Search Results (130)

Maintenance scheduling of hydropower units is essential for ensuring the operational security and stability of large-scale cascaded hydropower systems and for improving the efficiency of water energy utilization. This study takes the Cascaded Hydropower System of the Lower Jinsha River (CHSJS) as a representative case, identifying four key challenges facing maintenance planning: multi-dimensional influencing factor coupling, spatial and temporal conflicts with generation dispatch, coordination with transmission line maintenance, and compound uncertainties of inflow and load. To address these issues, four strategic recommendations are proposed: (1) identifying and quantifying the impacts of multi-factor influences on maintenance planning; (2) developing integrated models for the co-optimization of power generation dispatch and maintenance scheduling; (3) formulating coordinated maintenance strategies for hydropower units and associated transmission infrastructure; and (4) constructing joint models to manage the coupled uncertainties of inflow and load. The strategy proposed in this study was applied to the CHSJS, obtaining the weight of the impact factor. The coordinated unit maintenance arrangements of transmission line maintenance periods increased from 56% to 97%. This study highlights the critical need for synergistic optimization of generation dispatch and maintenance scheduling in large-scale cascaded hydropower systems and provides a methodological foundation for future research and practical applications. Full article

The proposed study incorporates a stochastic demand side management (SDSM) strategy for a self-sufficient residential system powered from a PV source with a hybrid battery–hydrogen storage system to minimize the total degradation costs associated with key components, including Li-io batteries, fuel cells, and PEM electrolyzers. The uncertainty in demand forecasting is addressed through a scenario-based generation to enhance the robustness and accuracy of the proposed method. Then, stochastic optimization was employed to determine the optimal operating schedules for deferable appliances and optimal water heater (WH) settings. The optimization problem was solved using a genetic algorithm (GA), which efficiently explores the solution space to determine the optimal operating schedules and reduce degradation costs. The proposed SDSM technique is validated through MATLAB 2020 simulations, demonstrating its effectiveness in reducing component degradation costs, minimizing load shedding, and reducing excess energy generation while maintaining user comfort. The simulation results indicate that the proposed method achieved total degradation cost reductions of 16.66% and 42.6% for typical summer and winter days, respectively, in addition to a reduction of the levelized cost of energy (LCOE) by about 22.5% compared to the average performance of 10,000 random operation scenarios. Full article

Increasing concern over climate change has made Carbon Capture and Storage (CCS) an important tool. Operators use deep geologic reservoirs as a form of favorable geological storage for long-term CO₂ sequestration. However, the success of CCS hinges on the integrity of wells penetrating these formations, particularly legacy wells, which often exhibit significant uncertainties regarding cement tops in the annular space between the casing and formation, especially around or below the primary seal. Misalignment of cement plugs with the primary seal increases the risk of CO₂ migrating beyond the seal, potentially creating pathways for fluid flow into upper formations, including underground sources of drinking water (USDW). These wells may not be leaking but might fail to meet the legal requirements of some federal and state agencies such as the Environmental Protection Agency (EPA), Railroad Commission of Texas (RRC), California CalGEM, and Pennsylvania DEP. This review evaluates the impact of CO₂ exposure on cement and casing integrity including the fluid transport mechanisms, fracture behaviors, and operational stresses such as cyclic loading. Findings revealed that slow fluid circulation and confining pressure, primarily from overburden stress, promote self-sealing through mineral precipitation and elastic crack closure, enhancing well integrity. Sustained casing pressure can be a good indicator of well integrity status. While full-physics models provide accurate leakage prediction, surrogate models offer faster results as risk assessment tools. Comprehensive data collection on wellbore conditions, cement and casing properties, and environmental factors is essential to enhance predictive models, refine risk assessments, and develop effective remediation strategies for the long-term success of CCS projects. Full article

