Abstract

Solar ground source heat pump systems offer asustainable solution that reduces energy consumptionand environmental impact. This study develops anintegrated solar ground source heat pump system,evaluating its exergy and environmental impacts. Resultsshow that the exergy efficiency is 49.16%, with a totalexergy destruction of 111.43 kW. The environmentalimpact is greatest during construction, followed byusage, disposal, and maintenance. This study providesvaluable insights for optimizing renewable energyintegration in building systems and serves as a guide forthe design of similar energy-efficient projects.

Abstract

Evaporative cooling using hygroscopic compositehydrogels and radiative cooling are widely studiedpassive thermal management strategies for photovoltaic(PV) modules. This work explores a synergistic approachthat integrates evaporative cooling through ahygroscopic hydrogel on the back side with radiativecooling on the front side of PV modules. A highlyaccurate heat and mass transfer model is developed andvalidated to simulate three configurations: radiativecooling only; evaporative cooling only on the back side;and the combined use of both techniques. Resultsdemonstrate that the hybrid configuration achieves thebest cooling performance, yielding an averagetemperature reduction of 3.2 °C and power generationefficiency (PCE) increase by 1.2%, which corresponds toa 500% and 26% relative improvement in PCE comparedto the radiative-cooling-only and evaporative-cooling-only cases, respectively. Notably, radiative cooling hasonly a minor influence on the hydrogel’s absorption anddesorption dynamics, increasing nighttime waterabsorption by about 1%, while the enhanced radiationextends the duration of the positive temperaturereduction effect by 10%. Radiative cooling can beconsidered as a supplementary method coupled with PVmodules, where evaporative cooling serves as theprimary passive cooling mechanism.

Abstract

How to maximize the use of renewable energy toachieve the most efficient utilization of energy resourcesis increasingly drawing attention. Against this backdrop,this study proposes a rule-based energy managementstrategy optimized by the Sparrow Search Algorithm fora novel diesel-hydrogen hybrid power system,investigating the impact of varying power source limitsthrough simulation. The findings demonstrate thatcontrolling the diesel engine power limit to 40–45 kWand the fuel cell power limit to 5–10 kW maximizes therenewable energy proportion and achieves significantenergy conservation, establishing an optimal balancebetween sustainability and dynamic performance

Abstract

The wake interactions between wind turbines in largewind farms significantly impact optimal layout andpower generation. Therefore, it is crucial to accurateprediction of turbine wake development. In this study,we utilize Large Eddy Simulation (LES) coupled with theactuator line method to investigate the flow of wakesdownstream of a scaled wind turbine model and theNREL 5MW turbine. To improve the accuracy of wake simulations, we propose a novel difference schemecalled the linear upwind stabilized transport (LUST) scheme, which we compare to the second-order centraland upwind schemes. Our results demonstrate that the LUST scheme provides proper numerical dissipation forwind turbine wake simulations, leading to improve accuracy in predicting mean velocity and turbulent intensity when compared to experimental measurements. Furthermore, the LES results indicate that the dissipation introduced by the LUST scheme stabilizes numerical simulations while still capturingsmall scales in the flow field. These findings highlight the effectiveness of the LUST scheme in accurately simulating wind turbine wakes and emphasize the importance of proper numerical dissipation in obtaining reliable results.

Abstract

Ambitious renewable expansion in ecologically fragile regions must reward not only scale but also match with environmental limits. We present a general coupling–coordination framework that evaluates clean-energy build out and ecological integrity on the same decisionfooting. For each location we derive energy efficacy 𝑆and ecological efficacy 𝐸. Match quality is measured by asymmetric, bounded coupling function 𝐶; overall development uses an ecology-prioritised index 𝑇; theirgeometric blend 𝐷 yields a single coordination score that enforces explicit scale thresholds (𝐷2 ≤ 𝑇) and achievesits upper envelope only along the balance line 𝑆= 𝐸. Weclassify cells into coordination tiers (D1–D5) and label the dominant subsystem (ecology-led, balanced, energy-led) to generate rank–driver codes that translate analysis intoaction. Applied to the Qinghai–Tibet Plateau, historical assets (n=1,606) cluster in the mid-to-high corridor—91.90% well coordinated (D4), 6.04% high-quality (D5),2.05% barely coordinated (D3)—while internal imbalance is common (ecology-led 60.77%, balanced13.88%, energy-led 25.34%), implying that unilateral expansion would depress coupling. A resource-based assessment shows a resilient, ecology-led landscape (median 𝐷 =0.777, mean 𝐷 =0.767, mean 𝐶 =0.959; mean𝑆 =0.499, mean 𝐸 =0.695), with province-scale contrasts: Gansu high- 𝐶 but single-driver; Qinghai high-level andmore balanced; Tibet tri-modal with ridge-adjacent optima and energy-pressured pockets; Xinjiang potential constrained by grid/flexibility; Yunnan and Sichuan coordination limited by engineering accessibility. The diagnostics point to three robust pathways—close the lagging subsystem, expand near the balance ridge, and pace build intensity with grid, storage and ecological restoration—providing a generalisable, auditable instrument for high-quality clean-energy growth under ecological redlines.

