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A proposed set of indicators for evaluating the performance of the operation and maintenance of photovoltaic plants
Abstract
As photovoltaic plants (PV) age, the need for efficient monitoring of operations & maintenance (O&M) increases, helping to understand the situation of the plant, identify problems and propose solutions for future strategies. In this context, the objective of this paper is to propose a set of key performance indicators (KPIs), responsable to evaluate O&M performance in PV power plants, considering their importance and complexity mensuration levels. After refining and validating a set of KPIs using Delphi method with industry specialists, the KPIs are classified by energy performance assessment and O&M services assessment. Subsequently, the levels of impor- tance and complexity of measurement are evaluated with stepwise weight assessment ratio analysis (SWARA) method. Finally, after obtaining this information, an importance-complexity matrix is developed, thus contem- plating the proposed set of KPIs. This study presents a comprehensive set of 25 KPIs capable of quantitatively measuring the O&M performance of a PV plant. The results show that the KPIs, Performance Ratio and Spare Parts Availability are the most important in this evaluation, and KPI Contractual Availability presents greater complexity in measuring its parameters. Based on the importance-complexity matrix, it is possible to verify that the KPI, Schedule Compliance stands out compared to the others, presenting a high level of importance and a low level of complexity in its measurement. The information resulting from this study seeks to help PV plant man- agers to select the appropriate KPIs to measure the status of the O&M management of the PV plant.
... This is the ratio of the average load to the highest demand for a given period of time [30]. Since the average load is always less than the maximum demand, L f is always less than unity. ...
... A low value of OCLF indicates that factors outside of management control are not significantly contributing to loss of capacity due to unplanned events. The mathematical model for OCLF is given as [30]: Where P Loss (beyond management control) = MW capacity of losses within management control × downtime of Load Loss. ...
This paper presents the analysis of key performance indicators and some effective improvement strategies on four gas turbine generators (GTG) of Nigeria's power plant company in for a period of four years (2019-2022). The investigation used the NERC/IEEE Standard 762 (2006) generator performance indices amongst other calculated key performance indices to evaluate the collected data. The research methodology was done through the collection of data using questionnaires, operational records, and plant data sheets recorded by operators in the power station and data analysis using Excel software, and then constructive optimization techniques were recommended for each key performance factor. From the result obtained, the operational performance shows an average energy generation of approximately 389.71 GWh. The equipment availability factor averaged 54.08%, indicating moderate reliability. The energy availability factor was notably lower, averaging 11.99%, suggesting significant room for improvement. The capacity factor averaged 9.39%, while the plant use factor was relatively high at 80.59%, demonstrating efficient operational usage. The load factor was low, with an average of 0.10, pointing to potential underutilization of capacity. The shortfall in performance levels is attributed to less plant availability due to overdue overhauling of some units resulting in frequent breakdowns/failures, obsolete technology, aging plant equipment, instability of the national grid system, and disruption in gas supply among others. The recommended strategic techniques are designed to address the specific challenges associated with each factor, thereby enhancing the overall operational performance of the power plant and other power generation companies for maximum productivity.
... Para isso, é preciso considerar vários fatores que influenciam o custo da eletricidade gerada pela FV, como o custo do combustível, o custo de operação e manutenção, e o horário de funcionamento (Rediske et al., 2022). O setor FV está em constante crescimento e, por isso, as atividades de operação e manutenção (O&M) se tornam cada vez mais relevantes (Rediske et al., 2024). A qualidade e a confiabilidade da energia gerada por um sistema FV dependem diretamente da gestão de O&M, que também é fundamental para garantir a rentabilidade dos investimentos. ...
A gestão de operação e manutenção (O&M) de usinas solares fotovoltaicas (FVs) é essencial para manter o alto rendimento e o baixo custo de geração desses sistemas, que estão em crescente expansão nos últimos anos. Além dos aspectos técnicos, o desempenho financeiro de O&M também é um fator importante para monitorar a viabilidade e a rentabilidade dos sistemas de geração de energia solar FV. No entanto, nem sempre os gestores dispõem de indicadores chaves de desempenho (KPIs) financeiros adequados para essa finalidade. Neste trabalho, propõe-se uma metodologia para identificar e ponderar os KPIs financeiros mais relevantes para a avaliação do desempenho financeiro de O&M de usinas FVs. Para isso, foi realizada uma revisão sistemática de literatura (RSL) sobre os principais indicadores financeiros utilizados na avaliação de desempenho de sistemas FVs, bem como uma pesquisa com especialistas do setor para obter suas opiniões utilizando ao método Delphi, e verificado suas preferências com o auxílio do método SWARA. Os resultados apresentam cinco indicadores que são importantes para os gestores considerarem monitorar em seus empreendimentos, sendo o KPI Custo equivalente de peças de reposição o mais importante, com 22,96% de peso.
... In order to reach this goal, it is necessary to monitor the photovoltaic system to identify operational failures and/or the need for corrective or preventive maintenance [3][4][5][6][7][8][9]. In the Brazilian context, the Standard ABNT NBR 16274:2014 [10] defines the minimum required procedures to evaluate the performance of a Grid-Tie Photovoltaic System (GTPVS) since the early operation. ...
This work presents the results of research aimed at evaluating the performance of the photovoltaic system connected to the electrical grid at the University of Brasília (UnB), Brazil. Following the guidelines established by the Brazilian Standard for Performance Monitoring and Analysis of Grid-connected Photovoltaic Systems, it was possible to evaluate the system’s performance by determining the Performance Ratio (PR) indicator. The operating temperatures were estimated using measured values of the ambient temperature and solar irradiation. These data were collected by a nearby solarimetric station. Next, the theoretical energy injected into the electrical grid was determined based on calculations of the Direct Current (DC) power at the inverter input and the Alternating Current (AC) power at the inverter output. To this end, the coefficients of the inverter efficiency curve were considered as well as a loss scenario, as recommended. With these results, as well as the information about the total photovoltaics (PV) system AC production obtained from the inverter supervisory system, it was possible to determine the average annual PR achieved and compare the theoretical and practical results obtained. The main contribution of this paper is the performance evaluation of a 125 kWp grid-connected photovoltaic system at the University of Brasília (UnB), assessed using Brazilian Standards for performance monitoring and analysis. The system, installed on the rooftop of the UED building, consists of 298 Canadian Solar HiKu CS3W-420P modules with a 15-degree north pitch angle facing geographic north. It interfaces with the grid through two three-phase inverters, model CSI-75K-T400 (74.76 kWp) and a CSI-50KTL-GI (50.4 kWp). The results showed that the system with a 50kW inverter had an average PR of 78%, while the system with a 75 kW inverter showed a PR variation from 56% to 93%. The information obtained in this work will be used to develop computational tools capable of monitoring and evaluating, in real time, the performance of photovoltaic systems and ensuring that the expected financial return is achieved through the use of preventive and corrective maintenance actions in a timely manner.
... Para isso, é preciso considerar vários fatores que influenciam o custo da eletricidade gerada pela FV, como o custo do combustível, o custo de operação e manutenção, e o horário de funcionamento (Rediske et al., 2022). O setor FV está em constante crescimento e, por isso, as atividades de operação e manutenção (O&M) se tornam cada vez mais relevantes (Rediske et al., 2024). A qualidade e a confiabilidade da energia gerada por um sistema FV dependem diretamente da gestão de O&M, que também é fundamental para garantir a rentabilidade dos investimentos. ...
A gestão de operação e manutenção (O&M) de usinas solares fotovoltaicas (FVs) é essencial para manter o alto rendimento e o baixo custo de geração desses sistemas, que estão em crescente expansão nos últimos anos. Além dos aspectos técnicos, o desempenho financeiro de O&M também é um fator importante para monitorar a viabilidade e a rentabilidade dos sistemas de geração de energia solar FV. No entanto, nem sempre os gestores dispõem de indicadores chaves de desempenho (KPIs) financeiros adequados para essa finalidade. Neste trabalho, propõe-se uma metodologia para identificar e ponderar os KPIs financeiros mais relevantes para a avaliação do desempenho financeiro de O&M de usinas FVs. Para isso, foi realizada uma revisão sistemática de literatura (RSL) sobre os principais indicadores financeiros utilizados na avaliação de desempenho de sistemas FVs, bem como uma pesquisa com especialistas do setor para obter suas opiniões utilizando ao método Delphi, e verificado suas preferências com o auxílio do método SWARA. Os resultados apresentam cinco indicadores que são importantes para os gestores considerarem monitorar em seus empreendimentos, sendo o KPI Custo equivalente de peças de reposição o mais importante, com 22,96% de peso.
