Renewable Energy Generation Systems Research Articles

A novel soft‐switching quadratic high voltage gain trans‐inverse DC/DC converter

AbstractThis paper suggests a new non‐isolated high step‐up DC/DC converter with soft‐switching performance and low input current ripple for renewable energy generation systems. In this introduced circuit, a three‐winding coupled‐inductor combined with a quadratic boost circuit and voltage multiplier cell to obtain high voltage gains. The advanced features of the proposed topology are its ultra‐high voltage gain, soft‐switching performance, low voltage stress across the switching components, continuous input current with low ripple, and the existence of a common ground between the input source and output load. Due to the partial trans‐inverse property in the suggested topology, higher voltage gains can be achieved under smaller turns ratios of the coupled in comparison to the other typical topologies, which leads to efficiency improvement. Moreover, in this introduced circuit, the voltage stress on the single power switch is mitigated using a regenerative clamp circuit. The theory and operating principle of the suggested converter, along with the steady‐state analysis and efficiency interpretation of the proposed converter are discussed in detail. Finally, to verify the theoretical analysis, a 200 W (25 V/400 V) sample prototype is established.

Reactive Power Optimization in Distribution Networks of New Power Systems Based on Multi-Objective Particle Swarm Optimization

The new power system effectively integrates a large number of distributed renewable energy sources, such as solar photovoltaic, wind energy, small hydropower, and biomass energy. This significantly reduces the reliance on fossil fuels and enhances the sustainability and environmental friendliness of energy supply. Compared to distribution networks in traditional power systems, the new-generation distribution network offers notable advantages in improving energy efficiency, reliability, environmental protection, and system flexibility, but it also faces a series of new challenges. These challenges include potential harmonic issues introduced by the widespread use of power electronic devices (such as inverters for renewable energy generation systems and electric vehicle charging stations) and the voltage fluctuations and flickering caused by the intermittency and uncertainty of renewable energy generation, which may affect the normal operation of electrical equipment. To address these challenges, this study proposes an optimization model aimed at minimizing network losses and voltage deviations, utilizing traditional capacitor adjustments and static var compensators (SVCs) as optimization measures. Furthermore, this study introduces an improved version of the multi-objective particle swarm optimization (MOPSO) algorithm, specifically enhanced to address the unique challenges of reactive power optimization in modern distribution networks. The test results show that this algorithm can effectively generate a large number of Pareto optimal solutions. The application of this algorithm on a 33-node network case study demonstrates its advantages in reactive power optimization. The optimization results highlight the effectiveness and feasibility of the proposed improved algorithm in the application of distribution network reactive power optimization, offering users a uniform and diverse range of reactive compensation solutions.

Analysis for the Implementation of Distributed Renewable Energy Generation Systems for Areas of High Vulnerability Due to Hillside Movements: Case Study of Marianza-Cuenca, Ecuador

This research presents a renewable energy system that takes advantage of the energy potential available in the territory. This study emerges as a relevant option to provide solutions to geological risk areas where there are buildings that, due to emergency situations at certain times of the year during deep winter, are a target of danger and where its inhabitants would find it difficult to abandon their properties. The record of mass movements covering the city of Cuenca-Ecuador and part of the province has shown that the main triggering factor of this type of movement comprises the geological characteristics of tertiary formations characterized by lithological components that become unstable in the presence of water and due to their slopes being pronounced. Hybrid systems are effective solutions in distributed electricity generation, especially when it comes to helping people and their buildings in times of great need and the required electricity generation is basic. A hybrid photovoltaic, wind and hydrokinetic system has been designed that supplies electrical energy to a specific area on the opposite geographical side that is completely safe. The renewable energy system is connected to the public electricity grid available on site; however, in the event of an emergency the grid is disconnected for safety and only the hybrid system will work with the support of a battery backup system. In this study, the Homer Pro simulation tool was used and its results indicate that renewable systems that include PV, HKT and WT elements are economically viable, with a COE of USD 0.89/kWh.

