Large-scale Photovoltaic Systems Research Articles

Enhancing Low Voltage Ride-Through Resilience using Empowering Solar Energy Strategies

The rapid advancement of Low Voltage Ride-Through (LVRT) technology has been highlighted by the exponential rise of photovoltaic (PV) generation systems. An extensive analysis of LVRT methods specifically designed for PV generating systems is presented in this research. Important topics like main causes, current control schemes, power balance and overall solutions are the main focus of the review. Moreover, it provides comprehensive insights and elaborates on integrated solutions intended for large-scale PV systems. The objective of this research is to improve the resilience and reliability of renewable energy sources, especially solar photovoltaic systems, against grid disruptions and voltage changes by investigating LVRT approaches in solar power systems. Enhancing the integration of solar power systems into current electrical grids, reducing downtime, and optimizing system performance are the objectives in order to accelerate the shift to a more resilient and sustainable energy infrastructure.

Optimal Power Flow with Integrated Large Scale PV Systems: Case of the Algerian solar field

Optimal Power Flow with Integrated Large Scale PV Systems: Case of the Algerian solar field

Design and operational control methodology for large-scale photovoltaic system in green port application

ABSTRACT Due to the complex-shading and ununiform-corrosion problems caused by the oceanic climate, the working conditions of photovoltaic (PV) system in port are poor. In this study, some effective PV topologies are developed and compared under typical oceanic conditions. In order to improve the output of port PV system, a novel maximum power point tracking (MPPT) method is developed, in which the convolutional neural network (CNN) and bidirectional long short time memory network (BiLSTM) are introduced to forecast the real-time optimal operation point. Finally, the developed topology and MPPT method are verified by a comprehensive set of case studies. Experimental results show that the total-cross-tied (TCT) and gradient (GT) topologies can achieve the best performance. However, by considering the complex structure and higher failure rate due to the large number of joints in TCT topology, the PV arrays installed on the roofs, in the free space of green belt, freight yard and near the seaside are suggested to be connected in GT topology to balance operation efficiency, failure rate and maintenance costs. Moreover, the developed CNN-BiLSTM-based MPPT method and PV topology can effectively work together, and the average forecasting error is just 3.482%, which outperforms the compared state-of-the-art methods significantly.

LCL-T Module Reduction of Offshore Photovoltaic Power Generation System Based on Current Feedback-routh Approximation Method

The rapid development of offshore photovoltaic power generation has attracted more and more attention. Aiming at the problem of poor output power quality caused by the randomness and intermittentness of offshore photovoltaic power generation, LCL filters and isolation transformers are widely used on the AC side of photovoltaic power generation systems to improve the power quality of Grid-connected. Although this method has a good suppression effect on harmonics and DC components, its higher order will result in a larger amount of calculation, especially in the simulation of large-scale photovoltaic grid-connected systems, the operating speed will be too slow. In order to increase the simulation rate while ensuring the quality of the output power, the high order LCL-T (LCL filter and isolation transformer) module of the photovoltaic power generation system is reduced in order. When the transformer is not saturated, the LCL-T module reduction method based on the weighted current feedback method is adopted, and the purpose of simplification is achieved through the zero-pole cancellation of the transfer function; When the transformer is saturated, the LCL-T module reduction method based on the Routh approximation method is adopted, Use advanced mathematics formulas to reduce the order of high-order system equivalent processing. The PSCAD/EMTDC simulation software verifies the off-grid characteristics and simulation rate of the reduced-order model.

