Search Results (182)

To address the need for intelligent scheduling and model integration under spatiotemporal variability and uncertainty in water systems, this study proposes a hybrid correction method for pump characteristic curves that integrates data-driven techniques with an affine modeling framework. Steady-state data are extracted through adaptive filtering and statistical testing, and representative operating conditions are identified via unsupervised clustering. An affine transformation is then applied to the factory-provided characteristic equation, followed by parameter optimization using the clustered dataset. Using the Hongze Pump Station along the eastern route of the South-to-North Water Diversion Project as a case study, the method reduced the mean blade angle prediction error from 1.73° to 0.51°, and the efficiency prediction error from 7.32% to 1.30%. The results demonstrate improved model accuracy under real-world conditions and highlight the method’s potential to support more robust and adaptive hydrodynamic scheduling models, contributing to the advancement of sustainable and smart water resource management. Full article

Leak management in water distribution networks (WDNs) is essential for minimising water loss, improving operational efficiency, and supporting sustainable water management. However, effectively identifying, preventing, and locating leaks remains a major challenge owing to the ageing infrastructure, pressure variations, and limited monitoring capabilities. Leakage management generally involves three approaches: leakage assessment, detection, and prevention. Traditional methods offer useful tools but often face limitations in scalability, cost, false alarm rates, and real-time application. Recently, artificial intelligence (AI) and machine learning (ML) have shown growing potential to address these challenges. Deep Reinforcement Learning (DRL) has emerged as a promising technique that combines the ability of Deep Learning (DL) to process complex data with reinforcement learning (RL) decision-making capabilities. DRL has been applied in WDNs for tasks such as pump scheduling, pressure control, and valve optimisation. However, their roles in leakage management are still evolving. To the best of our knowledge, no review to date has specifically focused on DRL for leakage management in WDNs. Therefore, this review aims to fill this gap and examines current leakage management methods, highlights the current role of DRL and potential contributions in the water sector, specifically water distribution networks, identifies existing research gaps, and outlines future directions for developing DRL-based models that specifically target leak detection and prevention. Full article

Distributed renewable energy sources with significant output fluctuations can negatively impact the power grid stability when it is connected to the power grid. Therefore, it is necessary to develop a capacity configuration method that improves the output stability of highly uncertain energy sources such as wind and photovoltaic (PV) power by integrating pumped storage units. In response, this study proposes a capacity configuration method for a cascade small hydropower-pumped storage–wind–PV complementary system. The method utilizes the regulation capacity of cascade small hydropower plants and pumped storage units, in conjunction with the fluctuating characteristics of local distributed wind and PV, to perform power and energy time-series matching and determine the optimal capacity allocation for each type of renewable energy. Furthermore, an optimization and scheduling model for the cascade small hydropower-pumped storage–wind–PV complementary system is constructed to verify the effectiveness of the configuration under multiple scenarios. The results demonstrate that the proposed method reduces system energy deviation, improves the stability of power output and generation efficiency, and enhances the operational stability and economic performance of the system. Full article

The short-term scheduling of pumped-storage hydropower plants is characterised by high dimensionality and nonlinearity and is subject to multiple operational constraints. This study proposes an intelligent scheduling framework that integrates an Atomic Orbital Search (AOS)-optimised Long Short-Term Memory (LSTM) network with the Deep Deterministic Policy Gradient (DDPG) algorithm to minimise water consumption during the generation period while satisfying constraints such as system load and safety states. Firstly, the AOS-LSTM model simultaneously optimises the number of hidden neurons, batch size, and training epochs to achieve high-precision fitting of unit flow–efficiency characteristic curves, reducing the fitting error by more than 65.35% compared with traditional methods. Subsequently, the high-precision fitted curves are embedded into a Markov decision process to guide DDPG in performing constraint-aware load scheduling. Under a typical daily load scenario, the proposed scheduling framework achieves fast inference decisions within 1 s, reducing water consumption by 0.85%, 1.78%, and 2.36% compared to standard DDPG, Particle Swarm Optimisation, and Dynamic Programming methods, respectively. In addition, only two vibration-zone operations and two vibration-zone crossings are recorded, representing a reduction of more than 90% compared with the above two traditional optimisation methods, significantly improving scheduling safety and operational stability. The results validate the proposed method’s economic efficiency and reliability in high-dimensional, multi-constraint pumped-storage scheduling problems and provide strong technical support for intelligent scheduling systems. Full article