... Typically, both physical examination and wages payment for construction workers are executed by the contractor internally, making it challenging to observe externally. As a result, it is hard to obtain the values of indicators a, h, and i. • Measurability: Whether the indicator can be measured by observers without effort [42,45]. For indicator h, it is nearly impossible to count the number of bank accounts verified by the contractor. ...
Overage construction workers have received widespread attention due to the higher safety risk. The balance of interests among the contractor, supervisor, and regulator forms a game, whose dynamics are tightly linked to the contractor’s risk perception. This study aims to construct a tripartite evolutionary game model with risk perception integrated, thus proposing tailored supervisory strategies for supervisors and regulators. Unlike the traditional scale-based approach, a behavior-based method is developed to measure the contractor’s risk perception, which improves the interpretability of results and avoids complex questionnaire surveys. The simulation results reveal a clear correlation between the contractor’s risk perception and behavior. It is recommended that supervisors increase penalties and the initial possibility of Rigorous Inspection properly, and regulators may consider enhancing credit-based future returns and losses. Notably, certain measures may not be applicable to all contractors. Hence, supervisors and regulators should identify their risk perception before adopting supervisory strategies.
... The data processing uses algorithms, heuristics, neural networks and other technologies to determine the optimum operation point considering several variables, such as weather information, grid capacity, energy prices, and current energy generation [189,190]. These systems can also participate in Blockchains and other DLTs used by decentralised local systems. ...
The evolution of technology and the diversification of service offerings have significantly influenced how energy is produced, distributed, stored, and consumed in cities. These new platforms are part of the Smart Energy concept and are aligned with the UN's 7th SDG by creating new business models that promote the growth of investment in infrastructure for clean energy technology. This development has several drivers, along with the various services offered to users, such as energy forecasting, intelligent measurement, and demand response programs. In this sense, this article aims to identify the development of technological drivers of Smart Energy, map collaborative networks among these drivers, and discuss best management practices for establishing collaborative relationships. The research was developed from a systematic literature review and a meta-analysis carried out with the aid of statistical software. Six key drivers and 20 collaborative drivers were identified. These drivers integrate four collaborative networks structured to offer an understanding of how energy systems are organised under the concept of Smart Energy. From this, the article provides insights into the development, implementation, and management of these networks and drivers to accelerate the diffusion of Smart Energy to users at all levels.
This study presents a comprehensive analysis of the energy performance and economic feasibility of optimal
power generation systems, including an electrical network and a grid-connected PV/battery system, designed to
meet the electricity demands of an industrial building in Saudi ‘Arabia’s Eastern region. The building, located in
Abqaiq City, spans approximately 29,000 m2 and has an average annual electrical consumption of 6,500 MWh.
The primary focus is on assessing the economic feasibility of these systems under local grid conditions. Simulations
using PVsyst software were conducted for three configurations: grid-connected PV and PV with battery.
The findings indicate that bifacial PV modules increase energy harvesting in coastal arid climate regions, with
specific production ranging from 2,235 to 2,158 kWh/kWp/year and a PR between 95.9 % and 92.6 %. The gridconnected
PV system is more feasible under industrial electricity tariffs, with a levelized cost of energy of
4,233,274, an ROI of 426.5 %, and a payback period of 4.7 years. These systems
achieve energy savings of approximately 66 % for grid-connected PV and 89 % for hybrid PV/battery systems.
Additionally, commercial and global tariffs significantly enhance the NPV and ROI of both systems, reducing the
payback period and improving economic feasibility. Under commercial tariffs, the grid-connected PV system
excels in ROI and payback period, while the hybrid system outperforms in NPV under global tariffs. By
addressing economic and climate challenges in harsh coastal environments, this work provides valuable insights
for sustainable investment decisions in an industrial city context.
Besides being limited in quantity, conventional energy sources also emit toxic gases. The Photovoltaic (PV) Solar System is one of the most energizing green energy sources. Around the globe, solar panels are being installed on barren land as well as on the roofs of buildings to generate electricity. An education institute in northern India recently took a step in this direction by installing a grid-tied 100 kWp solar power plant. The installed PV panels are tilted at an angle of 30° and mounted on the roof of the building. The actual PV plant system’s performance differs from the performance under laboratory conditions. Hence, performance evaluation of real outdoor plants becomes essential, especially when the plant is commissioned in different situations, such as roof-mounted systems. Many softwares can estimate the plant’s performance evaluation, but their reliability is not yet proven. This paper examines the performance evaluation of grid-tied PV plants between January 2019 and December 2019 in accordance with the IEC 61724 standard. Moreover, the results of the actual plant have also been compared with the results from the PV*Syst software that simulates the real-time behavior of the plant. Further, in order to evaluate the power plant’s performance, this paper analyzes the various parameters of the PV plant, including reference yield, final yield, and performance ratio of the PV plant. An evaluation of the module’s performance indicates that it has produced 101.57 MWh of energy over 1 year, with a performance ratio of 0.60. It is evident from the comparative analysis that rooftop solar panels are an economically viable and technologically feasible means of providing electricity in the northern parts of India. By taking such measures, the institutes or offices can protect the environment and save money by becoming microgrids. The proposed project provides a roadmap for installing rooftop photovoltaic plants in populated cities without occupying additional land.
This study aims to estimate the performance and losses of a 50 MW photovoltaic (PV) utility-scale after 12 years of operation. The PV plant has monocrystalline and polycrystalline silicon modules and is located in the central region of Spain with an annual insolation of 1976 kWh/m². Monitoring data over the entire year 2020 has been analyzed and filtered to assess the performance results following the IEC 61724 standard guidelines. The annual average reference yield, final yield, performance ratio and capacity utilization factor are of 5.44 h/d, 4.28 h/d, 79.24%, and 19.77%, respectively. Besides the experimental analysis, this work improves the estimation of the daily performance ratio, especially in days with low insolation. Two different modelling approaches have been assessed and compared. In first place, a physical model has been adopted, based on the most common losses, and including an exponential expression to account for low irradiance losses. In second place, statistical models have been used, with either multiple linear regressions or random forest algorithms. In contrast with other published models which require many inputs, the best accuracy has been reached with the random forest model using only the ambient temperature and solar irradiance as predictors, obtaining a RMSE of 1% for the PR and for the energy production.
Due to the overall declining costs of photovoltaic systems, market players in the operation and maintenance sector are under increasing price pressure when offering their services. The automation and standardization of maintenance and failure tickets as well as their statistical and economical evaluation are key to ensure optimal yield and long lifetime. A thorough understanding of typical faults, classified through a standardized taxonomy, can be a pathway of developing location and technology specific decision support, offering cost‐time efficient solutions to reduce component downtime and costs in case of failure appearance. A useful method for this approach is the Cost Priority Number, a methodology to assess technical failures and their economic impact in energy systems. In this work, this method is further improved to be applied to individual use cases to make it useful in the actual operation of PV plants. A standardized ticket taxonomy for the operational phase of PV systems has been developed where more than 35,000 PV systems' tickets have been statistically evaluated, and a fully automated methodology to calculate the cost of individual maintenance tickets has been developed. We developed several key tools for integrating a data‐driven decision support into existing PV O&M platforms based on O&M ticket evaluation. The foundation is the TRUST‐PV Risk Matrix, a standardized categorization framework for tickets. Based on that, more than 35,000 O&M tickets have been statistically evaluated to calculate frequency and probability of failures appearing in the field. The economic impact of these failures is determined through the development of a methodology calculating the cost of individual PV system tickets.
This review work presents an overview of the innovations shaping today’s photovoltaic (PV) operations and maintenance sector by summarising literature and current research. After a brief introduction to the market dynamics and state-of-the-art best practices, relevant insights are provided into emerging fields and key research directions are identified, such as the adaptation and application of the building information modelling concept and digital twins, which are topics already proven to help other industries to render processes more efficient, reduce costs and risks throughout the entire lifecycle of a project. Moreover, it explores new approaches on Supervisory Control and Data Acquisition architectures for remote monitoring of PV assets, highlighting the promising role of 5G wireless technologies such as Narrow Band Internet of things. Finally, concerned about the growing amount of PV waste due to the exponential growth of installed capacity on a global scale, this article covers relevant Circular Economy approaches being adapted to PV, pointing out the most significant research and development efforts that are pushing towards a more sustainable, environmentally friendly and economically viable end of life management for modules and balance of system.
