Search Results (288)

To address fault-induced boundary variations and conflicting commands among heterogeneous controllers in microgrid clusters with high distributed generation penetration, this paper proposes a multilayer multiagent control strategy based on cross-coordinated multiagent control and conflict coordination. The method uses a hierarchical distributed hybrid architecture. Local grid-forming (GFM) energy storage and photovoltaic (PV) converters provide autonomous voltage source support, microgrid coordination controllers generate distributed candidate commands, and the system-level coordination controller performs event-triggered arbitration. Unlike consensus-based cooperative control with fixed exchanged variables, the proposed method enables overlapping supervisory authority, weighted command fusion, explicit conflict classification, and feasible command projection under resource, state-of-charge (SOC), ramping, and load priority constraints. Direction, capacity, and objective conflicts are resolved through system-level arbitration, which converts multiple candidate commands into a single executable command. Comparative simulations show that the proposed method reduces frequency and voltage deviations, shortens power recovery time, improves SOC balancing among energy storage units, and enhances constrained hydropower coordination compared with conventional droop control and one-to-one hierarchical control. These results verify its effectiveness in improving dynamic stability and coordinated support capability in microgrid clusters. Full article

Hydropower is a crucial component of renewable energy, and sediment erosion is a key factor affecting the operation of impulse turbines, with erosion inside the nozzle being particularly prominent and leading to reduced unit efficiency. This paper investigates the distribution patterns of energy dissipation and erosion locations inside the nozzle under varying particle sizes, based on numerical simulation and entropy production theory. The results indicate that small particle sizes (0.02 mm) exhibit good fluidity, uniform flow velocity distribution, and a small high-entropy-production region. As particle size increases (0.1 mm, 0.3 mm), fluidity gradually deteriorates, the flow field becomes more turbulent, and the high-entropy-production region expands. When the turbulent kinetic energy exceeds 10 m²/s², the entropy production rate increases sharply. A significant negative correlation is observed between entropy production rate and erosion rate; smaller particle sizes correspond to more severe erosion. Erosion on the needle is primarily due to friction, while erosion on the nozzle is primarily due to impact. High erosion levels on both the nozzle and needle are concentrated within a particle velocity range of [80, 100], and the erosion rate within this speed range shows a sharp upward trend. Full article

Pump-as-turbine (PAT) units have been widely used for energy recovery in water-supply networks, petrochemical systems, and small hydropower applications; however, their turbine-mode performance is often limited because most commercial pumps are originally designed for pumping conditions. To improve the hydraulic performance of a low-specific-speed PAT, this study developed a surrogate-assisted multi-objective optimization framework combining three-dimensional computational fluid dynamics (CFD), design of experiments, a Kriging surrogate model, and a multi-objective genetic algorithm. Five key impeller geometric parameters, including blade inlet angles, blade wrap angles, and impeller outlet diameter, were selected as design variables, and turbine-mode efficiency was maximized under a head constraint of H ≥ 24 m at the rated condition of 1450 r/min. The results showed that the optimized design increased efficiency from 72.34% to 84.42% while satisfying the head requirement. Comparative analyses of pressure and velocity fields in the impeller and volute further revealed that the performance improvement was mainly associated with enhanced flow-field uniformity and reduced local hydraulic losses. A dedicated PAT test rig was finally established to experimentally validate the optimized design. Full article

