Photovoltaic Plants Research Articles

Positive sequence component based directional relaying for lines integrated with solar photovoltaic plant

Different fault current characteristics of converter interfaced solar photovoltaic plants as compared to the synchronous generator based sources impose challenges to the commonly used directional relaying methods. In this work, the issues with phasor based directional relay algorithms are analysed for the solar plant connected lines. A directional relaying method is proposed for such connectivity using the phase angle difference between positive sequence voltage and the line current measured at the local bus relay. Using PSCAD/EMTDC simulations the proposed method is tested for solar plant connected 39 bus system and validated using field data. The accuracy of the proposed method is also evaluated on a real-time simulation platform. Comparative analysis with the conventional directional relaying methods reveals the superiority of the proposed method.

Polymer encapsulation impact on potential-induced degradation in PV modules revealed by a multi-modal field study

Potential-induced degradation is a significant issue affecting the performance and reliability of photovoltaic systems, particularly in large-scale installations. Understanding the mechanisms underlying the formation of potential-induced degradation is crucial for developing effective mitigation strategies and ensuring the long-term sustainability of solar energy technologies. During the inspection of a 1.2 MWp photovoltaic power plant, various imaging methods, electrical measurements, spectroscopic characterization methods, and monitoring data analyses were utilized and combined. We observed that two percent of the photovoltaic modules at the string ends exhibited the characteristic checkerboard pattern in infrared or electroluminescence imaging, implicating issues on twelve percent of all strings. This uneven distribution of potential-induced degradation-affected modules suggests additional key factors are at play. Investigations of the backsheet and ethylene-vinyl acetate using near-infrared reflectance analysis revealed that one backsheet type and two different ethylene-vinyl acetate types were present in the installed solar panels. Yet, only one type of ethylene-vinyl acetate correlated with potential-induced degradation. The same type also exhibited a higher rate of oxidative degradation. Scanning electron microscopy and energy-dispersive X-ray spectroscopy revealed a significantly increased sodium ion content in the ethylene-vinyl acetate and glass of the potential-induced degradation-affected modules. Our findings highlight the crucial role of polymer encapsulation in the development of potential-induced degradation and underscore the importance of careful polymer selection in the design of photovoltaic module technologies.

Photovoltaic array fault detection based on a new model of series equivalent resistance

To address the difficulty of detecting photovoltaic array faults using photovoltaic array model parameters in photovoltaic power stations, a photovoltaic array fault detection method based on a new series equivalent resistance model is proposed. In this method, first, a mathematical model of the series equivalent resistance of a photovoltaic module is established based on the single-diode model of the photovoltaic module using the characteristics of this module when it operates at the maximum power point, short circuit and open circuit. Second, a new model of the series equivalent resistance of the photovoltaic array is obtained based on the mathematical model of the series equivalent resistance of the newly constructed photovoltaic module, and a series equivalent resistance fault detection model is proposed under short-circuit, degradation and shadow faults. Finally, the proposed new model of the series equivalent resistance of photovoltaic arrays is compared with three existing mathematical models to verify its validity and accuracy. Simultaneously, the series equivalent resistance fault detection model is verified in the case of short-circuit, degradation and shadow faults of photovoltaic arrays.

Comparison of Parabolic Troughs and Solar Photovoltaic as Solar Field Technology in Dispatchable Solar Power Plants

The use of thermal energy storage (TES) technologies, as means to provide dispatchability to thermally driven solar power production, has long stood as the main argument for the interest and potential competitiveness of Concentrated Solar Power (CSP) when compared to other non-dispatchable alternatives. Whereas reductions of the Levelized Cost of Electricity (LCOE) are observed for CSP in recent years, the notable LCOE reduction observed along this decade in large-scale solar photovoltaics (PV) plants is set to break the taboo of "power-to-heat-to-power" approaches, as its potential economic performance outcasts the associated thermodynamic inefficiency. The possibility of using available resistive heating technologies and components in PV-TES combinations, renders the configuration of a conventional CSP plant suitable for the replacement of a thermal conversion solar field by a photovoltaic field presenting further the possibility of delocalization and/or spatial distribution of the solar field (e.g. on a Carnot Battery configuration). As dispatchability no longer stands as an exclusive argument in favor of CSP over PV, the present article addresses the boundary conditions for the competitiveness of each technology as the champion of dispatchable solar power fields. The impact of both land and electrical heater costs variation in the variation of LCOE for PV-TES plants is much more modest than that observed for the impact of CSP solar field cost variations in the variation of LCOE for CSP plants, which leans for the latter and at present costs, towards better competitiveness for plant designs with a storage capacity in excess of 10.0 FLH (for GHI values in excess of 2100-2200 kWh∙m-2∙year-1).

