Solar Photovoltaic Plant Research Articles

Internet of Things Platform for Photovoltaic Maintenance Management: Combination of Supervisory Control and Data Acquisition System and Aerial Thermal Images

Suitable maintenance management plants of solar photovoltaic plants are required for global energy demands. The volume and variety of data acquired by thermographic cameras carried by unmanned aerial vehicles and Supervisory Control and Data Acquisition Systems increase the complexity of fault detection and diagnosis. The maintenance industry is requiring novel fault detection techniques that can be implemented in Internet of Thing platforms to automate the analysis and increase the suitability and reliability of the results. This paper presents a novel platform built with PHP, HTML, CSS and JavaScript for the combined analysis of data from Supervisory Control and Data Acquisition Systems and thermal images. The platform is designed. A real case study with thermal images and time series data from the same photovoltaic plant is presented to test the viability of the platform. The analysis of thermal images showed a 97% of accuracy for panel detection and 87% for hot spot detection. Shapelets algorithm is selected for time series analysis, providing an 84% of accuracy for the pattern selected by user. The platform has proven to be a flexible tool that can be applied for different solar plants through data upload by users.

54-Pulse VSC Based High-Power Grid Interfaced Solar PV Plant With DC-Coupled Battery Energy Storage and Distributed MPPT

This article presents a solar photovoltaic plant with dc-coupled battery energy storage (BES) system configured with a high-power 54-pulse voltage source converter (VSC) and distributed maximum power point tracking boost dc–dc converters. Dc-coupled BES allows storing excess solar energy and dispatching the same during nonsolar hours, thus improving VSC utilization and reducing the payback period. The 54-pulse VSC is configured with three sets of identical 18-pulse VSCs, which are developed with two-level converters and a medium voltage (MV) transformer. The MV transformer is configured with series-connected different phase-shifted zigzag and star primary windings and delta secondary windings. The VSC configuration has lower voltage and current harmonics, and reduces switching losses. The 54-pulse VSC is operated with 90–110% of voltage variation to provide 0–100% of nominal reactive power (capacitive and reactive) support to the grid. The solar PV-BES plant is simulated in MATLAB-Simulink and then real-time implementation is done in OPAL-RT to evaluate its steady state, harmonics, and dynamic responses.

Multicriteria Decision Method for Siting Wind and Solar Power Plants in Central North Namibia

We demonstrate the application of geomatics tools (remote sensing and geographic information systems) for spatial data analysis to determine potential locations for wind and solar photovoltaic (PV) energy plants in the Central North region of Namibia. In accordance with sustainable development goal 7 (affordable and clean energy) and goal 13 (climate action), the Namibian government has committed to reducing reliance on fossil fuels. In support of this, suitable locations for renewable energy plants need to be identified. Using multi-criteria decision-making and the analytical hierarchy process, sites were selected considering topographical, economic, climatic, and environmental factors. It was found that the highest potential for solar PV energy plants is in the northwest, southwest, and southern regions of the study area, whereas only the northwest region is highly suitable for wind power plants. These results were substantiated by comparison with global suitability maps, with some differences due to the datasets used. The findings can be used as a guide by governments, commercial investors, and other stakeholders to determine prospective sites for the development of renewable energy in Central North Namibia.

Electric Power Generation from Solar Photovoltaic Technology: Is It Marketable in Pakistan?

Solar photovoltaic systems are prohibitively expensive in terms of installation costs. Power from them is also available intermittently—only when energy from the sun is available. On the other hand, PV systems are free of the ever-rising costs of input fuel. They also incur much less operation and maintenance costs and are supposed to have a longer lifetime than, for example, a fossil fuel power plant. Thus using solar-PV power looks uneconomical in the short term, but may be profitable in the long term. It is, therefore, interesting to identify the factors that can make investment in solar PV power generation acceptable. This paper carries out a financial analysis of installing a 10 MW solar photovoltaic power generation plant for sale of electricity to a grid. It compares the levelised cost of this mode of energy generation as compared to a fossil fuel plant. It also calculates the cost of electricity generation and tariff for power from this plant. It then identifies the factors that can make the investment in a grid-scale solar PV plant more favourable than investment in other conventional and non-renewable sources.

