Search Results (233)

Aiming at the economic problems of industrial users with wind power, photovoltaic, and small hydropower resources in clean energy consumption and trading with superior power grids, this paper proposes a distributed pumped storage capacity optimization configuration method considering clean energy systems. First, considering the maximization of the investment benefit of distributed pumped storage as the upper goal, a configuration scheme of the installed capacity is formulated. Second, under the two-part electricity price mechanism, combined with the basin hydraulic coupling relationship model, the operation strategy optimization of distributed pumped storage power stations and small hydropower stations is carried out with the minimum operation cost of the clean energy system as the lower optimization objective. Finally, the bi-level optimization model is solved by combining the alternating direction multiplier method and CPLEX solver. This study demonstrates that distributed pumped storage implementation enhances seasonal operational performance, improving clean energy utilization while reducing industrial electricity costs. A post-implementation analysis revealed monthly operating cost reductions of 2.36, 1.72, and 2.13 million RMB for wet, dry, and normal periods, respectively. Coordinated dispatch strategies significantly decreased hydropower station water wastage by 82,000, 28,000, and 52,000 cubic meters during corresponding periods, confirming simultaneous economic and resource efficiency improvements. Full article

Under the “dual carbon” goals, the low-carbon economic dispatch of integrated energy systems (IES) faces multiple challenges, including suboptimal economic efficiency, excessive carbon emissions, and limited renewable energy integration. While traditional green certificate trading (GCT) enhances renewable energy adoption, its emission reduction effect remains inadequate. Conversely, standalone carbon emission trading (CET) effectively curbs emissions but often at the expense of increased operational costs, making it difficult to achieve both economic and environmental objectives simultaneously. To address these limitations, this study proposes an innovative green certificate trading–tiered carbon emission trading (GCT–CET) synergistic mechanism integrated with demand-side flexible load optimization, developing a low-carbon dispatch model designed to minimize total system costs. Simulation experiments conducted with the CPLEX solver demonstrate that, compared to individual GCT or CET implementations, the proposed coordinated mechanism effectively combines renewable energy incentives (through GCT) with stringent emission control (via stepped CET), resulting in a 47.8% reduction in carbon emissions and a 5.4% decrease in total costs. Furthermore, the participation of flexible loads enhances supply–demand balancing, presenting a transformative solution for achieving high-efficiency and low-carbon operation in IES. 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

This study focuses on the integrated scheduling issues of imaging and data transmission for Earth observation satellites, where each target needs to be imaged and transmitted within a feasible time window. The scheduling process also takes into account the constraints of satellite energy and storage capacity. In this paper, a mixed-integer linear programming (MILP) model for the integrated scheduling of imaging data transmission has been proposed. The MILP model was validated through numerical experiments based on simulation data from SuperView-1 series satellites. Additionally, some neighborhood mechanisms are designed based on the characteristics of the problem. Based on the neighborhood mechanisms, the rule-based large neighborhood search algorithm (RLNS) was designed, which constructs initial solutions through various scheduling rules and iteratively optimizes the solutions using multiple destroying and repairing operators. To address the shortcomings of the overly regular mechanism of the destruction and repair operator for large neighborhood search, we design a genetic algorithms (GA) for tuning the heuristic scheduling rules. The calculation results demonstrate the effectiveness of RLNS and GA, highlighting their advantages over CPLEX in solving large-scale problems. Full article

In light of the current challenges posed by complex multi-agent interactions and competing interests in integrated energy systems, an economic optimization operation model is proposed. This model is based on a two-layer game comprising a one-master–many-slave structure consisting of an energy retailer, energy suppliers, and a user aggregator. Additionally, it considers energy suppliers to be engaged in a non-cooperative game. The model also incorporates a carbon trading mechanism between the energy retailer and energy suppliers, considers integrated demand response at the user level, and categorizes users in the community according to their energy use characteristics. Finally, the improved differential evolutionary algorithm combined with the CPLEX solver (v12.6) is used to solve the proposed model. The effectiveness of the proposed model in enhancing the benefits of each agent as well as reducing carbon emissions is verified through example analyses. The results demonstrate that the implementation of non-cooperative game strategies among ESs can enhance the profitability of ES1 and ES2 by 27.83% and 18.67%, respectively. Furthermore, the implementation of user classification can enhance user-level benefits by up to 39.51%. Full article