Abstract

To address the challenges in distribution network protection caused by high-penetration distributed generation (DG) integration, this study proposes a multi-source information fusion-based fault location algorithmfor distribution networks. A dynamic current matrix forfault zones and a topology-adaptive node differential matrix are constructed, and the fault location matrix isderived through matrix multiplication. Grounded in the current differential principle for distribution networkfault localization, logical analysis is performed on the fault discrimination matrix. PSCAD simulation demonstrated that the criterion is capable of accurately locating fault nodes. The algorithm exhibits advantages such as simplicity in principle, low memory consumption, high computational efficiency, and superior accuracy, demonstrating strong practicality. It provides a robustsolution for active distribution networks with high-penetration renewable energy.

Abstract

Efficient temperature regulation is essential formaintaining the sensitivity and noise performance ofspaceborne infrared detectors under varyingobservation modes. This paper proposes an ActiveDisturbance Rejection Controller (ADRC) with dynamicparameter tuning based on the Soft Actor-Critic (SAC)reinforcement learning algorithm, aiming to optimize thetemperature control performance and disturbancerejection capability of spacecraft systems. A thermaltransfer model was established, and the Soft Actor-Critic(SAC) algorithm was introduced to dynamically tuneparameters (system gain, controller bandwidth andobserver bandwidth) by maximizing a weighted sum ofthe reward and policy entropy. This approach enablesreal-time estimation and compensation of totaldisturbances. Simulation results demonstrate that, intemperature tracking tasks, the proposed methodreduces the integral of absolute error (IAE) by 14.61%and 14.87%, and shortens the settling time by 36.36%and 58.47%, compared to fixed-parameter ADRC and PIDcontrollers, respectively. Under external periodicdisturbances, the proposed controller improves controlaccuracy by 15.6% and 13.6%. Monte Carlo robustnesstests further show that, under ±5% parameterperturbations, the method exhibits small fluctuations inIAE, settling time, and overshoot. The proposed SAC-ADRC strategy provides a promising solution for rapidand high-precision thermal regulation of infrareddetectors in aerospace applications.

Abstract

The increasing energy demand for air conditioningsystems presents substantial economic andenvironmental challenges, making effective airconditioning load monitoring crucial. Traditionalintrusive load monitoring techniques were costly andchallenging to implement, whereas non-intrusive loadmonitoring (NILM) technology offered a cost-effectivealternative. This study constructed the CN-AC-NILMdataset, customized for the characteristics of Chineseelectrical appliances and cultural usage patterns, andsubsequently developed an RNN-attention model for airconditioning systems, with a focus on evaluating theimpact of the attention mechanism on the model’sperformance. Initially, the optimal input feature set forthe air conditioning load disaggregation predictionmodel was determined using the forward featureselection method based on the dataset, followed by anevaluation of performance differences among variousRNN models. Subsequently, it was observed that in thecomparative analysis of baseline models applied to airconditioning system for NILM, LSTM exhibited higheraccuracy than GRU, and bidirectional modelsoutperformed unidirectional models. Furthermore,after introducing the attention mechanism in thebaseline models, the average accuracy of RNN modelsincreased by 21.09%, accompanied by a 5.33% increasein average training time. Finally, the effects of attentionmechanisms and bidirectional structure on the modelswere compared, revealing that the introduction of theattention mechanism outperformed the bidirectionalstructure, and the LSTM-Attention exhibited the bestoverall performance for NILM of the air conditioningsystems. Notably, Attention mechanisms enhancedmodel interpretability through the visualization ofattention weights. By improving load disaggregationaccuracy and interpretability, this attention-enhancedmodel enables more precise energy management for airconditioning systems. It reduces electricity costs,optimizes energy use, and supports demand-sidemanagement, offering substantial economic savings andlowering environmental impact, especially in highenergy-demand settings.

Abstract

Charging stations, as essential infrastructure for smart scheduling and energy management in smart cities, significantly improve the operational efficiency and intelligence of urban energy systems. This study proposes a load forecasting model that integrates LSTM and Transformer networks, introducing the Mamba module as a critical bridge to fuse temporal features extracted by both models, thereby constructing a superior knowledge representation to enhance prediction accuracy. First, LSTM and Transformer networks are used separately to extract latent temporal features from the load data. Subsequently, the Mamba module fuses and enhances these features to capture finer-grained information. Comprehensive experimental results demonstrate that the proposed method achieves an average prediction accuracy of 93.4% on data from multiple electric vehicle charging stations, without substantially increasing computational overhead.

Abstract

Combined heating and power integrated energy system (CHP-IES) enables efficient coordination and dispatch of multiple energy sources, enhancing renewable energy utilization and overall efficiency, thus serving as a key technical approach to support the low-carbon transition under the dual carbon goals. First, the paper develops a detailed model of key components in the system, including gas turbines, gas-fired boilers, electrical energy storage, and thermal energy storage, thereby capturing the multi-energy flow coupling relationships within CHP-IES. Second, a pricing mechanism based on the Stackelberg game is established, in which the distributed energy resource (DER) operator acts as the leader and the users serve as followers. The CHP-IES dynamically determines electricity and heat prices based on market signals, while users adjust their flexible electricity and heat loads in response to price signals. Finally, the proposed method is validated through a case study.