Optimization of Photovoltaic Panel Array Configurations to Reduce Lift Force Using Genetic Algorithm and CFD

Aerodynamic lift force acting on the solar structure is important while designing the counterweight for rooftop-mounted solar systems. Due to their unique configuration, the load estimated for solar structures using international building codes can be either higher or lower than the actual. Computational Fluid Dynamics(CFD) simulations haveproven to be an efficient tool for estimating wind loads on solar panels for design purposes and identifying critical design cases. Computational Fluid Dynamics (CFD) simulations usually require high computation power, and slight changes in geometry to find optimum configuration can be time-consuming. An optimization method to minimize lift force effects on solar photovoltaic (PV) arrays installed on rooftops usesthe Computational Fluid Dynamics (CFD)and genetic algorithms proposed in this paper. The tilt angle and pitch between two rows of solar panels were parameterized, and a genetic algorithm was used to search for aconfiguration resulting in minimum wind lift force acting on the solar photovoltaic plant. Only combinations with a performance ratio >80% were considered. Three different rooftopphotovoltaic (PV) plant layout configurations were analyzed in this research. Two rows of photovoltaic (PV) panel arrays wereconsidered for optimization in the 2D domain using ANSYS Fluent. Results showed that the difference in wind-liftforce between optimized configurations against that with maximum lift force configuration for all three cases above was fifty percent.

Combining data envelopment analysis and Random Forest for selecting optimal locations of solar PV plants

Solar photovoltaic (PV) energy has emerged as a potential alternative to carbon-based energies to meet the Paris agreement commitment. This study investigates the effect of environmental variables on the efficiency of solar PV panels. Data Envelopment Analysis (DEA) is used to estimate efficiencies of 91 solar PV panels located in Australia during the time period 2010–2020. The effects of environmental variables on the estimated efficiencies are quantified using the truncated regression model. Random forest is then used to predict efficiency of solar PV panel in every city of Australia. The results allow to determine the most suitable location and regions for solar PV energy production in Australia. This study provides an interesting and easily interpretable tool for policy decision makers.

Optimal Reactive Power Dispatch by Success History Based Adaptive Differential Evolution Salp Swarm Algorithm

In this study, a novel hybrid algorithm success history-based adaptive differential evolution salp swarm algorithm (SHADE-SSA) is proposed to solve two different cases of IEEE 30 bus reactive power dispatch problems integrated with thermal generators, wind farms and solar photovoltaic plants. Real power loss minimization and voltage deviation minimization are considered as main objectives in the present work. The performance and robustness of the proposed hybrid SHADE-SSA algorithm are compared with the results of five different metaheuristic algorithms for the same test system and consider the same control variables and constraints. The results of the simulation of the proposed algorithm conform to the effective choice for the solution of optimal reactive power dispatch problems of power systems.

Generation of clean water in dry deserts based on photo-voltaic solar plants

In Latin America the renewable energy has increased in 40GW, during the last twenty years; in this perspective countries as Perú, Chile, Brazil, Bolivia and Colombia have extensive desert with the highest irradiance and large field available for this technology. One example is the Atacama Desert and its extension in Chile and Peru; however, it has some challenges as the water availability. Therefore, the clean water availability for industry and agriculture is a problem, only 2.5% of the world's water is fresh water, this value is decreasing every year for agriculture and industrial process. Our methodology has been applied during 2019 to 2021 with a real case study, one water machine has generated 128,000 L of water per year, >230% of efficiency compared with traditional techniques with the same energy consumption: its contribution with a correlational study of 92.33%.

