Search Results (1,640)

Park-level integrated energy systems with high shares of wind and photovoltaic power face pronounced seasonal source–load mismatches, renewable energy curtailment, and low-carbon operation challenges. This paper proposes a capacity planning method considering seasonal salt-cavern hydrogen storage and adaptive representative days. An electricity–heat–cooling–hydrogen coupled system is established by integrating renewable generation, conventional conversion units, short-term storage, electrolyzers, fuel cells, and salt-cavern hydrogen storage, together with waste-heat recovery and tiered carbon trading. To represent interseasonal hydrogen transfer under representative-day modeling, a seasonal hydrogen inventory formulation based on weighted net hydrogen changes is developed, considering cushion gas, storage bounds, injection and withdrawal efficiencies, and flow-rate limits. A season-specific adaptive K-medoids method based on CRITIC evaluation is further proposed to determine the number of representative days, while zero-weight extreme days are introduced to verify capacity feasibility under boundary conditions. The optimization objective is to minimize annualized total cost. Case studies show that removing seasonal hydrogen storage increases total system cost by 23.63%, raises wind and photovoltaic curtailment from 1.81% to 13.09%, and increases carbon emissions by 13.74%. The proposed method improves economic, renewable-energy-utilization, and low-carbon performance. Full article

With the rapid penetration of electric vehicles (EVs) and renewable energy generation in distribution networks, the coordinated scheduling of flexible EV loads and uncertain renewable resources has become a critical research focus in modern power systems. This study investigates the collaborative optimal dispatch of thermal units, EVs, and renewable power generation. Different from conventional closed-loop game-based bi-level optimization, this paper constructs a transmission–distribution integrated scheduling framework and proposes a sequential hierarchical progressive optimization strategy for EV charging and discharging dispatch to fully tap the cross-level coordination potential of power grids. The upper transmission layer optimizes the joint operation of thermal units, wind power, and photovoltaic units to minimize the overall power supply cost, where the inequality power balance constraint is reasonably adopted to reserve power regulation margin for renewable fluctuation and meet practical engineering operation requirements. To effectively address the severe uncertainty of renewable power output without relying on accurate probability distribution information, information gap decision theory (IGDT) is employed to realize robust scheduling with risk-averse and opportunity-seeking decision adaptability. In the lower distribution layer, a theoretically grounded nodal electricity price (NEP) model integrating node loss sensitivity (NLS) and node load rate (NLR) is applied to substitute iterative power flow calculation, which realizes the spatial optimal allocation of EV charging and discharging nodes while significantly improving computational efficiency. The proposed framework comprehensively minimizes network power loss and user charging cost. Finally, extensive simulations based on the IEEE 33-node distribution system verify the effectiveness, computational superiority, and robustness of the proposed sequential hierarchical coordinated scheduling strategy. Full article

With the high-proportion integration of renewable energy, integrated energy systems face greater demands regarding renewable energy utilisation, power balancing, and operational efficiency. By aggregating distributed generation, energy storage and load resources, integrated energy systems can provide effective support for multi-energy coordinated scheduling. This paper proposes a 24 h day-ahead multi-objective optimal scheduling framework for an integrated hydro–wind–photovoltaic–storage energy system based on multi-objective particle swarm optimisation (MOPSO). Firstly, this paper establishes mathematical models for wind power, photovoltaic (PV), hydropower, and energy storage units. Subsequently, it incorporates the outputs of hydropower, wind power, PV, and storage, along with the charging and discharging of energy storage and the process of purchasing electricity from and selling electricity to the main grid, into a unified optimisation model. The objectives are to maximise economic benefit and variable renewable energy utilisation while minimising the peak-to-valley difference in residual load. To address the conflicts between these multiple objectives, a MOPSO algorithm combined with a normalised weighted scoring method is employed to select a compromise optimal solution. Results from case studies based on typical days of the four seasons and various operational strategies demonstrate that the proposed method can rationally allocate the outputs of different energy sources, reduce the system’s dependence on the main grid, and improve variable renewable energy utilisation, thereby providing a reference for the optimal scheduling of integrated energy systems. Full article

