Search Results (125)

With the goal of achieving carbon neutrality, promoting the clean and low-carbon transformation of energy assets, as exemplified by existing thermal power units, has emerged as a pivotal challenge in addressing climate change and achieving sustainable development. Arrangements and technologies such as the electricity–carbon–certificate multi-market, microgrids with direct green power connections, and carbon capture and storage (CCS) retrofitting provide favorable conditions for facing the aforementioned challenge. Based on an analysis of how liquid-storage CCS retrofitting affects the flexibility of thermal power units, this manuscript proposes a bi-level optimization model and solution method for capacity allocation for grid-connected microgrids, while considering CCS retrofits under multi-markets. This approach overcomes two key deficiencies in the existing research: first, neglecting the relationship between electricity–carbon coupling characteristics and unit flexibility and its potential impacts, and second, the significant deviation of scenarios constructed from real policy and market environments, which limits its ability to provide timely and relevant references. A case study in southern China demonstrates that first, multi-market implementation significantly boosts microgrids’ investment in and absolute consumption of renewable energy. However, its effect on reducing carbon emissions is limited, and renewable power curtailment may surge, potentially deviating from the original intent of carbon neutrality policies. In this case study, renewable energy installed capacity and consumption rose by 17.09% and 22.64%, respectively, while net carbon emissions decreased by only 3.32%, and curtailed power nearly doubled. Second, introducing liquid-storage CCS, which decouples the CO₂ absorption and desorption processes, into the capacity allocation significantly enhances microgrid flexibility, markedly reduces the risk of overcapacity in renewable energy units, and enhances investment efficiency. In this case study, following CCS retrofits, renewable energy unit installed capacity decreased by 24%, while consumption dropped by only 7.28%, utilization hours increased by 22%, and the curtailment declined by 78.05%. Third, although CCS retrofitting can significantly reduce microgrid carbon emissions, factors such as current carbon prices, technological efficiency, and economic characteristics hinder large-scale adoption. In this case study, under multi-markets, CCS retrofitting reduced net carbon emissions by 86.16%, but the annualized total cost rose by 3.68%. Finally, based on the aforementioned findings, this manuscript discusses implications for microgrid development decision making, CCS industrialization, and market mechanisms from the perspectives of research directions, policy formulation, and practical work. Full article

With the rapid growth of demands in marine resource exploitation, environmental monitoring, and maritime safety, cooperative operations based on Unmanned Surface Vehicles (USVs) and Unmanned Aerial Vehicles (UAVs) have emerged as a promising paradigm for intelligent ocean missions. UAVs offer flexibility and high coverage efficiency but suffer from limited endurance due to restricted battery capacity, making them unsuitable for large-scale tasks alone. In contrast, USVs provide long endurance and can serve as mobile motherships and energy-supply platforms, enabling UAVs to take off, land, recharge, or replace batteries. Therefore, how to achieve cooperative path planning and energy replenishment scheduling for USV–UAV systems in complex marine environments remains a crucial challenge. This study proposes a USV–UAV cooperative path planning and energy replenishment optimization method based on BeiDou high-precision positioning. First, a unified system model is established, incorporating task coverage, energy constraints, and replenishment scheduling, and formulating the problem as a multi-objective optimization model with the goals of minimizing total mission time, energy consumption, and waiting time, while maximizing task completion rate. Second, a bi-level optimization framework is designed: the upper layer optimizes the USV’s dynamic trajectory and docking positions, while the lower layer optimizes UAV path planning and battery replacement scheduling. A closed-loop interaction mechanism is introduced, enabling the system to adaptively adjust according to task execution status and UAV energy consumption, thus preventing task failures caused by battery depletion. Furthermore, an improved hybrid algorithm combining genetic optimization and multi-agent reinforcement learning is proposed, featuring adaptive task allocation and dynamic priority-based replenishment scheduling. A comprehensive reward function integrating task coverage, energy consumption, waiting time, and collision penalties is designed to enhance global optimization and intelligent coordination. Extensive simulations in representative marine scenarios demonstrate that the proposed method significantly outperforms baseline strategies. Specifically, it achieves around higher task completion rate, shorter mission time, lower total energy consumption, and shorter waiting time. Moreover, the variance of energy consumption across UAVs is notably reduced, indicating a more balanced workload distribution. These results confirm the effectiveness and robustness of the proposed framework in large-scale, long-duration maritime missions, providing valuable insights for future intelligent ocean operations and cooperative unmanned systems. Full article

