Solar Photovoltaic Plant Research Articles

Random forest machine learning algorithm based seasonal multi‐step ahead short‐term solar photovoltaic power output forecasting

AbstractTo maintain grid stability, the energy levels produced by sources within the network must be equal to the energy consumed by customers. In current times, achieving energy balance mainly involves regulating the electrical energy sources, as consumption is typically beyond the control of grid operators. For improving the stability of the grid, accurate forecasting of photovoltaic power output from largely integrated solar photovoltaic plant connected to grid is required. In the present study, to improve the forecasting accuracy of the forecasting models, onsite measurements of the weather parameters and the photovoltaic power output from the 20 kW on‐grid were collected for a typical year which covers all four seasons and evaluated the random forest techniques and other techniques like deep neural networks, artificial neural networks and support vector regression (reference in this study). The simulation results show that the proposed random forest technique for the forecasting horizon of 15 and 30 min is performing well with 49% and 50% improvements in the accuracy respectively over reference model for the study location 22.78°N, 73.65°E, College of Agricultural Engineering and Technology, Anand Agricultural University, Godhra, India.

Barriers and variable spacing enhance convective cooling and increase power output in solar PV plants

When the temperature of solar photovoltaic (PV) modules rises, efficiency drops and module degradation accelerates. Thus, it is beneficial to reduce module operating temperatures. Previous studies of solar power plants have illustrated that incoming flow characteristics, turbulent mixing, and array geometry can strongly impact convective cooling, as measured by the convective heat transfer coefficient h. In the fields of heat transfer and plant canopy flow, previous work has shown that system-scale arrangement modifications—e.g., variable spacing, barriers, or windbreaks—can passively alter the flow, enhance turbulent mixing, and influence convection. However, researchers have not yet explored how variable spacing or barriers might enhance convective cooling in solar power plants. Here, high-resolution large-eddy simulations model the air flow and heat transfer through solar power plant arrangements modified with missing modules and barrier walls. We then perform a control volume analysis to evaluate the net heat flux and compute h, which quantifies the influence of these spatial modifications on convective cooling and, thus, module temperature and power output. Installing barrier walls yields the greatest improvements, increasing h by 3.4%, reducing module temperature by an estimated 2.5 °C, and boosting power output by an estimated 1.4% on average. These findings indicate that incorporating variable spacing or barrier-type elements into PV plant designs can reduce module temperature and, thus, improve PV performance and service life.

An Analytic Hierarchy Process based approach for assessing the performance of photovoltaic solar power plants

The key performance indicators are crucial for monitoring the performance of photovoltaic solar plants, thus significantly enhancing their overall efficiency. This critical role underscores the importance of making informed decisions regarding the evaluation criteria of these indicators, which would streamline the PV installations evaluation process. Despite the significant importance of this role, the weighting and prioritization of KPI selection criteria in the specific field of solar plants have not been addressed in the literature. The article aims to address this gap by developing a methodology that identifies the most relevant aspects for evaluating the performance of photovoltaic plants, while taking into account specific needs and evaluation contexts, integrating a hierarchical decomposition tree of criteria, which serves as a framework to guide criterion prioritization through collaborative weighting with the thematic team. This methodology incorporates the Analytic Hierarchy Process, a multicriteria analysis approach designed to assist decision-makers in addressing their specific needs, ensuring optimal alignment with the characteristics of the optimization problem, whether it involves single or multiple objectives.

Uncertainty-aware estimation of inverter field efficiency using Bayesian neural networks in solar photovoltaic plants

Solar inverters are one of the most important components in a Photovoltaic plant. Their main function is to convert the DC power produced by the solar modules into AC power that can be injected into the grid. Although inverter efficiency has reached exceptionally high values, thanks to recent technological advancements, it is typically measured at dedicated laboratories under strict testing conditions, which makes its validation after deployment extremely challenging, both from a logistic and financial point of view. This paper presents a methodology for the calculation of inverter field efficiency based on Bayesian neural networks. The goal of the neural network is to model inverter efficiency and its variance as a function of the inverter's operational state. Results show that an optimised Bayesian neural network can effectively model inverter efficiency with small reconstruction errors and negligible bias. Furthermore, the model has been proven useful to replicate the calculation of the European efficiency along with a full uncertainty characterisation.

