Search Results (402)

China is expanding hydropower capacity as a key climate change mitigation strategy, yet greenhouse gas (GHG) emissions from reservoirs can substantially offset this benefit. The influence of specific environmental drivers on these emissions remains poorly understood, and previous studies have rarely quantified their relative importance under multifactorial conditions. To fill this gap, this study quantifies CO₂, CH₄, and N₂O emissions from 79 major hydroelectric reservoirs across China—representing over 60% of national hydropower generation—by integrating the G-res model and the IMAGE-DGNM model. We then employ a random forest (RF) model to evaluate the significance and marginal effects of 15 environmental drivers. Results show that reservoir-specific properties collectively explain 40.37% of the variance in total GHG emissions, and reservoir area emerges as the overwhelmingly dominant driver (MDI importance score = 1.41), far exceeding other key variables such as NH₄⁺ concentration, dissolved oxygen, altitude, water temperature, catchment area, total phosphorus, and air temperature (all with MDI importance > 0.5). Partial dependence analysis further reveals that emissions rise sharply with expanding reservoir area, NH₄⁺ concentrations above 0.15–0.2 mg/L, and catchment areas in the 360,000–680,000 km² range, while elevated dissolved oxygen (6–9 mg/L) and higher altitude suppress emissions. This study moves beyond simple emission inventories by providing a national-scale, data-driven attribution of reservoir GHG emissions to interacting environmental factors, thereby offering actionable insights for sustainable hydropower planning. Full article

Recent developments and trends in power systems have increased the importance of dynamic modeling and monitoring of system components. Increased penetration of renewable energy sources and battery storage systems makes grid operation challenging. Being environment-friendly and fast-responding, hydroelectric power plants will participate in the generation as a balancing factor while introducing inertia. They will operate dynamically—as a reserve in frequency regulation and load-generation balancing— due to the intermittent characteristics of wind and photovoltaics (PVs). Therefore, their condition monitoring and health assessment should be performed regularly or in real time to ensure that the plant is ready whenever needed. In this research, a data-driven condition monitoring method is introduced in which the health status of the water conduit system is assessed from the turbine’s startup process. The proposed “PbyGate Analysis” method briefly obtains the expected behavior and healthy/anomalous operation regions from the historical data. Then the unit is monitored in real time with the online SCADA measurements. The method is developed and tested on three different hydroelectric turbine data. Startups are tagged as healthy or anomalous with 84.5% accuracy. Full article

Large-scale drinking water treatment plants contribute to environmental burdens through energy consumption, chemical use, and sludge generation. However, Life Cycle Assessment applications to full-scale drinking water treatment plants remain limited in Morocco and other Global South contexts, where site-specific operational data are often scarce. This study assesses the environmental performance of an existing conventional drinking water treatment plant in Al-Hoceima, northern Morocco, using full-scale operational data and a Life Cycle Assessment (LCA) approach based on the ISO 14040/14044 framework. The assessment was performed using OpenLCA v1.11 and the ReCiPe 2016 Midpoint (H) method, with a functional unit of 1 m³ of treated drinking water. The results show that the operational phase dominates the environmental impacts, mainly due to sludge generation and electricity consumption. Two improvement scenarios were therefore evaluated: sludge recycling and the integration of a hydroelectric turbine as an on-site renewable energy option. Both scenarios showed potential to reduce environmental impacts while improving resource efficiency and long-term economic performance. By integrating environmental and techno-economic analyses, this study provides a practical decision-support framework for the sustainable transformation of conventional drinking water treatment plants in Morocco and comparable developing regions. Full article

Pressurized pipelines are critical components in hydraulic engineering systems, including urban water supply networks and hydroelectric power plants. These systems are susceptible to air entrapment during operations such as filling and emptying, which can reduce the effective flow area and trigger critical pressure surges or sub-atmospheric conditions. One-dimensional approaches, namely the Rigid Water Column (RWC) and Elastic Water Column (EWC) models, are the most widely used due to their balance between physical accuracy and computational practicality. EWC models have been widely used to analyze transient phenomena in pipe filling and water hammer processes; however, their application to emptying operations is limited. For this reason, this study develops an EWC-based formulation for emptying operations and assesses pressure behavior through a dimensionless analysis for different air pocket configurations. The developed model couples the Method of Characteristics (MOC) with a polytropic air pocket model, enabling the representation of wave propagation effects that RWC-based models cannot capture. The formulation is verified against 24 experimental cases, yielding a mean absolute error of 0.35% in minimum pressure prediction. The results show that dimensionless air pocket ratios $x_0/L_T$ between 0.17 and 0.83 produce minimum pressures between 0.309 and 0.877 $p_{atm}^{*}$, confirming that smaller initial air pocket volumes generate the most severe depressurization conditions. The inclusion of an air valve in the most critical scenario effectively prevents sub-atmospheric pressure development, underscoring the protective role of air admission devices. These findings provide a dimensionless framework for characterizing transient pressure risk during pipeline emptying across different operational conditions. Full article

