Search Results (1,822)

Park-level integrated energy systems (IESs) are increasingly challenged by rapid electrification and higher penetration of renewable energy, which exacerbate source–load imbalances and scheduling uncertainty. This study proposes a unified framework that couples high-accuracy cooling and heating load forecasting with day-ahead coordinated optimization for an office park in Tianjin. The forecasting module employs correlation-based feature selection and variational mode decomposition (VMD) to capture multi-scale dynamics, and a sparrow search algorithm (SSA)-driven long short-term memory network (LSTM), with hyperparameters globally tuned by root mean square error to improve generalization and robustness. The scheduling module performs day-ahead optimization across source, grid, load, and storage to minimize either (i) the standard deviation (SD) of purchased power to reduce grid impact, or (ii) the total operating cost (OC) to achieve economic performance. On the case dataset, the proposed method achieves mean absolute percentage errors (MAPEs) of 8.32% for cooling and 5.80% for heating, outperforming several baselines and validating the benefits of multi-scale decomposition combined with intelligent hyperparameter searching. Embedding forecasts into day-ahead scheduling substantially reduces external purchases: on representative days, forecast-driven optimization lowers the SD of purchased electricity from 29.6% to 88.1% across heating and cooling seasons; seasonally, OCs decrease from 6.4% to 15.1% in heating and 3.8% to 11.6% in cooling. Overall, the framework enhances grid friendliness, peak–valley coordination, and the stability, flexibility, and low-carbon economics of park-level IESs. Full article

The rapid growth of PV generation in the distribution grid has necessitated PV curtailment to prevent overvoltage violations, and this has raised fairness issues as some are curtailed disproportionately to others. This paper proposes an adaptive PV curtailment scheme that balances fairness with energy sales using a Curtailment Index (CI) employed to reallocate curtailed energy between PV systems. The CI-based approach dynamically adapts each inverter’s output in real time to provide voltage compliance while ensuring that no individual PV system experiences an overburden of curtailment. The method is evaluated through MATLAB simulations on a three-PV test distribution network and validated experimentally on the PAGLIA ORBA solar microgrid, where its performance is compared to equal-curtailment and unfair strategies. The findings indicate that the adaptive method helps integrate high PV penetration more equitably and efficiently, ensuring stable grid operation while minimizing financial losses for PV owners. Full article

The increasing deployment of islanded microgrids in disaster-prone and infrastructure-constrained regions has elevated the importance of resilient energy storage systems capable of supporting autonomous operation. Grid-forming energy storage (GFES) units—designed to provide frequency reference, voltage regulation, and black-start capabilities—are emerging as critical assets for maintaining both energy adequacy and dynamic stability in isolated environments. However, conventional storage planning models fail to capture the interplay between uncertain renewable generation, time-coupled operational constraints, and control-oriented performance metrics such as virtual inertia and voltage ride-through. To address this gap, this paper proposes a novel distributionally robust optimization (DRO) framework that jointly optimizes the siting and sizing of GFES under renewable and load uncertainty. The model is grounded in Wasserstein-metric DRO, allowing worst-case expectation minimization over an ambiguity set constructed from empirical historical data. A multi-period convex formulation is developed that incorporates energy balance, degradation cost, state-of-charge dynamics, black-start reserve margins, and stability-aware constraints. Frequency sensitivity and voltage compliance metrics are explicitly embedded into the optimization, enabling control-aware dispatch and resilience-informed placement of storage assets. A tractable reformulation is achieved using strong duality and solved via a nested column-and-constraint generation algorithm. The framework is validated on a modified IEEE 33-bus distribution network with high PV penetration and heterogeneous demand profiles. Case study results demonstrate that the proposed model reduces worst-case blackout duration by 17.4%, improves voltage recovery speed by 12.9%, and achieves 22.3% higher SoC utilization efficiency compared to deterministic and stochastic baselines. Furthermore, sensitivity analyses reveal that GFES deployment naturally concentrates at nodes with high dynamic control leverage, confirming the effectiveness of the control-informed robust design. This work provides a scalable, data-driven planning tool for resilient microgrid development in the face of deep temporal and structural uncertainty. Full article