Modeling and Deep Reinforcement Learning Based Control Parameter Tuning for Voltage Source Converter in a Renewable Energy Generation System

Modeling and Deep Reinforcement Learning Based Control Parameter Tuning for Voltage Source Converter in a Renewable Energy Generation System

Analysis of Grid-tied Solar Photovoltaic Energy Generation under Uncertain Atmospheric Conditions Using Adaptive Neuro-fuzzy Control System

The grid-tied photovoltaic (PV) power system has remained the most practical and sustainable configuration among renewable energy generation systems. Although uncertainties persist in solar irradiance and temperature, the grid-tied system faces transient instability issues during maximum power point tracking, adversely affecting power quality and resulting in substantial costs. To overcome this issue, we proposed analyzing the grid-tied system under uncertain atmospheric conditions based on an adaptive neuro-fuzzy control system (ANCS). This control scheme incorporates a hybrid learning algorithm and undergoes evaluation across various operating conditions. The obtained results demonstrate the effectiveness of the learning algorithm in maintaining a fast convergence speed. Consequently, this capability ensures the consistent preservation of sufficient power quality in the power system without any discernible transient impact. Furthermore, the investigation reveals the significant impact of solar radiation and temperature on the performance of the solar grid-tied PV system. Specifically, temperature alone contributes to over 15% power reduction when reaching 45 °C. As the temperature decreases to 5 °C at 1000 W/m2 irradiance, the ANCS influences an increase in the system's power generation from 100.72 kW at 25 °C to 103.01 kW.

Proceeding of catalytic water splitting on Cu/Ce@g-C3N4 photocatalysts: an exceptional approach for sunlight-driven hydrogen generation.

Increasing energy demands and environmental problems require carbon-free and renewable energy generation systems. For this purpose, we have synthesized efficient photocatalysts (i.e., g-C3N4, Cu@g-C3N4, Ce@g-C3N4 and Cu/Ce@g-C3N4) for H2 evolution from water splitting. Their optical, structural and electrochemical properties were investigated by UV-Vis-DRS, PL, XRD, FTIR, Raman and EIS methods. Their surface morphologies were evaluated by AFM and SEM analyses. Their chemical characteristics, compositions and stability were assessed using XPS, EDX and TGA techniques. Photoreactions were performed in a quartz reactor (150 mL/Velp-UK), whereas hydrogen generation activities were monitored using a GC-TCD (Shimadzu-2014/Japan). The results depicted that Cu/Ce@g-C3N4 catalysts are the most active catalysts that deliver 23.94 mmol g-1 h-1 of H2. The higher rate of H2 evolution was attributed to the active synergism between Ce and Cu metals and the impact of surface plasmon electrons (SPEs) of Cu that were produced during the photoreaction. The rate of H2 production was optimized by controlling various factors, including the catalyst amount, light intensity, pH, and temperature of the reaction mixture. It has been concluded that the current study holds promise to replace the conventional and costly catalysts used for hydrogen generation technologies.

Multi-objective capacity optimization method for renewable energy generation systems based on artificial bee colony algorithm

Multi-objective capacity optimization method for renewable energy generation systems based on artificial bee colony algorithm

Multi-objective capacity optimisation method for renewable energy generation systems based on artificial bee colony algorithm

Multi-objective capacity optimisation method for renewable energy generation systems based on artificial bee colony algorithm

Iridium Oxide Nanorods Supported on Sb-Doped SnO2 As Highly Active Oxygen Evolution Catalysts for PEM Water Electrolysis