Stability-Constrained Contingency Analysis for Modern Power Systems

Modern power systems comprise diverse nonlinear components, and an increasingly large number of low inertia renewable power sources necessitate the modernization of conventional power system security measures. Contingency analysis (CA), a routine process for power system operators, ensures grid security under unforeseen circumstances by identifying potential issues and enabling proactive measures for uninterrupted power flow. Electromechanical oscillations (EMOs) in the power system that are a threat to stability must be regularly monitored and mitigated. An online hierarchical EMO index integrating time and frequency response analysis can be utilized for system stability assessment. The integration of an EMO index threshold into contingency analysis is presented in this paper to enhance system security. This new approach is referred to as the stability-constrained contingency analysis (SCCA). Typical results for a modified two-area, four-machine power system with large solar photovoltaic plants simulated on a real-time digital simulator (RTDS) are presented. These results demonstrate that SCCA flags potential issues that can arise from EMOs for certain contingencies, whereas traditional CA does not, as it solely considers bus voltage limits and line ratings.

Integration of Solar Photovoltaic Plant in the Eastern Sumba Microgrid Using Unit Commitment Optimization

Integrating renewable energy sources (RES) into island microgrids is usually done to provide a cost-effective electricity supply. The integration process is carried out by scheduling generating unit operations with a unit commitment (UC) scheme to ensure low system operating costs. This article discusses developing a UC optimization method for integrating solar photovoltaic plants in Indonesia’s Eastern Sumba microgrid power system. The scope of this study is the optimization algorithm of the UC, which consists of a priority list (PL) for the UC stage and an economic dispatch (ED) that relies on a genetic algorithm (GA) to minimize total operating costs (TOC). The results show that the PL-GA algorithm performs better than the extended priority list (EPL), and combinations of genetic algorithm and Lagrange, by applying continuous problem dispatch and improved binary GA hourly dispatch to meet ramping constraints. The application of RES incentive programs, such as carbon taxes and incentives for RES generation in calculating the TOC, shows an improvement in the financial feasibility analysis of the internal rate of return (IRR) and net present value (NPV) of actual projects in Indonesia.

Solar photovoltaic panel production in Mexico: A novel machine learning approach

This study examines the potential for widespread solar photovoltaic panel production in Mexico and emphasizes the country's unique qualities that position it as a strong manufacturing candidate in this field. An advanced model based on artificial neural networks has been developed to predict solar photovoltaic panel plant metrics. This model integrates a state-of-the-art non-linear programming framework using Pyomo as well as an innovative optimization and machine learning toolkit library. This approach creates surrogate models for individual photovoltaic plants including production timelines. While this research, conducted through extensive simulations and meticulous computations, unveiled that Latin America has been significantly underrepresented in the production of silicon, wafers, cells, and modules within the global market; it also demonstrates the substantial potential of scaling up photovoltaic panel production in Mexico, leading to significant economic, social, and environmental benefits. By hyperparameter optimization, an outstanding and competitive artificial neural network model has been developed with a coefficient of determination values above 0.99 for all output variables. It has been found that water and energy consumption during PV panel production is remarkable. However, water consumption (33.16 × 10−4 m3/kWh) and the emissions generated (1.12 × 10−6 TonCO2/kWh) during energy production are significantly lower than those of conventional power plants. Notably, the results highlight a positive economic trend, with module production plants generating the highest profits (35.7%) among all production stages, while polycrystalline silicon production plants yield comparatively lower earnings (13.0%). Furthermore, this study underscores a critical factor in the photovoltaic panel production process which is that cell production plants contribute the most to energy consumption (39.7%) due to their intricate multi-stage processes. The blending of Machine Learning and optimization models heralds a new era in resource allocation for a more sustainable renewable energy sector, offering a brighter, greener future.