The increased penetration of power electronics interfaced resources in modern power systems is unlocking new opportunities and challenges. New concepts like multiport converters can further enhance the efficiency and power density of power electronics-based solutions. The triple active bridge is an isolated multiport converter with soft switching and high voltage gain that can integrate different sources, storage, and loads, or act as a building block for modular systems. However, the triple active bridge suffers from power flow cross-coupling, which affects its dynamic performance if it is not removed or mitigated. Unlike the extensive literature on two-port power converters, studies on modeling and control comparison for multiport converters are still lacking. Therefore, this paper presents and compares different modeling and decoupling control approaches applied to the triple active bridge converter, highlighting their benefits and limitations. The converter operation and modulation are introduced, and modeling and control strategies based on the single phase shift power flow control are detailed. The switching model, generalized full-order average model, and the reduced-order model derivations are presented thoroughly, and a comparison reveals that first harmonic approximations can be detrimental when modeling the triple active bridge. Furthermore, the model accuracy is highly sensitive to the operating point, showing that the generalized average model better represents some dynamics than the lossless reduced-order model. Furthermore, three decoupling control strategies are derived aiming to mitigate cross-coupling effects to ensure decoupled power flow and improve system stability. To assess their performance, the TAB converter is subjected to power and voltage disturbances and parameter uncertainty. A comprehensive comparison reveals that linear PI controllers with an inverse decoupling matrix can effectively control the TAB but exhibit large settling time and voltage deviations due to persistent cross-coupling. Furthermore, the decoupling matrix is highly sensitive to inaccuracies in the converter’s model parameters. In contrast, linear active disturbance rejection control and sliding mode control based on a linear extended state observer achieve rapid stabilization, demonstrating strong decoupling capability under disturbances. Furthermore, both control strategies demonstrate robust performance under parameter uncertainty. Full article

The increasing integration of power-electronic devices, such as voltage source converter-based high-voltage direct current (VSC-HVDC) systems and inverter-interfaced renewable energy sources (RESs), has rendered conventional short-circuit current (SCC) calculation methods inadequate. This paper proposes a novel analytical model that explicitly incorporates the current-limiting control dynamics of voltage source converters to accurately determine SCCs. The key contribution is a simplified yet accurate formulation that captures the transient behavior during faults, offering a more realistic assessment compared to traditional quasi-steady-state approaches. The proposed model was rigorously validated through electromagnetic transient (EMT) simulations and large-scale case studies. The results demonstrate that the method reduces the SCC calculation error to below 4%. Furthermore, when applied to the real-world provincial power grids of ZJ and JS, all computations converged within 10 iterations, confirming its robust numerical stability. These findings offer valuable insights for protection coordination studies and verify the model’s effectiveness as a reliable tool for planning future power systems with high power-electronics penetration. Full article

Grid frequency is a critical factor for the stability of a power system. However, with the penetration of massive dynamic load requirements and information and communication infrastructure, grid frequency is vulnerable to cyberattacks on the load side. In this paper, we model dynamic load-altering attacks (LAAs) on grid frequency and propose a control-based mitigation strategy. First, the dynamic grid-frequency model for frequency-sensitive loads is constructed. Then, the vulnerability of grid frequency to dynamic LAAs is analyzed using eigenvalue sensitivity analysis. To design the mitigation strategy, a stability condition with the first-order dynamic model is derived. Further, a second-order dynamic model is constructed to illustrate the joint impact of dynamic LAAs and the control strategy on eigenvalues, thereby revealing insights into mitigating factors for maintaining grid frequency stability. Finally, we conduct extensive simulations to evaluate the vulnerability of grid frequency under dynamic LAAs and to validate the effectiveness of the mitigation strategy. Full article

During perforation operations, high-speed jet penetration into the casing-cement sheath-formation assembly damages the bonding interfaces, resulting in fluid flow along these interfaces within the wellbore. This can compromise the wellbore seal integrity and shorten the lifespan of the oil and gas well. To address this, a numerical model was developed using fluid-solid coupling algorithms, combined with a cohesive zone damage model and the ALE algorithm. The model was employed to analyze the dynamic damage evolution of the bonding interfaces during the jet penetration process and quantify the effects of the cement sheath’s mechanical parameters (shear modulus and compressive strength) and geological stress on the axial damage length and area. The results indicate that both the casing-cement sheath and cement sheath-formation interfaces exhibit significant damage, with the former showing a larger damage area under identical mechanical conditions; as the cement sheath’s shear modulus increases, the damaged area at the casing-cement sheath interface expands, while that at the cement sheath-formation interface reduces. Conversely, an increase in the cement sheath’s compressive strength reduces the damage extent at both interfaces, as does elevated geological stress. Based on engineering cases, different cement slurry types were compared to minimize perforation-induced interface damage. This study provides theoretical and practical guidance for optimizing cement selection and assessing bonding interface integrity during perforation. Full article