Off-grid photovoltaic (PV) hydrogen production systems must coordinate rapidly varying PV power, battery energy, and the operating states of multiple alkaline water electrolyzers. Inappropriate coordination may lead to PV curtailment, frequent unit switching, and persistent workload concentration on a small number of electrolyzers. This paper develops an efficiency- and load-balanced operation (ELBO) scheme as an improved rule-based supervisory strategy rather than an online optimization method. ELBO adopts a two-level decision structure. A planned number of online electrolyzers is first determined from the moving-average PV power and the reference power associated with high single-unit efficiency. This planned count is then corrected using real-time PV power, battery state of charge, and the previous electrolyzer states. The controller adjusts the powers of the online units before changing their number, uses the battery to bridge temporary power deficits, and distributes the remaining adjustable power under the operating and ramp-rate constraints. Five representative PV profiles selected from one year of measured data were used to compare ELBO with PV-following operation (PFO), multi-electrolyzer coordinated operation (MECO), and an offline mixed-integer linear programming (MILP) benchmark. ELBO produced 1328 kg of hydrogen, which was 8.85% and 6.07% higher than PFO and MECO, respectively. Its overall PV-to-hydrogen efficiency and PV utilization reached 65.2% and 94.9%, respectively, with 36 start–stop events. MILP produced 1345 kg of hydrogen, only 1.28% more than ELBO, but required the complete future PV sequence. Ablation analysis further shows that the planned-count layer, moving-average filtering, battery-supported retention, and load-balancing allocation contribute to different and complementary aspects of capacity matching, operating continuity, and workload distribution. The results indicate that the benefit of ELBO arises from the ordered coordination of these supervisory functions and that it provides a practical compromise between operating performance, workload distribution, information requirements, and computational complexity under the representative conditions considered. Full article

Collaborative intelligent sensing in the Social Internet of Things (Social IoT) relies on distributed AI-enabled sensors to support complementary information sharing, multimodal perception, and real-time autonomous decision-making. Under high-load conditions, mismatches between resource provisioning and sensing quality of experience (QoE) can significantly degrade system performance in applications such as smart cities. To address this issue, this paper proposes a service priority-aware collaborative sensing support framework based on a joint next-generation passive optical network (NG-PON) and cooperative intelligent service-based radio access network (CIS-RAN) architecture. The framework enables edge AI-driven inference and distributed sensor collaboration in heterogeneous Social IoT environments. Service-slice-specific priority weights are assigned to optical network units (ONUs) and wavelengths according to the QoE requirements and latency sensitivity of sensing tasks, allowing dynamic wavelength tuning that prioritizes high-impact collaborative services. The utility of a centralized intelligent processing pool is formulated to achieve priority-consistent and efficient resource coordination under collaborative constraints. In addition, a multi-agent AI-driven optimization framework is employed to derive adaptive resource allocation strategies that incorporate service priorities while satisfying stringent service-level agreements (SLAs). Simulation results show that the proposed framework improves system-level proxy metrics, including total utility, wavelength satisfaction, and resource utilization, compared with representative baseline schemes. Full article

This paper studies performance analysis and load frequency control (LFC) for a Takagi–Sugeno (T-S) fuzzy power system equipped with an energy storage unit. A corresponding T-S fuzzy representation of the system is constructed. Subsequently, a new lemma of slack Lyapunov function is constructed, which relaxes the conventional positive-definiteness requirement on the quadratic form, provides additional flexibility in the LMI formulation, and yields less conservative stability conditions. In addition, a genetic-algorithm-based outer search is employed to optimize the scalar parameter $\varrho$, while the associated LMIs are solved using the LMI solver, thereby reducing the conservatism of the stability conditions and enlarging the maximum allowable delay bound. Simulation cases are finally presented to confirm the feasibility and effectiveness of the proposed methods. The proposed method increases the maximum allowable delay bound by $2.6920\%$, $4.0220\%$ and $4.0528\%$ compared with the reference method for the tested controller parameters. Full article

