IET Renewable Power Generation最新文献

The growing penetration of renewable energy resources could be a major concern in terms of the primary frequency response of the grid. Nevertheless, this can potentially be addressed by the inertial response of the grid-connected DFIG-based offshore wind farms through VSC-HVDC-VSC (voltage-sourced converter). However, the inherent nonlinearity of this system leads to unsatisfactory performance when controlled with linear controllers. This paper proposes a nonlinear control strategy specifically designed to enable robust inertial support under the grid frequency events. A detailed nonlinear model is developed for the entire system, including the wind turbine, DFIG converters, HVDC transmission system, and grid interface. Unlike conventional omission of nonlinear terms, the output feedback linearisation (FL) is applied to transform the coordinates of the nonlinear system into a new coordinate representing a linear structure while fully retaining the original nonlinearities, thereby ensuring accurate dynamic representation. Furthermore, a sliding mode control (SMC) strategy is proposed to ensure robust system performance in the face of load variations and uncertainties in the engaged system and grid parameters. The stability of the system's internal dynamics and the sliding surface in the SMC is demonstrated using Lyapunov's method. The performance of the proposed controller is validated through various simulations under different scenarios by employing the IEEE 39-bus test system.

This paper proposes a dual-input single-output high step-up converter for use in renewable energy systems. In this converter, the boost and SEPIC structures are integrated. To achieve high voltage gain with a low component count and high power density, the SEPIC second inductor is coupled with the boost inductor. A passive clamp circuit absorbs the leakage inductance energy and recycles it to the load. Thus, the switch voltage stress at the turn-off moment is limited. The switch voltage stress decreases, and the converter benefits from a common ground between the input and output. Moreover, all the switch sources are connected to the ground. Hence, the converter control circuit is simple. In this converter, all diodes turn off at the ZCS condition, and the reverse recovery problem is eliminated. In this article, the proposed converter operating principles are investigated, and the converter is analyzed. To demonstrate the advantages of the converter, the proposed converter is compared with its counterparts. The experimental results for the 100 W prototype confirm the results of the theoretical analysis.

Interest in green hydrogen production using electrolysers powered by renewable energy has grown in recent years, driven by applications in energy storage and various industrial sectors. Scaling-up alkaline water electrolyser (AWE) stacks by increasing the number of series-connected cells, and elevating the operating voltage (1000–1500 V) can reduce overall capital cost and improve the efficiency of power converters. However, shunt currents remain a key challenge, preventing scaling-up of AWE, while reducing the energy efficiency, especially under partial load conditions. This study developed an equivalent circuit model of the stack to investigate the impact of shunt currents on a large-scale AWE. The model was verified with measurement data of an industrial AWE system, and the simulations were carried out in the MATLAB/Simulink environment. The main goal of this work was the shunt currents modelling, based on a resistance components network of each cell, considering the geometric design of fluid ports and manifolds, and the presence of gas bubbles in outlet channels at the anode and cathode. Impact of increasing the equivalent resistance of fluid ports was studied across stacks of various lengths, containing a larger number of series-connected cells. The reduction of shunt currents can significantly improve the energy efficiency of AWE systems.

Constructing an all-DC offshore wind farm with DC power generation, DC collection, and DC transmission is an important direction for the development of offshore wind power. The offshore DC wind turbine generator is the core equipment of the all-DC wind farm, and establishing its simulation models at different time scales is of great significance for conducting source-grid coordinated research. Currently, worldwide, the all-DC offshore wind farm is still in the research and development stage, with no actual engineering applications yet. Existing research mostly focuses on the topology of high-capacity DC/DC converters suitable for offshore DC wind turbines, often only paying attention to the control characteristics of the DC/DC converter, lacking research on the overall control strategy and the simulation model of the offshore DC wind turbine. This paper first compares several typical schemes of DC collection for offshore DC wind farms, pointing out that the parallel two-stage voltage boost scheme is the most technically and economically viable at this stage. It then selects a modular combination-type DC/DC converter topology based on the LLC resonant converter, presents the complete structure of the offshore DC wind turbine using this topology, establishes an electromagnetic transient model of the offshore DC wind turbine based on the LLC resonant converter, and designs its control strategy under small and large disturbances. Through simulation examples, the model's ability to maximize wind energy capture during normal operation and ensure safe and stable operation during faults is verified. Subsequently, based on the average models of the AC/DC converter and the DC/DC converter, an electromechanical transient model of the offshore DC wind turbine is established, its control strategy is designed, and the accuracy of the electromechanical transient model is verified by comparing the simulation results with those of the electromagnetic transient model. Finally, the paper presents the different application scenarios of the two established models at different time scales in the grid-connected operation research of the offshore DC wind power system and looks forward to the future research focus. The research in this paper can provide a certain reference for the field of modelling and analysis techniques for grid-connected offshore DC wind power systems.

Due to the low emission production and the fast-response nature of natural gas-fired units (NGFUs), installation of these generators has increased in the electricity system, and as a result, the interdependency of gas and electricity systems has intensified. However, uncertainties have brought new challenges to the coordinated operation of the two systems. This paper proposes a new tri-layer model to include uncertainties in the coordinated scheduling of gas-electricity networks. In the first layer, wind uncertainty is handled through the stochastic method. In the second layer, the IGDT method is used to manage electrical load uncertainty, and in the third layer, the optimal values for the objective functions of the previous two layers are simultaneously derived by the fuzzy method. Also, three practical solutions (flexible resources) are provided to increase gas network flexibility: (1) using natural gas storage, (2) implementing a demand response program in the gas network and (3) using the generators with the ability to change their fuel (dual-fuel generators). Also, carbon capture systems (CCS) are integrated with traditional units to decrease emissions of these generators. Numerical tests illustrate that the simultaneous use of flexible resources alongside CCS leads to more reduction in the total cost and emission, and prevents load shedding in the operation of gas-electricity networks. As well, with this new hybrid fuzzy-IGDT-stochastic model, both the objective function and uncertainty radius are optimised.