Pumped storage hydropower plants are important resources for scheduling urban energy storage, which realize the conversion of electric energy through upper and lower reservoir capacities. Dynamic forecasting of reservoir capacities is crucial for scheduling pumped storage and maximizing the economic benefits of pumped storage hydropower plants. In this work, a hybrid forecast network is proposed for both the upper and lower reservoir capacities of a pumped storage hydropower plant. A bidirectional long- and short-term memory network (BiLSTM) is designed as the baseline for the prediction model. A convolutional neural network (CNN) and Squeeze-and-Excitation (SE) attention mechanism are designed to extract local features from raw time series data to capture short-term dependencies. In order to better distinguish the effects of different data types on the reservoir capacity, the correlation between data and reservoir capacity is analyzed using the Spearman coefficient, and a multi-branch forecast model is established based on the correlation. A fusion module is designed to weight and fuse the branch prediction results to obtain the final reservoir capacities forecast model, namely, Multi-Branch Attention–CNN–BiLSTM. The experimental results show that the proposed model exhibits better forecast accuracy in forecasting the reservoir capacity compared with existing methods. Compared with BiLSTM, the $MAPE$ of the forecast values of the reservoir capacities of the upper and lower reservoirs decreased by $1.93\%$ and $2.2484\%$, the $RMSE$ decreased by $16.9887{\mathrm{m}}^3$ and $14.2903{\mathrm{m}}^3$, and the $R^2$ increased by $0.1278$ and $0.1276$, respectively. Full article

To achieve low-carbon economic dispatch and collaborative optimization of carbon capture efficiency in power systems, this paper proposes a flexible carbon capture power plant and generalized energy storage collaborative operation model under a dynamic carbon quota mechanism. First, adjustable carbon capture devices are integrated into high-emission thermal power units to construct carbon–electricity coupled operation modules, enabling a dynamic reduction of carbon emission intensity and enhancing low-carbon performance. Second, a time-varying carbon quota allocation mechanism and a dynamic correction model for carbon emission factors are designed to improve the regulation capability of carbon capture units during peak demand periods. Furthermore, pumped storage systems and price-guided demand response are integrated to form a generalized energy storage system, establishing a “source–load–storage” coordinated peak-shaving framework that alleviates the regulation burden on carbon capture units. Finally, a multi-timescale optimization scheduling model is developed and solved using the GUROBI algorithm to ensure the economic efficiency and operational synergy of system resources. Simulation results demonstrate that, compared with the traditional static quota mode, the proposed dynamic carbon quota mechanism reduces wind curtailment cost by 9.6%, the loss of load cost by 48.8%, and carbon emission cost by 15%. Moreover, the inclusion of generalized energy storage—including pumped storage and demand response—further decreases coal consumption cost by 9% and carbon emission cost by 17%, validating the effectiveness of the proposed approach in achieving both economic and environmental benefits. Full article

Natural Gas Liquid (NGL) pumps are critical assets in oil and gas operations, where unplanned failures can result in substantial production losses. Traditional maintenance approaches, often based on static schedules and expert judgement, are inadequate for optimising both availability and cost. This study proposes a novel Artificial Intelligence (AI)-based methodology and digital tool for optimising NGL pump maintenance using limited historical data and real-time sensor inputs. The approach combines dynamic reliability modelling, component condition assessment, and diagnostic logic within a unified framework. Component-specific maintenance intervals were computed using mean time between failures (MTBFs) estimation and remaining useful life (RUL) prediction based on vibration and leakage data, while fuzzy logic- and rule-based algorithms were employed for condition evaluation and failure diagnoses. The tool was implemented using Microsoft Excel Version 2406 and validated through a case study on pump G221 in a Saudi Aramco facility. The results show that the optimised maintenance routine reduced the total cost by approximately 80% compared to conventional individual scheduling, primarily by consolidating maintenance activities and reducing downtime. Additionally, a structured validation questionnaire completed by 15 industry professionals confirmed the methodology’s technical accuracy, practical usability, and relevance to industrial needs. Over 90% of the experts strongly agreed on the tool’s value in supporting AI-driven maintenance decision-making. The findings demonstrate that the proposed solution offers a practical, cost-effective, and scalable framework for the predictive maintenance of rotating equipment, especially in environments with limited sensory and operational data. It contributes both methodological innovation and validated industrial applicability to the field of maintenance optimisation. Full article

As the share of renewable energy in modern power systems continues to grow, its inherent uncertainty and variability pose severe challenges to grid stability and the accuracy of traditional thermal power dispatch. To address this issue, this study fully exploits the fast response and flexible operation of variable-speed pumped storage (VS-PS) by developing a day-ahead scheduling model for a wind–photovoltaic–thermal–VS-PS system. The optimization model aims to minimize system operating costs, carbon emissions, and thermal power output fluctuations, while maximizing the regulation flexibility of the VS-PS plant. It is assessed using the improved multi-objective jellyfish search (IMOJS) algorithm, and its effectiveness is demonstrated through comparison with a fixed-speed pumped storage (FS-PS) system. Simulation results show that the proposed model significantly outperforms the traditional FS-PS system: it increases renewable energy accommodation capacity by an average of 68.51%, reduces total operating costs by 14.13%, and lowers carbon emissions by 3.63%. Full article