To address the insufficient flexibility in power systems caused by renewable energy output uncertainty during extreme weather events, a coordinated source–network–load–storage (SNLS) dispatch method that combines a flexibility supply–demand balance approach with a partitioned grid framework is proposed to achieve the effective enhancement of operational resilience. Firstly, a convolution method is employed to aggregate net load forecast error distributions, and expected flexibility demand metrics are introduced to construct a probabilistic model of compound weather impacts, thereby improving flexibility requirement quantification. Secondly, uncertainties arising from extreme meteorological conditions are considered, and an integrated economic dispatch model for the partitioned grid is established based on chance-constrained reserves and regulation capability envelopes, in order to co-optimize generation costs, demand response, and expected flexibility insufficiency penalties. Then, inter-zone power exchange and spatiotemporal unit commitment dynamics are introduced to optimally redistribute spatial generation surpluses and load deficits, so that a system-wide flexibility supply–demand balance is enabled. Finally, simulations are conducted on the real-world Guangdong 500 kV transmission network under typhoon, heatwave, and rainstorm scenarios, and the results demonstrate the effectiveness of the proposed method in eliminating flexibility deficits, reducing total dispatch costs, and capturing distinct weather-adaptive operational patterns. Full article

In the context of the new power system, coal-fired units are transitioning into peaking units. This study investigates the combustion characteristics and NO_X emissions of a 600 MW opposed wall-fired boiler within a load range of 50% to 20%, and further analyzes the impact of burner operation modes on boiler performance at the 20% ultra-low load. The results indicate that as the boiler load decreases from 50% to 20%, the average temperature in the primary combustion zone drops from 1634.3 K to 1457.0 K, and the ignition distance extends from 0.228 m to 0.260 m, leading to a significant decline in combustion stability. Notably, at the 20% ultra-low load, although the drop in temperature suppresses the formation of thermal NO_X, the flow short-circuiting caused by the shrinking of the recirculation zone results in pulverized coal particles missing the optimal reduction window; the formation pathway dominated by fuel NO_X causes the NO_X concentration at the furnace outlet to surge to 670.6 mg/m³. Furthermore, the burner operation modes significantly influence boiler performance at the 20% ultra-low load. While ensuring combustion stability, operating the lower-tier burners effectively reduces NO_X emissions by up to 21.6%. Considering both combustion stability and NO_X emissions, prioritizing the operation of lower-tier burners is recommended. This study reveals the underlying mechanisms behind the surge in NO_X concentrations at ultra-low loads of 20% and proposes optimal burner operation strategies, providing a theoretical foundation for the clean and stable operation of boilers during deep peak shaving. Full article

Modern power systems increasingly depend on energy storage devices to manage fluctuations in renewable generation and load demand. Coordinating multiple heterogeneous storage units in a grid-level system while enforcing individual state-of-charge (SoC) limits constitutes a complex, high-dimensional control problem that cannot be resolved by conventional proportional-sharing schemes. This work formulates the Distributed Optimal Energy Management (DOEM) problem for a grid comprising n parallel storage units with power-dependent efficiency and heterogeneous capacities. Optimality conditions are derived using Pontryagin’s Minimum Principle (PMP) and a smooth penalty function is introduced to handle hard SoC constraints without state-space discretisation. For the practically important class of lossless storage devices, an explicit closed-form control law is obtained, in which each unit is dispatched proportionally to its storage capacity. Numerical validation is performed on the Israeli power grid, modelling three pumped-hydro systems with a combined capacity of 8.0 GWh, using MATLAB/Simulink R2018b. Across the base net-load scenario and four additional load profiles, the cost achieved by the proposed method matches the dynamic programming (DP) benchmark within 1.1%, while the maximum state-of-charge violation is limited to 0.64% of total capacity at the default penalty setting. Computationally, the proposed update requires only 2.21 s for nine storage units compared to 59.30 s for DP, a 26.8-fold speedup, and scales with $O\left(n\right)$ arithmetic operations per time step. The results confirm a clear pathway to optimal, safe, and scalable real-time control of large-scale heterogeneous energy storage ensembles. Full article