Aiming at the problem that the regular maintenance method of the photovoltaic power generation system cannot comprehensively consider the optimization of maintenance cost, availability and profit during the maintenance period. On the basis of considering the operating state of equipment and the influence of weather, a novelly dynamic and combined maintenance model of photovoltaic power plants based on component correlation and availability is proposed. Firstly, an association set is established from the component correlation. The maintenance decision is made with the association set as the basic unit. Then through the improved Markov state transition process, the state of the equipment and the association set is predicted, and its availability is determined. Finally, the maintenance model is established with the lowest maintenance cost and highest availability as the objective function. The model comprehensively considers the influence of component correlation, availability and weather factors on the maintenance strategy. The simulation of matlab proves that the model can effectively reduce maintenance times and maintenance costs. Taking a photovoltaic power station as an example, by setting different scenarios, comparing the model with the regular maintenance model that does not consider correlation, availability and weather factors, the economy and effectiveness of the maintenance model are verified.
Electricity consumption in Brazil increases significantly, making feasibility studies to diversify the Brazilian electricity mix with other renewable sources become essential. However, the electricity market continues to be primarily driven by government or regulatory incentives and economic status. With the coronavirus pandemic since the beginning of 2020, the market has been undergoing significant changes that cause uncertainty in consumer investments in grid‐connected photovoltaic systems. This article presents an economic feasibility analysis of photovoltaic system installation for the Brazilian residential sector, estimating a cash and term investment and comparing the viability of the investment before the global pandemic (December 2019) with the pandemic scenario (April 2021) for each of the 27 Brazilian capitals. The baseline scenario for economic feasibility analysis calculations considered a power system of 4 kW. The results show that the effects of the pandemic in the electricity market were positive in the feasibility of investing in grid‐connected photovoltaic systems. Also, financing the system increases the chances of profitability. The current scenario is possibly the most attractive ever experienced by potential investors. However, this favorable scenario could decline in the coming years due to climatic and governmental factors.
The number of large photovoltaic (PV) power plants is increasing around the world. Energy sale usually follows demand contracts with clearly defined obligations, subject to nonsupply penalties. Not supplying the amount of contracted energy is a critical issue to PV plant performance, which can be mitigated with operation and maintenance (O&M) good practices. Furthermore, as the PV plant ages, O&M practices become increasingly important to improve or maintain a good performance. Performance assessment is a complex process that involves several parameters to collect, indicators to measure, and action plans to solve the identified problems. The objective of this study is to identify relevant factors in performance evaluation of O&M practices of medium to large PV plants. Through a systematic literature review and Delphi method with specialists, 33 key performance indicators (KPIs) were determined and classified in operation or maintenance categories, and further in technical or economic subcategories. This paper also organized a set of parameters that PV plant managers should collect to determine the KPIs and evaluate the O&M practices performance. Efficient management of O&M practices results in an indirect increase in generation capacity, as well as ensuring compliance with electricity supply contracts. Photovoltaic plant monitoring
Data collection and review are the building blocks of academic research regardless of the discipline. The gathered and reviewed data, however, need to be validated in order to obtain accurate information. The Delphi consensus is known as a method for validating the data. However, several studies have shown that this method is time-consuming and requires a number of rounds to complete. Until now, there has been no clear evidence that validating data by a Delphi consensus is more significant than by a general consensus. In this regard, if data validation between both methods are not significantly different, then just using a general consensus method is sufficient, easier, and less time-consuming. Hence, this study aims to find out whether or not data validation by a Delphi consensus method is more significant than by a general consensus method. This study firstly collected and reviewed the data of sustainable building criteria, secondly validated these data by applying each consensus method, and finally made a comparison between both consensus methods. The results showed that seventeen of the valid criteria obtained from the general consensus and reduced by the Delphi consensus were found to be inconsistent for sustainable building assessments in Cambodia. Therefore, this study concludes that using the Delphi consensus method is more significant in validating the gathered and reviewed data. This experiment contributes to the selection and application of consensus methods in validating data, information, or criteria, especially in engineering fields.
In this paper, a novel software package, called RoboPV, is introduced for autonomous aerial monitoring of PV plants. RoboPV automatically performs aerial monitoring of PV plants, from optimal trajectory planning to image processing and pattern recognition for real-time fault detection and analysis. RoboPV consists of four integrated components: boundary area detection, path planning, dynamic processing, and fault detection. To design an optimal flight path, aerial images of PV plants, which have been collected from experimental flights, are given as inputs to a developed encoder-decoder deep learning architecture to extract boundary points of PV plants automatically. Then, a novel path planning algorithm is conducted by RoboPV to design an optimal flight path with full coverage of whole regions of the PV plant. Aerial images are analysed in real-time during the flight by a high precise neural network trained for automatic fault detection. In this study, several decision-making and maneuver algorithms were developed for various real-world flight conditions to improve the performance of RoboPV during an autonomous aerial inspection. RoboPV is a modular processing library that can be installed on any microcomputer processor with a low computational power. Moreover, supporting the MAVLink communication protocol enables RoboPV to connect with an intelligent Pixhawk flight autopilot and navigate a wide range of multi-rotors. To demonstrate the performance of RoboPV, a six degrees of freedom dynamic model of a multi-rotor is developed in a SIMULINK environment with a defined aerial monitoring mission on three different real megawatt-scale PV plants. The results prove that RoboPV can execute the autonomous aerial inspection with an overall accuracy of 93% for large-scale PV plants.
Photovoltaic solar energy is a fast-growing renewable energy that needs reliable condition monitoring systems to ensure the productivity of solar plants. Unmanned aerial vehicles are widely implanted to reduce maintenance costs in photovoltaic plants, leading suitable information for fault detection and diagnosis. This paper presents a novel condition monitoring system for photovoltaic panels composed by a radiometric sensor embedded in an unmanned aerial vehicle. A new contribution based on the field of view analysis with infrared sensors is proposed according to the measurement conditions, and the increment of unmanned aerial vehicle positioning reliability by a real-time kinetic navigation system. Several scenarios are designed for testing the reliability and suitability of the system. The results show significant improvements of the proposed approach compared with the reference based on GPS navigation system, higher than 65% for 83% of the cases, minimizing the errors and increasing the accuracy of the inspection, avoiding the analysis of areas outside the panels. The application of real time kinematic ensures a suitable field of view definition in 97% of the cases, while inaccurate results are detected with GPS in more than 20% of the experiments.
Most photovoltaic (PV) plants conduct operation and maintenance (O&M) by periodical inspection and cleaning. Such O&M is costly and inefficient. It fails to detect system faults in time, thus causing heavy loss. To ensure their operations are at an ideal state, this work proposes an unsupervised method for intelligent performance evaluation and data-driven fault detection, which enables engineers to check PV panels in time and implement timely maintenance. It classifies monitoring data into three subsets: ideal period A, transition period S, and downturn period B. Based on A and B datasets, we build two non-continuous regression prediction models, which are based on a tree ensemble algorithm and then modified to fit the non-continuous characteristic of PV data. We compare real-time measured power with both upper and lower reference baselines derived from two predictive models. By calculating their threshold ranges, the proposed method achieves the instantaneous performance monitoring of PV power generation and provides failure identification and O&M suggestions to engineers. It has been assessed on a 6.95 MW PV plant. Its evaluation results indicate that it is able to accurately determine different functioning states and detect both direct and indirect faults in a PV system, thereby achieving intelligent data-driven maintenance.
Observing the growing energy demand of modern societies, many countries have recognized energy security as a looming problem and renewable energies as a solution to this issue. Renewable hydrogen production is an excellent method for the storage and transfer of energy generated by intermittent renewable sources such as wind and solar so that they can be used at a place and time of our choosing. In this study, the suitability of 15 cities in Fars province, Iran, for renewable hydrogen production was investigated and compared by the use of multiple multi-criteria decision-making methods including ARAS, SAW, CODAS, and TOPSIS. The obtained rankings were aggregated by rank averaging, Borda method, and Copeland method. Finally, the partially ordered set ranking technique was used to reach a general consensus about the ranking. The criteria that affect hydrogen production were found to be solar energy potential, wind energy potential, population, air temperature, natural disasters, altitude, relative humidity, land cost, skilled labor, infrastructure, topographic condition, and distance from main roads. These criteria were weighted using the best–worst method (BWM) based on the data collected by a questionnaire. Solar energy potential was estimated using the Angstrom model. Wind energy potential was estimated by using the Weibull distribution function for each month independently. The results of the multi-criteria decision-making methods showed Izadkhast to be the most suitable location for renewable hydrogen production in the studied area.