With the increasing penetration of renewable energy generation in energy systems, power and district heating systems (PHSs) continue to encounter challenges with wind and solar curtailment during scheduling. Further integration of renewable energy generation can be achieved by exploring the flexibility of existing systems. Therefore, this study systematically explores the deep transfer modifications of a specific thermal power plant based in Liaoning, China, and the operational characteristics of the heating supply system of a particular heating company. In addition, the overall PHS operational performance is analyzed. The results indicate that both absorption heat pumps and solid-state electric thermal storage technologies effectively improve system load regulation capabilities. The temperature decrease in the water medium in the primary network was proportional to the pipeline distance. When the pipeline lengths were 1175 and 14,665 m, the temperature decreased by 0.66 and 3.48 °C, respectively. The heat exchanger effectiveness and logarithmic mean temperature difference (LMTD) were positively correlated with the outdoor temperature. When the outdoor temperature dropped to −18 °C, the heat exchanger efficiency decreased to 60%, and the LMTD decreased to 17.5 °C. The study findings provide practical data analysis support to address the balance between power supply and heating demand. Full article

Optimizing well spacing is crucial for enhancing the production efficiency and economic returns of tight oil development. The limited understanding of hydraulic fracture geometry and properties poses significant challenges in designing well spacing for tight oil reservoirs. In this study, we proposed an integrated workflow for optimizing well spacing in tight oil reservoirs. Geological and geomechanical models were established to form the basis for numerical reservoir simulation and dynamic fracture modeling. A multi-staged, multi-clustered fracture propagation simulation of horizontal wells was conducted by a hydraulic fracturing simulator with matched actual field pumping schedules. The differences between fracture propagation simulation results and field monitoring results, including micro-seismic testing and distributed temperature sensing (DTS) monitoring, were analyzed. The geological model and fracture propagation simulation results were integrated into an efficient numerical reservoir simulator. A material balance method for fracturing fluids leak-off was proposed and utilized to equivalently calculate the actual oil–water distribution after fracturing and to complete the historical matching water cuts of all wells. Subsequently, the inter-well drainage area and pressure interference were evaluated. By employing this integrated workflow, the production performance of six wells (three well pairs) at different well spacings was simulated over a 15-year period, and their estimated ultimate recoveries (EURs) were predicted. When well spacing was less than the optimal distance, oil production dropped significantly. Ultimately, it was determined that reasonable well spacing for this block was 250 m. In future well pattern designs, well spacing smaller than the current value should be used. Full article

Improving energy efficiency in post-primary-school buildings is crucial for decarbonisation, yet existing strategies often focus on costly renovations, rather than operational optimisations. This study addresses the research gap by investigating how targeted adjustments in building operation can achieve significant energy savings without major renovations while maintaining user comfort. This research employs the interdisciplinary ENERGE Project framework and a five-step methodology that integrates technical and behavioural approaches to identify savings opportunities. Central to the approach is an energy audit, which analyses building performance, benchmarks consumption against local standards, and categorises energy use to prioritise interventions. The methodology involves planning, implementing, and evaluating savings strategies with stakeholder engagement. Educational buildings were selected as pilot sites due to their important building stock and potential for dissemination. The results of a case study with empirical validation in Luxembourg demonstrate significant energy-saving opportunities, particularly in baseload consumption. By adopting reduced operational modes during unoccupied periods, energy use was minimised without compromising comfort. Monitoring revealed substantial reductions in electricity consumption, with an additional 5% savings achieved by adjusting light levels in common areas to meet standard requirements. Moreover, adapting the operational schedules of pumps and ventilation systems in a swimming pool to actual usage patterns yielded estimated savings of 12 MWh/a. These findings highlight the potential to achieve meaningful energy savings without requiring high investments or deep renovations, which in many cases face performance gaps. Success relies on adaptable operational settings and active engagement of the entire stakeholder chain to realise sustainable and impactful energy-saving measures. Furthermore, the saving measures tested in educational buildings can be replicated in the residential sector. Full article