Fixed automated (unmanned) above-water radiometric measurements are subject to unavoidable sky conditions and surface perturbations, leading to significant uncertainties in retrieved water surface remote sensing reflectances (R_rs(λ), sr⁻¹). This study evaluates various above-water R_rs(λ) glint correction methods using a comprehensive dataset collected at the Royal Netherlands Institute for Sea Research (NIOZ) Jetty Station located in the Marsdiep tidal inlet of the Dutch Wadden Sea, the Netherlands. The dataset includes in-situ water constituent concentrations (2006–2020), inherent optical properties (IOPs) (2006–2007), and above-water hyperspectral (ir)radiance observations collected every 10 min (2006–2023). The bio-optical models were validated using in-situ IOPs and utilized to generate glint-free remote sensing reflectances, R_rs,ref(λ), using a robust IOP-to-R_rs forward model. The R_rs,ref(λ) spectra were used as a benchmark to assess the accuracy of glint correction methods under various environmental conditions, including different sun positions, wind speeds, cloudiness, and aerosol loads. The results indicate that the three-component reflectance model (3C) outperforms other methods across all conditions, producing the highest percentage of high-quality R_rs(λ) spectra with minimal errors. Methods relying on fixed or lookup-table-based glint correction factors exhibited significant errors under overcast skies, high wind speeds, and varying aerosol optical thickness. The study highlights the critical importance of surface-reflected skylight corrections and wavelength-dependent glint estimations for accurate above-water R_rs(λ) retrievals. Two showcases on chlorophyll-a and total suspended matter retrieval further demonstrate the superiority of the 3C model in minimizing uncertainties. The findings highlight the importance of adaptable correction models that account for environmental variability to ensure accurate R_rs(λ) retrieval and reliable long-term water quality monitoring from hyperspectral radiometric measurements. Full article

Increasing pressures on groundwater in the last decades have led to a deterioration in the quality of groundwater for human consumption around the world. Beyond the essential evaluation of groundwater dynamics and quality, analyzing the situation from the perspective of the Ecosystem Services Assessment (ESA) approach can be useful to support aquifer management plans aiming to recover aquifers’ capacity to provide good quality water. This work illustrates how to implement the ESA using groundwater flow and nitrate transport modelling for evaluating future trends of the provisioning service Groundwater of Good Quality for Human Supply. It has been applied to the Medina del Campo Groundwater Body (Spain), where the intensification of agricultural activities and groundwater exploitation since the 1970s caused severe nitrate pollution. Nitrate status and future trends under different fertilizer and aquifer exploitation scenarios were modelled with MT3DMS coupled to a MODFLOW model calibrated with piezometric time series. Historical land use and fertilizer data were compiled to assess nitrogen loadings. Besides the uncertainties of the model, the results clearly show that: (i) managing fertilizer loads is more effective than managing aquifer exploitation; and (ii) only the cessation of nitrogen application by the year 2030 would improve the evaluated provisioning service in the long term. The study illustrates how the ESA can be incorporated to evaluate the expected relative impact of different management actions aimed at improving significant groundwater services to humans. Full article

Against the backdrop of the steady advancement of the national rural revitalization strategy and the dual-carbon goals, the low-carbon transformation of rural energy systems is of critical importance. This study first proposes a comprehensive architecture for rural energy supply systems, incorporating four key dimensions: investment, system configuration, user demand, and policy support. Leveraging the abundant wind, solar, and biomass resources available in rural areas, a low-carbon optimization model for rural energy system operation is developed. The model accounts for diverse load characteristics and the integration of elevated water towers, which serve both energy storage and agricultural functions. The optimization framework targets the multi-energy demands of rural production and daily life—including electricity, heating, cooling, and gas—and incorporates the stochastic nature of wind and solar generation. To address renewable energy uncertainty, the Fisher optimal segmentation method is employed to extract representative scenarios. A representative rural region in China is used as the case study, and the system’s performance is evaluated across multiple scenarios using the Gurobi solver. The objective functions include maximizing clean energy benefits and minimizing carbon emissions. Within the system, flexible resources participate in demand response based on their specific response characteristics, thereby enhancing the overall decarbonization level. The energy storage aggregator improves renewable energy utilization and gains economic returns by charging and discharging surplus wind and solar power. The elevated water tower contributes to renewable energy absorption by storing and releasing water, while also supporting irrigation via a drip system. The simulation results demonstrate that the proposed clean energy system and its associated operational strategy significantly enhance the low-carbon performance of rural energy consumption while improving the economic efficiency of the energy system. Full article