With the increasing installation of renewable energy generation systems such as solar and wind power, efficient energy storage devices are required to overcome the problem of fluctuations in the availability of electric energy. In particular, the splitting of water by electrolysis to produce storable hydrogen and oxygen appears to be one of the most promising approaches for large-scale energy storage. Proton exchange membrane water electrolysis (PEMWE) has received a great deal of attention due to its ability to respond quickly to renewable energy fluctuations and to operate at high current densities. However, a key limitation of PEMWE is the high cost associated with the use of high loadings of noble metal Ir catalysts at the anode electrode, which is necessary due to the slow kinetics of the oxygen evolution reaction (OER). Therefore, reducing the Ir loading at the OER electrode is essential to realize large-scale applications of PEMWE. This requires the development of highly active OER catalysts to accelerate the reaction or reduce the overpotential. Herein, we report a remarkably active OER catalyst of Ir oxide with nanorod-like structure supported on antimony-doped tin oxide (Sb-SnO2), showing an order of magnitude increase in Ir mass-specific activity than a commercial IrOx catalyst at 1.5 V vs. RHE. We also investigated the temperature dependence of the OER activity, as a way to elucidate the mechanism of enhanced catalysis, from both experimental and theoretical perspectives.The Sb-SnO2 support material (Sb dopant content 4 at.%) with fused-aggregate network structure was prepared by flame pyrolysis method.1 The as-prepared support powder was annealed at 700 oC for 2 h in air using a rotary kiln furnace. Sb-SnO2 supported IrOx nanorods (NRs) catalyst was synthesized by a colloidal method.2 The obtained IrOx NRs/Sb-SnO2 powder was heat-treated at 350 °C in N2 atmosphere for 2 h and at 150 °C in 1% H2 (N2 balance) for 2 h. The Ir loading was determined to be 49.3 wt.% by inductively coupled plasma optical emission spectrometry (ICP-OES). The OER activity was evaluated in 0.1 M HClO4 at 25 to 80 oC using the rotating disk electrode (RDE) technique.2,3 From Figure 1a, it can be seen that the Sb-SnO2 support particles present an interconnected network structure, which is considered to be beneficial to enhance the electron conduction and gas diffusion in the electrocatalytic reaction. On the support, the majority of IrOx nanoparticles are shown to have coalesced, forming rodlike structures. Figure 1b shows OER polarization curves (iR-corrected and normalized to the Ir mass) of the IrOx NRs/Sb-SnO2 catalyst compared to commercial IrOx catalyst (TANAKA Precious Metals). It is clear that the IrOx NRs/Sb-SnO2 catalyst significantly outperforms the IrOx reference catalyst over the entire potential range, and the OER onset potential of the IrOx NRs/Sb-SnO2 catalyst is lower. At 1.5 V (Figure 1c), the IrOx NRs/Sb-SnO2 catalyst exhibits a mass activity (MA) of 0.40 A mg-1 Ir, which is about 10 times higher than that of the IrOx reference catalyst. This dramatically enhanced activity makes it possible to significantly reduce the Ir loading on the anode of PEMWE. Experimental observations and density functional theory (DFT) calculations suggest that the nanorod geometry and strong metal-support interactions may play an important role in lowering the energy barrier for the crucial first step of the OER, thereby enhancing the OER activity. Acknowledgement This work was partly supported by the JSPS KAKENHI (23H02059) and by the project from the New Energy and Industrial Technology Development Organization (NEDO) of Japan.

Strategies for Low CO2-emission Schools in Norway

Regional and national strategies targeting climate change are a driver for reducing greenhouse gas emissions. Municipalities play an important role towards a sustainable society and in restructuring needed to reach the 2030 European Climate targets. They often require that new buildings shall be zero emission and that the building’s carbon footprint shall be reduced compared to a reference building, starting with public buildings. To reach low or zero emission buildings, the production of renewable energy must equal or outweigh the CO2-emissions associated with the building. The energy consumption of the building should be decreased before designing on-site renewable energy generation systems. This is ensured by following a holistic energy design approach: energy conservation, reducing electrical energy, efficient energy management and producing renewable energy. Although Norway has had a focus on low and zero emission buildings, there is a limited number of low CO2-emission schools. These are designed and constructed by various actors. Therefore, the practical experience and knowledge in the industry is scattered and still in development. This paper aims to summarize practical strategies for low CO2-emission schools in Norway. Several case studies were analyzed to identify common traits and interviews were conducted to identify opportunities, challenges and strategies.

Real-time inspection and fault detection for large photovoltaic arrays based on drones and deep learning algorithms

In recent years, the installation of renewable energy generation systems based on photovoltaic (PV) panels has experienced massive increments and PV parks with thousands of panels are now becoming commonplace. Yet, there are some challenges, like inspection and fault detection. Lately, these operations have been approached using drones. This project adds the use of deep learning, more specifically proposes the convolutional neural network (CNN) algorithm, the YOLOv5 model and Real-Time Messaging Protocol (RTMP) protocol to achieve real-time detection of PV panels failures. The YOLOv5 model was trained by sets sorted into 9 different categories including fault and abnormal objects’ coverage. This multi-class classification system was investigated by a variety of evaluation indexes to show effectiveness and accuracy. The system was also examined with its different fault classes. The performance results demonstrate that the mean average precision could reach up to 98% with a good training set, confirming the feasibility of proposed approaches.

Development of Energy Storage Systems for High Penetration of Renewable Energy Grids

As the proportion of renewable energy generation systems increases, traditional power generation facilities begin to face challenges, such as reduced output power and having the power turned off. The challenges are causing changes in the structure of the power system. Renewable energy sources, mainly wind and solar energy cannot provide stable inertia and frequency regulation capability. Ultimately, the power system’s emergency response capability to face an N-1 is reduced, which leads to a reduction in system stability. Therefore, the application technology of the battery energy storage system is used to support the impact of changes in the new power system structure. This paper designed control technologies based on the WECC second-generation generic model, namely, dynamic regulation, steady regulation, and virtual inertia regulation. The models and control strategies are verified on Taiwan’s 2025 power system target conditions, which consider the expected capacities for battery energy storage systems, and renewable energy sources with different load and N-1 fault levels. According to the simulation results, the capabilities of the RoCoF limitation, frequency nadir, frequency recovery, and system oscillation regulation are evaluated in the proposed strategies. Finally, the analysis results can help power operators make informed decisions when selecting and deploying battery energy storage systems.

