Search Results (113)

The large-scale integration of new energy is an inevitable trend to achieve the low-carbon transformation of power systems. However, the strong randomness of wind power, photovoltaic power, and loads poses severe challenges to the safe and stable operation of systems. Existing studies demonstrate insufficient integration and handling of source-load bilateral uncertainties in wind–solar–fossil fuel storage complementary systems, resulting in difficulties in balancing economy and low-carbon performance in their energy storage configuration. To address this insufficiency, this study proposes an optimal energy storage configuration method considering source-load uncertainties. Firstly, a deterministic bi-level model is constructed: the upper level aims to minimize the comprehensive cost of the system to determine the energy storage capacity and power, and the lower level aims to minimize the system operation cost to solve the optimal scheduling scheme. Then, wind and solar output, as well as loads, are treated as fuzzy variables based on fuzzy chance constraints, and uncertainty constraints are transformed using clear equivalence class processing to establish a bi-level optimization model that considers uncertainties. A differential evolution algorithm and CPLEX are used for solving the upper and lower levels, respectively. Simulation verification in a certain region shows that the proposed method reduces comprehensive cost by 8.9%, operation cost by 10.3%, the curtailment rate of wind and solar energy by 8.92%, and carbon emissions by 3.51%, which significantly improves the economy and low-carbon performance of the system and provides a reference for the future planning and operation of energy systems. Full article

With the increasing integration of renewable energy and smart technologies in residential energy systems, proactive household energy management (HEM) have become critical for reducing costs, enhancing grid stability, and achieving sustainability goals. This study proposes a ultra-short-term forecasting-driven proactive energy consumption optimization strategy that integrates advanced forecasting models with multi-objective scheduling algorithms. By leveraging deep learning techniques like Graph Attention Network (GAT) architectures, the system predicts ultra-short-term household load profiles with high accuracy, addressing the volatility of residential energy use. Then, based on the predicted data, a comprehensive consideration of electricity costs, user comfort, carbon emission pricing, and grid load balance indicators is undertaken. This study proposes an enhanced mixed-integer optimization algorithm to collaboratively optimize multiple objective functions, thereby refining appliance scheduling, energy storage utilization, and grid interaction. Case studies demonstrate that integrating photovoltaic (PV) power generation forecasting and load forecasting models into a home energy management system, and adjusting the original power usage schedule based on predicted PV output and water heater demand, can effectively reduce electricity costs and carbon emissions without compromising user engagement in optimization. This approach helps promote energy-saving and low-carbon electricity consumption habits among users. Full article

With the continuous expansion of global trade, achieving sustainable maritime transport optimization and operations has become a key strategic direction for transforming maritime transport companies. To summarize the current state of research and identify emerging trends in sustainable maritime transport optimization and operations, this study systematically examines representative studies from the past decade, focusing on three dimensions, technology, management, and policy, using data sourced from the Web of Science (WOS) database. Building on this analysis, potential avenues for future research are suggested. Research indicates that the technological field centers on the integrated application of alternative fuels, improvements in energy efficiency, and low-carbon technologies in the shipping and port sectors. At the management level, green investment decisions, speed optimization, and berth scheduling are emphasized as core strategies for enhancing corporate sustainable performance. From a policy perspective, attention is placed on the synergistic effects between market-based measures (MBMs) and governmental incentive policies. Existing studies primarily rely on multi-objective optimization models to achieve a balance between emission reductions and economic benefits. Technological innovation is considered a key pathway to decarbonization, while support from governments and organizations is recognized as crucial for ensuring sustainable development. Future research trends involve leveraging blockchain, big data, and artificial intelligence to optimize and streamline sustainable maritime transport operations, as well as establishing a collaborative governance framework guided by environmental objectives. This study contributes to refining the existing theoretical framework and offers several promising research directions for both academia and industry practitioners. Full article