The increasing penetration of renewable energy, such as photovoltaic generation, makes it essential to enhance power system dynamic performance through improved grid-forming control strategies. In the grid-forming control system, the virtual synchronous generator control (VSG) is currently widely used. However, the inertia (J) and damping (D) in the traditional VSG are fixed values, which can result in large overshoots and long adjustment times when dealing with disturbances such as load switching. To address these issues, this paper proposes an adaptive virtual synchronous generator (VSG) control strategy for grid-side inverters, which is accomplished by adaptively adjusting the VSG’s inertia and damping. Firstly, we established a photovoltaic-storage VSG grid-forming system; here, the photovoltaic power is boosted through a DC-DC converter, and the energy storage is connected to the common DC bus through a bidirectional DC-DC converter. We analyzed how J and D shape the system’s output characteristics. Based on the power-angle characteristic curve, the tanh function was introduced to design the control function, and a JD collaborative adaptive control (ACL) strategy was proposed. Finally, simulation experiments were conducted under common working conditions, such as load switching and grid-side voltage disturbance, to verify the results. From the results shown, the proposed strategy can effectively improve the response speed of the system, suppress system overshoot and oscillation, and, to a certain extent, improve the dynamic performance of the system. Full article

Transcranial focused ultrasound is a promising noninvasive technique for neuromodulation in neurological and psychiatric disorders, but accurate prediction of acoustic transmission through the skull remains a major challenge. In this study, we present a five-layer numerical human head model that integrates frequency-dependent acoustic parameters with nonlinear time-explicit dynamics to realistically capture ultrasound propagation. The model explicitly represents skin, trabecular bone, cortical bone, and brain, each assigned experimentally derived acoustic properties across a clinically relevant frequency range (0.5–5 MHz). Numerical simulations were performed in the frequency domain and time-explicit to quantify sound transmission loss and focal depth under high-intensity and high-frequency stimulation. The results show the effect of frequency, radius of curvature, and skull thickness on maximum pressure ratio, focal depth, and focus zone inside the brain tissue. Findings indicate that skull geometry, particularly radius of curvature and thickness, strongly influences the focal zone, with thinner skull regions allowing deeper penetration and reduced transmission loss. Comparison of the frequency-domain model with the time-explicit model demonstrated broadly similar trends; however, the frequency-domain approach consistently underestimated transmission loss and was unable to capture nonlinear effects such as frequency harmonics. These findings highlight the importance of nonlinear, time-explicit modeling for accurate transcranial ultrasound planning and suggest that the proposed framework provides a robust tool for optimizing stimulation parameters and identifying ideal target zones, supporting the development of safer and more effective neuromodulation strategies. Full article

With the rapid growth of electricity demand in industrial parks and the increasing penetration of renewable energy, vehicle-to-grid (V2G) technology has become an important enabler for mitigating grid stress while improving charging economy. This paper proposes a multi-objective rolling linear-programming-model-based predictive control (LP-MPC) method for coordinated electric vehicle (EV) scheduling in industrial park microgrids. The model explicitly considers transformer capacity limits, EV state-of-charge (SOC) dynamics, bidirectional charging/discharging constraints, and photovoltaic (PV) generation uncertainty. By solving a linear programming problem in a receding horizon framework, the approach simultaneously achieves load peak shaving, valley filling, and EV revenue maximization with real-time feasibility. A simulation study involving 300 EVs, 100 kW PV, and a 1000 kW transformer over 24 h with 5-min intervals demonstrates that the proposed LP-MPC outperforms greedy and heuristic load-leveling strategies in peak load reduction, load variance minimization, and charging cost savings while meeting all SOC terminal requirements. These results validate the effectiveness, robustness, and economic benefits of the proposed method for V2G-enabled industrial park microgrids. Full article

The large-scale integration of renewable energy sources introduces significant uncertainty into modern power systems, posing new challenges for reliable and economical operation. Effective scheduling therefore requires accurate monitoring of uncertainty and efficient handling of nonlinear system dynamics. This paper proposes an optimization-based scheduling method that combines sensor-informed monitoring of photovoltaic (PV) uncertainty with advanced processing of nonlinear hydropower characteristics. A detailed hydropower model is incorporated into the framework to represent water balance, reservoir dynamics, and head–discharge–power relationships with improved accuracy. Nonlinear constraints and uncertainty are addressed through a unified approximation scheme that ensures computational tractability. Case studies on the modified IEEE −39 system show that the proposed method achieves effective multi-source coordination, reduces operating costs by up to 2.9%, and enhances renewable energy utilization across different uncertainty levels and PV penetration scenarios. Full article