Brazil is currently undergoing changes to regulations on distributed generation (DG), specifically for solar energy micro-generation. The changes proposed by the Brazilian Regulatory Agency suggest that only the cost of energy be compensated to investors. The service costs and other charges related to energy tariffs must be divided among consumers. Investors with existing in-stallations and class entities have contested these proposals, calling them “sun-fees”. To date, no scientific papers have been published discussing these changes. The new regulations propose an end to cross subsidies, where all consumers (even those who do not have DG) pay for the trans-mission and distribution systems. This study compares the economic feasibility of mi-cro-generation before and after implementing the new standards proposed by the Regulatory Agency. We used data on average electrical energy demand, energy price, and solar radiation in different regions. The national averages were used as a base comparison with other scenarios. The results show that projects are viable for all analyzed scenarios, however, after implementing the proposed changes, the discounted payback time is extended. This, however, does not make projects unfeasible.
Both theoretical study, experimental validation, and outdoor performance assessment of a single grid-connected photovoltaic plant installed on the roof of the car parking located in Renewable Energy Applied Research Unit (URAER), Ghardaia, southern of Algeria have been carried out in this paper. The plant is made up of 16 micro-amorphous modules of 125 Wp and one SMA sunny boy inverter of 3 kW. The results presented were based on experimental data recorded after about three and a half years; from May 18, 2015 to December 31, 2018. The first part of this work, modeling, and validation by the experimental data of photovoltaic array (DC side) based on the Linear Reoriented Coordinates Method (LRCM) and grid-connected inverter (AC side) based on the model developed at Sandia National Laboratories. The results show a good agreement between measured and calculated data (current, voltage, power, and efficiency). The second part of the work is to investigate the operational performance and efficiency of the PV plant from the measured daily power generation and weather as recommended in the International Electro Technical Commission IEC 61724. The performance-computed parameters are based on monthly average daily values, including DC and AC energy generated, reference yield array yield final yield temperature losses PV, inverter and system efficiency, capacity factor performance ratio, and others as defined in IEC. The total output energy PV installation generated during the measured period was of 12944, 44 kWh and the total energy output delivered into URAER internal grid was 12292, 16 kWh. Furthermore, the average daily array yield, final yield, and reference yield varied from a minimum to a maximum value of 2.62 kWh/kWp-6.41 kWh/kWp, 2.51kWh/kWp-6.29kWh/kWp, and 2.39kWh/kWp-6.06kWh/kWp, respectively. In addition, the average daily PV module efficiency, system efficiency, and inverter efficiency varied from a minimum to a maximum value of 8.21-9.00%, 7.56-8.35%, and 94-95%, respectively. The array capture losses, system losses, capacity factor, and performance ratio varied from a minimum to a maximum value of 0.053 kWh/kWp-0.42 kWh/kWp, 0.1206 kWh/kWp-0.31kWh/kWp, 10.30%-26.10%, 79.13%-82.52%, respectively. These results highlight the relatively good performance of PV plants installed in the southern region of Algeria.
This study presents the actual monitoring data and performance analysis results obtained from the 11.15-MW grid-connected Shagaya Photovoltaic Park, Kuwait (latitude 29.2°N, longitude 47.04°W). The plant was specifically built to compare two PV technologies: a Thin-Film (TF) installation of 5.5 MW and a Polycrystalline (PC) silicon installation of 5.6 MW. The plant has been designed as a pilot testbed to evaluate the performance of different grid-tied PV technologies under a hot desert environment. A 25-month monitoring period has provided important information that will be used to prepare the design of upcoming GW-size PV projects at Shagaya. Performance parameters of the PV system evaluated here include: final yield, reference yield, array yield, system losses, array capture losses, cell temperature losses, PV module efficiency, system efficiency, inverter efficiency, performance ratio, and capacity factor. Comparison between the TF and PC PV subsystems reveals no significant difference between the two technologies regarding annual reference yields (1805 kWh=kW for TF and 1810 kWh=kW for PC), performance ratios (80.0% for TF and 80.2% for PC), or system losses (20.04% for TF and 19.81% for PC). The PC panels are affected by slightly higher module capture losses due to their higher negative temperature coefficient. It is found, most importantly, that the two PV subsystems have very similar performance behavior when they have similar capacity and operate under the same Shagaya desert environment with the same stringent operational and maintenance strategies. One disadvantage of the TF subsystem, however, is that it requires 18.5% more land area than the PC subsystem to match the installed capacity. The soiling of panels is substantial and requires monthly cleaning with water, which is scarce in a desert environment. A smaller total panel area for a similar power output could constitute a significant economic advantage for the PC technology under such conditions.
The fault detection applied to a large amount of small distributed PV systems needs to be simple, cost-effective, and reliable. This work presents a fault detection procedure applied to distributed PV system fleets, based on a novel performance indicator, designated as Performance to Peers (P2P), that can be constructed on the sole basis of the comparison of the energy production data of several neighboring PV systems. This article explains how to construct this performance indicator and how to use it to carry out automatic fault detections. This fault detection procedure has been developed in the context of the performance analysis carried out on approximately 6000 PV installations located in Europe, and it is illustrated and discussed through real application cases. The P2P has been shown to be more stable than the Performance Ratio (PR), in particular in the presence of sub-par metadata on the PV systems, and it thus constitutes a more robust performance indicator for fault detection. The stability of P2P is characterized by an Absolute Median Deviation (MAD) that is typically of 10% for hourly data and 5% for daily data. The application of P2P to fault diagnosis is illustrated on four categories of faults that are among the most frequently observed on PV systems. The main limitations of this novel methodology are discussed, and several future lines of research are suggested.
Soiling losses are a major concern for remote power systems that rely on photovoltaic energy. Power loss analysis is efficient for the monitoring of large power plants and for developing an optimal cleaning schedule, but it is not adapted for remote monitoring of standalone photovoltaic systems that are used in rural and poor regions. Indeed, this technique relies on a costly and dirt sensitive irradiance sensor. This paper investigates the possibility of a low-cost monitoring of cleaning interventions on photovoltaic modules during daytime. We believe that it can be helpful to know whether the soiling is regularly removed or not, and to decide if it is necessary to carry out additional cleaning operations. The problem is formulated as a classification task to automatically
identify the occurrence of a cleaning intervention using a time window of temperature, voltage and current measurements of a photovoltaic array. We investigate machine learning tools based on Logistic Regression, Support Vector Machines, Artificial Neural Networks and Random Forest to achieve such classification task. In addition, we study the influence of the temporal resolution of the signals and the feature extraction on the
classification performance. The experiments are conducted on a real dataset and show promising results with classification accuracy of up to 95%. Based on the results, three implementation strategies addressing different practical needs are proposed. The results may be particularly useful for non-governmental organizations, governments and energy service companies to improve the maintenance level of their photovoltaic facilities.
In this paper, the authors propose an UAV-based automatic inspection method for photovoltaic plants analyzing and testing a vision-based guidance method developed to this purpose. The maintenance of PV plants represents a key aspect for the profitability in energy production and autonomous inspection of such systems is a promising technology especially for large utility-scale plants where manned techniques have significant limitations in terms of time, cost and performance. In this light, an ad hoc flight control solution is investigated to exploit available UAV sensor data to enhance flight monitoring capability and correct GNSS position errors with respect to final target needs. The proposed algorithm has been tested in a simulated environment with a software-in-the loop (SITL) approach to show its effectiveness and final comparison with state of the art solutions.
The size and the complexity of photovoltaic solar power plants are increasing, and it requires an advanced and robust condition monitoring systems for ensuring their reliability. This paper proposes a novel method for faults detection in photovoltaic panels employing a thermographic camera embedded in an unmanned aerial vehicle. The large amount of data generated by these systems must be processed and analyzed. This paper presents a novel approach to identify panels to detect hot spots, and to set their locations. Two novels region-based convolutional neural networks are unified to generate a robust detection structure. The main contribution is the combination of thermography and telemetry data to provide a response of the panel condition monitoring. The data are acquired and then automatically processed, allowing fault detection during the inspection. A detailed description of the methodology is presented, including the different stages to build the neural networks, i.e. the training process, the acquisition and processing of data and the outcomes generation. A thermographic inspection of a real photovoltaic solar plant is done to validate the proposed methodology. The accuracy, the efficiency and the performance of the approach under different real scenarios are evaluated statistically obtaining satisfactory results.