With the increasing integration of photovoltaic (PV) generation, short-term scheduling of hydro–PV–battery energy storage systems (HPBS) faces growing challenges due to the stochastic variability of PV output, the temporal coupling of hydropower operation, and the accumulation of deviations during the real-time execution of day-ahead schedules. This paper proposes a bi-level coordinated scheduling framework that integrates day-ahead mixed-integer quadratic programming (MIQP) with intraday Soft Actor–Critic (SAC)-based correction. In the upper layer, MIQP generates a 24 h baseline schedule subject to unit output limits, mutually exclusive charging/discharging logic, and operational constraints. In the lower layer, SAC performs bounded real-time residual correction for hydropower and battery storage around the MIQP baseline, while a deviation-triggered replanning mechanism forms a closed-loop process of planning, execution, correction, and replanning. Comparative experiments under the tested setting show that SAC achieves better overall performance than Deep Deterministic Policy Gradient (DDPG), Twin Delayed Deep Deterministic Policy Gradient (TD3), and Proximal Policy Optimization (PPO). Typical-day evaluations under dry-, normal-, and wet-season conditions show that, in the selected case studies, the proposed MIQP + SAC framework achieves better performance than standalone MIQP and MIQP-Replan, which refers to a deviation-triggered MIQP re-optimization strategy, in load tracking, PV curtailment reduction, and hydro-storage coordination. These results indicate the effectiveness of the proposed framework for short-term HPBS scheduling under representative operating 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 the key problems of low-frequency oscillation and insufficient regulation accuracy at the Point of Common Coupling (PCC) in hydro–wind–photovoltaic hybrid systems, which are caused by the randomness of wind and photovoltaic output, the water-hammer effect of hydropower units, and multi-source power coupling, a joint control strategy based on Fractional-Order Proportional Integral Derivative (FOPID) and Co-evolutionary Multi-objective Particle Swarm Optimization (CMOPSO) is proposed. First, a small-signal transfer function model of the system covering photovoltaic inverters, doubly fed induction generators (DFIGs), hydropower units and voltage-source converter-based high-voltage direct current (VSC-HVDC) converter stations is established to accurately characterize the water-hammer effect and multi-source dynamic coupling characteristics. Second, a Caputo-type FOPID controller is designed. Compared with traditional integer-order controllers with limited tuning flexibility, the FOPID controller utilizes its five degrees of freedom to address specific multi-source coupling challenges. This precisely compensates for the non-minimum phase lag caused by the water-hammer effect in hydropower units via the fractional derivative link, and effectively smooths the impact of stochastic wind–solar fluctuations on PCC voltage through the memory characteristics of the fractional integral link. This multi-parameter regulation mechanism prevents a trade-off between response speed and overshoot suppression, achieving effective decoupling of complex multi-source dynamic interactions. Third, a dual-objective optimization framework with the Integral of Time-weighted Absolute Error (ITAE) and Oscillatory Disturbance Risk Index (ODRI) as the objectives is constructed. The multi-population co-evolution mechanism of the CMOPSO algorithm is adopted to solve the Pareto-optimal solution set, realizing the coordinated optimization of dynamic response accuracy and oscillation instability risk. Finally, comparative simulations are carried out on the Simulink platform with traditional PI/FOPI controllers and optimization algorithms such as Multi-objective Particle Swarm Optimization based on the Decomposition/Simple Indicator-Based Evolutionary Algorithm (MPSOD/SIBEA). The results show that the proposed strategy can effectively suppress low-frequency oscillations in the range of 0~30 Hz. Compared with the traditional PI controller, the PCC voltage overshoot is reduced by more than 40%, the oscillation decay time is shortened by 33%, the ITAE and ODRI indices are decreased by 12.58% and 2.47%, respectively, and the stability of DC bus voltage is significantly improved. Its robustness and comprehensive control performance are superior to existing methods, providing an efficient and stable control scheme for power electronics-dominated complex new energy grid-connected systems. Full article

Hydropower is a major renewable energy source, and improving the operational efficiency of existing hydropower systems has become essential. The objectives of this study are to (a) develop a specific energy-based hydropower efficiency method at a daily scale; (b) establish a comparable and general indicator for hydropower reservoir operation planning; (c) propose an operational strategy and practical decision support tool that maximizes generation performance under identical generation discharge constraints. We develop a method to estimate specific energy for different power output levels using the power output and discharge relationship and construct operating combinations based on the output range with the highest specific energy. We applied it to a single day and extended it to an entire month, using Hwacheon hydropower dam in South Korea. The results show that the daily increase ranged from 2.73 to 18.4 MWh, and the total monthly cumulative increase was 177.39 MWh. This corresponds to a potential increase of about 2.1 GWh in electricity generation. This approach achieves higher energy generation than observed operational performance. A specific energy-based operational strategy can consistently improve generation performance across varying hydrologic conditions. Specific energy provides a practical decision support tool for improving generation performance under water resource constraints. Full article