This paper proposes a stepwise expert-teaching reinforcement learning framework for intelligent frequency control in hydro–thermal–wind–solar–compressed air energy storage (CAES) integrated systems under high renewable energy penetration. The proposed method addresses the frequency stability challenge in low-inertia, high-volatility power systems, particularly in Southwest China, where large-scale renewable-energy-based energy bases are rapidly emerging. A load frequency control (LFC) model is constructed to serve as the training and validation environment, reflecting the dynamic characteristics of the hybrid system. The stepwise expert-teaching PPO (SETP) framework introduces a stepwise training mechanism in which expert knowledge is embedded to guide the policy learning process and training parameters are dynamically adjusted based on observed performance. Comparative simulations under multiple disturbance scenarios are conducted on benchmark systems. Results show that the proposed method outperforms standard proximal policy optimization (PPO) and traditional PI control in both transient response and coordination performance. Full article

The growing penetration of wind power has led to a continuous decline in system rotational inertia, posing serious challenges to the stability of next-generation power systems. Moreover, the strong dependence of wind generation on weather conditions, particularly the increasing frequency of extreme wind events, further exacerbates system vulnerability, making stability enhancement under adverse conditions an urgent research priority. To address this issue, this study proposes a virtual inertia-based control strategy for hybrid wind–storage systems, formulated through transfer function modeling of wind turbines, thermal generators, and energy storage units. By appropriately simplifying the dynamic characteristics of individual components, a comprehensive system-level transfer function model is developed to characterize the frequency response of the hybrid system. Virtual inertia support is provided by controlling the outputs of wind and storage units. A conventional wind–energy storage hybrid system without a virtual inertia control strategy was developed for comparison to evaluate the frequency regulation performance against the proposed system. Simulation studies under large load disturbance scenarios demonstrate that the hybrid wind–storage system achieves a smaller frequency nadir and faster steady-state recovery compared with standalone wind power system and a conventional wind–energy storage hybrid system without a virtual inertia control strategy. Notably, even under extreme wind conditions requiring complete curtailment of wind turbines, the energy storage unit continues to deliver virtual inertia, thereby maintaining system stability, superior to the conventional wind–energy storage hybrid system without virtual inertia control. These findings highlight the enhanced reliability and dynamic performance of wind–storage hybrid systems in mitigating frequency deviations within high-renewable environments, while also demonstrating the proposed control strategy’s robust adaptability to extreme weather conditions. The proposed approach offers valuable insights into strengthening the operational resilience of future low-carbon power systems. Full article

As wind power penetration continues to increase, the sub-synchronous control interaction (SSCI) problem caused by the interaction between doubly fed induction generators (DFIGs) and series-compensated transmission lines has become increasingly prominent, posing a serious threat to power system stability. To address this problem, this research proposes a centralized sub-synchronous oscillation damping controller (CSODC) for wind farms. First, a DFIG impedance model was constructed based on multi-operating-point impedance scanning and a Taylor series expansion, achieving impedance prediction with an error of less than 2% under various power conditions. Subsequently, a CSODC comprising a sub-synchronous damping calculator (SSDC) and a power electronic converter is designed. By optimizing feedback signals, phase shift angles, gain parameters, and filter parameters, dynamic adjustment of controllable impedance in the sub-synchronous frequency band is achieved. Frequency-domain impedance analysis demonstrates that the CSODC significantly enhances the system’s equivalent resistance, reversing it from negative to positive at the resonance frequency point. Time-domain simulations validated the CSODC’s effectiveness in scenarios involving series capacitor switching and wind speed disturbances, demonstrating rapid sub-synchronous current decay. The results confirm that the proposed strategy effectively suppresses sub-synchronous oscillations across multiple scenarios, offering an economical and efficient solution to stability challenges in high-penetration renewable energy grids. Full article

High-speed impact is a critical mechanism for structural damage. The infrared signatures generated during fragment formation provide essential data for damage assessment, protective system design, and target identification. This study investigated an aluminum alloy blunt projectile penetrating a target plate by employing smoothed particle hydrodynamics to simulate the debris ejection thermal and infrared properties. The infrared signatures of the debris clouds were calculated using Mie scattering theory under a spherical particle approximation. The reverse Monte Carlo methodology was applied to solve the radiative transfer equations and quantify the infrared emission characteristics. The infrared radiation characteristics of the debris cloud were investigated for projectile impact velocities of 800, 1000, and 1200 m/s. The results showed that the anterior debris regions reached peak temperatures of approximately 1200 K, with a temperature rise of 150–200 K per 200 m/s velocity increase behind the target. The medium-wave (3–5 μm) infrared intensity of the debris cloud was higher than the long-wave (8–12 μm) infrared intensity. The development of debris clouds enhanced the dispersion effect and slowed the increase in infrared radiation intensity in the same time interval. This study provides theoretical foundations for the dynamic infrared radiation characteristics of fragments generated by high-velocity projectile impacts. The infrared radiation characteristics within typical spectral bands can be utilized to assess hit probability and kill effectiveness. Full article