Solar PV cleaning techniques contribute to Sustainable Development Goals (SDGs) using Multi-criteria decision-making (MCDM): Assessment and review

• The types of cleaning techniques for Photovoltaic (PV) panels are briefly reviewed. • The Sustainable Development Goals (SDGs) and their associated targets are mapped to each cleaning technique. • SDGs 6 and 7 are the most influenced SDGs by the cleaning techniques. • The TOPSIS method is employed to compare the cleaning techniques and rank them from most favored to least favored. • Manual cleaning of the PV panels is the highest ranked cleaning technique according to the TOPSIS ranking. The efficiency and power output of photovoltaic (PV) panels are vital to the solar PV plant. Apart from overheating, and natural shading, some geographical locations are more susceptible to objectionable dust accumulation on the surfaces of PV panels. Consequently, specific cleaning techniques are required to mitigate the accumulated dust and restore the plant's efficiency. The most popular PV panel cleaning techniques include natural, manual, automatic, and electrostatic cleaning. Each cleaning technique is associated with both positive and negative impacts. Hence, this paper proposes a methodology that underlines the impact of each cleaning technique and relates it to the Sustainable Development Goals (SDGs) and their associated targets. The results exhibit the goals related to energy (SDG 7, 9,11, 12, & 13) and water (SDG 6) were the most affected SDGs by choice of cleaning technology and thus needed to be carefully considered before their deployment. This paper then employs a multi-criteria decision-making method Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), to compare these cleaning techniques and find the optimal cleaning method concerning the SDGs. This method prioritizes the alternatives by considering the 18 criteria set and their relative significance to the SDGs. The TOPSIS technique showed that the manual cleaning technique outperformed the other cleaning techniques and obtained the highest ranking.

IoT-Based Decentralized Energy Systems

In traditional energy production at large-scale, conventional methods are being used, including fossil fuels. This in turn leads to greenhouse gas emissions (e.g., carbon dioxide or CO2) that cause environmental concerns, but also those traditional methods rely on traditional distribution systems, which are burdened with high transmission losses. This paper focuses on a new concept in the energy sector that undergoes transformation from a traditional centralized system to a decentralized one. In reaching sustainability goals, such as net-zero emissions, the energy sector is incorporating renewable energy sources into the energy system. This requires transformation that combines big conventional energy producers with multiple small- and large-scale energy producers (rooftop photovoltaic panels, wind farms and solar plants) in one system. This enormous transformation is a difficult task, but with recent advancements in information and communication technologies, digitalization, the Industry 4.0 paradigm and Internet of Things technology, it is feasible to achieve. This paper provides a review based on keyword bibliometric analysis, and although it cannot be considered exhaustive or conclusive, it provides a picture of the current international research.

Techno-economic assessment of soiling losses in CSP and PV solar power plants: A case study for the semi-arid climate of Morocco

Many sites suitable for photovoltaic (PV), concentrated solar power (CSP) or hybrid CSP/PV plants are located in the desert or arid and semi-arid areas, where they can suffer from soiling accumulation on the surfaces of solar collectors. The aim of this research is to assess the impact of soiling on the annual performance of CSP and PV solar power plants from a technical and economic perspective. The yield analysis of 1MWe linear Fresnel CSP and 1MWe PV solar power plant is performed under the semi-arid climate of Morocco. One year of ground-based and quality-checked meteorological data, as well as one year of soiling data collected from two soiling monitoring sensors (a Tracking Cleanliness Sensor (TraCS) and a DustIQ) are considered in the analysis. The optical soiling loss is 5 to 6 times higher in case of CSP compared to PV, with an annual average daily of 1.21 %/day and 0.24 %/day, respectively. The total annual energy loss due to soiling, considering a monthly cleaning frequency, is equivalent to 17.76 % and 1.95 % for CSP and PV, respectively. The energy loss depends not only on the optical efficiency drop due to soiling, but also on the available solar resources at the location. The LCoE increases significantly if the impact of soiling is accounted, especially for CSP technology. The analysis of the different cleaning profiles reveals that the cleaning frequency of 3 weeks gives the highest profitability for both PV and CSP solar plants.