The first distributed mini-generation photovoltaic system in the state of Piauí was installed on the Floriano campus of the Federal Institute of Piauí. The system consists of 660 photovoltaic modules and occupies an area of approximately 1.122 m
2
of roof. This article aims to evaluate the performance of the first year of operation, which runs from May 2016 to June 2017, compared to technical and economic aspects. The technical analysis was done through the calculation of the indexes of merit: Final Productivity Performance Rate and Capacity Factor. In the economic viability analysis were used the concepts of Net Present Value, Internal Rate of Return, Payback and Cost-Benefit Ratio. The results demonstrate that the system has technical feasibility, since it presented a good generation in its first year of operation, with production superior to other systems installed in public educational institutions of Brazil. In addition, it has shown a positive balance in the Net Present Value, but with a long Payback and a very high Cost-Benefit Ratio, which may compromise its economic viability. However, in view of the institution's final activity, the installation of the photovoltaic system will bring benefits beyond the reduction of electricity consumption, if used in teaching, research and extension activities.
In this paper, we address the problem of asset performance monitoring, with the intention of both detecting any potential reliability problem and predicting any loss of energy consumption efficiency. This is an important concern for many industries and utilities with very intensive capitalization in very long-lasting assets. To overcome this problem, in this paper we propose an approach to combine an Artificial Neural Network (ANN) with Data Mining (DM) tools, specifically with Association Rule (AR) Mining. The combination of these two techniques can now be done using software which can handle large volumes of data (big data), but the process still needs to ensure that the required amount of data will be available during the assets' life cycle and that its quality is acceptable. The combination of these two techniques in the proposed sequence differs from previous works found in the literature, giving researchers new options to face the problem. Practical implementation of the proposed approach may lead to novel predictive maintenance models (emerging predictive analytics) that may detect with unprecedented precision any asset's lack of performance and help manage assets' O&M accordingly. The approach is illustrated using specific examples where asset performance monitoring is rather complex under normal operational conditions.
The main purpose of this paper is to design a scientific based probabilistic model based on
Markov chains, calculate reliability indicators such as Mean Time Between Failure (MTBF) and Mean Time
To Failure (MTTF) based on probabilities, compute failure rates based on statistics data and to provide an
algorithm to calculate the maximum number of interruptions and the maximum duration of one interruption
for a Photovoltaic Power Plant (PV-PP) meant to improve the Operation and Maintenance (O&M) activities
and optimize the stocks of the spare parts. Over almost two years, events are recorded at PV-PP Agigea with
an installed power of 0.5 MW. The predictive maintenance of future events and stockpile sizing at the
PV-PP Agigea are developed taking into account the maintenance activities carried out at the plant's
components since the commissioning of the PV-PP Agigea. Based on the PV-PP data and the intervention
reports that consist in the incidents recorded between February 2016 and December 2017, it is intended to
determine by statistical methods the following basic reliability indicators: failure rate, usually symbolized by
λ, defined as the average number of failures on time unit, and the maximum number of interruptions (Nmax)
eliminated through repairs or replacements during the reference period, determined for a certain level of risk.
For several years, fault diagnosis of photovoltaic (PV) plants has been manually performed by the human operator by a visual inspection or automatically, by evaluating electrical measures collected by sensors mounted on each PV module. In recent years, a notable interest of the scientific community has been devoted towards the definition of algorithms able to automatically analyse the sequence of images acquired by a thermal camera mounted on board of an unmanned aerial vehicle (UAV) for early PV anomaly detection. In this paper, we define a model-based approach for the detection of the panels, which uses the structural regularity of the PV string and a novel technique for local hot spot detection, based on the use of a fast and effective algorithm for finding local maxima in the PV panel region. Finally, we introduce the concept of global hot spot detection, namely a multi-frame recognition of PV faults which further improves the anomaly detection accuracy of the proposed method. The algorithm has been designed and optimized so as to run in real-time directly on an embedded system on board of the UAV. The accuracy of the proposed approach has been experimented on several video sequences with a standard protocol in terms of Precision, Recall and F-Score, so that our dataset and our quantitative results can be used for future comparisons and to evaluate the reliability of computer vision techniques designed for thermographic PV inspection.
There is a noticeable shortage of solar energy related information in Sub-Saharan Africa, especially about Concentrated Solar Power (CSP) technology. The Tanzanian official generation expansion plan till 2040 showed high dependency on coal and gas fired power plants and a negligible role of renewables other than large hydropower. This study investigates the techno-economic feasibility of CSP technology in Tanzania, through modelling Parabolic Trough and Solar Tower CSP technologies using the System Advisor Model developed by the U.S. National Renewable Energy Laboratory. Under debt interest rate of 7%, the levelized cost of electricity (LCOE) for the modelled Solar Tower plants ranges from 11.6 to 12.5 ¢/kWh while for the modelled Parabolic Trough plants ranges from 13.0 to 14.4 ¢/kWh. The LCOE increases significantly from 14.4 ¢/kWh (at 7% debt interest rate, for government-led projects) to 25.9 ¢/kWh (at 18% debt interest rate, for private investors-led projects). The study concludes that the feasibility of CSP in Tanzania is strongly dependent on the financing conditions. Policy mechanisms to de-risk CSP projects' investment through accessing lower debt interest rate are required to ensure the competitiveness of CSP projects in the country.
The energy assessment of the PV power systems is carried out by using different types of performance indicators that benchmark the output of these systems against the PV panel maximum output at hypothetical operation conditions. In this paper, a comparative analysis of six types of performance indicators is conducted and a new performance indicator which considers PV panel slope and orientation is proposed. The proposed indicator is benchmarking the PV system actual output against the maximum output of the same system if it would operate in two axis tracking mode. The proposed performance indicator is used to develop a friendly user calculator of PV system output that can be used by, energy providers and PV system installers to evaluate the output of the PV grid connect network. The advantage of the developed calculator is highlighted by a case study that estimates energy capacity of different residential rooftop PV systems installed in a residential suburb in Sydney.
Key performance indicators (KPI) are tools for measuring the progress of a business towards its goals. Although wind energy is now a mature technology, there is a lack of well-defined best practices to assess the performance of a wind farm (WF) during the operation and maintenance (O&M) phase; processes and tools of asset management, such as KPIs, are not yet well-established. This paper presents a review of the major existing indicators used in the O&M of wind farms (WFs), as such information is not available in the literature so far. The different stakeholders involved in the O&M phase are identified and analysed together with their interests, grouped into five categories. A suggestion is made for the properties that KPIs should exhibit. For each category, major indicators that are currently in use are reviewed, discussed and verified against the properties defined. Finally, we propose a list of suitable KPIs that will allow stakeholders to have a better knowledge of an operating asset and make informed decisions. It is concluded that more detailed studies of specific KPIs and the issues of their implementation are probably needed.
This paper presents a specific method to improve the reliability of the equipment and the quality of power supplied to the electrical systems with the frequency and voltage control of a thermoelectric plant, to guarantee a more stable system. The method has the novelty of combining Total Productive Maintenance (TPM) using only four pillars, with Electrical Predictive Maintenance based in failure analysis and diagnostic. It prevents voltage drops caused by excessive reactive consumption, thus guaranteeing the company a perfect functioning of its equipment and providing a longer life of them. The Maintenance Management Program (MMP) seeks to prevent failures from causing the equipment to be shut down from the electrical system, which means large financial losses, either by reducing billing or by paying fines to the regulatory agency, in addition to prejudice the reliability of the system. Using management tools, but applying only four TPM pillars, it was possible to achieve innovation in power plants with internal combustion engines. This study aims to provide maintenance with a more reliable process, through the implantation of measurement, control and diagnostic devices, thus allowing the management to reduce breakdown of plant equipment. Some results have been achieved after the implementation, such as reduction of annual maintenance cost, reduction of corrective maintenance, increase of MTBF (Mean Time between Failures) and reduction of MTTR (Mean Time to Repair) in all areas. Probabilistic models able to describe real processes in a more realistic way, and facilitate the optimization at maximum reliability or minimum costs are presented. Such results are reflected in more reliable and continual power generation.
This study aims to build a photovoltaic (PV) plant maintenance and operation system, using an unmanned aerial vehicle (UAV) carrying a thermal imager to take images. In the proposed system, the infrared (IR) image was used for detecting PV module thermal defects, and the RGB image was used for detecting module surface defects. The two images were employed to cross validate the causes for module defects.