Integration and performance analysis of optimal large-scale hybrid PV and pump hydro storage system based upon floating PV for practical application

The widespread use of green energy sources creates a significant demand for energy storage. Hybrid floating photovoltaic (FPV) and pumped hydro storage (PHS) represent one of the most dependable and cost-effective solutions, which uses the PV system on the water body combined with a pair of lakes with different heights. This study focuses on the load side as well as the PHS capacity factor and aims to lower the cost of energy (COE) by raising the PHS capacity factor. Since building a PHS requires a significant upfront investment, doing so will also lower the COE and enable the acquisition of PHS for large-scale power production, which will guarantee power access in large cities. To simulate the FPV-PHS system, the multi-objective genetic algorithm (MOGA) is employed. Sufficient power management is a prerequisite for achieving system reliability in the best possible hybrid energy system design and implementation. Considering the 60-year system lifespan, the net present cost (NPC) analysis shows that, out of all the communities evaluated, the FPV-PHS system has the lowest NPC and COE. For the optimal configuration (FPV (Block A) 105 MW, PHS 80 MW, FPV (Block B) 357 MW, and the current hydropower plant), the expected NPC and energy costs for implementing the hybrid energy system (HRS) at the chosen location are $44,737,613 and $40/MWh, respectively. The existing FPV system spans 4.65 km2 and lowers the evaporation fraction by 17,279,400 m3. The hybrid FPV-PHS system reduces annual CO2 emissions by 581,830 tons. Our research reveals that a hybrid floating PV and pump storage hydropower system offers more steady clean electricity, implying a great significance for power grid infrastructure.

A Novel Circuit Configuration for the Integration of Modular Multilevel Converter With Large-Scale Grid-Connected PV Systems

A Novel Circuit Configuration for the Integration of Modular Multilevel Converter With Large-Scale Grid-Connected PV Systems

Integration method of large-scale photovoltaic system in distribution network based on improved multi-objective TLBO algorithm

The increasing integration of distributed generations brings great challenges to the power grid. In this paper, a distributed photovoltaic (PV) integration methodology in distribution network is established for large-scale PV penetration. Firstly, a PV integration model was formulated with the aim of maximizing PV integration capacity and enhancing the voltage profile. Specially, the PV large-scale integration model for county-wide promotion is proposed by considering various typical integration scenarios. Additionally, a novel improved multi-objective Teaching-Learning based optimization (TLBO) algorithm, namely, IM-TLBO, was proposed to seek an optimal Pareto front of the PV integration model. The IM-TLBO algorithm innovatively incorporates the elite reverse learning search strategy to enhance exploration in the solution space. Moreover, the differentiated teaching guided by optimal individual and central location is employed to improve the efficiency of the “teaching” process. Meanwhile, a cyclic crowded sort deletion based on crowding distance is developed to enhance the diversity of elite individuals and the distribution characteristics of the Pareto Frontier. Finally, the performance of IM-TLBO is tested in benchmark functions. Also, a simulation case in IEEE 33 bus system is performed to verify the proposed PV integration method. It is observed that the proposed method in this paper can not only realize the overall optimal integration of roof distributed PV, but also improve voltage profile. The results of IM-TLBO are compared to other classical algorithms, and it is shown that IM-TLBO outperformed them in terms of convergence, distribution and diversity.

Internal Power Balancing of an MMC-Based Large-Scale PV System under Unbalanced Voltage Sags

Internal Power Balancing of an MMC-Based Large-Scale PV System under Unbalanced Voltage Sags

A New Low-Cost Internet of Things-Based Monitoring System Design for Stand-Alone Solar Photovoltaic Plant and Power Estimation