Residential electric water heaters have the potential to significantly contribute to the balancing of the grid by providing frequency services. However, this entails a large-scale, challenging control problem subject to several uncertainties. In this paper, we perform the first real-life validation of secondary frequency reserve provision with a cluster of residential thermal loads in a near-commercial setting. We adopt an aggregate-and-dispatch control approach, which combines a scalable optimization step enabled by a reduced-order model with a real-time dispatch step. To handle the uncertainty related to service activation, we incorporate chance constraints in the optimization model and reformulate it as a robust problem. We validate the control approach under the assumption of perfect merit order knowledge in different stages, with a cluster of up to 600 electric water heaters, and show that this pool is able to effectively provide reserves, and that the integration of the chance constraints is beneficial for performance. Full article

Global surface pressure, terrestrial water storage models, and seabed pressure grids provide valuable support for studying the mechanisms of the nonlinear motion behind GNSS stations. These data allow for the precise identification and analysis of displacement effects caused by environmental loads. This study analyzes GNSS coordinate time series data from 186 ITRF reference stations worldwide over a 10-year period, thoroughly examining the magnitude, spatial distribution, and impact of hydrological, atmospheric, and non-tidal oceanic loading on nonlinear motion. The results indicate that the atmospheric loading effects had a magnitude of approximately ±5 mm in the up (U) direction and ±1 mm in the east (E) and north (N) directions. Moreover, the impact of atmospheric loading on station displacements was more pronounced in high-latitude regions compared with mid- and low-latitude regions. Secondly, the hydrological loading showed a magnitude of approximately ±5 mm in the U direction and ±0.8 mm in the E and N directions, with inland areas causing larger displacements than coastal regions. Furthermore, the non-tidal oceanic loading induced displacements with magnitudes of approximately ±0.5 mm in the E and N directions and ±2 mm in the U direction, significantly affecting stations in the nearshore areas more than inland stations. Subsequently, this study analyzes the corrective effects of environmental loads on the coordinate time series. The average correlation coefficients between the E, N, and U directions and the coordinate time series were 0.35, 0.31, and 0.52, respectively. After removing the displacements caused by environmental loads, the root mean square (RMS) values of the coordinate time series decreased by 85.5% in the E direction, 77.4% in the N direction, and 89.8% in the U direction, with average reductions of 6.2%, 4.4%, and 16.7%, respectively. Lastly, it also comprehensively assesses the consistency between environmental loads and coordinate time series from the perspectives of the optimal noise model, velocity and uncertainty, and amplitude and phase. This study demonstrates that the geographic location of a station is closely related to the impact of environmental loads, with a significantly greater effect in the vertical direction than that in the horizontal direction. By correcting for environmental loads, the accuracy of the coordinate time series can be significantly enhanced. Full article

In recent years, with the steady growth of load demand in distribution networks, the fluctuation and uncertainty of power loads have significantly increased. Meanwhile, the rising penetration of photovoltaic generation has further exacerbated the challenges of power system accommodation capability. To enhance photovoltaic accommodation capability and realize the secure and economic operation of distribution networks, a multi-time scale hierarchical coordinated optimization operation strategy for distribution networks with aggregated distributed energy storage has been proposed. First, the regulation requirements of aggregated distributed energy storage are analyzed, and a distributed energy storage aggregation model is established based on an inner approximate Minkowski Sum. Subsequently, a multi-time scale optimization operation model considering source-load uncertainties for day-ahead, intra-day, and real-time stages is developed based on a stepped carbon emission cost model. Then, a power allocation method within the aggregated distributed energy storage based on the water-filling algorithm is presented. Finally, a practical distribution network in a demonstration county in China is used as a case study to validate the proposed method. The results demonstrate that the proposed strategy effectively reduces system operation costs while improving photovoltaic accommodation capacity and enhancing the reliability of system operation. Full article