In Part I, the PV plant information pattern was created, and the Taiwan PV plant (1,482 PV modules, 410 kW) was taken as an example. The PV system image feature points were detected by using the Scale Invariant Feature Transform (SIFT), in order to solve the feature variation problems, such as image luminance, rotation, and zoom in/out. The same feature points of multiple local power plant images were matched. Afterwards, the optimal number of feature points was calculated by homography transformation and random sample consensus (RANSAC) to form the PV plant panorama by image stitching. The PV plant panorama background noise was removed by image hue. The module segmentation of PV systems was performed by using image luminance, and the PV module was geometrically reconstructed by using morphology. The PV module edge contour was extracted by the Laplace operator to obtain the perimeter, area, and centroid features. The quantity and positions of PV modules were recognized and calculated to form the PV plant information pattern. In Part II, the PV module defect recognition and classification system was built. The defects in seven PV plants in Taiwan were collected, and the image features were enhanced using a convolutional neural network (CNN). Besides using the convolution layer to capture the image features, Max Pooling and local response normalization were used to enhance the image features. Color space transform was used to intensify the color features, increase the accuracy of the classification modules, and recognize and position the PV module defects. The IR image hot spot recognition accuracy was 100%. The classification accuracy of eight modules, including one normal module and seven defect modules, is 97.52%. The classification accuracy of six modules, including the appearances of one normal module and five defects in RGB images, is 99.17%. The classification accuracy of 14 defects in IR thermal images and RGB images is 97.52%. The causes of defects were cross validated by IR thermal image and RGB image. This study applied the K-fold cross validation to select the optimal model, and the recognition time of one image was shorter than 0.3 s, which is lower than the camera time constant. The results show that the system is applicable to real-time detections. In Part III, the PV plant defect information pattern was created. The PV module with defects was labeled during detection, and the defects in the power plant PV module and the positions thereof were obtained, which would be favorable for PV plant maintenance.
The main element of the work was the economic analysis resulting from the use of a photovoltaic installation cooperating with the installation of a heat pump in one of the single-family houses. It should be emphasized that the cooperation of photovoltaics and heat pumps, aimed at ensuring thermal comfort in the building and generating electricity, e.g. for the operation of a heat pump, it is one of the most economically advantageous technologies. In this work, the authors focused only on the assessment of the payback time of the sample photovoltaic installation, operating in Poland. Based on the analysis of the investment and operating costs of the installation, its profitability was demonstrated, while ensuring clean, practically maintenance-free energy production.
The long-term performance monitoring and characterization of field-exposed solar photovoltaic (PV) modules are essential for efficient power generation. This paper is an attempt at performance evaluation of the amorphous silicon (a-si), Heterojunction Intrinsic Thin layer (HIT), and Multi-Crystalline (mc-si) technologies after twelve years of outdoor exposure in the composite climate of India. Characterization techniques such as Visual Inspection, Infra-Red (IR) Thermography, Electroluminescence (EL) Imagining, and Electrical characterization have been carried out. I–V measurements, power losses, current degradation, and voltage degradation calculations are carried out. The percentage of electrical decline is 8.61%, 2.73%, and 29.08%, for Isc, Voc, and PMP respectively in a-si solar modules. For HIT modules 1.97%, 0.68%, and 0.48% for Isc, Voc, and VMP respectively. Finally, for the mc-si PV modules, these are 3.76%, 0.5%, and 1.44% for Isc, Voc, and PMP respectively over 12 years. The average degradation rates are found to be 1.24%/year, 0.14%/year, and 1.50%/year for a-si, HIT, and mc-si modules respectively. The EL imagining of the a-si modules shows the localized shunt over the surface of the modules as well as disconnected cell interconnects whereas no such defects are over the m-ci and HIT modules.
In 2021 a favorable environment reemerged in Romania for the development of greenfield large scale photovoltaic plants, after all the developments for large scale projects were halted at the end of 2016 when the former subsidy scheme ended. The conclusion of long-term power purchase agreements, a regulatory framework for contracts for difference, grants opportunities, the decreasing cost of technology, rising energy prices and sustainability commitments are all creating a promising context for greenfield development of solar photovoltaics in Romania. This paper covers an existing gap in the scientific literature by assessing technically and economically the viability of large scale photovoltaics in Romania. The paper shows the yield and performance gap between the major regions in Romania and how this gap affects the financial return and the levelized costs of electricity. The assessment shows that using 1-axis tracking is improving the specific yield with 10–13% in comparison with a fixed tilt technology and bifacial modules add an additional 3% to the yield. It was identified that 1-axis tracking improves the captured price in comparison with fixed tilt with up to 4.5 EUR/MWh in the long-term. The same methodology can be applied for updating the assessment when cost or revenue assumptions are changing. The modelling and analysis framework can be used in the technical and economic benchmarking of real projects in Romania and gives a flavor of what can be expected in terms of pricing for long term power purchase agreements, auctions for contracts for difference, but also for the expected returns if the projects remain merchantly exposed.
By 2035, Egypt pursues to generate 22% of the total electricity from photovoltaic power plants to meet the national spreading demand for electricity. The Egyptian government has implemented feed-in tariffs (FiT) support program to provide the economic incentives to invest in the PV power plants. The present study is carried out to evaluate the techno-economic feasibility of a large-scale grid-connected photovoltaic (LS GCPV) of the Benban Solar Park with a total capacity of 1600 MW AC producing annual electricity of 3.8 TWh. The characteristics of PV panels considering the meteorological data of Benban Solar Park are evaluated. Additionally, the reduction of greenhouse gas (GHG) emissions due to constructing Benban Solar Park is assessed. As well, the influences of annual operation and maintenance cost and the interest rate on the electricity cost and the payback period are evaluated. The results indicate that the electricity cost is about 8.1 US¢/kWh with 10.1 years payback period, which is indeed economically feasible with an interest rate of 12%. Furthermore, the Benban Solar Park will avoid annually almost 1.2 million tons of greenhouse gas. Finally, based on the techno-economic analysis, the improvement directions for the feasibility analysis based on agrivoltaic systems are proposed.
In the last few years, a considerable growth of rooftop photovoltaic systems has been experienced in Brazil, and according to the Ten-Years Energy Plan, developed by the Brazilian Energy Research Company, it is expected to increase even more in the coming years. As a result, the Brazilian Regulatory Agency (ANEEL) has been actively working in the sector, acting to smooth the impacts on Distribution Companies (DisCos) and prosumers. The two major negative impacts of the current Distributed Generation (DG) regulation in Brazil are the cross-subsidy for consumers to prosumers (i) since non-PV owners subsidize network costs that prosumers avoid paying, and the “death spiral” (ii) in which a DisCo lost a considerable share of the market continuously. To overcome these issues, the Brazilian government approved Law 14300, which includes a new compensation scheme for energy injected into the grid. In this way, the main objective of this paper is to conduct a technical-economic analysis of photovoltaic systems with this new structure by considering the impact on both sides: new investors in DG and DisCos. The analysis is compared against the previous regulation and the results proved a detriment to economic viability for prosumers as the amount paid in the Net-Metering Scheme increases, reducing the interest in the investment and the economic impact on the DisCos. On the other hand, when the amount paid in the Net-Metering Scheme gets reduced, it increases the interest of consumers to invest in DG and the DisCo's market losses. The present work quantifies these statements and indicates the appropriated regulation, which is in between these two extremes.
Net energy metering (NEM) systems have been promoting the growth of photovoltaic (PV) distributed generation worldwide. However, the economic and financial sustainability of this policy has been questioned due to the possibility of triggering the utility death spiral. In this context, this study presents a method to quantify the economic impact of NEM policies considering utilities and non-adopter consumers' points of view. The proposed method employs a cost-benefit risk analysis to obtain the present value of accumulated utility cash flows. Electricity tariff, PV system unit price, and PV systems average installed power varying based on the Monte Carlo method are considered in the computational simulations. The Bass diffusion model is utilized to forecast the number of PV adopters. A univariate sensitivity analysis is carried out to determine how the key parameters affect the economic analysis. The method was applied in a case study that contemplates 98% of the Brazilian distribution market, considering three different scenarios of PV adoption. The results showed that, if the current NEM policy continues, the payback of micro- and mini-PV systems will decrease significantly over the years, culminating in a high average present value of the accumulated cash flow. This method allows researchers and policymakers worldwide to reassess their NEM regulations to stimulate the sustainable development of PV systems and to avoid cross-subsidies between prosumers and other consumers.
