IET Renewable Power Generation最新文献

A major obstacle standing in the way of full-scale adoption of renewable energy sources is their intermittency and seasonal variability. To better understand the power generation dynamics, the effect of air density due to temperature on power and energy generation figures was modelled. The model uses historical ERA5 data and considers changes in weather patterns in a subarctic climate where seasonal changes are most pronounced. The power generation figures of using a mean and a dynamic air density value were compared and the results show that power generation estimates may be under- and overestimated by on average 5% up to 10% in winter and summer, respectively. This can have implications for the sizing of power transmission infrastructure and energy storage in both on-grid and off-grid applications as well as power availability. The topic is highly relevant in the Nordic countries where roughly 10% of new global added wind capacity is installed annually.

Adopting aggregation techniques in power sector modelling led to disregarding the key characteristics of regions in terms of resource use, which may not completely capture the bottlenecks in the energy transition. This study provides a holistic approach to estimate its impact on the transition of the European power system from the perspective of energy return on investment (EROI) by using six energy transition scenarios based on three different spatial representations. The findings indicate that EROI trends are highly dependent on the spatial representation, technology selection and energy mix. Further additional capacities of complementary technologies along with an upsurge in renewable capacities drive EROI values down. Disregarding the physical distances in the energy modelling results in large EROI enhancement due to the artificial smoothing effect of the aggregation method. EROI values of the aggregated scenarios remain between 18 and 24 by 2050. In the case of 20 independent sub-regions, the lowest EROI is obtained at about 14 by 2050, due to the limitation on optimal resource utilisation. Interconnection of the sub-regions, which represents the best proximation to the real situation, increases the EROI to 17 by 2050.

With the advancement of industrial low carbonization and electrification, emerging industrial production technologies have the characteristics of high-power consumption and significant impact load. In the context of increasing global climate change, frequent extreme weather events have brought serious challenges to the balance of power and electricity in the power system, and have a significant impact on the production scheduling of industrial users, especially industrial users using electrified production technology. First, based on the representative high-power industrial users of hydrogen reduction steel plants and internet datacentres (IDC), this paper establishes a flexible resource scheduling optimization model for single industrial users, and considers the collaborative relationship between multi-user flexible resources to establish a collaborative scheduling optimization model for industrial users. Then, considering the charging and discharging characteristics of electric vehicles (EV) in extreme scenarios, the redundant capacity of EVs is aggregated by EV aggregators and sold to industrial users, and a collaborative scheduling optimization model of EV aggregators and industrial users is established. Finally, the effectiveness of the proposed model and algorithm is verified by simulation analysis. Compared with the traditional industrial user production optimization, the proposed model can tap the potential of multi-agent scheduling operation in extreme scenarios.

Synchronous stability is crucial for the safety and operation of AC power systems. However, most of the current researches focused on the stability of grid-connected converters, and that of renewable equipment still lacked. In this article, the impact of the additional inertia control (AIC) on the permanent magnet synchronous generator (PMSG) is studied. It is found that with the AIC, the machine-side converter dynamics of the PMSG cannot be ignored, and the system dominant dynamics shifts from the electromagnetic to electromechanical timescales. This article develops a simplified model for the single-PMSG infinite-bus system with the AIC within the electromechanical timescale, and reveals the transient synchronization stability mechanism from three aspects: the machine-network interface, transient dominant variable, and interaction between the synchronization loop and the power imbalance loop. Finally, this article analyzes the swing characteristics of the PMSG system, and uncovers the relationship between the energy transmission and synchronization. These findings are supported by wide experimental verification and can provide the deeper physical insight and theoretical basis for the transient synchronous stability analysis of renewable-dominated new-type power systems.

The power control system structures for a doubly-fed generator (DFIG) are proposed. The classical field oriented control and the feedback control with the multi-scalar variables were considered. The generator is working in the AC grid connection mode. The rotor side of the generator is connected to the current source converter (CSC); the stator is directly related to the AC grid. The static feedback linearization using the multi-scalar variables of DFIG is proposed to increase active and reactive power control accuracy. The proposed control structure allows to linearize the generator system, and decoupled between the control paths. The proposed approach can be called voltage control because one of the control variables is the voltage in the DC-link of the CSC. The simulation and experimental investigations in the 2 kW DFIG system consider the AC grid voltage dips, confirming that the proposed control system remains stable.

