Photovoltaic Plants Research Articles

Case Study on Photovoltaic System: Impact of Solarimetric Stations on Simulations and Anomaly Detection

Local solarimetric stations (LSS) are essential for collecting data to evaluate photovoltaic (PV) plant performance and improve simulation accuracy. When unavailable, commercial solar databases (CSD), typically derived from satellite-based typical years, are used. This study compared the impact of LSS and CSD data on PV simulations and explored the use of LSS data for anomaly detection. Two LSS were analyzed: one at a small-scale PV system (minigeneration in Brazil) and another at a large-scale PV plant. The minigeneration station measured irradiance components to calculate Plane of Array (POA) irradiance, while the large-scale station directly measured POA. For the minigeneration system, simulations using LSS data showed a lower discrepancy (0.21%) compared to CSD (3.13%). For the large-scale plant, a -5.99% discrepancy using LSS revealed anomalies in energy generation. MAE and RMSE improved significantly with LSS for the large-scale system, with MAE decreasing from 660.25 MWh (CSD) to 348.08 MWh. Additionally, an unsupervised anomaly detection flagged 2.88% and 4.47% of data for two inverters, showcasing LSS potential for predictive models. These findings suggest that while LSS data are valuable for PV plant performance analysis, their effectiveness may depend on spectral range, averaging intervals, and irradiance transposition in simulations.

An Evaluation of Eco-Environmental Quality Based on the Fused Remote Sensing Ecological Index (FRSEI) in the Jinjie Coal Mine Area, Northwest China

This study focused on the Jinjie mining area by addressing the severe resource and environmental issues arising from excessive coal mining in Shenmu County, Northwest China. Multi-source remote sensing data from GF-1 and Landsat 8 OLI were utilized in this study. Specifically, the fused remote sensing ecological index (FRSEI) was constructed to conduct a detailed evaluation and analysis of the eco-environmental quality in the mining area from 2013 to 2023. The results indicated that the overall eco-environmental quality of the Jinjie mining area exhibited a trend of initial improvement, followed by degradation, and then improvement again over the decade. The eco-environmental quality of the mine pits and their surrounding areas was significantly lower than the overall level, confirming the destructive impact of coal mining on the eco-environment. Meanwhile, as the study area, Shenmu County is actively utilizing coal mining subsidence areas to develop the photovoltaic power generation industry, aiming to achieve green and low-carbon transformation. Although the construction of photovoltaic power plants initially led to the degradation of the condition of vegetation and the FRSEI, both gradually improved after the plants were operational. Furthermore, by comparing the FRSEI with the remote sensing ecological index (RSEI) calculated solely using Landsat 8 OLI data, we found a high degree of similarity between the two, thereby validating the stability and accuracy of the FRSEI. This study not only provides high-precision data support for eco-environmental monitoring in mining areas but also highlights the potential of multi-source remote sensing data fusion technology in improving monitoring accuracy, further providing a scientific basis for formulating sustainable development strategies specifically for the eco-environment in mining areas.

Solar parking lot capacity: an abundant dual-use alternative to meet demand for the renewable energy transition

Abstract Most utility-scale solar facilities (USSFs) in the United States (US) are planned or installed in grasslands, pastures, agriculture land, and timberland, with aggressive future expansion plans. Investor-owned electricity providers have lobbied aggressively to remove obstacles to approval of USSFs on agricultural lands and for limits on regulations that support distributed solar installation. Rural USSF expansion may have potentially significant local, state, and regional impacts on agricultural production and the economy, property values and affordability, and conservation of habitats, connectivity, and endangered species. The potential use of parking lots in urban and suburban areas for solar development to meet demands is poorly studied and parking lot solar has not gained much traction in the US. The objective of this research was to estimate whether solar PV systems installed in parking lots can meet electricity demands across multiple urban and suburban metropolitan areas within the US. Parking lot data across four municipalities with variation in latitude, solar radiation, and population density was obtained, along with modeled annual electricity demand by sector. Parking lot area and potential generating capacity was calculated for canopy and carport designs using standard and premium efficiency panels, and annual electricity production was estimated using the National Renewable Energy Laboratory’s PVWatts Calculator. Premium efficiency canopy designs can meet ~100% of demand in some large car dependent low to medium density municipalities. High density urban areas may receive marginal direct benefit from parking lot solar development within their boundaries. Dual-use of lands without any ecosystem or productive value can substantially contribute to renewable energy mixes to meet demands.