This research focuses on addressing the limitations of conventional physical sensors and developing a virtual water flow rate prediction technology. With HVAC systems being increasingly adopted, research on optimizing control settings based on load variations is critical. Existing systems often operate based on peak load conditions, leading to energy overconsumption in partial load scenarios. Physical sensors used for water flow measurement face challenges such as installation difficulties in constrained spaces and increased costs in large buildings. Virtual water flow rate prediction technology offers a cost-effective solution by leveraging in situ measurement data instead of extensive physical sensors. To achieve this, a test bed with a pump, valve, and heat pump was used, controlled via a BAS. Water flow rate was measured using an ultrasonic flow meter, and differential pressure was recorded using pressure gauges. Equations were developed to replace differential pressure values with valve opening rates and pump speeds by deriving performance curves and differential pressure ratio equations. Measurement uncertainty was calculated to assess the reliability of the experimental setup. Various test numbers were created to evaluate the virtual water flow rate model under controlled conditions. The results showed that relative errors ranged from 0.32% to 10.54%, with RMSE, MBE, and CvRMSE meeting all threshold criteria. The virtual water flow rate model demonstrated strong predictive accuracy and reliability, supported by an R² value close to 1. This research confirms the effectiveness of the proposed model for reducing the dependence on physical sensors while enabling accurate water flow rate predictions in HVAC systems. Full article

Lake water pollution under anthropogenic influences exhibits characteristics of high uncertainty, rapid evolution, and complex control challenges, presenting substantial threats to ecological systems and human health. Quantitative risk prediction provides crucial support for water quality deterioration prevention and management. This study employed the Copula Bayesian Network model to conduct a comprehensive risk assessment of water quality in Qilu Lake, China (2010–2020), incorporating inter-indicator correlations and multiple uncertainty sources. Analysis revealed generally “worse” water quality conditions (5.10 ± 1.35) according to established index classifications, with predicted probabilities of reaching “deteriorated” status ranging from 11.80% to 47.90%. Significant spatial and temporal variations in water quality and pollution risk were observed, primarily attributed to intensive agricultural non-point source loading and water resource deficiency. The study established early warning thresholds through key indicator concentration predictions, particularly for the southern region where “deteriorated” risk levels corresponded to specific ranges: TN (3.42–8.43 mg/L), TP (0.07–1.29 mg/L), and COD_Cr (27.75–67.19 mg/L). This methodology effectively characterizes lake water quality evolution while enabling risk prediction through key indicator monitoring. The findings provide substantial support for water pollution control strategies, risk management protocols, and regulatory decision-making for lake ecosystem administrators. Full article

This paper examines the effectiveness of a pumped storage hydropower plant (PSHP) when combined with other plants. System 1 examines the contribution of the PSHP to reducing fuel costs for thermal power plants. System 2 examines the optimization of operations for power systems with energy storage and uncertain renewable energies to maximize total profit based on four test system cases: Case 1: neglect the PSHP and consider wind and solar certainty; Case 2: consider the PSHP and wind and solar certainty; Case 3: neglect the PSHP and consider wind and solar uncertainty; and Case 4: consider the PSHP and wind and solar uncertainty. Cases 1 and 2 focus on systems that assume stable power outputs from these renewable energy sources, while Cases 3 and 4 consider the uncertainty surrounding their power output. The presence of a PSHP has a key role in maximizing the system’s total profit. This proves that Case 2, which incorporates a PSHP, achieves a higher total profit than Case 1, which does not include a PSHP. The difference is USD 17,248.60, representing approximately 0.35% for a single day of operation. The total profits for Cases 3 and 4 are USD 5,089,976 and USD 5,100,193.80, respectively. Case 4 surpasses Case 3 by USD 10,217.70, which is about 0.2% of Case 3’s total profit. In particular, the PSHP used in Cases 2 and 4 is a dispatching tool that aims to achieve the highest profit corresponding to the load condition. The PSHP executes its storage function by using low-price electricity at off-peak periods to store water in the reservoir through the pumping mode and discharge water downstream to produce electricity at periods with high electricity prices using the generating mode. As a result, the total profit increases. A modified slime mould algorithm (MSMA) is applied to System 2 after proving its outstanding performance compared to the jellyfish search algorithm (JS), equilibrium optimizer (EO), and slime mould algorithm (SMA) in System 1. Full article

