IET Renewable Power Generation最新文献

Conventional methods for identifying voltage sag sources are difficult to categorize accurately due to the complexity of transmission lines and the influence of noise. In order to solve the problem of difficulty in recognizing voltage sag sources of transmission lines under different locations, this paper proposes a new method for transmission line fault diagnosis based on modified wavelet denoising and multi-location information convolution transformer. The improved wavelet denoising method proposed in this paper solves the problems of discontinuity and bias of the traditional wavelet denoising method, and is able to better reconstruct the original voltage signal in a strong noise environment. In addition, multi-location information convolution transformer adopts a new model combining multi-location information convolution and multi-scale convolution transformer, which realizes the combination of global information and local context information capturing ability under multi-location faults. This paper validates the method through simulation experiments and practical situations, and the results show that the method can well classify and identify the type and location of voltage sag sources in transmission lines.

The harmonic problems caused by non-linear factors of the grid connected inverter (GCI) system are more complicated, including both non-characteristic harmonics emitted by the dead-time and the changes in harmonic impedance characteristics. Harmonics interact with the changing impedance, and even cause harmonic amplification and resonance. The harmonic deterioration of the multiple GCIs system is serious. To analyse the mechanism and way of harmonic deterioration in grid-connected system caused by nonlinear factors, the active impedance models of single inverter and multiple GCIs system including dead-time effect and digital control delay are established first. In view of this, the influence mechanism of non-linear factors on system stability is explored. The improved modal analysis method is used to traverse the network series parallel resonance caused by nonlinear factors. The results show that both the dead-time effect and digital control delay reduce phase margin of the system, resulting in the resonant frequency shift and the resonant peak increase. When the harmonic excitation source interacts with the complex network under the influence of nonlinear factors, it will lead to further deterioration of harmonics. Finally, based on the MATLAB and RT-LAB hardware-in-the-loop simulation platforms, a multiple GCIs system model is built to verify the correctness of the established active impedance model and harmonic deterioration analysis.

This paper addresses essential aspects of decision-making and management in energy resources. To achieve this, a tri-objective model is proposed that seeks to find the best solution within the basic constraints framework of the optimization problem so that all three proposed objective functions can approach their ideal point. The uncertainty is used for the photovoltaic and wind power plants’ output in the proposed multi-objective optimization problem as a scenario-based stochastic approach. The proposed objective functions are realizing the operating cost, the amount of emission produced by generation resources, the amount of load-shedding, and the maximum participation of responsive demands in the management program. The idea of employing plug-in electric vehicle (PHEV) units in the form of intelligent parking lots within the network is also included in the proposed study, which can increase network flexibility and help improve the main features of the network. A modified IEEE 83-bus test system is used to ensure the accuracy and effectiveness of the proposed model. The properties of PHEVs significantly affect the simulation results and compensate for the uncertainty associated with renewable energy sources. Randomly considering the parameters of PHEVs can also realistically bring the results of power management more realistic. In addition, the multi-objective problem defined for each scenario is solved by the augmented epsilon-constraint method with the correlation coefficient concept for the network under study, and the Pareto front curves are obtained separately and the best solution is extracted by a proper decision-making method.

Distributed cooperative control strategies for DC microgrids have been rapidly evolving in recent years. However, introducing a cyber layer to enhance robustness, scalability, and reliability also exposes the system to potential cyber-attacks. The damage inflicted by such attacks on the system performance can be catastrophic, reaching a point where it may devastate the system normal operation. By using model reference adaptive control (MRAC), this article proposes a resilient approach that does not require an accurate model of the system and despite the uncertainties for detecting false data injections into the reference DC voltage and simultaneously mitigating their adverse effects on the system stability and performance. The proposed technique employs an observer to detect possible false data injections in an online manner. By emulating the behavior of an ideal reference model, the MRAC ensures adaptive adjustments of the control parameters over time to mitigate the negative effects of potential attacks effectively and despite non-idealities such as measurement noise, parameter variations, and environmental changes in DC microgrids, the MRAC effectively manages false data injection attacks. Simulation studies are conducted using diverse scenarios involving a three-node DC microgrid to show the effectiveness of the proposed method.

The cascaded H-bridge multi-level converter is the main topology of high-voltage and high-capacity converter in renewable energy power generation, energy storage system and reactive power compensation equipment. The parameter design of cascaded H-bridge multi-level converters directly determines the material cost and performance. Taking the output current distortion rate and the ripple voltage of the capacitor of the cascaded H-bridge multi-level converter as the constraint conditions, this paper proposes an overall parameter optimal design method of the cascaded H-bridge multi-level converter based on the optimization of the total material cost. Finally, the effectiveness of the proposed parameter design method is verified by the simulation result.