The main purpose of the study is to examine the experimental and simulation performance of a 6 MWp grid-connected photovoltaic power plant during a specific period. A specific analysis technique was applied based on the IEC 61,724 standards to assess the effect of climatic factors. The treated data resulting from monitoring for 2 consecutive years (Jun 2017–Jun 2019) was analyzed on a daily and monthly basis in order to evaluate the performance trends of the solar PV system under climatic conditions such as an arid desert. Numerous measurement metrics are used in this respect, including the energy yields, performance ratio (PR), capacity factor (CF), and losses. The performance results obtained are compared with the PVsyst simulation, where findings of this study show that the actual data from the photovoltaic plant production closely matches the expected data collected using the PVSyst software. The average monthly yield of the PV array and the final yield were 5.1 and 4.7 h/d, respectively. The average performance ratio (PR) for the rows and the PV system was 90 and 84%, respectively. The average monthly efficiency of the PV array and the system were 12.68 and 11.75%, respectively. By comparing the results of the performance parameters of this installation with the results reported by different systems operating in various conditions, a desert climate may demonstrate to be slightly favorable. The experimental findings obtained during field operations illustrate how environmental parameters have a significant effect on both energy generation performance and system losses, where the Tm > 42 °C & PR < 70% the energy generated is relatively low even though the availability of solar irradiation, and also a correlation between the monthly average module temperature and the performance ratio with a correlation value of R2 = 0.90.
Uzbekistan is a developing country where energy demand is rising, and it has many problems in the energy supply sector. This issue leads to a mismatch between energy consumption and supply in this country. Most of the country’s energy supply comes from natural gas. A major part of Uzbekistan’s energy demand can be provided by solar power. The purpose of this study is to determine the position of using solar energy to generate hydrogen in 13 provinces of Uzbekistan. This study is performed by using the Stepwise Weight Assessment Ratio Analysis (SWARA) for criteria weighting and using the Weighted Aggregated Sum Product Assessment (WASPAS), the COmplex PRoportional Assessment of alternatives (COPRAS), the Evaluation Based on Distance from Average Solution (EDAS), and the Weight Sum Model (WSM) for ranking locations. The results of criteria weighting with SWARA show that the top three most important criteria are solar radiation, sun hours, and average wind speed with weights of 0.248, 0.2, and 0.154, respectively. All ranking methods identify Bukhara province as the most suitable place in Uzbekistan for solar-powered hydrogen production. It is estimated that 1539.2 MW of solar power and 24.92 tons of hydrogen are produced annually in Bukhara.
Many cities across the world are committing to deep decarbonisation efforts. While solar photovoltaics (PV) will play a critical role in this pursuit, the role of rooftop and facade-integrated PVs within the urban landscape is yet to be fully understood. This work presents an analysis into the solar energy harvesting potential of PVs integrated as building rooftops, walls, and windows at various spatial resolutions that range from city to building scale within the City of Melbourne, Australia, as a contemporary case study. It further investigates the relationship between calculated electricity production from such PVs with the urban morphology, seasonal variation and the measured electricity consumption by the local distribution network service providers. The results indicate that PV rooftops are responsible for the largest share of the city’s solar energy potential. However, for individual blocks with high densities of high-rise and glazed buildings, it is shown that the PV potential from windows becomes more prominent. The technical workflow presented here will enable different cities to facilitate decision-making on the PV implementation in urban environments.
Observing the growing energy demand of modern societies, many countries have recognized energy security as a looming problem and renewable energies as a solution to this issue. Renewable hydrogen production is an excellent method for the storage and transfer of energy generated by intermittent renewable sources such as wind and solar so that they can be used at a place and time of our choosing. In this study, the suitability of 15 cities in Fars province, Iran, for renewable hydrogen production was investigated and compared by the use of multiple multi-criteria decision-making methods including ARAS, SAW, CODAS, and TOPSIS. The obtained rankings were aggregated by rank averaging, Borda method, and Copeland method. Finally, the partially ordered set ranking technique was used to reach a general consensus about the ranking. The criteria that affect hydrogen production were found to be solar energy potential, wind energy potential, population, air temperature, natural disasters, altitude, relative humidity, land cost, skilled labor, infrastructure, topographic condition, and distance from main roads. These criteria were weighted using the best-worst method (BWM) based on the data collected by a questionnaire. Solar energy potential was estimated using the Angstrom model. Wind energy potential was estimated by using the Weibull distribution function for each month independently. The results of the multi-criteria decision-making methods showed Izadkhast to be the most suitable location for renewable hydrogen production in the studied area.
This paper presents a comparative analysis of the performance of three grid-connected photovoltaic power plants, of about 2kWp for each plant, using the principal component analysis (PCA) method. These systems include three silicon technologies. The analysis is based on the performance parameters described in the international standard IEC 61724. To perform this comparative analysis, the energy production, the operational and the meteorological data are first collected for a period of time. The performance evaluation of PV plants is then performed based on several performance indicators such as Final Yield, Performance Ratio, System Losses, Capture Losses, Array Efficiency and Capacity Factor. Using the PCA method, the correlation between the performance parameters and the meteorological variables is then studied and analyzed. The resulting analysis shows that the Polycrystalline silicon technology is the most performing one. The annual average values of the Performance Ratio were found to be 86.66% for the polycrystalline against 84.76% and 83%, for the monocrystalline and amorphous, respectively. For the daily data, the PCA method reveals that the Performance Ratio is independent of the solar irradiation but it has a slight correlation with temperature and System Losses and a strong correlation with Capture Losses. The result shows also that the temperature acts slightly on the amorphous compared to the crystalline ones.
Nowadays, plenty of data is continuously pouring from the PhotoVoltaic Power Plants (PV) monitoring systems and sensors that could be successfully handled by big data technologies. This paper proposes a methodology that automatically collects the data logs from sensors installed on PV arrays, inverters and weather stations, checks the health status of the PV components, forecasts the generated power for each inverter based on its real operating conditions and the predicted irradiance and finally provides useful insights of the PV system based on the Key Performance Indicators (KPI) using big data technologies. The Ultra-Short-Term Forecast (USTF) algorithm provides the estimations of irradiance and generated power for the next 30 min and is applied on a sliding time window interval. The algorithm uses a Feed-Forward Artificial Neural Network (FF-ANN) and, to significantly reduce the number of iterations, we propose a backtracking adjustment of the learning rate that enables faster convergence reducing the computational time that is essential for USTF. Two data sets from PV Agigea 0.5 MW and PV Giurgiu 7.5 MW, located in the South-East and South of Romania, that consist in data logs from inverters and arrays, are used for simulation. The exhaustive analyses are performed for PV Agigea (including KPI calculation), while PV Giurgiu data set was mainly used to check the scalability and replicability of the algorithm.
This paper presents an experimental performance analysis based on results attained from monitoring a 9.5 kWp photovoltaic grid-connected for 3 years; from 2016 to 2018. This system is composed of three 3.2 kWp sub-systems installed on the flat roof of the Renewable Energy Development Institute (CDER) in Algeria. This plant covers electricity needs required by the lab and feeds the excess into the low voltage supply grid. This study assesses the monthly average and annual performance normalized parameters of the PV system by analyzing and evaluating reference yield, array yield, final yield, system losses, array losses, PV module efficiency, system efficiency, inverter efficiency, and performance ratio of the whole system as well as the three sub-systems by following the IEC 61724 guideline. Results show that the annual average, reference yield, array yield, and final yield, are 4.67, 3.50, 3.37 h/day respectively. While the array and system losses are 1.16 and 0.13 kWh/kWp/day respectively. Moreover, the PV module efficiency, system efficiency, inverter efficiency are 8.62%, 8.29%, and 96% respectively. These results show that the system is below acceptable performance ratio rates and thus an inspection of the system must be conducted in order to diagnose the reasons behind the system's low productivity. Our study has found that the main attributes contributing to decreased PV outputs are near shading and type of used inverter (i.e.: Transformerless Inverter). However, the system's performance is still considered satisfactory given it has been functioning for more than 14 years while achieving a performance ratio equivalent to 70%, concluding that the PV grid-connected investment is very promising in this site.