Under the guidance of carbon peaking and carbon neutrality goals, the power industry is transitioning toward environmentally friendly practices. With the increasing integration of intermittent renewable energy sources (RES) and the enhanced self-regulation capabilities of grids, traditional distribution networks (DNs) are transitioning into active distribution networks (ADNs). To fully exploit the synergistic optimization potential of the “source-grid-load-storage” system in electricity-carbon coupling scenarios, leverage user-side flexibility resources, and facilitate low-carbon DN development, this paper proposes a low-carbon optimal scheduling strategy for ADN incorporating demand response (DR) priority. Building upon a bi-directional feedback mechanism between carbon potential and load, a two-layer distributed robust scheduling model for DN is introduced, which is solved through hierarchical iteration using column and constraint generation (C&CG) algorithm. Case study demonstrates that the model proposed in this paper can effectively measure the priority of demand response for different loads. Under the proposed strategy, the photovoltaic (PV) consumption rate reaches 99.76%. Demand response costs were reduced by 6.57%, and system carbon emissions were further reduced by 8.93%. While accounting for PV uncertainty, it balances the economic efficiency and robustness of DN, thereby effectively improving system operational safety and reliability, and promoting the smooth evolution of DN toward a low-carbon and efficient operational mode. Full article

In the context of the intensifying global climate crisis, the power industry, as a significant carbon emitter, urgently needs to promote low-carbon transformation using market mechanisms. In this paper, a multi-objective stochastic optimization scheduling framework for regional power grids integrating carbon trading (CET) and green certificate trading (GCT) is proposed to coordinate the conflict between economic benefits and environmental objectives. By building a deterministic optimization model, the goal of maximizing power generation profit and minimizing carbon emissions is combined in a weighted form, and the power balance, carbon quota constraint, and the proportion of renewable energy are introduced. To deal with the uncertainty of power demand, carbon baseline, and the green certificate ratio, Monte Carlo simulation was further used to generate random parameter scenarios, and the CPLEX solver was used to optimize scheduling schemes iteratively. The simulation results show that when the proportion of green certificates increases from 0.35 to 0.45, the proportion of renewable energy generation increases by 4%, the output of coal power decreases by 12–15%, and the carbon emission decreases by 3–4.5%. At the same time, the tightening of carbon quotas (coefficient increased from 0.78 to 0.84) promoted the output of gas units to increase by 70 MWh, verifying the synergistic emission reduction effect of the “total control + market incentive” policy. Economic–environmental tradeoff analysis shows that high-cost inputs are positively correlated with the proportion of renewable energy, and carbon emissions are significantly negatively correlated with the proportion of green certificates (correlation coefficient −0.79). This study emphasizes that dynamic adjustments of carbon quota and green certificate targets can avoid diminishing marginal emission reduction efficiency, while the independent carbon price mechanism needs to enhance its linkage with economic targets through policy design. This framework provides theoretical support and a practical path for decision-makers to design a flexible market mechanism and build a multi-energy complementary system of “coal power base load protection, gas peak regulation, and renewable energy supplement”. Full article

Under the dual carbon goal, in order to promote the consumption of new energy and reduce carbon emissions in power systems, this paper proposes a new distributed robust low-carbon optimization scheduling method for power systems based on LCA carbon emissions. Firstly, the carbon emissions of energy consumption in the power system are calculated based on the LCA method; secondly, a distributed robust optimization scheduling model is established with the goal of minimizing carbon emissions and economic costs. The model is linearly solvable through the description of uncertain parameters and the transformation of the model. Finally, the optimization results of the example scenario indicate that the distributed robust low-carbon optimization scheduling method based on LCA carbon emissions can effectively reduce carbon emissions and costs compared to traditional methods, providing theoretical support for the new low-carbon optimization strategy of power systems. Full article

Accelerating the low-carbon transition of China’s coal-fired power sector is essential for advancing national sustainability goals and fulfilling global climate commitments. This study introduces an integrated, data-driven analytical framework to facilitate the sustainable transformation of the coal power sector through coordinated unit-level retirements, new capacity planning, and targeted retrofits. By combining a comprehensive unit-level database with a multi-criteria evaluation framework, the analysis incorporates environmental, technical, and economic factors into decision-making for retirement scheduling. Scenario analyses based on the China Energy Transformation Outlook (CETO 2024) delineate both baseline and ideal carbon neutrality pathways. Optimization algorithms are employed to identify cost-effective retrofit strategies or portfolios, minimizing levelized carbon reduction costs. The findings reveal that cumulative emissions can be reduced by 10–14.9 GtCO₂ by 2060, with advanced technologies like CCUS and co-firing contributing over half of retrofit-driven mitigation. The estimated transition cost of 6.2–6.7 trillion CNY underscores the scale of sustainable investment required. Sensitivity analyses further highlight the critical role of reducing green hydrogen costs to enable deep decarbonization. Overall, this study provides a robust and replicable planning tool to support policymakers in formulating strategies that align coal power sector transformation with long-term sustainability and China’s carbon neutrality commitments. Full article