Methodology for Fault Detection Applied in Photovoltaic Plants Based on Inverter Power Curve Analysis

The maintenance of photovoltaic plants is crucial to ensure their proper performance, longevity, and efficiency, while also enhancing the return on investment and contributing to sustainable energy production. In such context, this paper proposes a methodology to characterize the main issues in photovoltaic plants, based on the analysis of the proposed inverter output power curve. Through power curve analysis, the most common anomalies and faults encountered in inverters are identified, providing a valuable tool for the early detection of operational issues. Additionally, power curve analysis allows a comparison between projected and actual values of generated energy, offering an index of system energy losses. Lastly, an economic analysis is presented, ranking inverters based on the magnitude of their energy losses, from highest to lowest. This analysis provides important insights for prioritizing maintenance actions and allocating resources to enhance generation returns.

Evaluation of the quality of the electrical energy supplied by a grid-connected solar photovoltaic plant using machine learning and data mining algorithms

La presencia de plantas solares fotovoltaicas para la producción de electricidad implica la disminución del uso de combustibles fósiles, y la reducción de las emisiones contaminantes. La disponibilidad de la energía solar depende de las condiciones climáticas, por lo que los parámetros de la energía eléctrica a entregar podrían verse afectados. El objetivo de esta investigación es mostrar una metodología basada en la ciencia de datos para la evaluación de la calidad de la energía de plantas solares fotovoltaicas conectadas a la red, considerando los estándares vigentes. Se aplica a una planta de 260 kWp del Instituto Nacional de Estándares y Tecnología de los Estados Unidos. Los parámetros utilizados son la distorsión armónica total, fluctuaciones y desbalance de voltaje, frecuencia eléctrica, y factor de potencia. Casi el 100% de los registros cumplen con los límites establecidos por los estándares para los parámetros, a excepción del factor de potencia, con un 63,56%. Del modelo de clasificación del factor de potencia se obtuvo que la potencias aparente y activa, y la frecuencia, son las variables más importantes. Del algoritmo de descubrimiento de subgrupos, se obtuvo que la irradiancia solar aparece en el 40% de los subgrupos, y la frecuencia en el 50%.

Integrating GIS and AHP for Photovoltaic Farm Site Selection: A Case Study of Ikorodu, Nigeria

Integrating GIS and AHP for Photovoltaic Farm Site Selection: A Case Study of Ikorodu, Nigeria

Influence of Limestone Dust on PV Panel Efficiency in a Small Solar Park in Bulgaria

Influence of Limestone Dust on PV Panel Efficiency in a Small Solar Park in Bulgaria

Site Suitability Analysis for Green Hydrogen Production Using Multi-Criteria Decision-Making Methods: A Case Study in The State of Ceará, Brazil

– Brazil is leveraging its extensive potential in solar, wind, and hydro energy to establish itself on the global map of green hydrogen production—a pivotal component in the ongoing energy transition. The economic viability of green hydrogen hinges on optimal locations with abundant renewable resources, space for solar or wind farms, access to water, and the potential for exporting to major demand centers. In this context, this study evaluates the potential for green hydrogen production in the State of Ceará, Brazil, using multi-criteria decision-making (MCDM) methods, which are designed to prioritize conflicting tangible and intangible criteria to determine the best decision alternatives. The additive ratio assessment (ARAS), simple additive weighting (SAW), combinative distance-based assessment (CODAS), and technique for order performance by similarity to ideal solution (TOPSIS) methods are evaluated for decision-making purposes, with weighting and aggregation relying on the best-worst method (BWM) and ensemble ranking. Criteria considered in the analysis include suitable areas for wind and solar farms, wind and solar energy potentials, water availability, surface temperature, electrical infrastructure, population, and local gross domestic product (GDP). The municipalities with the highest potential for renewable hydrogen production identified are Tauá (638 kton/year), Araripe (471 kton/year), and Beberibe (462 kton/year). According to the results, Araripe emerges as the most suitable municipality for green hydrogen production in Ceará.