Under the guidance of the ‘dual carbon’ goals, the installed capacity of wind power continues to grow, increasing wind power penetration levels (WPPLs) and posing challenges to system frequency stability. Therefore, it is essential to study the control of wind farms operating in de-loading mode to participate in system frequency regulation (SFR). This paper proposes a power dispatching method for a de-loading operated wind farm that participates in power SFR considering the wake effect. It begins by grouping wind turbines (WTs) considering the wind's incoming angle and wake effects, which simplifies computational needs compared with controlling individual WTs. The method sets a priority for power distribution to maximise the use of WTs’ overspeed de-loading capacity, effectively increasing rotor kinetic energy and reducing pitch angle adjustments. This approach avoids complex optimisations and wind speed measurement for each WT, significantly boosting system robustness. To assess the effectiveness of this method, simulations using the EMTP-RV simulator were conducted under various wind speed angles, disturbance levels and WPPLs. The results indicate that the proposed strategy enhances the WF's ability to regulate system frequency and decreases the need for pitch adjustments.

The increasing reliance on digital infrastructures in power systems, combined with the rising penetration of renewable energy sources (RES), has heightened their vulnerability to sophisticated cyber-physical attacks, particularly false data injection a ttacks (FDIAs). These attacks exploit state estimation processes to disrupt grid operations while remaining undetected. This paper presents a novel multi-layered optimization framework to enhance the resilience of cyber-physical power systems against FDIAs under uncertain attack scenarios. The framework employs a tri-level Stackelberg optimization approach to model the interactions between defenders, attackers, and system operations. The defender's strategy focuses on optimal resource allocation and adaptive decoy placement to misdirect attacker efforts while minimizing operational costs. The middle level simulates attacker strategies using generative adversarial networks (GANs) to generate stealthy and adaptive attack vectors. The lower level incorporates physical and operational constraints of the grid, ensuring realistic scenario modeling. Advanced methodologies, including multi-agent deep reinforcement learning (MADRL), Bayesian inference, and distributionally robust optimization, are integrated to address dynamic uncertainties and evolving attack patterns. The proposed framework is validated on a modified IEEE 123-bus system with synthesized attack scenarios, demonstrating significant improvements in grid resilience. Results indicate an average reduction in attack success rates by 40% and an enhancement in resilience metrics by 35%, achieved through optimized defense budget allocation and adaptive decoy strategies. This research contributes to the field by bridging game theory, robust optimization, and machine learning, offering a comprehensive solution to ensure the security and reliability of modern power systems under extreme cyber-physical threats.

Hybrid wind-wave energy systems, integrating floating offshore wind turbine (FOWT) and wave energy converters (WECs), have received much attention in recent years due to its potential benefits in increasing the power harvesting density and reducing the levelized cost of electricity (LCOE). Recent studies show that advanced model-based control strategies have the great potential to significantly improve their overall control performance. However, the performance of these advanced control strategies relies on the computationally efficient control-oriented models with sufficient fidelity, which are normally difficult to derive due to the complexity of the hydro-, aero-dynamic effects and the couplings. In most available results, the hybrid wind-wave energy system models are established by using the boundary element method (BEM), devoting to understanding the hydrodynamic responses and performance analysis. However, such models are complex and involved in relatively heavy computational burden, which cannot be directly used for the advanced model-based control methods in practice. To overcome this issue, this paper proposes a control-oriented model of the hybrid wind-wave energy system with six degrees of freedom (DOFs). First, the Newton's second law and fluid mechanics are employed to characterize the motion behavior of the hybrid wind-wave energy system with the coupled aero-hydro-mooring dynamics. Then, a novel adaptive parameter estimation algorithm with simple low-pass filter approach is developed to estimate the system unknown coefficients. Different from the conventional parameter estimation methods, such as gradient descent method and recursive least-squares (RLS) method, the estimated parameters can be driven to their true values with guaranteed convergence. Finally, numerical analysis using the AQWA and MATLAB are applied to validate the fidelity of the control-oriented model under different wind and wave conditions. The results indicate that the control-oriented model predicts the motion response accurately in comparison to the BEM-based model. Overall, the results pave the way for designing advanced hybrid wind-wave energy system control method.

Wind power is one of the most widely available renewable energy sources (RES). However, due to the intermittent nature of wind, the output power of wind turbines (WTs) is always variable. In WTs, at speeds lower than the rated wind speed, the goal is to maximise the power extracted from the wind. At higher wind speeds, the goal is to keep the WT's power constant at rated value; that is typically done by the WT's pitch control system. The operation of the pitch system has a delay due to WT's blades and rotor inertia and limited pitch rate, which may lead to output power fluctuations. Superconducting magnetic energy storage (SMES) has fast response and high efficiency. This paper explores the application of SMES to compensate for the pitch system delay in output power smoothing of a permanent magnet synchronous generator (PMSG)-based WT. It is verified that the SMES properly compensates for the pitch lag by absorbing the surplus power and releasing it at power shortage intervals, particularly when pitch control returns the blades to their initial position. In the meantime, the pitch system reduces the SMES coil current and prevents it from saturation, which allows selecting an optimal/practical coil size for the SMES.