Advancements in rechargeable batteries and environmental awareness campaigns have highlighted the importance of electric vehicles (EVs) in recent times. The influx of EVs has posed challenges in almost all areas of technology, including demand-side management (DSM). Extra generating units are switched ON to meet the resultant higher electricity demand, thus reducing the sustainability of the system. To overcome this challenge, effective DSM techniques integrating renewable energy sources are proposed to efficiently utilize the existing generating capacity. The primary goal is to fairly distribute available resources among smart homes and EV owners using the Shapley value and tau value. In this work, two scenarios are examined. First, a community energy storage (CES) approach is adopted to maximize CES revenue, reduce the grid peak-to-average ratio (PAR), and minimize electricity costs. Second, a coordinated group of EVs is utilized to minimize the impact of charging loads during peak hours while concurrently reducing EV charging costs. Simulation results show a reduction in the grid PAR from 2.468 to 1.799, or 27.1%, together with an average reduction of approximately 3% in the electricity cost of participating smart homes. In the EV scenario, optimal scheduling reduces total charging expenditure by 24.8% and lowers the system peak by 2.65% relative to uncoordinated charging of the same fleet, with the total cost distributed among the vehicles by the Shapley value. Benchmarking against a proportional-to-demand rule shows that the tau-value allocation coincides with proportional sharing, whereas the Shapley allocation shifts 4.2% of the allocation away from the household contributing most to the system peak. The framework provides a fair and individually rational cost allocation layer for community-scale peer-to-peer energy markets. Full article

Pumped storage hydropower (PSH) is a key regulating resource for renewable energy integration and power system stability. Due to frequent start-stop operations, deep peak-load regulation, and bidirectional operating conditions, stator winding insulation degradation, rotor inter-turn short circuits, and end-winding vibration have become the dominant failure modes of pumped storage units. Conventional monitoring systems are limited by single-source sensing, asynchronous data acquisition, high misdiagnosis rates, and maintenance decisions that rely heavily on expert experience, making traditional periodic maintenance increasingly inadequate. To address these challenges, this study proposes an intelligent diagnosis and decision support system based on multi-source data fusion and hybrid intelligent algorithms. An Intelligent Electronic Device (IED)-based condition monitoring platform is developed by integrating multiple sensing technologies. Complete Variational Mode Decomposition (CVMD) and Kernel Principal Component Analysis (KPCA) are employed to extract representative features from multi-physical-field data, while an attention-enhanced Long Short-Term Memory (LSTM) network is introduced for accurate fault identification. In addition, adaptive time-alignment and joint denoising algorithms are developed to improve data quality and diagnostic robustness. A predictive maintenance framework incorporating health assessment and remaining useful life prediction is further established to optimize maintenance scheduling. Results demonstrate that the proposed system achieves a fault prediction accuracy of over 90% and reduces annual maintenance costs by approximately 15–20%. The proposed framework provides an effective solution for intelligent operation and maintenance of modern pumped storage units. Full article

This study presents the development and validation of an intelligent error compensator based on Long Short-Term Memory (LSTM) recurrent neural networks for dynamic weighing systems in copper concentrate belt conveyors. Conventional weighing systems fail to capture nonlinear temporal dynamics, leading to measurement inaccuracies during container filling operations. The methodology comprised data acquisition from load cells, speed sensors, and inclinometers; systematic hyperparameter optimization; and evaluation using Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), and coefficient of determination (R²). Hyperparameter optimization identified an optimal configuration with one LSTM layer (20 units, learning rate 0.001, window size 20 steps). Evaluation on an independent test set showed that the compensator reduced MAPE from 8.5% (uncompensated system) to 3.01%, representing a 64.6% improvement, and reduced RMSE from 12.3 to 4.2 tons (65.9% improvement), with an R² of 0.95. Feature importance analysis confirmed physical consistency, with load cell voltage as the dominant predictor (42%). These results demonstrate that LSTM-based compensation significantly enhances weighing accuracy. The study provides a replicable framework for industrial metrology modernization, contributing to sustainable mining operations through material loss reduction and logistics optimization. While the proposed model has been validated offline using historical data, its deployment in the live production environment remains pending. Full article

