IET Renewable Power Generation最新文献

This article presents a detailed method for reactive power valuation exclusively from the generator-side point of view. This study is performed on a 1.5 MVA doubly-fed induction generator and two main generator-side parameters are introduced as the effective variables. These variables include power loss and active power capacity. In this article, in constant apparent power, the produced/consumed reactive power of the generator in exchange with the power grid is studied in detail and the operational characteristics of the generator including active/reactive power, power factor, and efficiency are reported. Using these functional characteristics, the power loss increment factor and the active power capacity reduction factor are proposed. Finally, the reactive power valuation factor that is the main output of this article is presented and the results are compared with the current reactive power pricing method in Iran. The results show that the proposed method can be useful for a more accurate analysis of reactive power valuation problem from the generator point of view.

Thermoelectricity is a versatile clean energy technology; however, its in/efficiency has been a question of debate. Thus, this practical study focuses on thermoelectric devices' (TEDs') performance when operated as thermoelectric generators (TEGs). Sixteen identical TEDs (TEC-12706 of the same made and model) were operated as TEGs under the same experimental setup and test modalities with a hotside temperature between ∼20°C and ∼100°C and a coldside temperature of ∼20°C, to practically and comparatively examine the TEGs voltage production and energy conversion in/efficiency. The findings revealed that, while it's already common knowledge that a TEG output voltage is proportional to its temperature difference as evident in all the TEGs used in the study; however, as the TEGs temperature difference proportionally increases, some of the TEGs relatively produced less, the same and more output voltages at certain temperature differences compared to others. For example, TEG1 and TEG3 produced almost the same output voltages throughout; however, at ∼100°C hotside temperature, TEG3 produced 3.16 V, whereas TEG1 produced 2.90 V. While such widespread TEGs discrepancies can be mostly attributed to bad manufacturing/poor workmanship, they are usually misconstrued by some as a generic inherent thermoelectricity technology limitations which this study highlights.

The proliferation of photovoltaic (PV) systems connected to low voltage (LV) distribution networks can have detrimental impacts on waveform distortion. This is caused by the power electronic interface, with voltage source converter (VSC) technology being by far the most prevalent. As such, proper emission models, which can account for the non-linear operation of VSCs used in PV systems have to be developed and investigated for the assessment of harmonic distortion in LV networks. This paper compares different frequency domain models (FDMs), specifically, methods based on frequency coupling matrices and an analytical method based on a harmonically coupled impedance matrix, for the type of single-phase PV systems typically found in LV distribution networks. Two case studies are presented to compare the models in terms of computational complexity and accuracy, with results showing that models accounting for the interaction between same order harmonics are sufficiently accurate.

An inverter-level analysis of a large photovoltaic (PV) plant is evaluated over four years to investigate the long-term performance and degradation caused by wind and temperature effects. The multi-megawatt utility-scale PV plant is located in a semi-arid region in South Africa. The degradation rate is determined using the performance ratio as a comparison metric between the different inverters. The degradation from the first year of operation up to the fourth year shows that different areas of the PV plant have varying degradation rates. The spatial degradation analysis indicates that inverter blocks operating at higher ambient temperatures and lower wind frequency measurements show higher rates of degradation compared to inverter groups operating at lower temperatures and higher wind frequency. Notably, the lowest degradation rates correlate positively with wind direction and frequency from the North and North-East, indicative of a cooling influence on the PV modules. The weather-corrected performance analysis indicates that the most prominent wind direction (North) has the highest mean performance ratio, further supporting the claims that the cooling effect of wind improves performance and efficiency. The widening gap between the best- and worst-performing inverters annually underscores the premise that specific inverters' PV modules degrade at disparate rates within the PV plant. The results of this work present future designs of PV plants that could potentially be optimised to take advantage of wind as a cooling tool, which may further enhance the longevity and sustainability of large PV installations.

Mixed potential theory is frequently employed for analysing large-disturbance stability. The precision of the current stability criterion for DC microgrids controlled by virtual DC motors (VDCM), which relies on mixed potential theory, is inadequate. This is primarily because the criterion does not account for the control parameters of the DC bus voltage control link and the virtual DC motor link. To address these issues, the paper initially formulates the control aspect of the VDCM using a controlled current source. Subsequently, it derives the system's mixed potential function from the model, enabling the development of a stability criterion that includes the control parameters of the DC bus voltage control link, the virtual DC motor link, and the current tracking link. It also clarifies how parameters not directly included in the criterion affect system stability. Through simulations and experimental validations, it is demonstrated that the proposed stability criterion effectively captures the impact of control parameters on system stability and precisely delineates the system's power stability boundary, offering insights for system parameter optimization.