Taguchi ve ANOVA Analizi Kullanılarak Fotovoltaik Enerji Santrallerinde Dizi Tasarımının Optimizasyonu

Fossil fuels, predominant in fulfilling current energy demands, are implicated in global warming, prompting a global shift towards renewable energy sources. Among these, photovoltaic (PV) solar power plants have garnered significant attention, experiencing a rapid surge in installed power capacity. However, a notable drawback of PV solar power plants is their considerable spatial footprint, emphasizing the pivotal role of efficient space utilization and shading mitigation in their design. Notably, pitch distance, array design, and PV type emerge as critical parameters influencing the performance of these power plants during installation. In the present study, eight distinct PV solar power plant designs were conceptualized, incorporating four different PV array configurations (2P-3P-2L-3L) and two PV types (monofacial-bifacial), each with specified orientations (portrait-landscape). Other parameters were held constant across designs. Leveraging PVsyst software, simulations were conducted for each design, yielding crucial performance metrics, including the annual energy output delivered to the grid (E-grid), performance ratio (PR), and associated CO2 emissions. Subsequently, a Taguchi analysis facilitated optimization based on these results. The outcome of this analysis identified the optimal PV array design as 3D and the optimal PV type as bifacial. Further insight was gained through an ANOVA analysis, revealing the substantial contributions of parameters to overall variability. Specifically, PV type exhibited a significant contribution of 65.27%, while PV array configuration contributed 34.72% to the observed variability in plant performance. These findings not only enhance the understanding of PV power plant design intricacies but also underscore the paramount significance of array design in achieving heightened efficiency and sustainability.

Optimal power flow calculation in hybrid power system involving solar, wind, and hydropower plant using weighted mean of vectors algorithm

One of the most complex and motivating issues in power system is optimal power flow (OPF), which is a constrained optimization problem characterized by non-linearity and non-convexity. From these specifications, researchers competed in the past decades to find optimal solutions to OPF problem while keeping system stability. This paper presents an efficient optimization approach to deal with OPF problem in the hybrid renewable energy systems involving wind turbines, solar photovoltaic and small hydropower plant using optimization method depends on weighted mean of vectors INFO. Total generation cost, active power losses, and combined cost and emission are the principle goal, taking into account both reserve and penalty cost appropriate to over and under estimation respectively in the generation cost model. To evaluate the performance of INFO in solving OPF problem, modified IEEE 30-bus and IEEE 57-bus test systems will be utilized. The obtained results are compared with several algorithms such as Gorilla troop optimizer GTO, artificial ecosystem-based optimization AEO, Barnacles Mating Optimizer BMO for the same test systems keeping the same conditions. Simulation results have indicated the superiority of INFO while respecting all constraints. INFO can minimize total generation cost to 788.9417 $/h for IEEE 30-bus and 5259.2040 $/h for IEEE 57-bus. The results demonstrate clearly that the INFO is a highly efficient algorithm that is an encouraging tool for solving OPF problem. The promising findings highlight the potential of the INFO algorithm to smoothest the integration of RES, and its role in promoting sustainable energy solutions. Furthermore, the one-way analysis of variance (ANOVA) test, a statistical approach, was employed to evaluate the superiority of the proposed algorithm and to highlight a certain level of confidence to our study.

An AHP-GIS combination for site suitability analysis of hydrogen production units from CSP &PV solar power plants in Morocco

The main objective of this study is to evaluate Morocco's potential for hosting large-scale solar-based hydrogen production facilities, from both Photovoltaic (PV) and Concentrated Solar Power (CSP) technologies. To achieve this, a comprehensive approach is employed, combining the Analytical Hierarchy Process (AHP) methodology with Geographic Information System (GIS) software to create precise land suitability maps for two hydrogen production scenarios: PV-based (H2PV) and CSP-based (H2CSP) units. By identifying the highly suitable areas, two reference locations, one for each scenario, are selected based on their exceptional irradiance records. These reference sites serve as focal points for a detailed techno-economic assessment.The results have demonstrated that opting for the installation of H2PV hydrogen units represents the most advantageous strategic choice for Morocco to efficiently producing green hydrogen. This decision is well-supported by several key factors. Notably, the availability of abundant land significantly favors the H2PV scenario, with a considerably larger portion of highly suitable areas compared to CSP (20.3 % compared to 7.4 %). Moreover, a 1 MW of H2PV has the capacity to produce an annual hydrogen output of 48.5 tons/year, with a Levelized Cost of Hydrogen (LCOH2) amounting to 4.972$/kg.