The increasing demand for solar photovoltaic systems that generate electricity from sunlight stems from their clean and renewable nature. These systems are often deployed in remote areas far from urban centers, making the remote monitoring and early prediction of potential issues in these systems significant areas of research. The objective here is to identify maintenance requirements early and predict potential problems within the system. In this study, a cost-effective Internet of Things-based remote monitoring system for solar photovoltaic energy systems is presented, along with a machine learning-based photovoltaic power estimator. An Internet of Things-compatible data logger developed for this system gathers critical data from the photovoltaic system and transmits them to a server. Real-time visualization of these data is facilitated through web and mobile monitoring interfaces. The measured data encompass current, voltage, and temperature information originating from the photovoltaic generator and battery, alongside environmental parameters such as temperature, radiation, humidity, and pressure. Subsequently, these acquired data are employed for photovoltaic power estimation using machine learning techniques. This enables the estimation of potential issues within the photovoltaic system. In the event of a problem occurring within the photovoltaic system, users are alerted through a mobile application. Early detection and intervention assist in preventing power loss and damage to system components. When evaluating the results according to performance assessment criteria, it was observed that the random forests algorithm yielded the best results with an accuracy rate of 87% among the machine learning methods such as linear regression, support vector machine, decision trees, random forests, and k-nearest neighbor. When prediction models using other algorithms were ranked in terms of success, decision trees exhibited an accuracy rate of 81%, k-nearest neighbor achieved 79%, support vector machine reached 67%, and linear regression achieved 64% accuracy. In conclusion, the developed monitoring and estimation system, when integrated with web and mobile interfaces, has been demonstrated to be suitable for large-scale photovoltaic energy systems.

Assessment of Floating Photovoltaic (FPV) Systems as an Alternative Electricity Generation Source: A Case Study from Sudan

The deployment of large-scale photovoltaic (PV) systems has gained global attention due to technological advancements and cost reductions. Floating PV (FPV) is becoming a preferred alternative, offering efficiency and environmental benefits. It can be installed on dams lakes and reservoirs, generating clean energy without competing for land use. Despite the promising prospects of floating PV, there has been a lack of analysis on its implementation in Sudan. To fill this gap, the present study delves into the feasibility and potentiality of FPV at Merowe Dam and Khashm El Girba Dam in Sudan, providing crucial insights for the first time. The developed systems were evaluated using PVsyst. The results show that investing in FPV systems on dam reservoirs significantly improves power system reliability while generating additional clean energy, meeting the rising demand, and reducing carbon emissions. This highlights the potential of FPV systems as a promising solution for sustainable energy growth, mitigating environmental concerns, and ensuring a greener future.

An experimental investigation on coalescing the potentiality of PCM, fins and water to achieve sturdy cooling effect on PV panels

Improving the efficiency of silicon photovoltaic (PV) panels is of utmost importance to achieve Sustainable Development Goal 7. The energy loss aided by the heat generation of PV panel is the leading cause of the degradation of PV panels' efficiency as well as its longevity. A novel cooling system with PCM, fins and still water is propounded in this research as a solution. The proposed configuration utilizes the latent heat of fusion of PCM and the latent heat of evaporation of water to achieve effective cooling in a passive approach. HS 29 PCM is chosen in context with the location and system thermal characteristics. The results highlight that the per day output of the proposed configuration is 8.12% and 9.39% higher than the PV panel without cooling for the observed two selected days respectively. The maximum power enhancement was 20.25% and on average 8.57% improvement is obtained relative to reference PV. The thermal characteristics revealed that the temperature of the cooled panel was below 50 °C while the PV panel without cooling reached about 73 °C. The average and maximum temperature drops are observed to be 10.14 °C and 16.7 °C respectively. The maximum enhancement in power conversion efficiency was 20.13% when compared to the uncooled PV panel. Furthermore, corrosion analysis and thermal stress assessment are performed to ensure the longevity of the system. The energy saving is scaled to a 1 MW large-scale PV system and the results show ∼367 MWh of annual energy savings and ∼ 2 ktoe of reduction in CO2 emissions. The inclusion of a cooling strategy significantly improves the performance of the PV panel and contributes further in attaining SDG 7.

Structural Response Analysis for Multi-Linked Floating Offshore Structure Based on Fluid–Structure Coupled Analysis