In this article, a three-stage, two-level voltage regulation scheme based on receding horizon control (RHC) principles is proposed for active distribution networks. The distribution system consists of photovoltaic (PV) generators, electric vehicles (EVs), and electrical loads. Electric vehicle aggregators (EVA) act as intermediary between the prosumers and the network operators. The devised framework aims to coordinate various voltage control equipment according to their slow response (1^st stage) or fast (2^nd stage) response to voltage variations. In the 3^rd stage, the electric vehicle charge scheduling is accomplished and all these stages constitute to form upper-level operation. Further, an effort has been made to maximize the profit of EVA for performing ancillary services during EV charge scheduling. Moreover, EVAs incorporate demand response programs to enhance network stability. While 1^st and 2^nd stages are formulated as non-linear programming problems, the 3^rd stage is formulated as a mixed-integer non-linear problem. The problems are optimized using the CPLEX solver in the general algebraic modeling system (GAMS) environment. The lower control level is implemented by following a few rules that adjust the local Q(V) characteristics embedded in the power electronics interfaced devices. The obtained results exhibit that the devised framework not only helps in voltage regulation but also the EV owners and the EVA. Later, the profit earned by EVAs is computed for slow and fast charging schemes.

Impaired power quality is known to increase the power consumption of water electrolysis cells without affecting the hydrogen production rate. Owing to a lack of large-signal dynamic water electrolyzer models, simulations on the topic often consider only the static polarization curve omitting actual cell dynamics. This article aims to bridge the gap by experimentally studying the dynamic phenomena leading to additional power consumption of a polymer electrolyte membrane water electrolyzer cell using sinusoidal current ripple. The effect of ripple amplitude is analyzed with high-speed current and voltage waveform measurements, and the frequency dependence is determined using electrochemical impedance spectroscopy. The complex cell impedance is found to be the only parameter needed for determining the additional power consumption at frequencies above $��30��\mathrm{Hz}�$. This finding enables a simple prediction of additional power consumption for arbitrary current waveforms at frequencies relevant for water electrolyzer rectifiers. At frequencies below $��30��\mathrm{Hz}�$, the static polarization curve begins to influence the voltage waveform of the specific cell, thereby reducing the predictive power of the impedance model. The results prove the use of the static polarization curve is generally inaccurate for modeling water electrolysis power consumption with ripple current.

As a newly widely used energy source, hydrogen has not yet been integrated with other energies efficiently, leading to low energy efficiency and high operating costs. This paper develops a novel cooperation model to coordinate electricity, hydrogen, and district heating systems and reduce operating costs. First, the mathematical model of electricity, hydrogen, and district heating systems coupled by electrolysers is established, and the bidirectional heat exchange (BHE) between hydrogen production and district heating networks (DHNs) is proposed. Then, a dual-layer model predictive control (DLMPC) method is proposed for the integrated electricity, hydrogen, and heating microgrid (IEHHM) scheduling. The upper layer aims to deal with the hourly power variation and determine the IEHHM operation schedules, and the lower layer revises the power of electrolysers and combined heat and power (CHP) plants to follow the real-time power variation of renewable energy generations (REGs). Simulation results show that: (1) BHE improves the thermal dynamics of electrolysers and DHNs, enhancing operational flexibility; (2) The DLMPC method enables timely adjustments to the dispatch schedule, reducing operating costs by responding to multi-time-scale power variations from REGs.

To keep pace with the construction of the new-type power system, virtual synchronous generator control, as a classical method of virtual inertia control, has been widely adopted due to its electromechanical characteristics similar to synchronous generator. However, the introduction of rotor motion equations leads to low-frequency oscillation issues in virtual synchronous generator units similar to synchronous machines. To address this challenge, this paper constructs the Phillips-Heffron model of the virtual synchronous generator grid-connected system and analyses the mechanism of low-frequency oscillation in virtual synchronous generator through the damping torque method. Subsequently, a virtual dual-input power system stabilizer is proposed by drawing inspiration from the design principles of the traditional dual-input power system stabilizer to suppress low-frequency oscillations in the power system. The structure of the virtual dual-input power system stabilizer is provided, and the phase compensation method is used to optimize the parameters of the virtual dual-input power system stabilizer. Finally, the effectiveness of the proposed virtual dual-input power system stabilizer is verified by simulation comparison.

The employment of behind-the-meter solar photovoltaic (PV) systems has gained increasing popularity in recent years as more individuals and organizations aim to reduce their reliance on conventional grid-connected power sources and take advantage of the environmental and economic benefits of solar power. However, precisely estimating the potential output of PV systems is a challenging task, since most of the PV systems used in residential properties have been installed behind the meter. Consequently, electric power companies are limited to accessing only the recorded net electricity consumption. This article introduces an innovative approach to estimate behind-the-meter PV power generation within a large region, utilizing a limited representative subset. The proposed framework integrates Missforest, that is, a robust tool for missing data imputation, with a hybrid application of K-Means, Pearson Correlation Coefficient, and Principal Component Analysis, for the precise selection of representative PV sites. Additionally, it leverages the Informer model, a cutting-edge deep learning-based time series model, to link the relationship between the PV power generation at representative sites and the total PV power output on the entire region. To conduct a case study, the power output of 367 PV sites and solar radiation measured at 105 weather stations in Taiwan were collected and analyzed. The application of this comprehensive methodology demonstrates a notable advancement in the estimation of “invisible” PV power generation in comparison to other established techniques.