Machine learning for monitoring and classification in inverters from solar photovoltaic energy plants

The efficiency of solar energy farms requires detailed analytics and information on each inverter regarding voltage, current, temperature, and power. Monitoring inverters from a solar energy farm was shown to minimize the cost of maintenance, increase production and help optimize the performance of the inverters under various conditions. Machine learning algorithms are techniques to analyze data, classify and predict variables according to historic values and combination of different variables. The 140 kWp photovoltaic plant contains 300 modules of 255 W and 294 modules of 250 W with smart monitoring devices. In total the inverters are of type SMA Tripower of 25 kW and 10 kW. The 590 kWp photovoltaic plant contains 1312 Trina solar 450 W modules. In total the four inverters are SMA Sunny Tripower type of 110–60 CORE 2 with rated power of 440 kW were analyzed and several supervised learning algorithms were applied, and the accuracy was determined. The facility enables networked data and a machine learning algorithm for fault classification and monitoring was developed, energy efficiency was calculated and solutions to increase energy production and monitoring were developed for better reliability of components according to the monitorization and optimization of inverters.

Innovative method for the brine treatment by electrokinetic principles integrated with solar photovoltaic plants

With the growing world population and industrial production, the demand for water has been continuously increasing. By 2030, it was estimated that 60.0 % of the world population will not have access to freshwater, which is about 2.50 % of the total global water. For this, a total of over 17,000 operational desalination plants have been constructed worldwide. However, the key barriers to expansion of the desalination treatments are the brine production and energy consumption. In fact, the brine production is 50.0 % higher than the freshwater, and its treatments could account for 5.0–33.0 % of total desalination cost. Here, a new theoretical approach for brine treatments integrated to solar photovoltaic plants (PVs) to supply renewable energy to the whole system has been proposed. This approach consists in combining electrokinetic and electrochemical phenomena to dilute the brine, by using an alkaline clay with high buffering power. This method substantially desalinates the brine to produce new treated seawater, using clean energy, optimizing energetic and management costs. Some hypotheses and secondary effects should validate the model, e.g., relatively high Ca2+ promotes the electro-migration; the Cl2 production reduces the Cl− concentrations; and the production of H2 can be used to store energy. A practical example for PVPs design is shown.

Aeroelastic instability mechanisms of single-axis solar trackers

Single-axis solar trackers are currently the most commonly used racking system in utility scale solar photovoltaic (PV) plants. The last decade has seen significant advances in the understanding of single-axis tracker aerodynamics after significant failures have occurred due to aerodynamic instabilities. The current study focuses on the aeroelastic mechanisms causing these divergent instabilities. The analytical background as a function of static tilt angle is presented for the different instabilities. The role of aerodynamic stiffness and aerodynamic damping are examined through rigid sectional model testing and these results are used to perform numerical stability estimates using multiple theoretical approaches. These results are compared with aeroelastic model testing of a single-axis solar tracker over a wide range of static tilt angles. These analytical, numerical, and experimental approaches are used to assess the static tilt angles governed by stiffness-driven and damping-driven aerodynamic instabilities. The small size and light weight of single-axis trackers makes them more susceptible to turbulent gusts. This aspect has been addressed in the current study through the concept of the structurally averaged wind speed and the effective gust velocity factor. Comparison to the experimental results suggests that stiffness-driven torsional instability can respond quite rapidly to the passage of turbulent gusts.