Recently, offshore structures for eco-friendly energy, such as wind and solar power, have been developed to address the problem of insufficient land space; in the case of energy generation, they are designed on a considerable scale. Therefore, the scalability of offshore structures is crucial. The Korea Research Institute of Ships & Ocean Engineering (KRISO) developed multi-linked floating offshore structures composed of floating bodies and connection beams for floating photovoltaic systems. Large-scale floating photovoltaic systems are mainly designed in a manner that expands through the connection between modules and demonstrates a difference in structural response with connection conditions. A fluid–structure coupled analysis was performed for the multi-linked floating offshore structures. First, the wave load acting on the multi-linked offshore floating structures was calculated through wave load analysis for various wave load conditions. The response amplitude operators (RAOs) for the motions and structural response of the unit structure were calculated by performing finite element analysis. The effects of connection conditions were analyzed through comparative studies of RAOs and the response’s maximum magnitude and occurrence location. Hence, comparing the cases of a hinge connection affecting heave and pitch motions and a fixed connection, the maximum bending stress of the structure decreased by approximately 2.5 times, while the mooring tension increased by approximately 20%, confirmed to be the largest change in bending stress and mooring tension compared to fixed connection. Therefore, the change in structural response according to connection condition makes it possible to design a higher structural safety of the structural member through the hinge connection in the construction of a large-scale multi-linked floating offshore structure for large-scale photovoltaic systems in which some unit structures are connected. However, considering the tension of the mooring line increases, a safety evaluation of the mooring line must be performed.

Floating photovoltaic systems homogenize the waterbird communities across subsidence wetlands in the North China Plain

Photovoltaic (PV) power generation provides an environmental-friendly alternative to fossil fuels, but the potential impacts of large-scale PV systems on wildlife have become a hotspot. In the North China Plain, floating photovoltaic (FPV) systems have been extensively installed across subsidence wetlands created by underground coal mining. However, there have been no studies investigating the effects of FPV systems on waterbird communities. During the wintering and migrating seasons of 2016–2017 and 2021–2022, we carried out waterbird surveys in subsidence wetlands with and without FPV systems in the Huainan-Huaibei coal mining area in the North China Plain to examine the effects of FPV systems on waterbird communities. After installation of FPV systems, waterbird numbers increased, but Simpson diversity and Pielou evenness decreased, with no significant changes in species richness. The presence of FPV systems resulted in decrease in the proportion of diving birds but increase in the proportion of vegetation gleaners. The FPV systems leaded to decrease in the proportion of herons in October but increase in other months. Besides the effect of FPV systems, we also found that Simpson diversity and the proportion of ducks increased, while the occurrence rate of shorebirds and gulls decreased from 2016 to 2021. Our results indicate that the cover of FPV systems may homogenize waterbird communities across subsidence wetlands. This study provides the first information on the effects of the FPV systems on waterbirds in artificial wetlands and highlights the need for further research to understand the ecological implications of FPV installations in wetland ecosystems.

Fuzzy logic controller-based enhancement for grid-connected large-scale PV systems

: In recent years, considerable scholarly attention has been directed toward photovoltaic (PV) power generation, driven by the imperative to ameliorate the ecological ramifications inherent in conventional electricity generators. The economic viability of PV generation is intrinsically contingent upon both the cost-efficiency of the cells employed and the quantum of energy that the arrays have the capacity to deliver over their operational lifespan. This scholarly exposition elucidates an exploratory inquiry into the intricate realm of modeling and simulating solar PV systems, conjoined with the distribution grid via the employment of a Fuzzy Logic Controller (FLC). The inquiry delves into the nuanced realm of the influence wielded by fluctuations in solar radiation upon the system's operational efficacy, concurrently proffering a novel energy storage framework engineered to counterbalance the power quality predicaments stemming from the intermittent nature of solar energy. The empirical manifestations gleaned from simulations evince the system's conceptual prowess, effectuating an amelioration in the comprehensive operational dexterity of the solar PV system, thereby embellishing energy capture efficiency and mollifying the perturbing power quality predicaments. A comparative investigation, encompassing varied conditions, substantiates the prowess of the proposed methodology vis-à-vis the fixed perturbation Maximum Power Point Tracking (MPPT) algorithm. Empirical observations emanating from these investigations conspicuously affirm the feasibility and distinct advantages underpinning the purview of the proposed methodology.