Performance and Parametric Studies of a Non-Compact Hybrid PV-CSP Power Plant Integrated by a Supercritical Rankine Cycle and an Electric Heater in Series

This study focuses on performing a parametric analysis of the non-compact concentrated solar power (CSP)-photovoltaic (PV) hybrid plant combined with an electric heater (EH) and a Supercritical Rankine Cycle as well as a multi-objective optimization of different technologies. The power plant sizing is evaluated in a baseload dispatch scenario of 165 MWe for a location in Seville and considering combinations of the PV installed power, CSP solar multiple (SM), thermal energy storage (TES) capacity and EH power. Studied parametric combinations are optimized for minimizing the Levelized Cost of Electricity (LCOE) and maximizing the capacity factor (CF), with the ultimate goal of achieving the Pareto front. The results of the study show that certain combinations, such as PV=450 MWe, SM=2, EH=250 MW, and TES=12 h, have a favourable balance between the LCOE and the CF (LCOE=0.0979 €/kWh, CF=0.702). In addition, the results obtained through the optimization process, in particular by considering the Pareto frontier, indicate that among the different technologies, the CSP-PV-TES-EH configuration has the lowest LCOE and the highest FC.

Reflux Power Optimization of a Dual-Active Hybrid Full-Bridge Converter Based on Active Disturbance Rejection Control

The dual-active hybrid full-bridge (H-FDAB) DC–DC converter has great potential in medium-voltage high-power photovoltaic power station applications by introducing a three-level bridge arm to increase the output voltage range. However, its mathematical model and optimum modulation schemes have not been fully explored. Under the traditional PI control, the H-FDAB DC–DC converter will produce significant reflux power, which will lead to a decrease in converter efficiency and output voltage fluctuation. On this basis, this paper proposes a reflux power optimization strategy for an H-FDAB DC-DC converter based on active disturbance rejection control (ADRC). Firstly, the structure and power characteristics of the H-FDAB DC–DC converter are analyzed, and the relationship among the reflux power, the transmission power, and the phase shift angle is derived. Secondly, to reduce the complexity of the control calculation, upon the foundation of dual phase-shifting modulation, the Karush–Kuhn–Tucker (KKT) condition is used to solve for the phase shift angle that corresponds to the minimum reflux power. Simultaneously, we develop an ADRC loop utilizing an extended state observer (ESO) for the real-time estimation of system states. We also consider the sudden changes in input voltage, load switching, and transmission power fluctuations caused by reflux power optimization strategies as system disturbances and compensate for them accordingly. Finally, the experiments conclusively validate the designed control strategy’s correctness and feasibility.

Life cycle climate performance of photovoltaic power plant for Turkey’s solar power conditions

ABSTRACT In this study, a solar power plant with an output power of 10 kW AC was designed according to the Turkish Electricity Legislation. Silicon-based photovoltaic modules were used in the design. The solar power plant’s annual and lifetime electricity generation was calculated for 81 provinces of Turkey. The study contributes significantly to the literature by evaluating the environmental impacts of energy sources in a more realistic way. According to the electricity generation calculations, the power plant in Trabzon generates a minimum of 13,037 kWh of electricity per year. In comparison, the power plant in Antalya generates a maximum of 18,562 kWh. Over the plant’s life, Antalya will generate 464,040 kWh of electricity and Trabzon will generate 325,929 kWh. The life cycle climate performance technique was used to understand the environmental impacts during the installation phase, and these values were determined to be 5759 kgCO2. Emission values from electricity generation vary between 0.01241 and 0.01767 kgCO2 per kWh. These data provide a comprehensive assessment of the environmental impacts of solar power plants. This study contributes to the literature by investigating the potential and environmental effects of solar energy in different regions of Turkey in detail. In particular, presenting the emission values of electricity generation from photovoltaic systems for each province provides valuable data that can be used in shaping energy policies and developing environmental strategies. This unique study will provide a scientific basis for planning and implementing solar energy investments in Turkey and contribute significantly to developing energy policies and environmental strategies.

