Search Results (467)

The ramping requirement in new power systems primarily stems from net load variations and forecast errors of renewable energy and load. Designing an equitable cost allocation mechanism for ramping services based on these factors facilitates incentives for generation and load to actively reduce ramping demands, thereby alleviating system ramping pressure. Accordingly, this paper proposes a fair ramping cost allocation mechanism based on the ramping responsibility coefficients of market participants. Under this mechanism, a market-oriented operation model for wind–hydro-storage joint operation is established to verify its effectiveness in market applications. First, a ramping cost allocation mechanism is constructed based on ramping responsibility coefficients. According to the responsibility coefficients of market participants for deterministic and uncertain ramping requirements, ramping costs are allocated to the corresponding contributors in proportion to the ramping demands caused by net load variations, load forecast deviations, and renewable energy forecast deviations. Specifically, for costs arising from renewable energy forecast errors, an allocation mechanism is designed based on the difference between the declared error range and the actual error. Second, within this allocation framework, hydropower and storage (including cascade hydropower and hybrid pumped storage) are utilized as flexible resources to mitigate wind power uncertainty and reduce its ramping costs. A two-stage day-ahead and real-time bi-level game model for wind–hydro-storage cooperative decision-making is developed. The upper level optimizes bilateral trading and market bidding strategies for wind–hydro-storage, while the lower level simulates the market clearing process. Through Stackelberg game modeling, joint optimal operation of wind–hydro-storage is achieved, ensuring mutual benefits. Finally, simulation results validate that the proposed ramping cost allocation mechanism can guide renewable energy to improve output controllability through economic signals. Furthermore, the bilateral trading and coordinated market participation of wind–hydro-storage realize win–win outcomes, reduce the ramping cost allocation for wind power by 23.10%, effectively narrow peak-valley price differences, and enhance market operational efficiency. Full article

Conventional thermoelectric generators (TEGs) suffer from thermal resistance introduced by ceramic substrates and thermal interface materials, which limits the achievable temperature gradient across the junctions and reduces conversion efficiency. To overcome this limitation, a pin-fin integrated thermoelectric device (iTED) is proposed, in which the hot-side heat exchanger is incorporated directly into the hot-side interconnector, eliminating the ceramic and associated greases. An explicitly coupled thermal-fluid-electric finite-volume model is developed in ANSYS Fluent’s user-defined scalar (UDS) environment to quantify the simultaneous thermal-fluid-electric behavior of the iTED for inlet temperatures of 350 $\le T_{\mathrm{in}}\mathrm{K}\le$ 650, Reynolds numbers of 3000 $\le \mathrm{Re}\le$ 15,000, and load resistances ranging from 0.01 to ${10}^6$% of the internal device resistance ($R_{\mathrm{int}}$), for a fixed cold-side temperature of 300 K. The model is validated against established tube-bank correlations (2.2% agreement in pumping power) and a one-dimensional Explicit Thomson Model (1.2–6.9% agreement across all electrical system response quantities). Compared with an equivalently sized conventional TEG, the iTED achieves a 4.6-fold higher maximum power output (23.9 [W] vs. 5.2 [W] at Re = 15,000), a 2.8-fold higher thermal conversion efficiency (8.1% vs. 2.9%), and a 4.8-fold higher performance index (7.8 [-] vs. 1.6 [-] at Re = 3000), all at $T_{\mathrm{in}}$ = 650 K. A performance index analysis reveals that lower Reynolds numbers and higher inlet temperatures maximize the net power benefit, delineating the operational envelope in which the iTED produces more electrical power than is needed for fluid pumping. These findings demonstrate that device-level restructuring—specifically, the elimination of interfacial thermal resistance via integrated pin-fin heat exchangers—can yield performance improvements comparable to or exceeding those achievable through material advances alone. Full article