Intelligent maximum power point tracking for coastal photovoltaic system concerning the corrosion and aging of modules

Due to the corrosion and aging caused by the special oceanic environment, the characteristic of coastal photovoltaic (PV) system significantly drift after years of operation. In this study, the maximum power point tracking (MPPT) problem for coastal PV system is addressed and a novel MPPT methodology based on deep neural network (DNN) integrated with the corrosion evaluation index (CE-index) and dynamic training-sample (DTS) mechanism is developed. To be specific, the detailed effect of corrosion and aging for the PV modules installed in coastal areas is comprehensively analysed, and a composite indicator for evaluating the PV parameter drift, namely CE-index, is proposed. Then, a novel DNN-based offline MPPT methodology for the large-scale coastal PV system is developed, in which the DTS mechanism is also introduced for overcoming the effect caused by PV module corrosion and aging phenomenon. Finally, the optimal length of DTS for different degrees of CE-index is comprehensively verified by case studies. Experimental result shows that the developed DNN-based MPPT methodology can accurately forecast the maximum power point (MPP) voltage for large-scale coastal PV-system with robust performance, and cooperation of the developed DTS-mechanism and CE-index corrosion evaluation strategy can also effectively overcome the disturbance caused by the harsh oceanic environment.

Design of Cascaded Multilevel Inverter and Enhanced MPPT Method for Large-Scale Photovoltaic System Integration

The key goal of this effort is to develop an efficient control system for a three-phase cascaded H-bridge multilevel inverter powered by the photovoltaic (PV) system. The power for the system is generated through the use of PV modules, which serve as DC inputs for the cascaded H-bridge multilevel inverter. The authors aim to achieve a nearly sinusoidal signal at the voltage level and are specifically focused on minimizing the total harmonic distortion (THD) to the smallest possible value. Hence, an advanced N-level space vector modulation (SVM) is developed to ensure an appropriate control for the cascaded inverter. The aim is to design an effective control strategy to increase inverter efficacy and, thus, supply the best output quality. In addition, a robust approach to the maximum power point (MPP) tracking (MPPT) technique is developed based on an adaptive perturb and observe (P&O) algorithm to ensure superior tracking of the MPP. The developed algorithm eliminates 90% of the power curve area in the search space process and only maintains 10% of the area that includes the MPP. Each PV system employs its own improved MPPT control. The numerical results confirm that the enhanced P&O algorithm attains a precise response with superior efficiency and a fast response under the fast alteration of environmental conditions. Hence, the energy loss is reduced. The simulation results validate the effectiveness of this study, highlighting the high efficiency of the control strategy and the enhanced performance of the proposed scheme with lesser THD values.

A machine-learning digital-twin for rapid large-scale solar-thermal energy system design

In many industrialized regions of the world, large-scale photovoltaic systems now contribute a significant part to the energy portfolio during daylight operation. However, as energy demands peak shortly before sunset and persist for several hours afterwards, the integration of solar-thermal systems is extremely advantageous as a green “bridge” energy source. Accordingly, this work develops a digital-twin model to track and optimize the flow of incoming solar power through a complex solar-thermal storage system, consisting of a large array of adaptable mirrors, an optical-receiver and a power distribution system for customers to extract energy. Specifically, the solar power flow is rapidly computed with a reduced order model of Maxwell’s equations, based on a high-frequency decomposition of the irradiance into multiple rays that experience mirror reflections, losses and ultimately receiver absorption and customer delivery. The method allows for rapid testing (in microseconds) of the performance of large numbers of mirror-receiver layout configurations in design space, over extremely long time periods, such as weeks, months and years, using a genetic-based machine-learning digital-twin framework, which integrates submodels for: •optics and tracking of the Fresnel multi-mirror system,•thermal absorption of the optical energy by the receiver and•optimal operating temperatures balancing radiative losses with heat storage. The overall machine-learning digital-twin optimizes the configuration layout to balance meeting customer demands and operational efficiency. Numerical examples are provided to illustrate the approach. Finally, a deep-learning algorithm is developed and applied to the create an Artificial Neural-Net representation, which allows for even further simulation speedup.