Economic Impacts of Performance Optimization in Large-Scale Solar Farms: A Case Study Using Artificial Neural Networks in Eastern Malaysia

Economic Impacts of Performance Optimization in Large-Scale Solar Farms: A Case Study Using Artificial Neural Networks in Eastern Malaysia

Design and Simulation of Smart Grid Based on Solar Photovoltaic and Wind Turbine Plants

Design and Simulation of Smart Grid Based on Solar Photovoltaic and Wind Turbine Plants

An interpretable hybrid spatiotemporal fusion method for ultra-short-term photovoltaic power prediction

An interpretable hybrid spatiotemporal fusion method for ultra-short-term photovoltaic power prediction

A Study on the Impact of Different PV Model Parameters and Various DC Faults on the Characteristics and Performance of the Photovoltaic Arrays

PV systems play a vital role in the global renewable energy sector, and they require accurate modeling and reliable performance to maximize the output power. This research presents a thorough analysis and discussions on the effects of different PV models’ parameters and certain specific faults on the performance and behavior of the photovoltaic systems under different temperature and irradiation conditions. It provides a detailed analysis of how several parameters affect the performance of the PV arrays, for instance, the series resistance, shunt resistance, photocurrent, reverse saturation current, and the diode ideality factor. These parameters were extracted mathematically and verified with the help of wide-ranging simulations and practical experiments. Additionally, the investigation of the effect of DC faults, including line-to-line, line-to-ground, partial shading, and complete shading faults on PV arrays, provides important fundamentals for fault detection and classification, thus improving the efficiency and protection of PV systems. It can, therefore, be stated that the outcomes of this research will assist in the enhancement of PV systems in terms of design, operation, and maintainability of photovoltaic plants, as well as contribute positively to the advancement of sustainable solar energy technology.

A Multi-Objective Optimization Approach for Solar Farm Site Selection: Case Study in Maputo, Mozambique

Solar energy is an important source of clean energy to combat climate change issues that motivate the establishment of solar farms. Establishing solar farms has been considered a proper alternative for energy production in countries like Mozambique, which need reliable and clean sources of energy for sustainable development. However, selecting proper sites for creating solar farms is a function of various economic, environmental, and technical criteria, which are usually conflicting with each other. This makes solar farm site selection a complex spatial problem that requires adapting proper techniques to solve it. In this study, we proposed a multi-objective optimization (MOO) approach for site selection of solar farms in Mozambique, by optimizing six objective functions using an improved NSGA-II (Non-dominated Sorting Genetic Algorithm II) algorithm. The MOO model is demonstrated by implementing a case study in KaMavota district, Maputo city, Mozambique. The improved NSGA-II algorithm displays a better performance in comparison to standard NSGA-II. The study also demonstrated how decision-makers can select optimum solutions, based on their preferences, despite trade-offs existing between all objective functions, which support the decision-making.

A Photovoltaic Fault Diagnosis Method Integrating Photovoltaic Power Prediction and EWMA Control Chart

The inevitability of faults arises due to prolonged exposure of photovoltaic (PV) power plants to intricate environmental conditions. Therefore, fault diagnosis of PV power plants is crucial to ensure the continuity and reliability of power generation. This paper proposes a fault diagnosis method that integrates PV power prediction and an exponentially weighted moving average (EWMA) control chart. This method predicts the PV power based on meteorological factors using the adaptive particle swarm algorithm-back propagation neural network (APSO-BPNN) model and takes its error from the actual value as a control quantity for the EWMA control chart. The EWMA control chart then monitors the error values to identify fault types. Finally, it is verified by comparison with the discrete rate (DR) analysis method. The results showed that the coefficient of determination of the prediction model of the proposed method reached 0.98. Although the DR analysis can evaluate the overall performance of the inverter and identify the faults, it often fails to point out the specific location of the faults accurately. In contrast, the EWMA control chart can monitor abnormal states such as open and short circuits and accurately locate the string where the fault occurs.

Near-Ground wind field characteristics of tracking photovoltaic systems based on field measurements

The study conducts field measurements at a tracking photovoltaic power station in Zhongwei City, Ningxia Hui Autonomous Region, investigating near-ground incoming wind characteristics, spatial coherence, and their impact on the flow field of the tracking photovoltaic system. Actual measurements indicate a low dispersion degree in the roughness index of this area, with an average roughness index of α = 0.1228, which is consistent with the characteristics of open land and desert areas. The incoming wind speed profile largely complies with European wind load specifications, with turbulence intensity measured at different heights exceeding other wind load specifications, primarily due to increased sand movement contributing to turbulence in desert areas. Additionally, this study proposes calculation formulas for measured turbulence intensity, integration scale, and gust factor, offering valuable insights into the incoming wind field characteristics in this region. Furthermore, the investigation into spatial coherence and decay parameters revealed the superior performance of the Krenk model in high frequency bands and over long distances. Lastly, the tracking photovoltaic array induces noticeable shading effects, altering flow field characteristics significantly, with a diminished impact on wind flow velocity as the tilt angle of the photovoltaic panel decreases. These studies serve as a valuable reference for gaining a deeper understanding of wind field characteristics, wind load calculation, and the optimal design of tracking photovoltaic systems.