48-Pulse Voltage-Source Converter Based on Three-Level Neutral Point Clamp Converters for Solar Photovoltaic Plant

The demand for high-power converters for use in large-scale solar photovoltaic (PV) plants increases as the plant size continues to grow. The multipulse voltage-source converter (VSC) configurations are the viable option for high-power solar PV converter applications because they improve conversion efficiency due to low switching losses and allow direct integration with a medium-voltage (MV) grid. In this article, a 48-pulse VSC based on the fundamental frequency switching (FFS) three-level neutral point clamp (NPC) converter with a start–delta transformer configuration is used for large-scale solar PV plant application. It is also used as a PV-static synchronous compensator (STATCOM) to provide the required reactive power support to the grid to regulate the voltage. The solar PV plant configuration for a 100-MW capacity is modeled in MATLAB and implemented in the real-time OPAL-RT platform to verify its design and controls. The harmonics, static, and dynamic performances are demonstrated in detail.

Energetic, economic, and greenhouse gas emissions assessment of biomass and solar photovoltaic systems for an industrial facility

The industrial sector is responsible for nearly 25% of direct and more than 35% of cumulative global energy-related greenhouse gas (GHG) emissions. This study evaluates the feasibility and comparative analysis of on-site solar photovoltaic (PV) and biomass-fed boilers based on historical energy consumption and demand to reduce dependence on grid-supplied electricity and coal-fired boiler for a rice processing industrial plant. The analysis includes energetic, economic, and GHG emissions assessments and covers (a) Solar PV systems, (b) Biomass fuels, and (c) Co-firing scenarios. Financial analysis of solar PV considers both company-owned and bank-financed systems of fixed and double axis tracking technologies. Biomass fuels include 100% wood, 100% rice husk, and a 50%–50% wood-rice husk mix. Historical energy consumption shows that annual electricity demand is 4,479 MWh, costing $537,480 and producing 2,307 tCO 2 emissions. Feasibility of 1MW, company-owned fixed axis solar PV plant shows a yearly electricity production of 1,544 MWh with 0.031 $/kWh levelized cost and reducing 733 tCO 2 with a payback period of 2.8 years. The base case scenario for boiler operations consists of 3,746 tons of coal burning, costing $427,075 and producing 11,840 tCO 2 emissions. Rice husk fuel study shows a 19% fuel cost savings, a 1.7-year payback period, and 11,612 tCO 2 GHG emission reductions. It is concluded that biomass-based rice husk fuel feedstock is the most suitable renewable energy source (RES) for this industrial facility and can reduce 82% of cumulative annual GHG emissions. Furthermore, energy efficiency improvements and off-site carbon credits provide additional opportunities to achieve net-zero GHG emissions for plant operations.

Analysis of a Multi-Generation Renewable Energy System With Hydrogen-Fueled Gas Turbine

Abstract Solar energy is considered one of the most affordable renewable resources for meeting current energy demands and mitigating environmental problems. However, the exploitation of solar energy is challenging because of both diurnal and seasonal variations. Power-to-hydrogen technologies can play a key role to counterbalance the variation of solar irradiance. Moreover, hydrogen-fueled gas turbines are considered promising technologies to decarbonize the electricity sector. To tackle these concerns, this paper presents a multigeneration energy system operated in island mode in which a hydrogen-fueled gas turbine is coupled with a solar photovoltaic plant, an electrolyzer, an absorption chiller, electric and thermal energy storage, as well as a hydrogen storage. Therefore, the energy system is 100% based on renewable energy. The sizes of the components are optimized by maximizing the exploitation of renewable energy sources, while the supply of electricity from the national grid must be null. Moreover, the effect of ambient conditions on the optimal sizing is also investigated by considering the thermal, cooling, and electrical energy demands of two case studies located in two different climatic zones. The paper demonstrates that the adoption of hydrogen-fueled gas turbines coupled with power-to-hydrogen technologies can effectively support the transition toward a clean energy supply. Moreover, this study provides a procedure for the optimal sizing of a multigeneration energy system fully based on solar energy, by also demonstrating that both photovoltaic (PV) panel area and hydrogen storage volume are feasible, if compared to the considered district layout.