With the increasing penetration of distributed energy resources, distribution networks face elevated risks of power disruptions, which call for rapid and flexible emergency response mechanisms. There are not enough traditional emergency generator vehicles, and they are not highly adaptable when it comes to operations, which makes it hard to meet changing dispatching needs. Electric vehicles (EVs), on the other hand, can be used as distributed emergency resources that can be dispatched through vehicle-to-grid (V2G) interaction. Electric vehicle charging stations (EVCSs), on the other hand, are integrated energy storage units that use existing charging infrastructure to provide on-site grid support. To address this gap, this study proposes a comprehensive V2G-powered pre- and post-disaster coordination framework for enhancing distribution network resilience, with three core novelties: first, a refined individual EV model considering dual power and energy constraints is developed, and the Minkowski summation method is applied to accurately quantify the real-time aggregate regulation potential of EVCSs for the first time; second, a two-stage robust optimization model is formulated for pre-event strategic planning, which jointly optimizes EVCS participant selection and distribution network topology to address photo-voltaic (PV) power generation uncertainties; third, a multi-source collaborative dynamic scheduling model is constructed for post-disaster recovery, which explicitly incorporates the spatiotemporal dynamics of EVs and coordinates EVCSs, gas turbine generators (GTGs) and other resources for the first time. We carried out simulations on a modified IEEE 33-bus system with a 10 h extreme fault scenario. The results show that the proposed strategy raises the average critical load recovery ratio to 97.7% (2% higher than traditional deterministic optimization), lowers the total load shedding power by 0.2 MW and the load reduction cost by 19,797.63 CNY, and gives a net V2G power output of 3.42 MW (86.9% higher than the comparison strategy). The proposed V2G-enabled coordinated pre- and post-disaster fault recovery strategy significantly improves the resilience of distribution networks compared to traditional methods. This makes it easier and faster to recover from extreme disaster scenarios, with the overall load recovery rate reaching 91.8% and the critical load restoration rate staying above 85% throughout the recovery process. Full article

The integration of hydrogen into natural-gas-fired gas turbines represents a promising transitional pathway for reducing greenhouse gas emissions in industrial power generation. This study presents a comparative thermodynamic and environmental assessment of a Solar Titan 130 gas turbine operating in combined heat and power (CHP) mode under two fueling conditions: conventional natural gas and a hydrogen-enriched CH₄/H₂ (80/20 vol.%) blend. The analysis combines validated operational data for natural gas with analytical thermodynamic modeling for the blended-fuel scenario to evaluate key performance indicators, including thermal efficiency, specific fuel consumption, power output, and carbon dioxide emissions. The results indicate that hydrogen enrichment leads to an increase in thermal efficiency from 34.1% to 36.6% and a reduction in specific CO₂ emissions by approximately 13.7%, while maintaining similar thermal input within the adopted steady-state modeling framework. Compressor power consumption decreases, and net electrical output increases slightly under hydrogen-enriched operation, contributing to improved overall energy performance. Although the hydrogen-blended regime is assessed through modeling, the findings suggest that moderate hydrogen addition can enhance efficiency and environmental performance in industrial gas turbines without fundamental structural redesign of the turbine core, assuming appropriate fuel supply and control system adaptation. The study provides practical insights into the feasibility of hydrogen-assisted operation in existing CHP installations and supports its role in near-term decarbonization strategies. Full article

With the deepening implementation of the dual-carbon strategy, the penetration rates of distributed power sources and flexible loads in new distribution grids continue to rise, posing significant challenges to system security and stability due to output fluctuations and randomness. To enhance voltage quality and achieve low-carbon economic operation in distribution grids, this paper proposes a multi-objective optimization model for Distributed Energy Storage System allocation. The model integrates power quality, economic benefits, and net carbon emissions. To efficiently solve this high-dimensional nonlinear problem, an improved Multi-Objective Gray Wolf Optimization algorithm is proposed. It employs a chaotic map to initialize the population, enhancing global distribution uniformity. A nonlinear convergence factor is introduced to dynamically balance global exploration and local exploitation. A dynamic grouping collaboration strategy is designed, combining Lévy flight and the elite crossover strategy to enhance search capability and convergence accuracy. Simulations on an IEEE 33-node system show that the improved MOGWO-optimized energy storage scheme reduces average voltage deviation by 37.0%, total operating costs by 7.0%, and net carbon emissions by 4.1%, compared to a no-storage scenario. Compared to the standard MOGWO algorithm, the proposed method achieves further optimization across all objectives, validating its effectiveness and superiority in realizing coordinated energy storage planning that balances safety, economy, and low-carbon goals. Full article