This paper presents a joint optimization framework for multi-period planning in a Manufacturing–Remanufacturing–Transport Supply Chain (MRTSC), focusing on carbon emission reduction and economic efficiency. A novel Mixed Integer Linear Programming (MILP) model is developed to coordinate procurement, production, remanufacturing, transportation, and returns under environmental constraints, aligned with carbon tax policies and the Paris Agreement. To address uncertainty in future demand and the number of returned used products (NRUP), a two-stage approach combining forecasting and optimization is applied. Among several predictive methods evaluated, a hybrid SARIMA/VAR model is selected for its accuracy. The MILP model, implemented in CPLEX, generates optimal decisions based on these forecasts. A case study demonstrates notable improvements in cost efficiency and emission reduction over traditional approaches. The results show that the proposed model consistently maintained strong service levels through flexible planning and responsive transport scheduling, minimizing both unmet demand and inventory excesses throughout the planning horizon. Additionally, the findings indicate that carbon taxation caused a sharp drop in profit with only limited emission reductions, highlighting the need for parallel support for cleaner technologies and more integrated sustainability strategies. The analysis further reveals a clear trade-off between emission reduction and operational performance, as stricter carbon limits lead to lower profitability and service levels despite environmental gains. Full article

With the sharp increase in the number of products and the development of the remanufacturing industry, disassembly lines have become the mainstream recycling method. In view of the insufficient research on the layout of multi-form disassembly lines and human factors, we previously proposed a linear-U-shaped hybrid layout considering the constraints of employee posture and a Duel-DQN algorithm assisted by Large Language Model (LLM). However, there is still room for improvement in the utilization efficiency of workstations. Based on this previous work, this study proposes an innovative layout of U-shaped and circular disassembly lines and retains the constraints of employee posture. The LLM is instruction-fine-tuned using the Quantized Low-Rank Adaptation (QLoRA) technique to improve the accuracy of disassembly sequence generation, and the Dueling Deep Q-Network(Duel-DQN) algorithm is reconstructed to maximize profits under posture constraints. Experiments show that in the more complex layout of U-shaped and circular disassembly lines, the iterative efficiency of this method can still be increased by about 26% compared with the traditional Duel-DQN, and the profit is close to the optimal solution of the traditional CPLEX solver, verifying the feasibility of this algorithm in complex scenarios. This study further optimizes the layout problem of multi-form disassembly lines and provides an innovative solution that takes into account both human factors and computational efficiency, which has important theoretical and practical significance. Full article

This study addresses the multi-period, multi-item, single-stage capacitated lot sizing problem (CLSP) in a parallel machine environment with machine eligibility constraints under a make-to-order production policy. A mixed-integer linear programming (MILP) model is developed to minimize total operational costs, including production, overtime, extra shifts, inventory holding, and backorders. The make-to-order setting introduces additional complexity by requiring individualized customer orders, each with specific due dates and product combinations, to be scheduled under constrained capacity and setup requirements. The model’s performance is evaluated in the context of a real-world production planning problem faced by a manufacturer of cold-formed steel profiles. In this setting, parallel forming machines process galvanized sheets of cold-rolled steel into a variety of profiles. The MILP model is solved using open-source optimization tools, specifically the HiGHS solver. The results show that optimal solutions can be obtained within reasonable computational times. For more computationally demanding instances, a runtime limit of 300 s is shown to improve solution quality while maintaining efficiency. These findings confirm the viability and cost-effectiveness of free software for solving complex industrial scheduling problems. Moreover, experimental comparisons reveal that solution times and performance can be further improved by using commercial solvers such as CPLEX, highlighting the potential trade-off between cost and computational performance. Full article