Electric vehicles (EVs) are gradually gaining high penetration in transportation due to their low carbon emissions and high power conversion efficiency. However, the large-scale charging demand poses significant challenges to grid stability, particularly the risk of transformer overload caused by random charging. It is necessary that a coordinated charging strategy be carried out to alleviate this challenge. We propose a hierarchical charging scheduling framework to optimize EV charging consisting of demand prediction and hierarchical scheduling. Fuzzy reasoning is introduced to predict EV charging demand, better modeling the relationship between travel distance and charging demand. A hierarchical model was developed based on NSGA-II, where the upper layer generates Pareto-optimal power allocations and then the lower layer dispatches individual vehicles under these allocations. A simulation under this strategy was conducted in a residential scenario. The results revealed that the coordinated strategy reduced the user costs by 21% and the grid load variance by 64% compared with uncoordinated charging. Additionally, the Pareto front could serve as a decision-making tool for balancing user economic interest and grid stability objectives. Full article

Environmental sustainability is receiving growing global attention, making the development of low-carbon and green transportation increasingly important. Low-carbon policies offer significant advantages in incentivizing energy conservation and reducing emissions in the transportation sector; however, it is vital to consider the impacts of regional differences on the implementation effect of low-carbon policies. This paper explores multimodal transportation route optimization under a carbon tax policy. First, a bi-objective route optimization model is constructed, with the goal of minimizing total transportation cost and time, while accounting for uncertain demand, fixed departure schedules, and regional differences. Trapezoidal fuzzy numbers are used to represent uncertain demand, and a fuzzy adaptive non-dominated sorting genetic algorithm is designed to solve the bi-objective optimization model. The algorithm is then tested on differently sized networks and on real-world transportation networks in eastern and western China to validate its effectiveness and to assess the impacts of regional differences. The experimental results show the following. (1) When considering transportation tasks at different network scales, the proposed fuzzy adaptive non-dominated sorting genetic algorithm outperforms the NSGA-II algorithm, achieving minimum differences in percentages of cost and time of 9.25% and 7.72%, respectively. (2) For transportation tasks assessed using real-world networks in eastern and western China, an increase in the carbon tax rate significantly affects carbon emissions, costs, and time. The degree of carbon emission reduction varies depending on the development of the regional transportation network. In the more developed eastern region, carbon emissions are reduced by up to 44.17% as the carbon tax rate increases. In the less developed western region, the maximum reduction in carbon emissions is 14.37%. The carbon tax policy has a more limited impact in the western region compared to the eastern one. Therefore, formulating differentiated carbon tax policies based on local conditions is an effective way to maximize the economic and environmental benefits of multimodal transportation. Full article

Hybrid tractors, as an efficient and environmentally friendly power system, are gradually becoming an important technical choice in the agricultural field. Compared to conventional powertrain systems, hybrid electric powertrains can achieve a 15–40% reduction in fuel consumption. By optimizing the engine operating range and incorporating electric-only driving modes, these systems further contribute to a 20–35% decline in CO₂ emissions, along with a significant mitigation of nitrogen oxides (NOx) and particulate matter (PM) emissions. In this paper, the energy management technology of hybrid tractors is reviewed, with emphasis on the energy scheduling between the internal combustion engine and electric motor, the optimization control algorithm, and its practical performance in agricultural applications. Firstly, the basic configuration and working principle of hybrid tractors are introduced, and the cooperative working mode of the internal combustion engine and electric motor is expounded. Secondly, the research progress of energy management strategies is discussed. Then, the application status and challenges of hybrid power systems in agricultural machinery are discussed, and the development trend of hybrid tractors in the fields of intelligence, low carbonization, and high efficiency in the future is prospected. This paper extracts many experiences and methods from the references over the years and provides a comprehensive evaluation. Full article