Clouds are a key factor affecting solar radiation, and their dynamic variations directly cause uncertainty and fluctuations in photovoltaic (PV) power output. To improve PV power prediction accuracy, this paper proposes an enhanced short-term photovoltaic power forecasting approach based on a hybrid neural network architecture using features extracted from satellite cloud images. First, a dual-layer image fusion method is developed for satellite cloud images from different wavelengths and spectral bands, effectively improving fusion accuracy. Second, texture descriptors derived from the Gray-Level Co-occurrence Matrix and multiscale information obtained via the wavelet transform are employed for feature extraction from fused images. Combined with a residual network (ResNet), an optical flow method, as well as an LSTM-based temporal modeling module, multidimensional features of the predicted cloud images are obtained. An improved Bayesian optimization (IBO) algorithm is then employed to derive the optimal fused features, thereby improving the matching between cloud image features and PV power. Third, an enhanced hybrid architecture integrating a convolutional neural network and long short-term memory units with a multi-head self-attention mechanism is developed. Numerical weather prediction (NWP) meteorological features are incorporated, and a tilted irradiance model is introduced to calculate the solar irradiance received by PV modules for use in near-term photovoltaic power forecasting. Finally, measurements collected at a photovoltaic power plant located in Hebei Province are used to validate the proposed method. The results show that, relative to the SA-CNN-MSA-LSTM and BO-CNN-LSTM models, the developed approach lowers the RMSE to an extent of 22.56% and 4.32%, while decreasing the MAE by 24.84% and 5.91%, respectively. Overall, the proposed model accurately captures the characteristics of predicted cloud images and effectively improves PV power prediction accuracy. Full article

To boost the power and conversion efficiency of a polygonal automobile exhaust thermoelectric generator (AETEG), an innovative protrusion-type disturbance is introduced to the original sickle-shaped fins in this work. A coupled multiphysics field model integrating fluid, thermal, and electrical fields was constructed, a net power framework was formulated, and the protrusion structure parameters of protrusion radius and spacing were optimized. At a flow velocity of 40 m/s and an inlet temperature of 600 K, simulation results reveal that increasing the protrusion radius and protrusion spacing effectively improves the heat capture capability and the overall performance of the AETEG system. Simultaneously, the backpressure inside the heat exchanger increases, accompanied by a decline in temperature uniformity at the hot side of the thermoelectric modules (TEMs). Based on the designed multiple performance metrics, the optimal protrusion configuration is finally set as R = 8 mm, D_tg = 8 mm, and D_hf = 5.5 mm. Compared with the original AETEG system with sickle-shaped fins, the optimized protrusion design enhances the TEMs’ average hot-side temperatures by 5.11%, increases the output power by 42.22%, and improves the net power by 76.48%. Additionally, this optimization results in a 13.44% improvement in conversion efficiency and a 40.65% enhancement in net efficiency. Full article

We demonstrate an all-polarization-maintaining (PM) mode-locked thulium-doped fiber laser operating in the net-normal-dispersion regime based on a figure-9 nonlinear amplifying loop mirror (NALM) configuration. A chirped fiber Bragg grating (CFBG) and a commercial PM dispersion-compensating fiber (PM-DCF) are incorporated into the figure-9 cavity, providing a large normal net dispersion and enabling stable dissipative-soliton mode-locking. Under stable dissipative-soliton operation, the laser delivers a maximum output power of 53.6 mW at a repetition rate of 12.31 MHz, corresponding to a pulse energy of 4.3 nJ. The output spectrum has a central wavelength of ~1952 nm with a 3 dB bandwidth of ~11 nm. The all-PM laser oscillator directly generates a fs pulse without extra-cavity compression, achieving a pulse duration of 545 fs at the CFBG arm. Moreover, stable fundamental mode-locking is verified by a high radio-frequency signal-to-noise ratio (SNR) exceeding 82 dB and a long-term root-mean-square (RMS) power fluctuation of 0.45% over two hours. To the best of our knowledge, this represents the highest output power generated from an all-PM-fiber figure-9 laser oscillator in the 2 μm band, alongside fs-pulse operation. This high-power, compact, stable and environment-insensitive fs-pulsed laser source shows great potential as an ideal seed for biomedical imaging and mid-infrared frequency combs. Full article