In the context of electricity and carbon markets, with the in-depth research of virtual power plants and to realize the mutual assistance of electric energy in different regions within the same distribution network, a scheduling strategy of virtual power plant alliance based on dynamic electricity and carbon pricing using the Master–Slave game is proposed. Firstly, an interactive framework of virtual power plant alliance is designed in which the alliance operator formulates the electricity and carbon prices, and each user entity formulates the operation plan according to the prices. Secondly, the information gap decision theory is adopted to handle the uncertainties on the source–load side. Based on the Master–Slave game and source–load interaction, an economic optimal dispatching model for the virtual power plant alliance is established. Finally, the particle swarm optimization algorithm nested with the CPLEX solver is used to solve the model, and the rationality and effectiveness of the proposed strategy are demonstrated through case analysis. The simulation results show that, after considering the electricity energy interaction and dynamic electricity–carbon pricing, the daily operation cost of the virtual power plant alliance was reduced by 47.7%, carbon emissions decreased by 24.6%, and comprehensive benefits increased by 77.2%. Full article

The sea–rail intermodal container terminal serves as a key transportation hub for green logistics, where efficient resource coordination directly enhances multimodal connectivity and operational synergy. To address limited storage capacity and trans-shipment inefficiencies, this study innovatively proposes a resource-sharing strategy between the seaport and the railway container terminal, focusing on the joint allocation of yard space and internal trucks. For indirect trans-shipment operations between ships, the port, the railway container terminal, and trains, a mixed-integer programming model is formulated with the objective of minimizing the container trans-shipment cost and the weighted turnaround time of ships and trains. This model simultaneously determines yard allocation, container transfers, and truck allocation. A two-layer hybrid heuristic algorithm incorporating adaptive Particle Swarm Optimization and Greedy Rules is designed. Numerical experiments verify the model and algorithm performance, revealing that the proposed method achieves an optimality gap of only 1.82% compared to CPLEX in small-scale instances while outperforming benchmark algorithms in solution quality. And the shared yard strategy enhances ship and train turnaround efficiency by an average of 33.45% over traditional storage form. Sensitivity analysis considering multiple realistic factors further confirms the robustness and generalizability. This study provides a theoretical foundation for sustainable port–railway collaboration development. Full article

This paper considers the problem of consistent routing with minimum service (ConVRPms). ConVRPms aims to determine the minimum cost routes for each day of a planning horizon. In particular, the goal is to satisfy all individual demands and serve every customer via a single driver, with times that do not differ by more than $L$ time units. There is a fleet of homogeneous vehicles that start from a single depot. In this paper, a heuristic algorithm for ConVRPms is proposed. The algorithm is based on classical constructive heuristics and the tabu search metaheuristic. The proposed algorithm has been tested on benchmark instances from the literature. The experimental results show that the proposed approach produces high-quality solutions within computing times considerably less than those observed with CPLEX. The proposed algorithm can optimally solve instances with 20 customers and a planning horizon of three days, producing more economical solutions in some of the larger instances and those requiring hourly consistency ($L=1 \mathrm{h}$). Full article

Within the framework of “dual carbon”, intending to enhance the use of green energies and minimize the emissions of carbon from energy systems, this study suggests a cost-effective low-carbon scheduling model that accounts for stepwise carbon trading for an integrated electricity, heat, gas, cooling, and hydrogen energy system. Firstly, given the clean and low-carbon attributes of hydrogen energy, a refined two-step operational framework for electricity-to-gas conversion is proposed. Building upon this foundation, a hydrogen fuel cell is integrated to formulate a multi-energy complementary coupling network. Second, a phased carbon trading approach is established to further explore the mechanism’s carbon footprint potential. And then, an environmentally conscious and economically viable power dispatch model is developed to minimize total operating costs while maintaining ecological sustainability. This objective optimization framework is effectively implemented and solved using the CPLEX solver. Through a comparative analysis involving multiple case studies, the findings demonstrate that integrating electric-hydrogen coupling with phased carbon trading effectively enhances wind and solar energy utilization rates. This approach concurrently reduces the system’s carbon emissions by 34.4% and lowers operating costs by 58.6%. Full article