Optimal Allocation Strategy of Virtual Inertia and Damping for Improving the Small-signal Stability

The application of the virtual synchronous generator (VSG) control technology to engage the inertia response of renewable energy generation systems is an effective means of coping with the problem of weak inertia in power systems. To deal with the problem of small-signal stability in the weak inertia power system, this paper studies how the strength of virtual inertia and damping affect the small-signal stability, and proposes an optimal allocation strategy of virtual inertia and damping for improving the small-signal stability. The optimal allocation model of virtual inertia is constructed to minimize the energy imbalance of the system under small-signal by selecting the virtual inertia and damping as optimization variables. Regarding the aforementioned model, the evolutionary algorithm is used to obtain the optimal allocation of virtual inertia and damping. Simulations on a virtual synchronized microgrid system show the effectiveness of the proposed strategy in improving small-signal stability.

A Control Design Technology of Isolated Bidirectional LLC Resonant Converter for Energy Storage System in DC Microgrid Applications

This paper presents a new control method for a bidirectional DC–DC LLC resonant topology converter. The proposed converter can be applied to power the conversion between an energy storage system and a DC bus in a DC microgrid or bidirectional power flow conversion between vehicle-to-grid (V2G) behavior and grid-to-vehicle (G2V) behavior. Furthermore, such a converter can be applied to energy storage systems for decentralized renewable energy generation systems, such as solar and wind power. In addition, this converter can be combined with a bidirectional inverter to allow energy storage in the system to improve the safety, stability, and power quality of the microgrid. In the proposed circuit structure, we use a bidirectional DC–DC LLC, which has the advantages of a higher voltage conversion ratio, lower part count, simpler control than similar converters such as DAB, CLLC, and L–LLC converters, and bidirectional power flow and electrical isolation. Specifically, to extend the battery life, it can be employed as a control strategy for discharging the energy stored in the battery (SOC) and reducing the temperature rise generated by the internal solid electrolyte interphase (SEI) when discharging the battery under the variation in distributed energy resource (DER) generation and load demand. To realize the bidirectional power conversion without using any auxiliary inductor and only changing the control strategy, the forward step-down power conversion was based on pulse frequency modulation (PFM) control, and the reverse step-up power conversion was based on pulse width modulation (PWM) control. In this paper, we introduce the bidirectional converter topology and its control strategy for the DC microgrid battery energy storage system. Finally, a 500 W prototype is built to verify the effectiveness of the proposed converter.

Performance investigation of tempered glass based photovoltaic panel integrated with back cooling hollow chamber

ABSTRACT At present, the whole world is facing a huge fuel crisis and those who adopt the extraction of energy from renewable sources are getting much benefit both environmentally and economically. Photovoltaic systems are the most mature and scaled renewable energy generation systems worldwide. However, the optimal condition for maximizing the output from Photovoltaic (PV) panels is characterized by low temperatures and high irradiation levels. With the increasing temperature and irradiation, the solar panel becomes warmer and thus the efficiency drops. Hence, by cooling the PV panels, the efficiency can be increase. Various thermal collector designs and different water flowing methods (Top cooling & Back cooling) has been employed to cool the panels. In this research study, an experiment of building a new PVT panel attached to a hollow chamber (Thermal Collector) at the back of the panel is carried out. In hollow chamber the warm water was kept inside and release that when the water was hot enough. This experiment was conducted in various outdoor conditions. On average, the electrical efficiency was measured 8.7 to 9.9% higher than the simple PV system. Maximum overall efficiency was recorded as 76%. The experiment also figured out due to low irradiation in cloudy weather the output power drops significantly.

Optimal location selection for a distributed hybrid renewable energy system in rural Western Australia: A data mining approach

The adverse effects of coal and gas energy production with the subsequent rapid increase in energy consumption emphasize the importance for Australia to adopt more renewable energy sources to counteract these dismissive contributions to climate change. This work presents a data mining approach for optimally selecting the best locations for installing a distributed hybrid renewable energy generation system for rural regions in Western Australia. The K-Means and K-Medoids clustering algorithms were used to divide the constructed dataset into clusters. In total, 69 locations were selected for the overall dataset, proceeding with the filtering process. The returned cluster data were graphically rendered on a Western Australia map for the region. The clustering algorithms were evaluated using the Dunn index, such that K-Means performed to a higher degree than K-Medoids, given our dataset's nature. After passing the generated clusters to HOMER software to generate the potential wind and solar energy output for each centroid, K-Medoids produced a set of locations that generated higher solar and wind energy on average. However, due to the reduced internal validation, K-Medoids might not be as valuable as K-Means, it does not cluster the data points very well, and within-cluster location energy requirements are not considered in our study.