Selection and Optimization of Hybrid Power Systems in Remote Areas of China

Providing electricity to remote areas is one of the most important measures to improve the living conditions of local inhabitants. Clean and reliable ways of electricity generation to meet the electricity demands of residents in remote areas of China are considered proceeding from resource, technical and economic factors. To minimize the total net present cost (NPC) and levelized cost of energy (COE), a hybrid power system comprising a diesel power plant (DPP), a solar photovoltaic plant (SPPP), a wind power plant (WPP), and a pumped storage power plant (PSPP) has been designed by using the HOMER software to meet the electricity demands of residents in Xiaoqindao Township (Chandao County, China). The system includes a 150 kW DPP 1, a 140 kW DPP 2, a 529 kW SPPP, a 50 kW WPP, a 200 kW PSPP (with the upper reservoir volume equal to 10000 m3 and head equal to 98 m), and a 201 kW inverter. The renewable sources account for 91.6% of the hybrid power system total electricity output. Along with meeting the electricity demands of residents, the hybrid system efficiently reduces the pollutant emissions caused by electricity consumption, which is important for efficient use of renewable energy in remote areas.

Computation and analysis of anisotropic solar radiation model for India

Abstract The solar radiation data are a very important parameter to forecast generation from any solar PV (Photo Voltaic) plant to be installed at a suitable location, though it is not readily available for many locations because of the costly measurement instruments, and their maintenance, and calibration issues. The solar irradiation data available from NASA and Meteonorm has the prediction topology of consolidating 20–25 years old irradiation data which has the question of reliability. Furthermore, at various locations in India, the irradiance value of Meteonorm is overestimated than actual. In this research study an attempt has been made to develop an algorithm for the anisotropic solar radiation model by coalescing Gopinathan empirical parameter, Klucher anisotropic model, and Collares-Pereira & Rabl (GKCR) empirical parameter which gives the best results of forecasted daily and monthly solar radiation at almost every location in India. The computed results are compared with Meteonorm and the NASA database for three distinct locations (New Delhi, Chennai, and Kolkata) at different azimuths and tilt angles. The results obtained i.e., solar tilted radiation and tilt gain were very close to that simulated using PVsyst software using Meteonorm and NASA database. The computed results were compared using various statistical tools with measured irradiance values for four distinctly located solar PV plants. The computed result shows the least error compared to actual data for two locations. Employing the GKCR empirical algorithm mentioned in the research article, hourly irradiance data for the next day, for any specific tilt and azimuth angle can be estimated using available weather forecast data (sunshine duration) and can circumvent the penalty levied by the Load dispatch Centre. Furthermore, by using monthly data anticipated by the algorithm, the question of data reliability of chargeable Meteo sources working on coalescing data of antiquated years like NASA and Meteonorm can be solved.

Impact of Time Resolution on Curtailment Losses in Hybrid Wind-Solar PV Plants

Curtailment losses for large-scale hybrid wind–solar photovoltaic (PV) plants with a single grid connection point are often calculated in 1 h time resolution, underestimating the actual curtailment losses due to the flattening of power peaks occurring in shorter time frames. This paper analyses the curtailment losses in hybrid wind–PV plants by utilising different time resolutions of wind and PV production while varying the grid cut-off power, wind/solar PV farm sizes, and shares of wind/PV capacity. Highly resolved 1 s measurements from the operational wind farm and pyranometer are used as an input to specialized wind and PV farm power production models that consider the smoothing effect. The results show that 15 min resolution is preferred over 1 h resolution for large-scale hybrid wind–PV plants if more accurate assessment of curtailment losses is required. Although 1 min resolution additionally increases the estimation accuracy over 15 min resolution, the improvement is not significant for wind and PV plants with capacity above approx. 10 MW/10 MWp. The resolutions shorter than 1 min do not additionally increase the estimation accuracy for large-scale wind and PV plants. More attention is required when estimating curtailment losses in wind/PV plants with capacity below approx. 10 MW/10 MWp, where higher underestimation can be expected if lower time resolutions are used.