The use of electric vehicles in urban transportation is increasing daily due to their energy efficiency and environmental friendliness. In last-mile logistics, route optimization must consider charging station locations while balancing operational costs and customer satisfaction. In this context, solutions for cost-oriented route optimization have been presented in the literature. On the other hand, customer satisfaction is also important for third-party logistics companies. This study discusses the Capacitated Electric Vehicle Routing Problem with Time Windows (CEVRPTW) encountered in last-mile logistics. This article defines the objective function of minimizing total tardiness and compares the routes between the service provider logistics company and the customer receiving the service. In this study, the CEVRPTW was solved for the minimum total tardiness objective function with the hybrid adaptive large neighborhood search (ALNS) algorithm. The success of ALNS was proven by comparing the differences between the optimal solutions obtained with the CPLEX Solver and the ALNS solutions. Tardiness objective function-specific operators for ALNS are proposed and supported by local search and VNS algorithms. The findings of this study contribute to the literature by analyzing the balance trade-offs between customer-oriented and cost-oriented and the effect of time windows on the number of vehicles. Full article

In line with the strategic plan for emerging industries in China, renewable energy sources like wind power and photovoltaic power are experiencing vigorous growth, and the number of electric vehicles in use is on a continuous upward trend. Alongside the optimization of the distribution network structure and the extensive application of energy storage technology, the active distribution network has evolved into a more flexible and interactive “source–grid–load–storage” diversified structure. When electric vehicles are plugged into charging piles for charging and discharging, it inevitably exerts a significant impact on the control and operation of the power grid. Therefore, in the context of the extensive integration of electric vehicles, delving into the charging and discharging behaviors of electric vehicle clusters and integrating them into the optimization of the active distribution network holds great significance for ensuring the safe and economic operation of the power grid. This paper adopts the two-stage “constant-current and constant-voltage” charging mode, which has the least impact on battery life, and classifies the electric vehicle cluster into basic EV load and controllable EV load. The controllable EV load is regarded as a special “energy storage” resource, and a corresponding model is established to enable its participation in the coordinated control of the active distribution network. Based on the optimization and control of the output behaviors of gas turbines, flexible loads, energy storage, and electric vehicle clusters, this paper proposes a two-layer coordinated control model for the scheduling layer and network layer of the active distribution network and employs the improved multi-target beetle antennae search optimization algorithm (MTTA) in conjunction with the Cplex solver for solution. Through case analysis, the results demonstrate that the “source–grid–load–storage” coordinated control of the active distribution network can fully tap the potential of resources such as flexible loads on the “load” side, traditional energy storage, and controllable EV clusters; realize the economic operation of the active distribution network; reduce load and voltage fluctuations; and enhance power quality. Full article

Optimizing the operation of active distribution networks (ADNs) has become more challenging because of the uncertainty created by the high penetration level of distributed photovoltaic (PV). From the convex optimization perspective, this paper proposes a two-layer optimization model to simplify the solution of the ADN optimal operation problem. Firstly, to pick out the ADN “key” nodes, a “key” nodes selection approach that used improved K-means clustering algorithm and two indexes (integrated voltage sensitivity and reactive power-balance degree) is introduced. Then, a two-layer ADN optimization model is built using various time scales. The upper layer is a long-time-scale model with on-load tap-changer transformer (OLTC) and capacitor bank (CB), and the lower layer is a short-time-scale optimization model with PV inverters and distributed energy storages (ESs). To take into account the PV users’ interests, maximizing PV active power output is added to the objective. Afterwards, under the application of the second-order cone programming (SOCP) power-flow model, a linearization method of OLTC model and its tap change frequency constraints are proposed. The linear OLTC model, together with the linear models of the other equipment, constructs a mixed-integer second-order cone convex optimization (MISOCP) model. Finally, the effectiveness of the proposed method is verified by solving the IEEE33 node system using the CPLEX solver. Full article