Active distribution networks (ADNs) are consistently being developed as a result of increasing penetration of distributed energy resources (DERs) and energy transition from fossil-fuel-based to zero carbon era. This penetration poses technical challenges for the operation of both transmission and distribution networks. The determination of the active/reactive power capability of ADNs will provide useful information at the transmission and distribution systems interface. For instance, the transmission system operator (TSO) can benefit from reactive power and reserve services which are readily available by the DERs embedded within the downstream ADNs, which are managed by the distribution system operator (DSO). This article investigates the important factors affecting the active/reactive power flexibility area of ADNs such as the joint active and reactive power dispatch of DERs, dependency of the ADN's load to voltage, parallel distribution networks, and upstream network parameters. A two-step optimization model is developed which can capture the P/Q flexibility area, by considering the above factors and grid technical constraints such as its detailed power flow model. The numerical results from the IEEE 69-bus standard distribution feeder underscore the critical importance of considering various factors to characterize the ADN's P/Q flexibility area. Ignoring these factors can significantly impact the shape and size of Active Distribution Networks (ADN) P/Q flexibility maps. Specifically, the Constant Power load model exhibits the smallest flexibility area; connecting to a weak upstream network diminishes P/Q flexibility, and reactive power redispatch improves active power flexibility margins. Furthermore, the collaborative support of reactive power from a neighboring distribution feeder, connected in parallel with the studied ADN, expands the achievable P/Q flexibility. These observations highlight the significance of accurately characterizing transmission and distribution network parameters. Such precision is fundamental for ensuring a smooth energy transition and successful integration of hybrid renewable energy technologies into ADNs.

The large scale deployment of modern wind turbines and the yearly increase of installed capacity have drawn attention to their operation and maintenance issues. The development of highly reliable and low-maintenance wind turbines is an urgent demand in order to achieve the low-carbon goals, and the arrival of fault diagnosis provides assurance for its satisfactory operation and maintenance. Numerous statistical studies have pointed out that generator failures are a main cause of wind turbine system downtime. The generator, as one of the core components, converts rotating mechanical energy into electrical energy. However, the generators can hardly operate reliably towards the end of the turbine life owing to the variable-speed conditions and harsh electromagnetic environments. This article first provides a comprehensive and up-to-date review of the electrical and mechanical failures of various parts (stator, rotor, air gap and bearings) of the generator. Then the fault characteristics and diagnostic processes of generators are investigated, and the principles and processes of fault diagnosis are discussed. Finally, the application of four categories of model-based, signal-based, knowledge-based and hybrid approaches to wind turbine generator fault diagnosis is summarized. The comprehensive review shows that the hybrid approach is now the leading and most accurate tool for real-time fault diagnosis for wind turbine generators. A qualitative and quantitative assessment of algorithm performance using false alarm rates is proposed. The methodology can subsequently be applied to the wind industry.

The advancement of Photovoltaic technology has undergone rapid acceleration in recent years. Nonetheless, the most significant drawback of Photovoltaic is its intermittence, making it an obvious source of power fluctuation. This study proposes a novel scheme for real-time or intraday PV power forecasting by adopting two predictive models, namely, White-box and Combination. The White-box model is implemented employing mathematical calculations and statistics called Exceedance Probability. Meanwhile, the Combination model is an aggregation of several predictive models' outputs including the White-box model and benchmark ones by dynamically adjusting the weight coefficient of each model based on their forecasting accuracy. The experimental results, which are verified on two PV systems corresponding to two case studies located at Vietnam and Australia, indicate that the two proposed models outperform other referenced models as $�\mathrm{nMAPE}$ improves approximately 40% and 38% in terms of the first and second case study, respectively. In particular, the White-box model shows superiority by updating the forecast every 10 min, which can adapt to the fluctuation of weather conditions whereas the Combination one yields acceptable precision, indicating its flexible application.

This article aims to investigate different dc-bus voltage balancing control modes for a modular HVDC drive train applied to offshore wind energy. The modular structure discussed consists of a stacked polyphase bridge converter connected to the modules of a segmented generator. Three voltage control modes are analysed: non-voltage balancing, voltage balancing allowing overcurrent, and voltage balancing with power derating. Assembly imperfections and operation differences can cause parametric deviations across modules leading to generation unbalance. When operating with voltage balancing with power derating, none has ac overcurrent or dc overvoltage. However, when the turbine is already operating at nominal power, a reduction in total power is required for this to occur. On the other hand, non-voltage balancing or voltage balancing allowing overcurrent could bring more generated power to the system as the optimal output power allows dc overvoltage or ac overcurrent in some of the modules, respectively. Therefore, there is a trade-off between efficiency and oversizing the system's components. A sensitivity analysis is performed to identify the expected dc-bus voltage deviation. Experimental results in a low-power prototype validate the voltage balancing control modes and a case study demonstrates the impact on energy yield in an offshore wind turbine.

The increasing penetration of grid-following voltage source converters (GFL-VSCs) in the power grid has changed the frequency dynamics of the system. GFL-VSC follows the frequency of the terminal bus using a phase-locked loop, therefore it does not establish the frequency in the same manner as a synchronous generator. However, previous research has demonstrated that GFL-VSC without additional frequency controls not only tracks the terminal frequency during the system dynamic process bus also has an influence on it, and derives the relationship between GFL-VSC and terminal frequency through a simplified model. This paper further derives a more generic relationship considering the influences of q-axis current and grid voltage amplitude variation, which were ignored in previous research. The relationship has proven to be effective and valid. Several influencing factors are analysed to describe the ability to modify terminal frequency. Then the effect of GFL-VSCs on the frequency distribution of power grid is studied. The frequency divider formula is improved by incorporating the relationship, which provides a frequency estimation formula applicable for system with multiple GFL-VSCs. Two system-level simulations verify the conclusion and related influencing factors are analysed.