The objective of this work is to present sustainability analysis and performance evaluation of six hydropower units through exergy-based indices. The method of exergy analysis, based on the first and second laws of thermodynamics, was utilized to evaluate system irreversibility and environmental impact. The Exergy Efficiency, Sustainability Efficiency Index (SEI), and Exergy Ecological Index (ECEI) were determined and plotted in MATLAB. The efficiency and exergy performance results show that Unit 6 had the highest exergy efficiency at 89.3%, and Unit 1 had the least at 82.1%. The values of SEI and ECEI showed that elevated exergy efficiency contributes to increasing sustainability and ecological performance in parallel. The results demonstrate that exergy analysis can provide a broader and more accurate measure of system performance than energy analysis in hydroelectric power systems. The approach shows that local reference environmental conditions must be incorporated to establish system equilibrium. It also suggests that exergy analysis should be used as a standard tool for the optimization and performance management of hydropower plants. Its integration would help the operators take measures against malfunction, minimize losses and improve the environmental and thermodynamic sustainability of energy systems. Full article

In recent years, the industrial decarbonization in the cement sector has introduced secondary environmental impact due to an increase in power and water demand. Deploying carbon capture, utilization, and distributed storage requires an uninterrupted supply of power and water to achieve net-zero targets. However, the traditional static optimization algorithms seem insufficient in addressing the high-frequency and dynamic renewable networks. To overcome these issues, this work develops a dynamic water-energy-carbon trade-off optimization model for industrial decarbonization, with the deployment of Carbon Capture, Utilization, and Storage system in the cement sector within a United Kingdom industrial cluster. The key objective is to quantify and control the secondary burden that low-carbon interventions can impose on electricity systems and local water resources. Firstly, the Water-Energy-Carbon problem is treated as a tri-lemma, which is formulated as a continuous Markov Decision Process. Then the optimization problem is solved via a Soft Actor-Critic Deep Reinforcement Learning algorithm under coupled and resource-constrained abstraction inputs. This work further introduces the Water-Carbon Mitigation Penalty Index as a diagnostic metric for measuring the marginal increase in water burden associated with carbon mitigation. The results show that unmanaged distributed carbon-mitigation pathways increase local hydrological stress by 2.15–5.17% relative to baseline operating conditions. Although the proposed algorithm successfully reduces the nexus cost by up to 70.5% and achieves 13.83% carbon reduction by shifting from freshwater abstraction to reclaimed municipal wastewater and by coordinating operation with low-carbon hydropower availability. These results show that dynamic AI-based scheduling can support net-zero transitions while reducing pressure on regional hydro-ecological systems. Full article

The main purpose of electrical transmission and distribution networks is to carry electrical energy from the places where it is produced to the places of consumption, where the energy is used. Electrical energy is produced in power plants by generating units, which convert a form of primary energy into electrical energy. Primary energy comes from a number of sources, such as fossil fuels, nuclear energy, hydropower, wind, and solar. The carbon neutrality targets set by the European Union and several countries around the world have driven a transformation characterized by the gradual replacement of synchronous thermal generation based on fossil fuels with Renewable Energy Sources (RES), such as wind and solar. The energy transition, while necessary to achieve the established targets, introduces significant challenges to the stability of Electrical Power Systems (EPS) and electrical grids, since RES do not yet contribute to stability at levels comparable to the generating units of large thermal power plants, whether in terms of inertia, which has seen a notable reduction in recent years, or in voltage control or short-circuit power. This article presents and discusses solutions to mitigate the effect of this reduction in inertia in power plants using synchronous compensators and synthetic inertia emulation using battery storage. 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