Connected and Autonomous Vehicles (CAVs) are expected to reshape future transportation systems. During the long transition period, in which CAVs and human-driven vehicles (HVs) coexist, deploying CAV-dedicated lanes offers a promising approach to enhancing overall efficiency, but raises concerns about distributional fairness. This study develops a system-level evaluation framework that integrates bi-level network capacity optimization with practical planning constraints to determine optimal lane-deployment strategies. The bi-level model aims to maximize network reserve capacity at the upper level, while it captures mixed-traffic flow distribution under the lower-level user equilibrium (UE) principle. Both levels are constrained by CAV market penetration (MPR), social equity, and budget bound considerations. To ensure computational tractability, nonlinear relationships are linearized through Piecewise Linear Approximation (PLA), converting the original Mixed-Integer Nonlinear Programming (MINLP) model into a Mixed-Integer Linear Programming (MILP) formulation solvable by standard optimization solvers. Numerical experiments on the Sioux Falls network demonstrate that increasing MPR and dedicated lane deployment can substantially improve network capacity by up to 36% compared with the baseline, with diminishing marginal benefits as deployment scale excesses. Incorporating equity constraints further reduce the HV–CAV cost gap, promoting fairer outcomes without significant efficiency loss. These findings offer quantitative evidence on the efficiency–equity trade-offs in CAV-dedicated lanes planning and provide practical implications for policymakers in developing sustainable strategies. Full article

The hybrid energy storage system (HESS) that combines battery with hydrogen storage exploits complementary power/energy characteristics, but most studies optimize capacity and operation separately, leading to suboptimal overall performance. To address this issue, this paper proposes a bi-level co-optimization framework that integrates deep reinforcement learning (DRL) and mixed integer programming (MIP). The outer layer employs the TD3 algorithm for capacity configuration, while the inner layer uses the Gurobi solver for optimal operation under constraints. On a standalone PV–wind–load-HESS system, the method attains near-optimal quality at dramatically lower runtime. Relative to GA + Gurobi and PSO + Gurobi, the cost is lower by 4.67% and 1.31%, while requiring only 0.52% and 0.58% of their runtime; compared with a direct Gurobi solve, the cost remains comparable while runtime decreases to 0.07%. Sensitivity analysis further validates the model’s robustness under various cost parameters and renewable energy penetration levels. These results indicate that the proposed DRL–MIP cooperation achieves near-optimal solutions with orders of magnitude speedups. This study provides a new DRL–MIP paradigm for efficiently solving strongly coupled bi-level optimization problems in energy systems. Full article

The rapid proliferation of lithium-ion batteries in electric vehicles and grid-scale energy storage systems has underscored the critical need for advanced battery management systems, particularly for accurate state of health (SOH) monitoring. In this study, a hybrid data-driven framework incorporating the whale optimization algorithm (WOA) for Bidirectional Long Short-Term Memory (BiLSTM) networks is introduced. The framework extracts battery aging-related features based on incremental capacity (IC) and differential voltage (DV), which are used as inputs to the SOH prediction model. Then, the BiLSTM network is optimized by WOA to improve convergence performance and model generalization. To further quantify the prediction uncertainty, the Bootstrap approach was used to construct SOH prediction intervals for various confidence levels. Experimental results based on the Oxford dataset show that the proposed WOA-BiLSTM model outperforms the baseline methods including standard LSTM, BiLSTM, and BiGRU. Model performance is evaluated using the root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). In addition, the integration of Bootstrap enables flexible and reliable interval prediction. The results show that PICP reaches 1 at the 90% confidence level and exceeds 0.85 at the 80% confidence level, with PINAW and CWC metrics validating the interval quality. The proposed method provides accurate point prediction and robust uncertainty quantification, offering a promising tool for smart battery health management. Full article