Reversing LeTID in PV power plants: a feasibility study

Reversing LeTID in PV power plants: a feasibility study

A numerical simulation study of microclimate in PV power plant using coupled WRF-PVCM

A numerical simulation study of microclimate in PV power plant using coupled WRF-PVCM

“site suitability analysis for integrating large-scale hybrid photovoltaic and concentrated solar power plants in Cameroon using Monte Carlo, analytic hierarchy process, and geographic information system methods”

“site suitability analysis for integrating large-scale hybrid photovoltaic and concentrated solar power plants in Cameroon using Monte Carlo, analytic hierarchy process, and geographic information system methods”

Short-term solar irradiance forecasting using deep learning models

Population growth and evolving consumer technology have resulted in an ever-increasing demand for energy and power. Traditional energy sources such as coal, oil, and gas are not only quickly depleting but have also contributed to global pollution. As a result, the demand for renewable energy for power generation has increased tremendously. Short-term solar irradiance is a critical area in renewable energy for the optimal operation and power prediction of grid-connected photovoltaic (PV) plants and other solar energy applications. However, solar irradiance is complex to handle due to the nonuniform characteristics of inconsistent weather conditions. Deep Learning techniques have shown outstanding performance in modeling these complexities. In this paper, short-term solar forecasting models are proposed using deep learning to reliably predict the amount of solar irradiance for optimal power generation. Furthermore, it is also evaluated whether the model can forecast the amount of Global Horizontal Irradiance (GHI) within one hour given the current recorded features including air temperature, azimuth, cloud opacity, and zenith. The data for Penang, Malaysia is used in this research. A Dense Neural Network (DNN) with 32 units achieved a validation MAE of 21.33 and MSE of 1343.68 in the 6th fold. Long-Short Term Memory (LSTM) with 256 units achieved a validation MAE of 8.23 and MSE of 246.98 in the 7th fold. On test data, the DNN achieved MAE and MSE of 31.71 and 2560.80 respectively whereas the LSTM model achieved MAE and MSE of 5.78 and 106.65 respectively.

Maximum penetration level and optimal placement of photovoltaic power station in distribution network

Maximum penetration level and optimal placement of photovoltaic power station in distribution network

Numerical analysis of evaporation reduction in floating photovoltaic power plants: influence of design parameters

Numerical analysis of evaporation reduction in floating photovoltaic power plants: influence of design parameters

Intelligent Control of the Dynamic Voltage Restorer for Fault Ride through Capability Enhancement of a Grid-connected Photovoltaic Power Plant in accordance with Recent Moroccan Grid Code

Background: The recent development of small-scale, decentralized generation from renewable sources and the fall in the price of the equipment needed for this operation have given a new role to the distribution networks, which is to collect the energy produced by the smallest generation plants and deliver it to the end customers. However, the national Grid Codes present technical requirements in terms of FRT and particularly LVRT and HVRT which are imposed on PV plants connected to medium voltage distribution networks, to ensure the energy needed by the loads connected to the network at the time of the failure and especially sensitive ones. Methods:: In this paper, an intelligent neural network approach is applied to the DVR control circuit to enable the requirements of the sensitive load connected near the PCC, and the system is tested in the presence of a non-linear load to demonstrate its efficiency for all situations. The proposed strategy is based on the implementation of an improved ANFIS and ANN control which are compared to a tuned PI controller, the approach intends to meet the technical requirement of the recently approved Grid Code in Morocco. The simulation is performed using MATLAB Simulink. Results: The proposed approach brings great improvement to the load side voltage waveforms, and numerical experiment findings demonstrate that it can successfully guarantee the technical requirements of the electrical grid code. Conclusion: The results obtained show better behavior of the system using ANFIS and ANN control strategy in the presence of a nonlinear load and a significant improvement of the voltage THD.

The value of AC optimal power flow models in the assessment of distribution grid electric vehicle and photovoltaic power plant hosting capacity – case study in Bosnia and Herzegovina

The value of AC optimal power flow models in the assessment of distribution grid electric vehicle and photovoltaic power plant hosting capacity – case study in Bosnia and Herzegovina

Landslide risk on photovoltaic power stations under climate change

To achieve the net-zero carbon dioxide emission goals, the number of solar photovoltaic (PV) power stations (PPSs) installed worldwide has increased. An increasing number of PPSs are exposed to natural hazards, such as landslides. However, the socioeconomic impact of landslide risk on PPSs has rarely been assessed nationally. In this study, we assessed the landslide risk for PPSs by combining statistical susceptibility and physical-based hazard analyses under three representative concentration pathways (RCPs). We found that the cumulative landslide-susceptible area (LSA) during the current, 2040s, and 2090s periods could vary depending on RCP scenarios: 2895 (RCP2.6), 3417 (RCP6.0) and 5492 km2 (RCP8.5). Especially, under RCP6.0, due to the spatial match with the distribution of LSA and the PPSs, the landslide risk on PPSs could become about 60 million dollars per year. This loss would be 24 million dollars more than the risk of RCP2.6. This study provides insights into the economic loss of PPSs from landslides in the Republic of Korea and suggests that severe climate change would bring a significant increase in this economic loss, which may occur in other countries with large mountainous areas.