Semi-supervised adversarial discriminative learning approach for intelligent fault diagnosis of wind turbine

Wind turbines play a crucial role in renewable energy generation systems and are frequently exposed to challenging operational environments. Monitoring and diagnosing potential faults during their operation is essential for improving reliability and reducing maintenance costs. Supervised learning using data-driven techniques, particularly deep learning, offers a viable approach for developing fault diagnosis models. However, a significant challenge in practical wind power equipment lies in the scarcity of annotated samples required to train these models effectively. This paper proposes a semi-supervised fault diagnosis approach specifically designed for wind turbines, aiming to address this challenge. Initially, a semi-supervised deep neural network is constructed using adversarial learning, where a limited set of annotated samples is used in conjunction with a vast amount of unannotated samples. The health status features present in the unannotated samples are leveraged to capture a generalized representation of the underlying features. Subsequently, a metric learning-guided discriminative features enhancement technique is employed to improve the separability of different manifolds, thereby enhancing the performance of the semi-supervised training process. By employing this methodology, it becomes possible to develop a fault diagnosis model with superior accuracy using only a limited amount of annotated samples. Comprehensive fault diagnosis experiments were conducted on a wind turbine fault dataset, revealing the efficacy and superiority of the presented methodology.

A Novel Three-Winding Coupled Inductor-Based High Step-Up DC-DC Converter for Renewable Energy Application

This paper presents a novel nonisolated high step-up dc-dc converter for renewable energy generation system. The converter combines a three-winding coupled inductor, voltage multiplier cells, and two power switches with simultaneous operation. Compared with existing high step-up converters, the proposed converter can achieve high voltage gain under a low number of turns ratio while avoiding a large duty cycle. Moreover, the passive clamp circuit recycles leakage inductor energy and reduces the voltage spike of switches. Continuity of input current and existence of a common ground between the input energy source and load make the converter suitable for renewable energy applications. Also, the proposed converter shares high voltage gain with low voltage stress on semiconductors (switches/diodes), improved compactness and high efficiency. The operating principles and steady-state analysis of the proposed converter are illustrated. Then the theoretical efficiency analysis, and performance comparison with other converters are discussed. Finally, a 200 W prototype with 380 V output voltage is established based on the design guidelines, and experimental results verify the accuracy of the theoretical analysis.

Design and validation of a multilevel voltage source inverter based on modular H-bridge cells

Generation, power conversion and subsequent integration of renewable energy generation systems, such as solar photovoltaic or wind, require an efficient power conversion system that can provide sufficient quality energy according to technical standards (e.g. IEEE 519–2022). In this context, this paper focuses on the analysis, design and experimental validation of a multilevel voltage source inverter (VSI) scheme based on H-bridge cells with a modular and scalable structure for its application in power electronic converter circuits. The designed and assembled experimental setup is a versatile platform for testing experimentally varied control strategies and power converter configurations, such as the number of levels (3, 5, 7 levels) and phases (single-phase or three-phase). Therefore, the hardware design process proposed for the H-bridge cell and the measurement and conditioning circuits for voltage and current signals necessary for implementing the control algorithms are explained in detail. Moreover, a quantitative analysis of the operation of the design was carried out from measurements made with the experimental platform to verify its correct operation. Among the analysed parameters, the generated harmonics level stands out, quantified by calculating the total harmonic distortion and the mean square error between the reference signals and the measured values.

A Multicriteria Optimal Operation Framework for a Data Center Microgrid Considering Renewable Energy and Waste Heat Recovery: Use of Balanced Decision Making

The rapid development of data centers (DCS) leads to a huge challenge in their energy consumption and environmental impact. It is promising to establish DC microgrids (DCMGs) for solving these issues, considering utilizing renewable energy (RE) generation and waste heat recovery systems. However, the efficient energy management of a DCMG is a topic to be pursued. In this article, we propose a multicriteria optimal operation framework for the DCMG by coordinatively scheduling the energy supply and demand. In this framework, multiple criteria are comprehensively considered via a multicriteria optimization (MCO) approach. Then, we adopt an augmented constraint algorithm to address the corresponding MCO problem. Afterward, a balanced decision-making (BDM) method is further proposed to determine the optimal scheduling solution. Finally, a case study and results analysis verify the effectiveness of the proposed multicriteria optimal operation framework for the DCMG.