The wind–solar–storage–thermal combined power generation system is one of the key measures for China’s energy structure transition, and rational capacity planning of each generation entity within the system is of critical importance. First, this paper addresses the uncertainty of wind and photovoltaic (PV) power outputs through scenario-based analysis. Considering the diversity of generation entities and their complex interest demands, a bi-level capacity optimization framework based on game theory is proposed. In the upper-level framework, a game-theoretic method is designed to analyze the multi-agent decision-making process, and the objective function of capacity allocation for multiple entities is established. In the lower-level framework, multi-objective optimization is performed on utility functions and node voltage deviations. The Nash equilibrium of the non-cooperative game and the Shapley value of the cooperative game are solved to study the differences in the capacity allocation, economic benefits, and power supply stability of the combined power generation system under different game modes. The case study results indicate that under the cooperative game mode, when the four generation entities form a coalition, the overall system achieves the highest supply stability, the lowest carbon emissions at 30,195.29 tons, and the highest renewable energy consumption rate at 53.93%. Moreover, both overall and individual economic and environmental performance are superior to those under the non-cooperative game mode. By investigating the capacity configuration and joint operation strategies of the combined generation system, this study effectively enhances the enthusiasm of each generation entity to participate in the energy market; reduces carbon emissions; and promotes the development of a more efficient, environmentally friendly, and economical power generation model. Full article

Environmental pollution caused by shipping has always received great attention from the international community. Currently, due to the difficulty of fully electrifying medium- and large-scale ships, the hybrid energy ship power system (HESPS) will be the main type in the future. Considering the economic and long-term energy efficiency of ships, as well as the uncertainty of the output power of renewable energy units, this paper proposes an improved design for an integrated power system for large cruise ships, combining renewable energy and a hybrid energy storage system. An energy management strategy (EMS) based on time-gradient control and considering load dynamic response, as well as an energy storage power allocation method that considers the characteristics of energy storage devices, is designed. A bi-level power capacity optimization model, grounded in comprehensive scenario planning and aiming to optimize maximum return on equity, is constructed and resolved by utilizing an improved particle swarm optimization algorithm integrated with dynamic programming. Based on a large-scale cruise ship, the aforementioned method was investigated and compared to the conventional planning approach. It demonstrates that the implementation of this optimization method can significantly decrease costs, enhance revenue, and increase the return on equity from 5.15% to 8.66%. Full article

With the rapid growth in the penetration of Electric Vehicles (EVs), their large-scale uncoordinated charging behavior presents significant challenges to the hosting capacity of traditional distribution networks (DNs). The novelty of this paper lies in its methodology, which integrates a Markov Chain Monte Carlo (MCMC) method for realistic load profiling with a bi-level optimization framework for Time-of-Use (TOU) pricing, whose effectiveness is then rigorously evaluated through an Optimal Power Flow (OPF)-based assessment of the grid’s hosting capacity. First, to compensate for the limitations of historical data, the MCMC method is employed to simulate the uncoordinated charging process of a large-scale EV fleet. Second, the bi-level optimization model is constructed to formulate a globally optimal TOU tariff that maximizes charging cost savings for EV users. At the same time, its lower-level simulates the optimal economic response of the EV user population. Finally, the change in the minimum daily hosting capacity is calculated based on the OPF. Case study simulations for IEEE 33-bus and IEEE 69-bus systems demonstrate that the proposed model effectively shifts charging loads to off-peak hours, achieving stable user cost savings of 20.95%. More importantly, the findings reveal substantial security benefits from this economic strategy, validated across diverse network topologies. In the 33-bus system, the minimum daily capacity enhancement ranged from 174.63% for the most vulnerable node to 2.44% for the strongest node. In the 69-bus system, vulnerable nodes still achieved a significant 78.62% improvement. This finding highlights the limitations of purely economic assessments and underscores the necessity of the proposed integrated framework for achieving precise, location-dependent security planning. Full article