System downtime and unplanned outages massively affect plant productivity, therefore RAMS (Reliability, Availability, Maintainability and Safety) disciplines, together with fault diagnosis and condition monitoring, are mandatory in energy applications. This paper focuses on the optimization of a maintenance plan for a yaw system used in an on-shore wind turbine. A complete Reliability Centered Maintenance procedure is applied to the system to identify which maintenance action is the optimal solution in terms of cost, safety, and availability. The scope of the research is to propose a new customized decision-making diagram inside the reliability centered maintenance assessment to reduce the subjectivity of the procedure proposed in the standard and saving cost by optimizing maintenance decisions making the projects more cost-efficient and cost-effective. The paper concludes by proposing a new diagnostic method based on a datadriven condition monitoring system to efficiently monitor the health and detect damages in the wind turbine by means of measurements of critical parameters of the tested system. The paper highlights how a reliability analysis during the early phase of the design is a very helpful and powerful means to guide the maintenance decision and the data-driven condition monitoring.
We investigate the photovoltaic (PV) power losses due to soiling for Lahore, Pakistan for solar panels. Optimized cleaning schedules are proposed incorporating the effect of solar panels' tilt angle and the method (manual vs. automatic) for cleaning. Output power losses and dust accumulation on solar panels were measured at variable tilt angles for a period of 120 days at an open roof top location in Lahore. The relative soiling losses for monofacial vs. bifacial (constructed by stacking two back to back monofacial) solar panels were compared for two different panel orientations, i.e., south faced tilted panels vs. East/ West faced vertical panels. We found that the soiling rate for Lahore was consistently around 0.8% per day for 30 tilted panel (for the measurement period between October to January), which is among one of the highest soiling rates reported for various urban locations across South Asian and Gulf regions. A dust accumulation rate of 0:01 À 0:02mg=cm 2 per day was recorded for panels that were fixed at 30 tilt. The variation for soiling/dust deposition rates was found to be negligible for different dry periods spanning between October and January. The chemistry and composition of the dust were analyzed using scanning electron microscopy (SEM), X-ray diffraction (XRD), and, electron dispersive x-ray (EDX) spectroscopy. Large contents of carbon and quartz were found in the dust collected from the samples through EDX and XRD analysis. High carbon contents in the accumulated dust are attributed to air pollutants and could be a contributing factor for the high soiling rate. For manual cleaning, the optimal cleaning schedule was calculated to be about once per week for panels at 30 tilt, and, once every three weeks for panels at 90 tilt.
Amid all renewable energies, solar PV is of particular interest, mainly in Africa. Mauritania is an example of African countries which, gives great concern to produce electricity via PV installations. This study is carried out on the performance evaluation of a 954,809 kWp photovoltaic array made up of micro-amorphous silicon situated in Nouakchott (capital of Mauritania) at Sheikh Zayed solar power plant. The measures of one year of operation from September 2014 to August 2015 were evaluated according to the IEC 61724. The results obtained demonstrate that the photovoltaic array performances depend on both insolation and environmental conditions. The array capture loss ranges vary from a minimum value of 1.63 h/day to a maximum value of 2.46 h/day. So, the system loss is relatively stable, with an average value of 0.12 h per day. The monthly performance ratio varies from 0.61% in August to 0.71% in November, with a monthly average value of 0.66%. The monthly average capacity factor achieves its maximum and minimum in October (20.54%) and January (11.66%), respectively. The energy generated by the PV array (Edc) and the energy fed to the utility grid (Eac) during November moth, are affected by the insolation and the module temperature. However, wind speed variation does not influence those energies. Two linear models, depending on insolation and module temperature, are proposed for the evaluation of Edc and Eac during this month. These laters present a coefficient of determination (R²) of 0.96.
Condition monitoring and fault diagnosis of photovoltaic modules are essential to ensure the efficient and reliable operation of large-scale photovoltaic plants. This article presents an algorithmic solution for the rapid and sensitive detection of photovoltaic modules with multiple visible defects by an image analyzing apparatus mounted onto an unmanned aerial vehicle. The proposed solution is composed of three stages to efficiently and accurately analyze various forms of module defects. First, the Kirsch operator is employed to identify the anomalous regions, which can significantly reduce the computational complexity, and error rate. Afterward, a deep convolutional neural network is adopted to extract defect features. Finally, a multiple classification support vector machine is developed to facilitate the defects detection decision-making. The proposed solution is extensively evaluated by the comprehensive dataset collected from real-world solar photovoltaic plants. The experimental results clearly demonstrate the effectiveness of our solution for photovoltaic modules diagnosis with multiple visible defects.
Purpose
The purpose of this paper is to enhance the performance of maintenance in a solar power plant by implementing the proactive maintenance (PaM) strategy, measured by the availability and the total maintenance workload.
Design/methodology/approach
The prior maintenance strategy was reviewed, and then the strategy was adjusted to focus on PaM. Failure modes and effects analysis (FMEA) was a tool for analyzing the severity and occurrence of the failure modes and effects. Then, the Why‒Why analysis was used for investigating the root causes of failures. The countermeasures were drawn, and the preventive maintenance (PM) plan was revised and carried out. The total maintenance, the PaM and reactive maintenance workload, was obtained, and then the improvements were determined. The values of availability were also obtained.
Findings
Previously, the appeared maintenance strategy was not clearly defined. It seemed to have reactive maintenance coupled with PM; it was checked once a year, and corrective actions were made when something wrong was found. Then the management team observed an increase in the reactive maintenance workload, whereas the values of availability were not consistent and tended to drop. After implementing the new maintenance strategy, PaM, the total maintenance workload decreased 14 percent in one year. The average availability of the solar power plant improved from 0.9943 to 0.9969, and the values of availability had better consistency.
Practical implications
The PaM can be applied to solar power plant without limiting the prior maintenance strategy and the complexity of production or machinery. The solar power plant is a quite simple production, and most machines consist of electrical equipment and electrical circuits. The PaM supports to analyze the failure modes, the consequence of the failure events and failure effects, and to decide what should be done. Importantly, PaM can reduce total maintenance workload while the value of availability is higher and consistent.
Originality/value
This paper states how to successfully implement the PaM for the solar power plant. Previously, the plant did not have a clearly defined maintenance strategy; it was checked once a year, and it was corrected when abnormalities were detected. The PaM strategy provides tools and processes for failures and effects analysis. Although there was a more workload of PM, the total maintenance workload decreased, even in the first year.
Nowadays renewable energies are becoming more important in the generation of electricity. Fossil resources do not present a sustainable option for the future since they are non-renewable sources of energy that contribute to environmental pollution.
Within the sources of renewable generation, photovoltaic energy is the most used, and this is due to a large number of solar resources existing throughout the planet. At present, the greatest advances in photovoltaic systems (regardless of the efficiency of different technologies) are focused on improved designs of photovoltaic systems, as well as optimal operation and maintenance. This work intends to make a review of the photovoltaic systems, where the design, operation and maintenance are the key points of these systems. Within the design, the critical components of the system and their own design are revised. Regarding the operation, it is reviewed the general operation and the operation of hybrid systems, as well as the power quality. Finally, in relation to the maintenance of PV systems, it has been studied their performance, thermography and electroluminescence, dirt, risks and failure modes.
This paper provides a critical literature review of the impact of snow accumulations on photovoltaic (PV) system electricity generation. The review quantifies the impact of snow, identifies factors that influence the generation loss, examines existing snow impact estimation techniques, and identifies mitigation strategies to reduce the impact of snow accumulations. Reported annual and monthly electricity generation losses resulting from snow accumulations on photovoltaic systems show that annual electricity generation losses were less than 10% in most climates; however, monthly generation losses throughout the winter were generally higher than 25%. The influences of climatic characteristics and system characteristics on the impact of snow were examined individually, and the codependence between influence factors was also discussed where relevant. Estimation techniques for electricity generation loss due to snow cover were summarized. Relatively accurate estimates are achievable by several models when considering ambient temperature, solar irradiance, and snow depth. Ten mitigation methods were identified as having the potential to reduce the impact of snow on PV system electricity generation and were discussed qualitatively. This review provides system designers and operators with the information required to identify how to manage the effect of snow on PV systems and highlights the need for researchers to develop ways to reduce and predict the impact.
Solar Photovoltaic systems are widely accepted as alternate energy sources across the world. Grid Tied PV (GTPV) and Grid Interactive (GIPV) PV systems are the two configurations in which PV generation is integrated with Grid. Later is more complex due to inclusion of battery as storage and connected load, along with Grid import/export. In this paper, performance of a 40 kWp GIPV system, installed in India, is presented. The system under study comprises of PV, Grid, and Battery bank and connected load. Performance parameters like reference yield, array yield, final yield, performance ratio, and capacity factor are derived using standards IEC 61724. Also presented is Annual energy yield of the plant and Annual efficiencies of PV array, inverter and system. The effect of temperature on PV array and inverter performance is also evaluated. All these parameters are evaluated from real time annual data.