Profitability of battery storage in hybrid hydropower–solar photovoltaic plants

Increased energy demand and rapid environmental changes triggered by global greenhouse gas emissions have forced numerous countries to consider renewable energy sources (RES) as possible alternatives to conventional fossil-fuel energy sources. Due to the inherent uncertainty, intermittency, and generally uncontrollable power generation by single-source renewable power plants, hybrid power plants (HPPs) incorporating several mutually complementary RES have lately gained much interest. Integrating battery storage systems with such HPPs has the potential to run them more similarly to conventional fossil-fueled power plants, providing controllable power generation, and reducing its variability. Given such a future scenario and the lack of existing detailed studies, this paper investigates the profitability potential for a viable business case for battery storage integration with utility-scale hybrid hydropower–solar photovoltaic (PV) plants. The study presented here is based on a hypothetical, two-reservoir cascaded hydropower plant in Sub-Saharan Africa. The role of the battery is assessed by considering the overall profitability of the HPP when participating in capacity markets, ancillary services, and energy arbitrage. The relationship between the value stacking of battery services and its impact on battery life has been critically examined. This study provides estimates on increased profitability, cost-optimal battery capacities, battery degradation estimates, and the HPP-battery interoperability aspects under various hydropower and electricity market operating scenarios. Batteries will likely increase cost-effectiveness by co-optimization with PV-system as well as power market contracts. In this case, adding a battery increased the profitability by about 2% when combining revenues from capacity markets and ancillary services.

Profile Optimisation in Real Distribution Networks (Case Study Long MV Feeder)

Abstract In the dynamic field of power systems, integrating distributed generation (DG) sources like solar photovoltaic (PV) plants is crucial for enhancing reliability and fostering sustainability. However, this integration poses voltage profile management challenges in electrical grids. This study investigates voltage profile optimization in Bosnia and Herzegovina’s Gračanica network, focusing on a medium voltage feeder with 68 buses. Using DIgSILENT PowerFactory software, six scenarios with different configurations of solar plants are analyzed for their impact on voltage profiles and power losses. Results show that while DG integration offers benefits, incorrect sizing or placement can increase power losses. Optimal DG benefits are linked to specific sizes and locations. This research emphasizes the need for balancing PV generation with load demands and provides insights for optimal PV plant size and output to minimize negative impacts. These findings aid energy planners and policymakers in implementing distributed solar PV in medium voltage networks.

Local positive sequence component based protection of series compensated parallel lines connecting solar photovoltaic plants

Local positive sequence component based protection of series compensated parallel lines connecting solar photovoltaic plants

An Assessment of Solar Micro-Grid System in the Islands of Bangladesh for Sustainable Energy Access

The isolated islands in the northern Bay of Bengal face difficulties accessing electricity from the central grid and use fossil fuel-based generators, which causes health risks, environmental damage, and high expenses. So, this study aims to replace these fossil fuel-based power sources with Solar Photovoltaic (SPV) microgrids to provide continuous power to remote islands and contribute to reducing emissions. A specific location on the island of Manpura is selected for the SPV plant installation, considering land availability and vulnerability to erosion. Solar power density and other technical parameters such as Direct Normal Irradiation, Global Horizontal Irradiation, Diffuse Horizontal Irradiation, Global Tilted Irradiation, the optimum tilt angle of Photovoltaic (PV) modules, air temperature, and terrain elevation are analyzed using some prominent online analytical tools named “Global Solar Atlas (GSA)”, “Photovoltaic Geographical Information System (PVGIS)” etc. Based on these datasets, a 10kW ground-mounted PV system using monocrystalline silicon solar panels is designed for off-grid operation. The developed system can generate 14.808 MWh of energy per year. This design’s environmental and social impacts are critically analyzed considering the Sustainable Development Goals (SDGs). Compared with conventional energy sources, the results show that the 10kW microgrid SPV system can reduce CO2 emissions by 284 tons. Financial analysis shows the recovery period of the investment based on electricity production at around 8.8 years, making solar PV microgrids a viable option for remote areas like Manpura Island.