Against the backdrop of increasing penetration of renewable energy sources such as wind and solar power, coupled with intermittent regional power restrictions, ensuring the quality of power transmission has become increasingly critical. The volatility and uncertainty of wind and photovoltaic output exacerbate dynamic fluctuations in net load on the grid side, necessitating hydroelectric units to undertake more frequent Automatic Generation Control (AGC) regulation tasks in complementary hydro–wind–solar operations. However, frequent regulation processes significantly intensify the operational stress on actuating mechanisms within the governor system, thereby accelerating wear and degradation of equipment such as hydraulic turbine servomotors. This study employs modeling and simulation to investigate the influence and mechanistic role of key control parameters in the AGC process on the wear of hydraulic turbine servomotors. Utilizing pulse count and pulse width metrics, a reasonable quantification of this impact is established. A multi-objective optimization framework for AGC parameters is constructed, and frontier solutions are selected based on quantified equipment wear values. Simulation results indicate that the optimized parameters achieve a balanced performance in terms of settling time, steady-state performance, and comprehensive dynamic metrics during power closed-loop transition processes. This approach effectively mitigates the actuation intensity of servomotors while satisfying regulation quality requirements, thereby enhancing the overall performance of the power closed-loop adjustment process. Full article

To address dependence on external power and the limited capability of conventional hydroelectric units to detect low-amplitude vibrations, this work introduces a self-contained, highly accurate monitoring device. The design incorporates a magnetically levitated configuration, with triboelectric films placed on both the upper and lower faces of the floating magnet. Under minor oscillations, magnetic repulsion increases the relative displacement between the friction layers, producing a substantial voltage that permits low-level vibration sensing. A surrounding induction coil responds to the levitated pole’s vertical motion; this motion intersects the magnetic flux, generating a current that provides stable energy for wireless data transmission. Experimental outcomes confirm a detection limit of 0.1 mm. At an amplitude of 1 mm and a load of 1000 Ω, the system achieves a maximum output of 9 mW and a power density of 1.587 W/m², ensuring reliable power. This configuration provides a new pathway for monitoring vibrations in hydroelectric turbine generators. Full article

There are abundant energy resources in remote areas of China, such as photovoltaics and small hydropower. With uncertain factors such as sunshine and climate, hydroelectric and photovoltaic power generation face prominent problems such as large output power fluctuations, unstable energy transmission, and difficulty in multi-converters’ synchronous control for primary frequency regulation. This article proposes an adaptive dynamic programming (ADP) control method and energy management strategies for multi-converters under primary frequency regulation, in order to address the problems of large-scale access to new energy. Firstly, a parameter online optimization design is proposed based on ADP controller to improve the dynamic performance of the system and the power quality of the output currents of multiple converters. Secondly, in order to achieve energy optimization management of multiple converters, a multimodal collaborative optimization control strategy is proposed to achieve energy optimization control and comprehensive management of the entire system. Finally, the effectiveness of the proposed ADP and energy management strategies are verified by simulation. Full article

Variable renewable generation is increasing the need for hydropower plants to provide fast and flexible grid support, which places new demands on plant-level dynamic models used for monitoring, control, and operational decision-making. This need is especially important for hydroelectric systems, where turbine and generator dynamics are strongly coupled, nonlinear, and time-varying, making accurate real-time representation difficult. To address this problem, this paper develops a digital twin (DT) framework for a synchronous generator–Kaplan turbine system using an explicit separation of slow turbine dynamics and fast generator dynamics. The turbine subsystem is represented by a six-coefficient model, whose parameters are identified offline using particle swarm optimization, while the generator subsystem is updated online through an extended Kalman filter for real-time state and parameter estimation. These models are integrated within a closed-loop simulation that includes a proportional–integral–derivative–double-derivative governor and excitation system, allowing the DT to track plant behavior under realistic operating conditions. Unlike prior studies that treat turbine and generator modeling separately or rely mainly on simulated inputs, the proposed framework is validated using real operational data from a hydropower plant. Results show that the DT reproduces terminal voltage, active power, and reactive power with a normalized root mean square error of approximately 5%. This hybrid offline–online formulation constitutes the main contribution of the work, providing an adaptive and practically deployable DT for hydropower systems with direct relevance to control improvement, performance monitoring, and grid-support applications under high renewable penetration. Full article