Accurate fault diagnosis of hydropower units is crucial for ensuring the efficient and complete utilization of hydropower resources. Existing diagnostic methods predominantly consider either single-sensor or single-scale multi-sensor fusion, failing to fully exploit the effective information within monitoring data. Furthermore, they neglect the correlation between different sensors and faults during fusion diagnosis, thereby limiting the diagnostic performance of fusion models. To address this, this paper proposes a multi-sensor data fault diagnosis method based on multi-scale fusion. First, a feature extraction model is constructed to extract shallow-level features from multi-sensor signals across multiple dimensions. Subsequently, an attention-based feature fusion network is designed to extract and fuse multi-depth features, yielding high-quality deep-fused features. Finally, an information-entropy-based decision fusion strategy is established to effectively enhance the model’s diagnostic performance. Experimental validation on the public rotating machinery fault dataset and the hydropower unit fault dataset yielded diagnostic accuracies of 96.42% and 99.28%, respectively, demonstrating the significant effectiveness and robustness of the proposed method. Full article

As a key part of the new power system, hydropower units (HPUs) are capable of maintaining the stability of system frequency through the flexible conversion of operating conditions. Fixed control parameters are generally adopted by existing HPU governors, which cannot meet the requirements of variable operating conditions, and the flexibility of hydropower regulation is thus restricted. Therefore, an adaptive optimal control strategy for units in frequency regulation mode is proposed for a large hydropower station in this paper. Firstly, a segmented linearized mathematical model for HPU frequency regulation is established. On this basis, objective functions under frequency and load perturbation are constructed. Control parameters under each operating condition are optimized via an improved multi-objective particle swarm optimization based on the objective functions. The nonlinear relationship between optimal control parameters and operating conditions is fitted to obtain the adaptive adjustment strategy. Comparative verification with the fixed-parameter strategy shows that the proposed strategy improves comprehensive performance (frequency adjustment and recovery time) under 48 operating conditions. The improvement rate exceeds 50% under large opening conditions, with an overall average of 51.01%, fully proving its superiority. Full article

Frequent flash floods threaten human well-being, hydropower infrastructure, and ecosystems. However, the long-term evolution of flash flood patterns over recent decades remains insufficiently understood, particularly in data-scarce high-altitude regions. Using multi-source remote sensing data integrated with historical disaster records and field investigations, this study examined the spatiotemporal evolution and driving factors of flash floods across the Qinghai–Tibet Plateau (QTP). The results indicate that flash floods have increased exponentially, which may be influenced by disaster management policies, with peaks in July–August and frequent occurrences from April to September. The seasonal trajectory of the center of gravity of flash floods from April to September exhibited a clear directional pattern. Regions with the highest disaster density were concentrated in the headwaters of five major rivers, including the Yarlung Zangbo, Jinsha, Nu, Lancang, and Yellow Rivers. Shapley Additive Explanation (SHAP) and Random Forest analyses reveal that soil moisture, anthropogenic intensity, and seasonal runoff variability are the dominant driving factors. With ongoing socioeconomic development, intensified human activities have become a key contributor to the increasing frequency of flash floods. These findings highlight the value of remote sensing-based assessments for flash flood monitoring and early warning and provide scientific support for risk mitigation, loss reduction, and the advancement of water-related targets under the United Nations’ Sustainable Development Goals. Full article

In power systems with high renewable penetration, day-ahead hydropower scheduling is increasingly dispatched for power balancing and residual-load smoothing, which leads to frequent ramping and power-output fluctuations, thereby increasing the likelihood of operating in vibration zones (VZs). Vibration zones can significantly affect the safe and reliable operation of hydropower units and have therefore become a key operational concern in day-ahead scheduling. Using plant-provided VZ information and the three-zone classification adopted in practice, operating conditions are partitioned into a safe operating zone (SOZ), a restricted operating zone (ROZ), and a forbidden operating zone (FOZ). Operation is unrestricted in the SOZ; operation in the ROZ is allowable only for short durations; operation in the FOZ is prohibited. The three zones are generally non-convex and may contain holes. To handle non-convex feasible regions, an optimal convex partition (OCP) is employed to represent the SOZ and ROZ as unions of convex subregions. The operating point is then enforced via convex combination constraints, yielding a mixed-integer linear programming (MILP) model solved by a commercial MILP solver. Case studies demonstrate that the proposed approach improves the trade-off between operational safety and residual load smoothing performance, providing a practical framework for vibration-zone-aware day-ahead scheduling of large-scale hydropower plants with complex VZs. Full article