To enhance the operational efficiency of container multimodal transportation and mitigate carbon emissions during freight transit, this study investigates carbon emission-conscious multimodal transportation route optimization models and solution methodologies. Addressing the path optimization challenges under uncertain conditions, triangular fuzzy numbers are employed to characterize transportation time uncertainty, while a scenario-based robust regret model is formulated to address freight price volatility. Concurrently, the temporal value attributes of cargo are incorporated by transforming transportation duration into temporal costs within the model framework. Through the implementation of four distinct low-carbon policies, carbon emissions are either converted into cost metrics or established as constraint parameters, thereby constructing an optimization model with total cost minimization as the objective function. For model resolution, fuzzy chance-constrained programming is adopted for defuzzification processing. Subsequently, a multi-strategy improved whale optimization algorithm (WOA) is developed to solve the formulated model. Numerical case studies are conducted to validate the proposed methodology through comparative analysis with conventional WOA implementations, demonstrating the algorithm’s enhanced computational efficiency. The experimental results confirm the model’s capability to adapt multimodal transportation schedules for cargo with varying temporal value attributes and effectively reduce CO₂ emissions under different carbon reduction policies. This research establishes a comprehensive decision-making framework that provides logistics enterprises with a valuable reference for optimizing low-carbon multimodal transportation operations. Full article

Focusing on issues related to SDG 11 (Sustainable Cities and Communities) and SDG 13 (Climate Action), this study aligns with the framework of the 2030 Agenda for Sustainable Development. This study explores the barriers unique to the industry that obstruct the adoption of low-carbon emission solutions in Jordan’s multi-family residential buildings. Multi-family apartments constitute 73% of the total housing stock and account for over 80% of all residential structures. A total of eight main barriers that are preventing the implementation of low-carbon emission techniques were evaluated. The Fuzzy Delphi Method was utilized to gather insights from the Consultancy Council members of the Jordan Housing Investors Association. The results suggest that a major obstacle is the insufficient knowledge among end-users regarding environmental concerns, along with financial limitations, resulting in a lack of enthusiasm for low-carbon multi-family apartments. Moreover, insufficient cooperation between consultants and contractors leads to subpar constructability, which is worsened by the prevailing conventional procurement method that prioritizes cost and schedule above environmental consequences. To further investigate, it is advisable to examine the utilization of contemporary procurement methods, such as Design–Build and Construction Management and modern family contracts such as NEC4 in the housing industry of Jordan. These alternative methods have the potential to solve the current difficulties by promoting more effective and environmentally friendly building practices. 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

To promote low-carbon transformation and achieve carbon peak and neutrality in the energy field, this study proposes an operational optimization model considering the energy- and load-side dual response (ELDR) mechanism for electrothermal coupled virtual power plants (VPPs) containing a carbon capture device. The organic Rankine cycle (ORC) waste heat boiler (WHB) is introduced on the energy side. The integrated demand response (IDR) of electricity and heat is performed on the load side based on comprehensive user satisfaction (CUS), and the carbon capture system (CCS) is used as a flexible resource. Additionally, a carbon capture device operation mode that makes full use of new energy and the valley power of the power grid is proposed. To minimize the total cost, an optimal scheduling model of virtual power plants under ladder-type carbon trading is constructed, and opportunity-constrained planning based on sequence operation is used to address the uncertainty problems of new energy output and load demand. The results show that the application of the ELDR mechanism can save 27.46% of the total operating cost and reduce CO₂ emissions by 45.28%, which effectively improves the economy and low carbon of VPPs. In particular, the application of a CCS in VPPs contributes to reducing the carbon footprint of the system. Full article

To achieve low-carbon economic dispatch and collaborative optimization of carbon capture efficiency in power systems, this paper proposes a flexible carbon capture power plant and generalized energy storage collaborative operation model under a dynamic carbon quota mechanism. First, adjustable carbon capture devices are integrated into high-emission thermal power units to construct carbon–electricity coupled operation modules, enabling a dynamic reduction of carbon emission intensity and enhancing low-carbon performance. Second, a time-varying carbon quota allocation mechanism and a dynamic correction model for carbon emission factors are designed to improve the regulation capability of carbon capture units during peak demand periods. Furthermore, pumped storage systems and price-guided demand response are integrated to form a generalized energy storage system, establishing a “source–load–storage” coordinated peak-shaving framework that alleviates the regulation burden on carbon capture units. Finally, a multi-timescale optimization scheduling model is developed and solved using the GUROBI algorithm to ensure the economic efficiency and operational synergy of system resources. Simulation results demonstrate that, compared with the traditional static quota mode, the proposed dynamic carbon quota mechanism reduces wind curtailment cost by 9.6%, the loss of load cost by 48.8%, and carbon emission cost by 15%. Moreover, the inclusion of generalized energy storage—including pumped storage and demand response—further decreases coal consumption cost by 9% and carbon emission cost by 17%, validating the effectiveness of the proposed approach in achieving both economic and environmental benefits. Full article