A Deep Learning-Based Dual-Scale Hybrid Model for Ultra-Short-Term Photovoltaic Power Forecasting

Ultra-short-term photovoltaic (PV) power forecasting is crucial in the scheduling and functioning of contemporary electrical systems, playing a key role in promoting renewable energy integration and sustainability. In this paper, a novel hybrid model, termed AI_VMD-HS_CNN-BiLSTM-A, is introduced to tackle the challenges associated with the volatility and unpredictability inherent in PV power output. Firstly, Akaike information criterion variational mode decomposition (AI_VMD) integrates the Akaike information criterion with variational mode decomposition (VMD) and reduces data complexity, enhancing grid optimization and energy efficiency. The adaptive selection of optimal parameters enhances VMD decomposition performance, supporting sustainable energy management. Secondly, the hierarchical scale-transform convolutional architecture (HS_CNN) supplements the traditional convolutional neural network (CNN) with two channels featuring distinct dilation rates, thereby extracting dual levels of time-scale information for a more comprehensive data representation. Finally, a bidirectional long short-term memory neural network (BiLSTM) with an attentional mechanism combines past and future data to enable more accurate forecasts, aiding in carbon reduction and smart grid advancements. Experimentation with data from the Alice Springs PV plant in Australia demonstrates that the proposed AI_VMD-HS_CNN-BiLSTM-A model exhibits superior adaptability and accuracy in multiple time-scale forecasting compared to the baseline models. This approach is important for decision-making and scheduling in grid-connected photovoltaic systems, enhancing energy resilience and promoting the sustainable development of renewable energy.

Optimizing the bidding strategy and assessing profitability of over-install renewable plants equipped with battery energy storage systems

The over-installation of renewable energy sources (RES) can enhance the profits of RES producers by increasing the total exporting energy; however, RES power curtailments must be applied under over-production of RES plants to ensure their contracted maximum export capacity limits. Battery energy storage systems (BESSs) can be used to reduce the RES curtailments and therefore enhance the profits of producers. This work develops a bidding strategy as a scenario-based stochastic optimization scheme to maximize the expected profits of producers in electricity markets considering battery degradation and maximum export capacity constraints. Using the proposed scheme, a financial analysis is performed to assess the profitability for over-installing hybrid RES plants by calculating the net present value and internal rate of return of the project under different BESS costs. Simulation results, using real data from a wind and photovoltaic plant, demonstrate the effectiveness of the proposed stochastic scheme in enhancing the profit of producers compared to the corresponding deterministic scheme. The performed financial analysis, using the net present value metric, indicates that a maximum over-installation of 150%–170% and 160%–180% for the wind and photovoltaic plant, respectively, is profitable, while the usage of a BESS is profitable when its cost is under 200,000 €/MWh.

Multi-Criteria Decision Making for Selecting the Location of a Solar Photovoltaic Park: A Case Study in UAE

The high availability of solar energy in the Gulf Cooperation Council (GCC) makes it the most attractive source of energy in this region, especially due to the global shift toward eco-friendly systems. A significant increase in the implementation of solar PV projects has been noticed in the United Arab Emirates (UAE). For this reason, this study conducted a multi-criteria decision-making process to compare four locations for building a solar PV park in the UAE, namely, Abu Dhabi, Dubai, Sharjah, and Umm Al Quwain. Various criteria were taken into account, including the solar radiation, wind speed, distance from the electricity grid, distance from seaports, and land cost. A hybrid AHP-TOPSIS model was employed to evaluate the criteria weights and alternatives’ scores, which were also compared with the no priority-TOPSIS to check the effects of the criteria weights on the overall ranking. According to the findings, Dubai and Abu Dhabi were ranked first and second, with final scores of 0.7108 and 0.4427, respectively, when using the AHP-TOPSIS model. Furthermore, Umm Al Quwain scored slightly less than that of second place, with a value of 0.4252. The top two ranked alternatives were consistent between the two compared models, as Dubai also performed the best among all locations, which confirmed the reliability of the proposed approach and verified the obtained results and rankings.

