IET Renewable Power Generation最新文献

The increasing complexity of modern DC microgrids, driven by the integration of renewable energy and storage units, makes their analysis and simulation both challenging and time-consuming. Dynamic equivalencing has emerged as an effective solution to reduce model complexity, accelerate simulations and improve understanding of system dynamics. This paper presents a circuit-based equivalent model for a grid-connected DC microgrid using a grey-box approach that bridges physical modelling and data-driven identification. The proposed model is operating-point independent and capable of accurately reproducing both small- and large-signal dynamics. It preserves the original circuit structure, while its parameters are identified from measurement data at the point of common coupling and MATLAB/Simulink simulations. Parameter estimation is carried out under diverse scenarios — including load variations, solar irradiance fluctuations and short-circuit faults — using a genetic algorithm for optimal identification. The studied microgrid comprises multiple photovoltaic units, battery storage systems and resistive loads; in the equivalent model, one representative unit of each type is employed with a scaled capacity. The proposed model is also benchmarked against a conventional black-box model. Validation results show that the proposed grey-box equivalent can faithfully reproduce the dynamic behaviour of the detailed microgrid, achieving an R² index above 90% across all scenarios, demonstrating its suitability for future control and operational studies. This makes the proposed equivalent particularly useful for controller design, stability assessment and real-time simulation of DC microgrids.

State estimation (SE) is a crucial tool for the secure operation of transmission systems, which are susceptible to false data injection attacks (FDIAs) that can bypass the bad data detection mechanism. The increasing penetration and distribution of renewable energy sources (RESs) reduce the predictability of normal grid behaviour, thereby weakening the performance of conventional detection methods by providing the opportunity for stealthy attacks. This paper presents a new FDIA detection method based on hybrid machine learning (HML), leveraging soft and hard clustering before the classification-based anomaly detection. It effectively distinguishes between attack and normal samples in grids with various types (wind and solar), distributions, and penetration levels of RESs. The suggested feature engineering enables clustering, using a combination of fuzzy C-means (FCM) and K-means, to better organise the database so that a desirable performance can be achieved by the random forest (RF)-based classifier. The model is comprehensively tested on the IEEE 14-bus system under 22 RES scenarios, showing robust accuracy across diverse grid conditions. Additionally, the scalability of the method is validated on the IEEE 118-bus system. The method outperforms recent approaches with average detection accuracies of 99.66% and 99.04% on the small and large systems, respectively.

This paper explores an innovative control strategy for photovoltaic-based shunt active power filters (PV-SAPF), aiming to enhance power quality, support load supply, and inject low-harmonic current into the grid. Unlike existing approaches that rely solely on MPPT-controlled inverters for optimal PV performance, this method integrates an optimal operating point tracking (OOPT) algorithm with a harmonic identification method (HIM), enabling comprehensive system functionality. The control scheme employs a HIM modules to generate reference currents, facilitating harmonic mitigation, reactive power compensation, and load support under varying energy production conditions. MATLAB/Simulink simulations demonstrate the effectiveness of this approach in maintaining grid stability during both energy surplus and deficit scenarios. When energy exceeds load demand, the PV-SAPF supplies the load and injects less than 1% THD into the grid; during underproduction, it partially supplies the load and mitigates harmonics. The results adhere to IEEE Std 519-2022 standards, showcasing the system's ability to improve power quality and grid harmony. Notably, this strategy differs from previous methods by removing the need for MPPT-controlled converters and allowing compatibility with any harmonic identification technique, thus offering a versatile and efficient solution for integrating PV systems into the power grid.