A systematic review of grid-connected photovoltaic and photovoltaic/thermal systems: Benefits, challenges and mitigation

Solar energy is the powerhouse where all potential and classified renewable energies lug their sources. The energy transformation from the Sun to electricity requires an adequate control scheme to maximise the generated power and enhance the system efficiency. Besides, more than half of solar irradiation on conventional Photovoltaic (PV) panels is lost. The PV thermal (PV/T) modules have been introduced to convert the lost irradiation to heat. Thus, a systematic review of system components, development, and strategies for grid-connected solar PVs plants is presented. Two solar PVs, traditional PV and PV/T, are evaluated. Each grid-tied PV component is considered a subsystem to analyse the potential improvement of grid-connected PVs. This is from solar resources to grid-tied PV inverter techniques. An intensive assessment of the system improvements is presented to evaluate PV plants’ benefits, challenges, and potential solutions. The improvement trends for the novel generation of grid-connected PV systems consist of applying innovative approaches. It is also found that intelligent strategies optimally ensure the overall efficiency of grid-tied PVs using real-time control and measurement under innovative applications and technologies. These methods effectively assist in enhancing grid-tied diverse solar power approaches. Therefore, this paper would offer a significant foundation for advanced research into the subject of grid-tied PV and PV/T and their innovation and/or technology development.

A photovoltaic power prediction approach enhanced by feature engineering and stacked machine learning model

The rapid depletion of world reserves of fossil fuels escalates energy costs, raises concerns regarding energy supplies, and increases climate impacts. The deployment of renewable energy has therefore become a worldwide trend. Among the other known forms of energy, solar energy is unquestionably the cleanest. In an era of widespread deployment of solar photovoltaic plants, research and development activities have made significant progress in assessing and investigating the behavior of solar installations in order to increase their reliability; however, there is still a local component that has not been thoroughly considered in many climate areas, including the semi-arid one. Understanding the distinctive features of each environment as well as how they affect photovoltaic power is the first step towards developing intelligent and data-driven maintenance algorithms suited to the region’s environmental context. This study proposes a stacked machine learning model making hourly predictions of two PV systems varying in size and age. Three machine learning algorithms were compared with a baseline linear regression model and a reference physical model for the PV prediction. The algorithms were also evaluated in terms of the input predictors. The findings demonstrate that the stacked ensemble model outperforms the individual models chosen for comparison in both systems and offers new perspectives for improving PV prediction in this area.

Converting data into knowledge with RCA methodology improved for inverters fault analysis

In the last years, the knowledge management methodology increased the perspective and deeply analysis in the energy evaluation, with great emphasis in the training of the maintenance teams and early detection of failure modes; these inefficiencies detection is associated to patterns recognition with expert systems. Several energy brands, utilities, universities, and design companies investigated about this problem with limits in the integration between maintenance team knowledge and the degradation of the energy equipment. Therefore, our findings are a new approach of the root cause analysis (RCA) improved with the knowledge management perspective, associated to the failure mode analysis for 164 inverters in photo-voltaic solar plant by using twenty-one failures modes; by incorporate the graph theory called Erdös–Rényi graphs with a quantitative methodology and qualitative evaluation with the knowledge management method in the root cause analysis; the dataset evaluated has 120,561 signals associated to 3,014,025 patterns, during the period from 2018 to 2021 in a PV solar plant. In this new root cause analysis method, the knowledge management is analyzed as a complement for the solution for sudden failure modes and early degradation.