The increasing uncertainty of distributed energy resources (DERs) challenges the secure and resilient operation of low-voltage distribution networks (LVDNs). Flexible interconnection via power-electronic devices enables controllable links among LVDAs, supporting capacity expansion, reliability, load balancing, and renewable integration. This paper proposes a two-stage robust optimization framework for flexible interconnection planning in LVDNs. The first stage determines investment decisions on siting and sizing of interconnection lines, while the second stage schedules short-term operations under worst-case wind, solar, and load uncertainties. The bi-level problem is reformulated into a master–subproblem structure and solved using a column-and-constraint generation (CCG) algorithm combined with a distributed iterative method. Case studies on typical scenarios and a modified IEEE 33-bus system show that the proposed approach mitigates overloads and cross-area imbalances, improves voltage stability, and maintains high DER utilization. Although the robust plan incurs slightly higher costs, its advantages in reliability and renewable accommodation confirm its practical value for uncertainty-aware interconnection planning in future LVDNs. Case studies on typical scenarios and a modified IEEE 33-bus system demonstrate that under the highest uncertainty the proposed method reduces the voltage fluctuation index from 0.0093 to 0.0079, lowers the autonomy index from 0.0075 to 0.0019, and eliminates all overload events compared with stochastic planning. Even under the most adverse conditions, DER utilization remains above 84%. Although the robust plan increases daily operating costs by about $70, this moderate premium yields significant gains in reliability and renewable accommodation. In addition, the decomposition-based algorithm converges within only 39 s, confirming the practical efficiency of the proposed framework for uncertainty-aware interconnection planning in future LVDNs. Full article

Under the background of “double carbon” and sustainable development, aimed at the problem of resource capacity planning in the integrated energy system (IES), at improving the economy of system planning operation and renewable energy (RE) consumption, and at reducing carbon emissions, this paper proposes a multi-objective bi-level sustainability planning method for IES considering the bilateral response of supply and demand and hydrogen utilization. Firstly, the multi-energy flow in the IES is analyzed, constructing the system energy flow framework, studying the support ability of hydrogen utilization and the bilateral response of supply and demand to system energy conservation, emission reduction and sustainable development. Secondly, a multi-objective bi-level planning model for IES is constructed with the purpose of optimizing economy, RE consumption, and carbon emission. The non-dominated sorting genetic algorithm II (NSGA-II) and commercial solver Gurobi are used to solve the model and, through the simulation, verify the model’s effectiveness. Finally, the planning results show that after introducing the hydrogen fuel cells, hydrogen storage tank, and bilateral response, the total costs and carbon emissions decreased by 29.17% and 77.12%, while the RE consumption rate increased by 16.75%. After introducing the multi-objective planning method considering the system economy, RE consumption, and carbon emissions, the system total cost increased by 0.34%, the consumption rate of RE increased by 0.6%, and the carbon emissions decreased by 43.61t, which effectively provides reference for resource planning and sustainable development of IES. Full article

This study explores the potential for reducing CO₂ emissions in China’s express freight sector by promoting a modal shift from air and road transport to high-speed rail (HSR) through the implementation of a carbon tax policy. A bi-level optimization model is employed to analyze the decision-making processes of three key stakeholders: the government, HSR operators, and shippers. The government aims to maximize consumer surplus while reducing CO₂ emissions through a carbon tax policy; HSR operators seek to maximize transportation profit; and shippers select the most efficient transportation mode based on cost and service considerations. A solution algorithm combining particle swarm optimization, the CPLEX solver, and a custom convergence procedure is designed to solve the bi-level programming model and determine the optimal carbon tax rate. The findings from the Beijing–Shanghai corridor case study indicate that a well-designed carbon tax policy, when integrated with robust HSR services, can effectively encourage a modal shift towards HSR. The extent of emission reduction is influenced by both the capacity of HSR infrastructure and the stringency of the carbon tax policy. This research highlights the importance of addressing asymmetries in transportation mode preferences and market demands. The integration of carbon tax policies with HSR services not only mitigates emissions but also promotes greater symmetry and efficiency within the transportation network. Full article