Modelling of Large-Scale Photovoltaic Power Generation System Based on PSCAD and Analysis of Its Stability

In this paper, based on the study of PV power generation principles and mathematical models of PV cells, PSCAD simulation modelling is performed for a large-scale PV plant with required output over 100 MW, and its operational characteristics are analysed. An improved Newton iteration, conductance increment method and semi-aggregate equivalent control algorithm are used to achieve maximum power point tracking (MPPT) for large-sized photovoltaic electricity plant. Finally, the simulation analysis of the large-scale photovoltaic electricity generation system under normal operation and light disturbance is conducted to validate the validity and stability of the model.

Decarbonizing Telecommunication Sector: Techno-Economic Assessment and Optimization of PV Integration in Base Transceiver Stations in Telecom Sector Spreading across Various Geographically Regions

Renewable energy is considered to be sustainable solution to the energy crisis and climate change. The transition to renewable energy needs to be considered on a sectoral basis and one such sector that can potentially decarbonized with renewable energy is the telecommunication sector. Several base transceiver stations (BTS) in remote regions have unstable electric supply systems. Diesel generators (DG) are a common solution to energy problems on such telecommunication sites. However, they have high fuel costs on the global market and contribute to high carbon emissions. Hybrid renewable energy systems may provide a stable power output by integrating multiple energy sources, essential for supplying a dependable and uninterrupted power supply in the context of the telecom sector, notably base transceiver stations (BTS). Deploying such a system might also help BTS, which relies mainly on diesel generators with battery storage backup, reduce operational costs and environmental problems. This study presents the framework for large-scale photovoltaic system penetration based on techno-economic analysis (based on actual on ground data with least assumptions) in base transceiver stations (BTS) encapsulating telecom sector spread across various geographical regions. The proposed framework includes a mathematical model complemented with system design in HOMER software tool. The techno-economic aspects of the study were spread across 2, 12 and 263 sites, along with comparison analysis of photovoltaic system installation with and without energy storage devices, respectively. The sites included both on-grid and off-grid sites, which were exposed to high levels of power outages and subjected to reliance on costly and environmentally hazardous diesel generators. Optimization results showed that the photovoltaic system with a diesel generator and battery storage system provide a promising solution to the energy problem, with an average decrease in LCOE of 29%, DG hour’s reduction by 82% with 92% reduction in carbon emission and a reduction in NPC of 34% due to the high availability of solar. The techno-economic analysis indicated that optimized photovoltaic system and storage results in both on–off grid BTS sites with better options, amid low cost of energy and free accessibility of solar. Moreover, the results spread across geographical regions aiming at a reliable and environmentally friendly option that reduces load on utility grid across on-grid BTS sites and substantial overall reduction in diesel usage.

Influence of off-grid/grid-connected operation on stability of large-scale photovoltaic system

Off-grid or grid-connected operation of large-scale photovoltaic system may cause stability problems. When the system performs off-grid operation, energy storage, as balance node, regulates power and frequency, then also influences stability. Under grid-connected operation, the external power system balances power and frequency regulation, and energy storage only equilibrium wave power. Although a detailed model for a single photovoltaic generation system has been established, the connection of numerous photovoltaic units is difficult to model and simulate, and it can easily cause dimension disaster. In order to analyze stability effectively of off-grid/grid-connected operation of large-scale photovoltaic system, we first establish the equivalent model of a photovoltaic farm through the method of dynamic aggregation of wind turbines, and then obtain indicators affecting the stability considering the reactance matrix of the transmission line. When the real part of the oscillation mode sensitivity index is greater than zero, oscillation mode approaches the imaginary axis, indicating that the PV system stability deteriorates. Conversely, the PV system is regionally stable. Finally, the effects of the number of photovoltaic units and transmission line length on stability are theoretically analyzed, and the theoretical derivation is validated through simulations. More PV units and a longer transmission line increase the likelihood of instability through the sensitivity of the oscillation mode.