The compound energy system is an important component of zero-carbon buildings. Due to the complex form of the system and the difficult-to-capture characteristics of thermo-electric coupling interactions, the operation control of the zero-carbon building’s energy system is difficult in practical engineering. Therefore, it is necessary to carry out relevant optimization methods. This paper investigated the current research status of the control and scheduling of compound energy systems in zero-carbon buildings at home and abroad, selected a typical zero-carbon building as the research object, analyzed its energy system’s operational data, and proposed an operation scheduling algorithm based on day-ahead flexible programming and intraday rolling optimization. The multi-energy flow control algorithm model was developed to optimize the operation strategy of heat pump, photovoltaic, and energy storage systems. Then, the paper applied the algorithm model to a typical zero-carbon building project, and verified the actual effect of the method through the actual operational data. After applying the method in this paper, the self-absorption rate of photovoltaic power generation in the building increased by 7.13%. The research results provide a theoretical model and data support for the operation control of the zero-carbon building’s compound energy system, and could promote the market application of the compound energy system. Full article

Efficient irrigation management is crucial for optimizing water use and productivity in agriculture, particularly in water-scarce regions. This study evaluated the effectiveness of soil-based and weather-based irrigation management using a low-cost (DIY) Internet of Things (IoT) capacitive soil moisture sensor on drip-irrigated lettuce. A field experiment was conducted to compare water productivity and water use efficiency between the two management approaches. The soil-based system utilized real-time data from IoT sensors to guide irrigation scheduling, while the weather-based system relied on evapotranspiration data. The IoT-enabled system used 28.8% less water and reduced the pumping hours by 16.2% compared with the conventional weather-based methods. In terms of crop water productivity (CWP), the IoT system reached 16 kg/m³, which was 52.5% higher than the conventional method (10.5 kg/m³). Furthermore, the developed DIY sensor was compared with existing commercial soil moisture sensors, namely, Teros 54 and Drill& Drop. The developed prototype demonstrated reliability and accuracy comparable to other commercial sensors, with an R² = 0.6, validating its utility for enhanced data-driven irrigation, giving its initial low cost (USD 62). These findings highlight the potential of low-cost soil-based IoT systems in enhancing irrigation efficiency and supporting sustainable agriculture, particularly in resource-limited settings. Full article

The Haidian District was, historically, rich in water resources. However, with urban development, the groundwater levels have declined, and most rivers have lost their ecological baseflows. To restore the aquatic ecosystems, the district has implemented a cyclic water network and advanced water replenishment projects. Nonetheless, the existing replenishment strategies face challenges, such as an insufficient scientific basis, lack of data, and high energy consumption. There is an urgent need to develop a scientifically robust ecological water replenishment system and optimize pump station scheduling to enhance water resource management efficiency. This study addresses the ecological water replenishment needs of seasonal rivers by integrating the Literature method, Rainfall-Runoff method, and R2cross method to develop a comprehensive approach for calculating the ecological flow and water depth. The proposed method simultaneously meets the ecological functionality and landscape requirements of seasonal rivers. Additionally, the SWMM model is employed to design intelligent pump station scheduling rules, optimizing the replenishment efficiency and energy consumption. Through field measurements and data collection, the ecological water demands of the river channels in different areas are assessed. Using a hydrodynamic model, the dynamic variations in the ecological flow and water depth are simulated. For the Cuihu, Daoxianghu, and Yongfeng areas, this study reveals that the current replenishment volume is insufficient to meet the landscape and ecological needs of the rivers. Most rivers require a 20–30% increase in water levels, with the Dazhai qu needing a substantial rise from 0.17 m to 0.3 m, representing an increase of 76%. Additionally, the results demonstrate that intelligent pump station scheduling can significantly reduce operating costs and energy consumption by dynamically adjusting the replenishment timing and flow rates. This approach optimizes the intervals between equipment activation and deactivation, thereby balancing ecological and energy-saving goals. This research not only provides technical support for the precise calculation of ecological replenishment volumes and the intelligent management of pump stations, but also offers scientific references for water resource management in similar regions. The findings will enhance the ecological functions and landscape quality of the rivers in the Haidian District while promoting refined and intelligent regional water resource management. Moreover, this study presents innovative solutions and theoretical foundations for water resource regulation under the backdrop of climate change. Full article

The use of pumps in water distribution networks is very useful when there is a need for additional pressure head. However, the functioning of pumps can be influenced by the presence of private storage tanks in the network, which alters the way the users draw water due to their compensation ability. This condition is very common in areas affected by the historical scarcity of water resources or intermittent supply (Mediterranean Area, Arabian Peninsula, etc.). This paper studies the effects of private tanks on the performance of pumps in a network model, considering different retention times and evaluating possible effects on background leakages. A sample network and two real water distribution networks in the UAE will be analyzed. The results show that low retention time (i.e., 12 h) leads to a decrease in pump running time, thus lowering the energy consumption and carbon footprint, which gives a sustainable solution. These results, therefore, suggest that considering the presence of private storage tanks for the pump design in network models is of crucial economic importance, as well as for efficient designs and sustainable water distribution systems. Full article