Aiming at the problems of frequency fluctuation caused by load changes and low utilisation rate of renewable energy during the operation of isolated microgrids, a flexible power allocation strategy based on model predictive control (MPC) in which photovoltaic (PV) and energy storage (ES) jointly participate in frequency regulation is proposed. Taking the minimum frequency deviation and the highest energy utilisation as indexes, the optimisation objective function in the rolling time domain was constructed, and the weights of photovoltaic and energy storage output terms in the objective function were adjusted in real-time to optimise their respective outputs. A fuzzy adaptive controller (FAPPT) is designed, which makes the photovoltaic array run in the fuzzy adaptive power tracking mode with power reserve adjustable up and down flexibly; and realises bidirectional regulation of microgrid frequency by adaptive increase or decrease of power reserve. Compared with the simulation of traditional MPPT and FAPPT control, it is verified that the proposed strategy makes the system frequency more stable, improves the energy utilisation rate, and has the characteristics of low dependence on model parameters.

Over recent decades, solar photovoltaic (PV) technologies have transformed the energy market, becoming a cornerstone of renewable energy systems. Ensuring the reliability of critical components within PV systems is essential to maximise their lifespan and minimise unexpected failures and downtime. Predictive maintenance, which leverages equipment condition modelling to anticipate faults and schedule maintenance, has emerged as a promising approach. However, forecasting PV equipment faults remains complex due to the indirect measurability of equipment status and the susceptibility of systems to various adverse conditions that may compromise system performance. In response to these challenges, there has been growing research interest in developing predictive analytics tools to optimise operational management. This study provides a comprehensive review of PV system design, key components, operation, different faults and maintenance strategies. Furthermore, it conducts a comparative analysis of artificial intelligence, including machine learning and deep learning models, to evaluate their performance in predictive maintenance applications for PV systems. This article reviews and analyses both established and emerging techniques used in PV systems, with particular emphasis on their effectiveness in addressing predictive maintenance. Its findings aim to inform the development of advanced fault prediction methods to improve the reliability and efficiency of solar PV systems. In addition, the paper serves as a valuable reference for researchers in this field, offering a clear overview of current approaches. It also identifies the main challenges and outlines key recommendations for future research directions, helping to guide innovation and progress in PV system maintenance and performance.

To address the slow response and large errors of traditional sliding mode controllers when integrating hydrogen fuel cells into microgrids, a control strategy based on the RBF-improved ultra-spiral sliding mode algorithm is proposed. In this scheme, a dual closed-loop PI control method is used to ensure the quality of the output voltage from the boost converter. In the main circuit, PQ control is applied to obtain the desired current, which is then fed into the ultra-spiral sliding mode controller along with the actual output current of the circuit. To overcome the difficulty in selecting sliding mode control parameters, the RBF algorithm is used to fit appropriate parameters. The effectiveness of the proposed control scheme is verified through MATLAB/Simulink simulations. Simulation results show that the proposed control scheme outperforms traditional PI control.

With the rapid integration of renewable energy sources, particularly wind and solar power, grid-connected converters have become essential interfaces between renewable energy systems and the power grid. Among the various control strategies, grid-following (GFL) and grid-forming (GFM) controls are commonly used, with GFM converter playing a crucial role in enhancing grid stability. However, traditional GFM converter often rely on linear control methods, which struggle with nonlinear grid dynamics and transient faults. To address these challenges, passivity-based control (PBC) has emerged as a promising solution for improving system stability. This paper proposes a novel passivity-based control strategy for GFM converter, incorporating the impact of the control delays. Using the port-controlled Hamiltonian (PCH) model, we design a feedback controller based on interconnection and damping assignment passivity-based control (IDA-PBC), ensuring system stability. Additionally, a frequency-domain D-partition method is introduced to derive the stability region and boundary of the controller under time delays, providing clear tuning criteria. The proposed strategy offers an improved approach for large-scale renewable energy integration, enhancing converter stability and performance. The results contribute to advancing passivity-based control theory and its practical application in renewable energy systems.