Performance assessment of a 1 MW grid-connected rooftop solar photovoltaic (GCRPV) plant on an institutional building in composite climate of Northern India

In recent years, rooftop solar photovoltaic systems have gained significant attention for the curtailment of electricity bills and the reduction of carbon footprints. In the study, the operating performance of a 1 MW grid-connected rooftop solar photovoltaic (GCRPV) plant was assessed using actual recorded data installed on the university buildings in Lucknow, India, using IEC 61724. The real performance of GCRPV was compared with three simulation softwares: PVsyst, SAM, and PVLIB. The objective of the research is to evaluate the simulation software’s accuracy to predict the operating performance of large-scale solar PV plants. The results reveal significant variations in final yield: PVLIB estimated 5.09 h/day, PVsyst 3.73 h/day, and SAM 4.64 h/day, against the actual value of 3.45 h/day. Annual energy generation projections by PVLIB, SAM, and PVsyst were 1858.80, 1699.92, and 1365.79 MWh, respectively, compared to the actual value of 1255.77 MWh. Plant efficiencies and performance metrics are comparable with Indian installations. These findings offer valuable insights into the performance and potential of solar photovoltaic systems in similar climates, providing a foundation for site selection and system optimisation in India and similar regions worldwide.

Techno-economic analysis of floating solar photovoltaic plant in reservoir-based hydropower station in India: a case study

ABSTRACT India's grid stability and sustainable energy transition encounter challenges posed by the unpredictable nature of solar power and the low plant load factor (PLF) of hydroelectric stations caused by water scarcity. It is worth noting that traditional energy sources still account for 78% of the country's energy demand. This study aims to present a solution to these problems by analysing the feasibility of a floating solar photovoltaic (FSPV) plant on the reservoir of Tehri hydro plant, Uttarakhand, India. The study determined that a 175 MW FSPV system has the potential to generate 298 million units (MU) of energy and reduce CO2 emissions by 265 million kg. The estimated increase in hydropower production by preventing water loss through evaporation amounts to 7.05 million units per year. The estimated tariff for the proposed 175 MW FSPV is 5.25 US Cents/Unit, in accordance with current regulations. In addition, the System Advisor Model (SAM) software calculates the net present value and payback duration of capital invested to be 18.05 million US$ and 8.8 years, respectively. The analysis also determined that there is no need for additional transmission infrastructure to evacuate FSPV power from the existing Tehri hydro plant. The proposed model of FSPV with hydropower stations enables the reservoirs to function as virtual batteries, hence optimising power generation. Furthermore, the viability of FSPV installations have been assessed at sixteen reservoir-based hydropower stations with a capacity factor less than 30%. Annually, installing FSPV systems at these locations may yield total power output of 11446 MW and generate 17310 MU of electricity. The integration of these sites would lead to substantial reductions in CO2 emissions, amounting to 15406 million kg, as well as an increase in hydropower generation by 341.30 million units per year.

Usage of non-invasive technique to diagnose a photovoltaic plant

ABSTRACT The present work utilizes an intelligent technique to lower photovoltaic systems’ operating and maintenance costs. In 2013, inverter manufacturer Huawei pioneered this concept with the development of multi-MPPT (Maximum Power Point Tracking) string inverters covering 14 different types of faults of photovoltaic systems. So far, the main advantage that the authors of this work have identified when using Huawei’s “Smart I-V Curve Diagnosis” function is the ease with which mismatch situations can be perceived and located in the photovoltaic plant, without having to install individual optimizers in each of the modules. This methodology required a specific data logger and a software license. Additionally it is only available for large inverters, over 100 kW. The “Smart” function indicates the faulty string. To identify the damaged module, the authors used thermographic images. The diagnostic approach showed that all strings achieved the nominal. Fill Factor (FF) of 78%. Commissioning tests concluded all strings were normal, but I4S1 and I4S2 tests required repetition due to their unique configuration. A fault in module 1 of I4S7, undetected during commissioning, was later confirmed by Smart I-V Curve Diagnosis and infrared thermography.