. Second, a dynamic carbon emission calculation method, considering electricity sources, was developed by combining the results of the optimal power flow calculation with carbon emission flow theory. Finally, the effectiveness of the approach in this article was verified by the IEEE 14-bus system example and a provincial power grid, ensuring strict adherence to the conservation principle of carbon emissions between the supply and demand sides and satisfying power flow constraints. Full article

The traditional blast furnace ironmaking process is the most widely used ironmaking process globally, yet it is associated with significant drawbacks, including high energy consumption and carbon emissions. To achieve low-carbon ironmaking, researchers have developed hydrogen ironmaking, which is capable of achieving lower CO₂ emissions. Nevertheless, the distribution behavior of impurities has been less studied in the existing research on hydrogen ironmaking. Therefore, in this study, the factors affecting the slag properties and distribution of impurity elements during hydrogen ironmaking were investigated using FactSage, and smelting experiments were carried out. The results show that temperature has the greatest influence on the distribution behavior of the impurities, and excessively elevated temperatures result in the ingress of a significant quantity of impurities into the reduced iron. Reduced iron with a purity of 98.52% was obtained under the conditions of 10%, 10%, 2%, and 2% ratios of CaO, SiO₂, MgO, and Al₂O₃, respectively, a hydrogen flow rate of 12 mL/min, and a temperature of 1400 °C; Lg L Mg, Lg L Al, Lg L Si, and Lg L Ca were 2.72, 2.41, 3.36, and 2.45, respectively (“L” stands for slag-to-metal ratio). The slag was mainly dominated by the silicate, and the iron was mainly lost in the form of mechanical inclusions in the slag. This study will enrich the basic theory of hydrogen ironmaking and is of great significance for the realization of carbon neutralization. Full article

Cold chain logistics is an industry that generates high levels of carbon emissions. In the context of a low-carbon economy, it is crucial to recognize the low-carbon competitiveness of regional cold chain logistics and to implement effective measures to guide the development and improvement of their low-carbon competitiveness. This is essential for transitioning the economic development model and promoting low-carbon economic growth. This article proposes a low-carbon competitiveness evaluation model known as the Triangular Fuzzy–Gray Correlation Evaluation Model. This model is based on the Triangular Fuzzy Theory and Gray System Theory. According to the calculated logistics low-carbon competitiveness index, a scatter plot is used to rank and classify the evaluation objects. This method utilizes triangular fuzzy numbers as evaluation levels and further expands upon them by introducing the concept of gray correlation in group decision making. By constructing relative closeness based on curve similarity, the improved method possesses a strong ability to capture information and objectivity compared to traditional models. The selected critical indicators cover four significant aspects: low-carbon environment, low-carbon flow service capability, energy consumption in cold chain logistics, and low-carbon energy transition. Empirical research is being conducted using relevant data from Henan in 2022. The measured results are divided into four levels of competition. Using the diamond model, this study analyzes the development of low-carbon cold chain logistics at different levels in each city and provides corresponding recommendations. Full article

The cleanliness of charging power determines whether electric vehicles can fully utilize their low-carbon properties. This paper, taking into account the impact of temperature on the energy consumption of electric vehicle air conditioning, uses the Monte Carlo algorithm to calculate the typical daily charging load of electric vehicle clusters in different seasons. Secondly, based on the Shapley value carbon responsibility allocation method, a reasonable range of carbon emission responsibilities for different electric vehicle agents is calculated, and a tiered carbon pricing method is proposed accordingly. Then, using carbon emission flow theory, we calculate the carbon emissions generated by the different agents’ charging amounts and corresponding carbon emission costs. Finally, a low-carbon incentive guidance model is constructed with the signal of tiered carbon prices and the goal of minimizing operating costs to re-optimize the charging load distribution of electric vehicles. Case studies showcase that the proposed method is effective in reducing power system carbon emissions and electric vehicle charging costs. Full article

Natural gas is an eco-friendly energy source with low carbon emissions, making it attractive globally. Understanding gas reservoirs is crucial for sustainable extraction and optimizing potential. However, the complicated fluid flow and production dynamics within intricate gas reservoirs, particularly those characterized by abnormally high pressures and tight porous media, remain partially understood and demand further investigation. In a tight porous medium subjected to high pressure, the assumption of constant permeability is no longer valid. Consequently, a novel composite seepage model has been developed in this study, which considers the responsiveness of permeability to stress. Perturbation theory is employed to address the inherent non-linearity demonstrated by the permeability modulus. The solution of dimensionless pressure responses under constant production conditions is accomplished in the Laplace domain by implementing integral transformation methods. Overall, a comprehensive model is provided to understand the production behaviors of tight gas reservoirs. Moreover, in order to comprehend the transient flow characteristics of tight gas reservoirs, log–log plots are generated through the Stehfest numerical inversion approach, with the flow regimes categorized based on the normalized time phases of the pressure curves. Parametric investigations reveal that stress sensitivity detrimentally affects permeability, resulting in more pronounced pressure declines during the intermediate and late flow phases. The transient seepage model elaborated in this study is able to consider the pertinent formation and well parameters. These interpreted parameters bear significance in designing hydraulic fracturing operations, assessing the potential of tight gas reservoirs, and ultimately enhancing gas production. The presented model not only enhances our understanding of the behavior of horizontal wells in stress-sensitive tight gas reservoirs but also makes a valuable contribution to the broader discussion on transient flow phenomena in petroleum engineering. Full article

Domestic regional synergistic emission reduction is important in achieving national climate goals. This study constructed a game theory-based model for regional synergistic emission reduction, modified the Basic Climate Game using the exact-hat algebra method, and expanded the game model using a general spatial equilibrium model to incorporate cross-regional economic impacts generated by emission reduction actions through factors and product flows. The formation of regional comparative advantages in emission reductions and their impact on synergistic emission reductions were revealed through regional characteristics such as emission elasticity, sectoral structure, regional trade shares, and green total factor productivity. A form of synergy was then proposed that utilizes the comparative advantages of different regions, allowing for synergistic emission reductions across different income regions and engagement with regions that are still at the carbon-peaking stage in cooperation. Moreover, the model was created to be as close to the economic reality as possible to provide a trade, industry, and economic growth policy that complements emission-reduction policies. Full article