Accurate power prediction is essential for assessing wind turbine performance under real-world operating conditions and for supporting condition monitoring and maintenance planning using SCADA data. Most existing approaches rely directly on raw SCADA signals, which may limit their ability to capture complex spatiotemporal dependencies among operational variables. To address this limitation, this paper proposes a novel SCADA-driven power prediction framework that transforms selected SCADA variables into multi-channel grayscale images and leverages an optimized LeNet-5–LSTM hybrid neural network for active and reactive power prediction. First, the SCADA dataset is analyzed to identify the most influential variables affecting power output. Six key variables are then selected, segmented, and encoded as 2D grayscale images, enabling the model to learn richer feature representations compared to conventional raw SCADA data-based methods. The proposed network combines convolutional layers for spatial feature extraction from SCADA data-based grayscale images with LSTM layers to capture temporal dependencies. Model training incorporates a customized loss function that integrates both data-driven supervision and physics-based constraints. The model is trained using 70% of the image-based dataset, with five independent runs to ensure robustness and reproducibility, while the remaining 30% is used for testing. The proposed approach is validated using SCADA data from three real-world cases: (i) a 2 MW Siemens wind turbine in Poland, (ii) a Vestas V52 wind turbine in Ireland, and (iii) the La Haute Borne wind farm in France, consisting of four wind turbines. The results demonstrate that the SCADA-based image representation enables the proposed LeNet-5–LSTM model to effectively learn discriminative feature patterns and achieve accurate active and reactive power predictions across different turbine types and operating conditions. Full article

Customer Segment Classification is a machine learning task in marketing analytics that involves assigning customers to predefined categories using features derived from historical transactional data. However, conventional approaches, such as statistical and clustering-based algorithms, may face challenges in fully capturing the nonlinear relationships in customer data, which can lead to limited insights and suboptimal segmentation outcomes. This paper introduces RFM-Net, an approach that integrates Deep Learning with Recency, Frequency, and Monetary (RFM) analysis for customer segment classification. By leveraging RFM features as input and labeled customer segments as output, we designed a specialized Convolutional Neural Network (CNN) model tailored for classification tasks. In the proposed method, labels are generated by a rule-based logic from RFM scores and then used as supervised ground truth. Accordingly, learning an expert-defined mapping is employed to model customer segmentation, rather than discovering a new segmentation structure. The proposed method enables businesses to classify customers into strategically meaningful segments such as Champions, Loyal Customers, At Risk, and Hibernating, thereby facilitating effective and targeted marketing strategies. Unlike traditional CNN architectures, RFM-Net offers a more compact, lightweight, and computationally efficient model with fewer layers and parameters, supporting improved interpretability and reduced risk of overfitting. Experimental results conducted on a real-world dataset demonstrated the effectiveness of RFM-Net with an accuracy of 94.33%. The results of this study showed a relative average increase of 13.17% compared to the results reported in previous studies on the same dataset. The core contribution of this research lies in combining the powerful generalization capabilities of deep learning with the effectiveness of RFM analysis, offering a robust solution for data-driven customer relationship management. Full article

To support the sustainable transition of power systems with high penetration of renewable energy, this study proposes an optimization model for critical peak pricing (CPP) that integrates photovoltaic (PV) utilization and consumer heterogeneity. With the aim of improving renewable energy consumption, reducing carbon emissions, and enhancing the long-term sustainability of distribution networks, electricity consumers are classified according to their diverse behavioral characteristics, and a differentiated CPP mechanism is designed accordingly. Time periods are dynamically segmented using fuzzy membership functions based on net load curves, enabling price signals to better align electricity demand with PV generation profiles. Consumer psychology is further incorporated to develop a user response model that reflects heterogeneous demand-side behavior. A multi-objective CPP optimization framework is established to balance the economic interests of electricity consumers, retailers, and other stakeholders, while simultaneously promoting renewable energy integration and system-level sustainability. The proposed model is solved using a genetic algorithm. Case studies demonstrate that the approach effectively smooths net load curves, encourages electricity consumption during periods of high PV output, improves economic benefits for all participants, and enhances carbon emission reduction performance. Finally, a sensitivity analysis under multiple scenarios is conducted to evaluate the robustness and sustainability implications of the proposed CPP mechanism. Full article