Design and Techno-economic Simulation of a Standalone PV Solar Power Plant for a Typical Housing Estate in Owerri, Nigeria

Most, if not all, of the estates in Owerri, Nigeria are planned without recourse to the United Nations Sustainable Development Goals (SDG) envisions 2030’s goals 7 and 11. SDG goal 7 (on affordable and clean energy) and goal 11 (on sustainable cities and communities) envision a world by 2030 where there will be universal access to affordable, reliable, and modern energy services; a substantial increase in the share of renewable energy in the global energy mix, etc. This research was done to depict the feasibility of a photovoltaic (PV) power plant for estates in Owerri, Nigeria. A standalone PV solar power plant for a typical 200 bungalow housing estate in Owerri, Nigeria was designed and simulated to study its techno-economic feasibility using PVsyst 7.3 simulation software. The study revealed that with a global horizontal irradiation of 4.70 kWh/m2/day reaching Owerri, Nigeria, a 468 kWp PV system is required to supply the electricity needs of an estate with an energy demand of 1,480 kWh/day. The system will generate a total of 615,168 kWh of electric energy in a year with a performance ratio of 70.4 % and a solar fraction of 96.8%. The designed PV solar power plant is highly feasible as the obtained results from the economic simulation showed that the system’s installation cost is 466,970 USD at a specific cost of 1.0 USD/kWp, and the net present value (NPV) is positive at 842,699.16 USD, 752,219.11 USD, and 698,107.07 USD, respectively for the 0%, 50%, and 80% loan financing cases. With an energy tariff of 0.15 USD/kWh, the return on investment, payback period, and levelized cost of energy were determined as 180.5%, 7.2 years, and 0.0698 USD/kWh, respectively for the 0% loan financing case.

IFSOLAR: the Innovative IFSULDEMINAS Program for the Acquisition of Solar Photovoltaic Plants

IFSOLAR is the project for the bidding, acquisition and installation of photovoltaic plants for the production of solar energy at IFSULDEMINAS, used by public institutions throughout Brazil, and involves SDG 7, 8, 9, 11, 12 and 13. Acquisition of photovoltaic solar plants conducted by IFSULDEMINAS are considered the largest ever carried out in Brazil, with a significant impact on the national energy matrix. The project began in 2016, with the bidding, acquisition and installation of photovoltaic panels on the campuses of 15 Federal Institutes in the country, with a capacity of 5,470 MWp. In 2018, 94 federal agencies participated or adhered to the new tender, moving BRL 220 million, with a capacity of 56 MWp. By the end of 2019, Brazil had approximately 2,100 MWp of distributed generation, and with just the last bid, IFSULDEMINAS contributed to the expansion of 2.7% of the total installed capacity in the country. The performance of IFSULDEMINAS in these innovative bidding processes led to the cost of bidding being reduced by 9% in the final value, for RDC 1/2016, and 41% for RDC 3/2018, reducing the cost for public coffers at BRL 162 million. The IFSOLAR project helped transform IFSULDEMINAS into a reference in the bidding, generation and research of photovoltaic solar energy in the public sector in Brazil.

Review of deep learning techniques for power generation prediction of industrial solar photovoltaic plants

Varying power generation by industrial solar photovoltaic plants impacts the steadiness of the electric grid which necessitates the prediction of solar power generation accurately. In this study, a comprehensive updated review of standalone and hybrid machine learning techniques for PV power forecasting is presented. Forecasting solar generation is of importance for the sustainability of grid power and also to achieve the UN sustainable development targets by 2030. The comparison of techniques shows that grouping datasets based on input feature similarity, results in higher accuracy. Long-Short Term Memory (LSTM) is found to perform better than other deep learning networks for all time horizons. The Gate Recurrent Unit (GRU), with few trainings, is found to be better for small datasets than LSTM. Based on the more complicated data patterns, a novel architecture of the Deep Learning Network model, with the capability to analyze and forecast is presented considering factors influencing industrial solar power generation. The study is of importance to researchers, solar industry, and electricity distribution companies for sustainable development worldwide.