With the widespread integration of renewable energy sources such as wind and solar power into power systems, their inherent unpredictability and fluctuations present significant challenges to grid stability and security. To address these issues, Battery Energy Storage Systems (BESSs) offer an effective means of enhancing renewable energy absorption and improving the overall system efficiency. This study proposes a coordinated planning method based on the improved bat algorithm (IBA) to tackle the challenges associated with integrating renewable energy into distribution networks. A bi-level optimization framework is introduced to coordinate the planning and operation of the distributed generation (DG) and BESS. The upper-level model focuses on selecting optimal sites and determining the capacity of wind turbines, photovoltaic arrays, and storage systems from an economic perspective. The lower-level model optimizes the curtailment of wind and solar energy and minimizes network losses based on the upper-level planning outcomes. Additionally, the lower-level model also coordinates the dispatch between renewable energy generation and storage systems to ensure the reliable operation of the system. To effectively solve this bi-level optimization model, we have improved the conventional bat algorithm. Simulation results show that the improved bat algorithm not only significantly enhances the convergence speed but also improves the voltage stability, with the photovoltaic utilization rate reaching 90.27% and the wind energy utilization rate reaching 92.18%. These results highlight the practical advantages and success of the proposed method in optimizing renewable energy configurations. Full article

Aiming at the incentive effect of real-time electricity prices on load demand response in the context of the electricity market, this paper proposed a dual layer optimization operation method for regional energy storage considering dynamic electricity prices and battery capacity degradation. The innovation of the proposed method lies in introducing user satisfaction and establishing real-time electricity price models based on fuzzy theory and consumer satisfaction, making dynamic electricity prices more realistic. At the same time, the proposed dual layer optimization operation model for regional energy storage has modeled the capacity degradation performance of energy storage batteries, which more accurately reflects the practicality of energy storage batteries. Finally, the particle swarm optimization (PSO) algorithm is utilized to efficiently optimize charging/discharging strategies, balancing economic benefits with battery longevity. The correctness of the proposed method is verified through simulation examples using MATLAB. Simulation results demonstrate that real-time electricity prices based on consumer satisfaction increase load demand response resources, resulting in stronger absorption of new energy sources, improving by 73.7%, albeit with reduced economic efficiency by 11.27%. While the real-time electricity prices based on fuzzy theory exhibit weaker absorption of new energy sources improving by only 36.4%, but achieve the best overall economic performance. Full article

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

Emergency material scheduling (EMS) is a core component of post-disaster emergency response, with its efficiency directly impacting rescue effectiveness and the satisfaction of affected populations. However, due to severe road damage, limited availability of resources, and logistical challenges after disasters, current EMS practices often suffer from uneven resource distribution. To address these issues, this paper proposes a priority-based, multi-stage EMS approach with drone coordination. First, we construct a three-level EMS network “storage warehouses–transit centers–disaster areas” by integrating the advantages of large-scale transportation via trains and the flexible delivery capabilities of drones. Second, considering multiple constraints, such as the priority level of disaster areas, drone flight range, transport capacity, and inventory capacities at each node, we formulate a bilevel mixed-integer nonlinear programming model. Third, given the NP-hard nature of the problem, we design a hybrid algorithm—the Tabu Genetic Algorithm combined with Branch and Bound (TGA-BB), which integrates the global search capability of genetic algorithms, the precise solution mechanism of branch and bound, and the local search avoidance features of Tabu search. A stage-adjustment operator is also introduced to better adapt the algorithm to multi-stage scheduling requirements. Finally, we designed eight instances of varying scales to systematically evaluate the performance of the stage-adjustment operator and the Tabu search mechanism within TGA-BB. Comparative experiments were conducted against several traditional heuristic algorithms. The experimental results show that TGA-BB outperformed the other algorithms across all eight test cases, in terms of both average response time and average runtime. Specifically, in Instance 7, TGA-BB reduced the average response time by approximately 52.37% compared to TGA-Particle Swarm Optimization (TGA-PSO), and in Instance 2, it shortened the average runtime by about 97.95% compared to TGA-Simulated Annealing (TGA-SA).These results fully validate the superior solution accuracy and computational efficiency of TGA-BB in drone-coordinated, multi-stage EMS. Full article