The mining industry is a key contributor to Western Australia’s economy, with over 130 mining operations that produce critical minerals such as iron ore, gold, and lithium. Ensuring a reliable and continuous energy supply is vital for these operations. This paper addresses the challenges and opportunities of integrating renewable energy sources into isolated power systems, particularly under uncertainties associated with renewable energy generation and demand. A robust optimization approach is developed to model a multi-source hybrid energy system that considers risk-averse, risk-neutral, and risk-seeking strategies. These strategies address power demand and renewable energy supply uncertainties, ensuring system reliability under various risk scenarios. The optimization framework, formulated as a mixed integer linear programming problem and implemented in Python using the Gurobi Optimizer, integrates renewable energy sources such as wind turbines, photovoltaic arrays, and demand response programs alongside traditional diesel generators, boilers, combined heat and power units, and water desalination. The model ensures reliable access to electricity, heat, and water while minimizing operational costs and reducing reliance on fossil fuels. A comprehensive sensitivity analysis further examines the impact of uncertainty margins and the value of a lost load on the total system cost, providing insights into how different risk strategies affect system performance and cost-efficiency. The results are validated through three case studies demonstrating the effectiveness of the proposed approach in enhancing the resilience and sustainability of isolated power systems in the mining sector. Significant improvements in reliability, scalability, and economic performance are observed, with the sensitivity analysis highlighting the critical trade-offs between cost and reliability under varying uncertainty conditions. Full article

Over recent decades, the global occurrence of pesticide residues in aquatic environments has been a pivotal issue; however, their trace-level concentrations necessitate the establishment of ultra-sensitive and reliable analytical approaches. To this end, the present study describes the optimization and validation of an LC-HRMS-based method for the accurate determination of 18 pesticides in river and sea water, accompanied by a measurement uncertainty estimation. This method was applied to analyze 17 real samples from agriculture and aquaculture-impacted areas in Greece and Albania. Different solid-phase extraction (SPE) protocols were tested. For the analysis, cutting-edge Orbitrap MS technology and MS/MS fragmentation, along with the use of matrix-matched calibration curves, provided unprecedented accuracy (<5 ppm) and sensitivity for the confirmation of positive detections. Regarding method performance, exceptional linearity was obtained; the limits of quantification ranged from 1.7 ng L⁻¹ to 90 ng L⁻¹, recoveries varied from 61% to 96% in river water, while slightly higher recoveries (60–111%) were observed in seawater. In all cases, repeatability and intra-laboratory reproducibility were below 15%. The measurement expanded uncertainty (U′, k = 2) was estimated considering precision and bias. MU% values were lower than 50% in all cases, as recommended in SANTE guidelines and applied to the quantified results. The matrix effect study exhibited negative values (<20%) for all compounds. Application to real samples showed a low pesticide contamination load that should not be underestimated. Full article

Introducing pumped storage to retrofit existing cascade hydropower plants into hybrid pumped storage hydropower plants (HPSPs) could increase the regulating capacity of hydropower. From this perspective, a capacity configuration optimization method for a multi-energy complementary power generation system comprising hydro, wind, and photovoltaic power is developed. Firstly, to address the uncertainty of wind and photovoltaic power outputs, the K-means clustering algorithm is applied to deal with historical data on load and photovoltaic, wind, and water inflow within a specific region over the past year. This process helps reduce the number of scenarios, resulting in 12 representative scenarios and their corresponding probabilities. Secondly, with the aim of enhancing outbound transmission channel utilization and decreasing the peak–valley difference for the receiving-end power grid’s load curve, a multi-objective optimization model based on the normal boundary intersection (NBI) algorithm is developed for the capacity optimization of the multi-energy complementary power generation system. The result shows that retrofitting cascade hydropower plants with pumped storage units to construct HPSPs enhances their ability to accommodate wind and photovoltaic power. The optimal capacity of wind and photovoltaic power is increased, the utilization rate of the system’s transmission channel is improved, and the peak-to-valley difference for the residual load of the receiving-end power grid is reduced. Full article