This study presents a nonlinear optimization framework for the hourly economic dispatch of interconnected multi-zone power systems integrating thermal, hydroelectric, wind, photovoltaic, and battery energy storage resources. The proposed formulation explicitly models zonal power balance, interzonal power exchange, thermal ramp-rate limits, battery state-of-charge dynamics, storage operating bounds, and hydroelectric energy quotas in order to minimize total system operating cost while preserving technical feasibility. The methodology was implemented in MATLAB and applied to a three-zone interconnected test system under two operating conditions: autonomous zonal operation and coordinated interconnected operation with battery storage support. The results show that the coordinated strategy reduces total operating cost from USD 8.23 million/day to USD 6.60 million/day, corresponding to a 19.8% reduction and an estimated annual saving of USD 595 million. In parallel, the optimized interconnected dispatch increases wind generation from 14.46 to 16.44 GWh/day and reduces thermal generation from 8.12 to 6.08 GWh/day, thereby improving the effective use of renewable resources. A complementary sustainability assessment further shows that coordinated operation increases the renewable share from 71.81% to 78.68%, decreases the carbon intensity of supplied electricity from 189.4 to 146.3 kgCO₂-e/MWh, and yields estimated avoided emissions of 1241.0 tCO₂-e/day. These findings demonstrate that the joint use of interzonal coordination and battery energy storage provides simultaneous economic, operational, and environmental benefits, thereby supporting sustainability-oriented operation of modern multi-zone power systems. Full article

Following the massive power cut in Spain on 28 April 2025, questions have been raised about the reliability of energy generation infrastructure in the face of the variability of renewable energy sources. On the other hand, the market penetration of electric vehicles and their charging requirements implies the need for knowledge about the availability of electric generation technologies. This research work presents a macro-level analysis of the availability factor of electricity generation mix, applied to the case of Spain and based on data collected between 2019 and 2024. Using hourly generation and installed capacity data, a methodology is developed to estimate the seasonal and daily availability of the main generation technologies: photovoltaic, solar thermal, wind, hydroelectric, nuclear, combined cycle, coal and others. The analysis reveals that conventional sources, such as nuclear and combined cycles, exhibit low variability, with daily fluctuation of less than 1%. In contrast, renewable sources show significant variability. Photovoltaic availability increases from 22.6% ± 1.3% in the early morning to 57.8% ± 0.6% during summer afternoons, while solar thermal energy reaches a maximum of 78.5% ± 1.3% under the same conditions. The results highlight the uncertainty generated by the high penetration of renewable energy and the challenges posed by balancing generation with demand, particularly given new consumption patterns influenced, for example, by electric vehicles, battery storage and green hydrogen, among others. The integration of probabilistic planning frameworks into infrastructure development and the extension of this analysis to the provincial level, together with the incorporation of restriction and self-consumption scenarios involving constraints and self-consumption, will help to ensure the robust operation of the grid in the future. Full article

The transition from traditional synchronous generators to intermittent renewable sources, combined with increasingly variable and difficult-to-control energy demand, is creating a growing need for large-scale reserves and energy storage. At the same time, reduced system inertia and evolving electricity market regimes are emerging as important challenges that may affect grid stability, reliability, and economic performance. Advanced storage technologies, particularly those with fast ramping and high-response capabilities, offer a potential means of providing near-instantaneous support in response to unexpected system disturbances or market signals, thereby helping to mitigate inertia-related risks. This paper investigates four technologies: pumped hydroelectric storage, battery energy storage systems, synchronous condensers, and special protection schemes, with a focus on their capability to deliver rapid responses to large-scale disturbances. The analysis is conducted using a deliberately simplified power system model to provide qualitative insights into system behavior and control interactions. The results indicate that automation-enabled responses to system imbalances, including support from synchronous condensers and the rapid activation of additional generation, can enhance system performance under disturbance conditions within the considered framework. These findings demonstrate the feasibility and potential value of such approaches; however, further validation using higher-fidelity models and system-specific data is required to quantify their operational and economic impacts. Full article