Modelling long-term operational dynamics of grid-connected hydro- photovoltaic hybrid systems

The development of variable renewable energy sources (VRESs), such as wind and solar photovoltaic (PV), has become a strategic choice for ensuring the security of electricity supplies and the decarbonization of power systems. The hybridization of hydropower with VRESs is an important measure for guaranteeing the smooth and cost-effective integration of VRESs into the power system. However, the highly variable weather- and climate-driven spatial and temporal fluctuations in VRES performance have significant potential to affect the optimal trajectory of hydropower operations. Therefore, it is essential to understand how hydropower responds to VRESs over long-term horizons, thereby allowing informed operation decisions to be made for hybrid generation systems (HGSs). In this study, a long- and short-term nested operation model is developed for hydropower operations considering PV integration. Specifically, the long-term model determines the day-by-day hydropower production over many years, and the short-term model simulates the day-ahead generation scheduling and real-time operation processes of the HGS. Performance metrics are established for evaluating the complementary operation in comparison with separate operation of hydropower and PV plants. China's two representative hydro-PV HGSs (Zhongyu and Longyangxia) are selected as case studies. The results indicate that the hybrid generation of hydropower and PV could, on average, increase total electricity generation by 11.33 %, reduce the load loss rate by 9.31 %, and decrease the PV curtailment rate by 36.60 %. However, hydro-PV hybridization may lead to an increase of 9.22 % in the water spillage rate and a decrease of 0.094 kWh/m3 in the water–electricity conversion coefficient. Therefore, hybrid generation of hydropower and PV could further enhance energy yield and supply reliability. Also, the importance of improving hydropower flexibility is highlighted.

Offshore solar photovoltaic potential in the seas around China

China has embarked on the promotion of offshore solar photovoltaic (PV) development along its coastal regions in pursuit of carbon neutrality. An evaluation of the inherent features and exploitative potential of offshore solar PV resource stands as a pivotal measure to the development and utilization of China's offshore solar PV resource. To this end, the tripartite objectives of this study unfold as follows: i) investigating offshore solar energy features, which include spatial distribution of PV resource and its monthly and hourly variations, along with an examination of climate change impacts; ii) analyzing ocean conditions that exert an influence on offshore solar PV farms; and iii) proposing criteria for assessing the suitability of offshore solar PV development, considering both resource characteristics and ocean conditions, thereby identifying the most promising areas for forthcoming offshore PV projects. The findings reveal that the South China Sea has the richest offshore solar PV resource and the least intra-annual fluctuation, despite challenging ocean conditions. Over the past 60 years, there has been a slight decrease in the solar resources in the majority of the South China Sea, in contrast to a marginal upswing observed in the Yellow Sea and Bohai Sea. Recommendations for future offshore solar PV development suggest considering the southwest waters of Hainan Island, where the proportion of annual PV power generation to power consumption of the island in 2022 is nearly 225%.

Performance Analysis of a 50 MW Solar PV Installation at BUI Power Authority: A Comparative Study between Sunny and Overcast Days

Ghana, being blessed with abundant solar resources, has strategically invested in solar photovoltaic (PV) technologies to diversify its energy mix and reduce the environmental impacts of traditional energy technologies. The 50 MW solar PV installation by the Bui Power Authority (BPA) exemplifies the nation’s dedication to utilizing clean energy for sustainable growth. This study seeks to close the knowledge gap by providing a detailed analysis of the system’s performance under different weather conditions, particularly on days with abundant sunshine and those with cloudy skies. The research consists of one year’s worth of monitoring data for the climatic conditions at the facility and AC energy output fed into the grid. These data were used to analyze PV performance on each month’s sunniest and cloudiest days. The goal is to aid in predicting the system’s output over the next 365 days based on the system design and weather forecast and identify opportunities for system optimization to improve grid dependability. The results show that the total amount of AC energy output fed into the grid each month on the sunniest day varies between 229.3 MWh in December and 278.0 MWh in November, while the total amount of AC energy output fed into the grid each month on the cloudiest day varies between 16.1 MWh in August and 192.8 MWh in February. Also, the percentage variation in energy produced between the sunniest and cloudiest days within a month ranges from 16.9% (December) to 94.1% (August). The reference and system yield analyses showed that the PV plant has a high conversion efficiency of 91.3%; however, only the sunniest and overcast days had an efficiency of 38% and 92%, respectively. The BPA plant’s performance can be enhanced by using this analysis to identify erratic power generation on sunny days and schedule timely maintenance to keep the plant’s performance from deteriorating. Optimizing a solar PV system’s design, installation, and operation can significantly improve its AC energy output, performance ratio, and capacity factor on sunny and cloudy days. The study reveals the necessity of hydropower backup during cloudy days, enabling BPA to calculate the required hydropower for a consistent grid supply. Being able to predict the daily output of the system allows BPA to optimize dispatch strategies and determine the most efficient mix of solar and hydropower. It also assists BPA in identifying areas of the solar facility that require optimization to improve grid reliability.