This paper investigates an Organic Rankine Cycle (ORC) system for low-to-medium temperature heat recovery using comparative thermodynamic, exergoeconomic and economic modelling. A working-fluid study considering environmental and thermodynamic perspectives is conducted. A 20 kW ORC unit is tested and used as a feasibility and trend-consistency reference to support the modelling assumptions and practical operating bounds. A parametric study then examines the effects of evaporator pressure, condensation temperature, superheat, subcooling and heat-exchanger pinch-point temperature differences on net power output, first- and second-law efficiencies, total product cost and total capital investment under prescribed boundary conditions. Multi-objective optimization is applied to identify Pareto-optimal trade-offs and representative compromise solutions. Results show an intermediate evaporator pressure maximizes net power output, while lower condensation temperature generally improves efficiency; superheat has limited efficiency impact but should ensure safe operation, and a small subcooling margin (around 3 °C) mitigates cavitation risk. The best overall performance is obtained with an evaporator pinch of 3 °C and a condenser pinch of 5–9 °C; tightening pinch constraints increases required heat-transfer area and makes heat exchangers the main cost bottleneck for high-efficiency solutions. Full article

Hybrid photovoltaic (PV) systems augmented by wind-induced energy contributions can improve energy reliability under variable atmospheric conditions. However, their performance remains highly sensitive to site-specific weather patterns, panel orientation, and system parameter selection. This study presents a computational optimization framework based on Differential Evolution (DE) to enhance the combined energy output of a hybrid PV–wind system using high-resolution reanalysis data. Hourly solar irradiance from NASA POWER and near-surface wind components from ERA5 were processed through a unified data ingestion and preprocessing pipeline supporting GRIB and NetCDF formats to evaluate seasonal and annual energy production. The optimization jointly adjusted PV tilt angle, effective PV area scaling, and a wind energy scaling parameter to maximize total energy yield. Case studies for San Antonio (TX), Denver (CO), and Albuquerque (NM) demonstrate seasonal energy gains of 36–57% and annual improvements of 36.9–56.2% relative to baseline fixed-parameter configurations. The results indicate that evolutionary optimization combined with reanalysis-driven energy modeling provides a robust and scalable approach for improving hybrid renewable energy performance across diverse climatic regions. Full article

To address the inherent mismatch between the fluctuating power output of renewable energy and the continuous production requirements of ammonia in off-grid wind–solar–hydrogen–ammonia systems, this paper proposes a “day-ahead–intraday–real-time” multi-time-scale coordinated optimization scheduling strategy. In the day-ahead layer, Wasserstein Distributionally Robust Optimization (WDRO) is employed to determine a conservative and stable baseline plan for ammonia load under high uncertainty of wind and solar output. The intraday layer utilizes Model Predictive Control (MPC) with a 2-h prediction horizon and 15-min rolling steps to correct short-term forecast deviations. The real-time layer achieves minute-level power balancing through priority dispatch and deadband control. Furthermore, hydrogen storage tanks serve as a material buffer between hydrogen production and ammonia synthesis, with their state variables transmitting across layers to achieve flexible multi-time-scale coupling. Simulation results demonstrate that, although this strategy slightly reduces the theoretical maximum ammonia yield, it completely avoids load-shedding risks. Compared with the deterministic scheduling (Scheme 1), which suffers a net loss due to severe penalty costs, the proposed strategy achieves a positive daily profit of CNY 277,700, representing an absolute increase of CNY 429,300. Furthermore, it provides an additional daily profit of CNY 65,800 compared to the stochastic optimization approach (Scheme 2), demonstrating superior economic robustness in off-grid environments. Full article

Accurate assessment of source-load complementarity and system regulation capacity is critical for secure dispatch and planning in high-penetration renewable power systems. Addressing limitations of existing methods—which rely heavily on static metrics, struggle to capture temporal and tail dependence characteristics, and provide insufficient support for dispatch decisions—this paper proposes a multi-level integrated evaluation framework. First, from a source—load matching perspective, we develop a novel complementarity metric, integrating real-time rate of change, temporal consistency, and tail dependency. An improved adaptive noise-complete set empirical mode decomposition combined with a hybrid Copula model is employed to isolate noise and to precisely quantify dynamic dependency structures. Second, we introduce the Minkowski measure and construct a net load fluctuation domain accounting for extreme fluctuations and coupling relationships. Subsequently, combining the Analytic Hierarchy Process (AHP) with probabilistic convolution enables multi-level comparative quantification of resource capacity and fluctuation domain requirements under varying confidence levels. Simulation results demonstrate that the proposed framework not only provides a more robust assessment of source-load complementarity but also quantitatively outputs the adequacy and risk level of system regulation capacity. This delivers hierarchical, actionable decision support for dispatch planning, significantly enhancing the engineering applicability of evaluation outcomes. Full article