Hydropower units operating in isolated grids are characterized by low rotational inertia and weak damping, making it difficult to balance rapid frequency regulation and overshoot suppression. To address this issue, this paper proposes a GA–SQP hybrid optimization control strategy for multiple operating conditions based on a high-fidelity nonlinear dynamic model. Deep feedforward neural networks are first employed to reconstruct the nonlinear torque and discharge characteristics of the hydro-turbine, providing smooth and continuously differentiable mappings for subsequent gradient-based optimization. An improved performance index combining the Integral of Time-Cubed Absolute Error (ITCAE) with a transient overshoot penalty is then formulated to suppress long-tail errors and prioritize smooth responses with reduced transient overshoot. A two-stage optimization framework is further developed, in which the Genetic Algorithm (GA) performs global exploration to identify a promising parameter region, followed by Sequential Quadratic Programming (SQP) for high-precision local refinement. Comparative simulations under low-, rated-, and high-head high-load conditions show that the proposed strategy achieves higher optimization accuracy with fewer iterative resources. Within the investigated operating range, the optimized controller maintains a very low overshoot level while preserving satisfactory response speed, effectively improving the balance between rapidity and stability in isolated-grid frequency regulation. Full article

Overpressure prediction is critical for safe and efficient drilling, yet remains challenging in complex basins with multiple genetic mechanisms. This study systematically investigates the overpressure origins in the Xihu Sag, East China Sea, a prolific hydrocarbon-bearing sag with widespread overpressure and complex pressure regimes. By integrating well logging data and direct pore pressure measurements from nine wells across three major structural units, the Western Slope Belt, the Western Sub-sag and the Central Inversion Belt, a multi-method diagnostic framework is employed. This combines Bowers’ effective stress analysis with sonic-density cross-plots to discriminate between loading and unloading mechanisms. Results show obvious vertical zoning of pore pressure—normal-pressure zone, overpressure zone, and pressure reversal zone—with distinct horizontal heterogeneity. Results reveal a distinct spatial differentiation in dominant overpressure mechanisms. In the Western Slope Belt, overpressure in the deep Pinghu Formation primarily results from a composite of undercompaction (creating initial pressure seals) and subsequent hydrocarbon generation-induced fluid expansion. In contrast, in the Central Inversion Belt and Western Sub-sag, overpressure is predominantly driven by hydrocarbon charging along faults coupled with tectonic compression, with minimal undercompaction signatures. Previous studies on overpressure genesis in the Xihu Sag have largely focused on the Western Slope Belt. This study expands the analytical scope to the Western Sub-sag and Central Inversion Belt, and conducts a systematic comparative analysis of overpressure genesis across multiple tectonic units. The value of this work lies in the systematic application of classical diagnostic methods to fill the regional research gap regarding the overpressure characteristics of the Huagang Formation and the composite nature of overpressure. With accurately constrained genetic mechanisms, the findings can provide support for optimized drilling fluid design and wellbore stability management, and effectively mitigate deep hydrocarbon exploration risks in this sag and analogous overpressured basins. Full article

The aim of this in vitro study is to evaluate the stress distribution of different definitive restorative materials designed with different connector dimensions using finite element analysis. Two adjacent prepared maxillary molars were designed digitally. Two-unit splinted fixed dental prostheses (FDPs) were designed with 4 different triangular connector dimensions (2 × 3, 3 × 3, 3 × 4, and 4 × 4 mm (width × length)). The tested materials are Gold Metal, Base Metal Alloy, Feldspathic Porcelain, Lithium Disilicate, Zirconia, and Zirconia-Reinforced Lithium Silicate. A total of 56 two-unit splinted crowns models were designed and evaluated using finite element analysis (FEA) in Autodesk Fusion 360. FEA demonstrated a non-linear relationship between connector size and performance, with the 3 × 4 mm design exhibiting optimal stress distribution and the highest safety factor. Among different materials, the base metal alloy showed the highest safety factor across all configurations, while zirconia and lithium disilicate performed comparably under static loading. The 3 × 4 mm connector demonstrated optimal performance across all tested materials. Base metal alloy exhibited the highest safety factor among all connector dimensions. Full article