This study explores the optimal balance between grid-forming (GFM) and grid-following (GFL) converter capacities within power stations to ensure stable operations. The investigation introduces a novel, generic modelling approach for analysing multiple converter systems in the wind and photovoltaic power plants. The method aims to elucidate the dynamic characteristics of the converters in power plants, particularly focusing on the continuity and existence of the equilibrium manifolds and their impact on system stability. Findings reveal a pronounced difference in the recovery capabilities of GFM and GFL following synchronization losses, highlighting an asymmetry in their abilities. Specifically, GFL converters exhibit more effectiveness in reinstating synchrony after synchronization losses caused by GFM. Conversely, GFM demonstrates a lesser capacity to recover from synchronization losses induced by GFL. Furthermore, analysis indicates that when the capacity ratio of GFL to the system's short-circuit capacity significantly exceeds that of GFM (exceeding a 1:5 ratio), the system experiences an absence of a stable equilibrium point, thereby affecting the synchronization stability of GFM. These conclusions have been validated through joint controller hardware-in-the-loop testing.

With the growth of the load in the electricity networks, sufficient investment in the generation and lines expansion should be made in order to provide the energy needed by consumers with the lowest possible investment and operation costs. This issue is especially important in distribution networks, which are faced with the uncertainties of renewable energy generation and the development of microgrids and related issues.

In this article, the planning of generation and lines expansion has been modeled with the aim of minimizing the total costs of microgrids, based on the cooperative approach. For this purpose, a bi-level model has been developed; on the upper level, microgrids make investment decisions with a cooperative approach, and a constrained stochastic formulation has been developed with considering operational uncertainties on the lower level. Also, in this article, in order to ensure the supply of critical loads in island conditions, the self-sufficiency index is defined. Three case studies have been considered to ensure the effectiveness of the developed model. In case 1, each microgrid will be able to supply its load only by generating of its units and purchasing from the retail market. In case 2, the possibility of trading with other microgrids in a non-cooperative approach will also be available to the microgrids operators, and in case 3, microgrids can exchange energy with other microgrids in a cooperative manner. The simulation results showed that due to the possibility of using nearby microgrid resources, the cost of microgrid load supply in case 2 was reduced by 4.84% compared to case 1. Also, this cost in case 3 was reduced by 5.23% and 0.38%, respectively, compared to cases 1 and 2, due to the use of a cooperative manner in microgrid load providing.

Stochastic programming is a competitive tool in power system uncertainty management. Traditionally, stochastic programming assumes uncertainties to be exogenous and independent of decisions. However, there are situations where statistical features of uncertain parameters are not constant but dependent on decisions, classifying such uncertainties as decision-dependent uncertainty (DDU). This is particularly the case with future power systems highly penetrated by multi-source uncertainties, where planning or operation decisions might exert unneglectable impacts on uncertainty features. This paper reviews the stochastic programming with DDU, especially those applied in the field of power systems. Mathematical properties of diversified types of DDU in stochastic programming are introduced, and a comprehensive review on sources and applications of DDU in power systems is presented. Then, focusing on a specific type of DDU, that is, decision-dependent probability distributions, a taxonomy of available modelling techniques and solution approaches for stochastic programming with this type of DDU and different structural features are presented and discussed. Eventually, the outlook of two-stage stochastic programming with DDU for future power system uncertainty management is explored, including both exploring the applications and developing efficient modelling and solution tools.

State of Charge (SoC) estimation for Lithium-Iron Phosphate (LFP) batteries is challenging due to a flat Open Circuit Voltage (OCV) curve and a well-known hysteresis effect. The authors built upon a previous study, which has shown that hysteresis in LFP is not an inherent characteristic but a very slow relaxation process when compared to other battery chemistries. Distribution of Relaxation Times (DRT) is used to deconvolve Electro-Impedance Spectroscopy (EIS) measurements and model the hysteresis effect. The extracted DRT parameters show good agreement at low frequencies with previous thermodynamic studies in both fresh and aged cell conditions. The proposed model, called hystimator, integrates the hysteresis characteristics into a physics-based Electro-Chemical Model (ECM). The validation results show a significant reduction in the Root Mean Square Error (RMSE) during real-world laboratory testing. This approach holds promise for SoC estimation in LFP battery cells, especially in embedded Battery Management System (BMS).

Recent years, the tremendous number of distributed photovoltaic are integrated into low-voltage distribution network, generating a significant amount of operational data. The centralized cloud data centre is unable to process the massive data precisely and promptly. Therefore, the operational status of distributed photovoltaic systems in low-voltage distribution network becomes difficult to predict. However, edge computing in the distribution network enable local processing of data to improve the real-time and reliability of the forecasting service. In this regard, this paper proposes a distributed photovoltaic short-term power forecasting model based on lightweight AI algorithms. Firstly, based on the Pearson correlation coefficient method, an analysis is conducted on the historical operational data in the network to extract important meteorological features that are correlated with the photovoltaic power output. Secondly, a distributed photovoltaic power forecasting model for the distribution network is constructed based on the Xception and attention mechanism. Finally, the model is trained using pruning, which involves removing redundant parts of the model, resulting in a compact and efficient forecasting model. By conducting validation on real-world datasets, the results demonstrate that the model presented in this article possesses a smaller size and higher forecasting accuracy compared to other state-of-the-art forecasting models.