With the advancement of digital hydropower stations, the requirements of real-time, high-precision industrial soft measurement of key power equipment operating status are attracting more and more attention. However, it is difficult to transfer energy to the monitoring sensor in strong electromagnetic environments. In this paper, a high-efficiency, high-power-density magnetic field energy harvester is proposed for monitoring sensors in hydropower stations, which captures the energy from the magnetic flux leakage of a hydroelectric generating set. Efficient magnetic energy capture is achieved by modeling material properties and optimizing the receiver’s magnetic core parameters via a Genetic Algorithm. The theoretical analysis of charging characteristics is given, and a Maximum Power Point Tracking (MPPT) control circuit is proposed, realizing high-efficiency energy conversion. Finally, an experimental planet is built. Under 70–130 Gs power-frequency magnetic fields, the system delivers 2.8–5.1 V open-circuit voltage, 66 mW maximum load power, and 6.5 mW/cm³ power density. Full article

This study empirically isolates the marginal CO₂ abatement efficiency of wind and solar power within the Greek electricity system, utilizing hourly dispatch data from August 2012 to December 2018—a period characterizing the grid’s “pre-saturation” technical potential. By employing an econometric framework to capture ex-post displacement dynamics, we identify a statistically significant but highly heterogeneous abatement impact across renewable technologies. Our analysis reveals that wind power consistently achieves higher carbon savings per MWh than solar photovoltaics, primarily by driving deeper displacement of carbon-intensive thermal baseload. Conversely, solar generation exhibits a stronger propensity to displace zero-carbon hydroelectric output and net imports, thereby dampening its domestic abatement efficiency. Furthermore, we demonstrate that the marginal emissions avoided are non-linear, fluctuating significantly with system load, interconnection flows, and renewable penetration levels. These findings establish an “unconstrained efficiency” benchmark for the Greek grid, providing the necessary counterfactual to evaluate the diminishing returns and curtailment penalties characterizing the high-penetration era of renewables. Full article

Energy use remains central to Colombia’s economic growth, yet its composition shapes the scale and direction of environmental outcomes. This study investigates how coal, oil, and hydroelectricity influence ecological degradation within the context of economic growth. The study applies cross-quantilogram and bootstrap Fourier Granger causality techniques to capture directional dependence and predictive causality across different quantiles, respectively. The findings show that the relationships are heterogeneous rather than uniform across the distribution. Economic growth exhibits a predominantly negative dependence on ecological footprint, suggesting that higher output is associated with lower ecological pressure under several environmental states. Hydroelectricity also shows a largely negative dependence, indicating its general contribution to environmental sustainability, although this effect weakens under extreme conditions. By contrast, the effects of coal and oil are more conditional and vary across quantiles, reflecting the complex role of fossil fuels in Colombia’s environmental dynamics. The bootstrap Fourier Granger causality results further reveal that causality is not constant across the distribution, but emerges only at specific quantiles. The central policy implication from this result lies in adopting an adaptive environmental strategy in which preventive measures dominate under low degradation, green-supportive policies are emphasized under moderate degradation, and stronger corrective interventions are implemented under high ecological stress. Full article

Hybrid hydro–solar microgrids offer a practical electrification option for remote and weak-grid communities by combining run-of-river hydropower with photovoltaic generation. However, their performance depends strongly on coordinated decisions across three layers: (i) system sizing and architecture, (ii) turbine selection and rating under variable river flow, and (iii) operational energy dispatch under time-varying solar resource and demand. This paper develops an optimization-driven planning framework for a run-of-river hydro–PV microgrid that co-optimizes component capacities and turbine-related design choices while enforcing time-series operational feasibility. Physics-based component models translate river discharge into hydroelectric output via turbine efficiency characteristics and operating limits, and compute PV generation and storage trajectories under dispatch and state-of-charge constraints. The planning problem is formulated as a multi-objective optimization that quantifies trade-offs among life-cycle cost, supply reliability (e.g., unmet-load metrics), and sustainability indicators (e.g., diesel-free operation or emissions when backup generation is present). A Pareto-optimal set of designs is obtained using a population-based multi-objective algorithm, and representative knee-point (balanced) solutions are selected to illustrate how turbine choice and dispatch strategy interact with seasonal hydrology and solar variability. The proposed approach supports transparent and robust design decisions for hybrid hydro–solar microgrids. Full article