With the expansion of renewable energy sources (RES), green power trading has garnered wide attention. Serving as a medium- and long-term trading (MLT), the green power trading acquiesces prior determination for contract energy and price. Green power trading can prevent market fluctuations and promote the consumption of RES. However, the green power trading is insufficient in reflecting the real-time value of RES and intractable in addressing the fluctuation of RES in the long period. Conversely, the spot market induces time-varying prices reflecting the real-time value of RES. The generation and consumption schedules can be adjusted flexibly to tackle the uncertainty of RES. Thus, it is necessary to develop effective measures to coordinate the spot market and the green power trading market. However, the integration of these two markets could cause settlement issues, requiring the coupling of electrical energy in the green power trading with electrical power in the spot time scale. Besides, the uncertainty of RES exhibits significant differences across monthly and daily timescales. Considering these, a two-stage rolling decomposition method for green power contracts (GPCs) considering the uncertainty and environmental value of RES is proposed in this paper. Firstly, the decomposition method is proposed, encompassing the preliminary allocation of monthly GPC and an optimal decomposition of daily GPC. Subsequently, the corresponding indices are established for quantitatively evaluating the electrical and environmental value of the proposed method. With the spot market clearing model, the evaluation indices are constructed from four aspects: contract execution rate, load-side green certificate issuance, generation-side revenue, and load-side cost. Finally, numerical simulations are carried out using the modified IEEE-39 test system to validate the effectiveness of the proposed method. The results show that the proposed method ensures the contract execution while increasing load-side green certificate issuance by 10.7% and generation-side revenue by 8.1% and decreasing load-side cost by 2.1%.

With the continuous development of the economy and society, the global energy demand and climate issues have become increasingly serious. As a result, the low-carbon economic optimization of comprehensive energy systems, considering multi-energy complementarity, has emerged as a critical research area. This paper presents a low-carbon economic scheduling model for a combined heat and power virtual power plant (CHP-VPP), incorporating two-stage flexible power-to-gas and carbon capture. Concentrating solar power equipment is used to realize the thermoelectric decoupling of the traditional virtual power plant, which promotes the improvement of solar energy utilization efficiency. The introduction of independent power-to-hydrogen production and methanation reactions, combined with hydrogen fuel cells and storage systems, enables the multi-path utilization of hydrogen energy. Vacuum pressure swing adsorption equipment is used to meet the requirements of the oxygen load. Moreover, a heat and power demand response mechanism is developed, whereby replaceable loads that do not affect user comfort can be adjusted based on the responsive power load. This allows for energy coupling and substitution to be achieved. The tiered carbon trading mechanism, in combination with carbon capture equipment, has been implemented with the objective of achieving low carbon emissions and optimal economic benefits within the park. The cases verify the dual advantages of the proposed model, demonstrating its capacity for energy saving and multi-purpose operation, as well as its suitability for low-carbon economy operation.

Recently, the energy management of DC microgrids, which consist of renewable energy sources, energy storage devices, and distributed loads, has been an important challenge. The challenge is effectively coordinating the dynamic behaviors of renewable energy sources such as solar photovoltaic (PV) system, hybrid energy storage system (HESS) including a battery and a supercapacitor, and loads simultaneously. This system incorporates a DC-DC boost converter (BC) on the PV side and bidirectional DC-DC converters (BDCs) on the energy storage side. While BC is used to maximize power utilization from the solar PV using maximum power point tracking method, BDCs, which are separately connected to the battery and supercapacitor, are employed for energy management to manage the DC-link voltage level by controlling the current of the energy storage devices. For this challenge, a bidirectional quasi-Z-source DC-DC converter (BQZSDC) provides more advantages compared to conventional BDC topologies. In this context, a novel energy management control scheme based on a fuzzy logic approach using the BQZSDC is proposed in this study, which aims to enhance the operational performance of a solar PV-integrated HESS for DC microgrid applications. A primary aim of the proposed control scheme is DC-link voltage stabilization under different factors such as solar irradiance changes, DC-link voltage variations and dynamic load change, which is crucial for maintaining the system performance. Battery and supercapacitor current coordination are also enabled by the proposed control approach. Through case studies, the proposed control approach is compared with the conventional controller to assess its superior performance. In terms of time-domain specifications, it eliminates oscillations, reduces settling time, and minimizes undershoot/overshoot, thereby ensuring satisfactory transient responsiveness and enhancing operational performance under various operating conditions.